# document

id: 7f0182702ee3ec52a7599e6a7b08fc1d
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 17537
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" \r\nMulti-level governance arrangements, public-private cooperation \r\nand robust urban-data platforms are among the factors enabling \r\nthe pursuit of these objectives within countries (Corfee-Morlot et al. \r\n2009; Gordon 2015; Creutzig et al. 2019; Yarime 2017)."


id: 151245d383ab7f153e930ff5deccba92
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
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vector_id: 822
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"Climate actions, including climate change mitigation and adaptation, \r\nare highly scale-dependent, and solutions are very context-specific. \r\nEspecially in developing countries, a  strong link exists between \r\nsustainable development, vulnerability and climate risks, as limited \r\neconomic, social and institutional resources often result in low \r\nadaptive capacities and high vulnerability. Similarly, the limitations \r\nin resources also constitute key elements weakening the capacity \r\nfor climate change mitigation (Jakob et al. 2014). The change to \r\nclimate-resilient societies requires transformational or systemic \r\nchanges, which also have important implications for the suite of \r\navailable sustainable-development pathways (Kates et al. 2012; \r\nLemos et al. 2013)"


id: 117195b0c4e23e60be51fefd050b2e28
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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" Thornton and Comberti (2017) point to the \r\nneed for social-ecological transformations to take place if synergies \r\nbetween mitigation and adaptation are to be captured, based on \r\nthe argument that incremental adaptation will not be sufficient \r\nwhen climate change impacts can be extreme or rapid and when \r\ndeep decarbonisation simultaneously involves social change \r\n(Chapter 18 in AR6 WGII)."


id: 9eafa4a7765e7516661b8b5bdf02e3f9
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 22505
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"As discussed in AR6 WGII, Section  18.4, there are synergies and \r\ntrade-offs between adaptation and sustainable development, as \r\nwell as between mitigation and sustainable development, which \r\nis supported by comprehensive assessments such as that by Dovie \r\n(2019) and Sharifi (2020). Links between mitigation and adaptation \r\noptions are identified in Chapter 18 in AR6 WGII, such as expected \r\nchanges in energy demand due to climate change interacting with \r\nenergy-system development and mitigation options, changes to \r\nagricultural production practices to manage the risks of potential \r\nchanges in weather patterns affecting land-based emissions and \r\nmitigation strategies, or mitigation strategies that place additional \r\ndemands on resources and markets"


id: d8e819fed6ab8f9f6972bb4712170ea8
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 31634
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" This increases the pressures on \r\nand costs of adaptation or ecosystem restoration linked to carbon \r\nsequestration and the benefits in terms of the resilience of natural \r\nand managed ecosystems, but it also could restrict mitigation options \r\nand increase costs. Chapter 3 of AR6 WGIII similarly concludes that \r\nthe connectedness and coherence of actions to mitigate climate \r\nchange could support the conservation and adaptation of ecosystems \r\nand meet the Sustainable Development Goals more widely."


id: 21c19ebcce301a4d62c705585b8cfe33
created_at: 2023-03-16 14:02:55
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"around the world. (Grafakos et al. 2019; Sanchez Rodriguez et al. \r\n2018) concluded that cities are an obvious place for the development \r\nof plans that can capture several synergies between sustainable \r\ndevelopment and climate-resilient pathways. (Kim and Grafakos \r\n2019; Landauer et al. 2019) similarly concluded that cities are an \r\nobvious platform for the development of integrated planning efforts \r\nbecause of the scale of policies and actions, which could potentially \r\nmatch the different policy domains. (Kim and Grafakos 2019) \r\nassessed the level of integration of mitigation and adaptation in \r\nurban climate change plans across 44 major Latin American cities, \r\nconcluding that the integration of climate change mitigation and \r\nadaption plans was very weak in about half the cities and that \r\nlimited donor finance was a main barrier. The authors also mention \r\nbarriers in relation to governance and the weakness or lack of legal \r\nframeworks"


id: 595a168ae48047299bd6892b255acfb9
created_at: 2023-03-16 14:02:55
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" The integration of SDGs with adaptation could help \r\nincrease the willingness of politicians to implement climate actions, \r\nas well as provide stronger arguments for investing the required \r\nresources (Sanchez Rodriguez et al. 2018)."


