# document

id: 8114bb7a0ceee1100572d4145e9fbe15
created_at: 2023-03-16 14:02:35
updated_at: 2023-03-16 14:02:35
content_type: text
index: climate_gpt_only_giec
vector_id: 17831
content:
"Climate risks are also strongly related to other inequalities, often \r\nbut not always intersecting with poverty. AR5 found with very high \r\nconfidence that differences in vulnerability and exposure arise from \r\nmulti-dimensional inequalities, often produced by uneven development \r\nprocesses. These inequalities relate to geographic location, as well \r\nas economic, political and socio-cultural aspects, such as wealth, \r\neducation, race/ethnicity, religion, gender, age, class/caste, disability \r\nand health status (Oppenheimer et al., 2014). Since AR5, a number of \r\nstudies have confirmed and refined this assessment, especially also \r\nregarding socioeconomic inequality and poverty (Hallegatte et  al., \r\n2016; Hallegatte and Rozenberg, 2017; Pelling and Garschagen, 2019; \r\nHallegatte et al., 2020). Poor people more often live in exposed areas \r\nsuch as wastelands or riverbanks (Garschagen and Romero-Lankao, \r\n2015; Winsemius et al., 2018)"


id: 337c137f3ee9aa9c13990185f1512186
created_at: 2023-03-16 14:02:35
updated_at: 2023-03-16 14:02:35
content_type: text
index: climate_gpt_only_giec
vector_id: 5518
content:
" Also, poor people lose more of their total \r\nwealth to climatic hazards, receive less post-shock support from their \r\noften-times equally poor social networks, and are often not covered by \r\nsocial protection schemes (Leichenko and Silva, 2014; Hallegatte et al., \r\n2016). Countries with high inequality tend to have above-average \r\nlevels of exposure and vulnerability to climate hazards (BEH UNU-EHS, \r\n2016). Many socioeconomic models used in climate research have \r\nbeen found to have a limited ability to capture and represent the poor \r\nat a larger scale (Rao et  al., 2019; Rufat et  al., 2019). However, an \r\nanalysis of 92 countries found that relative income losses and other \r\nclimate change impacts were disproportionately high among the \r\npoorest (Hallegatte and Rozenberg, 2017, see Section 16.2.6)"


id: 8d776ea17677891a89efaf0808a6e3dc
created_at: 2023-03-16 14:02:35
updated_at: 2023-03-16 14:02:35
content_type: text
index: climate_gpt_only_giec
vector_id: 19784
content:
" There \r\nhave also been advances in detecting and attributing the impacts of \r\nclimate change and vulnerability at household scale and specifically \r\non women's agency and adaptive capacity (Rao et  al., 2019). The \r\ndistribution of impacts and responses (adaptation and mitigation) \r\naffects inequality, not just between countries but also within countries \r\n(e.g., Tol, 2020) and between different people within societies. \r\nDistribution has so far largely been thought of in a geographical sense, \r\nbut identifying those most at risk requires an additional focus on the \r\nsocial distribution of impacts, responses, and resilience, as influenced \r\nfor instance by differential social protection coverage (Tenzing, 2020).\r\nMany climate responses interact with all of these global risk drivers. \r\nSome raise additional equity concerns about marginalising those most \r\nvulnerable and exacerbating social conflicts (Oppenheimer et  al"


id: 4609effd940541cf3fc4e8815e87c226
created_at: 2023-03-16 14:02:35
updated_at: 2023-03-16 14:02:35
content_type: text
index: climate_gpt_only_giec
vector_id: 8404
content:
", \r\n2019), leading to wider questions about the governance of climate \r\nrisks (and impacts) across scales. Hence, our assessment of impacts, \r\nresponses and risks is complemented by the assessment of governance \r\nand the enabling environment for risk management in Chapter 17, and \r\nof climate resilient development in Chapter 18."


id: 1f3f1d61fcc261e3d706471429dc8d86
created_at: 2023-03-16 14:02:35
updated_at: 2023-03-16 14:02:35
content_type: text
index: climate_gpt_only_giec
vector_id: 2355
content:
"compared to contributions from management changes?' (e.g., Ray et al., \r\n2015; Muller et al., 2017) and 'Can weather fluctuations explain part \r\nof the observed variability in annual national economic growth rates?' \r\n(e.g., Burke et al., 2015). Identification of weather sensitivity may also \r\naddress the effects of individual climate extremes, for example asking, \r\n'Was the observed outbreak of cholera triggered by an associated flood \r\nevent?' (e.g., Rinaldo et al., 2012; Moore et al., 2017b). It is important \r\nto note that sensitivity could be described in diverse ways and that, for \r\nexample, the fraction of the observed variability in a system explained by \r\nweather variability differs from the strength of the systems' response to \r\na specific change in a weather variable"


id: 1ca175dc125007e614b4b631b760757d
created_at: 2023-03-16 14:02:35
updated_at: 2023-03-16 14:02:35
content_type: text
index: climate_gpt_only_giec
vector_id: 1953
content:
" Nevertheless, all these different \r\nmeasures are integrated in the 'identification of weather sensitivity' \r\nassessment, where 'sensitivity' should not be considered a quantitative \r\none-dimensional mathematical measure."


id: 417e12d7c02a8a4a8727f3d52547a5ac
created_at: 2023-03-16 14:02:35
updated_at: 2023-03-16 14:02:35
content_type: text
index: climate_gpt_only_giec
vector_id: 7705
content:
"In line with the AR5 definition, 'changes in climate-related systems' \r\nhere refer to any long-term trend, irrespective of the underlying \r\ncauses; thus, an observed impact is not necessarily an observed \r\nimpact of anthropogenic climate forcing. For example, in this section, \r\nsea level rise is defined as relative sea level rise measured against \r\na land-based reference frame (tide gauge measurements), meaning \r\nthat it is driven not only by thermal expansion and loss of land ice \r\ninfluenced by anthropogenic climate forcing, but also by vertical land \r\nmovements. As attribution of coastal damages to sea level rise does not \r\ndistinguish between these components, it does not imply attribution \r\nto anthropogenic forcing. Where the literature does allow attribution \r\nof changes in natural, human or managed systems to anthropogenic \r\nclimate forcing ('joint attribution', Rosenzweig et  al., 2007), this is \r\nhighlighted in the assessment"


id: 4c226a3f8bacb48a0b20b9f509268b6e
created_at: 2023-03-16 14:02:35
updated_at: 2023-03-16 14:02:35
content_type: text
index: climate_gpt_only_giec
vector_id: 9401
content:
" Often the attribution of changes in \r\nthe natural, human or managed systems to anthropogenic forcing can \r\nbe done in a two-step approach where (i) an observed change in a \r\nclimate-related system is attributed to anthropogenic climate forcing \r\n('climate attribution') and (ii) changes in natural, human or managed \r\nsystems are attributed to this change in the climate-related system \r\n('impact attribution')."


id: 189e3680d94c888f7caa19f78c112320
created_at: 2023-03-16 14:02:35
updated_at: 2023-03-16 14:02:35
content_type: text
index: climate_gpt_only_giec
vector_id: 1369
content:
"For climate attribution, the main challenge is the separation of externally \r\nhuman forced changes in the climate-related systems from their internal \r\nvariability, while for impact attribution it often is the separation of \r\nthe effects of other external forcings (i.e., direct human influences or \r\nnatural disturbances) from the impacts of the changes in the climate\u0002related systems. Direct influences not related to changes in the climate\u0002related systems could, for example, be pollution and land use changes \r\namplifying biodiversity losses, intensification of fishing reducing fish \r\nstocks, and increasing protection reducing losses due to river floods. \r\nThe direct human or natural influences may counter the impacts of \r\nclimate change (e.g., climate change may have reduced flood hazards, \r\nbut exposure may have increased as people have moved to flood-prone \r\nareas, resulting in no change in observed damages)"


id: 3a3451189ac778ccb9c567fd8774f4e1
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 6523
content:
" Given the definition \r\nof impact attribution, this means that there may be an observed impact \r\nof climate change without the detection of a change in the natural, \r\nhuman or managed system. This is different from 'climate attribution', \r\nwhere detection and attribution usually are consecutive steps."


id: f5c0db87c00a44f39951d731aa1e3fe3
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 36183
content:
"By definition, the counterfactual baseline required for impact \r\nattribution cannot be observed. However, it may be approximated by \r\nimpact model simulations forced by a stationary climate, for example \r\nderived by de-trending the observed climate (Diffenbaugh et al., 2017; \r\nMengel et al., 2021), while other relevant drivers (e.g., land use changes \r\nor application of pesticides) of changes in the system of interest (e.g., a \r\nbird population) evolve according to historical conditions. To attribute \r\nto anthropogenic climate forcing, the anthropogenic trends in climate \r\nare estimated from a range of different climate models and subtracted \r\nfrom the observed climate (e.g., Abatzoglou and Williams, 2016, for \r\nchanges in the extent of forest fires or Diffenbaugh and Burke, 2019, \r\nfor effects on economic inequality) or the 'no anthropogenic climate \r\nforcing' baseline is directly derived from a large ensemble of climate \r\nmodel simulations not accounting for anthropogenic forcings (e"


id: 29e60626159334545dfd85c4d40cde1a
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 4024
content:
"g., \r\nKirchmeier-Young et al.., 2019b, for the extent of forest fires). In any \r\ncase, it has to be demonstrated that the applied impact models are \r\nable to explain the observed changes in natural, human or managed \r\nsystems by, for example, reproducing the observations when forced \r\nby observed changes in climate-related systems and other relevant \r\ndrivers."


id: 907dee9a300860c1bd1ec8defa23a6a3
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 20266
content:
"To date, explicit accounting for direct human or natural influences \r\nis often hampered by an incomplete understanding of the processes \r\nand limited observational data. There are, however, first studies \r\ndemonstrating the potential of detailed process-based or empirical \r\nmodelling that explicitly accounts for known variations in direct human \r\nor natural drivers and separate their effects from the ones induced \r\nby changes in the climate-related systems. Examples are Butler et al. \r\n(2018) for the separation of growing season adjustments from within \r\ngrowing season climate effects on US crop yields; Wang and Hijmans \r\n(2019) separating effects of shifts in land use from climate effects; \r\nJongman et al. (2015); Formetta and Feyen (2019) and Tanoue et al. \r\n(2016) for the separation of changes in exposure and vulnerability \r\nfrom climate effects on river floods; Kirchmeier-Young et al. (2019b) \r\nfor wildfire attribution; and Venter et  al"


id: c51ad6370cf571c4da0ad9bc6679caf8
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 28590
content:
" (2018) for the attribution \r\nof ecosystem structural changes to climate change versus other \r\ndisturbances."


id: 8b402a484be39b08ae0d38a26ca9470a
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 19434
content:
"The collapse or transformation of ecosystems is one of the most \r\nabrupt potential tipping points associated with climate change. \r\nClimate change has started to induce such tipping points, with the \r\nfirst examples including mass mortality in coral reef ecosystems (e.g., \r\nDonner et al., 2017; Hughes et al., 2018; Hughes et al., 2019) (high \r\nconfidence), and changes in vegetation cover triggered by wildfires \r\nwith climate change suppressing the recovery of the former cover \r\n(Tepley et al., 2017; Davis et al., 2019) (low confidence because of the \r\nstill limited number of studies). Another example of an abrupt change \r\nin an ecosystem triggered by a climate extreme is the shift from \r\nkelp- to urchin-dominated communities along parts of the Western \r\nNorth America coast (Rogers-Bennett and Catton, 2019; McPherson \r\net  al., 2021, see 'Marine ecosystems--Kelp forest', Table  SM16.22)"


id: 557698220d1dde6bc8c490d3c1a87e13
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 10921
content:
" \r\nThe loss of kelp forests was induced by a marine heatwave where \r\nanthropogenic climate forcing has been shown to have increased the \r\nprobability for an event of that duration by at least a factor of 33 \r\n(Laufkotter et al., 2020). Many terrestrial ecosystems on all continents \r\nshow evidence of significant structural transformation, including \r\nwoody thickening and 'greening' in more water-limited ecosystems, \r\nwith a significant role played by rising atmospheric CO2 fertilisation in \r\nthese trends (high confidence) (Fang et al., 2017; Stevens et al., 2017; \r\nBurrell et al., 2020). Climate change is identified as a major driver of \r\nincreases in burned areas in the Western USA (high confidence, see \r\n'Terrestrial ecosystems--Burned areas', Table SM16.22)."


id: 5acbebbabb6626befa631b0f1189a5ff
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 11799
content:
"Tropical cyclone activity', Table  SM16.21). However, anthropogenic \r\nclimate forcing has become the dominant driver of sea level rise (high \r\nconfidence) (see 'Coastal systems--Mean and extreme sea levels', \r\nTable SM16.21) and has increased the risk of coastal flooding, including \r\ninundation induced by tropical cyclones. In addition, anthropogenic \r\nclimate forcing has increased the amount of rainfall associated with \r\ntropical cyclones (high confidence) (Risser and Wehner, 2017; Van \r\nOldenborgh et  al., 2017; Wang et  al., 2018, for Hurricane Harvey in \r\n2017; Patricola and Wehner, 2018, for hurricane Katrina in 2005, Irma \r\nin 2017 and Maria in 2017, see 'Atmosphere--Heavy precipitation', \r\nTable SM16.21)"


id: 4a450b74dd8406f11c6e5169abb505d1
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 9092
content:
" Assuming that the extreme rainfall is a major driver \r\nof the total damages induced by the tropical cyclone, the contribution \r\nof anthropogenic climate forcing to the occurrence probability of the \r\nobserved rainfall (fraction of attributable risk) can also be considered \r\nthe fraction of attributable risk of the hurricane-induced damages or \r\nfatalities (Frame et al., 2020; Clarke et al., 2021, see 'Coastal systems--\r\nDamages', Table SM16.22). However, first studies do not only quantify \r\nthe change in occurrence probabilities but translate the actual change \r\nin climate-related systems into the additional area affected by flooding \r\nin a process-based way (Strauss et  al., 2021 for the contribution of \r\nanthropogenic sea level rise (SLR) to damages induced by Hurricane \r\nSandy; Wehner and Sampson, 2021 for the contribution increased \r\nprecipitation to damages induced by Hurricane Harvey) and attribute \r\na considerable part of the observed damage to anthropogenic climate \r\nforcing"


id: 27b61a22b802daffdf6331f856c2710b
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 3654
content:
" In addition, disruption of local economic activity in Annapolis, \r\nMaryland and loss of areas and settlements in Micronesia and Solomon \r\nIslands have been attributed to relative SLR (Nunn et al., 2017; Albert \r\net al., 2018; Hino et al., 2019), while permafrost thawing and sea ice \r\nretreat are additional drivers of observed coastal damages in Alaska \r\n(Albert et al., 2016; Smith and Sattineni, 2016; Fang et al., 2017)."


id: a18afc2f99263b4bac49b0f8a36146c0
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 22972
content:
"Water Availability\r\nObserved trends in high river flows strongly vary across regions but \r\nalso with the considered time period (Gudmundsson et  al., 2019; \r\nGudmundsson et al., 2021) as influenced by climate oscillations such \r\nas the El Nino-Southern Oscillation (Ward et al., 2014). On the global \r\nscale, the spatial pattern of observed trends is largely explained by \r\nobserved changes in climate conditions as demonstrated by multi\u0002model hydrological simulations forced by observed weather, while the \r\nconsidered direct human influences play only a minor role on global \r\nscale (Gudmundsson et  al., 2021, see 'Water distribution--Flood \r\ninduced economic damages', Table SM16.22). The annual total number \r\nof reported fatalities from flooding shows a positive trend (1.5% yr-1\r\nfrom 1960 to 2013, Tanoue et al"


id: dbd32d3008ac5272b7907d8f9d910c9e
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 32102
content:
", 2016) which appears to be primarily \r\ndriven by changes in exposure dampened by a reduction in vulnerability, \r\nwhile climate-induced increases in affected areas show only a weak \r\npositive trend on the global scale. However, the signal of climate change \r\nin flood-induced fatalities may be lost in the regional aggregation, \r\nwhere effects of increasing and decreasing hazards may cancel out. Thus, \r\na climate-driven increase in flood-induced damages becomes detectable \r\nin continental subregions with increasing discharge, while the signal \r\nof climate change may not be detectable without disaggregation \r\n(Sauer et  al., 2021, see 'Water distribution--Flood induced economic \r\ndamages', Table  SM16.22)"


id: 7946bc967231f2bc5503d66b3293fdb7
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 16647
content:
" Compared with river floods, the analysis \r\nof impacts of long-term changes in the climate-related systems on the \r\nreduction in water availability is much more fragmented and reduced to \r\nindividual case studies regarding associated societal impacts (see 'Water \r\ndistribution--Reductions in water availability + induced damages and \r\nfatalities', Table SM16.22). At the same time, weather fluctuations have \r\nled to reductions in water availability with severe societal consequences \r\nand high numbers of drought-induced fatalities and damages in \r\nparticular in Africa and Asia (see 'Water distribution--Reductions in \r\nwater availability + induced damages and fatalities', Table  SM16.23) \r\nand impacts on malnutrition (see 'Food system--Malnutrition', \r\nTable SM16.23). Although anthropogenic climate forcing has increased \r\ndroughts' intensity or probability in many regions of the world (medium \r\nconfidence), (see 'Atmosphere--Droughts', Table SM16"


id: 2396fbb6b1d3e183dd7c402738f5a8e6
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 3047
content:
"21) the existing \r\nknowledge has not yet been systematically linked to attribute long\u0002term trends in malnutrition, fatalities and damages induced by reduced \r\nwater availability to anthropogenic climate forcing or long-term climate \r\nchange. For impacts of individual attributable drought events, see \r\nTable  4.5 and 'Water distribution--Reductions in water availability + \r\ninduced damages and fatalities', Table SM16.23."


id: b5db6e79efdba8a881ce4bd3ce2a425f
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 26142
content:
"Crop yields respond to weather variations but also to increasing \r\natmospheric CO2, changes in management (e.g., fertilizer input, changes \r\nin varieties), diseases and pests. However, the weather signal is clearly \r\ndetectable in national and subnational annual yield statistics in main \r\nproduction regions (see 'Food system--Crop yields', Table  SM16.23). \r\nOver the last decades, crop yields have increased nearly everywhere \r\nmainly due to technological progress (e.g., Lobell and Field, 2007 \r\n[global]; Butler et al., 2018 [USA]; Hoffman et al., 2018 [Sub-Saharan \r\nAfrica]; Agnolucci and De Lipsis, 2019 [Europe]), with only minor areas \r\nnot experiencing improvements in maize, wheat, rice and soy yields. \r\nHowever, meanwhile, stagnation or decline in yields is also observed in \r\nparts of the harvested areas (high confidence) (~20-40% of harvested \r\nareas of maize, wheat, rice and soy with wheat being most affected) (Ray \r\net al., 2012; Iizumi et al., 2018)"


id: df6fac19770779e5d94db64ab48ab84f
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 32654
content:
" Evidence on the contribution of climate \r\nchange to recent trends is still limited (see 'Food system--Crop yields', \r\nTable SM16.22). Current global-scale process-based simulations forced \r\nby simulated historical and pre-industrial climate lack an evaluation \r\nto what degree simulations reproduce observed yields (Iizumi et  al., \r\n2018). Global-scale empirical approaches do not explicitly account for \r\nextreme weather events but growing season average temperatures \r\nand precipitation (e.g., Lobell et al., 2011; Ray et al., 2019). In addition, \r\nstudies are constrained by only fragmented information about changes \r\nin agricultural management such as growing season adjustments. Some \r\nof these limitations have been overcome in regional studies indicating a "


id: 55fcf9ed0179536c73d22f09b585d5db
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 11020
content:
"No conclusive evidence emerges regarding recent temporal trends in \r\nexcess mortality attributable to cold exposure (Vicedo-Cabrera et  al., \r\n2018b). Quantitative detection and attribution studies of temperature\u0002related mortality are still rare. One study (Vicedo-Cabrera et al. 2021), \r\nusing data from 43 countries, found that 37% (range 20.5-76.3%) of \r\naverage warm-season heat-related mortality during recent decades can \r\nbe attributed to anthropogenic climate change (medium confidence, \r\nsee 'Other societal impacts--Heat-related mortality', Table  SM16.22). \r\nStudying excess mortality associated with past heatwaves, such as \r\nthe 2003 or 2018 events in Europe, even higher proportions of deaths \r\nattributable to anthropogenic climate change have been reported for \r\nFrance and the UK (Mitchell et  al., 2016; Clarke et  al., 2021). Formal \r\nattribution studies encompassing cold-related mortality are quasi non\u0002existent. The very few studies from Europe and Australia (Christidis et al"


id: f555cb1f048d1a5ccb44068615336cf5
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 36121
content:
", \r\n2010; Astrom et al., 2013; Bennett et al., 2014) find weak impacts of \r\nclimate change on cold-associated excess mortality, with contradictory \r\noutcomes both towards higher and lower risks (low confidence, see \r\n'Other societal impacts--Heat-related mortality', Table SM16.22)."


id: 5b367cf878ecfbdec0aab33477dc552f
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 11857
content:
"Due to complex interactions with socioeconomic conditions, climate\u0002induced trends in crop yields and production do not directly transmit \r\nto crop prices, availability of food, or nutrition status. This complexity, \r\nin addition to the limited availability of long-term data, has so far \r\nimpeded the detection and attribution of a long-term impact of climate \r\nchange on associated food security indicators. However, in a few cases, \r\nobserved crop prices (e.g., domestic grain price in Russia and Africa, \r\nGotz et  al., 2016; Mawejje, 2016; Baffes et  al., 2019) are shown to \r\nbe sensitive to fluctuations in local weather through its impact on \r\nproduction (see 'Food system--Food prices', Table  SM16.23). In \r\naddition, there is growing evidence that climate extremes (in particular, \r\ndroughts) have led to malnutrition (in particular, stunting of children) \r\nin the historical period (medium confidence, see 'Food system--\r\nMalnutrition', Table SM16"


id: b3f2aa2b189ffedbf94f0919993759cc
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 25828
content:
"Infectious diseases with water-associated transmission pathways \r\nconstitute a large burden of disease globally. Since the AR5, the \r\nevidence has strengthened that waterborne diseases, and especially \r\ngastrointestinal infections, are highly to moderately sensitive to weather \r\nvariability (medium confidence, see 'Water distribution--Waterborne \r\ndiseases', Table SM16.23). Increased temperature and high precipitation, \r\nwith associated flooding events, have been shown to generally increase \r\nthe risk of diarrhoeal diseases. There are, however, a number of studies \r\nthat describe important exceptions and modifications to this general \r\nobservation. While high temperatures favour bacterial diarrhoeal \r\ndiseases, virally transmitted diarrhoea is on the contrary mostly \r\nassociated with low temperatures (Carlton et  al., 2016; Chua et  al., \r\n2021). Socioeconomic determinants, such as the existence of single\u0002household water supplies (Herrador et  al"


id: af979cf5857c2c9930385ce11734c85e
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 25141
content:
", 2015) or combined sewer \r\noverflows (Jagai et  al., 2017), have been shown to critically increase \r\nthe risk of gastrointestinal infections linked to heavy rainfall in high\u0002income countries. Also, for both low- and high-income countries it has \r\nbeen found that gastrointestinal diseases increase following a heavy \r\nrainfall event only if preceded by a dry period (Carlton et al., 2014; Setty \r\net al., 2018). Yet, so far there is no consistent evidence on the role of \r\ndroughts in favouring waterborne disease transmission (Levy et  al., \r\n2016). As exemplified by the large cholera outbreak following the 2010 \r\nearthquake in Haiti, the existence of functioning sanitation systems is \r\ncritical for preventing waterborne disease outbreaks, while climatic \r\nfactors (especially rainfall) are important in driving the transmission \r\ndynamics once the outbreak has started (Rinaldo et  al., 2012). Other \r\nsocioeconomic factors, such as human mobility and water management \r\nprojects (e.g"


id: 4d02b1a1b5a40a2cfe4ecde18a329d2d
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 9539
content:
", dam constructions), also modify the strength of the \r\nassociation between climatic factors and waterborne diseases, as shown \r\nby recent studies in Africa (Perez-Saez et al., 2015; Finger et al., 2016)."


id: 4934e425e1d869ea6131c3cac3a055c9
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 8906
content:
"There is nearly universal evidence that non-optimal ambient temperatures \r\nincrease mortality (high confidence), with notable heterogeneity only in \r\nthe shape of the temperature-mortality relationship across geographical \r\nregions but often sharply growing relative risks at the outer 5% of the \r\nlocal historical temperature distributions (Gasparrini et al., 2015; Guo \r\net al., 2018; Carleton et al., 2020; Zhao et al., 2021; see 'Other societal \r\nimpacts--Heat-related mortality', Table SM16.23). Significant advances \r\nhave been made since AR5 regarding the analysis of temperature\u0002related excess mortality in previously under-researched regions, such \r\nas developing countries and (sub)tropical climates (e.g South-East Asia: \r\nDang et al., 2016; Ingole et al., 2017; Mazdiyasni et al., 2017; South Africa: \r\nWichmann, 2017, Scovronick et  al., 2018; the Middle East: Alahmad \r\net al., 2019, Gholampour et al., 2019; and Latin America: Peres et al., \r\n2020)"


id: 6c60d6fb87abf88169c4c6304095c057
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 14678
content:
" Progress has also been made with regard to temporal changes \r\nin temperature-related excess mortality and underlying population \r\nvulnerability over time. Heat-attributable mortality fractions have \r\ndeclined over time in most countries owing to general improvements \r\nin health care systems, increasing prevalence of residential air \r\nconditioning, and behavioural changes. These factors, which determine \r\nthe susceptibility of the population to heat, have predominated over the \r\ninfluence of temperature change (see 'Other societal impacts--Heat\u0002related mortality', Table  SM16.22, De'Donato et  al., 2015; Arbuthnott \r\net al., 2016; Vicedo-Cabrera et al., 2018a). Important exceptions exist, \r\nfor example, where unprecedented heatwaves have occurred recently. "


id: 2e3533fb661b8839a810a9a8e02ce7b3
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 4709
content:
"Since the AR5, there has been significant progress regarding the \r\nidentification of economic responses to weather fluctuations: \r\nevidence has increased that extreme weather events such as tropical \r\ncyclones, droughts, and severe fluvial floods have not only caused \r\nsubstantial immediate direct economic damage (high confidence, see \r\n'Coastal Systems--Damages, Table  SM16.23, 'Water distribution--\r\nReductions in water availability + induced damages and fatalities', \r\nTable  SM16.23, and 'Water distribution--Flood-induced economic \r\ndamages', Table SM16.22) but have also reduced economic growth \r\nin the short term (year of, and year after event) (Strobl, 2011; Strobl, \r\n2012; Fomby et al., 2013; Felbermayr and Groschl, 2014, Loyaza et al. \r\n2012) (high confidence) as well as in the long term (up to 10-15 years \r\nafter event) (medium confidence) (Hsiang and Jina, 2014; Berlemann \r\nand Wenzel, 2016; Berlemann and Wenzel, 2018; Krichene et al., 2020; \r\nTanoue et  al"


id: d64d3c7891cd0ec60e678392d0b62776
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 31229
content:
", 2020, see 'Other societal impacts--Macroeconomic \r\noutput', Table SM16.23). Short- and long-term reductions of economic \r\ngrowth by extreme weather events affect both developing and \r\nindustrialised countries, but have been shown to be more severe \r\nin developing than in industrialised economies, thereby increasing \r\ninequality between countries (high confidence, see 'Other societal \r\nimpacts--Between-country inequality', Table  SM16.23). Further, \r\nextreme weather events have increased within-country inequality \r\nsince poorer people are more exposed and suffer relatively higher \r\nwell-being losses than richer parts of the population (medium \r\nconfidence, see 'Other societal impacts--Within-country inequality', \r\nTable  SM16.23)"


id: 4c1b30c7806286e5abc441ebd52df655
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 9398
content:
" Going beyond extreme weather events, economic \r\nproduction depends nonlinearly on temperature fluctuations: below \r\na certain threshold temperature, economic production increases \r\nwith temperature, whereas it decreases above a certain threshold \r\ntemperature (high confidence) (Burke et al., 2015; Pretis et al., 2018; \r\nKalkuhl and Wenz, 2020; Kotz et al., 2021)."


id: fbcc054240479bfa744876c6ab7f03c
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 37114
content:
"Vector-borne diseases constitute a large burden of infectious diseases \r\nworldwide and are highly sensitive to fluctuations of weather conditions \r\nincluding extreme events. Thus, both extreme rainfall and droughts have \r\nincreased infections (high confidence, see 'Other societal impacts--\r\nVector-borne diseases', Table  SM16.23). For example, in Sudan, \r\nanomalous high rainfall increased Anopheles mosquito breeding sites, \r\nleading to malaria outbreaks (Elsanousi et al., 2018), while in Barbados \r\nand Brazil, drought conditions in urban areas have enhanced dengue \r\nincidence due to changes in water storage behaviour creating breeding \r\nsites for Aedes mosquitoes around human dwellings (Lowe et al., 2018; \r\nLowe et al., 2021) . In the Caribbean and Pacific Island nations, weather \r\nextremes, such as storms and flooding, have led to outbreaks of dengue \r\ndue to disruption to water and sanitation services, leading to increased \r\nexposure to Aedes mosquito breeding sites (Descloux et al"


id: 39249368ed24cb8629dd0e8a64bfe92
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 6367
content:
", 2012; Sharp \r\net al., 2014; Uwishema et al., 2021). In South and Central America, and \r\nAsia, dengue incidence has been shown to be sensitive to variations in \r\ntemperature and the monsoon season in addition to variations induced \r\nby urbanisation and population mobility (high confidence [South \r\nand Central America]; medium confidence [Asia]; see 'Other societal \r\nimpacts--Vector-borne diseases', Table SM16.23)."


id: 81a4eb9f566e29a94903a00bf4b7db2a
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 17928
content:
"The attribution of changes in disease incidence to long-term climate \r\nchange is often limited by relatively short reporting periods often \r\nonly covering 10-15 years. Most studies then attribute trends in the \r\noccurrence of vector-borne diseases to the trends in climate across \r\nthe same observational period and do not refer to an early 'no climate \r\nchange' baseline climate. This means that they also capture trends \r\ninduced by longerterm climate oscillations. Nevertheless, we list them \r\nin Table  SM16.22 on 'impact attribution' to clearly distinguish them \r\nfrom the analysis of interannual fluctuations. The overall consistency of \r\ntheir findings across regions and time windows indicates that climate \r\nchange is an important driver of the observed latitudinal or altitudinal \r\nrange expansions of vector-borne diseases into previously colder areas \r\n(medium to high confidence, see 'Other societal impacts--Vector\u0002borne diseases', Table SM16.22)"


id: 9b023ec45bef798203aab7ad971ec989
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 21930
content:
" In highland areas of Africa and South \r\nAmerica, epidemic outbreaks of malaria have become more frequent \r\ndue to warming trends that allow Anopheles mosquitoes to persist at \r\nhigher elevations (Pascual et al., 2006; Siraj et al., 2014). In the USA, \r\nticks that transmit Lyme disease have expanded their range northwards \r\nbecause of warmer temperatures (high confidence; Kugeler et al., 2015; \r\nMcPherson et al., 2017; Lin et al., 2019; Couper et al., 2020; see 'Other \r\nsocietal impacts--Vector-borne diseases', Table SM16.22). In Southern \r\nEurope, climate suitability for Aedes mosquitoes, which transmit dengue \r\nand chikungunya, and Culex mosquitoes, which transmit West Nile \r\nvirus, has also increased and contributed to unprecedented outbreaks \r\nincluding the 2018 West Nile fever outbreak (medium confidence, "


id: cd8e3208addf3031b22023659dbf6f9d
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 29875
content:
"So far, there are few individual studies attributing observed economic \r\ndamages to long-term climate change except for damages induced \r\nby river flooding, droughts and tropical cyclones (see 'Coastal \r\nsystems--Damages', 'Water distribution--Flood-induced damages', \r\nand 'Water distribution--Reduction in water availability + induced \r\ndamages and fatalities', Table  SM16.22). In addition, the empirical \r\nfindings on the sensitivity of macroeconomic development to weather \r\nfluctuations and extreme weather events have been used to estimate \r\nthe cumulative effect of historical warming on long-term economic \r\ndevelopment (see 'Other societal impacts--Macroeconomic output', \r\nTable SM16"


id: c22e9d5c0cb9ea5f65b842ba325e9aa7
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 28135
content:
"22): anthropogenic climate change is estimated to have \r\nreduced gross domestic product (GDP) growth over the last 50 years, \r\nwith substantially larger negative effects on developing countries \r\nand in some cases positive effects on colder industrialised countries \r\n(low confidence) (Diffenbaugh and Burke, 2019). Globally, between\u0002country inequality has decreased over the last 50  years. Climate \r\nchange is estimated to have substantially slowed down this trend, that \r\nis, increased inequality compared with a counterfactual no-climate\u0002change baseline (low confidence) (Diffenbaugh and Burke, 2019). On \r\na regional level, decreasing rainfall trends in Sub-Saharan Africa may "


id: 266d1f202283507b9bbcce6d936c6326
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 3457
content:
"There are few studies directly attributing changes in conflict risk to \r\nclimate change in the modern era (van Weezel, 2020), preventing a \r\nconfident assessment of the effect of long-term changes in the climate\u0002related systems on armed conflict (see 'Other societal impacts--Social \r\nconflict', Table  SM16.22). However, a sizeable literature links the \r\nprevalence of armed conflict within countries to within- and between\u0002year variations in rainfall, temperature or drought exposure, often via \r\nreduced-form econometric analysis or statistical models that control \r\nfor important non-climatic factors, such as agricultural dependence, \r\nlevel of economic development, state capacity and ethnopolitical \r\nmarginalisation (see 'Other societal impacts--Social conflict', \r\nTable SM16.23). Overall, there is more consistent evidence that climate \r\nvariability has influenced low-intensity organised violence than major \r\ncivil wars (Detges, 2017; Nordkvelle et al., 2017; Linke et al., 2018)"


id: 3b90022215d4667f4ad7943f62b8ae7f
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 6723
content:
" \r\nLikewise, there is more consistent evidence that climate variability \r\nhas affected dynamics of conflict, such as continuation, severity and \r\nfrequency of violent conflict events, than the likelihood of initial \r\nconflict outbreak (Yeeles, 2015; Eastin, 2016; Von Uexkull et al., 2016, \r\nSection 7.2.7). Moreover, research suggests with medium confidence\r\n(medium evidence, medium agreement) that weather effects on \r\narmed conflict have been most prominent in contexts marked by a \r\nlarge population, low socioeconomic development, high political \r\nmarginalisation and high agricultural dependence (Theisen, 2017; \r\nKoubi, 2019; Buhaug et al., 2020; Ide et al., 2020)."


id: da5f84fe409eff08bdc19213b9faa04b
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 31879
content:
"However, new evidence has emerged since the AR5 that further \r\ndocuments widespread effects of weather fluctuations and extreme \r\nevents on migration (see 'Other societal impacts--Displacement \r\nand migration', Table  SM16.23). Numerous studies find significant \r\nlinks between temperature or precipitation anomalies, or extreme \r\nweather events such as storms or floods, and internal as well as \r\ninternational migration (Coniglio and Pesce, 2015; Cattaneo and Peri, \r\n2016; Nawrotzki and DeWaard, 2016; Beine and Parsons, 2017, for \r\ninternational migration; and IDMC, 2019, for internal displacement). \r\nInternal displacement of millions of people every year is triggered by \r\nnatural hazards, mainly floods and storms (IDMC, 2019)"


id: 7a310a250613c781e6b97351113af936
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 16795
content:
" The effects of \r\nweather fluctuations and extremes on migration are considered more \r\nimportant for temporary mobility and displacement than permanent \r\nmigration, and more influential on short-distance movement, including \r\nurbanisation, than international migration (McLeman, 2014; Hauer \r\net al., 2020; Hoffmann et al., 2020, Section 7.2.6). Importantly, these \r\nlinks are conditional on the socioeconomic situation in the origin; for \r\nexample, poor populations may be 'trapped' and not able to migrate in \r\nthe face of adverse climate or weather conditions (Black et al., 2013; \r\nAdams, 2016). Many studies have also explored the channels through \r\nwhich climate or weather influence migration, and have identified \r\nincomes in the agricultural sector as one of the main channels \r\n(Nawrotzki et al., 2015; Viswanathan and Kavi Kumar, 2015; Cai et al., \r\n2016a)"


id: f90940d809a641397151820a4a42dc42
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 36692
content:
" In particular, declines in agricultural incomes and employment \r\ndue to changed weather variability may foster increased rural-urban \r\nmovement, and the resulting pressures on urban wages in turn foster \r\ninternational migration (Marchiori et  al., 2012; Maurel and Tuccio, \r\n2016). Another possible but controversial channel is violent conflict, \r\nwhich may be fostered (though not exclusively caused) by adverse \r\nclimate conditions such as drought, and in turn lead to people seeking \r\nrefugee status, although evidence of such an indirect effect is weak \r\n(Brzoska and Frohlich, 2016; Abel et al., 2019; Schutte et al., 2021)."


id: dcf1fdf649019a29f0be2cdb4ed5e202
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 32256
content:
"Some studies also seek to evaluate potential indirect links between \r\nclimate and weather anomalies and prevalence of armed conflict via \r\nfood price shocks or forced migration. While there is robust evidence\r\nthat the likelihood of social unrest in the developing world generally \r\nincreases in response to rapid growth in food prices (Bellemare, 2015; \r\nRudolfsen, 2018), the magnitude of the climate effect on unrest via \r\nfood prices is less well established (Martin-Shields and Stojetz, 2019). \r\nSimilarly, research shows with high confidence that climate variability \r\nand extremes have affected human mobility (see 'Other societal \r\nimpacts--Displacement and migration', Table SM16.23), but there is \r\nlow agreement and limited evidence that weather-induced migration \r\nhas increased the likelihood of armed conflict (Section 7.2.7, Brzoska \r\nand Frohlich, 2016; Kelley et al., 2017; Selby et al., 2017; Abel, 2019)"


id: a9675c99712452df9e21068a74ee0434
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 24180
content:
" \r\nResearch on weather-related effects on interstate security generally \r\nconcludes that periods of transboundary water scarcity are more \r\nlikely to facilitate increased international cooperation than conflict \r\n(Bernauer and Bohmelt, 2020)."


id: 1d3f605d844265af7acc8f4e1f76368b
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 2042
content:
"Separating between climatic and non-climatic factors in impact attribution is often challenging, as highlighted by the debate surrounding \r\nthe causes of the Syrian civil war. During the years 2006-2010, the Fertile Crescent region in Eastern Mediterranean and Western Asia \r\nwas hit by the worst drought on meteorological record, compounding a consistent drying of the region over the past half century (Trigo \r\net al., 2010; Hoerling et al., 2012; Mathbout et al., 2018, SR15 BOX 3.2 (Hoegh-Guldberg et al., 2018a)). The magnitude of the multi-year \r\ndrought is estimated to have become two to three times more likely as a result of increased CO2 forcing (Kelley et al., 2015). The drought \r\nhad a devastating impact on agricultural production in the northeast of Syria. In 2007-2008 alone, average crop yields dropped by 32% \r\nin irrigated areas and as much as 79% in rain-fed areas (De Chatel, 2014), and herders in the northeast lost around 85% of their livestock \r\n(Werrell et al., 2015)"


id: ef73304900d79457a7fe01d84d8e7d78
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 35158
content:
" Successive years with little or no income eventually forced people to leave their farms in great numbers and seek \r\nemployment in less affected parts of the country, adding to existing pressures on housing, labour market and public goods provision \r\n(Gleick, 2014; Kelley et al., 2015). In March 2011, by which time the 'Arab Spring' uprisings had gained momentum and spread across much \r\nof the region, anti-regime protests broke out in Syria, first in the southern city of Dara'a and then in Damascus and throughout the country."


id: 1a53ca99803b2ace109945e5e969779a
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 1612
content:
"In addition, the protesters' demands centred around contentious political rather than economic issues, including release of political \r\nprisoners, ending of torture and indiscriminate violence by security forces, and abolishment of the near 50-year-old state of emergency \r\n(Selby et al., 2017; Ash and Obradovich, 2020). The mobilisation in Syria in the spring of 2011 also made explicit references to events \r\nacross the Middle East and North African region. Analyses of regional and social media and networks show a high level of interaction \r\nacross the Arab world, and the initial Syrian uprising adopted a mobilisation model and rhetorical frames similar to those developed in \r\nTunisia and Egypt (Leenders, 2013; 2014)"


id: 25bd9352ed4d019a6ffe5d2899aaab7d
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 3364
content:
" However, the Syrian uprising stands out in how it was met with overwhelming violent force by \r\nthe police and security forces, which changed the character of the resistance and opened up for militarisation of non-state actors that \r\nfurther escalated the conflict (Heydemann, 2013; Leenders, 2013; Bramsen, 2020)."


id: c846cac21e5c2fe0c92119ec416b3a47
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 29086
content:
"There is growing evidence of shifts in species distributions and \r\necosystem structure and functioning in response to climate change \r\n(Chapter 2). While many species are increasingly responding to climate \r\nchange, there is limited evidence that these responses will be fully \r\nadaptive, and for many species the rate of response appears insufficient \r\nto keep pace with the rate of climate change under mid- and high-range \r\nemissions scenarios (medium confidence). There is relatively limited, \r\nbut growing, empirical data to document adaptation of natural systems \r\nin the absence of human interventions. For example, Scheffers et al. \r\n(2016) reviewed climate responses across diverse species, reporting \r\nwidespread and extensive observed changes in organisms (genetics, \r\nphysiology, morphology), populations (phenology, abundance and \r\ndynamics), species (distributions) and ecosystems. A systematic review \r\nby Franks et al"


id: 3883f673450bee3189d196b4938838ad
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 6269
content:
" (2014) synthesised evidence from 38 empirical studies \r\nof changes in terrestrial plant populations, finding evidence to support \r\na mix of plastic and evolutionary responses. Boutin and Lane (2014) \r\nsimilarly reviewed adaptive responses in mammals, finding most \r\nspecies' responses to be due to phenotypic plasticity. Charmantier \r\nand Gienapp (2014) reviewed responses to climate change among \r\nbirds, finding emerging evidence that birds from a range of taxa show \r\nadvancement in their timing of migration and breeding in response \r\nto warming. Aragao et  al. (2018) reviewed adaptation responses in \r\nmarine systems, including 12  studies of live marine mammals. They \r\nobserved widespread evidence of shifting distributions and timing of \r\nbiological events (Chapter 2, Chapter 3, and Cross-Chapter Paper 1)."


id: 60f2b076eb4e156c3530dc378833bf03
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 12820
content:
"Some ecosystems and species' responses may be insufficient to \r\nkeep pace with rates of climate change. It is difficult to distinguish \r\nwhether adaptations are due to genotypic change or to phenotypic \r\nplasticity. Long-term natural adaptations will require the former, but \r\nthe latter may provide short-term coping mechanisms to 'buy time' \r\nto respond to climate changes or lay foundations for evolutionary \r\nadaptation. There is mixed evidence regarding evolutionary versus \r\nplastic responses, with relatively limited evidence of longer-term \r\nevolutionary responses of species that can be associated with climate \r\nchange. Similarly, it is difficult to assess whether responses are indeed \r\npotentially adaptive (e.g., coping, shifting, migrating) or simply \r\nreflective of impacts (e.g., stress, damage). Among mammal responses \r\nreviewed by Boutin and Lane (2014), for example, only 4 of 12 studies \r\nfound some evidence that responses were adaptive"


id: 1a9f862733556e10f49f7ee3e95e40bd
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 1666
content:
" Even where \r\nadaptive responses are occurring, they may not be sufficient to keep \r\npace with the rate of climate change. found, for example, that, among \r\nthe 12 studies in their review that directly assessed the sufficiency of \r\nresponses to keep pace with the rate of climate change, 8 concluded \r\nthat responses would be insufficient to avert extinction."


id: 6c8a5c7ee7fda7e0900811a71f90af9
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 14701
content:
"Adaptation-related responses are frequently motivated by a \r\ncombination of climatic and non-climatic drivers, and interact with \r\nother transitions to affect risk. For societal responses, it is difficult to \r\nsay whether they are triggered by observed or anticipated changes in \r\nclimate, by non-climatic drivers, or by a combination of all three. In the \r\ncase of observed impacts, assessment typically focuses on detection \r\nand attribution vis a vis a counterfactual of no climate change. \r\nWhile there has been some effort to attribute reduced climate risk to \r\nadaptation-related responses (Toloo et al., 2013a; Toloo et al., 2013b; \r\nHess et al., 2018; Weinberger et al., 2018), in many cases this has not \r\nbeen feasible given difficulties in defining adaptation and empirically \r\ndisentangling the contribution of intersecting social transitions and \r\nchanging risks"


id: 5d587ae3434ce9fa42feb4ba1a4a4d58
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 12208
content:
" Literature on adaptation-related response frequently \r\ndraws on theories of change to assess the likely contribution of \r\nadaptations to changes in risk, including maladaptation and co\u0002benefits."


id: 95ee92ea50833a95c172f3ab2c931784
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 21094
content:
"National and local governments are also frequently engaged \r\nin reported adaptation across most regions. In Africa and Asia, \r\nreported adaptations have been primarily associated with individuals, \r\nhouseholds, national governments, non-governmental organisations \r\n(NGOs), and international institutions, with more limited reporting of \r\ninvolvement from sub-national governments or the private sector (Ford \r\net al., 2015a; Ford and King, 2015; Hunter et al., 2020). Engagement by \r\nsub-national governments in adaptation is more frequently documented \r\nin Europe and North America (Craft and Howlett, 2013; Craft et al., 2013; \r\nBauer and Steurer, 2014; Lesnikowski et al., 2015; Shi et al., 2015; Austin \r\net al., 2016). Reporting of private sector engagement is generally low. \r\nCivil society participation in adaptations is reported across all regions"


id: 872ddfa54a4dfb3c2f8e5856baaef8e3
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 18801
content:
" \r\nConsistent with this, local governments are also widely reported \r\nin documented adaptation responses, particularly where municipal \r\njurisdiction is high, including cities, infrastructure, water and sanitation."


id: a8320dbfb93b4c700d5cae11659c3668
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 23982
content:
"Drought and precipitation variability are the most prevalent hazards \r\nin the adaptation literature, particularly in the context of food and \r\nlivelihood security. Adaptation frequently occurs in response to specific \r\nrapid or slow-onset physical events that can have adverse impacts on \r\npeople. In some cases, people adapt in anticipation of climate change in \r\ngeneral or to take advantage of new opportunities created by hazards \r\n(e.g., increased navigability due to melting sea ice). There is evidence \r\nthat prior experience with hazards increases adaptation response \r\n(Barreca et al., 2015). Following drought and precipitation variability, \r\nthe next specific hazards that are most frequently documented in the \r\nglobal adaptation literature are heat and flooding. Heat, while less \r\nsalient, appears to be a driver of adaptation across all regions and \r\nsectors (Stone Jr et al., 2014; Hintz et al., 2018; Nunfam et al., 2018)"


id: 794c5a6aed3fa5315d6128e2692c4d40
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 16651
content:
" \r\nDrought, extreme precipitation, and inland flooding are commonly \r\nreported in the context of water and sanitation (Bauer and Steurer, \r\n2015; Lindsay, 2018; Kirchhoff and Watson, 2019; Hunter et al., 2020; \r\nSimpson et al., 2020). Flooding is frequently reported as a key hazard \r\nfor adaptation in cities, followed by drought, precipitation variability, \r\nheat, and SLR (Broto and Bulkeley, 2013; Araos et al., 2016; Georgeson \r\net al., 2016; Mees, 2017; Reckien et al., 2018; Hunter et al., 2020)."


id: c9b6ed27c4c5891a45a9b1638a759f66
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 29292
content:
"Behavioural change is the most common form of adaptation. \r\nThe scientific literature presents extensive evidence of behavioural \r\nadaptation--change in the strategies, practices and actions that \r\npeople, particularly individuals and households, undertake to reduce \r\nrisk (Figure  16.5). This includes, for example, household measures to \r\nprotect homes from flooding, protect crops from drought, relocation \r\nout of hazard zones, and shifting livelihood strategies (Porter et  al., \r\n2014). This is followed by adaptation via technological innovation and \r\ninfrastructural development, nature-based adaptation (enhancing, \r\nprotecting or promoting ecosystem services) and institutional adaptation \r\n(enhancing multi-level governance or institutional capabilities). \r\nBehavioural adaptation is most frequently documented in Asia, Africa \r\nand Small Island States, and in the agriculture, health and development \r\nsectors"


id: 676d21baafa5e64071534447f701eb88
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 13860
content:
"The mix of adaptation response types differs across regions \r\nand sectors. Technological and infrastructural responses are widely \r\nreported in Europe, and globally in the context of cities and water \r\nand sanitation (Mees, 2017; Hintz et  al., 2018). Responses to flood \r\nrisk in Europe include the use of flood- and climate-resistant building \r\nmaterials, large-scale flood management, and water storage and \r\nirrigation systems (van Hooff et al., 2015; Mees, 2017). Technological \r\nand infrastructural responses are also documented to some extent in \r\nagriculture, including, for example, breeding more climate-resilient \r\ncrops, precision farming and other high-tech solutions such as genetic \r\nmodification (Makhado et  al., 2014; Fisher et  al., 2015; Costantini \r\net al., 2020; Fraga et al., 2021; Grusson et al., 2021; Naulleau et al., \r\n2021)"


id: 11eee5bf05f67c849c0337fcdf3400e3
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 320
content:
" While less common, institutional responses are more prominent \r\nin North America and Australasia as compared with other regions, \r\nand include zoning regulations, new building codes, new insurance \r\nschemes, and coordination mechanisms (Craft and Howlett, 2013; \r\nCraft et al., 2013; Parry, 2014; Ford et al., 2015b; Beiler et al., 2016; \r\nLesnikowski et  al., 2016; Labbe et  al., 2017; Sterle and Singletary, \r\n2017; Hu et al., 2018; Conevska et al., 2019). Institutional adaptations "


id: 7bd15eaed9a86c5e9cc43b1d8d10a5b4
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 17082
content:
"2018; Folkerts et al., 2020). Investment in flood protection, including \r\nbuilding design and monitoring and forecasting, have reduced flood\u0002related mortality over time and are cost-effective (Bouwer and Jonkman \r\n2018; Ward et al. 2017). Declining heat sensitivity, primarily reported \r\nin developed nations, has also been observed, and has been linked to \r\nair conditioning, reduced social vulnerability and improved population \r\nhealth (Boeckmann and Rohn, 2014; Chung et al., 2017; Kinney, 2018; \r\nSheridan and Allen, 2018). Formetta and Feyen (2019) demonstrate \r\ndeclining global all-cause mortality and economic loss due to extreme \r\nweather events over the past four decades, with the greatest reductions \r\nin low-income countries, and with reductions correlated with wealth"


id: 5a448367094c4d7b7f9995902f35e335
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 11706
content:
" \r\nStudies that correlate changes in mortality or economic losses with \r\nwealth indicators, to infer changes in vulnerability or exposure, lack \r\ndirect empirical measures of vulnerability or exposure and are limited \r\nin their ability to assess how indirect effects of extreme events (e.g., \r\nmorbidity, relocation, social disruption) may have changed or how \r\nchanges may redistribute risk across populations."


id: 2f493bc3caf041dda8e67f18a651624
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 4859
content:
"There remain persistent difficulties in defining and measuring \r\nadaptation effectiveness and adequacy for many climate risks. No \r\nstudies have systematically assessed the adequacy and effectiveness \r\nof adaptation at a global scale, across nations or sectors, or for \r\ndifferent levels of warming. There has, however, been progress in \r\noperationalising assessment of adaptation feasibility (Cross-Chapter \r\nBox  FEASIB in Chapter 18). Effectiveness of adaptation-related \r\nresponses reflects whether a particular response actually reduces \r\nclimate risk, typically through reductions in vulnerability and exposure \r\n(Figure  1.7 in Section  1.4). Some adaptation-related responses may \r\nincrease risk or create new risks (maladaptation) or have no or \r\nnegligible impact on risk. Adequacy of adaptation-related responses \r\nrefers to the extent to which responses are collectively sufficient \r\nto reduce the risks or impacts of climate change (Figure  1.7 in \r\nSection 1.4)"


id: db17486074e27e14dbb58ceed36f4565
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 31981
content:
"Based on the literature, the overall transformative nature of adaptation \r\nacross most global regions and sectors is low (high confidence)\r\n(Figure  16.6). Documented adaptations tend to involve minor \r\nmodifications to usual practices taken to address extreme weather \r\nconditions (high confidence). For example, changing crop variety or timing \r\nof crop planting to address floods or droughts, new types of irrigation, \r\npursuing supplementary livelihoods, and home elevations are widely \r\nreported but typically do not reflect radical or novel shifts in practice or \r\nvalues and are therefore considered low depth (high confidence) (see \r\nSM16.1 for more examples). Adaptations documented in the literature \r\nare also frequently focused on a single sector or small geographic \r\narea (high confidence). Actions taken by individuals or households are \r\ngenerally small in scope (Hintz et al., 2018; Hlahla and Hill, 2018) unless \r\nthey are widely adopted (e.g"


id: 6f330bb29e1d2e245735e2d6d898d7f0
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 15122
content:
"Adaptation-related responses are frequently motivated by a combination \r\nof climatic and non-climatic drivers, and interact with other transitions \r\nto affect risk. For societal responses, it is difficult to say whether they are \r\ntriggered by observed or anticipated changes in climate, by non-climatic \r\ndrivers or, as is the case in many societal responses, by a combination \r\nof all three. In the case of impacts, assessment typically focuses on \r\ndetection and attribution vis a vis a counterfactual of no climate change. \r\nWhile there has been some effort to attribute reduced climate risk to \r\nadaptation-related responses (Toloo et al., 2013a; Toloo et al., 2013b; \r\nHess et al., 2018; Weinberger et al., 2018), in many cases this has not \r\nbeen feasible given difficulties in defining adaptation and empirically \r\ndisentangling the contribution of intersecting social transitions and \r\nchanging risks"


id: a0bde5bdf110efdea6ae25b843551162
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 22836
content:
" Literature on adaptation-related response frequently \r\ndraws on theories of change to assess the likely contribution of \r\nadaptations to changes in risk, including maladaptation and co-benefits."


id: 1c81b48081585048a17d55666b4b526f
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 1940
content:
"Inter-regional risks from climate change--also called cross-border, transboundary, transnational or indirect risks--are risks that are \r\ntransmitted across borders (e.g., transboundary water use) and/or via teleconnections (e.g., supply chains, global food markets) (Moser \r\nand Hart, 2015). The risks can result from impacts, including compound or concurrent impacts, that cascade across several tiers, in ways \r\nthat either diminish or escalate risk within international systems (Carter et al., 2021). Risk transmission may occur through trade and \r\nfinance networks, flows of people (Cross-Chapter Box MIGRATE in Chapter 7), biophysical flows (natural resources such as water) and \r\necosystem connections. However, not only risks are transmitted across borders and systems; the adaptation response may also reduce \r\nrisks at the origin of the risk, along the transmission channel or at the recipient of the risk (Carter et al., 2021)"


id: a19d49b21ef7cf2c63cc188a112ae55d
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 22983
content:
" This cross-chapter box \r\ndiscusses four inter-regional risk channels (trade, finance, food and ecosystems) and how adaptation can govern these risks."


id: 50ee435b3794658e07dc8d9301cbf288
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 10182
content:
"Most commodities are traded on global markets, and supply chains have become increasingly globalised. For instance, specialised industrial \r\ncommodities such as semiconductors are geographically concentrated in a few countries (Challinor et al., 2017; Liverman, 2016). When \r\nclimatic events like flooding or heat affect the location of these extraction and production activities, economies are not only disrupted \r\nlocally but also across borders and in distant countries (high confidence), as exemplified by the Thailand flood 2011 that led to a shortage \r\nof key inputs to the automotive and electronics industry not only in Thailand but also in Japan, Europe and the USA (Figure Cross-Chapter \r\nBox INTEREG.1). For many industrialised countries like the UK, Japan, the USA and the European Union, there is increasing evidence that \r\nthe trade impacts of climate change are significant and can have substantial domestic impacts (medium confidence) (Nakano, 2017; Willner \r\net al., 2018, Section 13"


id: 4ec5a70a9d20235cb82b90a7c8c60dbf
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 9824
content:
"9.1; Benzie and Persson, 2019; Knittel et al., 2020). Enhanced trade can transmit risks across borders and thereby \r\namplify damages (Wenz and Levermann, 2016), but it can also increase resilience (Lim-Camacho et al., 2017; Willner et al., 2018)."


id: 71d374d711a31e7b329b5638a22448c2
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 15537
content:
"The export of major food crops like wheat, maize and soybeans from many of the world's water-scarce area--the Middle East, North Africa, \r\nparts of South Asia, North China Plains, southwest USA, Australia--to relatively water-abundant parts of the world carries a high virtual \r\nwater content (the net volume of water embedded in trade) (high confidence) (Hoekstra and Mekonnen, 2012; Dalin et al., 2017; Zhao \r\net al., 2019, Chapter 4). Both importing and exporting countries are exposed to transboundary risk transmission through climate change \r\nimpacts on distant water resources (Sartori et al., 2017; Zhao et al., 2019; Ercin et al., 2021). Climate change is projected to exacerbate \r\nrisk and add new vulnerabilities for risk transmission (medium confidence)"


id: 1f63cbbd0c4fce5793632872fe748349
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 2370
content:
" Rising atmospheric CO2 concentration is projected to decrease \r\nwater efficiency of growing maize and temperate cereal crops in parts of the USA, East and Mediterranean Europe, South Africa, Argentina, \r\nAustralia and Southeast Asia, with important implications for future trade in food grains (Fader et al., 2010). By 2050 (SRES B2 scenario), \r\nvirtual water importing countries in Africa and the Middle East may be exposed to imported water stress as they rely on imports of food \r\ngrains from countries which have unsustainable water use (Sartori et al., 2017). Until 2100, virtual trade in irrigation water is projected \r\nto almost triple (for SSP2-RCP6.5 scenarios) and the direction of virtual water flows is projected to reverse, with the currently exporting \r\nregions like South Asia becoming importers of virtual water (Graham et al., 2020)"


id: bd99c955496e55a3cd9b4b1cb6bdd06f
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 27398
content:
" An additional 10-120% trade flow from water-abundant \r\nregions to water-scarce regions will be needed to sustain environmental flow requirements on a global scale by the end of the century \r\n(Pastor et al., 2019). Exports of agricultural commodities contribute to deforestation, over-exploitation of natural resources and pollution, \r\naffecting the natural capital base and ecosystem services (Agarwala and Coyle, 2020; Rabin et al., 2020, Section 12.5.4)."


id: 25f1f13e2bba9b59936ca3b5b8e21072
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 3394
content:
"A key area of advancement since AR5 is how incremental adaptation \r\nand transformational adaptation relate to limits to adaptation. \r\nIncremental adaptation maintains 'the essence and integrity of a \r\nsystem or process at a given scale', while transformational adaptation \r\n'changes the fundamental attributes of a social-ecological system' \r\n(IPCC, 2018b). Both incremental and transformational adaptation may \r\nexpand the adaptive possibilities for a system, providing additional \r\nadaptation options after a system reaches a soft limit (Felgenhauer, \r\n2015; Pelling et  al., 2015; Termeer et  al., 2017, see also Chapters 1 \r\nand 17; Alston et al., 2018; Panda, 2018; Mechler and Deubelli, 2021). \r\nHowever, it is critical to note that adaptation, whether incremental \r\nor transformational, must support securing an actor's objectives or \r\nsystem's needs from intolerable risks. Once objectives or needs have \r\nbeen abandoned or transformed, a limit to adaptation has occurred"


id: bb3a98a3d0bce0b8d08422d078e353cf
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 27000
content:
" \r\nHowever, objectives or needs may change over time as values of a \r\nsociety change (Taebi et al., 2020), thus adding further complexity to \r\nassessing limits to adaptation."


id: bafddb47ef0d18606f12041216440b29
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 26958
content:
"An expanding volume of empirical research highlights existing \r\nadaptation constraints that may lead to soft limits in Small Island \r\nDeveloping States (SIDS). Investigation of national communications \r\namong 19 SIDS found that financial constraints, institutional \r\nchallenges and poor resource endowments were the most frequently \r\nreported as inhibiting adaptation for a range of climate impacts \r\n(Robinson, 2018b). Governance, financial and information constraints \r\nsuch as unclear property rights and lack of donor flexibility have led \r\nto hasty implementation of adaptation projects in Kiribati, whereas \r\nin Vanuatu and the Solomon Islands, limited awareness of rural \r\nadaptation needs and weak linkages between central governance \r\nand local communities have resulted in an urban bias in resource \r\nallocation (Kuruppu and Willie, 2015)"


id: a1c9e3e30435378c26decd3a0aa56cb
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 23020
content:
" Limited availability and use of \r\ninformation and technology also present constraints to adaptation; \r\nmany SIDS suffer from lack of data and established routines to identify \r\nlosses and damages, and the combination of poor monitoring of slow\u0002onset changes and influence of non-climatic determinants of observed \r\nimpacts challenges attribution (Thomas and Benjamin, 2018). The fact \r\nthat climate information is often available only in the English language \r\nrepresents another common constraint for island communities (Betzold, \r\n2015). Although Indigenous and local knowledge systems can provide \r\nimportant experience-based input to adaptation policies (Miyan et al., \r\n2017), socio-cultural values and traditions such as attachment to \r\nplace, religious beliefs and traditions can also constrain adaptation \r\nin island communities, particularly for more transformational forms of \r\nadaptation (Ha'apio et al., 2018; Oakes, 2019)."


id: 135d7b8fc705e0352d998b2dca4b2c0f
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 559
content:
"The term 'residual risk' was not assessed in detail in AR5 and was \r\nused interchangeably with other terms, including 'residual impacts', \r\n'residual loss and damage' and 'residual damage'. SR15 includes \r\ndiscussion of residual risks without an explicit definition and relates \r\nthese to L oss and D amage and limits to adaptation, concluding \r\nthat residual risks rise as global temperatures increase from 1.5degC to \r\n2degC. SRCCL refers to residual risks arising from limits to adaptation \r\nrelated to land management. Such residual risk can emerge from \r\nirreversible forms of land degradation, such as coastal erosion when \r\nland completely disappears, collapse of infrastructure due to thawing \r\nof permafrost, and extreme forms of soil erosion. SROCC advanced \r\nthe conceptualisation of residual risk and integrated it within the risk \r\nframework, defining residual risk as the risk that remains after actions \r\nhave been taken to reduce hazards, exposure and/or vulnerability"


id: 2c0376ba8272fbb880bcf93d9c132282
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 4357
content:
" \r\nResidual risk is therefore generally higher where adaptation failure, \r\ninsufficient adaptation or limits to adaptation occur. We use the SROCC \r\ndefinition of residual risk for our assessment in the following sections \r\nand identify residual risks that are associated with limits to adaptation."


id: 3d9a22dfe80a2c2626ec47aafa383c3
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 7046
content:
"McNamara et al. (2017) provides an example of community-scaled adaptation that highlights how constraints affect limits, the relationship \r\nbetween soft and hard limits, and the potential need to abandon or transform objectives. Community members of Boigu Island, Australia, \r\nare already adapting to perceived climate change hazards--including sea level rise and coastal erosion--to secure their objective of \r\nsustaining livelihoods and way of life in their current location. Existing seawall and drainage systems provide inadequate protection \r\nfrom flooding during high tides, leading residents to elevate their houses to prevent damages. However, these adaptation measures have \r\nproved to be insufficient. Standing saltwater for extended periods of time after floods has resulted in losses and damages, including \r\nerosion of infrastructure, increased soil salinity, and heightened public health concerns"


id: cbc94d95567a4a3cbda961c0d7f8cccb
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 29601
content:
" Additional adaptation efforts are constrained by \r\nscarcity of elevated land, which inhibits movement of infrastructure within the community, and lack of financial, technical and human \r\nassets to improve coastal protection measures."


id: 185c2c9c4342e096c2f8fae3f83310b0
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 1327
content:
"These constraints are leading to a soft limit to adaptation, where risks would become unreasonable as sea levels continue to rise and \r\npracticable and affordable adaptation options are limited to currently available approaches. This soft limit could be overcome through \r\naddressing constraints and allowing further adaptation to take place, such as providing financial, technical and human resources for more \r\neffective coastal protection and drainage systems that would reduce flooding. However, if the effectiveness of these new adaptation \r\nmeasures decreases as sea levels rise further and if constraints are not able to be overcome, another soft limit may be reached. Eventually, \r\nif constraints are not addressed, no further adaptation measures are implemented, and climate hazards intensify, the area could become \r\nuninhabitable"


id: 59e5c6449dec25d1d676ed8819eec482
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 11652
content:
" This would then be a hard limit for adaptation; there would be no adaptation options available that would allow the \r\ncommunity to sustain livelihoods and way of life in its present location. This hard limit to adaptation may necessitate abandoning the \r\nobjective of remaining in the community. The objective of the community may then transform to sustaining their livelihoods in a less \r\nvulnerable location, which would necessitate relocation. However, such transformation of the community's objectives may be hindered \r\nby the expressed resistance of residents to migrate, due to their strong sense of place."


id: ac750a566fd202863907f788344ed4dc
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 24670
content:
"Soft limits to adaptation for coastal flooding and erosion are already \r\nbeing experienced in Samoa owing largely to financial, physical and \r\ntechnological constraints (Crichton and Esteban, 2018). While sea walls \r\nhave been erected to minimise coastal erosion, these defences need \r\nregular upgrading and replacement as high swells, tropical cyclones \r\nand constant wave action erode their effectiveness. The high costs of \r\ninstalling, upgrading and enlarging such infrastructure has led to sea \r\nwalls only being used in specific locations, leaving communities that \r\nare beyond the extent of these measures exposed to inundation and \r\nerosion. Native tree replanting has also been implemented, but coastal \r\nflooding and erosion persist as large swells lead to high failure rates \r\nof replanting efforts"


id: 69983c0ee81a8bc406d09fcf34d734c2
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 14211
content:
" Across SIDS, adaptation to coastal flooding and \r\nerosion in particular is increasingly facing soft limits due to high costs, \r\nunavailability of technological options and limited physical space or \r\nenvironmental suitability for hard engineering or ecosystem-based \r\napproaches (Mackey and Ware, 2018; Nalau et al., 2018)."


id: a3b655cc53ba1148df9c5297df152324
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 23321
content:
"rise (Deutsch et al., 2018), increasing crop loss across Asia for which \r\nfarmers are already experiencing a variety of adaptation constraints, \r\nincluding financial, economic and technological challenges (Sada et al., \r\n2014; Tun Oo et al., 2017; Fahad and Wang, 2018). Extreme heatwaves \r\nare projected in the densely populated agricultural regions of South \r\nAsia, leading to increased risk of heat stress for farmers and resultant \r\nconstraints on their ability to implement adaptive actions (Im et  al., \r\n2017). However, socioeconomic constraints appear to have a higher \r\ninfluence on soft limits to adaptation in agriculture than biophysical \r\nconstraints (Thomas et  al., 2021). For example, an examination of \r\nfarmers' adaptation to climate change in Turkey found that constraints \r\nrelated to access to climate information and access to credit will \r\nlikely limit the yield benefits of incremental adaptation (Karapinar \r\nand Ozertan, 2020)"


id: 384734b6d458c5ac1fd94d254f9e215b
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 6231
content:
" In Nepal, conservation policies restrict traditional \r\ngrazing inside national parks, which promotes intensive agriculture and \r\nlimits other cropping systems that have been implemented as climate \r\nchange adaptation (Aryal et al., 2014)."


id: e6a12a970248ac02d211c6cf9138a2fe
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 33765
content:
"Palao et  al.. (2019) identify the possible need for transformational \r\nadaptation in Asian-Pacific agricultural practices due to changes in \r\nbiophysical parameters as global average temperatures rise. In this \r\ncontext, transformational adaptation would consist of changing \r\nfarming locations to different provinces or different elevations for \r\nthe production of specific crops or introducing new farming systems. \r\nNearly 50% of maize in the region along with 18% of potato and 8% \r\nof rice crops would need to either be shifted in location or use new \r\ncropping systems, with the most significant transformation being \r\nneeded in China, India, Myanmar and the Philippines. For maize \r\nsuitability by 2030, seven provinces in the east and northeast of China \r\nare projected to experience over 50% reduction in suitability, and two \r\nnorthern states in India may experience 70% reduction in suitability"


id: 14a91e2bab9e8f6035e46550762f46b0
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 757
content:
" \r\nCassava and sweet potato may play a critical role in food resilience in \r\nthese areas, as these crops are more resilient to climate change (Prain \r\nand Naziri, 2019)."


id: 1bde21781d9e03b59d1000b6018ff219
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 1843
content:
"Lack of financial resources is found to be a significant constraint that \r\ncontributes to soft limits to adaptation in agriculture across Asia. Although \r\nsmallholder farmers are currently adapting to climate impacts, lack of \r\nfinance and access to credit prevents upscaling of adaptive responses \r\nand has led to losses (Bauer, 2013; Patnaik and Narayanan, 2015; Bhatta \r\nand Aggarwal, 2016; Loria, 2016). Other constraints further contribute \r\nto soft limits, including governance and associated institutional factors \r\nsuch as ineffective agricultural policies and organisational capacities \r\n(Tun Oo et al., 2017), information and technology challenges such as \r\nlimited availability and access to technologies on the ground (Singh \r\net al., 2018), socio-cultural factors such as the social acceptability of \r\nadaptation measures that are affected by gender (Huyer, 2016; Ravera \r\net al., 2016), and limited human capacity (Masud et al., 2017)"


id: 5c243dc22426c05391c8ff7f84b03856
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 12010
content:
"(Klomp and Valckx, 2014). Substantial immediate output losses and \r\nreduced economic growth due to extreme events have been observed \r\nin both the short and long term (Section  16.2.3). Estimates of the \r\nduration of negative effects of climate-related disasters differ, with \r\nsome analyses suggesting that, on average, economies recover after \r\n2 years (Klomp, 2016) and others finding negative effects of cyclones \r\nto persist 15-20 years following an event (Hsiang and Jina, 2014; IMF, \r\n2017). Rising climate vulnerability has also been shown to increase \r\nthe cost of debt (Kling et  al., 2018). Rising climatic risks negatively \r\naffect developing countries' ability to access financial markets (Cevik \r\nand Jalles, 2020), and their disclosure may result in capital flight \r\n(Cross-Chapter Box FINANCE in Chapter 17). Overall, the direct and \r\nindirect economic effects of climate change represent a major risk to \r\nfinancial system stability (Section 11.5.2)"


id: 8531f13146c653125850dd044e266c37
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 18482
content:
" These risks and effects may \r\nfurther limit the availability of financial resources needed to overcome \r\nconstraints, in particular for developing countries."


id: e54b96e432f6de4adff36dcbbf3da7fc
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 33573
content:
"Sectoral studies indicate that climate impacts will result in higher \r\nlevels of losses and damages and decreases in income, thereby \r\nincreasing financial constraints (medium confidence). Yield losses \r\nfor major agricultural crops are expected in nearly all world regions \r\n(Figure 5.7). Decreases in estimated marine fish catch potential and \r\nlarge economic impacts from ocean acidification are expected globally, \r\nleading to the risk of revenue loss (Section 5.8.3). Losses of primary \r\nproductivity and farmed species of shellfish are expected in tropical \r\nand subtropical regions (Section 5.9.3.2.2). Economic losses have been \r\nobserved in the power generation sector and transport infrastructure \r\n(Section 10.4.6.3.8), including economic losses from floods in urban \r\nareas (Section  4.2.4.5). However, some positive sectoral climate \r\nchange impacts have been identified for the timber and forestry sector \r\n(Section 5.6"


id: 8b0432dcc9983184d28a148c438da4e0
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 19404
content:
"2), for primary productivity and farmed species of shellfish \r\nin high-latitude regions (Section 5.9.3.2.2) and for agriculture in high\u0002latitude regions (Section 5.4.1.1)."


id: 767c060bc081763d15e8b35313ac0174
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 16233
content:
"A key risk is defined as a potentially severe risk and therefore especially \r\nrelevant to the interpretation of dangerous anthropogenic interference \r\n(DAI) with the climate system, the prevention of which is the ultimate \r\nobjective of the UNFCCC as stated in its Article 2 (Oppenheimer et al., \r\n2014). Key risks are therefore a relevant lens for the interpretation of this \r\npolicy framing. The severity of a risk is a context-specific judgement based \r\non a number of criteria discussed below. KRs are 'potentially' severe \r\nbecause, while some could already reflect dangerous interference now, \r\nmore typically they may become severe over time due to changes in the \r\nnature of hazards (or, more broadly, climatic impact drivers; IPCC, 2021) \r\nand/or of the exposure/vulnerability of societies or ecosystems to those \r\nhazards. They also may become severe due to the adverse consequences \r\nof adaptation or mitigation responses to the risk (on the former, see \r\nSection 17.5"


id: f27ac660f6aa5ced3c9a3a683496417
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 35674
content:
"1; the latter is not assessed separately here, except as it \r\ncontributes to risks from climate hazards). Dangerous interferences in \r\nthis chapter are considered over the course of the 21st century."


id: e4fdcfbbff1190a5551e3cb7195b6b0b
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 33520
content:
"At the household or community level, climate impacts may increase \r\nfinancial constraints (high confidence). Impacts on agriculture \r\nand food prices could force between 3 and 16  million people into \r\nextreme poverty (Hallegatte and Rozenberg, 2017). Within-country \r\ninequality is expected to increase following extreme weather events \r\n(Section  16.2.3.6 and Chapter 8). Households affected by climate\u0002related extreme events may be faced with continuous reconstruction \r\nefforts following extreme events (Adelekan and Fregene, 2015) or \r\ndeclines in critical livelihood resources in the agriculture, fisheries and \r\ntourism sectors (Forster et  al., 2014, Section  3.5.1). Further erosion \r\nof livelihood security of vulnerable households creates the risk of \r\npoverty traps, particularly for rural and urban landless (Sections 8.2.1, \r\n8.3.3.1), for example in Malawi and Ethiopia (Section 9.9.3)"


id: 743709b42c8dd85796143bb2acdde816
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 15881
content:
" Levels of \r\nlabour productivity and economic outputs are projected to decrease as \r\ntemperatures rise particularly in urban areas (Section 6.2.3.1). At the \r\nsame time, higher utilities demand under higher urban temperatures \r\nexerts additional economic stresses on urban residents and households. \r\nSubstantial, negative impacts on the livelihoods of over 180 million \r\npeople are expected from changes to African grassland productivity \r\n(Section 5.5.3.1). In Western Uzbekistan, farmers' incomes are at risk \r\nof declining (Section 10.4.5.3). For SIDS, loss of livelihoods is expected \r\ndue to negative climatic impacts on coastal environments and \r\nresources (Section 3.5.1). Negative effects on households from extreme "


id: ea13d5a4b2d265dfa933988f1647c41b
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 34326
content:
"Figure 16.8 |  Constraints associated with limits by region and sector. Data from Thomas et al. (2021), based on 1682 scientific publications reporting on adaptation-related \r\nresponses in human systems. See SM16.1 for methods"


id: b9e1d8a2dc3d8c14b9cb4d3216f094d9
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 26785
content:
" Insufficient data: there is not enough literature to \r\nsupport an assessment (fewer than five studies available); Minor constraint: <20% of assessed literature identifies this constraint; Secondary constraint: 20-50% of assessed \r\nliterature identifies this constraint; Primary constraint: >50% of assessed literature identifies this constraint."


id: 9e6648000392944b47b647de173a9eea
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 22449
content:
"The authors of the sectoral and regional chapters and cross chapter \r\npapers of the WGII AR6 Report identified more than 120 key risks \r\n(SM16.7.4). Authors were asked to rely on the above definition and \r\ncriteria to identify risks that could potentially become severe according \r\nto changes in the associated hazards, the study systems' exposure \r\nand/or vulnerability, and important adaptation strategies that could \r\nreduce these risks (see SM16.3 for methodology). Wherever possible, \r\nidentification is based on literature that includes projected future \r\nconditions for all three components of risk and adaptation. Where \r\nliterature was insufficient, potential severity is based on current \r\nvulnerability and exposure to climate hazards and the expectation that \r\nhazards will increase in frequency and/or intensity in the future"


id: 1a500291c3ee02902c15ef7b7b3da17e
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 1609
content:
" This \r\napproach is more limited in that it does not consider future changes \r\nin exposure and vulnerability nor in adaptation, but has the benefit of \r\nbeing grounded in observed experience."


id: 56fb13347600be2427371fc64bdfa266
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 11163
content:
"defining 'severe' risks for each particular RKR. They also assess the \r\nconditions in terms of warming (more broadly, climatic impact drivers; \r\n(Ranasinghe et al., 2021), exposure/vulnerability and adaptation under \r\nwhich the RKR would become severe. For each of these dimensions, \r\nRKR teams considered generic levels ranging from High to Medium \r\nand Low. For warming levels, in line with WGI framing, High refers \r\nto climate outcomes consistent with RCP8.5 or higher, Low refers to \r\nclimate outcomes consistent with RCP2.6 or lower, and Medium refers \r\nto outcomes for scenarios between RCPs 2.6 and 8.5. For reference, \r\nthe full range of warming levels (across all climate models) associated \r\nwith RCP8.5 for the 2081-2100 period is 3.0-6.2degC; for RCP2.6 it is \r\n0.9-2.3degC; and for intermediate RCPs it is 1.8-3.6degC (Cross-Chapter \r\nBox  CLIMATE in Chapter 1)"


id: 9db04768270847737fb313ec24b29362
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 22337
content:
" For Exposure-Vulnerability, levels are \r\ndetermined by the RKR teams relative to the range of future conditions \r\nconsidered in the literature, for example based on the Shared \r\nSocioeconomic Pathways (SSPs) in which future conditions based on \r\nSSPs 1 or 5 represent Low exposure or vulnerability and those based \r\non SSPs 3 or 4 represent High exposure or vulnerability (O'Neill et al., \r\n2014; van Vuuren and Carter, 2014). For Adaptation, two main levels \r\nhave been considered: High refers to near maximum potential, and Low \r\nrefers to the continuation of today's trends. Despite being intertwined \r\nin reality, Exposure-Vulnerability and Adaptation conditions are \r\ndistinguished to help understand their respective contributions to \r\nrisk severity. Importantly, this assessment does not consider all risks, \r\nbut only those that can be considered severe given the definition and \r\ncriteria presented in Section 16.5.1"


id: b3b75315ce6278e4f4785fd904bd4a4d
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 25794
content:
" The assessment does not exclude \r\nthe possibility that severe risks are already observed in some contexts, \r\nand considers projected risks through the end of this century."


id: 3d29002095dad9ccc4220fb2bb1ec1be
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 6986
content:
"The RKRs are intended to capture the widest variety of KRs to \r\nhuman or ecological systems with a small number of categories that \r\nare easier to communicate and provide a manageable structure for \r\nfurther assessment. They expand the scope of some AR5 KR clusters \r\n(e.g., on coasts, health, food and water) and add new ones (e.g., \r\non peace and human mobility). The RKRs encompass a diversity of \r\ntypes of systems, including an example of a geographically defined \r\nsystem (RKR-A on coastal regions), ecosystem well-being and integrity \r\n(RKR-B), a cross-cutting issue relevant to several outcomes of concern \r\n(RKR-C on critical infrastructure) and several topics focused directly on \r\naspects of human well-being and security (RKR-D to RKR-H). This set \r\nof RKRs manages but does not eliminate overlap, instead providing \r\nalternative perspectives on underlying key risks that sometimes \r\ninclude complementary views on common risks"


id: 3e6af0ff2e4ad9f1ccd50a008498998f
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 7195
content:
" For example, the \r\nwater security RKR highlights the many key risks mediated by water \r\nquantity or quality, which are sometimes manifested as risk to food \r\nsecurity (RKR-F) or health (RKR-E)."