id: a8530fa9e122da02501d4f14c59c6c50
created_at: 2023-03-16 14:02:55
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"were asked about their willingness to change this balance and \r\nto commit more resources to projects that address both climate \r\nchange mitigation and adaptation. More than two thirds of those \r\ninterviewed, however, raised concerns that integrated projects \r\ncould be too complicated and that a greater alignment of financial \r\nmodels across different policy domains could entail greater \r\nfinancial risks. Another barrier mentioned in respect of finance was \r\nthat mitigation projects were primarily aimed at GHG emissions \r\nreductions, while adaptation projects had more national benefits \r\nand were also more suitable for community development and \r\npromoting equality and fairness. In an assessment of 201 projects \r\nin the forestry and agricultural sectors in the tropics, (Kongsager \r\net al"


id: 76b2dbe59312fc5f94201acca405f78
created_at: 2023-03-16 14:02:55
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" 2016), found that a majority of the projects contributed to \r\nboth adaptation and mitigation or at least had the potential \r\nto do so, despite the separation between these two objectives by \r\ninternational and national institutions."


id: 8fa76b37926709700c09614b437cc395
created_at: 2023-03-16 14:02:55
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"building materials sectors, as it could make the existing building \r\nstock more climate-resilient (Hallegatte 2009; de Wilde and Coley \r\n2012; Pyke et al. 2012) and thus also support implementation of \r\nthe Sendai Framework on Disaster Risk Reduction (United Nations \r\n2015b). Climate change in the form of extremely high temperatures, \r\nintense rainfall leading to flooding, more intense winds and/or \r\nstorms and sea level rises (SLRs) can seriously impact transport \r\ninfrastructure, including the operations and mobility of road, rail, \r\nshipping and aviation; Chapter 10 assesses the impacts on subsectors \r\nwithin transportation. At the same time, these sectors are major \r\ntargets for GHG mitigation options, and many countries are currently \r\nexamining what to do in terms of combined mitigation-adaptation \r\nefforts, using the need to mitigate climate change through transport\u0002related GHG emissions reductions and pollutants as the basis for \r\nadaptation action (Thornbush et al"


id: cde69228a3cd21e18e656596ed82fce8
created_at: 2023-03-16 14:02:55
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" 2013; Wang and Chen 2019). For \r\nexample, urban sprawl indirectly affects climate processes, increasing \r\nemissions and vulnerability, which worsens the ability to adapt \r\n(Congedo and Munafo 2014). Hence greater use of rail by passengers \r\nand freight will reduce the pressures on the roads, while having less \r\nurban sprawl will reduce the impacts on new infrastructure, often in \r\nmore vulnerable areas (IPCC 2019; Newman et al. 2017)."


id: 916432b393e45a9ec7bda2c8c4286859
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"The TWI2050 2019 report (IIASA 2019) assessed both the positive \r\nand negative impacts of digitalisation in the context of sustainable \r\ndevelopment. It found that efficiency improvements, reduced \r\nresource consumption and new services can support the SDGs, but \r\nalso that there were challenges, including in relation to equality, \r\nfacing the least-developed and developing countries because of their \r\nlow level of access to technologies. The necessary preconditions for \r\nsuccessful digital transformation include prosperity, social inclusion, \r\nenvironmental sustainability, protection of jobs and good governance \r\nof sustainability transitions. One negative impact of digitalisation \r\ncould be the rebound effects, where easier access to services could \r\nincrease demand and with it GHG emissions"


id: 24fde7d518bc969e308e5d71c0e62d73
created_at: 2023-03-16 14:02:55
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" Digitalisation in the \r\nmanufacturing sector could also provide a comparative advantage \r\nto developed countries due to the falling importance of labour costs, \r\nwhile the barriers to emerging economies seeking to enter global \r\nmarkets could accordingly be increased."