id: 9dfb64f3b4e4d862d512da645edb410e
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 22377
content:
"global warming especially at lower latitudes (Section  3.4.2.2) (high \r\nconfidence). In addition, depending on the local tide and sediment \r\nconditions, SLR associated with >1.5degC of global warming (SSP1-\r\n2.6; 3.4.2.5) is sufficient to initiate shifts to alternate states in some \r\nseagrass and coastal wetland systems (van Belzen et  al., 2017; El\u0002Hacen et al., 2018, Section 3.4.2.5, Cross-Chapter Box SLR in Chapter \r\n3), and submergence of some mangrove forests (Section  3.4.2.5). A \r\nstriking example of risks becoming severe at higher levels of warming \r\nis the one of coral islands with low elevation (Section 15.3.4, Box 15.1): \r\nthe risk of loss of habitability transitions from Moderate-to-High under \r\nRCP2.6 for most island types (urban and rural) to High-to-Very High \r\nunder RCP8.5 (Duvat et  al., 2021), even under a high adaptation \r\nscenario (Oppenheimer et al., 2019), partly due to declining sediment \r\nsupply (Perry et  al"


id: b8b5615986fe7454d6c184ec569c044f
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 26582
content:
"even under Low warming (high confidence). These systems comprise a \r\nlarge fraction of unique and endemic biodiversity, with species impacts \r\noften exacerbated by multiple drivers of global change (Chapter 2, \r\nChapter 3). Roughly one-third of all known plant species are extremely \r\nrare, vulnerable to climate impacts, and clustered in areas of higher \r\nprojected rates of anthropogenic climate change (Enquist et al., 2019). \r\nMuch evidence shows increased risk of the loss of 10% or more of \r\nterrestrial biodiversity with increasing anthropogenic climate change \r\n(Urban, 2015; Smith et  al., 2018) (medium confidence), likely with \r\n2degC warming above pre-industrial level (Chapter 2), with consequent \r\ndegradation of terrestrial, freshwater and ocean ecosystems (Oliver \r\net al., 2015) and adverse impacts on ecosystem services (Pecl et al., \r\n2017) and dependent human livelihoods (Dube et al., 2016). Adverse \r\nimpacts on biodiversity may show lagged responses (Essl et al"


id: e0a1dd60267b455682dcf23b16595f4b
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 32845
content:
", 2015), \r\nand loss of a substantial fraction of species could occur abruptly, \r\nsimultaneously across multiple taxa, below 4degC of global warming \r\n(Trisos et al., 2020)."


id: 7be20aeedbd46daaec43b85aede7e538
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 17094
content:
"Landscape- and larger-scale shifts in ecosystem structure and function \r\n(order-of-magnitude increases or abrupt decreases in cover and/or \r\nbiomass of novel growth forms or functional types) are occurring in non\u0002equilibrium ecosystems (systems which exist in multiple states, often \r\ndisturbance-controlled) in response to changing disturbance regime, \r\nclimate and rising CO2 (high confidence) Woody plant encroachment \r\nhas been occurring in multiple ecosystems, including subtropical and \r\ntropical fire driven grassland and savanna systems, upland grassland \r\nsystems, arid grasslands and shrublands (high confidence), leading \r\nto large-scale biodiversity changes, albedo changes, and impacts \r\non water delivery, grazing services and human livelihoods (medium \r\nconfidence). Expansion of grasses (alien and native) into xeric \r\nshrublands is occurring, causing increasing fire prevalence in previous \r\nfire-free vegetation (Cross-Chapter Paper 3)"


id: 521f7e7372441c40c8a6f16cb1d75ec1
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 10373
content:
" In tropical forests, \r\nrepeated droughts and recurrence of large-scale anthropogenic \r\nfires increase forest degradation, loss of biodiversity and ecosystem \r\nfunctioning (high confidence) (Anderson et  al., 2018b; Longo et  al., \r\n2020). Accelerated growth rates and mortality of tropical trees is also \r\nadversely affecting tropical ecosystem functioning (McDowell et  al., \r\n2018; Aleixo et al., 2019). Projected changes in ecosystem functioning, \r\nsuch as via wildfire (Section 2.5.5.2), tree mortality (Section 2.5.5.3) \r\nand woody encroachment under climate change (Chapter 2) would \r\nalter hydrological processes, with adverse implications for water yields \r\nand water supplies (Sankey et al., 2017; Robinne et al., 2018; Rodrigues \r\net al., 2019; Uzun et al., 2020)."


id: 6f9e50fb6977036e9b971c70f47f9262
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 15188
content:
"Mass population-level mortality (>50% of individuals or colonies \r\nkilled) and resulting abrupt ecological changes can be caused by \r\nsimple or compound climate extreme events, such as exceedance \r\nof upper thermal limits by vulnerable terrestrial species (Fey et  al., \r\n2015), who also note reduced mass mortality trends due to extreme \r\nlow thermal events; marine heatwaves that can cause mortality, \r\nenhance invasive alien species establishment, and damage coastal \r\necological communities and small-scale fisheries (high confidence) \r\n(Section 3.4.2.7); and increased frequency and extent of wildfires that \r\nthreaten populations dependent on habitat availability (like Koala \r\nBears, Lam et al., 2020). Abrupt ecological changes are widespread and \r\nincreasing in frequency (Turner et al., 2020), and include tree mortality \r\ndue to insect infestation exacerbated by drought, and ecosystem \r\ntransformation due to wildfire (Vogt et al., 2020)"


id: ad41b07241f9fa9302d9e818c1e19248
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 24798
content:
" Freshwater ecosystems \r\nand their biodiversity are at high risk of biodiversity loss and turnover \r\ndue to climate change (precipitation change and warming, including \r\nwarming of water bodies), due to high sensitivity of processes and \r\nlife histories to thermal conditions and water quality (Chapter 2) (high \r\nconfidence). In marine systems, heatwaves cause damages in coastal \r\nsystems, including extensive coral bleaching and mortality (very high \r\nconfidence) (Section  3.4.2.1), mass mortality of invertebrate species \r\n(low to high confidence, depending on system) (Sections  3.4.2.2, \r\nSection 3.4.2.5, Section 3.4.4.1), and abrupt mortality of kelp-forest \r\n(high confidence) (Section 3.4.2.3) and seagrass-meadow habitat (high \r\nconfidence) (Section  3.4.4.2). The biodiversity of polar seas shows \r\nstrong impacts of climate change on phenological timing of plankton \r\nactivity, Arctic fish species range contractions and species community \r\nchange (Table  SM16.22) (high confidence)"


id: 9f274094b9a66ff7bc8d29acc71dcf11
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 22576
content:
" Extreme weather events \r\nand storm surges exacerbated by climate change have severe and \r\nsudden adverse impacts on coastal systems, including loss of seagrass \r\nmeadows and mangrove forests (high confidence) (see Section 3.4.2.7, \r\nSection 3.4.2.8, Cross-Chapter Box EXTREMES in Chapter 2)."


id: bcc80d3ff5e5139d53058a096b980fa5
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 27273
content:
"The loss of a substantial fraction of biodiversity globally, abrupt impacts \r\nsuch as significant local biodiversity loss and mass population mortality \r\nevents, and ecological disruption due to novel species interactions \r\nhave been observed or are projected at global warming levels \r\nbelow 2degC (Chapter 2 Table SM2.5, Cross Chapter Box: EXTREMES in \r\nChapter 2, Section 2.4.4.3.1, Section 2.4.2.3.3) (medium confidence). \r\nSimple and compound impacts of extreme climate events are already \r\ncausing significant losses and damages in vulnerable ecosystems, \r\nincluding through the facilitation of important global change drivers of \r\necological disruption and homogenisation like invasive species (high \r\nconfidence). Severe impacts on human livelihoods and infrastructure, \r\nand valuable ecosystem services, are all projected to accompany these \r\nchanges"


id: b8d4a2b0907b8d43de5d7f80025f9bbe
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 26605
content:
" Adaptation potential for many of these risks is low due to \r\nthe projected rate and magnitude of change, and to the requirement \r\nof significant amounts of land for terrestrial ecosystems (Hannah \r\net al., 2020). Biodiversity conservation efforts may be hampered due \r\nto climate change impacts on the effectiveness of protected areas, \r\nwith high sensitivity of effectiveness to forcing scenario (medium \r\nconfidence). In addition, climate-related risks to ecosystems pose \r\nchallenges to ecosystem-based adaptation responses ('nature-based \r\nsolutions') (Section 2.1.3) (medium confidence)."


id: 50b975de953768fe6d090b0ecbfb38c5
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 10142
content:
"confidence). Damages from multiple climate hazards to transport, \r\nenergy, industry and social infrastructure in Europe could increase \r\n10-fold by the 2080s, from 3.4 EUR billion annually to date, and 15-\r\nfold for transport infrastructure, under Medium warming (A1B, \r\n~3degC by 2100) and with current adaptation levels, even if no further \r\nextension of the infrastructure in exposed areas is considered \r\n(Forzieri et al., 2018). Under High warming (RCP8.5) in 2100, the \r\npercent of roads in the USA that require rehabilitation due to high \r\ntemperatures and precipitation is expected to increase to 23-33%, \r\nrelative to 14% in 2100 when no climate change is considered \r\n(Mallick et  al., 2018). Projections of climate-induced changes in \r\nexposure are an incomplete measure of risk but in the absence \r\nof other metrics can serve as a proxy for the potential for severe \r\nimpacts. In the circumpolar Arctic, 14"


id: e931e1ec5d604ebdbcff2039b3c59b57
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 34209
content:
"8% of critical infrastructure \r\nassets would be affected by climate change under RCP8.5 by 2050, \r\nwith lifecycle replacement costs projected to increase by 27.7% if \r\ninfrastructure is to be preserved at current adaptation levels (Suter \r\net al., 2019). Under RCP8.5, the number of ports under high risk \r\nwill increase from 3.8% in the present day to 14.4% by 2100, as \r\na result of increased coastal flooding and overtopping due to SLR, \r\nas well as the heat stress impacts of higher temperatures (Izaguirre \r\net  al., 2021). In the UK under High warming (4degC), the number \r\nof clean and wastewater treatment sites located in the 1-in-75-\r\nyear floodplain will increase by a third relative to today by the \r\n2080s under current vulnerability and adaptation levels (Dawson \r\net al., 2018)"


id: e30566c3d72c4b0e2f4c390bf3fff3e2
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 33211
content:
" A global assessment of changing climate and water \r\nresources for electricity generation finds considerable reductions in \r\nusable hydropower and thermoelectric capacity by 2050 for a range \r\nof warming scenarios from Low to High, with absolute declines on \r\naverage for most (61-74%) of the world's hydropower resources \r\nand monthly maximum reductions above 30% of usable capacity \r\nfor over two-thirds of 1427 thermoelectric power plants worldwide \r\n(Van Vliet et al., 2016). Many studies find large technical potential \r\nfor coordinated adaptation-mitigation policies in the electricity \r\nsector to avoid a significant portion of projected climate change \r\nimpacts (e.g., a two-thirds reduction, and in some cases fully offset) \r\n(Ciscar and Dowling, 2014; Van Vliet et al., 2016; Gerlak et al., 2018; \r\nAllen-Dumas et al., 2019)."


id: 59355bc14c0979e877cfc88004f3a97c
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 11527
content:
"RKR-C includes risks associated with the breakdown of physical \r\ninfrastructure and networks which provide goods and services considered \r\ncritical to the functioning of societies. It encompasses infrastructure \r\nsystems for energy, water, transportation, telecommunications, health \r\ncare and emergency response, as well as compound, cascading and cross\u0002boundary risks resulting from infrastructure interdependencies (Birkmann \r\net  al., 2016; Fekete, 2019). Critical infrastructures such as transport \r\nor energy supply also play a central role in coping with climate risks, \r\nespecially in acute disaster situations in which the services of transport \r\ninfrastructure, communication technologies or electricity are particularly \r\nneeded, despite the fact that these very systems are themselves exposed \r\nto disaster impacts (Garschagen et  al., 2016; Pescaroli et  al., 2018)"


id: c0ac38fd7ed6c8c0d27b5db30d607477
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 27877
content:
" \r\nThe major hazards driving such risks are acute extreme events such as \r\ncyclones, floods, droughts or fires (high confidence), but cumulative and \r\nchronic hazards such as SLR are also considered."


id: aa1286faa1f96505df7aa827e9981a9f
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 24301
content:
"RKR-C is considered severe when the functioning of critical infrastructure \r\ncannot be secured and maintained against climate change impacts, \r\nresulting in the frequent and widespread breakdown of service delivery \r\nand eventually a significant rise of detrimental impacts on people (lives, \r\nlivelihoods and well-being), the economy (including averted growth) or \r\nthe environment (disruption and loss of ecosystems) above historically \r\nobserved levels. Severity in this RKR is assessed on two levels for (i) \r\ndirect impacts of climate change on infrastructure assets and networks \r\n(e.g., amount of port infrastructure damaged or destroyed by SLR, \r\nflooding and storms) on which most of the literature focuses, as well as \r\n(ii) indirect and cascading downstream impacts to people, economy and \r\nenvironment (Markolf et al., 2019; Pyatkova et al., 2019; Chester et al., \r\n2020), for which attribution is more difficult and uncertainties tend to \r\nbe much higher"


id: cfa5666104bcfda8e2ccacb8a1981c95
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 30202
content:
" Overall, the literature with quantified assessments of \r\nclimate change infrastructure risks remains to be less extensive than \r\nfor many other risks, particularly with regard to assessments focusing \r\non the Global South. While climate-related changes in hazards are \r\nwidely considered in the literature, changes in future exposure and \r\nvulnerability conditions are often not treated explicitly. In addition, \r\nthe severity of infrastructure risks also depends on future trends in the \r\ncapacity to maintain, repair and rebuild infrastructure and adapt it to \r\nnew hazard intensities (medium evidence, high agreement). These are \r\nmostly not quantified in a forward-looking manner in the literature; \r\nhowever, damage projections (see below) indicate a rapidly rising \r\ndemand for investment, straining the financial capacity of countries \r\n(medium evidence, high agreement)."


id: 27f290e39868b77223334860b097c6e
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 3695
content:
"(Cervigni et al., 2015). On the Dawlish railway section (UK), the \r\nnumber of days with line restrictions is set to increase by up to \r\n1170%, to as high as 84-120 yr-1 by 2100 due to 0.8 m SLR with \r\nHigh warming (Dawson et al., 2016). Next to the limited number of \r\nprojections or scenarios of indirect impacts, additional inferences \r\nfrom studies focusing on past and current impacts can be drawn. \r\nAlready today, climate-related impacts on transport and energy \r\ninfrastructure reach far beyond the direct impacts on physical \r\ninfrastructure, triggering indirect impacts on, for example, health \r\nand income (medium confidence). A case study of future flood \r\nhazard in Europe found that the indirect impact of a power outage \r\non the local economy is six to eight times greater than the direct \r\nflood damage and asset repair costs, due to the interruption of daily \r\neconomic activity (Karagiannis et al., 2019)"


id: a9e53e18bb32c32ce5e04fa07bb3561f
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 24265
content:
" In low- and middle\u0002income countries, the annual costs from infrastructure disruptions \r\nreach up to 300 billion USD for firms and 90 billion USD for private \r\nhouseholds, with natural hazards such as floods being responsible \r\nfor 10-70% of these disruptions, depending on the sectors and \r\nregions (Hallegatte et al., 2019). Power outages triggered by floods \r\nor droughts have also been found to have substantial health \r\nimplications, particularly among low-income populations (Klinger \r\net al., 2014), and shown to impede disaster recovery efforts and \r\nseverely disrupt local economies (Karagiannis et al., 2019; Nicolas \r\net al., 2019). In addition, risks associated with infrastructure have \r\nthe potential to become particularly severe when hazard-driven \r\ninfrastructure disruptions undermine the capacity of emergency \r\nresponse in disaster situations (limited evidence, high agreement)"


id: 856ed6fc58d9668536d3dc6207b275fa
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 18523
content:
" \r\nA study on the UK shows, for example, that even a small increase in \r\nminor road flooding leads to a disproportionately high disruption \r\nof the efficacy of emergency services (Yu et  al., 2020). Similar \r\nrisks have been found for rural areas, particularly in developing \r\ncountries (Alegre et al., 2020)."


id: 2e5087b26f4ca384077153adcaf3abe0
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 4725
content:
"Annual economic output losses in developing countries could exceed \r\nthe worst country-level losses during historical economic recessions \r\n(medium confidence). Assuming global warming of ~4degC by 2100, \r\nhistorical adaptation levels and high vulnerability, losses across Sub\u0002Saharan Africa may reach 12% of GDP by 2050 (Baarsch et al., 2020) \r\nand 80% by 2100 (Burke et  al., 2015), and ~9% on average across \r\ndeveloping countries by 2100 (Acevedo et  al., 2017). The largest \r\nestimates are debated and depend on assumptions about development \r\ntrends, adaptive capacity, and whether temperature impacts the level \r\nor growth rate of economic activity (Kalkuhl and Wenz, 2020). Severe \r\nrisks are more likely in (typically hotter) developing countries because \r\nof nonlinearities in the relationship between economic damages and \r\ntemperature (Burke et al., 2015; Acevedo et al., 2017)"


id: f0e35b640ff4397e43b81cfba458b8c5
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 35394
content:
" These risks are \r\nhighest in scenarios and countries with: a large portion of the workforce \r\nemployed in highly exposed industries (Acevedo et al., 2017); a high \r\nconcentration of population and economic activity on coastlines (Hsiang \r\nand Jina, 2014; Acevedo et al., 2017); and an increase in the frequency \r\nor intensity of disasters triggered by natural hazards (Berlemann and \r\nWenzel, 2018; Botzen et al., 2019). Whether baseline economic growth \r\nmay help avoid severe future risks is highly uncertain (Dell et al., 2012; \r\nBurke et al., 2015; Acevedo et al., 2017; Deryugina and Hsiang, 2017)."


id: 7323f21d3f3ed84b145a139347c11f33
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 15728
content:
"and vulnerability, and the rate at which hazard frequency or \r\nintensity is expected to increase (Otto et  al., 2017; Roy et  al., \r\n2018; Li et al., 2019, Section 8.5). Without effective adaptation \r\nmeasures, regions with high dependence on climate-sensitive \r\nlivelihoods--particularly agriculture and fisheries in the tropics \r\nand coastal regions--would be severely impacted even at low \r\nlevels of warming (high confidence) (Hoegh-Guldberg et  al., \r\n2018b; Roy et al., 2018). For example, it is estimated that 330-\r\n396 million people could be exposed to lower agricultural yields \r\nand associated livelihood impacts at warming between 1.5degC \r\nand 2degC (Byers et  al., 2018). Risks to the 200  million people \r\nwith livelihoods derived from small-scale fisheries would also be \r\nsevere, given sensitivity to ocean warming, acidification and coral \r\nreef loss occurring beyond 1.5degC (Cheung et al., 2018b; Froehlich \r\net al., 2018; Free et al., 2019; Barnard et al., 2021)"


id: fa681edc819621c2a9f90277b6ce3bff
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 36916
content:
" Livelihoods in \r\nhighly exposed locations, such as Small Island Developing States, \r\nlow-lying coastal areas, arid or semiarid regions, the Arctic, and \r\nurban informal settlements or slums, are particularly vulnerable \r\n(Ford et al., 2015c; Hagenlocher et al., 2018; Ahmadalipour et al., \r\n2019; Tamura et al., 2019). Within populations, the poor, women, \r\nchildren, the elderly and Indigenous populations are especially \r\nvulnerable due to a combination of factors, including gendered \r\ndivisions of paid and/or unpaid labour, as well as barriers in access \r\nto information, skills, services or resources (Bose, 2017; Thomas \r\net al., 2019b; Anderson and Singh, 2020; Adzawla and Baumuller, \r\n2021) (high confidence). Future structural transformation \r\ncould moderate risk severity by improving adaptive capacity, \r\ncreating livelihoods in less climate-sensitive sectors, or by \r\nenabling sustainable migration to less climate-sensitive locations \r\n(Henderson et  al., 2017; Roy et  al., 2018)"


id: b53c218aff19ee77161b935ebfc9fd86
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 26054
content:
" However, successful \r\nrisk moderation would depend upon simultaneous avoidance of \r\nboth climate-change-related and mitigation-related (Doelman \r\net  al., 2019; Fujimori et  al., 2019; Doelman et  al., 2020) or \r\nmaladaptation-related risks (Magnan et  al., 2016; Benveniste \r\net al., 2020; Schipper, 2020)."


id: 8dd56118e8390c3043c008340a986ff3
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 19845
content:
"A severe risk to health is the potential for a widespread, substantial \r\nworsening of health conditions due to climate change. We measure \r\nseverity in terms of the magnitude of mortality and morbidity. We \r\nconsider a severe mortality impact to be a sustained increase in the \r\ncrude mortality rate (CMR) of more than about 2-4 deaths per 10,000 \r\npeople yr-1. This range of increase is consistent with current mortality \r\nimpacts with substantial global effects, including traffic fatalities (CMR \r\nof 1.6/10,000 yr-1; IHME, 2019) and the COVID-19 pandemic (CMR \r\nof 4/10,000 yr-1, as of April 2021, expressed as an annualised rate; \r\nRitchie et  al., 2021). We use these global rates as thresholds in all \r\ncases, recognising that they reflect substantial variation across regions \r\nand sub-populations (for other points of comparison, see IHME, 2019). \r\nMorbidity impacts are measured in numbers of disease cases or hospital \r\nadmissions"


id: 1d47fbac8651186dbdb7276fcf788848
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 2048
content:
" We find that severe health impacts are projected to occur for \r\nparticular sub-populations and regions where vulnerability is currently \r\nhigh and is assumed to persist into the future; we focus our assessment \r\non these cases. In other cases, literature is either inadequate or does not \r\nsupport severe outcomes."


id: 34e5087effe5839d2f5b47e27b273083
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 5730
content:
"Climate change also could increase income inequality between \r\ncountries (high confidence) as well as within them (medium evidence, \r\nhigh agreement) resulting from and exacerbating impacts on aggregate \r\neconomic activity, poverty and livelihoods. Increasing inequality \r\nimplies larger impacts on the least well-off, threatens their ability to \r\nrespond to climate hazards, compromises basic principles of fairness \r\nand established global development goals, and potentially threatens \r\nthe functioning of society and long-term progress (Roe and Siegel, \r\n2011; Cingano, 2014; van der Weide and Milanovic, 2018). There is \r\nevidence that warming has slowed down the convergence in between\u0002country income in recent decades (Diffenbaugh and Burke, 2019). \r\nFuture impacts may halt or even reverse this trend during this century \r\nowing to high sensitivity of developing economies (Burke et al., 2015; \r\nPretis et al., 2018; Baarsch et al"


id: 64e67150fd7ebc3bdd478e4f2a29a7f2
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 13441
content:
", 2020), although projections depend \r\nas much or more on future socioeconomic development pathways \r\nand mitigation policies as on warming levels (Takakura et al., 2019; \r\nHarding et  al., 2020; Taconet et  al., 2020). Within countries, studies \r\nthat find adverse impacts on low-income groups imply an increase \r\nin inequality (Hallegatte and Rozenberg, 2017; Hsiang et  al., 2017), \r\nalthough evidence for long-term climate impacts on within-country \r\ninequality at global scale remains limited."


id: a0a0d299742310efa4a4fc0b56ef75ec
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 22813
content:
"With high warming, the CMR for malaria among children under the \r\nage of 1 year could increase by 5.2-10.1/10,000 yr-1 in Africa under \r\ncurrent vulnerability levels. This estimate assumes a net increase of \r\n70-130 million more people exposed to potential disease transmission \r\ndue to climate change in a high-warming scenario (RCP8.5, end of \r\ncentury) (Caminade et  al., 2014; Colon-Gonzalez et  al., 2021; Ryan \r\net al., 2020), representing a 14-27% increase in the current population \r\nat risk (Ryan et al., 2020), and assumes children under 1 year of age \r\nare facing the same crude mortality in the future as for the African \r\nregion today (IHME, 2019). The largest increase is observed in Eastern \r\nAfrica, where the population exposed could nearly double by 2080 \r\n(Ryan et al., 2020) without accounting for population growth, driven \r\nmainly by changes among previously unexposed populations at higher \r\naltitude areas (Colon-Gonzalez et  al., 2021)"


id: 3357f356f85bcc11445627a8c7361c46
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 5496
content:
" Actual future disease \r\nburden of malaria will be highly sensitive to regional socioeconomic \r\ndevelopment and the effectiveness of malaria intervention programs."


id: 1caaf1b7457e91efdd49b9fc6a2df2da
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 1958
content:
"Climate change will pose severe risks in terms of increasing the number \r\nof undernourished people, affecting tens to hundreds of million people \r\nunder High vulnerability and High warming, particularly among low\u0002income populations in developing countries (high confidence). Extreme \r\nweather events will increase risks of undernutrition even on a regional \r\nscale, via spikes in food price and reduced income (high confidence) \r\n(FAO et al., 2018, Hickey and Unwin, 2020; Mbow et al., 2019). The \r\ntiming of these impacts and our ability to respond to them vary based \r\non the level of GHG emissions and Shared Socioeconomic Pathways \r\n(SSP).. Under a low vulnerability development pathway (SSP1), climate \r\nchange starts posing a moderate risk to food security above 1degC of \r\nglobal warming (i.e., impacts become detectable and attributable to \r\nclimate-related factors), while beyond 2"


id: 47b8f546e434267c0683a074947c5db9
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 8664
content:
"5degC the risk becomes high \r\n(widespread impacts on larger numbers or proportion of population \r\nor area, but with the potential to adapt or recover) (Hurlbert et al., \r\n2019). Under high vulnerability-high warming scenario (i.e., SSP3-\r\nRCP6.0), up to 183 million additional people are projected to become \r\nundernourished in low-income countries owing to climate change by \r\n2050 (Mbow et al., 2019). Climate-related changes in food availability \r\nand diet quality are estimated to result in a crude mortality rate of \r\nabout 54 deaths per million people with about 2degC warming by 2050 \r\n(SSP2, RCP8.5), most of them projected to occur in South and East Asia \r\n(67-231 deaths per million depending on the country) (Springmann \r\net  al., 2016). In a medium vulnerability-high warming scenario "


id: d551f4657a402574c68565b4f65eccb8
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 31091
content:
"Climate change risks of micronutrient deficiency will become severe \r\nin high-vulnerability development pathways and in the absence of \r\nsocietal adaptation, leading to hundreds of millions of additional \r\npeople lacking key nutrients for atmospheric CO2 levels above \r\n500 ppm (high confidence) (Myers et  al., 2017; Nelson et  al., 2018; \r\nMbow et al., 2019). For example, concentration of many micronutrients \r\n(e.g., phosphorus, potassium, calcium, sulphur, magnesium, iron, zinc, \r\ncopper and manganese) can decrease by 5-10% under atmospheric \r\nCO2 concentrations of 690 ppm (3.5degC warming). The decline in zinc \r\ncontent is projected to lead to an additional 150-220 million people \r\naffected by zinc deficiency with increases in existing deficiencies in \r\nmore than 1  billion people (Myers et  al., 2017)"


id: 79e726bcb6fd587bd686f4a9e8403afa
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 16755
content:
" Similarly, decrease \r\nin protein and micronutrient content in rice due to a higher CO2\r\nconcentration (568-590 ppm) can lead to 600  million people with \r\nrice as a staple at risk of micronutrient deficiency by 2050 (Zhu et al., \r\n2018). Additionally, the impact on protein content of increased CO2\r\nconcentration (>500 ppm) can lead an additional 150 million people \r\nwith protein deficiency by 2050 (within the total of 1.4 billion people \r\nwith protein deficiency) in comparison with the scenario without \r\nincreased CO2 concentration (Medek et al., 2017)."


id: d8fae3afcfd6e1f93dd6a0e0cdd8a32c
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 31646
content:
"Risks associated with water scarcity have the potential to become \r\nsevere based on projections of large numbers of people becoming \r\nexposed to low levels of water availability per person, where 'water \r\navailability' includes fresh water in the landscape, including soil \r\nmoisture and streamflows, available for all uses including agriculture \r\nas a dominant sector. Approximately 1.6  billion people currently \r\nexperience 'chronic' water scarcity, defined as the availability of less \r\nthan 1000 m3\r\n of renewable sources of fresh water per person per \r\nyear (Gosling and Arnell, 2016). In this context, we define a severe \r\noutcome as an additional 1  billion people experiencing 'chronic' \r\nwater scarcity, relating to all uses of water, representing an increase \r\nof a magnitude comparable to current levels"


id: 9b996dfdf9df42b4be70720673e92d0d
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 22039
content:
" The global number of \r\npeople experiencing chronic water scarcity is projected to increase by \r\napproximately 800  million to 3  billion for 2degC global warming, and \r\nup to approximately 4 billion for 4degC global warming, considering the \r\neffects of climate change alone, with a 9 billion population (Gosling \r\nand Arnell, 2016). Severe outcomes are projected to occur even \r\nwith no changes in exposure: present-day exposure is defined here \r\nas 'medium' since either an increase or decrease in exposure could \r\nbe possible. Vulnerability is not quantified in the literature assessed \r\nhere, so in this assessment it is considered that severe outcomes \r\ncould occur with present-day levels of vulnerability, again defined \r\nhere as 'medium'. Particularly severe outcomes (i.e"


id: 92018e73e2fbf4d6a75f333f3864d01
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 20494
content:
", the high end \r\nof these ranges) are driven by regional patterns of climate change \r\nbringing severe reductions in precipitation and/or high levels of \r\nevapotranspiration in the most highly populated regions, leading \r\nto very substantial reductions in water availability compared with \r\ndemand. There is strong consensus across models that water scarcity is \r\nprojected to increase across substantial parts of the world even though \r\nprojections disagree on which specific areas would see this impact. \r\nMoreover, a projected decrease in water scarcity in some regions does \r\nnot prevent the increase in water scarcity in other regions becoming \r\nsevere. Hence there is high confidence that risks to water scarcity have \r\nthe potential to become severe due to climate change. Consequences \r\nof water scarcity include potential competition and conflicts between \r\nwater users (Vanham et  al"


id: d700685cfaa154ae2dada55803ce4241
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 31329
content:
", 2018), damaging livelihoods, hindering \r\nsocioeconomic development and reducing human well-being, for \r\nexample through malnutrition resulting from inadequate water \r\nsupplies leading to long-term health impacts such as child stunting \r\n(Cooper et  al., 2019). The avoidance of these consequences at high \r\nlevels of water scarcity would require transformational adaptations \r\nincluding large-scale interventions such as dams and water transfer \r\ninfrastructure (Greve etal2018)Since these require many years or "


id: de76253422a31d92d0fdd5911981a6cd
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 32502
content:
"providing freedom and mental well-being, loss of sea ice can be \r\nargued to represent environmental dispossession of these communities \r\n(Durkalec et al., 2015). Traditional ways of life are therefore threatened, \r\nand resulting changes would be transformative rather than adaptive. \r\nSimilarly, changes in streamflow affecting the availability of species for \r\ntraditional hunting can also negatively impact Indigenous communities \r\n(Norton-Smith et al.). Such changes are already being seen at current \r\nlevels of warming, but studies remain somewhat limited in number, so \r\nthis assessment is assigned medium confidence because of medium \r\nevidence and medium agreement. WGI conclude that it is virtually \r\ncertain that further warming will lead to further reductions in Northern \r\nHemisphere snow cover, and mass loss in individual glacier regions \r\nis projected to be between approximately 30% and 100% by 2100 \r\nunder high-warming scenarios (Chapter 4)"


id: 24e14d65c65c72463f8029e402549c6a
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 3246
content:
" Streamflows are projected \r\nto change in most major river basins worldwide by several tens of \r\npercent at 4degC global warming (Chapter 4)."


id: 347247d301b274a6a89bb453955b571d
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 5666
content:
"Risks associated with water-related extreme events and disasters \r\nhave the potential to become severe based on projections of large \r\nnumbers of people or high values of assets being affected. The risks \r\nto people from disasters can often only be quantified in terms of the \r\nhazard and exposure (the number of people affected), rather than the \r\nfull consequences such as number of deaths, injuries or other health \r\noutcomes, as these often depend on complex or unpredictable factors \r\nsuch as the effectiveness of emergency and humanitarian responses or \r\nthe access to healthcare. With approximately 50 million people per year \r\ncurrently affected by flooding (Alfieri et  al., 2017), we define severe \r\noutcomes as more than 100 million people affected by flooding. At 2degC \r\nglobal warming, between approximately 50  million and 150  million \r\npeople are projected to be affected by flooding, with figures rising to \r\n110  million to 330  million at 4degC global warming"


id: 7b13986e0e11e7af47445e5740d7bbf8
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 16960
content:
" These projections \r\nassume present-day population and no additional adaptation, so no \r\nchanges in exposure. Increased flood risk is projected by the WHO to \r\nlead to an additional 48,000 deaths of children under 15 years due to \r\ndiarrhoea by 2030, with Sub-Saharan Africa impacted the most (WHO, \r\n2014). Other consequences of floods that already occur include deaths \r\nby drowning, loss of access to fresh water, vector-borne diseases, \r\nmental health impacts, loss of livelihoods and loss of or damage to \r\nproperty. Many of these consequences depend on the vulnerability of \r\nindividuals, households or communities to flooding impacts, for example \r\nthrough the presence or absence of measures to safeguard health \r\nand livelihoods, such as through infrastructure services, insurance or \r\ncommunity support"


id: d53b03bd8db1d81d5b1ec177d74366b8
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 31076
content:
" The risks associated with these consequences \r\ncould increase if there were no local adaptations to counter the effect \r\nof increased levels of hazard by reducing exposure and/or vulnerability. \r\nClimate-related changes to extreme events that would lead to these \r\nsevere outcomes include increased frequency and/or magnitude of river \r\nfloods of flash floods due to heavy or long-lasting precipitation, rapid \r\nsnowmelt, or catastrophic failure of glacial lake moraine dams. These \r\nclimate conditions are projected to increase with global warming."


id: b71b92046c038d1c172a2fbe1b53993d
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 26341
content:
"There is strong potential for increases in water scarcity, flooding, loss of \r\nsnow and ice and changes in water bodies to lead to severe outcomes \r\nsuch as deaths from water-related diseases, drowning and starvation, \r\nlong-term health impacts arising from malnutrition and diseases, \r\nloss of property, loss of existence or access to places of cultural \r\nsignificance, loss of livelihoods and loss of aspects of culture especially \r\nfor Indigenous People with traditional lifestyles. The numbers of people \r\naffected are projected to range from hundreds of millions to several \r\nbillion, depending on the level of global warming and socioeconomic \r\nfutures"


id: 817be3d28d436792e8d1bce3a5838673
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 17898
content:
" A key aspect of the risk is the high uncertainty in future regional \r\nprecipitation changes in many regions of high vulnerability, including \r\nthe potential for large and highly impactful changes, for which it may \r\nnot be possible to provide adaptation measures before they become \r\nneeded, leading to a high likelihood of adaptation deficits."


id: 9e7c2e81d22fa4b4f041cf58ec5adb92
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 22467
content:
"Mobility is a universal strategy for pursuing well-being and managing \r\nhousehold risks (Section 7.2.6; Cross-Chapter Box MIGRATE in Chapter \r\n7, UN, 2018) and, where it occurs in a safe and orderly fashion, can \r\nreduce social inequality and facilitate sustainable development \r\n(Franco Gavonel et al., 2021). Involuntary mobility constitutes a key \r\nrisk because it implies reduced human agency with high potential for \r\nsignificant economic losses and non-material costs, an unequal gender \r\nburden, and amplified vulnerability to other RKRs (Schwerdtle et al., \r\n2018; Adger et al., 2020; Maharjan et al., 2020; Piggott-McKellar et al., \r\n2020). Climate change also may erode or overwhelm human capacity \r\nto use mobility as a coping strategy, producing involuntarily immobile \r\npopulations (Adams, 2016). A severe impact is when a large share of \r\nan affected population is forcibly displaced or prevented from moving, \r\nrelative to normal mobility patterns, at local to global scale"


id: 128ab7e749f15b4f17f48f432c478321
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 412
content:
" However, \r\nbecause mobility may be a favourable mechanism for reducing risk or \r\nan adverse outcome of risk, depending on the circumstances under \r\nwhich it occurs, it is not possible to specify a simple quantitative \r\nthreshold for when impacts become severe."


id: 5acb5851462d12522290765ed1e540f3
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 11798
content:
"Complex causal pathways and lack of long-term projection studies \r\npresently prevent making confident quantitative judgements about how \r\nrisks to peace and human mobility will materialise in response to specific \r\nwarming levels, development pathways and adaptation scenarios. \r\nLiterature concludes with medium confidence that risks to peace will \r\nincrease with warming, with the largest impacts expected in weather\u0002sensitive communities with low resilience to climate extremes and high \r\nprevalence of underlying risk factors (Theisen, 2017; Busby, 2018; Koubi, \r\n2019; von Uexkull and Buhaug, 2021). However, climate-driven impacts \r\non societies will depend critically on future political and socioeconomic \r\ndevelopment trajectories (limited evidence, high agreement), suggesting \r\nthat risks due to climate change are relevant primarily for highly \r\nvulnerable populations and for pessimistic development scenarios"


id: 57ad7442a82ac8babb5ba1157d62eed2
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 11293
content:
" \r\nOverall risks to peace may decline despite warming if non-climatic \r\ndeterminants are reduced sufficiently in the future."


id: 68c90a42e13918a93666ee9e481ee86b
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 14077
content:
"Climatic risks interact with economic, political and social drivers to \r\ncreate risks to human mobility both directly (through the threat of \r\nphysical harm and destruction of property and infrastructure) and \r\nindirectly (via adverse impacts on livelihood and well-being). Extreme \r\nweather events are leading causes of forced displacement (Cross\u0002Chapter Box MIGRATE in Chapter 7, IDMC, 2020). Projected increases in \r\nthe frequency and severity of extreme events ( Ranasinghe et al., 2021) \r\nin combination with future population growth in hazard-prone regions \r\n(e.g., Merkens et al., 2016) suggest that risks to mobility will increase \r\nin response to future global warming (Robalino et  al., 2015; Davis \r\net al., 2018; Rigaud et al., 2018). For example, moving from RCP2.6 \r\nto RCP8.5 (entailing ~0"


id: 7cc86913c74a884bb945c6a71473ec10
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 17230
content:
"5degC additional global warming by 2050) is \r\nprojected to increase internal migration by 2050 from 51 [31-72] \r\nmillion to 118 [92-143] million people across South Asia, Latin America \r\nand Africa (Rigaud et al., 2018), although those estimates principally \r\ncomprise migrants, whose decisions are also informed by non-climatic \r\ndrivers, rather than involuntarily displaced people. Global levels of \r\nflood displacement are estimated to increase by 50% with each 1degC \r\nwarming (Kam et al., 2021). Should future warming reduce adaptation \r\noptions for vulnerable populations ( Section 16.4), a consequence may \r\nbe higher levels of involuntary migration and immobility (Grecequet \r\net  al., 2017; Otto et  al., 2017). There is little evidence that climate\u0002driven mobility negatively affects peace (Brzoska and Frohlich, 2016; \r\nBurrows and Kinney, 2016; Freeman, 2017; Petrova, 2021)."


id: 3408416a0a95dd6d3784708ba10343e8
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 5601
content:
"Regular human mobility will continue regardless of climate change, \r\nbut mobility-related risks will increase with warming, notably in \r\ndensely populated hazard-prone regions, in small islands and low-lying \r\ncoastal zones, and among populations with limited coping capacity \r\n(RKR-A; Section CCP2.2.2; Chapter 7) (high confidence). Such risks can \r\nbecome severe even with limited levels of warming for populations \r\nwith low adaptive capacity and whose settlements and livelihoods are \r\ncritically sensitive to environmental conditions (medium evidence, high \r\nagreement). Likewise, risk of involuntary immobility could become \r\nsevere for highly vulnerable populations with limited resources, even \r\nwith moderate levels of warming (limited evidence, high agreement). \r\nCritically, population growth and shifting exposure will interact with \r\nwarming to shape these risks (Davis et al., 2018; Hauer et al., 2020; \r\nRobinson, 2020a)"


id: be7cf0ed1988d291a3e31dced5efd6f7
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 27523
content:
" Although climate-driven human mobility generally \r\ndoes not increase risks to peace (medium confidence), armed conflict \r\nis a major driver of forced displacement (high confidence)."


id: 33577f6715320caa54248e8abd3e6564
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 5495
content:
"the left of the set. The two scopes are 'broadly applicable', meaning applicable pervasively and even globally, and 'specific', meaning applicable to particular areas, sectors or groups \r\nof people. Details of confidence levels and scopes can be found in Section 16.5.2.3. In terms of severity condition levels (Section 16.5.2.3), for warming levels (coloured circles \r\nlabelled 'C' in the figure), High refers to climate outcomes consistent with RCP8.5 or higher, Low refers to climate outcomes consistent with RCP2.6 or lower, and Medium refers to \r\nintermediary climate scenarios. Exposure-Vulnerability levels are determined by the RKR teams relative to the range of future conditions considered in the literature. For Adaptation, \r\nHigh refers to near maximum potential and Low refers to the continuation of today's trends"


id: 49e27de56c736f82bbd9d2bb2a1af707
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 9027
content:
" Despite being intertwined in reality, Exposure-Vulnerability and Adaptation conditions \r\nare distinguished to help understand their respective contributions to risk severity."


id: 4473e058997229483b74173aa3d527e8
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 8193
content:
"Some ecological risks (Chapter 2) are particularly sensitive to warming. \r\nFor example, warm-water coral reefs are already experiencing High risk \r\nlevels and are expected to face Very High risks under 1.5degC of glob\u0002al warming (Hoegh-Guldberg et al., 2018a; Bindoff et al., 2019). Some \r\nsocietal risks, such as human mortality due to extreme heat, also are \r\nsensitive to warming. A medium-warming scenario (relative to high \r\nwarming) reduces projected global average mortality due to heat \r\nfrom seven deaths per 10,000 people yr-1 (7/10,000 yr-1) by 2100 to \r\n~1/10,000 yr-1, assuming high-vulnerability societal conditions (Carle\u0002ton et al., 2020). At the national level, without considering adaptation, \r\nreductions in a broader measure of mortality are projected across a \r\nrange of countries including Colombia, the Philippines, and several in \r\nEurope (Guo et al., 2018), and exposure of the US population to high\u0002mortality heatwaves is reduced by nearly half (Anderson et al., 2018a)"


id: f81f1a47604e3c596c9a46a6ceaa1f5e
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 36550
content:
" \r\nWithout considering changes in exposure or vulnerability, warming of \r\n1.5-2degC (compared with 4-5degC) reduces global mortality impacts from \r\nan increase of 2.1-13.0% to 0.1-2.2% (Gasparrini et al., 2017; Vicedo\u0002Cabrera et al., 2018a) and impacts in China from up to 4/10,000 yr-1\r\n(Weinberger et al., 2017) to 0.3-0.5/10,000 yr-1 (Wang and Hijmans, \r\n2019)."


id: 191cfb239a8c20d1c64cb2c455450ad5
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 1445
content:
"One approach to understand adaptation benefits for risk reduction is \r\nto contrast projected impacts for the same climate and development \r\nconditions but different levels of adaptation. For example, global-scale \r\ncoastal protection studies considering both RCPs and SSPs suggest \r\nthat, under a given RCP, the total flooded area may be reduced by 40% \r\nby using 1-m height dykes, compared with a no-adaptation baseline \r\n(Tamura et al., 2019). The global cost of SLR over the 21st century can be \r\nlowered by factor of two to four if local cost-benefit decisions consider \r\nmigration an adaptation option, in addition to hard protection (Lincke \r\nand Hinkel, 2021). Under a low-warming scenario, it is estimated that \r\nadaptation (i.e., changes in crop variety and planting dates) could \r\nreduce the total number of people at risk of hunger globally by about \r\n4%, and by about 10% in a high-warming scenario (Hasegawa et al., \r\n2014)"


id: b3b4cb8714412dce7c06c72dd9248d15
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 25789
content:
" Impacts on heat-related mortality would be cut from 10 to 7 \r\ndeaths per 10,000 people yr-1 in 2100 by adaptation actions beyond \r\nthose assumed to be driven by income growth (Carleton et al., 2020). \r\nIn a regional example, proactive adaptation efforts on infrastructure \r\n(especially roads, runways, buildings and airports) in Alaska, USA, \r\ncould reduce damage-related expenditure by 45% under medium or \r\nhigh warming (Melvin et al., 2017)."


id: 1efeed416a9e80121caff5d66dcdd3e
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 2316
content:
"Another approach infers the potential future effectiveness of \r\nadaptation based on current sensitivity of impacts to interventions. \r\nFor example, the future disease burden of malaria is likely to be \r\nhighly dependent on the future development of health services, \r\ndeployment of malaria programs and adaptation. Investments in \r\nwater and sanitation infrastructure are also recognised to have the \r\npotential to reduce severe risks of waterborne disease, although \r\nthese improvements likely need to provide transformative change \r\n(Cumming et al., 2019). The potential for severe risks may also be \r\nsubstantially reduced through the development of vaccines for \r\nspecific enteric diseases (Riddle et al., 2018), although most current \r\nvaccines target viral pathogens, incidence for which tends to be \r\ninversely correlated with ambient temperature (Carlton et al., 2016)"


id: e7600b5654ded95545b9472bb6005d44
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 33895
content:
" \r\nIn addition, international migration as well as forced movement \r\nof people across borders will be influenced by developments in \r\nlegal and political conditions (McLeman, 2019; Wrathall et  al., \r\n2019), but the fact that these developments are unknown strongly \r\nlimits any forecasts on the magnitude of adaptation benefits \r\n(Section 16.5.2.3.8)."


id: 152fb3b10a4df64aa9a5234dfe5f77e1
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 845
content:
"While climate change will increase risk to society and ecosystems, \r\nfuture exposure and vulnerability conditions will also greatly impact \r\noutcomes of concern directly. Global economic damages to coastal \r\nassets from tropical cyclones are projected to increase by more than \r\n300% due to coastal development alone, a much larger effect than \r\nprojected climate change impacts through 2100 even in RCP8.5 \r\n(Gettelman et al., 2018). Similarly, global crop prices are more than \r\nthree times more sensitive to alternative assumptions about changes \r\nin production technologies and demand than to alternative climate \r\noutcomes (Ren et al., 2016). Future water scarcity is driven mainly by \r\nboth demographic change and socioeconomic changes affecting water \r\ndemand and management"


id: 2e59b47657f8d696afacdae6328cc8e
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 4736
content:
" A measure of between-country inequality \r\n(Gini coefficient) would decline by more than 50% this century in \r\nlow-vulnerability conditions, but would double in a high-vulnerability \r\nfuture (Crespo Cuaresma, 2017), outweighing the effect of climate \r\n(Taconet et al., 2020). Similarly, the global prevalence of armed conflict \r\nwill roughly double this century in a high-vulnerability future, whereas \r\nit will drop by half in a low-vulnerability future (Hegre et al., 2016). In \r\nSub-Saharan Africa, assumptions about governance and political rights \r\nare estimated to be far more important to the future risk of violent \r\nconflict than climate change (Witmer et al., 2017)."


id: 96e877a4b8b2ade6cd5eed33e48192b2
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 21258
content:
"In terms of salt-water intrusion and salinisation, global comparative \r\nstudies are still lacking but the general processes are well understood \r\n(e.g., White and Kaplan, 2017), and research on individual deltas is on \r\nthe rise. In the Mekong Delta of Vietnam, one of the main rice-producing \r\ndeltas globally, salinity intrusion has been observed to extend around \r\n15 km inland during the rainy season and around 50 km during the dry \r\nseason (Gugliotta et al., 2017), resulting in rice yield losses of up to 4 \r\nt ha-1 yr-1 (Khat et al., 2018). SLR, along with the expansion of dams \r\nand dry season irrigation upstream, is expected to further increase the \r\nsalinity intrusion into the delta"


id: c9d97d7e04e3dfc8401b0a68c10bb3a9
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 29312
content:
" This creates additional risk for food \r\nproduction as rice and other crops might be pushed beyond their \r\nadaptation limits in terms of salt tolerance, potentially affecting many \r\nof the 282,000 agriculture-based livelihoods in the Mekong Delta \r\nand increasing the pressure for cost-intensive adaptation (Smajgl \r\net  al., 2015). Genua-Olmedo et  al. (2016) find for the Ebro that in \r\nhigh scenario (RCP8.5, and SLR of almost 1 m by 2100), SLR-induced \r\nsalinity intrusion will lead to almost a doubling of salinity levels and a \r\ndecrease of mean rice productivity by over 20% in a high-SLR scenario \r\nwith almost 1 m of SLR by the end of the century."


id: b73a0cb56967bed265be03f4c7663cde
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 26359
content:
"Large deltas, which are very dynamic risk hotspots of global importance \r\nand interest (Wigginton, 2015; Hill et al., 2020; Nicholls et al., 2020), \r\nserve well to illustrate how risk pathways develop over time, determined \r\nby climatic as well as non-climatic risk drivers and by adaptation. Deltas \r\noccupy less than 0.5% of the global land area but host over 5% of the \r\nglobal population (Dunn et al., 2019) and contribute major fractions of \r\nfood production in many world regions (Kuenzer et al., 2020). Future \r\nrisk in these areas is heavily driven by climate change but also greatly \r\ndepends on past, current and future socioeconomic changes which \r\ninfluence future trends in exposure, vulnerability and adaptive capacity \r\nof natural and human systems (high confidence) (Oppenheimer et al., \r\n2019)"


id: fc766bc58648d191cf41acf13b209ff7
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 37228
content:
" From a risk perspective, trends over the past decades have been \r\nunfavourable for many deltas, as most of them have experienced a \r\nsimultaneous intensification of hazards, rise in exposure and stagnation \r\nor only limited reduction in vulnerability, particularly in low-income \r\ncountries (high confidence) (Day et al., 2016; Tessler et al., 2016; Loucks, \r\n2019; Oppenheimer et al., 2019; Hill et al., 2020)."


id: be2e73bdec2bc028aa03d780871f75e4
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 27478
content:
"Next to the trends in hazards, future exposure of and in deltas is shaped \r\nparticularly by the increase of population and infrastructure and the \r\nintensification of land use. Over the recent years, the population has been \r\nrising in major deltas, roughly along with overall national population \r\ntrends (Szabo et al., 2016). In 2017, 339 million people lived in deltas \r\nwith a high exposure to flooding, cyclones and other coastal hazards \r\n(Edmonds et  al., 2020). Over 40% of the global population exposed \r\nto flooding from tropical cyclones lived in deltas, more than 90% of \r\nwhich in developing countries and emerging economies (ibid.). Looking \r\ninto the future, population in low-elevation coastal zones is expected \r\nto increase by 2050 across all SSPs with diverging developments in the \r\nsecond half of the century, and at the end of the century will reach well \r\nover 1 billion people in SSP3 (Jones and O'Neill, 2016; Merkens et al., \r\n2016)"


id: 529f229b3e28eb396ca455ae6d6b23f2
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 10458
content:
" A major part of this population is expected to reside in deltas \r\nwith large cities or mega-urban agglomerations such as the Pearl River \r\nDelta, China. One of the first studies using the SSP-RCP framework on \r\nthe delta scale suggests a strong increase in intensive agricultural land \r\nby the middle of the century in three SSPs (2, 3, 5) in the Volta Delta, \r\nGhana, while the Mahanadi, India, and the Ganges-Brahmaputra-\r\nMeghna do not show a significant further increase (Kebede et  al., \r\n2018). Hence, the amount of population and infrastructure as well as \r\nagricultural land is expected to rise further under certain SSPs, further \r\nincreasing the exposure to future climate hazards."


id: 5316fa01e29421cbb937a86909f31ceb
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 10540
content:
"Deltas face multiple interacting hazards, many of which over the past \r\ndecades have been intensified by local and regional anthropogenic \r\ndevelopments (e.g., the construction of dams, groundwater extraction, \r\nor agricultural irrigation practices) and most of which are expected \r\nto be exacerbated by climate change (high confidence) (Giosan \r\net al., 2014; Tessler et al., 2015; Tessler et al., 2016; Arto et al., 2019; \r\nOppenheimer et al., 2019). The most important hazards include SLR, \r\ninundation, salinity intrusion, cyclones, storms and erosion, many of \r\nwhich occur in combination. The potential for flooding and inundation \r\ndepends on the relative sea level rise (RSLR) which results from \r\nglobal and regional SLR as well as local subsidence within the deltas"


id: 8e16d9f38af852acd6453e92d9c1e87b
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 19887
content:
" \r\nSubsidence caused by natural and human drivers (mainly compaction \r\nand groundwater extraction) is currently the most important cause for \r\nRSLR in many deltas and can exceed the rate of climate-induced SLR \r\nby an order of magnitude (Oppenheimer et al., 2019). But in higher \r\nwarming scenarios the relative importance of climate-driven SLR is \r\nexpected to increase over time (Oppenheimer et al., 2019). In a global \r\nstudy covering 47 major deltas and assessing future trends of sediment \r\ndelivery across four RCPs, three SSPs (1,2,3) and a projection of future \r\ndam construction, Dunn et al. (2019) find most deltas (33 out of the \r\n47) will experience a mean decline of 38% in sediment flux by the end \r\nof the century when considering the average of the scenarios. Nienhuis \r\net al. (2020) find in a global assessment that some deltas have gained \r\nland through increased sediment load (e.g"


id: d2301151a6871c14cf9db8fbfad8ef53
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 30597
content:
", through deforestation), \r\nbut recent land gains are unlikely to be sustained if SLR continues to \r\naccelerate. According to the latest assessments, it is virtually certain \r\nthat global mean sea level will continue to rise over the 21st century, \r\nwith SLR by 2100 likely to reach 0.28-0.55 m in a an SSP1-1.9 and \r\n0.63-1.01 m in an SSP5-8.5 scenario relative to 1995-2014 (IPCC, \r\n2021). The combined effects of local subsidence and GMSL rise result \r\nin a significant increase in the potential for inundation of low-lying \r\ndeltas across all RCPs, with some variation according to regional sea \r\nlevel change rates, without significant further adaptation measures \r\n(very high confidence)."


id: d9bc38cebb077d308da71321732ad5d8
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 31773
content:
"living standards, food security and water security (RKR-D, RKR-F, RKR-G, \r\nrespectively). Increased poverty, increased hunger and limited access to \r\ndrinkable water are well-known drivers of poor health conditions. The \r\nrole of impact cascades is even more prominent in the case of peace \r\nand human mobility (RKR-H), even though the scientific literature \r\ndoes not conclude on any clear and direct climate influence on armed \r\nconflict and human migration. Rather, climate-induced degradation of \r\nnatural resources that are vital for subsistence agriculture and fisheries, \r\ntransformational and long-term consequences on livelihoods (e.g., \r\nnew risks, increasing precarious living conditions, gendered inequity, \r\netc"


id: 6307b1d2eff1903fef92309091a37392
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 13152
content:
"), and erosion of social capital due to exacerbated tension within \r\nand between communities are considered among the main drivers of \r\narmed conflicts and forced displacement, therefore highlighting links \r\nwith water security (RKR-G) and living standards (RKR-D), for example."


id: 9eef717b58a9a6d4a331460b8afc6c3e
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 22548
content:
"Multiple feedbacks between individual risks exist that have the \r\npotential to create cascades (WEF, 2018; IPCC, 2019c p. 680; Simpson \r\net  al., 2021) and then to amplify systemic risks and impacts far \r\nbeyond the level of individual RKRs (medium confidence). Scientific \r\nresearch, however, remains limited on whether such interactions \r\nwould result in increasing or decreasing the initial impact(s), and \r\nhence risk severity across systems. Given the scope of this chapter on \r\nincreasing risk severity, here we focus on assessing RKR interactions \r\nthat lead to increasing risk. Drawing directly on RKR assessments \r\n(Sections 16.5.2.3.2-16.5.2.3.8), this section cites those assessments \r\nrather than primary literature. The arrows in Figure 16.11 are derived \r\nfrom a qualitative analysis by three authors of Chapter 16 of the material \r\nprovided by chapters on KRs and RKR assessments (Section 16.5.2"


id: 7a517e65902800c96d68f54196ed2c42
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 16815
content:
"3), \r\nand do not result from any systematic and quantitative approach as \r\ndone in some recent studies (e.g., WEF, 2018; Yokohata et al., 2019)."


id: 6424c22d26bd395a3366f3834c70a0d7
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 13321
content:
"Interactions at the RKR level (Figure 16.11, panel A)--climate change \r\nwill combine with pre-existing socioeconomic and ecological conditions \r\n(grey blocks on the left-hand-side of panel A in Figure  16.10) to \r\ngenerate direct and second-order effects (black plain arrows) both on \r\nthe structure and/or functioning of ecosystems (RKR-B) and on some \r\nnatural processes such as the hydrologic cycle (RKR-G), for example. This \r\nthen translates into implications not only for biodiversity but also for \r\nnatural resources that support livelihoods, which will in turn affect food \r\nsecurity (especially food availability; RKR-F), water security (especially \r\naccess to adequate quantities of acceptable quality water; RKR-G) \r\nand the living standards of already vulnerable groups and aggregate \r\neconomic outputs at the global level (RKR-D)"


id: ca5f37a2c056646a3773cc0734bc5331
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 29402
content:
" CIDs (IPCC, 2021) will \r\nalso directly affect infrastructure that are critical to ensure some basic \r\nconditions for economies to function (RKR-C), for example through \r\ntransportation within and outside the country, energy production \r\nand international trade. Such disturbances to socioecological systems \r\nand economies pose climate-related risks to human health (RKR-E) as \r\nwell as to peace and human mobility (RKR-H). Indeed, while health \r\nis concerned with direct influence of climate change, for example \r\nthrough hotter air temperatures impacting morbidity and mortality or \r\nthe spatial distribution of disease vectors such as mosquitos, it is also \r\nat risk of being stressed by direct and secondary climate impacts on "


id: d9497272ee94dc26481f688d6ca17549
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 31698
content:
"Within the AR6 cycle, the three IPCC Special Reports have all \r\nconsidered the relationships between climate change impacts and \r\nactions and the SDGs. SR15 discussed priorities for sustainable \r\ndevelopment in relation to climate adaptation efforts (Section 5.3.1, \r\nSR15); synergies and trade-offs of climate adaptation measures \r\n(Section 5.3.2, SR15); and the effect of adaptation pathways towards \r\na 1.5degC warmer world (Section 5.3.3 SR15). The SRCCL considered \r\nimpacts of desertification on SDGs 1 (no poverty), 2 (zero hunger), 13 \r\n(climate), 15 (life on land) and 5 (gender) (IPCC, 2019a, Figure 3.9). \r\nTrade-offs and synergies between SDGs 2 (zero hunger) and 13 \r\n(climate action) at the global level were recognised (IPCC, 2019a, \r\nSection  5.6.6, Figure  5.16). Various integrated response options, \r\ninterventions and investments were also evaluated within the SDG \r\nframework (IPCC, 2019a, Section  6.4.3)"


id: c23456cce8c7a6c46b49b0607c4e2a69
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 28139
content:
" The SROCC (Chapter 5) \r\nconcluded that climate change impacts on the ocean, overall, will \r\nnegatively affect achieving the SDGs, with 14 (life below water) \r\nbeing most relevant (Singh et al., 2019)."


id: 1ae7bb13062a45e897dfc7e097cd4234
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 1702
content:
"There is high agreement in the literature that for addressing climate change risks SRM cannot be the main policy response to climate \r\nchange and is, at best, a supplement to achieving sustained net zero or net negative CO2 emission levels globally (de Coninck et al., 2018; \r\nMacMartin et al., 2018; Buck et al., 2020; National Academies of Sciences and Medicine, 2021b). SRM contrasts with climate change \r\nmitigation activities, such as emission reductions and carbon dioxide removal (CDR), as it introduces a 'mask' to the climate change \r\nproblem by altering the Earth's radiation budget, rather than attempting to address the root cause of the problem, which is the increase \r\nin greenhouse gases (GHGs) in the atmosphere. In addition, the effects of proposed SRM options would only last as long as a deployment \r\nis maintained--for example, requiring ca. yearly injection of aerosols in the case of SAI as the lifetime of aerosols in the stratosphere \r\nis 1-3 years (Niemeier et al"


id: b3ca0458a34b4d5945adc1fd81dcb84f
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 25802
content:
", 2011) or continuous spraying of sea salt in the case of MCB as the lifetime of sea salt aerosols in the \r\natmosphere is only about 10 d--which contrasts with the long lifetime of CO2 and its climate effects, with global warming resulting from \r\nCO2 emissions likely remaining at a similar level for a hundred years or more (MacDougall et al., 2020) and long-term climate effects of \r\nemitted CO2 remaining for several hundreds to thousands of years (Solomon et al., 2009)."


id: 58f1d479d477e74a67518791675ae8d2
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 11489
content:
"In the context of reaching the long-term global temperature goal of the Paris Agreement, there are different hypothetical scenarios \r\nof SRM deployment: early, substantial mitigation with no SRM, more limited or delayed mitigation with moderate SRM, unchecked \r\nemissions with total reliance on SRM, and regionally heterogeneous SRM. Each scenario presents different levels and distributions of \r\nSRM benefits, side effects and risks. The more intense the SRM deployment, the larger is the likelihood for the risks of side effects and \r\nenvironmental risks (e.g., Heutel et al., 2018). Regional disparities in climate hazards may result from both regionally deployed SRM \r\noptions such as GBAM, and more globally uniform SRM such as SAI (Jones et al., 2018a; Seneviratne et al., 2018b). There is an emerging \r\nliterature on smaller forcings of SAI to reduce global average warming, for instance, to hold global warming to 1.5degC or 2degC alongside \r\nambitious conventional mitigation (Jones et al"


id: 30609458474165421744e95658db385d
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 5040
content:
", 2018a; MacMartin et al., 2018), or bring down temperature after an overshoot (Tilmes \r\net al., 2020). If emissions reductions and CDR are deemed insufficient, SRM may be seen by some as the only option left to ensure the \r\nachievement of the Paris Agreement's temperature goal by 2100."


id: 74a3216f925349e89e8452a49139256
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 15945
content:
"There is low confidence and large uncertainty in projected impacts of SRM on crop yields due in part to a limited number of studies. \r\nBecause SRM would result in only a slight reduction in CO2 concentrations relative to the emission scenario without SRM (Chapter 5, \r\nWGI), the CO2 fertilisation effect on plant productivity is nearly the same in emissions scenarios with and without SRM. Nevertheless, \r\nchanges in climate due to SRM are likely to have some impacts on crop yields. A single study indicates MCB may reduce crop failure rates \r\ncompared with climate change from a doubling of CO2 pre-industrial concentrations (Parkes et al., 2015). Models suggest SAI cooling \r\nwould reduce crop productivity at higher latitudes compared with a scenario without SRM by reducing the growing season length, \r\nbut benefit crop productivity in lower latitudes by reducing heat stress (Pongratz et al., 2012; Xia et al., 2014; Zhan et al., 2019)"


id: 902a6f90d0c94520e91ef4c315a1a037
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 20216
content:
" Crop \r\nproductivity is also projected to be reduced where SAI reduces rainfall relative to the scenario without SRM, including a case where \r\nreduced Asian summer monsoon rainfall causes a reduction in groundnut yields (Xia et al., 2014; Yang et al., 2016). SAI will increase the \r\nfraction of diffuse sunlight, which is projected to increase photosynthesis in forested canopy, but will reduce the direct and total available \r\nsunlight, which tends to reduce photosynthesis. As total sunlight is reduced, there is a net reduction in crop photosynthesis with the result \r\nthat any benefits to crops from avoided heat stress may be offset by reduced photosynthesis, as indicated by a single statistical modelling \r\nstudy (Proctor et al., 2018). SAI would reduce average surface ozone concentration (Xia et al., 2017) mainly as a result of aerosol-induced \r\nreduction in stratospheric ozone in polar regions, resulting in reduced downward transport of ozone to the troposphere (Pitari et al"


id: 9049982a3283904765681d7e610bef5e
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 20235
content:
", 2014; \r\nTilmes et al., 2018). The reduction in stratospheric ozone also allows more UV radiation to reach the surface. The reduction in surface \r\nozone, together with an increase in surface UV radiation, would have important implications for crop yields but there is low confidence\r\nin our understanding of the net impact."


id: 88e69ea985c50fc3bac541ca6b3b3b8
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 19074
content:
"Few studies have assessed potential SRM impacts on human health and well-being. SAI using sulphate aerosols is projected to deplete \r\nthe ozone layer, increasing mortality from skin cancer, and SAI could increase particulate matter due to offsetting warming, reduced \r\nprecipitation and deposition of SAI aerosols, which would increase mortality, but SAI also reduces surface-level ozone exposure, which \r\nwould reduce mortality from air pollution, with net changes in mortality uncertain and depending on aerosol type and deployment \r\nscenario (Effiong and Neitzel, 2016; Eastham et al., 2018; Dai et al., 2020). However, these effects may be small compared with changes \r\nin risk from infectious disease (e.g., mosquito-borne illnesses) or food security due to SRM influences on climate (Carlson et al., 2020)"


id: 58bb3f835c1edf23ef2d26544174c941
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 11453
content:
" \r\nUsing volcanic eruptions as a natural analogue, a sudden implementation of SAI that forced the El Nino-Southern Oscillation (ENSO) \r\nsystem may increase risk of severe cholera outbreaks in Bengal (Trisos et al., 2018; Pinke et al., 2019). Considering only mean annual \r\ntemperature and precipitation, SAI that stabilises global temperature at its present-day level is projected to reduce income inequality \r\nbetween countries compared with the highest warming pathway (RCP8.5) (Harding et al., 2020). Some integrated assessment model \r\nscenarios have included SAI (Arino et al., 2016; Emmerling and Tavoni, 2018; Heutel et al., 2018; Helwegen et al., 2019; Rickels et al., \r\n2020) showing the indirect costs and benefits to welfare dominate, since the direct economic cost of SAI itself is expected to be relatively \r\nlow (Moriyama et al., 2017; Smith and Wagner, 2018). There is a general lack of research on the wide scope of potential risk or risk "


id: 7b157fc7748cae4a1f1d429e9afe09c8
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 16962
content:
"There is large uncertainty in terrestrial ecosystem responses to SRM. By decoupling increases in atmospheric greenhouse gas concentrations \r\nand temperature, SAI could generate substantial impacts on large-scale biogeochemical cycles, with feedbacks to regional and global \r\nclimate variability and change (Zarnetske et al., 2021). Compared with a high-CO2 world without SRM, global-scale SRM simulations \r\nindicate reducing heat stress in low latitudes would increase plant productivity, but cooling would also slow down the process of nitrogen \r\nmineralisation, which could decrease plant productivity (Glienke et al., 2015; Duan et al., 2020). In high-latitude and polar regions, SRM \r\nmay limit vegetation growth compared with a high-CO2 world without SRM, but net primary productivity may still be higher than pre\u0002industrial climate (Glienke et al., 2015)"


id: 53356b38214ced28daf61d2a29cb756d
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 10558
content:
" Tropical forests cycle more carbon and water than other terrestrial biomes, but large areas of \r\nthe tropics may tip between savanna and tropical forest depending on rainfall and fire (Beer et al., 2010; Staver et al., 2011). Thus, SAI\u0002induced reductions in precipitation in Amazonia and central Africa are expected to change the biogeography of tropical ecosystems in \r\nways different from both present-day climate and global warming without SAI (Simpson et al., 2019; Zarnetske et al., 2021). This would \r\nhave potentially large consequences for ecosystem services (Chapter 2 and Chapter 9). When designing and evaluating SAI scenarios, \r\nbiome-specific responses need to be considered if SAI approaches are to benefit rather than harm ecosystems. Regional precipitation \r\nchange and sea salt deposition over land from MCB may increase or decrease primary productivity in tropical rainforests (Muri et al., \r\n2015)"


id: 7e8ab819c30117b472624cf0ca4e6458
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 17473
content:
" SRM that fully offsets warming could reduce the dispersal velocity required for species to track shifting temperature niches, \r\nwhereas partially offsetting warming with SAI would not reduce this risk unless rates of warming were also reduced (Trisos et al., 2018; \r\nDagon and Schrag, 2019). SAI may reduce high-fire-risk weather in Australia, Europe and parts of the Americas, compared with global \r\nwarming without SAI (Burton et al., 2018). Yet SAI using sulphur injection could shift the spatial distribution of acid-induced aluminium \r\nsoil toxicity into relatively undisturbed ecosystems in Europe and North America (Visioni et al., 2020). For the same amount of global \r\nmean cooling, SAI, MCB and CCT would have different effects on gross and net primary productivity because of different spatial patterns \r\nof temperature, available sunlight, and hydrological cycle changes (Duan et al., 2020)"


id: a0fa63d3b8b37f354c085a97d9889279
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 22875
content:
" Large-scale modification of land surfaces for GBAM \r\nmay have strong trade-offs with biodiversity and other ecosystem services, including food security (Seneviratne et al., 2018a). Although \r\nexisting studies indicate SRM will have widespread impacts on ecosystems, risks and potential for risk reduction for marine and terrestrial \r\necosystems and biodiversity remain largely unknown."


id: 8f08b562208bb4d2643442d6add34897
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 20041
content:
"A sudden and sustained termination of SRM in a high CO2 emissions scenario would cause rapid climate change (high confidence; WGI \r\nChapter 4). More scenario analysis is needed on the potential likelihood of sudden termination (Kosugi, 2013; Irvine and Keith, 2020). \r\nA gradual phase-out of SRM combined with emission reduction and CDR could avoid these termination effects (medium confidence) \r\n(MacMartin et al., 2014; Keith and MacMartin, 2015; Tilmes et al., 2016). Several studies find that large and extremely rapid warming \r\nand abrupt changes to the water cycle would occur within a decade if a sudden termination of SAI occurred (McCusker et al., 2014; \r\nCrook et al., 2015b). The size of this 'termination shock' is proportional to the amount of radiative forcing being masked by SAI. A sudden \r\ntermination of SAI could place many thousands of species at risk of extinction, because the resulting rapid warming would be too fast for \r\nspecies to track the changing climate (Trisos et al"


id: b0ccc3640f15c6f2b3758046d19a0c3d
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 25335
content:
"Studies on the public perception of SRM have used multiple methods: questionnaire surveys, workshops, and focus group interviews \r\n(Burns et al., 2016; Cummings et al., 2017). Most studies have been limited to Western societies, with some exceptions. Studies have \r\nrepeatedly found that respondents are largely unaware of SRM (Merk et al., 2015). In the context of this general lack of familiarity, the \r\npublic prefers CDR to SRM (Pidgeon et al., 2012), is very cautious about SRM deployment because of potential environmental side effects \r\nand governance concerns, and mostly rejects deployment for the foreseeable future. Studies also suggest conditional and reluctant \r\nsupport for research, including proposed field experiments, with conditions of proper governance (Sugiyama et al., 2020). Recent studies \r\nshow that the perception varies with the intensity of deliberation (Merk et al., 2019), and that the public distinguishes different funding \r\nsources (Nelson et  al., 2021)"


id: 21d2c2a6f3c250e76ed62ed639735746
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 2757
content:
" Limited studies for developing countries show a tendency for respondents to be more open to SRM \r\n(Visschers et al., 2017; Sugiyama et al., 2020), perhaps because they experience climate change more directly (Carr and Yung, 2018). \r\nIn some Anglophone countries, a small portion of the public believes in chemtrail conspiracy theories, which are easily found in social \r\nmedia (Tingley and Wagner, 2017; Allgaier, 2019). Since researchers rarely distinguish different SRM options in engagement studies, there \r\nremains uncertainty in public perception."


id: f006a74ab1d6789becd489083a138e60
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 35271
content:
"There is broad literature on ethical considerations around SRM, mainly stemming from philosophy or political theory, and mainly focused \r\non SAI (Flegal et al., 2019). There is concern that publicly debating, researching and potentially deploying SAI could involve a 'moral \r\nhazard', with potential to obstruct ongoing and future mitigation efforts (Morrow, 2014; Baatz, 2016; McLaren, 2016), while empirical \r\nevidence is limited and mostly at the individual, not societal, level (Burns et al., 2016; Merk et al., 2016; Merk et al., 2019). There is \r\nlow agreement whether research and outdoors experimentation will create a 'slippery slope' towards eventual deployment, leading \r\nto a lock-in to long-term SRM, or can be effectively regulated at a later stage to avoid undesirable outcomes (Hulme, 2014; Parker, \r\n2014; Callies, 2019; McKinnon, 2019)"


id: 9216f424a3a388be1f9c0b998d840eb1
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 20509
content:
" Regarding potential deployment of SRM, procedural, distributive and recognitional conceptions \r\nof justice are being explored (Svoboda and Irvine, 2014; Svoboda, 2017; Preston and Carr, 2018; Hourdequin, 2019). With the SRM \r\nresearch community's increasing focus on distributional impacts of SAI, researchers have started more explicitly considering inequality \r\nin participation and inclusion of vulnerable countries and marginalised social groups (Flegal and Gupta, 2018; Whyte, 2018; Taiwo and \r\nTalati, 2021), including considering stopping research (Stephens and Surprise, 2020; National Academies of Sciences and Medicine, \r\n2021a). There is recognition that SRM research has been conducted predominantly by a relatively small number of experts in the Global \r\nNorth, and that more can be done to enable participation from diverse peoples and geographies in setting research agendas and research \r\ngovernance priorities, and undertaking research, with initial efforts to this effect (e.g"


id: d1762cd6c0044a2a79e18d54a6b8f4f2
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 30492
content:
", Rahman et al., 2018), noting unequal power \r\nrelations in participation could influence SRM research governance and potential implications for policy (Whyte, 2018; Taiwo and Talati, \r\n2021; Winickoff et al., 2015; Frumhoff and Stephens, 2018; Biermann and Moller, 2019; McLaren and Corry, 2021; National Academies of \r\nSciences and Medicine, 2021b)"


id: 2cdcf2833b535fae70685d0a98540d70
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 4492
content:
"Currently, there is no dedicated, formal international SRM governance for research, development, demonstration or deployment (see \r\nWGIII Chapter 14). Some multilateral agreements--such as the UN Convention on Biological Diversity or the Vienna Convention on \r\nthe Protection of the Ozone Layer--indirectly and partially cover SRM, but none is comprehensive, and the lack of robust and formal \r\nSRM governance poses risks (Ricke et al., 2013; Talberg et al., 2018a; Reynolds, 2019a). While governance objectives range broadly, \r\nfrom prohibition to enabling research and potentially deployment (Sugiyama et al., 2018b; Gupta et al., 2020), there is agreement that \r\nSRM governance should cover all interacting stages of research through to any potential, eventual deployment with rules, institutions \r\nand norms (Reynolds, 2019b)"


id: ace36f8aca19424b5d7a8e78bd2e8e6d
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 24747
content:
" Accordingly, governance arrangements are co-evolving with respective SRM technologies across the \r\ninteracting stages of research, development, demonstration and--potentially--deployment (Rayner et al., 2013; Parker, 2014; Parson, \r\n2014). Stakeholders are developing governance already in outdoors research, for example for MCB and OAC experiments on the Great \r\nBarrier Reef (McDonald et al., 2019). Co-evolution of governance and SRM research provides a chance for responsibly developing SRM \r\ntechnologies with broader public participation and political legitimacy, guarding against potential risks and harms relevant across a full \r\nrange of scenarios, and ensuring that SRM is considered only as a part of a broader portfolio of responses to climate change (Stilgoe, \r\n2015; Nicholson et al., 2018)"


id: ef952fc468fd543292bc162a0d4badf7
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 35191
content:
" For SAI, large-scale outdoor experiments even with low radiative forcing could be transboundary, and \r\nthose with deployment-scale radiative forcing may not be distinguished from deployment, such that MacMartin and Kravitz (2019) \r\nargue for continued reliance on modelling until a decision on whether and how to deploy is made, with modelling helping governance \r\ndevelopment. For further discussion of SRM governance, see Chapter 14, WGIII."


id: 66d7e8158c199f5343428f091220a7b4
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 13773
content:
"Many linkages between SDG 13 (climate action) and other SDGs have \r\nbeen identified (very high confidence) (Blanc, 2015; Kelman, 2015; \r\nNorthrop et  al., 2016; Hammill and Price-Kelly, 2017; ICSU, 2017; \r\nMugambiwa and Tirivangasi, 2017; Dzebo et  al., 2018; Major et  al., \r\n2018; Nilsson et al., 2018; Sanchez Rodriguez et al., 2018). In addition, \r\ninteractions between different climate change actions and SDGs, \r\nand interactions among SDGs themselves, have also been assessed \r\n(Nilsson et al., 2016; IPCC, 2018a; McCollum et al., 2018; Fuso-Nerini \r\net al., 2019; IPCC, 2019b; Cernev and Fenner, 2020). The Cross-Chapter \r\nBox GENDER in Chapter 18 assessment indicates the importance of \r\ngender considerations in achieving success and benefits in adaptation \r\nefforts"


id: 552d65cbd032e4d3924b11fab306d7a6
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 10861
content:
" Aligning climate change adaptation to the SDGs could bring \r\npotential co-benefits and increased efficiency in funding, and reduce \r\nthe gap between adaptation planning and implementation (very high \r\nconfidence) (IPCC, 2018a; Sanchez Rodriguez et al., 2018; IPCC, 2019b; \r\nIPCC, 2019a)."


id: ac0cc29f6867e12fbcb579b00e264b21
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 24590
content:
"Progress towards meeting the SDGs has been recognised to be \r\nable to reduce global disparities and support more climate resilient \r\ndevelopment pathways (IPCC WGII AR5, Chapter 13, p. 818; discussed \r\nfurther in Chapter 18). Nevertheless, we are still lagging in achieving the \r\n2030 Goals (OECD, 2019; Sachs et al., 2021), and this affects societal \r\nvulnerability, readiness and risk response capacities (IPCC, 2019a, \r\nChapters 6, 7, Chapters 6 and 8, this report). We assess the risk literature \r\nfor linkages between key risks (grouped by RKRs) and the indicators of \r\nthe SDGs (UN, 2015) using text analysis (details in SM16.5) to identify \r\nthe potential level of effect of different risks on the SDGs. Some 940 \r\ndocuments were analysed. The SDG status is associated with projected \r\nclimate hazards, also called climatic impact drivers (CIDs) (Ranasinghe \r\net  al"


id: e5908d3143fad95f2ed7f7121f999887
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 33625
content:
", 2021) (panel a), and RKRs (panel c), summarising hazard and \r\nexposure with vulnerability aspects, as expressed by challenges in \r\nachieving the SDGs (panel d), on a regional level (Figure 16.12)."


id: 7ff0dba7f9a00e21414e70887cc0753f
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 17675
content:
"The analysis of RKR linkages to SDGs is also useful in identifying gaps \r\nand susceptibilities, especially for developing future climate resilient \r\ndevelopment targets. This aspect is discussed further in Chapter 18. \r\nGaps may arise as SDG targets and indicators are not specifically \r\nfocused on systems affected by climate change risks or impacts. For \r\nexample, in the SRCCL Section 7.1.2, Hurlbert et al. (2019) noted the \r\nabsence of an explicit goal for conserving freshwater ecosystems and \r\necosystem services in the SDGs. Such gaps (Tasaki and Kameyama, \r\n2015; Guppy et al., 2019) are inevitable as the current SDG targets \r\nand indicators focus on overall sustainable development. As another \r\nexample, projected increases in frequency and intensity of hot \r\ntemperature extremes are likely to result in increased heat-related \r\nillness and mortality, yet heat extremes are not called out as an SDG \r\nindicator under SDGs 3 (good health and well-being) or 13 (climate \r\naction)"


id: 246e0d1cf8a123474128c0d4febeab11
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 3176
content:
" The gaps on climate-related metrics for impacts on health \r\nare just beginning to be evaluated (Lloyd and Hales, 2019, see also \r\nSection  7.1.6). The current SDG 13 (climate action) targets also do \r\nnot specifically track the possibility of differential impacts on society \r\nfrom disasters and extreme weather events (RFC2). For example, the \r\nfirst indicator (Section 13.1.1.1), 'Number of deaths, missing persons \r\nand directly affected persons attributed to disasters per 100,000 \r\npopulation', does not include any requirement for disaggregated data, \r\nunlike several other socioeconomic and population SDG indicators, \r\nmaking it difficult to track the different effects that climate-related \r\ndisasters are expected to have on men, women and children across \r\ndifferent segments of society, relevant for distributional impacts \r\n(RFC3) (see also Section 8.3, Cross-Chapter Box GENDER in Chapter \r\n18). The risk consequences identified and discussed in each RKR \r\n(Section 16.5"


id: 2c9e856d38e480d5e94c14a5d40a58ab
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 4450
content:
"2) provide useful entry points for identifying indicators \r\nand metrics for monitoring and evaluating specific impacts of key \r\nclimate change risks. In addition, the sector and region chapters have \r\nconsidered various adaptation responses relevant to the SDGs (see, for \r\nexample, Sections 3.6, 4.7.5, 5.13.3, 8.2.1.6, 10.6.1, 13.11.4, 14.6.3) \r\nwith relevant metrics for evaluation."