id: 5ece9cb0b03f80d3ccac8baa6fac1bc6
created_at: 2023-03-16 14:02:55
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"well managed. Nuclear power is assessed as having synergies with \r\nSDG 3 (good health and well-being) due to reduced air pollution, but \r\npotential trade-offs in relation to SDG 6 (clean water and sanitation) \r\ndue to high water consumption, and water consumption issues are \r\nalso possible in relation to many of the other mitigation options \r\nin the energy sector. Synergies are identified in relation to SDG 12 \r\n(responsible production and consumption) for nuclear power due \r\nto low material consumption. CCUS has been assessed as having \r\ntrade-offs in relation to SDG 1 (no poverty) due to high costs and \r\nSDG 6 (clean water and sanitation) due to high water consumption. \r\nSynergies are related to SDG 3 (good health and well-being), and to \r\nSDG 9 (industry, innovation and infrastructure) due to the facilitation \r\nof decarbonisation of industrial processes"


id: 59d599478a0c6ef9d53cf3e686ddb1a
created_at: 2023-03-16 14:02:55
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" Both synergies and trade\u0002offs could arrive in relation to SDG 12 (responsible production and \r\nconsumption), since some rare chemicals and other inputs could in \r\nsome cases be used with large-scale applications."


id: b58db343a8da768a75d634f54a29ba1e
created_at: 2023-03-16 14:02:55
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content:
"In conclusion, in most cases digitalisation options may have both \r\npositive synergistic impacts on mitigation and the SDGs and some \r\nnegative trade-offs. Energy-sector options are assessed primarily as \r\nhaving synergies, while some digitalisation options in transport could \r\nincrease the demand for emission-intensive modes of transport. \r\nDigital platforms for the sharing economy could have both positive \r\nand negative impacts depending on the goods and services that are \r\nactually exchanged (Cross-Chapter Box 6 in Chapter 7). Options related \r\nto agriculture and the water-energy-food nexus (WEFN) could help \r\nmanage resources more efficiently across sectors, which could create \r\nsynergies. Digitalisation can also raise a number of ethical challenges \r\naccording to (Clark et al. 2019)"


id: 71c6437612f9ae43e92e77d3cc94b66e
created_at: 2023-03-16 14:02:55
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" Wider public discussion of internet\u0002based activities was accordingly recommended, including topics such \r\nas the negotiation of online consent and the use of data for which \r\nconsent has not been obtained."


id: d7a2544d6876038d906d9cdf88650d6f
created_at: 2023-03-16 14:02:55
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"SDG 3 (good health and well-being) can be supported by many \r\nmitigation options in the agriculture, forestry and food sectors, \r\nprimarily due to the reduced environmental impacts, and the same \r\nis the case with SDG 14 (life below water) due to decreased nutrient \r\nloads, and SDG 15 (life on land) due to increased biodiversity, \r\nwith the caveat however, that SDGs 14 and 15 could have both \r\nsynergies and trade-offs dependent on land use. It is considered that \r\nthere could be both synergies and trade-offs in relation to SDG 8 \r\n(decent work and economic growth) due to competition over land \r\nuse related to the mitigation options reducing deforestation and \r\nreforestation and restoration, and the same is the case in relation \r\nto SDG 7 (affordable and clean energy) depending on the economic \r\noutcome of the mitigation options"


id: 762753e0063e5fa652318118ea751254
created_at: 2023-03-16 14:02:55
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" Similarly, the mitigation option \r\nof reduced CH4 and N2O emissions from agriculture are assessed as \r\nhaving mixed impacts on SDG 8 (decent work and economic growth), \r\nand SDG 9 (industry, innovation and infrastructure) depending on \r\ninnovative food production. The mitigation options of reforestation \r\nand forest management are assessed as having mixed impacts on "