id: c56dfc563a00bcfeef57c7422b6ed23c
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 28643
content:
"Linkages between the 17 SDGs and the eight RKRs (Figure  16.12 \r\nbottom left panel) are mapped to the regional SDG status (Figure 16.12 \r\nbottom right panel) and related to the CIDs (Figure  16.12 top left \r\npanel). Interconnections between CIDs and RKRs are complicated by \r\nthe possibility of concurrent weather events, extremes and longer\u0002term trends. Risks are compounded by existing vulnerabilities (Iwama \r\net al., 2016; Thomas et al., 2019b; Birkmann et al., 2021) and cascading \r\nconsequences (Pescaroli and Alexander, 2015; Pescaroli and Alexander, \r\n2018; Yokohata et al., 2019) (see, for example, Sections 3.4.3.5, 5.12, \r\n6.2.6, 7.2.2.2) as well as interactions. The level of challenges faced in \r\nattaining the SDGs is one metric for assessing vulnerability and lack of \r\ncapacity to manage risks (Cernev and Fenner, 2020). Other metrics are \r\nalso available (Parker et al., 2019; Garschagen et al., 2021b; Birkmann \r\net al., 2022). From Figure 16"


id: b89b8c36c891720ea7c39529834e20cc
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 26575
content:
"12, aside from SDG13 (climate action), the \r\nstrongest connections and risk challenges are with zero hunger (SDG2), \r\nsustainable cities and communities (SDG11), life below water (SDG14), \r\ndecent work and economic growth (SDG8), no poverty (SDG1), clean \r\nwater and sanitation (SDG6) and good health and well-being (SDG3) \r\n(high confidence). Other SDGs have strong linkages with specific RKRs, \r\nfor example, terrestrial and marine ecosystems with life on land (SDG15); \r\ninfrastructure (RKR-C) with industry, innovation and infrastructure (SDG9) \r\nand affordable and clean energy (SDG7); living standards (RKR-D) with "


id: 99ccff2dd674c44a59aacd49873f1061
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 21721
content:
"We draw important distinctions between RFC and RKR. First, RFCs \r\nassess risks that might be of global concern, while RKRs also include \r\nrisks that may be of concern only locally or for specific population \r\ngroups (Figure 16.13). RFCs focus on the full range of increasing risk, \r\nand locate transitions between four categories of risk: undetectable, \r\nmoderate, high, and very high. RKRs focus on severe risks, and attempt \r\nto elaborate when/where severe impacts may occur. RKR assessments \r\nfocus on the conditions under which some risks would become severe \r\nover the course of this century, while RFCs evaluate changes in risk \r\nlevels against gradual increase in temperature levels. The RKR analysis \r\nused specific definitions of severity including quantified thresholds \r\nwhere possible, and this is distinct from the approach based on the \r\ncombined elements of risk used in the RFC expert elicitation process"


id: 2abd72b7db92a2fdfbb64ba4ee41a83c
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 4151
content:
" \r\nSeverity as defined in the RKRs is associated with high or very high \r\nrisk levels but does not align precisely with either of those categories, \r\nand a further difference arises from a more explicit emphasis on \r\nirreversibility and adaptation limits in the very high risk category in the \r\nRFCs. Thus, RKR and RFC neither map directly to one another in terms \r\nof content, nor in terms of the response metric."


id: 6e7e30bf6774a14c25df086dd4d9fbb
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 15013
content:
"The treatment of vulnerability and adaptation is different in the RKR \r\nand RFC assessments. The RKR assessment considered specifically three \r\nalternative levels of vulnerability, whereas the RFC process did not \r\nexplicitly differentiate risk by level of vulnerability. Therefore, the global \r\nwarming levels at which the various RKR assessments identify risk of \r\nsevere impacts are not directly comparable to risk transitions identified \r\nin the RFC assessments. In addition, RKRs consider implications of \r\nlow versus high adaptation in order to illustrate the potential role of \r\nambitious adaptation efforts to limit risk severity; RFCs consider risks in \r\na no/low adaptation scenario only, although there is some discussion \r\nof the potential role of adaptation in assessing the transition to very \r\nhigh risk"


id: a18290b0f304165bb438e03d4c16a572
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 22969
content:
" Last, both RKRs and RFCs focus on the 21st century scale, \r\nthough recognising risk will continue to increase after 2100, but treat \r\nthis timing issue differently: RKRs assess severe risks over the course \r\nof this century and distinguish risks that are already severe, that will \r\nbecome severe by the mid-century, or that will become severe by the \r\nend of the century; while RFCs assess risk level irrespective of their \r\ntiming, but according to different temperature levels."


id: 603694d4033d5c2798befc56d5cb8083
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 12709
content:
"high, and very high) reached at various levels of global average \r\nwarming are informed by evidence of observed impacts illustrated \r\nin Section 16.2 and variations in individual key risks under different \r\nscenarios of climate change, socioeconomics and adaptation effort \r\nin Section  16.5. We follow the methodological advances from \r\nSRCCL Chapter 7 (Hurlbert et  al., 2019), which used an expert \r\nelicitation protocol for developing the burning embers (Zommers \r\net  al., 2020). Specifically, we used expert participants from within \r\nthe AR6 author team and a protocol based on the modified Delphi \r\ntechnique (Mukherjee et  al., 2015) and the Sheffield Elicitation \r\nFramework (Oakley and O'Hagan, 2010; Gosling, 2018). This \r\napproach (Figure  16"


id: 8e722e6db780b7120a89ac95bf043bb5
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 19934
content:
"14) includes a two-round elicitation process \r\nwith a first round of independent anonymous judgements about \r\nthe global warming level at which risk levels transition from one \r\nto the next, and a final round of group discussion and deliberation \r\nto develop consensus. The results are then reported, and additional \r\nreferences are made to findings from other relevant chapters in this \r\nreport. Then, authors who had not participated in the elicitation as \r\npart of independent appraisal review the results."


id: 24f7c228fb68db85ca53c14e43bc76cb
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 3257
content:
"The RFC assessment reflects the latest understanding of warming \r\nreported in WGI AR6. Global surface temperature was 1.09 [0.95 to \r\n1.20]degC higher in 2011-2020 than 1850-1900, with stronger warming \r\nover land (1.59 [1.34 to 1.83]degC) than over the ocean (0.88 [0.68 to \r\n1.01]degC) (WGI AR6 Cross Chapter Box 2.3 Table 1, Eyring et al. in Gulev \r\net al., 2021). Warming levels are commonly reported and studied in the \r\nimpacts literature using two scales of spatially averaged temperature rise, \r\nglobal surface air temperature (GSAT), commonly produced by General \r\nCirculation Models (GCMs) when projecting climate changes, and global \r\nmean surface temperature (GMST), commonly used in empirical studies. \r\nBoth have the same reference point of pre-industrial of 1850-1900. \r\nThe ember diagrams presented here use GSAT, which is consistent with \r\nmost literature of projected risk (largely based on the output of climate \r\nmodels)"


id: c2f772fce6b46b2b3465682d210035b5
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 28255
content:
"representing high and very high biodiversity risk, respectively (medium \r\nconfidence) (Section 2.5.4). Similarly, WGI AR6 finds that 'Over the 21st \r\ncentury and beyond, abrupt and irreversible regional changes in the \r\nwater cycle, including changes in seasonal precipitation, streamflow \r\nand aridity, cannot be excluded'. Thus, information about irreversibility \r\nprovides information about the potential outcome of temperature \r\novershoot scenarios. Other types of losses, such as loss of human or \r\nspecies life, are irreversible even if the loss process ceases in the future. \r\nThe less resilient a system is, the more likely it is to suffer irreversible \r\ndamage during a temperature overshoot; the more resilient it is, the \r\nmore likely it is to be able to withstand the overshoot or recover \r\nafterwards. Very high levels of risk, as assessed here in the RFC, are \r\nassociated with a wide range of criteria for risk assessment including \r\nirreversibility"


id: f4774b5a70f57e1ab270c1615198596b
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 35965
content:
" While not all very high risks are irreversible, in general, \r\nrisks reaching a very high level include a component of irreversible \r\nrisks that would persist during and after an overshooting of a given \r\ntemperature level."


id: 2fbe49e1191618758dfe1527bc66dfe9
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 4929
content:
"systems respond when a transient level of warming is first reached, \r\nand then further, how they continue to develop if that transient level of \r\nwarming is then maintained indefinitely). We note that this important \r\nfactor is captured in the RFC assessment (and ember diagrams), since the \r\ntiming of risk accrual is one of the criteria for the assessment of the level \r\nof risk (Section 16.5.1). Risks that are known to evolve only over very \r\nlong-time scales contribute less to the level of risk than those which are \r\nknown to occur rapidly. This is because SLR also depends on the dynamics \r\nof global warming, including the rate of change of radiative forcing, and \r\ntime lags of several decades, including between atmospheric and ocean \r\nwarming, and in reaching equilibrium sea level state (Oppenheimer \r\net al., 2019; Fox-Kemper et al., 2021)"


id: 4ba9a131725f34e6ebda90f1e021375c
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 9322
content:
" However, longer-term risks that \r\nwould arise if those transient temperatures were maintained are also \r\nincluded, and this is particularly important in RFC5 (large-scale singular \r\nevents). Note that risks that take place over a very long time scale are \r\nconsidered to be of lower concern than more imminent risks. However, \r\nchanges of very large magnitude can still be very important even if far \r\naway in time, especially if these changes are irreversible (or reversible \r\nonly on extremely long time scales) (see Section 16.5.1)."


id: c5544cc979575cd1c18db1e51d48286f
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 28627
content:
"Although the embers do not indicate the decade in which certain \r\nrisks are projected to occur, clearly this depends strongly on the level \r\nof mitigation action as well as the degree of adaptation. Hence, the \r\nember diagram (Figure 16. 15 ) is shown alongside a graphic illustrating \r\npossible global temperature time series emerging from alternative future \r\nscenarios assessed by WGI AR6 which imply different levels of mitigation \r\neffort. For example, in a scenario with a high level of mitigation effort \r\n(SSP1-1.9) reaching net zero emissions in the 2050s, it is extremely likely\r\nthat global warming remains below 2degC and more than 50% likely that it \r\nwill remain below 1.6degC (AR6 WGI 4.3.1.1, Meinshausen et al., 2020). On \r\nthe other hand, a level of 2degC warming is extremely likely to be exceeded \r\nduring the 21st century under the three scenarios assessed by WGI AR6 \r\nin which GHG emissions do not fall below current levels before mid\u0002century (i.e., SSP2-4.5, SSP3-7.0, SSP-8"


id: 10cfdad7b78976cff1de6e0032d17eab
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 120
content:
"5) (WGI AR6 4.3.1.1, Lee et al., \r\n2021). WGI AR6 has assessed that 'global surface temperature averaged \r\nover 2081-2100 is very likely to be higher by 1.0degC-1.8degC under the \r\nlowest CO2 emission scenario considered in this report (SSP1-1.9) and \r\nby 3.3degC-5.7degC under the highest CO2 emission scenario (SSP5-8.5)'. \r\nHowever, almost all scenarios assessed by IPCC AR6 WGI reach 1.5degC \r\nglobal warming level in the early 2030s (WGI AR6 SPM, IPCC, 2021)."


id: 86f4c543226b79994c5c7f52d746496d
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 18767
content:
"The ember diagrams in Figure 16. 15 capture only the risks arising from \r\nexposure of vulnerable socio-ecological systems to climatic hazards \r\nacross a range of socioeconomic futures. They do not capture any \r\nrisk component arising solely from changes in population or level of \r\ndevelopment. Importantly, they also do not capture additional risks \r\nthat may arise from the human response to climate change, including \r\nclimate change mitigation or unintended negative consequences of \r\nadaptation-related responses (i.e., maladaptation) (Section  17.5.1). \r\nSuch risks are discussed in SRCCL Chapter 7, for example, adverse \r\neffects of the very large-scale use of land and water for primary \r\nbioenergy production on food production and biodiversity (Hurlbert \r\net al., 2019). Contributions of mitigation or maladaptation to risk can \r\nbe important, however, and are discussed further in the context of \r\nspecific RFCs in Section 16.6.3"


id: 760ee5b90d191a85abbc47375620ef22
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 16178
content:
" In general, such components of risk are \r\ndifficult to quantify, and can be minimised by good design of climate \r\nchange mitigation and adaptation. Thus, the effect is excluded from \r\nthe ember diagrams to allow a clearer representation of the accrual of \r\nclimate change risk with global warming."


id: 2c7be0f41496394d3251742a27af6e4a
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 4429
content:
"AR5 Oppenheimer et al. (2014) defined 'emergent risk' as a risk that \r\narises from the interaction of phenomena in a complex system. While \r\nemergent risk is a relevant consideration for this RFC risk assessment, \r\nthis type of risk has not been explicitly accounted for in the burning \r\nember assessment because of the limited literature basis. Unlike known \r\nor identified risks, emergent risks are characterised by the uncertainty \r\nof consequences and/or probabilities of occurrence"


id: e6bb4518522489d7a59800732c8ccf84
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 33786
content:
" The International \r\nRisk Governance Council (IRGC) suggests three categories of emergent \r\nrisks: (1) high uncertainty and a lack of knowledge about potential \r\nimpacts and interactions with risk-absorbing systems; (2) increasing \r\ncomplexity, emergent interactions and systemic dependencies that \r\ncan lead to nonlinear impacts and surprises; and (3) changes in \r\ncontext (for example, social and behavioural trends, organisational \r\nsettings, regulations, natural environments) that may alter the nature, \r\nprobability and magnitude of expected impacts. Feedback processes "


id: 50dc5f006ee6bafab7b5eba7240a5dae
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 10172
content:
"The concept of temperature overshoot, defined as 'exceedance of a \r\nspecified global warming level followed by a decline to or below that \r\nlevel during a specified period of time' is a relevant consideration for \r\nthis RFC risk assessment; however, the effect of overshoot has not \r\nexplicitly been considered in the burning ember assessment because \r\nof the limited literature basis. However, despite the lack of directly \r\nassessed overshoot scenarios, the current literature provides several \r\nsalient examples of irreversible changes that are projected to occur \r\nonce global temperatures reach a particular level. For example, coral \r\nreefs are unable to survive repeated bleaching events that are too close \r\ntogether, leading to irreversible loss of the reefs even if bleaching were \r\nto cease (see Section 16.6.3.1 RFC1). Species extinction is irreversible, \r\nand Chapter 2 assesses that, at ~1.6degC, >10% of species are projected \r\nto become endangered as compared with >20% at ~2"


id: c92addd53ddfd20a6ced8e79bec0f71
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 29212
content:
"In this section, we present the results of the expert elicitation in the \r\nform of the burning embers diagram, alongside a description of the \r\nrecent literature and scientific evidence for each of the RFCs in turn. The \r\nconsensus transition values are illustrated in Figure 16. 15 , an updated \r\nversion of the burning embers diagram that describes the additional \r\nrisk due to climate change for each RFC when a temperature level \r\nis reached and then sustained or exceeded (Table SM16.20 presents \r\nthe consensus values of the transition range and median estimate in \r\nterms of global warming level by risk level for each of the five RFC \r\nembers). The shading of each ember provides a qualitative indication \r\nof the increase in risk with temperature, and we retain the colour \r\nscheme employed in the most recent versions of this figure, where \r\nwhite, yellow, red and purple indicate undetectable, moderate, high \r\nand very high additional risk, respectively"


id: 5d166a2eb0b61ca5b03ea2bca7237c07
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 12162
content:
" These transitions were \r\nassessed under conditions of low to no adaptation compared with \r\ntoday, in accordance with definitions provided in 16.3 (i.e., adaptation \r\nconsists of fragmented, localised, incremental adjustments to existing \r\npractices), though the effect of adaptation on risk for individual RFCs \r\nand related literature is discussed further below."


id: c692f406cd14f62b248122f67a32cabf
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 28810
content:
"The transitions here are informed by these assessments, along with \r\nthe assessment in Chapter 2 on species high extinction risk and on \r\necosystem transitions. It also draws substantially from information \r\nin Cross-Chapter Paper 1 and Table  SM16.22 on risks to unique \r\nand threatened biological systems. Some unique and threatened \r\nsystems, such as coral reefs and sea-ice-dependent ecosystems, were \r\nalready showing attributable impacts with high confidence (see \r\nTable SM16.22 , Cross-Chapter Paper 1 and Chapter 2) based on data \r\ncollected in the mid to latter 20th century, when global warming of \r\n0.5degC above pre-industrial levels had taken place, as noted already in \r\nAR3. In this AR6 assessment, the temperature range for the transition \r\nfrom undetectable to moderate risk is still located at a median value \r\nof 0.5degC above pre-industrial levels, with very high confidence. Since \r\nimpacts were first detected in coral reef systems in the 1980s when \r\nwarming of ~0"


id: d487aec06d058215626f9078c41cda78
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 30963
content:
"4degC of global warming had occurred (SR15 Chapter \r\n3), this provides the temperature at which the transition begins. The \r\nSeptember Arctic sea ice volume has declined by 55-65% between \r\n1979 and 2010 (AR6 WGI, Schweiger et al., 2019) as global warming \r\nincreased from around 0.36degC in 1979 to around 0.9degC in 2010. These \r\nprovide evidence of a start to the transition from undetectable to \r\nmoderate risk at 0.4degC above pre-industrial levels. Recent evidence of \r\nobserved impacts on mountaintop ecosystems and sea-ice-dependent \r\nspecies, and of range shifts in multiple ecosystems during 1990-2000, \r\nwhich AR6 WGI now assesses as corresponding to a global warming \r\nof 0.69degC (see WGI AR6 Cross-Chapter Box 2.3, Figure 1, Gulev et al., \r\n2021), provides evidence for an upper limit to this transition of 0.7degC \r\nwith very high confidence. Overall, the transition is located at a median \r\nof 0.5degC with lower and upper limits of 0.4degC and 0"


id: 3426755dec05e2a6bb4b795a7de6af6c
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 5618
content:
"This RFC addresses the potential for increased damage to or irreversible \r\nloss of a wide range of physical, biological and human systems that \r\nare unique (i.e., restricted to relatively narrow geographical ranges and \r\nhave high endemism or other distinctive properties) and are threatened \r\nby future changes in climate (Smith et  al., 2001; Smith et  al., 2009; \r\nOppenheimer et al., 2014). The specific examples of such systems given in \r\nprevious IPCC assessment reports has remained broadly consistent, with \r\nAR4 including 'coral reefs, tropical glaciers, endangered species, unique \r\necosystems, biodiversity hotspots, small island states, and indigenous \r\ncommunities' (Smith 2009), AR5 including 'a wide range of physical, \r\nbiological, and human systems that are restricted to relatively narrow \r\ngeographical ranges' and 'are threatened by future changes in climate' \r\n(Smith et  al"


id: 300a8264cc6c5788dc1410449e2597f1
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 4987
content:
", 2001), and SR15 Chapter 3 including 'ecological and \r\nhuman systems that have restricted geographic ranges constrained by \r\nclimate related conditions and have high endemism or other distinctive \r\nproperties. Examples include coral reefs, the Arctic and its Indigenous \r\nPeople, mountain glaciers and biodiversity hotspots'. In this cycle, we \r\nretain the definition used in SR15 as most explicit and inclusive of the \r\nprevious definitions."


id: 3d26c1f9f5f9cee88ea1eb9c8c7cf20
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 6984
content:
"(b) Embers are shown for each RFC, assuming low to no adaptation (i.e., adaptation is fragmented, localised, incremental adjustments to existing practices). The dashed horizontal \r\nline denotes the present global warming of 1.09degC (IPCC WGI Figure SPM.8a ) which is used to separate the observed, past impacts below the line from the future projected risks \r\nabove it. RFC1 Unique and threatened systems: ecological and human systems that have restricted geographic ranges constrained by climate-related conditions and have high \r\nendemism or other distinctive properties. Examples include coral reefs, the Arctic and its Indigenous People, mountain glaciers and biodiversity hotspots. RFC2 Extreme weather \r\nevents: risks/impacts to human health, livelihoods, assets and ecosystems from extreme weather events such as heatwaves, heavy rain, drought and associated wildfires, and \r\ncoastal flooding"


id: 5e511f8be7b96db9122dd8755dad2166
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 12371
content:
" RFC3 Distribution of impacts: risks/impacts that disproportionately affect particular groups owing to uneven distribution of physical climate change hazards, \r\nexposure or vulnerability. RFC4 Global aggregate impacts: impacts to socio-ecological systems that can be aggregated globally into a single metric, such as monetary damages, \r\nlives affected, species lost or ecosystem degradation at a global scale. RFC5 Large-scale singular events: relatively large, abrupt and sometimes irreversible changes in systems \r\ncaused by global warming, such as ice sheet disintegration or thermohaline circulation slowing. Comparison of the increase of risk across RFCs indicates the relative sensitivity of \r\nRFCs to increases in GSAT. The levels of risk illustrated reflect the judgements of IPCC author experts from WGI and WGII."


id: 2e01e58b7ce795c7391c82f985041d46
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 4673
content:
"mortality (sometimes associated with insect damage and wildfire) has \r\ncaused the mortality of up to 20% of trees in western North America, \r\nthe African Sahel, and North Africa, linked to a warming of 0.3-0.9degC \r\nabove pre-industrial levels, and is implicated in more than 100 other \r\ncases of drought-induced tree mortality in Africa, Asia, Australia, Europe, \r\nand North and South America (high confidence). Species in biodiversity \r\nhotspots already show changes in response to climate change (CCP1, \r\nhigh confidence). Roman-Palacios and Wiens (2020) attribute local \r\nextinctions of several taxonomic groups between the latter 20th century \r\nand 2003-2012, (corresponding to warming of less than 0.85degC) to \r\nclimate-change-related temperature extremes for up to 44% (0-75%) \r\nof species"


id: 8c77a9d730e2aa62f7052972b59c1e69
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 19619
content:
" Widespread declines of up to 35% in the species richness \r\nof the unique pollinator group, bumble bees, between 1901-1974 \r\nand 2000-2014 are also attributed to climate change, via increasing \r\nexceedance of their thermal tolerance limits across Europe and North \r\nAmerica (Soroye et al., 2020). The first extinctions attributed to climate \r\nchange have been now detected with the present 1.2degC warming, \r\nincluding that of the Bramble Cay melomys (Melomys rubicola), a sub\u0002species of the lemuroid ringtail possum (Hemibelideus lemuroides), and \r\ngolden toad (Incilius periglenes) (Chapter 2). An increasing frequency \r\nor unprecedented occurrence of mass animal mortality due to climate\u0002change-enhanced heatwaves has also been observed in recent years on \r\nmore than one continent, including temperature-vulnerable terrestrial \r\nbirds and mammals in South Africa and Australia (Ratnayake et  al., \r\n2019; McKechnie et al., 2021)"


id: 3a09b14adf8de69ac9865413080369ae
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 6505
content:
" There have also been 90% declines in sea\u0002ice-dependent species such as sea lions and penguins in the Antarctic \r\n(Table SM16.22 ). A strong effect of climate change on the observed \r\ncontraction of ranges of polar fish species and strong expansion of \r\nranges of arcto-boreal or boreal fish was observed between 2004 and \r\n2012 (Frainer et al.., 2017). Even if current human-driven habitat loss \r\nis excluded, many hotspots are projected to cease to be refugia (i.e., to \r\nremain climatically suitable for >75% of the species they contain which \r\nhave been modelled), at 1.0-1.5degC (Cross-Chapter Paper 1)."


id: 5429616355575335fb18e53a39aa4ad2
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 10704
content:
"action is not effective if the unique and threatened systems in question \r\nreach a hard limit to adaptation (as in the case of the loss of Arctic \r\nsummer sea ice, the submergence of a small island, the contraction \r\nand elimination of a species' climatic niche from a mountaintop, or the \r\ndegradation of a coral reef) (Section  16.4). Furthermore, adaptation \r\nbenefits deriving from restoration rapidly diminish with increasing \r\ntemperature (Cross-Chapter Paper 1). One study quantifies how land \r\nmanagement (in terms of protecting existing ecosystems or restoring \r\nlost ones) might reduce extinctions in biodiversity hotspots or globally \r\nsignificant terrestrial biodiversity areas more generally (Warren et al., \r\n2018b)"


id: e0d913e127a0d1d6b85a1ad7b60229cc
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 32875
content:
" While the latter suggests that substantial benefits can result \r\nglobally in terrestrial systems, allowing less unique systems to persist at \r\nhigher levels of warming but only under a high adaptation scenario in \r\nwhich globally applied terrestrial ecosystem restoration and protected \r\narea expansion takes place, this is less likely for many of the unique \r\nand threatened terrestrial systems which are more vulnerable than \r\nthe globally significant biodiversity areas treated in that study (which \r\nexcludes coral reefs and Arctic sea-ice-dependent systems). Such high \r\nlevels of adaptation globally are likely infeasible owing to competition \r\nfor land use with food production (Portner et al., 2021). Novel targeted \r\nadaptation interventions for coral reefs such as artificial upwelling \r\nand local radiation management show some promise for reducing the \r\nadverse effects of thermal stress and resulting coral bleaching (Condie \r\net al"


id: 6768588a2d98c0d1353e0724225eb7a7
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 13857
content:
", 2021), but are far from implementation (Sawall et  al., 2020; \r\nKleypas et  al., 2021). Larger benefits in this RFC could theoretically \r\naccrue only if adaptation action became ubiquitous and extensive, \r\nwhich experts considered infeasible at the scales required. Small island \r\ncommunities are confronted by socio-ecological limits to adaptation \r\nwell before 2100, especially those reliant on coral reef systems for \r\ntheir livelihoods, even for a low-emissions pathway (Chapter 3) (high \r\nconfidence). At warming levels beyond 1.5degC, the potential to reach \r\nbiophysical limits to adaptation due to limited water resources are \r\nreported for small islands (medium confidence) and unique systems \r\ndependent on glaciers and snowmelt (Chapter 4) (medium confidence)."


id: 295b7fd30db858f452b4accce8688fd4
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 3946
content:
"extinction increases with warming in all climate change projections, \r\nfor all native species studied in biodiversity hotspots (Cross-Chapter \r\nPaper 1, high confidence), being roughly threefold greater for endemic \r\nthan more widespread species for global warming of 3degC above pre\u0002industrial levels than 1.5degC) (Manes et al., 2021, Cross-Chapter Paper \r\n1) (medium confidence). The Arctic is projected to be practically ice free \r\nin September in some years for global warming of between 1.5degC and \r\n2degC (WGI AR6 Section 9.3.1.1, Fox-Kemper et al., 2021), undermining \r\nthe persistence of ice-dependent species such as polar bears, ringed \r\nseals and walrus (Meredith et  al., 2019), and adversely affecting \r\nIndigenous communities. Warming of 1.5degC is also assessed (Chapter \r\n3) to reduce the habitability of small islands, due to the combined \r\nimpacts of several key risks (high confidence)"


id: 95b0619fb851959243ec3de1593df056
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 21045
content:
" Hence, the transition \r\nfrom high to very high risk in these systems is assessed to occur with \r\nhigh confidence beginning at 1.2degC, passing through a median value of \r\n1.5degC, and completing (i.e., reaching its upper bound) at 2degC warming."


id: 5810363b364aac6b26c74d4018054e2c
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 11358
content:
"Assessment of a high level of risk requires a higher level of magnitude, \r\nseverity and spatial extent of the risks. Events prior to that already \r\nhad substantial impacts, such as the 2003 European heatwave (IPCC \r\nSREX Chapter 9). Examples of impactful events in the early 2010s (at \r\nca. 0.95degC of global warming; WGI AR6 Chapter 2, Gulev et al., 2021) \r\ninclude the 2010 Russian heatwave (Barriopedro et al., 2011) and the \r\n2010 Amazon drought (Lewis et al., 2011). Later impactful events include, \r\namong others, the 2013 heatwave in eastern China (Sun et al., 2014), the \r\n2017 tropical cyclone Harvey (Risser and Wehner, 2017; Van Oldenborgh \r\net al., 2017) and the 2018 concurrent North Hemisphere heatwaves in \r\nEurope, North America and Asia (Vogel et al., 2019). Very recent events \r\nwith severe and unprecedented impacts attributed to anthropogenic \r\nclimate change indicate that thresholds to high risks may already \r\nhave been crossed at recent levels of global warming (ca. 1.1-1"


id: 3f0d2f6865c60c2d9680f21b0f00d239
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 7305
content:
"2degC), \r\nincluding the Siberian fires and the 2019 Australian bushfires that were \r\nlinked to extreme heat and drought conditions (Van Oldenborgh et al., \r\n2017) and extreme precipitation linked to increased storm activity in the \r\nUSA (Van Oldenborgh et al., 2017). Severe and unprecedented impacts \r\noccurred with current low levels of adaptation (Section 16.2.3.4). The \r\nglobal-scale risk of wildfire considerably degrading ecosystems and \r\nincreasing illnesses and death of people has been assessed to transition \r\nfrom undetectable to moderate over the range 0.6-0.9degC with high \r\nconfidence (Chapter 2, Table SM2.5, Figure 2.11)."


id: 43e9a7bacad4e616b660159a51173e1a
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 8098
content:
"AR5 identified a transition from undetectable to moderate risk below \r\n'recent' temperatures (i.e., during 1986-2005, which then corresponded \r\nto a global warming of 0.6degC above pre-industrial levels). SR15 \r\nSection 3.5.2.2 (Hoegh-Guldberg et al., 2018b) concluded that differences \r\nof 0.5degC in global warming led to detectable changes in extreme weather \r\nand climate events on the global scale and for large regions. IPCC WGI \r\nAR6 Chapter 11 confirms this assessment and concludes that 'new \r\nevidence strengthens the conclusion from SR15 that even relatively small \r\nincremental increases in global warming (+0.5degC) cause statistically \r\nsignificant changes in extremes on the global scale and for large regions'.\r\nSubstantial literature is available for comparisons at +1"


id: cabd399b5ac53a4b8312567d8be2b207
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 29457
content:
"5degC versus +2degC \r\nof global warming, but the conclusions are assessed to also apply at \r\nlower global warming levels and smaller increments of global warming \r\ngiven the identified linearity of regional responses of several extremes in \r\nrelation to global warming (Seneviratne et al., 2016; Wartenburger et al., \r\n2017; Tebaldi and Knutti, 2018) and the identification of emergence of \r\nglobal signals in climate extremes for global warming levels as small as \r\n0.1degC (Seneviratne and Hauser, 2020, WGI AR6, Chapter 11, Figure 11.8; \r\nWGI Cross-Chapter Box 12.1). Further analyses are consistent with this \r\nassessment, based on model simulations (Fischer and Knutti, 2015; \r\nSchleussner et  al., 2017; Kirchmeier-Young et  al., 2019a; Seneviratne \r\nand Hauser, 2020) and observational evidence (Zwiers et al., 2011; Dunn \r\net al., 2020). A global warming of +0.5degC above pre-industrial conditions \r\ncorresponds approximately to climate conditions in the 1980s (Chapter 2, \r\nFigure 2"


id: 65deb0e1897ac18d2aa415f83cc08e30
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 13613
content:
"11), a time frame at which detectable changes in some extremes \r\nwere established at the global scale based on observations (Dunn et al., \r\n2020). Heat-related mortality has also been assessed to have increased \r\nconsiderably because of climate change (Ebi et al., 2021; Vicedo-Cabrera "


id: 3e97590c699474522b0a25be9a7d35d0
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 7231
content:
"increase in the probability of compound events, such as an increase in \r\nconcurrent heatwaves and droughts (high confidence) (WGI AR6 Chapter \r\n11). There is medium confidence that weather conditions that promote \r\nwildfires (fire weather) have become more probable in southern Europe, \r\nnorthern Eurasia, the USA and Australia over the last century (WGI AR6 \r\nChapter 11; SRCCL Chapter 2, Jolly et al., 2015; Abatzoglou and Williams, \r\n2016). Furthermore, food security and livelihoods are being affected by \r\nshort-term food shortages caused by climate extremes (Section 5.12.1; \r\nChapter 16, Food Security RKR) which have affected the productivity of \r\nall agricultural and fishery sectors (high confidence). The frequency of \r\nsudden food production losses has increased since at least mid-20th \r\ncentury on land and sea (medium evidence, high agreement)"


id: f1e06bf53a76878ca6c9060d868d917d
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 35572
content:
" Droughts, \r\nfloods and marine heatwaves contribute to reduced food availability and \r\nincreased food prices, threatening food security, nutrition and livelihoods \r\nof millions (high confidence). Changes in sea surface temperatures drive \r\nsimultaneous variation in climate extremes, increasing the risk of multi\u0002breadbasket failures (Cai et al., 2014; Perry et al., 2017). Droughts induced \r\nby the 2015-2016 El Nino, partially attributable to human influences \r\n(medium confidence), caused acute food insecurity in various regions, \r\nincluding eastern and southern Africa and the dry corridor of Central \r\nAmerica (high confidence). Human-induced climate change warming \r\nalso worsened the 2007 drought in southern Africa, causing food \r\nshortages, price spikes and acute food insecurity in Lesotho (Verschuur \r\net al., 2021)"


id: 16d931909cb12f61e9ee26cd80716d57
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 1117
content:
" In the fisheries and aquaculture sector, marine heatwaves \r\nare estimated to have doubled in frequency between 1982 and 2016, \r\nas well as increasing in intensity and length, with consequences for fish \r\nmortality (Chapter 5; Smale et al., 2019; Laufkotter et al., 2020). In the \r\nnortheast Pacific, a recent 5-year warm period impacted the migration, \r\ndistribution and abundance of key fish resources (high confidence). \r\nAt 1degC warming, the number of people affected by six categories of \r\nextreme events was found to have already increased by a factor of 2.3 \r\nrelative to pre-industrial (Lange et al., 2020)."


id: ed9415f9d551b122c2aae1747bfffcfa
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 34843
content:
"As in RFC1, one of the criteria for identification of very high risks is \r\nlimits to adaptation. Though the literature explicitly considering societal \r\nadaptation to extreme weather events is limited, there is evidence \r\nthat investments in hydro-meteorological information, early-warning \r\nsystems and anticipatory forecast-based finance are a cost-effective \r\nway to prevent some of the most adverse effects of extreme events \r\n(Coughlan de Perez et  al., 2016; Fakhruddin and Schick, 2019; Merz \r\net al., 2020). Despite a lack of systematic methods for assessing general \r\nadaptation effectiveness, there is some evidence of risk reduction for \r\nparticular places and hazards, especially flood and heat vulnerability \r\n(Section  16.3.2.4), including investment in flood protection, building \r\ndesign and monitoring and forecasting, air conditioning, reduced social \r\nvulnerability, and improved population health"


id: 3bac131833b860167da8404f0a4dfa9c
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 6744
content:
" One study finds declining \r\nglobal mortality and economic loss due to extreme weather events \r\nover the past four decades (Formetta and Feyen, 2019) especially in \r\nlow-income countries. Using SSP2 as a proxy for expanded adaptation, \r\nEbi et al. (2021) assess that the transition to high risk for heat-related \r\nmortality increases to 1.8degC (compared with 1.5degC with less adaptation \r\nunder SSP3). There is evidence of adaptation avoiding heat-related \r\nmortality at low levels of global warming, using early-warning and \r\nresponse systems and sustainable alterations of the thermal environment \r\nat the individual, building, urban and landscape levels (Jay et al., 2021). \r\nDespite the evidence that adaptation can reduce risks of heat stress, the \r\nimpact of projected climate change on temperature-related mortality \r\nis expected to be a net increase under a wide range of climate change \r\nscenarios, even with adaptation (Chapter 7, high confidence)"


id: 643ac2c70fd304aa73a66ea7635f323e
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 13339
content:
" Much \r\nof the adaptation literature focuses on coping with long-term gradual \r\nclimate change and largely does not take into account the increased \r\ndifficulty of adapting to climate extremes and general higher variability \r\nin climate that is projected to occur in the future. However, expanding \r\nand more coordinated adaptation, including wider implementation and \r\nmulti-level coordination, has the potential to reduce the risks to crops \r\nfrom heatwaves at intermediate (but not high) levels of warming.(IPCC \r\nAR5 Ch7, Ahmed et al., 2018; Ahmed et al., 2019, Section 16.3.2.2; EEA, \r\n2019; Raza et al., 2019; Tripathi and Sindhi, 2020).\r\nThe transition from high to very high risk for the RFC2 was not assessed \r\nin the AR5 or in SR15. Some new evidence suggests, however, that \r\nvery high risks associated with weather and climate extremes would\r\nbe reached at higher levels of global warming"


id: 329756d84989830994bffcc676e39d30
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 5379
content:
" In particular, changes \r\nin several hazards would be more widespread and pronounced at 2degC \r\ncompared with 1.5degC global warming, including increases in multiple \r\nand concurrent extremes (IPCC WGI AR6 SPM; IPCC WGI AR6 Chapter \r\n11, IPCC WGI AR6 Chapter 12). On average over land, high temperature "


id: 58e945bf9495006f0a3ab6e30fca018b
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 11483
content:
"events that would have occurred once in 50 years in the absence of \r\nanthropogenic climate change are projected to become 13.9  times \r\nmore likely with 2degC warming, and 39.2  times more likely with 4degC \r\nwarming (IPCC AR6 WGI SPM Figure  SPM.6, IPCC, 2021), indicating \r\na nonlinear increase with warming. Chapter 2 assessed that risk of \r\nwildfire transitions from moderate to high over the range 1.5degC to 2.5degC \r\nwarming (medium confidence, Table SM2.5 , Figure 2.11). The intensity \r\nof heavy precipitation events increases overall by about 7% for each \r\nadditional degree of global warming (IPCC AR6 WGI SPM), while their \r\nfrequency increases nonlinearly. Events that would have occurred once \r\nevery 10 years in a climate without human influence are projected to \r\nbecome 1.7 times more likely with 2degC warming, and 2.7 times more \r\nlikely with 4degC warming (IPCC AR6 WGI SPM Figure SPM.6)"


id: db707e1368d8815710c98c914d7ef074
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 32038
content:
" Several \r\nAR6 regions are projected to be affected by increases in agricultural and \r\necological droughts at 2degC of global warming, including western North \r\nAmerica, central North America, northern Central America, southern \r\nCentral America, the Caribbean, northern South America, northeastern \r\nSouth America, South American Monsoon, southwestern South America, \r\nsouthern South America, West and Central Europe, the Mediterranean, \r\nwestern Southern Africa, eastern Southern Africa, Madagascar, \r\neastern Australia and southern Australia (IPCC WGI AR6, Chapter 11, \r\nSeneviratne et al., 2021). This is a substantially larger number compared \r\nwith projections at 1.5degC (IPCC WGI AR6, Chapter 11, Seneviratne et al., \r\n2021). In these drying regions, events that would have occurred once \r\nevery 10 years in a climate without human influence are projected to \r\nhappen 2.4 times more frequently at 2degC of global warming (IPCC WGI \r\nAR6 SPM Figure SPM.6)"


id: ccfb65bfb3ad832a486169e59c535966
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 29798
content:
" Urban land exposed to floods and droughts \r\nis very likely to have more than doubled between 2000 and 2030, \r\nand the risk of flooding accelerates after 2050 (Chapter 4). At 2degC of \r\nglobal warming, there are also significant projected increases in fluvial \r\nflood frequency and resultant risks associated with higher populations \r\nexposed to these flood risks (Alfieri et al., 2017; Dottori et al., 2018)."


id: e4f5d0628b858dc6238fb46c08f2052f
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 33510
content:
"Based on the available evidence, we assess that there is medium \r\nconfidence that the transition to very high risk would happen at a \r\nmedian value 2degC of global warming, considering the increased risk for \r\nbreadbasket failure and irreversible impacts associated with changes \r\nin extremes at this warming level (e.g., damages to ecosystems, health \r\nimpacts, severe coastal storms), but that due to the disproportionate \r\nincreases in risk between 1.5degC and 2degC this transition begins already \r\nat 1.8degC. The higher range for this transition is set with medium \r\nconfidence at 2.5degC in this low/no adaptation scenario, owing to the \r\nfurther projected nonlinear increases in risks associated with high \r\ntemperature events above 2degC (WGI AR6 Figure SPM.6, IPCC, 2021; \r\nCross-Chapter Box 12.1, Ranasinghe et al"


id: 3d2684a58eee7e819ffb68d996a6d799
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 6983
content:
", 2021), and also the limits to \r\nadaptation associated with dealing with a rapid escalation of extreme \r\nweather events globally during this century; extreme events are \r\nparticularly difficult to adapt to and thus more often exceed hard limits \r\nto adaptation, particularly in natural ecosystem settings (Section 16.4)."


id: 9564054791907a60eb88263af10b4dbc
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 21000
content:
"Heat-related mortality is assessed to increase from high to very high \r\nby 3degC under SSP3, a socioeconomic scenario with large challenges to \r\nadaptation (Ebi et al., 2021). SRCCL assessed that very high risks would be \r\nreached in association with wildfire above 3degC of global warming (IPCC, \r\n2019a). Chapter 2 has assessed that risk of fire weather itself transitions \r\nfrom high to very high over the range 3degC to 4.5degC warming (medium \r\nconfidence, Table  SM2.5, Figure  2.11). Matthews et  al. (2017) show \r\nthat, at 1.5degC of global warming, about 40% of all megacities would be \r\naffected at least 1 d yr-1 with a heat index above 40.6degC (i.e., with 40.6degC \r\n'feels-like' temperatures, accounting for moisture effects). This number \r\nwould reach about 65% of megacities at 2.7degC and close to 80% at 4degC. \r\nIn addition, there is evidence for a higher risk of concurrent heat extremes \r\nat different locations with increasing global warming (Vogel et al"


id: 3e0031a388cf22580e80340da6234536
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 7114
content:
", 2019), \r\nmeaning that several cities could be affected by deadly heatwaves \r\nsimultaneously. Laufkotter et  al. (2020) found that marine heatwave \r\nevents would become annual to decadal events under 3degC of global \r\nwarming, with consequences for aquaculture (Chapter 5). Gaupp et al. \r\n(2019) conclude that risks of simultaneous crop failure across worldwide \r\nbreadbasket regions, due to changes in maximum temperatures in the \r\ncrop-growth-relevant season or cumulative precipitation in relevant time \r\nframes, increase disproportionately between 1.5degC and 2degC of global \r\nwarming. Populations exposed to extreme weather and climate events \r\nmay consume inadequate or insufficient food, leading to malnutrition \r\nand increasing the risk of disease (Chapter 5, high confidence). Hence, "


id: fd295be28e821710385836caa128fbeb
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 37344
content:
"nutrition-related health (Springmann et  al., 2016; Lloyd et  al., 2018; \r\nSulser et al., 2021) and exposure to stressors such as drought, floods \r\n(Alfieri et al., 2017; Hirabayashi et al., 2021) and extreme heat (Dosio \r\net al., 2018; Harrington et al., 2018; Sun et al., 2019). One study (Gaupp \r\net al., 2019) found that the risk of simultaneous crop failure in maize is \r\nestimated to increase from 6% to 40% at 1.5degC relative to the historical \r\nbaseline climate. In particular, further research on projected regional \r\nyield declines of wheat and maize between 1.5degC and 2degC, especially \r\nin Africa, has accrued Asseng et al. (2015), including in Ethiopia (Abera \r\net  al., 2018) with associated economic effects (Wang et  al., 2019). \r\nOptimum maize production areas in East Asia are projected to reduce in \r\narea by 38% for global warming of 1.5-2.0degC (He et al., 2019). A study \r\nof Jamaica also estimated that warming of less than 1"


id: 6e9fab0e779995aad194919c08dc9822
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 15030
content:
"5degC will have an \r\noverall negative impact on crop suitability and a general reduction in \r\nthe range of crops, but above 1.5degC, irreversible changes to Jamaica's \r\nagriculture sector were projected (Rhiney et al., 2018)."


id: 3b082fc495ef822b46e2624c091186c
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 6646
content:
"Very high risk implies limited ability to adapt. Adaptation potential not \r\nonly differs across sectors and regions, but also occurs on different \r\ntime scales depending on the nature and implementation level of the \r\nadaptation option under consideration and the system in which it is \r\nto be deployed. The costs of adaptation actions that would be needed \r\nto offset projected climate change impacts for major crop production \r\nare projected to rise once global warming reaches 1.5degC (Iizumi et al., \r\n2020). It has been estimated that the number of additional people \r\nat risk of hunger with 2.0degC global warming could be reduced from \r\n40 million to 30 million by raising the level of adaptation action (Baldos \r\nand Hertel, 2014), but beyond this level of warming residual impacts \r\nare projected to escalate (Iizumi et al., 2020)"


id: 9275a83ac601bc3499dd78f95e8daf16
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 20564
content:
" Chapter 5 assessed the \r\npotential of existing farm management practices to reduce yield losses, \r\nfinding an average 8% loss reduction in mid-century and 11% by end\u0002century (Section 5.4.4.1), which is insufficient to offset the negative \r\nimpacts from climate change, particularly in currently warmer regions \r\n(Section 5.4.3.2). The literature indicates that, globally, crop production \r\nmay be sustained below 2.0degC warming with adaptation, but negative \r\nimpacts will prevail at 2.0degC warming and above in currently warm \r\nregions (Section  5.4.4.1). Importantly, residual damage (that which \r\ncannot be avoided despite adaptation) is projected to rise around \r\n2.0degC global warming (Iizumi et al., 2020). Evidence of constraints and \r\nlimits for food, fibre and other ecosystem products for the different \r\nregions is evident for the various regions (Section 16.4.3.1) indicating \r\nlimited ability to adapt"


id: 275b505057a1bd257abf06a54cfd5cff
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 3595
content:
" Adaptation costs are also higher relative to \r\nGDP in low-income countries, for example for the building of sea dikes \r\n(Brown et al., 2021)."


id: 5ded321524fce6886d954957c24be165
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 12312
content:
"Very high risks to crop production are assessed to occur in Africa above \r\n~2.5degC warming (Figure 9.6) and below 4degC in Europe (Figure 13.29 \r\n). Very high risks of mortality and morbidity due to heat extremes and \r\ninfectious disease are assessed to occur in Africa with 2.5degC warming \r\n(Figure 9.6); heat stress, mortality and morbidity in Europe are assessed \r\nto reach a very high level of risk at ~3.2degC (Figure 13.30). Heat-related \r\nmortality risk and cascading effects on cities both transition to a very \r\nhigh level by ~2.5degC warming in Australasia (Figure  11.7). Risks to \r\nwater scarcity in North America are projected to reach very high levels \r\nwith 3.5degC warming (Figure 14.4). Hence, this assessment concludes \r\nwith medium confidence that a transition from high to very high risks, \r\nin terms of distribution of impacts, begins at 2degC global warming, \r\nwith a full transition to very high risks completed by 3.5degC"


id: f22690f968913ba8f59677ffc65df44d
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 35629
content:
" However, \r\nit should be noted that many studies upon which this assessment \r\nhas been based have not taken into account the impacts of extreme \r\nweather events and oscillations in sea surface temperatures; hence, \r\nrisks at a given level of global warming might be underestimated in \r\nthe literature."


id: d0e4098e0eebce296f8df48a7ddb1dda
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 30400
content:
"In previous reports, the transition from high to very high risk for the \r\ndistribution of impacts was not assessed due to limited available \r\nliterature, but there is now sufficient evidence to do so. A range of \r\nliterature quantifies the increasing regional probability of drought as \r\ncompared with the present day, with projected increases in the area \r\nexposed to drought (Carrao et al., 2018; Pokhrel et al., 2021), as well as \r\nin the duration (Naumann et al., 2018) and frequency of droughts, with \r\nhigher warming levels. Naumann et al. (2018) showed that, for drying \r\nareas, drought durations are projected to rise from 2 months per degC \r\nbelow 1.5degC to 4.2 months per degC near 3degC warming. Most of Africa, \r\nAustralia, southern Europe, southern and central USA, Central America, \r\nthe Caribbean, northwest China, and parts of Southern America are \r\nprojected to experience more frequent droughts"


id: 6b5686249968ec40a4199505d557633c
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 14506
content:
" Adverse effects of \r\nclimate change on food production are projected to become much \r\nmore severe (Section  5.4.3.2) when global temperatures rise more \r\nthan 2degC globally, but there are predicted to be much more negative \r\nimpacts experienced sooner on food security in low to mid-latitudes \r\n(Richardson et al., 2018a) (Section 5.4.1). For instance, climate change \r\nby 2050 is projected to increase the number of people at risk of hunger \r\nby between 8 and 80 million with 2-3degC warming compared with no\u0002climate-change conditions (Baldos and Hertel, 2014; Hasegawa et al., \r\n2018; Nelson et al., 2018; Janssens et al., 2020). In addition to effects \r\nupon crop yield, agricultural labour productivity, food access and \r\nfood-related health are projected to be negatively impacted by 2-3degC \r\nwarming (Springmann et al., 2016; de Lima et al., 2021). Regionally, \r\nsubstantial regional disparity in risks to food production is projected "


id: 8daf1c8d1704a06ea53cf8422fef6ce1
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 19814
content:
"In AR5 Section 19.6.3.5 (Oppenheimer et al., 2014), the transition from \r\nundetectable to moderate risk was assessed between 1.6degC and 2.6degC \r\nabove pre-industrial levels (i.e., 1degC and 2degC above the 1986-2005 \r\nlevel) based on impacts to both Earth's biodiversity and the overall \r\nglobal economy with medium confidence. The risk transition between \r\nmoderate and high risk was set around 3.6degC above pre-industrial \r\nlevels (i.e., 3degC above the 1986-2005 level), based on literature \r\nfinding extensive species vulnerability and biodiversity damage with \r\nassociated loss of ecosystem goods and services at 3.5degC (Foden et al., \r\n2013; Warren et al., 2013). In SR15 Section 3.5.2.4 (Hoegh-Guldberg \r\net al"


id: cae79b071581701afc15d729a54cc093
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 29484
content:
", 2018b), economic literature on potential socioeconomic threshold \r\nevents and empirical studies of global economic damages, combined \r\nwith new evidence on biome shifts, extinction risk, species range loss \r\n(especially noting the integral role of insects in ecosystem function) \r\nand ecosystem degradation, were assessed, and the upper bound of \r\nthe transition to moderate risk was lowered to 1.5degC warming above \r\npre-industrial levels, and the transition from moderate and high risk \r\nwas lowered to between 1.5degC and 2.5degC (medium confidence). The \r\nboundary between high risk and very high risk was not assessed in "


id: 4b1ebb6728f6d813a4b4e9b73b749310
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 9241
content:
"Since AR5, many new global estimates of the aggregate, economy-wide \r\nrisks of climate change have been produced, though, as was the case \r\nin AR5, these continue to exhibit a low level of agreement, including \r\nfor today's level of global warming, due primarily to differences in \r\nmethods. Cross-Working Group Box ECONOMIC in this chapter includes \r\na more thorough discussion of advancements and limitations of global \r\neconomic impact estimates and methodologies, finding significant \r\nvariation in estimates that increases with warming, indicating higher risk \r\nin terms of economic costs at higher temperatures (high confidence). \r\nClimate change has been found to exacerbate poverty through declines \r\nin agricultural productivity, changes in agricultural prices and extreme \r\nweather events (Hertel and Lobell, 2014; Hallegatte and Rozenberg, \r\n2017)"


id: 575584a87fcd29647dc48212e2d152c2
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 11230
content:
" In terms of biodiversity risks, the literature indicates that losses \r\nin terrestrial and marine ecosystems increase substantially between \r\n1.5degC and 2degC of warming (Hoegh-Guldberg et al., 2018b). Since SR15, \r\nfurther evidence of degradation of biodiversity and ecosystem services \r\nand ocean acidification at the global aggregate level has continued to \r\naccrue due to climate change (see Chapter 2)."


id: 42e7ba46726067bcb8d6ffbf437717fb
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 7917
content:
"Section 16.5.2.3.4 (RKR-D, underlying key risk on poverty) reports that, \r\nunder medium warming pathways, climate change risks to poverty \r\nwould become severe if vulnerability is high and adaptation is low \r\n(limited evidence, high agreement). At and beyond 1.5degC, approximately \r\n200  million people with livelihoods derived from small-scale fisheries \r\nwould face severe risk, given sensitivity to ocean warming, acidification \r\nand coral reef loss (Cheung et  al., 2018a; Froehlich et  al., 2018; Free \r\net  al., 2019). Warming between 1.5degC and 2degC could expose 330-\r\n396 million people to lower agricultural yields and associated livelihood \r\nimpacts (Byers et al., 2018; Hoegh-Guldberg et al., 2018a), due to a high \r\ndependency of climate-sensitive livelihoods to agriculture globally (World \r\nBank, 2020)"


id: 774aa224ea3c0ffa42dc2cabba20f390
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 16328
content:
" Models project that climate change will increase the number \r\nof people at risk of hunger in 2050 by 8-80 million people globally, with \r\nthe range depending on the level of warming (1.5-2.9degC) and SSPs \r\n(Nelson et al., 2018; Mbow et al., 2019; Janssens et al., 2020). Higher \r\natmospheric concentrations of carbon dioxide reduce the nutritional \r\nquality of wheat, rice and other major crops, potentially affecting millions \r\nof people at a doubling of carbon dioxide relative to pre-industrial \r\n(very high confidence) (Section  7.3.1). Global ocean animal biomass \r\nis projected to decrease on average by 5% per 1degC increase; hence, a \r\n2.5degC level of warming is associated with ~13% decline in ocean animal \r\nbiomass, which would considerably reduce marine food provisioning, \r\nfisheries distribution and revenue value, with further consequences for \r\necosystem functioning (Chapter 5, medium confidence)."


id: 22050c14f7ab0f737de5d3851cd68c6f
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 2798
content:
"For this RFC, the transition from undetectable to moderate risk to \r\nglobal aggregate impacts is assessed with medium confidence to occur \r\nbetween 1.0degC (start of transition) and 1.5degC (completion of transition) \r\nwith a median judgement of transition at 1.3degC, based on evidence of a \r\ncombination of economic consequences, widespread impacts to climate\u0002sensitive livelihoods, changes in biomes, and loss of terrestrial and marine \r\nbiodiversity. The start of the transition from undetectable to moderate \r\nrisk is located at recent temperatures based on observed impacts \r\nto biodiversity (Section  16.2.3.1). Experts noted aggregate impacts \r\non biodiversity are detectable, with damages that have had global \r\nsignificance (e.g., drought, pine bark beetles, coral reef ecosystems)"


id: 814c3632356b9deaff8b9175aafba15e
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 17866
content:
" \r\nConsistent with the start of this transition at 1degC, a similar elicitation \r\nconducted in Chapter 2 assessed that risks to biodiversity globally have \r\nalready transitioned to a moderate level with 1degC warming, while risks \r\nof widespread tree mortality are already moderate with 0.9degC warming \r\nand moderate risks of ecosystem structure change began with warming \r\nof 0.5degC (Table  SM2.5, Figure  2.11). Human-induced warming has \r\nslowed growth of agricultural productivity over the past 50 years in mid\u0002and low latitudes (Chapter 5; Hurlbert et al., 2019). Although there is \r\nnot yet strong evidence of attributable loss of life and livelihoods at the \r\nglobal level (Sections 16.5.2.3.4, 16.5.2.3.5), experts found that regional \r\nevidence of such observed impacts was still relevant to defining the \r\nbeginning of the transition (e.g., Table SM16.22, Chapter 9)"


id: 4b75e5d6dabe6ab8dd358eac80b98725
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 9287
content:
" Informing \r\nthe median value and upper bound of the transition to moderate risk, \r\nempirical studies and scenario analyses have found that regions with \r\nhigh dependence on climate-sensitive livelihoods like agriculture, \r\nfisheries and forestry would be severely impacted even at low levels of \r\nwarming under conditions of low adaptation (RKR-D, Lobell et al., 2011; \r\nHoegh-Guldberg et al., 2018b).\r\nThe transition to high risk is assessed with medium confidence to occur \r\nbetween 1.5degC (start of transition) and 2.5degC (completion of transition) \r\nwith a median judgement of transition at 2.0degC. Though economic \r\nestimates exhibit wide variation and low agreement at warming levels "


id: b841f9b11a3991a9ff5535b0e7330bc1
created_at: 2023-03-16 14:02:36
updated_at: 2023-03-16 14:02:36
content_type: text
index: climate_gpt_only_giec
vector_id: 26521
content:
"Losses in terrestrial and marine biodiversity increase substantially beyond \r\n1.5degC of warming (Hoegh-Guldberg et al., 2018b). Section 16.5.2.3.2 \r\n(RKR-B, risks to terrestrial and marine ecosystems) finds that substantial \r\nbiodiversity loss globally, abrupt local ecosystem mortality impacts, and \r\necological species disruption are all projected at global warming levels \r\nbelow 3degC, with insular systems and biodiversity hotspots at risk below \r\n2degC (medium confidence). Insects play a critical role in providing vital \r\necosystem services that underpin human systems, with major losses \r\nof their climatically determined geographic range at 2degC warming \r\nimplying adverse effects on ecosystem functioning. Consistent with \r\nthe transitions presented here, a similar burning ember developed in \r\nChapter 2 assessed a transition from moderate to high risks globally \r\nfor marine and terrestrial biodiversity (e.g"


id: 254c90b976a63d63363863776919d0bd
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 3295
content:
", widespread death of trees, \r\ndamages to ecosystems, and reduced provision of ecosystem services, \r\nand structural change, including biome shifts) beginning between 1.0degC \r\nand 2.0degC warming ( Table SM2.5, Figure 2.11)."


id: a69da734c17325cf07eb7dd2a0b0a0
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 23740
content:
"Though explicit treatment of adaptation is limited in the RFC4 impacts \r\nliterature (i.e., studies that compare risks for specific adaptation scenarios \r\nin terms of globally aggregated impacts with quantified findings), \r\nthere is evidence of the potential for investments in improved hydro\u0002meteorological information and early-warning systems to avoid some \r\nof the most adverse social and economic impacts from extreme weather \r\nevents in both developed and developing countries, with benefits at a \r\nglobally significant level (Hallegatte, 2012). Studies of adaptation in the \r\nagriculture sector (e.g., changing crop variety, timing of crop planting, \r\nnew types of irrigation, etc.) and infrastructure (e.g., coastal protection, \r\nhardening of critical infrastructure, flood and climate-resistant building \r\nmaterials and water storage) show large potential benefits in terms of \r\nreduced impacts to lives and livelihoods (van Hooff et al., 2015; Mees, \r\n2017)"


id: 9eafc0cf1c1a43f4eb94c1f7f29d093c
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 22506
content:
" At higher warming levels, however, potential adaptations to \r\naddress biodiversity loss are expected to be limited due to the projected \r\nrate and magnitude of change as well as the resources required (Hannah \r\net al., 2020)."


id: 85d0583cdbc03fb9963d867d7d6a447e
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 18596
content:
"This RFC, large-scale singular events (sometimes called tipping points or \r\ncritical thresholds), considers abrupt, drastic and sometimes irreversible \r\nchanges in physical, ecological or social systems in response to smooth \r\nvariations in driving forces (accompanied by natural variability) \r\n(Oppenheimer et al., 2014; O'Neill et al., 2017). SR15 Section 3.5.2.5 \r\npresented four examples, including the cryosphere (West Antarctic ice \r\nsheet, Greenland ice sheet), thermohaline circulation (slowdown of \r\nthe Atlantic Meridional Overturning Circulation), the El Nino-Southern \r\nOscillation (ENSO) as a global mode of climate variability, and the role \r\nof the Southern Ocean in the global carbon cycle (Hoegh-Guldberg \r\net al., 2018b)"


id: 51b709cd8e6dfd0d0de37a1a0bc4376e
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 10320
content:
" While most of the literature assessed here focuses on \r\nthe resultant changes to climate-related hazards such as sea level rise, \r\nin this assessment, evidence about the implications of accelerated \r\nsea level rise for human and natural systems is also considered. If sea \r\nlevel rise is accelerated by ice sheet melt, the associated impacts are \r\nprojected to occur decades earlier than otherwise, directly affecting \r\ncoastal systems including cities and settlements by the sea (CCP2) and \r\nwetlands (Chapter 2). The associated disruption to ports is projected \r\nto severely compromise global supply chains and maritime trade with \r\nlocal-global geo-political and economic consequences. To compensate \r\nfor this acceleration, adaptation would need to occur much faster and \r\nat a much greater scale than otherwise, or indeed than has previously \r\nbeen observed (CCP2)"


id: c649dbb0fabc7bd22f19ef35b99c10bc
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 28775
content:
" The costs of accommodating port growth and \r\nadapting to sea level rise amount to USD 22-768 billion before 2050 \r\nglobally (medium evidence, high agreement) (see Sections  2.1, 2.2; \r\nCross-Chapter Box SLR in Chapter 3)."


id: 72d763befc1c19bcb931fbc58793651c
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 15682
content:
"The transition to very high risks is assessed to occur within a range of \r\n2.5-4.5degC with medium confidence over the range, and low confidence\r\nassessed over a narrowed 'best estimate' range of 2.7-3.7degC. The \r\nlower end of the range reflects the loss of an increasingly large \r\nfraction of biodiversity globally. Chapter 2 has assessed a transition \r\nfrom high to very high risks globally for biodiversity (marine and \r\nterrestrial) completing at ~2.5degC warming, noting widespread death \r\nof trees, damages to ecosystems, and reduced provision of ecosystem \r\nservices over the temperature range 2.5-4.5degC ( Table  SM2.5, \r\nFigure 2.11) and, similarly, a transition from high to very high risks of \r\necosystem structure change (including biome shifts) between 3degC and \r\n5degC warming ( Table SM2.5, Figure 2.11)"


id: cb90e706b0d5be7b585c47ef82619998
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 29574
content:
" A global study of 115,000 \r\ncommon species projects climatically determined geographic range \r\nlosses of over 50% in 49% of insects, 44% of plants and 26% of \r\nvertebrates with global warming of 3.2degC, implying an associated \r\neffect on provisional and regulating ecosystem services that support \r\nhuman well-being, including pollination and detritivory (Warren \r\net al., 2018a). The risk of abrupt impacts on ecosystems as multiple \r\nspecies approach tolerance limits simultaneously is projected to \r\nthreaten up to 15% of ecological communities with 4degC of warming \r\n(Trisos et al., 2020). Under a 4degC warming scenario, models project \r\nglobal annual damages associated with SLR of $31,000 billion yr-1 in \r\n2100 (Brown et al., 2021)"


id: ebb303f6fa0b0a06234d8190eca8ce5c
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 34582
content:
"In terms of global economic impact, while an emerging economic \r\nliterature is addressing many gaps and critiques of previous damage \r\nestimates for high warming (e.g., Jensen and Traeger, 2014; Burke et al., \r\n2015; Lontzek et al., 2015; Moore and Diaz, 2015; Lemoine and Traeger, \r\n2016; Moore et  al., 2017a; Cai and Lontzek; Takakura et  al., 2019, \r\ndiscussed further in Cross-Working Group Box ECONOMIC; Carleton \r\net  al., 2020; Mejean et  al., 2020; Rode et  al., 2021), there remains \r\nwide variation across disparate methodologies, though the spread \r\nof estimates increases with warming in all methodologies, indicating \r\nhigher risk in terms of economic costs at higher temperatures (high \r\nconfidence). Section 16.5.2.3.4 (RKR-D) finds that risks to aggregate \r\neconomic output would become severe at the global scale at high \r\nwarming (~4"


id: 6022565e8eae4f5ca6adc10f9e3d9fde
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 12700
content:
"This Cross-Working Group Box assesses literature estimating the potential global aggregate economic costs of climate change and the \r\nsocial cost of carbon (SCC), where the former are sometimes referred to as estimates of global 'climate damages' and the latter are \r\nestimates of the potential monetised impacts to society of an additional metric ton of carbon dioxide emitted to the atmosphere. These \r\nmeasures include the economic costs of climate change that could be felt in market sectors such as agriculture, energy services, labour \r\nproductivity and coastal resources, as well as non-market impacts such as other types of human health risks (including mortality effects) \r\nand ecosystems. Global economic impacts estimates can inform decisions about global climate management strategy, while SCC estimates \r\ncan inform globally incremental emissions decisions"


id: 40ea1339008695975cd46639ccd0e75
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 7600
content:
" In practice, economic damage estimates have been used to explore economically \r\nefficient ('economically optimal') global emissions pathways (e.g., Nordhaus and Moffat, 2017), while SCCs have been used to inform \r\nfederal and state-level policy assessment in some countries (Greenstone et al., 2013; Rose and Bistline, 2016), but the type of SCC and \r\napplication matters (Rose, 2017). This literature has been assessed in previous WGII reports (e.g., Arent et al., 2014), and this box serves \r\nthis need for this report. The assessment in this box was performed jointly across WGII and WGIII, building on the foundation of WGII AR6 \r\nChapter 16's 'Risk to living standards' assessment (Section 16.5.2.3.4), which includes consideration of severe risks to global aggregate \r\neconomic output, and WGIII AR6 Chapter 3's assessment of the benefits of mitigation"


id: 3bc172a09386b13d2d65179af2f2ed42
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 6758
content:
" It also informs Chapter 16's global aggregate \r\nimpacts Reasons for Concern and supports Chapter 18's assessment of global emissions transitions, risk management and climate resilient \r\ndevelopment. In keeping with the broad risk framing presented in Chapter 1 of this report, other lines of evidence regarding climate risks, \r\nbeyond monetary estimates, should be considered in decision making, including key risks and Reasons for Concern."


id: f94ef5218d1ddecc8e23b60598928c16
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 36732
content:
"Methods for estimating global economic costs of climate impacts\r\nThere are several broad approaches to estimating climate damages, including biophysical process models, structural economic models, \r\nstatistical methods (also called empirical or econometric) and hybrid approaches, with each methodology having strengths and \r\nweaknesses. Process models simulate physical, natural science and/or engineering processes and their response to climate variables, \r\nwhich are then monetised (e.g., Anthoff and Tol, 2014; Sieg et al., 2019; Narita et al., 2020). Process approaches have the advantage \r\nof being explicit and interpretable, though they can be computationally intensive; may omit relevant impact channels, interactions and \r\nmarket dynamics affecting valuation; and often lack a rigorous empirical basis for calibration (Fisher-Vanden et al.)"


id: e3d4f9c169f05c7fda6affb0f6a7629a
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 33323
content:
" Structural economic \r\nmodelling represents climate impacts on inputs, production, household consumption, aggregate investment, and markets for economic \r\nsectors and regional economies (e.g., Reilly et al., 2007; Roson and Van der Mensbrugghe, 2012; Anthoff and Tol, 2014; Dellink et al., 2019; \r\nTakakura et al., 2019), often using computable general equilibrium (CGE) frameworks. Structural models can evaluate how market and \r\nnon-market impacts might enter and transmit through economies, and adaptation responses within input and output markets, consumer \r\nand investment choices, and inter-regional trade (e.g., Darwin and Tol, 2001; Dellink et  al., 2019; Takakura et  al., 2019). Statistical \r\nmethods estimate economic impacts in a given sector (e.g., Auffhammer, 2018) or in aggregate (e.g., Dell et al., 2014; Burke et al., 2015; \r\nHsiang et al., 2017; Pretis et al., 2018; Kahn et al"


id: dbb3369569ad5bc487049446edaebead
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 32082
content:
", 2019), inferred from observed changes in economic factors, weather and climate, with \r\nresponses and net results constrained by available data. Since AR5, hybrid approaches have taken different forms to integrate process, \r\nstatistical and/or structural methods, and represent a potentially promising means of leveraging the strengths of different approaches \r\n(e.g., Moore and Diaz, 2015; and Hsiang et al., 2017; Moore et al., 2017a; Ricke et al., 2018; Yumashev et al., 2019; Chen et al., 2020b). \r\nThere is also a small literature that uses expert elicitation to gather subjective assessments of climate risks and potential economic \r\nimpacts (Nordhaus, 1994; IPCC, 2019a; Pindyck, 2019)."


id: 150ebbb2627c67c58d62a696f1cac274
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 819
content:
"Additional methodological challenges to address (see, for instance, EPRI, 2021; Piontek et al., 2021) include how to capture and represent \r\nuncertainty and variability in potential damage responses for a given climate and societal condition, combine estimates from different methods \r\nand sources (including aggregating independent sectoral and regional results), assess sensitivity and evaluate robustness of estimates \r\n(including sensitivity to model specification), capture interactions and spillovers between regions and sectors, estimate societal welfare \r\nimplications (versus gross domestic product [GDP] changes) of market and non-market impacts, consider distributional effects, represent \r\nmicro- and macro-adaptation processes (and adaptation costs), specify non-gradual damages and nonlinearities, and improve understanding \r\nof potential long-run economic growth effects"


id: 2348087a6db7dfb503c90236deac3250
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 3002
content:
" Note that the treatment of time preference, risk aversion and equity considerations have \r\nimportant welfare implications for the aggregation of both potential economic impacts and climate change mitigation costs."


id: 4150ff50f53dd37bc144bf3955d10a7e
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 7674
content:
"In addition to updated and new methods and estimates, newer literature has explored non-gradual damages, such as climatic and \r\nsocioeconomic tipping points (Lontzek et al., 2015; Mejean et al., 2020), potential damage to economic growth (e.g., Burke et al., 2015; \r\nMoore and Diaz, 2015), valuing uncertainty in potential damages (Jensen and Traeger, 2014; Lemoine and Traeger, 2016; Cai and Lontzek) \r\nand representing adaptation (Takakura et al., 2019; Carleton et al., 2020; Rode et al., 2021). Going forward, to help advance science \r\nand decisions, a key research priority is to understand and evaluate methodological strengths and weaknesses in damage estimation, \r\nand reconcile the differences affecting comparability in such a way that it informs use of the different lines of evidence"


id: 32e66d40e3dbe6f5e34431eb206c244e
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 5426
content:
" This will require \r\ngreater transparency and assessment of details and assumptions in individual methods, communication and evaluation of alternatives \r\nfor specifying or calibrating climate damage functional representations with respect to climate and non-climate drivers and potential \r\nnonlinearities, including evaluating data sufficiency for levels within and beyond observations and for characterising physical system \r\ndynamics, and evaluating the sensitivity of results to model specification and input parameter choices (Cropper et al., 2017). Improving \r\nthe robustness of economic impact estimates is an active area of research. Below we describe the latest estimates."


id: 1d8a0dbd726228517040d816c090456f
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 2086
content:
"Global aggregate economic impact estimates (Figure Cross-Working Group Box  ECONOMIC.1) are generally found to increase with \r\nglobal average temperature change, as well as vary by other drivers, such as income and population and the composition of the economy. \r\nMost estimates are nonlinear with higher marginal economic impacts at higher temperature, although some recover declining marginal \r\neconomic impacts, and functional forms cannot be determined for all studies. The drivers of nonlinearity found in economic impact \r\nestimates, and the differences in nonlinearity across estimates (e.g., convex versus concave, degree of curvature), are not well understood, \r\nwith methodology construction, assumptions and data all being potential factors. Relative to AR5, there have been more estimates and \r\ngreater variation in estimates, including some recent estimates significantly higher than the range reported in AR5. For most of the \r\nstudies shown in Figure Cross-Working Group Box ECONOMIC"


id: 985ba284d6db0f6ff40858715ede0d6d
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 21481
content:
"1, the visible variation within a study represents alternative socioeconomic \r\nprojections and climate modelling, not economic impacts response uncertainty for a given socioeconomic and climate condition. Response \r\nuncertainty could be significant, as indicated by some of the results shown in the figure (e.g., Burke et al., 2015; Rose et al., 2017), but \r\nmethodological differences in how uncertainty is characterised (model specification, errors and confidence intervals versus distributions \r\nof results) limit comparability and assessment. Note that modelling factors between global temperature change and the economic impact \r\ncalculation, such as regional temperature pattern assumptions or assumed SLR dynamics, can also impact calculated estimates (e.g., \r\nWarren et al.., 2021 PAGE09 estimates versus those in Rose et al.., 2017, Chen et al.., 2020 PAGE-ICE estimates versus Burke et al.., 2015)."


id: facf5d3477887f681eac80c810cd5099
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 36982
content:
"Figure Cross-Working Group Box ECONOMIC.1 |  Global aggregate economic impact estimates by global warming level (annual % global GDP loss \r\nrelative to GDP without additional climate change). Top row panels present estimates by methodology type: (a) statistical modelling, (b) structural modelling and \r\n(c) meta-analyses, with all estimates from a paper in the same colour and estimates from methodologies other than that highlighted by the panel in grey for reference. \r\nSecond row left panel (d) presents AR5 estimates. Second row right panel (e) presents all estimates in one figure, with the same colours as panels (a-d) using outlined \r\ndots for the statistical modelling estimates, solid dots for structural modelling estimates, and triangles for meta-analysis estimates. In all panels, lines represent functions, \r\nwith dashed and dotted lines 5th and 95th percentile functions from structural modelling"


id: d91b73893914431636e8f0acdeba5c98
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 31665
content:
" To avoid duplication, estimates from papers using the economic impacts \r\nestimates or model formulations already represented in the figure are not included (e.g., Diaz and Moore, 2017; Chen et al., 2020b; Glanemann et al., 2020; Warren et al., \r\n2021). The exception is Burke et al. (2018), with the different estimates shown representing variation across climate scenarios for a given aggregate economic impacts \r\nspecification from Burke et al. (2015)--the 'pooled, short run' statistical specification. Results shown for the latter are estimates with the author's different statistical \r\nmodel specifications (and a fixed climate scenario, SSP5). From top to bottom, the Burke et al. (2015) estimates are for the 'pooled, long run', 'differentiated, long run', \r\n'pooled, short run' (authors' base case) and 'differentiated, short run' statistical specifications. For Howard and Sterner (2017), the authors' preferred function is shown"


id: d371afc35f323972d9922df5a689fd9
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 30789
content:
" \r\nOverall, estimates shown in the figure can correspond to different future years, reflecting different socioeconomic conditions and climate pathways to a global warming \r\nlevel. Global average temperature change bars relative to the period 1850-1900 are shown below the economic cost estimates to provide context to potential future \r\nwarming. Shown are the WGI AR6 assessed best estimates and 90% intervals for the illustrative emissions scenarios considered for the near term 2021-2040, mid-term \r\n2041-2060 and long term 2081-2100."


id: 9b0700e6c3e51e640c51020d1e82f8ee
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 21935
content:
"Differences in methodology type and scope complicate comparison, assessment and synthesis (Cropper et al., 2017; Diaz and Moore, \r\n2017; EPRI, 2021; Piontek et  al., 2021). In particular, structural economic modelling and empirical aggregate output modelling are \r\nfundamentally different, which has been identified as an issue affecting the comparability of results (Cropper et al., 2017). The different \r\nmethodologies affect outcomes, with global aggregate estimates based on statistical methodologies typically higher than those from \r\nstructural modelling (Figure Cross-Working Group Box ECONOMIC.1). This is, in part, due to the relationships in observational data \r\ncaptured by statistical modelling, assumed persistence of impacts in statistical modelling, broader adaptation responses in structural \r\nmodelling, and differences in the representation of future societies and how they might evolve, respond and interact"


id: dd0e57f7620f241c40de73c273ad9776
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 32277
content:
" Within statistical \r\nmodelling, results are also found to be very sensitive to the statistical model specification (e.g., Burke et al., 2015; Newell et al., 2021). \r\nWithin structural modelling, differences in representations of biophysical changes and economic structural dynamics contribute to \r\ndifferences across structural estimates (e.g., Rose et al., 2017)."


id: 3a0aa96f6a2d7d66047a60f903c6c4c4
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 6507
content:
"The global economic impact estimates discussed in the previous section serve as a key input into the calculation of the value of potential \r\nnet damages caused by a marginal ton of carbon dioxide emissions, or the SCC. To compute an SCC, damage estimates are commonly \r\ncombined in a multi-century modelling framework with socioeconomic and emissions projections, a physical model of the climate, \r\nincluding a SLR component, and assumptions about the discount rate, with current frameworks having highly stylised representations of \r\nthese components"


id: db27668d8253e07b0043d9b4f4029e55
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 31993
content:
" Though we do not present quantitative estimates here, due to the challenge of comparability, for economic impacts \r\nmethodologies (as discussed above) as well as other SCC estimation elements, large variations in SCC estimates are found in the \r\nliterature assessed due to, among other things, differences in modelling component representations, input and parameter assumptions, \r\nconsiderations of uncertainty, and discounting, inflation, and emissions year (e.g., Tol, 2009; Tol, 2018; Pezzey, 2019; Iese et al., 2021). \r\nThere are also different 'variants' of SCC estimates that differ conceptually, and in magnitude, depending on the reference condition for \r\nevaluating the impact of a marginal metric ton--is it being evaluated relative to a no-climate-policy baseline, an economically efficient \r\npathway that weighs the benefits and costs of emissions mitigation, or a pathway based on a particular climate policy or goal such as \r\n2degC or a concentration target (Rose et al"


id: 568b957c383fc324203ee8bc2a528a1d
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 11088
content:
", 2017)? The variant of SCC has implications for its applicability to different policy contexts (Rose \r\nand Bistline, 2016)."


id: b28ddeb7dce157a5d8fa354cd4b91d56
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 25613
content:
"The literature has also assessed the application of aggregate economic impact cost and SCC estimates (Rose and Bistline, 2016; Rose \r\net al., 2017; Kaufman et al., 2020) and identified conceptual and technical issues that need to be considered when using results to inform \r\npolicy decisions. These issues include: accounting for endogenous marginal benefits and socioeconomic conditions in evaluating policies \r\nwith non-incremental global emissions implications; consistency in assumptions and treatment of uncertainty across benefit and cost \r\ncalculations; fully accounting for the streams of both mitigation costs and benefits over time; avoiding inefficiently valuing or pricing \r\nemissions more than once across policies and jurisdictions; and accounting for emissions leakage to capture net climate implications"


id: 26d4c4a8dd62d359db6e9cae6975d82b
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 3504
content:
" \r\nFurthermore, concerns about the robustness of estimates have led some to recommend considering alternatives, such as using marginal \r\nmitigation cost estimates based on modelling of policy goals instead of the SCC (e.g., Rose, 2012; Pezzey, 2019; Kaufman et al., 2020), \r\nalthough this comes with its own set of assumptions and technical challenges."


id: d16c8315aeb7aefda40bccf35cb9b120
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 30487
content:
"AR6 provides new evidence that relates to the location of the \r\ntransition from undetectable to moderate risk. At the time of SR15, \r\nobservations were suggesting that MISI might already be taking place \r\nin some parts of the WAIS, while AR5 supported assessment of an \r\nadditional MISI contribution to SLR of several additional tenths of a \r\nmetre over the next two centuries. Since SR15, new observations (WGI \r\nAR6 Section 9.4.2.1, Fox-Kemper et al., 2021) support the assessment \r\nof enhanced grounding line retreat and subsequent mass loss through \r\nbasal melt in various parts of Antarctica, and year 2100 sea level \r\nprojections for the RCP8.5 scenario have increased by 10-12 cm \r\nowing to ice dynamics. However, the onset of MISI is driven by ocean \r\nwarming in specific locations (ice cavities beneath floating ice shelves), \r\nand the relation between these ocean temperatures and global mean \r\ntemperature is indirect and ambiguous"


id: ac3bc02bb4cd1bd54cea725bac3871b7
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 24634
content:
" In addition, MISI implies a self\u0002sustaining instability in the absence of further forcing. Because forcing \r\nis still increasing, it cannot be unambiguously assessed whether MISI \r\nis driving the observed retreat of grounding lines in the WAIS, or \r\nwhether this retreat is a purely forced response (and would stop if \r\nthe warming stops) or is just a manifestation of natural variability in \r\nupwelling of warmer waters on the Antarctic continental shelves and, \r\nas a result, is just a temporary effect. Consistent with SROCC, AR6 \r\nstates with medium confidence that sustained mass losses of several \r\nmajor glaciers in the Amundsen Sea Embayment (ASE) are compatible \r\nwith the onset of MISI, but that whether unstable WAIS retreat has \r\nalready begun or is imminent remains a critical uncertainty."


id: 7476a016de0189138d8df914a528364d
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 15919
content:
"In the Amazon Forest, increases in tree mortality and a decline in the \r\ncarbon sink are already reported (Brienen et al., 2015; Hubau et al., \r\n2020), and old-growth Amazon Rainforest may have become a net \r\ncarbon source for the period 2010-2019 (Qin et al., 2021). Estimates \r\nwhich include land use emissions indicate the region may have become \r\na net carbon source (Gatti et al., 2021). Fire activity is an important \r\ndriver, and both bigger fires (Lizundia-Loiola et al., 2020) and longer \r\nfire season (Jolly et al., 2015) have been reported in South America, \r\nalthough this is strongly linked to land use and land use change as \r\nwell as climate (Kelley et al., 2021), and indeed land use change may \r\nbe a stronger driver of potential loss of the Amazon Forest than climate \r\nchange"


id: b337f3019aad2daec104cd13d56d4e73
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 25711
content:
" The risk of climate-change-related loss of the Amazon Forest is \r\nassessed already above 'undetectable', but has only emerged over the \r\nlast few years, when global warming had reached 1degC, and is linked \r\nto land use as well as GSAT levels. Chapter 2 has assessed ecosystem \r\ncarbon loss from tipping points in tropical forest and loss of Arctic \r\npermafrost, and finds a transition to moderate risk over the range \r\n0.6-0.9degC (medium confidence). Specifically, WGII AR6 Table  SM2.5 "


id: fbf68b92daf0eb6da03f44eb2eb5fc4c
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 37136
content:
"and 2-6 m at 2.0degC peak warming (WGI AR6 TS, Box TS.4 Figure 1; \r\nArias et  al., 2021). This is an important feature of the approach to \r\nthis RFC (i.e., it is not primarily focused on implications for the next \r\n100-200  years). In addition, since the AR5, there is new evidence \r\nabout the Last Interglacial (LIG), when global mean temperature was \r\nabout 0.5-1.5degC above the pre-industrial era. AR6 assesses that it is \r\nvirtually certain that sea level was higher than today at that time, likely \r\nby 5-10 m (medium confidence) (B.5.4 WGI AR6 SPM,(IPCC, 2021)). \r\nMid-Pliocene temperatures of 2.5degC (about 3 million years ago when \r\nglobal temperatures were 2.5-4degC higher) also provide evidence as \r\nan upper limit for the transition to high risk associated with long\u0002term equilibrium SLR of 5-25 m (WGI AR6 SPM B.5.4). Projected SLR \r\nfor 2300 in an RCP8.5 or SSP5-8.5 scenario (consistent with a peak \r\nwarming range of 4-6degC, varies between 1.7-6.8 m and 2.2-5"


id: 8ab770a2cb81c399ab4bd65d15894778
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 19366
content:
"9 m, \r\nrespectively (WGI AR6 TS Box  TS.4, Arias et  al., 2021), and when \r\naccounting for marine ice cliff instability taking place on Antarctica, \r\nthese numbers may increase to a range of 9.5-16.2 m (WGI AR6 TS \r\nBox TS.4, Arias et al., 2021)."


id: 669eeb569eefab6f59a7064062892112
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 13738
content:
"CMIP6 climate models project drying in the Amazon--especially in \r\nJune-July-August, irrespective of future forcing scenario, but which \r\nincreases with GSAT/higher scenarios (Lee et  al., 2021). For higher \r\nGSAT levels, Burton et  al. (2021) explore different forcing scenarios \r\nand found, regardless of scenario, burned area increases markedly \r\nwith GSAT. New understanding of the role of vegetation stomata will \r\nact to exacerbate this drying (Richardson et al., 2018b). A transition \r\nto high risk of savannisation for the Amazon alone was assessed to \r\nlie between 1.5degC and 3degC with a median value of 2.0degC. A mean \r\ntemperature increase of 2degC could reduce Arctic permafrost area ~15% \r\nby 2100 (Comyn-Platt et al., 2018). Chapter 2 has assessed ecosystem \r\ncarbon loss from tipping points in tropical forest and loss of Arctic \r\npermafrost, and finds a transition from moderate to high risk over \r\nthe range 1.5degC to 3degC with a median of 2degC (medium confidence,\r\nTable SM2"


id: 9079c4dbd6ff168307e3eeccb2b96721
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 20263
content:
"5, Figure 2.11). Its assessment of the transition from high \r\nto very high risk is located over the range 3-5degC (low confidence,\r\nTable SM2.5, Figure 2.11) based on the potential for Amazon Forest \r\ndieback between 4degC and 5degC temperature increase above the pre\u0002industrial period (Salazar and Nobre, 2010)."


id: 9fcfb6618f091b8735f4214664b4dd84
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 22683
content:
"There is high confidence in the existence of threshold behaviour of \r\nthe Greenland Ice Sheet in a warmer climate (WGI AR6 Ch 9, Fox\u0002Kemper et al., 2021); however, there is low agreement on the nature \r\nof the thresholds and the associated tipping points. Similarly, the \r\nlikelihood for accelerated and irreversible mass loss from Antarctica \r\nincreases with increasing temperatures, but thresholds cannot yet be \r\nunambiguously identified. By the year 2100, sea level projections (AR6 \r\nWGI Figure SPM.8 (IPCC, 2021)) now range from 0.57 m (0.37-0.85 m) \r\nfor the SSP1-1.9 scenario to 1.35 m (1.02-1.89 m) for the SS5-8.5 \r\nscenario and become 1.99 m for the latter scenario (1.02-4.83 m) in the \r\ncase of low-likelihood, high-impact outcomes resulting from ice sheet \r\ninstability, for which there is limited evidence"


id: 731bf562c2b478828248a23b89dad239
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 15724
content:
" It should be noted that \r\ninclusion of such low-likelihood, high-impact outcomes dominated by \r\nnot-well-understood processes affecting ice dynamics on the large ice \r\ncaps of Greenland, and in particular Antarctica, would also enhance \r\nthe sea level projections for other scenarios, but to a lesser extent for \r\nincreasingly weaker forcing. No quantitative assessment of their effect \r\nin other scenarios than SSP5-8.5 yet exists as such simulations with ice \r\nsheet models have not been carried out, or only in a very limited amount."


id: 636d2c8cb8b234096e0f0e80a4db9845
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 13215
content:
"Lack of evidence on the potential for adaptation to adequately reduce \r\nrisk is a critical gap in our ability to assess global risk transitions at \r\nthe RFC level, but not the only gap. In some cases, such as RFC1, \r\nthe widespread nature and rapid speed of the escalating risks, in \r\ncombination with limited ability to adapt, means that transitions \r\nto high risk may occur despite medium or even high levels of \r\nadaptation. Risks that are largely natural and not widely mediated \r\nby human vulnerability are thus less likely to have risk transitions \r\nthat shift under higher societal adaptation. Risk transitions that \r\nare mediated through human systems, such as distribution impacts, \r\nfor example, are more likely to shift in response to adaptation as \r\nimpacts are strongly mediated through vulnerability within human \r\nsystems, but such a shift is difficult to quantify given knowledge \r\ngaps in the literature (Section 16.3)"


id: 5e2d264047dc11e567ff7edc20876812
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 12342
content:
" However, in some circumstances, \r\nexpanded global adaptation could slow some of these transitions \r\n(low confidence); in the case of RFC2, RFC3 and RFC4, the literature \r\nsuggests that coordinated global adaptation could increase the \r\nglobal temperature at which risks transition from moderate to high, \r\nfor example the prevention of mortality associated with heat stress \r\nwithin RFC2."


id: e82aac498e8dcabbfc04631d3f119470
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 34032
content:
"A higher level of adaptation, applied globally and effectively, could \r\nhave larger benefits for several RFC, either postponing the onset of \r\na high level of risk until a higher level of warming is reached (and \r\nallowing time for mitigation efforts) or allowing a system to survive \r\na temporary overshoot of a lower temperature threshold. Adaptations \r\nare likely to have significant potential to reduce risks (Magnan et al., \r\n2021), in particular for risks mediated through human systems. \r\nHowever, there is limited evidence available to assess the extent to \r\nwhich current or potential adaptations are or would be adequate in \r\nreducing climate risks at different levels of warming, and adaptation \r\nimplications for risk transitions will be highly localised"


id: 1f91cb3e0048baadea0c2c54f4120431
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 2397
content:
" Pathways \r\nand opportunities for risk management and adaptation actions with \r\ntransformational potential are discussed in Chapter 17, together with \r\nenabling factors, governance frameworks, financing, success factors, \r\nand monitoring and evaluation discussed in Chapter 18, supporting \r\nsustainable system transitions and leading to options for climate \r\nresilient development pathways."


id: 9dfd9405ed0b0c4da908e26ff9f3cbce
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 22381
content:
"The IPCC defines key risks related to climate change as potentially severe risks that are relevant to the primary goal \r\nof the United Nations Framework Convention on Climate Change treaty to avoid 'dangerous human interference \r\nwith the climate system', and whatever the scale considered (global to local). What constitutes 'dangerous' or \r\n'severe' risks is partly a value judgement and can therefore vary widely across people, communities or countries. \r\nHowever, the severity of risks also depends on criteria like the magnitude, irreversibility, timing, likelihood of the \r\nimpacts they describe, and the adaptive capacity of the affected systems (species or societies). The Working Group \r\nII authors use these criteria in various ways to identify those risks that could become especially large in the future \r\nowing to the interaction of physical changes to the climate system with vulnerable populations and ecosystems \r\nexposed to them"


id: 5c30be77d1ebb99ac0e50912912d5448
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 12019
content:
" For example, some natural systems may be at risk of collapsing, as is the case for warm-water \r\ncoral reefs by mid-century, even if global warming is limited to +1.5degC. For human systems, severe risks can include \r\nincreasing restriction of water resources that are already being observed; mortality or economic damages that are \r\nlarge compared with historical crises; or impacts on coastal systems from SLR and storms that could make some \r\nlocations uninhabitable."


id: ca5284c0296c672b6a655a02e8765e24
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 29392
content:
"These representative key risks are expected to increase in the coming decades and will depend strongly not only \r\non how much climate change occurs, but also on how the exposure and vulnerability of society changes, as well \r\nas on the extent to which adaptation efforts will be effective enough to substantially reduce the magnitude of \r\nsevere risks. The report finds that risks are highest when high warming combines with development pathways with \r\ncontinued high levels of poverty and inequality, poor health systems, lack of capacity to invest in infrastructure, \r\nand other characteristics making societies highly vulnerable. Some regions already have high levels of exposure and \r\nvulnerability, such as in many developing countries as well as communities in small islands, Arctic areas and high \r\nmountains; in these regions, even low levels of warming will contribute to severe risks in the coming decades"


id: 3de91d47d0f7f5bba1261976efe349ec
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 7100
content:
" Some \r\nrisks in industrialised countries could also become severe over the course of this century, for example if climate \r\nchange affects critical infrastructure such as transport hubs, power plants or financial centres. In some cases, such \r\nas coral reef environments and areas already severely affected by intense extreme events (e.g., recent typhoons or \r\nwildfires), climate risks are already considered severe."


id: 92f8a9676ffa2324e55e2d32251f1c55
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 20642
content:
"Adaptation-related responses are actions that are taken with the intention of managing risks by reducing \r\nvulnerability or exposure to climate hazards. While mitigation responses aim to reduce greenhouse gas emissions \r\nand slow warming, adaptations respond to the impacts and risks that are unavoidable, either due to past emissions \r\nor failure to reduce emissions. However, while these responses intend to reduce risks, it is difficult to determine \r\nprecise levels of risk reduction that can be attributed to adaptation. Changing levels of risk as well as other actions--\r\nsuch as economic development--make it challenging to definitively connect specific levels of risk reduction with \r\nadaptation. Although it is not feasible to assess the adequacy of adaptation for risk reduction at global or regional \r\nlevels, evidence from specific localised adaptation projects do show that adaptation-related responses reduce \r\nrisk"


id: 28e157b6d5c5160f7954fc254439c185
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 3863
content:
" Moreover, many adaptation measures offer near-term co-benefits related to mitigation and to sustainable \r\ndevelopment, including enhancing food security and reducing poverty."


id: 65aeba269f48ec6606daebea795f1be1
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 13576
content:
"While adaptation is important to reduce risk, adaptation cannot prevent all climate impacts from occurring. \r\nAdaptation has soft and hard limits, points at which adaptive actions are unable to prevent risks. Soft limits can \r\nchange over time as additional adaptation options become available, while hard limits will not change as there are \r\nno additional adaptive actions that are possible. Soft limits occur largely due to constraints--factors that make it \r\nharder to plan and implement adaptation, such as lack of financial resources or insufficient human capacity. Across \r\nregions and sectors, the most challenging constraints to adaptation are financial and those related to governance, \r\ninstitutions and policy measures. Limited funding and ineffective governance structures make it difficult to plan \r\nand implement adaptation-related responses which can lead to insufficient adaptation to prevent risks"


id: 1fc7ae2708cc91d5879acfc7d44238d8
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 2433
content:
" Small-scale \r\nfarmers and coastal communities are already facing soft limits to adaptation as measures that they have put in \r\nplace are not enough to prevent loss. If constraints that are limiting adaptation are addressed, then additional \r\nadaptation can take place and these soft limits can be overcome. Evidence on limits to adaptation is largely focused \r\non terrestrial and aquatic species and ecosystems, coastal communities, water security, agricultural production, and \r\nhuman health and heat."


id: 6cdd249d25a83ac437218fd24045d962
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 14749
content:
"Global mean temperature has already risen by more than 1degC, and that also means that the impacts of climate change \r\nbecome more visible. Many natural and human systems are sensitive to weather conditions. Crop yields, river floods \r\nand associated damages, ecosystems such as coral reefs, or the extent of wildfires are affected by temperatures \r\nand precipitation changes. Other factors also come into play. So, for example, crop yields around the world have \r\nincreased over the last decades because of increasing fertilizer input, improved management and varieties. How do \r\nwe detect the effect of climate change itself on these systems, when the other factors are excluded? This question \r\nis central for impact attribution. 'Impact of climate change' is defined as the difference between the observed \r\nstate of the system (e.g"


id: d0b4e25fb11cfc4fc16ff22c6c61bd81
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 30367
content:
", level of crop yields, damage induced by a river flood, coral bleaching) and the state of the \r\nsystem assuming the same observed levels of non-climate-related drivers (e.g., fertilizer input, land use patterns or \r\nsettlement structures) but no climate change."


id: 129c7aa7c38fa135e71da25772cef058
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 427
content:
"Overall, however, the extent of adaptation-related responses globally is low. On average, responses tend to be \r\nlocal, incremental, fragmented, and consistent with Business-As-Usual practices. There are no global regions or \r\nsectors where the overall adaptation-related response has been rapid, widespread, substantial and has overcome or \r\nchallenged key barriers. The extent of adaptation thus remains low globally, with significant potential for increased \r\nscope, depth, speed and the challenging of adaptation limits. Examples of low-extent adaptations include shifts by \r\nsubsistence farmers in crop variety or timing, household flood barriers to protect houses and gardens, and harvesting \r\nof water for home and farm use. In contrast, high-extent adaptation means that responses are widespread and \r\ncoordinated, involve major shifts from normal practices, are rapid, and challenge existing constraints to adaptation"


id: 44def38fb11c900af50c32a193dbad5a
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 8256
content:
" \r\nExamples of high-extent adaptations include planned relocation of populations away from increasingly flood-prone \r\nareas, and widely implemented social support to communities to prevent migration or displacement due to climate \r\nhazards."


id: 912e8b3436fb4de70909798110ebf134
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 20378
content:
"A picture speaks a thousand words. The use of images to share ideas and information to convey scientific \r\nunderstanding is an inclusive approach for communicating complex ideas. A risk variation diagram is a simple way \r\nto present the risk levels that have been evaluated for any particular system. These diagrams take the form of bar \r\ncharts where each bar represents a different category of risk. The traffic light colour system is used as a basis for \r\ndoing the risks, making it universally understandable. These diagrams are known colloquially as 'burning ember' \r\ndiagrams, and have been a cornerstone of IPCC assessments since the Third Assessment Report, and further developed \r\nand updated in subsequent reports. The fact that the diagrams are designed to be simple, intuitive and easily \r\nunderstood with the caption alone has contributed to their longstanding effectiveness. Here, in Figure FAQ16.5"


id: 7185b9ef5687ff485ccab3113c1ed5a6
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 15495
content:
"1 \r\nbelow, we provide a simplified figure of this chapter's burning embers for five categories of global aggregate risk, \r\ncalled Reasons for Concern (RFCs), which collectively synthesise how global risk changes with temperature. The \r\ndiagram shows the levels of concern that scientists have about the consequences of climate change (for a specified \r\nrisk category and scope), and how this relates to the level of temperature rise."


id: a9496ece965457960aecb6801303942d
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 24162
content:
"Figure FAQ16.5.1 |  Simplified presentation of the five Reasons for Concern burning ember diagrams as assessed in this report (adapted from \r\nFigure 16.15). The colours indicate the level of risk accrual with global warming for a low-adaptation scenario. RFC1 Unique and threatened systems: ecological \r\nand human systems that have restricted geographic ranges constrained by climate-related conditions and have high endemism or other distinctive properties. \r\nExamples include coral reefs, the Arctic and its Indigenous People, mountain glaciers and biodiversity hotspots. RFC2 Extreme weather events: risks/impacts to \r\nhuman health, livelihoods, assets and ecosystems from extreme weather events such as heatwaves, heavy rain, drought and associated wildfires, and coastal \r\nflooding. RFC3 Distribution of impacts: risks/impacts that disproportionately affect particular groups owing to uneven distribution of physical climate change \r\nhazards, exposure or vulnerability"


id: 2163f6961489cf72577d2e2e394ad2f6
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 2682
content:
" RFC4 Global aggregate impacts: impacts to socio-ecological systems that can be aggregated globally into a single metric, \r\nsuch as monetary damages, lives affected, species lost or ecosystem degradation at a global scale. RFC5 Large-scale singular events: relatively large, abrupt and \r\nsometimes irreversible changes in systems caused by global warming, such as ice sheet disintegration or thermohaline circulation slowing."


id: b0504fb9e684622f900118af2cd95a81
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 25261
content:
"arrangements of public, private and community actors, is being \r\nincreasingly recognised as important across many decision\u0002making settings (high confidence). Public governance leadership has \r\nthe largest role for social safety nets, spatial planning and building codes \r\n(high confidence). Private sector governance is important for insurance \r\nand for minimising the stressors that can negatively impact ecosystems \r\nand their functions, especially in the absence of public regulations or \r\nenforcement (medium confidence). Communities and individuals play \r\nthe largest role in governance of adaptations to farming and fishery \r\npractices and ecosystem-based adaptations (medium confidence). \r\nInformal or individual-led decision-making is more common in food \r\nsecurity and livelihood-related adaptations, such as changes to diets, \r\nlivelihood diversification and seasonal migration (high confidence)"


id: 4ac9a9e45e05d0c6ab9e10967db0d043
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 9184
content:
" \r\nPeople who have experienced climate shocks are more likely to take \r\non informal adaptation measures, and in places where people are more \r\nexposed to extreme events, autonomous adaptation is more common \r\n(high confidence) {17.2.1, 17.3.2, 17.4.2}."


id: c25d9c13d5c9382a039a4cade12a0a18
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 28166
content:
"National and international legal and policy frameworks \r\nand instruments support the planning and implementation \r\nof adaptation and climate risk management across scales, \r\nespecially when combined with guidelines for action (medium \r\nconfidence). Nationally Determined Contributions (NDCs) have been \r\ndrivers of national adaptation planning, with cascading effects on \r\nsectors and sub-national action, especially in developing countries \r\n(high confidence). Nearly all developing countries (particularly \r\nSmall Island Developing States [SIDS]) that included an adaptation \r\ncomponent in their NDCs consider adaptation the most urgent aspect \r\nof their national climate change response (high confidence). A steady \r\nincrease in national and sub-national laws, policies and regulations \r\nthat mandate reporting and risk disclosure has promoted adaptation \r\nresponse across public agencies, private firms and community \r\norganisations (high confidence)"


id: b52c5a05e8d4562dda5cea5e1cad2ffa
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 26043
content:
" Greater adaptation is present where \r\nnational climate laws and policies require adaptation action from \r\nlower levels of government and include guidelines on how to do so \r\n(medium confidence) {17.4.2}."


id: f2ab808801fb41084ecb7e1e2478092e
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 35710
content:
"Recognition of the critical role of financing for adaptation \r\nand resilience as an important enabler for climate risk \r\nmanagement has strengthened (high confidence). Yet, since \r\nthe Intergovernmental Panel on Climate Change (IPCC) 5th \r\nAssessment Report (AR5), the gap between the estimated costs \r\nof adaptation and the documented (tracked) finance allocated \r\nto adaptation has widened (high confidence). Estimated global \r\nand regional costs of adaptation vary widely due to differences in \r\nassumptions, methods and data; the majority of more recent estimates \r\nare higher than the figures presented in AR5 (high confidence). A \r\nhigh proportion of developing country NDC adaptation contributions \r\nare conditional on external financial support, emphasising the \r\ncrucial role of international finance to achieving adaptation efforts \r\ncommensurate with climate risks (high confidence)"


id: e624e25c8ac73711cea71fc0587e5e9c
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 33703
content:
"Private sector financing for adaptation has been increasingly \r\npromoted as a response to realised adaptation finance needs \r\n(high confidence). However, private sector financing of \r\nadaptation has been limited, especially in developing countries \r\n(high confidence). Tracked private sector finance for climate change \r\naction has grown substantially since 2015, but the proportion \r\ndirected towards adaptation has remained small (high confidence); \r\nin 2018, these contributions were 0.05% of total climate finance and \r\n1% of adaptation finance. A key challenge for private sector financing \r\nof adaptation is demonstrating financial return on investment, as \r\nmany benefits of adaptation arise as avoided damages or public \r\ngoods, rather than direct revenue streams (medium confidence)"


id: fb933f231d57e9a956b9fa952753adf1
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 37080
content:
" \r\nLeveraging private finance in developing countries is often more \r\ndifficult because of risk (perceived and real) to investors, reducing \r\nthe pool of potential investors and/or raising the cost (interest) of \r\ninvestment (medium confidence) {17.4.3.; Cross-Chapter Box Finance \r\nin this Chapter}."


id: a4ca2b7436c7ecb9606b541ffc34edfd
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 23504
content:
"Decision support tools and decision-analytic methods are \r\navailable and are being applied for managing climate risks in \r\nvaried contexts, including where deep uncertainty is present \r\n(high confidence). These tools and methods have been shown \r\nto support deliberative processes where stakeholders jointly \r\nconsider factors such as the rate and magnitude of change \r\nand their uncertainties, associated impacts and timescales of \r\nadaptation needed along multiple pathways and scenarios of \r\nfuture risks (high confidence). However, comparative evidence \r\non the relative utility of different analytical methods in their use \r\nby decision makers for managing climate risks is an important gap \r\n(medium confidence). Nevertheless, robust decision-making, using \r\npathway analyses to determine 'no regrets' options among trade-offs, \r\nhas been shown to be a useful starting point under deep uncertainty \r\n(medium confidence)"


id: 5a73a2f2d5b629618e1e0a0501d2658e
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 11741
content:
" Methods for analysing options differ across geo\u0002political scales, with modelling studies being a particularly prominent \r\nmethod across scales from community and urban to regional and \r\nnational (high confidence) {17.3.1, 17.3.2, 17.6, Cross-Chapter \r\nBox DEEP in this Chapter}."


id: 3a6c14c88b555b9c16eb5ddbb703ffe
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 6556
content:
"Catalysing conditions and windows of opportunity can drive \r\nshifts in motivation and adaptation effort, stimulating more \r\nrapid uptake of existing and new adaptation options (medium \r\nconfidence). Decision makers can take advantage of windows of \r\nopportunity to promote rapid and effective responses in reactive \r\nand proactive cases. Disaster events or shocks such as wildfires, \r\ntropical cyclones, heatwaves or coral bleaching have catalysing \r\ncharacteristics (high confidence). Additional types of catalysing \r\nconditions include climate litigation and the presence of individuals \r\nand organisations that act as policy and decision innovators, including \r\ngovernment and business innovators in cities (medium confidence), \r\nstimulating action within and beyond their immediate contexts \r\n(medium confidence)"


id: 2aead5437ad15b3c91320eab2199351d
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 4179
content:
" Litigation on failure of government and business \r\nto adapt is becoming more frequent and is expected to increase as \r\nclimate impact attribution science matures further (high confidence) \r\n{Cross-Chapter Box LOSS in this Chapter; 17.4.5.2, 17.4.5.3}."


id: 5a0710ddc5b9bee3650798205e16b2e2
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 11678
content:
"Monitoring and evaluation (M&E) are essential for tracking \r\nadaptation progress and learning about adaptation success \r\nand maladaptation (high confidence). M&E application has \r\nincreased since AR5 at the local, project and national level, but \r\nis still at an early stage in most countries (high confidence) \r\nand underutilised as a way to assess adaptation outcomes \r\nat longer time frames (high confidence). About one-third of \r\ncountries have undertaken steps to develop national adaptation M&E \r\nsystems, but fewer than half of these are reporting on implementation \r\n(medium confidence). M&E, as well as tracking global progress \r\non adaptation, are confronted with a number of challenges (high \r\nconfidence), such as a comparability in what counts as adaptation \r\nand limited availability of data across scales"


id: 87a7bb7777fd104be0d4ddbbe6513c5d
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 18872
content:
" The relative strength \r\nand weaknesses of different approaches and their applicability have \r\nnot been systematically assessed, but the diversity of approaches \r\nbeing used could provide a more comprehensive assessment of \r\nglobal adaptation progress {17.5.2, Cross-Chapter Box PROGRESS in \r\nthis Chapter}."


id: 398d424daee40b5443bd711fb05e76d4
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 6430
content:
"Effective management of climate risks is dependent on \r\nsystematically integrating adaptations across interacting \r\nclimate risks, ensuring that measures of success include factors \r\nimportant to climate resilient development, and accounting \r\nfor the dynamic nature of climate risks over time (very high \r\nconfidence). Across the Working Group II report are examples of \r\nhow managing adaptations to reduce climate risks can negatively \r\nor positively affect sustainable development, thereby impacting the \r\npotential for climate resilient development. Climate risks can emerge \r\nat different rates and time horizons, and the interactions between \r\nrisks vary from region to region (very high confidence). The need to \r\nmanage these risks in an integrated manner is demonstrated by the \r\ndiverse and interacting impacts of climate risks on ecosystems, cities, \r\nhealth, and poverty and livelihoods, such as in the water-energy-\r\nfood nexus (high confidence)"


id: 83aa6b65cda0915d74650295c6ad6107
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 18248
content:
" Expertise and resources for integrated \r\nrisk management vary between the developed and developing \r\ncountries (high confidence). Integrated pathways for managing \r\nclimate risks will be most suitable when 'low regrets' anticipatory \r\noptions are established jointly across sectors in a timely manner, \r\npath dependencies are avoided in order to not limit future options for \r\nclimate resilient development, and maladaptations across sectors are \r\navoided (high confidence). National Adaptation Plans have potential \r\nto integrate participatory, iterative processes to monitor, review and \r\nupdate adaptations as knowledge, experience and resources become \r\navailable {17.6; Cross-Chapter Box DEEP in this Chapter}."


id: b8ba65a94b1cf78085a8e231e1e7b058
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 26586
content:
"Addressing the impacts and risks associated with observed and \r\nprojected climate change (Chapter 16) is fundamentally and \r\nintricately tied to the decision-making options available to manage \r\nthose risks. Climate risk decision-making focuses on the processes \r\nneeded to identify and characterise those risks as well as generate \r\nplans and policies to reduce the likelihood and/or magnitude of \r\nadverse potential consequences, based on assessed or perceived risks \r\n(derived from the definition of risk and risk management in Chapter \r\n1). This chapter presents an assessment of the evidence on climate \r\nrisk decision-making as a set of processes that involve a range of \r\nactors in different contexts resulting in diverse outcomes. The climate \r\nrisk decision makers and their actions are the central focus of the \r\nassessment"


id: ff90f2d81c7504dae99446cec12d4f9b
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 37754
content:
" The chapter is an assessment of the evidence of the \r\ndecision-making options that are available in practice, and functions \r\nas a central pivot point between the identification of key climate \r\nrisks (Chapter 16) and the means to integrate and leverage action on \r\nclimate risk decision-making into the broader requirements of climate \r\nresilient development pathways (Chapter 18). This section introduces \r\nthe main entry points on decision-making that have framed this \r\nassessment (Sections  17.1.1.1-17.1.1.5), as well as the key terms \r\nused to frame this assessment and its organisation in this chapter \r\n(Section 17.1.2)."


id: 2f8b022ad61d58aa1b66ba1d2f044df9
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 4898
content:
"Since AR5, significant advances have been made in regard to the \r\nunderstanding of the drivers of decision-making and contexts in which \r\nclimate risk decision-making takes place. Climate risk decision-making \r\ngenerally, and adaptation specifically, has been a focus within the IPCC \r\nspecial reports in the sixth assessment cycle. An overall goal of climate \r\nrisk management is to eliminate or reduce the risk to levels that are \r\nsocio-politically and economically acceptable. Risk management to an \r\nacceptable level may not be feasible because of limits or barriers to \r\nadaptation. Future potential risks are a more complex matter given \r\nthe need to define time scales and spatial extent, and uncertainties. \r\nIn the IPCC Special Report on Global Warming of 1.5degC (SR1.5) (IPCC, \r\n2018a), the risks associated with climate-related impacts were found \r\nto be higher under emission scenarios above 1"


id: eea51d0c73ab214fc686a24f48f3ed8d
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 35018
content:
"5degC, raising awareness \r\nfor the need to limit the impacts of warming through the acceleration \r\nof climate mitigation and both incremental and transformational \r\nadaptation (IPCC, 2018a)."


id: 8161fa3df6369a72f58d89da3ef8fb1b
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 17885
content:
"A central framing point is the connection between climate risk \r\ndecision-making and adaptation. Adaptation for human systems in \r\nthis report is introduced in Chapter 1 and defined in the Glossary \r\nas 'the process of adjustment to actual or expected climate and its \r\neffects, in order to moderate harm or exploit beneficial opportunities'. \r\nIn natural systems, adaptation is the process of adjustment to actual \r\nclimate and its effects; human intervention may facilitate adjustment \r\nto expected climate and its effects (see IPCC 6th Assessment Report \r\n[AR6] Glossary [Annex II]). In this chapter, we consider adaptations \r\nthat may be implemented by people, whether they be to support \r\nhuman, managed, or natural systems, and the processes and factors \r\nthat underpin adaptation in these diverse settings"


id: 5e2ef8d8f34869e2ae81390a36dd10c5
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 12344
content:
" Different types of \r\nadaptation have been distinguished in Chapter 1, including anticipatory \r\nversus reactive, autonomous versus planned, and incremental versus \r\ntransformational (IPCC WGII glossaries; Chapters 16-18). These \r\ndichotomies and interactions are assessed here. Implementation \r\nof adaptation through iterative risk management decision-making \r\nemphasises that anticipating and responding to climate change does \r\nnot consist of a single set of judgements at a single point in time, but \r\nrather an ongoing cycle of assessment, action, reassessment, learn and \r\nresponse (Chapter 1)."


id: 44b7b0a6e441fbc9e95e5172a7ea9a7c
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 8236
content:
"Another significant advance is that, instead of cataloguing decision\u0002making strategies, the literature has now evolved to the point \r\nwhere adaptation progress, effectiveness and efficiency can be more \r\nmeaningfully assessed through increased monitoring and evaluation \r\ncapacity, although the ability to measure success and effectiveness \r\nis not fully developed and is hampered by lack of data, agreed \r\nmethods and terms, and time to fully evaluate adaptation actions \r\n(see Sections 17.3.3 and 17.5, Cross-Chapter Box PROGRESS in this \r\nChapter). The ambition to describe effectiveness and success illustrates \r\nfurther maturation of the literature on climate risk decision-making \r\nas a system process"


id: 8efa9d6fa1ac6c7e547add337b9e2666
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 20031
content:
" Overall, the process of climate risk decision\u0002making remains dynamic, and the chapter attempts to assess a variety \r\nof proactive management approaches being developed and tested \r\nto address adverse, diverse and complex risks in a wide range of \r\ndeveloping and developed country contexts (Figure 17.1). The chapter \r\nprovides a synthesis of how these new approaches are reflected in the \r\nsectoral and regional chapters and cross-chapter papers of this report \r\n(Chapters 2-15; Cross-Chapter Papers 1-7). Specifically, the goal is to \r\nprovide a line of sight between the sectoral and regional chapters and \r\ncross-chapter papers' decision-making assessment to sections in this \r\nchapter. This synthesis also helps to present the varying and context\u0002driven character of adaptation strategies now in practice and being \r\nconsidered."


id: 54ab7fed1c978186a32ead4c1f6afaf3
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 10778
content:
"Since AR5, there has been an increasing ambition for adaptation, \r\nsignalled by growing attention to the adaptation gaps and deficits, \r\nwhich call for extensive and intensive levels of action (Chen et al., 2016; \r\nUNEP, 2017; Tompkins et  al., 2018; Valente and Veloso-Gomes, 2020; \r\nUNEP, 2021a), as well as increased attention to co-benefits between \r\nclimate risk reduction and other benefits, such as equity and biodiversity \r\nconservation (Colloff et  al., 2017, Section  17.5.1; Smith et  al., 2020). \r\nClimate risk decision-making as an object of study has emerged in a more \r\ncentral location within the literature as adaptation moves from planning \r\ninto the realm of practice"


id: 5f098940af8ba9be0df297ef0bdccd37
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 12495
content:
" The broad sense of urgency (summarised in \r\nWilson and Orlove, 2019; Wilson and Orlove, 2021) shows growth of the \r\nterm 'urgency' in both scholarly publications and the popular press since \r\n2014, building on earlier increases starting around 2005, and a dramatic \r\nspike of the terms 'climate crisis' and 'climate emergency'. Paralleling \r\nthis call for more extensive and rapid action is the emergence of the term \r\n'transformational' adaptation and decision-making. Transformational \r\nadaptation (defined and deeply examined in Chapters 1 and 16 and \r\nSection 17.2) highlights efforts that involve large-scale, systemic change \r\n(Wilson et al., 2020) and involves 'adapting to climate change resulting \r\nin significant changes in structure or function that go beyond adjusting \r\nexisting practices including approaches that enable new ways of \r\ndecision-making on adaptation' (IPCC, 2018a)"


id: 435f93724937d4ce041c6d4c1d350edf
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 8009
content:
" The complex relationship \r\nbetween incremental adaptation and transformational adaptation is \r\npresented and reviewed in Section 17.2. Furthermore, the literature since \r\nthe AR5 report has moved beyond the question of limits and barriers \r\nto adaptation as relevant aspects for decision-making to additionally \r\nassessing drivers of change, with increasing focus devoted to more \r\nnuanced and differentiated contexts for action."


id: d3b771fb234001aedfe12df7401539a3
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 30831
content:
"In AR6 (Chapter 16 and cross-chapter papers), over 100 key risks \r\nhave been identified across regions and sectors, which have the \r\npotential to manifest into severe impacts that are relevant to the \r\ninterpretation of UNFCCC Article 2, specifically on the objective \r\nto avoid dangerous anthropogenic interference with the climate \r\nsystem. These risks are likely2\r\n to become more severe under higher \r\nwarming scenarios and social-ecological conditions that yield high \r\nexposure and vulnerability to the associated climate-related hazards. \r\nIn this report, these key risks have been grouped into categories \r\nrepresented by eight overarching risks (called Representative Key \r\nRisks, RKRs) relating to: (1) coastal socio-ecological systems; (2) \r\nterrestrial and ocean ecosystems; (3) critical physical infrastructure, \r\nnetworks and services; (4) living standards; (5) human health; (6) \r\nfood security; (7) water security; and (8) peace and human mobility \r\n(Chapter 16)"


id: f8f6748391a7fee4a54727a503caf7fb
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 36679
content:
" Decision-making options for managing these risks, such \r\nas selecting the relevant adaptation options to implement, require an \r\nassessment of the local context in which these impacts are likely to \r\nbe experienced, as well as the local to global collective implications \r\nof those actions (Sections 17.2 and 17.5)."


id: 11dee2d783f9eebea08a84ff1383edc8
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 308
content:
"However, a wealth of information has recently become available on how success and effectiveness of adaptation could be assessed, \r\ndefined or investigated in certain settings (Patt and Schroter, 2008; Morecroft Michael et al., 2019; Tubi and Williams, 2021) or across \r\na larger set of adaptations (Hegger et al., 2012; Eriksen et al., 2015; Gajjar et al., 2019a; Owen, 2020; Singh et al., 2021). Accordingly, \r\nsuccessful adaptation is understood as effective adaptation, in that it reduces climate impacts, vulnerabilities and risk, and additionally \r\nbalances synergies and trade-offs across diverse objectives, perspectives, expectations and values (Eriksen et al., 2015; Juhola et al., 2016; \r\nGajjar et al., 2019a; Owen, 2020; Singh et al., 2021)"


id: fed3a3866d39449d9845d147eca7bc16
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 37634
content:
" Across this report, four factors are identified as enabling conditions of successful \r\nadaptation, which include a focus on recognitional, procedural and distributional justice as well as flexible and strong institutions that \r\nseek policy integration and account for long-term goals."


id: cd078d08e78353a199d0ce577e56f96d
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 29804
content:
"dimension of climate change, as described in Chapter 8, vulnerability, \r\ninequality and poverty influence these portfolios of adaptation and \r\ntransformational change. Particularly for change where residual risks \r\nremain that may lead to exceeding the limits of adaptation, increasingly \r\ntransformational adaptation and policy innovation will be important. \r\nSection  17.2.1 assesses options for climate risk management from \r\naround the world that reduce, manage or retain climate-related risks \r\nand assesses their contribution to reducing vulnerability and exposure, \r\nhow they are governed, and the benefits to humans and ecosystems. \r\nSection 17.2.2 presents portfolios of risk management, including the \r\ndesign principles and observed variations across the globe, before it \r\ndiscusses the need and potential for transformational adaptation to \r\ncomplement incremental adaptation, for which we present evidence \r\nacross the report for selected adaptation options and some key risks"


id: bb139f63f42a86bf6a196ceeb0dd6c2a
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 26976
content:
" \r\nThe Cross-Chapter Box LOSS in this Chapter synthesises recent literature \r\nand assesses key strands of the international policy dialogue on Loss \r\nand Damage, which discusses options that help to deal with impacts \r\nand residual risks in vulnerable countries."


id: f10eeff7ab65535a6256ec1e08c02d74
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 35423
content:
"Figure 17.1 |  Schematic representation of the climate risk management decision-making process as introduced in Chapter 1 (Figure 1.6) and the key \r\nelements of this chapter that address additional aspects of this process. In Chapter 17, climate risk management (middle box) is framed as the iterative response (i.e., \r\nwhat society could do and how it could be done) to the climate risks described in Chapter 16, with outcomes (ideally reduced risk) that can support (or perhaps hinder) climate \r\nresilient development, as assessed in Chapter 18. Decision makers from diverse contexts sit at the centre of the climate risk decision-making process and interact with and drive \r\nthese processes as they play out"


id: 706c02f229ce6d8d50459ed669824bfc
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 15308
content:
" The main sections of Chapter 17 (bottom panel of boxes) address a wide range of issues (keywords in bottom panel) that manifest at one or more \r\nstages of climate risk management processes, illustrated by icons for section numbers and Cross-Chapter Boxes in the interactive risk management process."


id: d7df8d78bdebed952ac480dfa91fe5dd
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 31492
content:
"To coordinate between a suite of applicable risk management \r\ninterventions, the concept of risk layering has been discussed and used in \r\n(financial) risk governance of disaster risk management (Mechler et al., \r\n2006; Cummins and Mahul, 2009; Clarke and Mahul, 2011) and climate \r\nrisk management (Lal et al., 2012; Mechler et al., 2014; Herron et al., \r\n2015; Schafer et al., 2016; Mechler and Deubelli, 2021). Incremental \r\nrisk prevention and preparedness as well as risk financing occurs \r\nwithin national systems. Over the years, regional cooperation, such as \r\nthrough the regional sovereign insurance pools in the Caribbean, the \r\nPacific and Africa, but also transboundary risk management elsewhere \r\nhave become more important (medium confidence) (see Martinez-Diaz \r\net al., 2019). Also, with risks increasingly experienced as severe and \r\nexistential (Boyd et al., 2017), global governance and solidarity have \r\nbeen invoked (see Linnerooth-Bayer et al"


id: 62c2e61d4fe814d34a7ce4ade515ef70
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 13108
content:
", 2019; Pill, 2021), largely as \r\npart of the policy discourse on Loss and Damage (Mechler et al., 2019) \r\nwith further momentum provided by discussions on the global goal of \r\nadaptation and recognition of climate risk as transboundary (Benzie \r\nand Persson, 2019; Cross-Chapter Box  INTERREG in Chapter 16). \r\nTransformational risk management has emerged where incremental \r\nand in situ adaptation is not effective in managing risks, such as for \r\nmanaged or strategic retreat for communities facing severe coastal \r\nand riverine flooding (Siders et al., 2019). Transformation has not been \r\nwell documented, including as to its governance (Section 17.2.2.5)."


id: 1a75b1e9d6b244a908398e982e72c063
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 1646
content:
"limited resources, and adaptation investments are more likely to \r\ninclude structural measures to reduce exposure, complemented by \r\nvulnerability-reducing measures and insurance protection (medium \r\nconfidence). While this typology is not universally representative of \r\nhigh-income areas (within or between countries), expensive exposure\u0002reduction measures tend to be easier to implement in high-income \r\ncountries. For example, flood protection is largest in countries with \r\nlarger amounts of public spending and least amounts of corruption \r\n(Scussolini et al., 2016). It is seen as more economically efficient to \r\ninvest in expensive protection measures in wealthy regions, under \r\ndifferent scenarios of sea level rise and river flooding, although these \r\ncalculations have equity and justice implications (Peduzzi, 2017; Lincke \r\nand Hinkel, 2018)"


id: 68a7548d28c35b866bfe475ed57751b8
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 14060
content:
" After flood events happen in regions with high \r\nlevels of protection, damages are comparatively limited, and people \r\ntend to continue living in close proximity to the protected river (Mard \r\net al., 2018). In contrast, flood displacement is higher in low-income \r\ncountries (Kakinuma et al., 2020)."


id: c68c88531601e55ae9a279ec5b70190a
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 28803
content:
"Several studies and many government planning documents reference \r\nhow people can benefit from a changed climate, beyond reducing \r\nrisks. For example, several regions are expecting an increase in \r\nvisitors to eco-tourism sites or national parks with a changing climate \r\n(Fisichelli, 2015; Lwasa, 2015). In Europe, several national adaptation \r\nplans include planning for potential benefits of a changing climate, \r\nincluding reduced winter mortality and improved conditions for \r\nhydropower (Biesbroek et  al., 2010). Recognising the need for \r\neconomic diversification, people working in certain industries, such as \r\ncoastal management, perceive climate change as a factor increasing \r\nthe need for their services (Fatoric et al., 2017). Northern countries \r\nare taking advantage of ice-free waters for shipping routes in the \r\nArctic (Eguiluz et al., 2016; Melia et al., 2016; IPCC, 2019c)"


id: 523fda38ed5af29ca7c47f14c58033d3
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 10391
content:
" In Africa, \r\nopportunistic adaptation has been observed by smallholder farmers, \r\nto plant crops that are better suited for a changing climate (Lalou \r\net  al., 2019). Similar agricultural adaptation in Pakistan has been \r\nassociated with improved food security and reduced poverty (Ali \r\nand Erenstein, 2017; Rahman et  al., 2020). In each of these cases \r\ndocumenting benefits, there are also potential negative impacts on \r\nother populations or ecosystems, such as ecosystem impacts from \r\nincreased Arctic shipping (Ng et al., 2018)."


id: 1953c221a465210b24cc65dfafbe6704
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 1469
content:
"Consequences of conflict that exacerbate vulnerability to climate change include: displacement, loss of access to employment leading to \r\nillegal livelihoods, gender-based violence, lack of land tenure, low literacy, poor access to social and health services, destruction, looting \r\nand theft of key assets, such as houses, food stocks and livestock, among others (Jaspars and Maxwell, 2009; Chandra et al., 2017; \r\nAnguita Olmedo and Gonzalez Gomez del Mino, 2019). Such impacts perpetuate cycles of poverty (World Bank, 2013), making conflict\u0002affected populations more susceptible to suffer from climate-related events (Basher, 2006; Coughlan de Perez et al., 2019). For example, \r\nin Mindanao, Philippines, poverty is closely linked to long-standing armed conflicts; both climate change and conflict have significantly \r\nincreased smallholder vulnerability, resulting in loss of livelihoods, financial assets, agricultural yield and the worsening of debt problems \r\n(Chandra et al., 2017)"


id: 5828d8c3df434c6016d7105a4553be34
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 11378
content:
" In Colombia, displacement induced by conflict has pushed the population to live in high-risk areas such as steep \r\nslopes susceptible to landslides and river banks exposed to flooding (Albuja and Adarve, 2011). This conflict-induced vulnerability, with \r\nlittle adaptation activity, has in turn resulted in climate-related disasters (Kuipers, 2019; Siddiqi et al., 2019)."


id: a180fd0377e4756a0553ce364b467731
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 22968
content:
"To address risks to livelihoods, conflict-sensitive livelihood programming has used vouchers to meet immediate needs, legal support \r\nto resolve land disputes, and disaster preparedness planning to identify safe places for displacement (Jaspars and Maxwell, 2009). \r\nFor example, cooperation in the Philippines between Moro Islamic Liberation Front and United Nations agencies included training of \r\nfarmers in disaster risk reduction, drought management and production of improved crop varieties to support a transition away from \r\nsubsistence farming (Walch, 2018). In Mali, negotiations on fertilizer access and safe transport to agricultural lands were brokered by the \r\nInternational Committee of the Red Cross, and in Afghanistan, conflict-sensitive approaches have promoted ecosystem-based adaptation \r\nto support reforestation (Walch, 2018; Mena and Hilhorst, 2020)"


id: 9847bb366bcb3a71bb21898516c78dd4
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 21472
content:
" Despite several examples of conflict-sensitive adaptation practices, \r\nlittle is known about the effectiveness of such efforts in reducing climate risks in these complex contexts (see Section 17.5 for further \r\ndiscussion of 'effectiveness')."


id: 55486cb52399762149c0f67782e9e178
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 10886
content:
"To distinguish incremental from transformational adaptation, Lonsdale \r\net  al. (2015), building on Mustelin and Handmer (2013), identify \r\ncriteria related to framing, learning and decision-making, space and \r\ntime, power, and type of change management. Tabara et al. (2019), \r\nadditionally discuss transformation in light of informing climate \r\npathways, strategies and solutions. Broadly considering these criteria, \r\nthey identify 12 dimensions with additional discussion of change \r\nwith regard to systems and dynamics, options and solutions, agency, \r\nand the consideration of equity (see also Chapters 1, 6, 18 for more \r\ndiscussion)"


id: 2256cd6b8f5ea4ff6d5f8457a669db72
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 2851
content:
" In particular, the following key aspects for understanding \r\nthe spectrum from incremental to transformational adaptation are \r\nof relevance: change, within or across the system; agency, single or \r\nheterogenous; a role for visioning and normative futures; the type of \r\nlearning required (from first order, business-as-usual, to second order); \r\nand how equity and distributional issues are explicit."


id: ccf82ceba34dcfe0fd4f34eda573c349
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 29794
content:
"Figure 17.6 organises global and regional findings for observed and \r\nprojected health risks from heat (RKR-E) from chapters across the \r\nreport and organises options according to findings on the potential for \r\ntransformational change as presented in Section 17.2 and Table 17.3. \r\nThe discussion shows that heat has become a significant health risk \r\nglobally, incurring severe mortality and morbidity in all world regions \r\nwith annual heat-related deaths estimated around 300,000 with \r\nmillions affected (high confidence) (Section 9.3.1). Evidence shows that \r\nadaptation and risk management can be effective in reducing (relative) \r\nrisks in developed countries, with inconclusive evidence in low-middle\u0002income states (Sections 9.2.4.1, 13.7.3, 13.6). In absolute terms, risk in \r\nterms of heat-related mortality and morbidity is projected to increase \r\nunder medium and high heating scenarios in many regions, even with \r\nimplemented adaptation"


id: 51b151679c27bdae6661bfa4828c870c
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 10314
content:
" By 2050 (compared with 1961-1991 and for \r\na mid-range emissions scenario), an excess of 94,000 deaths yr-1 is \r\nprojected globally as attributable to climate change (Section 9.3.1)."


id: df2cec7738cb61008c4f6b609ab996da
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 32614
content:
"The pathway to a decision may not be linear, depending on when and in \r\nwhat detail the decision-making or consultative group may need to be \r\nunderstanding the climate risk and its real-world context (sense-making,\r\nmodelling), has sufficient background to analyse and explore options for \r\nameliorating the risk (analysis, exploration), or is ready for interpreting \r\nthe analyses and deciding on the requirements and strategies for \r\nimplementing a chosen strategy (interpretation-implementation) \r\n(high confidence) (Figure  17.7; French et  al., 2020). The development \r\nof decision-support tools for climate risk management (Palutikof et al., \r\n2019a; Palutikof et al., 2019b) and more generally (Papathanasiou et al., \r\n2016), along with archives of experiences from practitioners (Watkiss \r\nand Hunt, 2013; Section 17.5; Bowyer et al"


id: 78cd6ccaf3e84a91c776f4919a02ab85
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 16566
content:
", 2014; French, 2020), means \r\nthat some aspects of the decision-making process can be circumvented \r\nor at least streamlined as that experience is re-used (high confidence)."


id: 36afc343ae536cfb4e7fd49961ffa856
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 5991
content:
"IPCC's first assessment of L&D in 2018 found residual risks to rise with further global warming leading to soft and hard adaptation \r\nlimits in some natural and human systems (e.g., coral reefs, human health, coastal livelihoods) (Roy et al., 2018). Sections 8.4.5.6, 16.4 \r\nand 17.2 corroborate these findings concluding that, depending on mitigation and adaptation pathways, residual risks in key systems \r\nin many regions will create potential for negative impacts beyond adaptation limits (medium confidence). The assessment in 2018 also \r\nnoted that there is 'not one definition of L&D'. This ambiguity has persisted, and a policy space for L&D has not clearly been delimited \r\n(high confidence). There is, however, coalescence in dialogue among academia, civil society and policy around a distinct set of themes \r\nas identified by stakeholder surveys as well as literature, methods and evidence reviews (Vanhala and Hestbaek, 2016; Boyd et al., \r\n2017; Mechler et al"


id: 39122e722351edb5d74dc5671663e3c0
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 6349
content:
", 2018; Calliari, 2019; McNamara and Jackson, 2019): risk management, limits to adaptation, existential risk, finance \r\nand support, including liability, compensation and litigation (Sections 8.3, 16.4; medium confidence; Figure Cross-Chapter Box LOSS.1). \r\nVarious advisory groups have been set up with participation of policy and experts from research, civil society and practice to help inform \r\nthe implementation of WIM workplans (UN, 2015; UN, 2019)."


id: f59c335d26b494c125de0cba652fcd16
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 36163
content:
"There is evidence that, without strong risk management and adaptation, losses and damages will continue to affect the poorest vulnerable \r\npopulations, potentially creating poverty traps (high confidence) (Sections 8.3, 8.4.5.6 and Tables 8.7, 17.2; Serdeczny, 2019; Tschakert \r\net al., 2019; Thomas et al., 2020). Research has started to develop global inventories on losses and damages, including on intangible \r\neffects (Tschakert et al., 2019; Otto et al., 2020), and engaged with the practice community for data collection. Practice has provided \r\nguidance to report on losses and damages in countries (I)NDCs (WWF & Practical Action, 2020). Yet, systematic risk assessments of \r\nclimate-related losses and damages including adaptation limits (see, e.g., Leal Filho and Nalau, 2018; Robinson, 2018) have remained \r\nscarce (Section 16.4; high confidence). Thus, many vulnerable countries lack comprehensive data at scale of risk management including \r\non economic (e.g"


id: 3d56b03dd8466f0f6dd22dfae2ae0fb2
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 7009
content:
", loss of livelihood assets and infrastructure) and non-economic losses and damages (e.g., culture, health, biodiversity), \r\nthus hampering effective risk management (Thomas and Benjamin, 2018; Martyr-Koller et al., 2021; Singh et al. 2021). Van den Homberg \r\nand McQuistan (2019) propose a losses and damages inventory also to be used to monitor how technologies may shape risks as well as \r\nadaptation limits. While early warning and other risk reduction options as well as risk retention considerations are being discussed, L&D \r\ndialogue has strongly focused on risk finance for residual risks, particularly through the donor-supported provision of public insurance \r\nsystems (Linnerooth-Bayer et al., 2019; Schafer et al., 2019; Broberg and Romera, 2020; Nordlander et al., 2020)."


id: 78adb9add9b9499332d96e992b80286
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 16548
content:
"The role of transformation in risk management for overcoming any soft limits to adaptation is seeing emerging attention (medium \r\nconfidence, limited evidence), and the TEG CRM has also been tasked to consider transformation. Relocation and retreat of assets \r\nand communities, where in situ adaptation is considered impossible, is increasingly being debated in research and practice, including \r\nin terms of finance and L&D implications (Section 8.4.4; Boston et al., 2021; Desai et al., 2021; Mach and Siders, 2021; van der Geest \r\nand van den Berg, 2021; Zickgraf, 2021). Livelihood transformation occurs where current livelihoods become unfeasible in the face \r\nof multiple climatic and non-climatic stressors (Section 8.3.4.1) requiring change within sectors (such as switching from cropping to \r\nlivestock rearing (Escarcha et al., 2020) or across sectors, when farming households relocate to offer labour elsewhere (Section 9.1; Rasel \r\net al., 2013)"


id: 5f0084d134b0739491e3d4cdfb3d5b25
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 12488
content:
" Biermann and Boas (2017) suggest revamping global governance systems to effectively address the protection and voluntary \r\nresettlement of those displaced by climate variability and change. A WIM taskforce on displacement is tasked to further advise on human \r\nmobility, including migration, displacement and planned relocation (UNFCCC, 2019a)."


id: 74db6509bd584f9aabe9d748c943a5e5
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 15978
content:
"Figure 17.7 |  Relationships between different processes of decision-making to manage climate-related risks in the real world, noting that, when appropriate, \r\nsome aspects may only require experience to be re-used. (1) Formulation of risks of concern and accompanying policies and objectives for managing those risks, forming \r\nprescriptive models for the decision maker. (2) Knowledge, understanding and observations of the real world are used to assess past and current impacts and future risks using \r\ndescriptive models, based on the perspectives and prescriptive models arising from (1). If not well formulated from other experience, processes in (1) and (2) interact to make sense \r\nof the world and what needs to be done. In iterative management, (1) and (2) also form the basis for monitoring, reviewing and evaluating effectiveness of adaptations. (3) Use \r\nof decision-support and decision-analytic tools to appraise costs and benefits of different options for ameliorating future risks"


id: 26287dda37c3eaf53f883d2a7ef234ef
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 3421
content:
" The double-headed arrow indicates where two-way \r\ninteractions occur between different activities (likely to be iterative, feedback and nonlinear processes); modelling and assessments are repeated and revised in tandem with the \r\nplanning and evaluation of options, based on interactions with the policymakers and stakeholders. (4) The decision maker, which may be a group of people, interacts with the \r\nevaluation of options (two-way interaction) and interprets the efficacy of the options and the implications for the real world, ultimately choosing one or more actions to satisfy the \r\npolicy objectives to manage the risks. (5) Implementation of the actions in the real world, which may be once-only actions or instigation of a feedback management system that \r\nenables ongoing adjustments to meet objectives."


id: 92e3f606ea7c50124afa2f15b400efda
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 20622
content:
"to support analysing and making of decisions generally (French, \r\n2020). These communities of decision analysts can act like broad\u0002based statisticians to advise on matching methods to the climate risk \r\nand its context, before individual decision specialists are consulted. \r\nSome scientific literature is presenting guides for choosing different \r\nmethods, tools and approaches (Shi et al., 2019). This sub-subsection \r\nprovides a summary guide for policy analysts and decision makers \r\nto help identify the classes of decision-analytic methods that may \r\nbe suitable for their context for managing climate risks. It focuses \r\non decision-analytic methods, noting that decision-support tools will \r\nunderpin many of these methods by organising information (Bourne \r\net  al., 2016; Papathanasiou et  al., 2016; Ceccato et  al., 2018; Hasse \r\nand Kind, 2018) or support modelling (Papathanasiou et  al., 2016; \r\nKwakkel, 2017; Gardiner et  al"


id: bd69cf6ddd963a19dcfaa9777736b0f2
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 27366
content:
", 2018), sometimes with a particular \r\ndecision-analytic process in mind (Hadka et al., 2015; Torresan et al., \r\n2016; Tonmoy et al., 2018)."


id: 45f5f78cf9390304588ebb11a27d9c7a
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 8392
content:
"The cognitive phase of 'analysing and exploring' uses models and \r\nexisting data and/or knowledge services as available to explore the \r\nrelevance/efficacy of adaptations to ameliorate risk or to meet other \r\nadaptation objectives, as well as possible flow-on effects of those \r\nactions (Section  17.3.1.4). Sensitivity and robustness analyses can \r\nbe useful if conditions are favourable to supplement the decision \r\nanalysis, setting bounds on some of the residual uncertainty (high \r\nconfidence) (Borgonovo and Plischke, 2016; Ferretti et  al., 2016). \r\nValidation of models and verification of data (Tittensor et al., 2018) are \r\nbecoming highlighted as important steps in this phase or in the sense\u0002making phase, particularly in their capacity to understand and test \r\ndecision makers and stakeholders' perceptions (medium confidence)"


id: 35ea0605d20f1c48833c637c6861ab7
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 5893
content:
" \r\nRandomisation methods, Bayesian methods, interval methods, multi\u0002criteria decision analysis (MCDA), decision-making under deep \r\nuncertainty (DMDU) and economic and financial approaches (e.g., Real \r\nOptions Analysis) are tools of choice in this phase (high confidence) \r\n(Table  17.4) (Abbas and Howard, 2015; Bendoly and Clark, 2016; \r\nBorgonovo and Plischke, 2016; Iooss and Saltelli, 2017; Korhonen and \r\nWallenius, 2020; Saltelli et  al., 2020). Decision-support tools in the \r\nprovision of data and/or modelling methods are regularly used in this \r\nand the sense-making phase (high confidence) (Section 17.3.1.2)."


id: c42fadaf5209260f56b1a5cc0015e7c8
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 28431
content:
"Many sensitivity analyses based on scenarios, including procedures \r\nto randomise across model uncertainty, relate to descriptive dynamic \r\nmathematical models with the user of the models characterised as an \r\nobjective observer (Borgonovo and Plischke, 2016; Ferretti et al., 2016; \r\nSymstad et al., 2017; French, 2020). Bayesian approaches enable these \r\ndescriptive analyses to take account of the subjective choices in model \r\nconstruction and implementation (Abbas and Howard, 2015; Sperotto \r\net  al., 2017; Jager et  al., 2018; Sperotto et  al., 2019; French, 2020). \r\nOrganising descriptive analyses and deciding on a suitable option \r\nacross a diversity of opinions among stakeholders use prescriptive \r\nprocesses, which can be supported with prescriptive modelling tools \r\n(Williamson and Goldstein, 2012; Gelman et  al., 2013; Abbas and \r\nHoward, 2015; Dias et al., 2018; Phan et al., 2019; Hanea et al., 2021)"


id: 2e2cb2b8211ea7f6fe60d42542b2dacb
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 4706
content:
" \r\nThese approaches are subjective, in that they are constrained or \r\ndirected by the particular views and emphases of the decision-making \r\ngroup (Gorddard et al., 2016). Not all tools are appropriate for all these \r\nactivities."


id: 9fa846de4f4880ba15cc3b61da52881a
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 22663
content:
"Decision makers will be better able to choose decision-analytic methods \r\nwhen they have an understanding of the types, scale and breadth of \r\nuncertainties around the climate risk (high confidence) (Symstad et al., \r\n2017). The Cynefin framework (Snowden, 2002; French, 2013) is a \r\npolicy-driven framework that broadly categorises the decision context \r\nof uncertainty within which decision makers and policy analysts may \r\nfind themselves (medium confidence) (Hurlbert et al., 2019; Helmrich \r\nand Chester, 2020). As Cynefin has helped frame previous IPCC \r\npresentations on contexts of uncertainty (Hurlbert et al., 2019) and has \r\na community of practice to consult on its use (French, 2020), it is used \r\nhere, also because it considers the uncertainty in knowledge around \r\ncause and effect in general terms, rather than specifically focusing on \r\nuncertainty in formal models"


id: 95b7a13e50735b076cd66f3b89cbe39e
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 21051
content:
" Helmrich and Chester (2020) show how \r\nCynefin can be used to frame climate adaptation decision-making in \r\nthe infrastructure sector."


id: 189522a374e1f22896037b07290baead
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 1361
content:
"Uncertainty does not just relate to what might happen given climate \r\ndrivers or adaptations, but also to how much one values potential \r\nconsequences (Butler et al., 2016; Beven et al., 2018a; Cross-Chapter \r\nBoc DEEP; Beven et  al., 2018b; French, 2020) (high confidence);\r\nthe balance between how particular decision analyses address \r\nuncertainties relating to the external world (descriptive models) and \r\nthose relating to the values driving the decision-making (prescriptive \r\nmodels) is important (Butler et  al., 2016)"


id: 711db8074f0c8b7637a451e6405fc0d2
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 15428
content:
" Some analyses partially \r\nignore uncertainties relating to the former in order to focus on conflicts \r\nin the values held by different stakeholders and help structure debate \r\n(Korhonen and Wallenius, 2020; French, 2020), while others build \r\nvery sophisticated models of the external world to predict potential \r\nconsequences, but in doing so lose transparency and risk becoming \r\nuntrustworthy black boxes to many stakeholders (low confidence) \r\n(Peterson and Thompson, 2020)."


id: 694ae283272c5938faa1f87267809fb3
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 14160
content:
"Classes of tools (Watkiss and Hunt, 2013; French, 2020) include \r\nBayesian methods, interval methods, decision-making under deep \r\nuncertainty (DMDU; see Cross-Chapter Box  DEEP in this Chapter), \r\ncost-benefit analyses, multi-criteria decision analysis, elicitation and \r\ngeneral decision support tools (Table  17.4). A summary guide for \r\npolicy analysts and decision makers is presented in Table 17.4 to help \r\nidentify the classes of decision-analytic methods that may be suitable \r\nfor their context for managing climate risks. The table summarises how \r\nwell the methods address the Cynefin context, the phase of decision\u0002making, the types of uncertainties that exist through the decision\u0002making process and the resources required. As terminology may vary \r\nbetween disciplines and research groups, suitable references to better \r\nexplain the methods within the class are provided"


id: a1b06dac95b4f7ec8223fb85bbe5d319
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 23000
content:
" Also, there may be \r\noverlap between the classes as individual methods are often paired \r\nwith other methods to address specific requirements and approaches \r\n(Buurman and Babovic, 2016; Haasnoot et al., 2019). In that respect, \r\nthese methods are referred to in the next section discussing advances \r\nin the different approaches to managing climate risks."


id: 4d55f581e55b3a131f828316c9283aac
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 9598
content:
"Much of the readily available literature on how uncertainties affect \r\ndecision-making relates to the uncertainty in the biophysical models, \r\nwith a recognition that the choice of tools will be influenced by \r\nthe types of uncertainty to be addressed (Le Cozannet et  al., 2017; \r\nSymstad et al., 2017; Beven et al., 2018a; Beven et al., 2018b; Durbach \r\nand Stewart, 2020b; French, 2020). While terminology varies among \r\ndisciplines, three types of uncertainty are important in understanding \r\nassessments of the future from descriptive models: epistemic \r\n(uncertainty in model construction relating to the lack of knowledge \r\nabout the system being represented), analytic (the degree to which a \r\nmodel fits observations, and its accuracy) and stochastic (the natural \r\nvariability or randomness in the system)"


id: c7a9099fe2cdbeea7a97f3aae49fcff2
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 28994
content:
" The probability of an event \r\narising in the future is determined from all three uncertainties, noting \r\nthat stochastic uncertainty is a property of the system rather than a \r\nlimitation of research (Le Cozannet et al., 2017; Beven et al., 2018a; \r\nBeven et al., 2018b)."


id: 76600f6863982e90bde2c70efa575a59
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 16214
content:
"Uncertainty in what constitutes a risk of concern is increasingly identified \r\nas important to consider when managing risk (Chapter 16; Butler et al., \r\n2016; Prober et al., 2017; French et al., 2020; Reis and Shortridge, 2020). \r\nThe uncertainty here arises from what is an acceptable risk. Acceptability \r\nrelates to the value or importance of the consequence, which may \r\ninclude moral and ethical uncertainties (Prober et  al., 2017), as well \r\nas how ambiguous the understanding of the consequence may be \r\nbetween different groups (Beven et al., 2018a; Beven et al., 2018b). The \r\ndevelopment of strategies to ameliorate risk will benefit from considering \r\nthese two uncertainties in specifying the risk to be managed (Prober \r\net al., 2017; French et al"


id: 3520aa98c109af215add51af3115a92a
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 5767
content:
", 2020) because they can help set boundaries on a \r\nrequired likelihood of success, rather than simply casting stakeholders or \r\ndecision makers as risk averse or risk tolerant, and can help identify and \r\naccept pathways of success (Gregory et al., 2012). This can be important \r\nwhen decisions need to be made well in advance of the actions needing \r\nto take effect, such as for many climate risks (Chapter 1; Chapter 16; \r\nSection 17.2.3; Cross-Chapter Box DEEP in this Chapter)."


id: b6ce9c4112689492df3bb90e953ee888
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 26285
content:
"Stakeholder engagement has become increasingly part of climate\u0002relevant decision processes (Orlove et  al., 2020). The degree of \r\nstakeholder engagement ranges from instructive and consultative to \r\ncooperative, which are equivalent to information exchange, influence \r\nand partners in decision-making (Sen, 2000; Cattino and Reckien, \r\nin press). Since the AR5, climate change adaptation and resilience \r\nliterature has seen an increase in participatory approaches that deepen \r\nengagement and overcome challenges, as well as making some \r\nassessments of their effectiveness (Newton Mann et al., 2017; Wamsler, \r\n2017; Esteve et  al., 2018), including structured interactions among \r\ndifferent types of stakeholders and the use of place-based boundary \r\norganisations to strengthen the interactions and heighten the awareness \r\nof the institutional context"


id: a799f4a90c97f735476ae2b49551c0f6
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 23877
content:
" A higher degree of public participation can \r\nlead to more transformational adaptation as well as to higher ambition \r\nfor local mitigation (medium confidence) (Section  17.4.4.2; Cattino \r\nand Reckien, in press). Challenges to stakeholder participation are \r\naccess to state-of-the-art science, capacity to recognise and respond \r\nto non-reliable or false climate science information, and the removal \r\nof cognitive and other biases (high confidence) (Gorddard et al., 2016; \r\nEngler et al., 2019; Fulton, 2021)."


id: 249dd41a221634fc0a7aa86db30271b
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 3204
content:
"Scenarios and pathways, combined with elicitation methods, are \r\nbecoming widely used to assess adaptation and resilience strategies (high \r\nconfidence) (Butler et al., 2016; Prober et al., 2017; Symstad et al., 2017; \r\nLawrence et al., 2019; Phan et al., 2019; Sperotto et al., 2019; Haasnoot \r\net al., 2020a). They can support the consideration of a wide range of \r\nalternative possible futures (Catenacci and Giupponi, 2013; Jager et al., \r\n2018), enabling identification of potential path dependencies caused \r\nby adaptation options (high confidence) (Pretorius, 2017; Haasnoot \r\net  al., 2020a). They can also increase the willingness of stakeholders \r\nto consider costly actions, by placing them within broader sequences \r\nof action (limited evidence) (Barnett et  al., 2014)"


id: 5dc09244f150a9980abb0a120ad81b5d
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 12276
content:
" The development, \r\nconsideration and understanding of scenarios can be enhanced by using \r\nvisualisation tools to better display storylines, enabling the discussion of \r\nalternative futures by participants in decision-making processes (limited \r\nevidence) (Winters et al., 2016)."


id: 420f2bd0105d4e395b6e12517c5761ee
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 7794
content:
"Participatory and elicitation approaches, where the concerns and \r\ninvolvement of a broader range of interest groups and stakeholders are \r\ntaken into account, can improve the effectiveness of decision-making \r\n(medium confidence) (Gregory et  al., 2012; Cvitanovic et  al., 2019). \r\nParticipatory planning includes a variety of co-generative strategies \r\nand approaches (e.g., qualitative scenario or adaptation pathway \r\ndevelopment) through which goals and objectives, knowledge and \r\nstrategy implementation and evaluation can be decided collaboratively \r\nbetween practitioners, policymaking, local interests and groups, \r\nand scientists (Butler et al., 2016; Prober et al., 2017; Symstad et al., \r\n2017). Specifically, for climate change adaptation, these decision\u0002making strategies can incorporate expert, Indigenous and local \r\nknowledge (high confidence) (Cross-Chapter Box  INDIG; Gustafson \r\net  al., 2016)"


id: a05e0b26d3a8d3143a8e8e6b92804900
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 22766
content:
" The challenge will be to bring together these different \r\nactors, as stakeholders tend to act within rather than among systems \r\nand procedures, and it is important that platforms are developed to \r\nintegrate data effectively (Rizzo et  al., 2020). Furthermore, reflexive \r\nand iterative risk management may further ensure acceptance by \r\nparticipating groups."


id: e785b33cd02ce40f00470c3285648cc6
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 33926
content:
"Since the AR5, robust decision-making methods are increasingly used \r\nto account for deep uncertainty in many climate-related risks (high \r\nconfidence) (Marchau et al., 2019; Table 17.4), particularly when decisions \r\nneed to be made well in advance of when the adaptations need to be \r\nimplemented (Cross-Chapter Box.5 in SROCC Chapter 1; Cross-Chapter \r\nBox DEEP in this Chapter). Reducing risk and building resilience under \r\nthe context of these types of wicked problems require asking 'what if' \r\nquestions about the future, remaining flexible in the face of uncertainty \r\nand seeking out policies that provide good outcomes no matter what the \r\nfuture climate might bring (high confidence) (Section 17.6; e.g., Larson \r\net al., 2015; Bhave et al., 2016; Bhave et al., 2018)"


id: 21866d1212b4edcda9160ec9c96ed93e
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 2698
content:
" In these cases, trade\u0002offs can be assessed and options can be prioritised through iterative \r\ndecision-making processes, such as multi-criteria decision-making, \r\nrobust decision-making and dynamic adaptation pathway planning \r\n(high confidence) (Table 17.4; Kwakkel et al., 2014; Kwakkel et al., 2016; \r\nShortridge et al., 2016; Lawrence and Haasnoot, 2017; Haasnoot et al., \r\n2019; Lempert, 2019; Roelich and Giesekam, 2019; Haasnoot et  al., \r\n2020a). They can address limitations of data-intensive robust decision\u0002making in developing countries (Daron, 2015), use proxy data to enable \r\nthe use of robust decisions in data-scarce contexts (Shortridge and \r\nGuikema, 2016; Ahmad et al., 2019), incorporate multiple-objectives into \r\nrobust decision-making (Singh et al., 2015), and supplement pathway \r\ndevelopment with real options analysis (Buurman and Babovic, 2016; \r\nSmet, 2017; Haasnoot et al., 2019; Lawrence et al., 2019)"


id: 52a063df9f5b4a35ac3116500d640cb4
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 10460
content:
" Often, there \r\nare close synergies between the application of these methods and using \r\nscenario analyses (Workman et al., 2021)."


id: de16afb4805118d5ae6b43d1119e25a6
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 32438
content:
"Since AR5, recognition of the importance of using integrated adaptation \r\nto improve climate risk management across the nexus between many \r\nsectors and across regions has increased (high confidence) (Harrison \r\net al., 2016; Challinor et al., 2018). This was highlighted in the Special \r\nReport on Climate Change and Land (Hurlbert et al., 2019); advanced \r\nplanning and integration of adaptation responses are needed over \r\nmany levels (medium confidence) (Gopfert et al., 2019; Section 17.6; \r\nWoodruff and Regan, 2019). The complexity of managing this nexus \r\nmay be compounded by the potential for antagonistic or synergistic \r\neffects among and between climate impacts, and changes arising from \r\nlocal sectoral activities and independent adaptation responses to those \r\nrisks (high confidence) (Crain et al., 2008; Piggott et al., 2015; Adger \r\net al., 2018; Brown et al., 2018; Stelzenmuller et al., 2018; Simpson \r\net al"


id: 21f1ce1d0b584dbd9894415cd5b84249
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 2786
content:
", 2021), such as the cross-sectoral demands for freshwater (Xue \r\net al., 2015; Azhoni et al., 2018). Integrated adaptation will also help \r\nfacilitate management of new and emerging risks, help identify when \r\nresponse plans may need to be changed in light of the dynamics of risk \r\nover time, and help identify solutions that are less likely to constrain \r\nfuture options for adapting to future needs (Wise et al., 2016)."


id: 60a1a3e9e39f3a51ff557c230aae8123
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 12772
content:
"To better communicate deep uncertainty, WGI AR6 complements projections of likely global mean sea level change, driven by processes \r\nin which there is at least medium confidence, with projections that incorporate ice-sheet processes in which there is low confidence\r\n(Section  9.6.3 in Fox-Kemper et  al., 2021). The latter are accompanied by storylines to highlight the physical processes that would \r\ngenerate extreme outcomes (Box 9.4 in Fox-Kemper et al., 2021). These low confidence projections and storylines are useful because the \r\nlikelihood of high-end (>1.5 m) global mean sea level (GMSL) rise in the 21st century is difficult to determine but important to consider \r\nin coastal settings (e.g., Cross-Chapter Paper 2; Cross-Chapter Box SLR in Chapter 3)"


id: bb59c52cda26ec525550d0ad649eccf3
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 27032
content:
" High-end GMSL rise by 2100 could be caused by \r\nearlier-than-projected disintegration of marine ice shelves, the abrupt, widespread onset of marine ice sheet instability and marine ice \r\ncliff instability around Antarctica, or faster-than-projected changes in the surface mass balance and dynamical ice loss from Greenland \r\n(Box TS.4 in Arias et al., 2021; Box 9.4 in Fox-Kemper et al., 2021). In a low-likelihood, high-impact storyline and a high CO2 emissions \r\nscenario, such processes could in combination contribute more than one additional metre of sea level rise by 2100 (Box TS.4 in Arias et al., \r\n2021; Section 9.6.3 and Box 9.4 in Fox-Kemper et al., 2021). Other hazards assessed in WGI AR6 that address similar aspects relevant \r\nfor decision-making under deep uncertainty include drought (Section 8.4.1.6 in Douville et al., 2021; Section 11.6.5 in Seneviratne et al., \r\n2021), flood (Section 8.4.1.5 in Douville et al., 2021; Section 11.5.5 in Seneviratne et al"


id: d35f9c78b441abd7c5a457251ad6256a
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 30778
content:
", 2021) and wildfire weather (days) (Section 11.8.3 \r\nand Box 11.2 in Seneviratne et al., 2021), among others."


id: 4f54aed30fd9a2717f28101430d0c8e1
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 9921
content:
"There are diverse examples of the practical application of deep uncertainty methods across different climate change hazards in many \r\nregions of the world. For instance, low-regret options have been used to address the impacts and risks of landslides and debris flows \r\nin mountains (Section CCP5.2.6). Their frequency and magnitude are already widely experienced (Section CCP5.2.6) and projected to \r\nincrease (Section CCP5.3.2.1). However, managing these associated risks also requires joint consideration of projected vulnerabilities \r\nand exposure of people and infrastructure, including the multiple and dynamic non-climate-related factors that are relevant for how the \r\nimpacts manifest in context, such as population growth and land use planning (CCP5.2.6). Here, context-specific deliberative processes \r\nare used that include scenarios to guide and specify preventive measures with higher effectiveness than protective (infrastructure) \r\nmeasures could achieve alone"


id: 2ba01f765624c08576b77840c0e76767
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 4297
content:
" Low-regret adaptation involves raising awareness and accounting for long planning horizons to address \r\nthe uncertainties associated with such risks, for instance in mountain regions, including education (Sections CCP5.4.1; CCP5.2.6), with \r\nco-benefits such as addressing changes in water availability for supply and demand (CCP5.4.1)."


id: 791b80efd3bf0ecb3678e9a972165e05
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 16615
content:
"Adaptation pathways have been used to address SLR and changes in extreme rainfall through flood risk and management (Cross-Chapter \r\nBox SLR in Chapter 3; CCP2; Sections 13.2, 11.3 and 11.7): for example, adaptive plans in the Netherlands (Van Alphen, 2016; Bloemen \r\net al., 2019), climate resilient development in Bangladesh (Hossain et al., 2018; Zevenbergen et al., 2018), adaptive spatial pathways for \r\ninfrastructure retreat and for flood risk management in New Zealand (Lawrence et al., 2019a; Kool et al., 2020) and adaptive strategies such \r\nas in the cities of London (Ranger et al., 2013; Hall et al., 2019), New York (Rosenzweig and Solecki, 2014) and Los Angeles (Aerts et al., \r\n2018a). This approach is mainstreamed into guidance documents such as the Climate Risk Informed Decision Analysis (CRIDA) (Mendoza \r\net al., 2018), national guidance and policy briefs to address coastal hazards and sea level rise planning in New Zealand (Lawrence et al., \r\n2018; Lawrence et al"


id: 5570ec4a12bb3a4046976c7c649330d4
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 10916
content:
", 2019b), planning for sea level rise in California (OCP, 2018) and synthesis documents by the government of Canada \r\non marine coasts (Lemmen et al., 2016). Furthermore, examples from the UK, New Zealand and the Netherlands point to the development \r\nof monitoring plans to detect signals for climate adaptation (Stephens et al., 2017; Haasnoot et al., 2018; Bloemen et al., 2019)."


id: 3c83cff58090ed34a145f6154550b81a
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 6887
content:
"Climate-smart planning, with a focus on keeping options open, can play a role in reducing species extinction rates (Sections 2.5, 2.6). \r\nWhen and where and for whom particular irreversible impacts will occur is deeply uncertain, for example the extinction of a species. Even \r\nat the lowest emissions scenarios, some local species will become extinct, but estimates of extinction risk are highly uncertain, typically \r\nvarying by factors of two to three even for one species (Section 2.5) (medium confidence). Risks of species' extinctions are lowered by \r\nreducing emissions, but keeping options open for as long as possible and avoiding irreversible actions are key to developing a climate\u0002resilient adaptive pathway so that real-time climate-driven changes can inform actions. Nature-based solutions (NBS) are emerging as \r\nkey players for mitigation"


id: df190ab54b976cd15cfcfc7a799659f
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 32593
content:
" With smart planning, NBS offer approaches that not only provide substantial mitigation, but also considerable \r\nadaptation benefit to biodiversity, and human health and well-being. Done poorly, such projects can result in large negative impacts on \r\nhumans and nature. An NBS climate-sensitive decision framework leading to 'win-win' solutions for mitigation and adaptation is shown \r\nin Figure 1 Cross-Chapter Box NATURAL in Chapter 2 (see also Sections 2.4.2.5, 2.5, 2.6, 5.4.4.4 and 5.14.1; Cross-Chapter Box ILLNES in \r\nChapter 2; Cross-Chapter Box COVID in Chapter 7)."


id: 1edc5fa42fcb5a8c9d72d2262f575b93
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 2298
content:
"Implicit to managing cross-sectoral interactions, including the nexus \r\nconcept, is that the interlinkages between multiple sectors are systemic, \r\nand therefore solutions to challenges arising from any one sector can \r\nonly be satisfactorily addressed by considering the connections to other \r\nsectors at the same time (Wichelns, 2017). Challenges for integrated \r\nadaptation include: (1) to sufficiently capture the complexities between \r\nthe nexus dimensions (Weitz et al., 2017); (2) to adequately consider the \r\ntime, costs and challenges of coordination and cooperation (Wichelns, \r\n2017); (3) to consider the political economy in which progress towards \r\nmore integrated solutions could take place, not only accounting for \r\ntechnological requirements (Leck and Roberts, 2015); (4) to obtain \r\nsufficient temporal or spatial data to capture the interactions between \r\nnatural and social processes (Shannak et  al"


id: f7687f4d2fb4d9c7f854bd2e723cd027
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 36438
content:
", 2018); (5) to connect \r\nthese considerations to decision-making and policy processes in order \r\nto gain insights into the conditions for collaboration and coordination \r\nacross sectors, including external dynamics and political and cognitive \r\nfactors determining change (Weitz et al., 2017); and (6) to develop a \r\ncoherent framework against which to assess results and observations \r\n(Crain et al., 2008; Wichelns, 2017)."


id: 978706c19383ad3ac887f82755b46a53
created_at: 2023-03-16 14:02:37
updated_at: 2023-03-16 14:02:37
content_type: text
index: climate_gpt_only_giec
vector_id: 21365
content:
"Even though there is no agreed definition of or typology for climate \r\nchange laws (Mehling, 2015), studies have tended to classify climate \r\nchange laws as being 'framework' or 'sectoral' (see Table  17.5 for \r\nexamples). Framework laws offer a comprehensive, unifying basis \r\nfor climate change policy, addressing multiple aspects or areas of \r\nclimate change mitigation or adaptation (or both) in a holistic and \r\noverarching manner (Townshend et al., 2011; Fankhauser et al., 2014; \r\nNachmany et al., 2015; Clare et al., 2017b); they are powerful levers \r\nfor setting national and sub-national agendas, creating climate change \r\ninstitutional structures, enabling policy implementation and driving \r\nthe passage of additional sectoral legislation and regulations (Clare \r\net al., 2017b). Prior to 2010, national framework laws tended to have a \r\nmitigation focus, while more recent laws or amendments thereto have \r\nan increased adaptation focus (Rumble, 2019b)"