id: 558111a692b948cf9c92f9884b585f94
created_at: 2023-03-16 14:02:55
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"The mitigation options in the transportation sector are assessed as \r\nhaving synergies with SDG 1 (no poverty) and SDG 3 (good health and \r\nwell-being) due to reduced environmental pollution, with exceptions \r\nin relation to pollution from biofuels and the risks of traffic accidents. \r\nTrade-offs are also mentioned in relation SDG 2 (zero hunger) where \r\nthe production of biofuels takes land away from food production. \r\nSynergies are assessed in relation to SDG 7 (affordable and clean \r\nenergy), SDG 8 (decent work and economic growth) and SDG 9 \r\n(industry, innovation and infrastructure). It is emphasised that some \r\nmitigation options, like the increased penetration of electric vehicles, \r\nrequire innovative business models, and that digitalisation and \r\nautomatic vehicles will support the socio-economic structures that \r\nimpede adoption of EVs and the urban structures that enable reduced \r\ncar dependence"


id: d5246bd807fc66904d567db08b473fd
created_at: 2023-03-16 14:02:55
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vector_id: 31064
content:
" In conclusion, there is a  need for investments in \r\ninfrastructure that can support alternative fuels for light-duty vehicles \r\n(LDVs). The large-scale electrification of LDVs requires the expansion \r\nof low-carbon power systems, while charging or battery-swapping \r\ninfrastructure is needed for some segments."


id: 3810afea012ce3c3a5779ae71593017b
created_at: 2023-03-16 14:02:55
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"The mitigation options in the industrial sector have been assessed \r\nprimarily as having synergies with meeting the SDGs. Several options, \r\nincluding energy efficiency, material recycling and electrification, are \r\nassessed has being able to create increased employment and business \r\nopportunities related to SDG 8 (decent work and economic growth), \r\nbut material-efficiency improvements could reduce tax revenues. \r\nElectrification is assessed as having many synergies with SDGs, such \r\nas supporting SDG 1 (no poverty), SDG 2 (zero hunger), and SDG 3 \r\n(good health and well-being). CCS applied in industry is assessed as \r\nhaving synergies in terms of the control of non-CO2 pollutants (such \r\nas sulphur dioxide), but increases in non-CO2 pollutants (such as \r\nparticulate matter, nitrogen oxide and ammonia)"


id: 2eb4cc699a95493693fef864afa8bfc
created_at: 2023-03-16 14:02:55
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" The conclusion is \r\nthat 15-25% additional energy will be required by CCS technologies \r\ncompared with conventional plants, implying that production costs \r\ncould increase significantly. For the industrial sector in general, it is \r\nconcluded that the balance between synergies and trade-offs between \r\nmitigation options and SDGs in industry depends on technology and \r\nthe scale of the sharing of co-benefits across regions, as well as on the \r\nsharing of benefits in business models over whole value chains."


id: 1bf4523f349635313f859f9a5da55411
created_at: 2023-03-16 14:02:55
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content:
"As for the urban mitigation options, the reviewed building-sector \r\nstudies reveal a lot of links between mitigation and the SDGs. Highly \r\nefficient building envelopes are expected to have synergies with the \r\nSDGs in all cases except those with potential trade-offs in relation to \r\nSDG 10 (reduced inequalities). Many SDG synergies are also identified \r\nfor the building design and performance, heating, ventilation and air \r\nconditioning, and efficient appliances mitigation options. However, \r\nsome trade-offs could appear in relation to SDG 8 (decent work and \r\neconomic growth) due to macroeconomic impacts of reduced energy \r\nconsumption, decreasing prices and stranded investments. Similar \r\nissues related to the economic impacts of reduced energy demand \r\nare also highlighted for all the other mitigation options, including \r\nfor the building sector"


id: 9cb553c2d6c713da7a41da9258a67f97
created_at: 2023-03-16 14:02:55
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vector_id: 22189
content:
" In relation to construction materials and the \r\ncircular economy, some trade-offs have been identified in relation to \r\nSDG 6 (clean water and sanitation) and SDG 15 (life on land) related \r\nto the use of bio-based materials."


id: 40d0f8625d3482c942badce3fc40ae3b
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 7581
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"and constituent materials (Garcia-Olivares et al. 2018). Transitions \r\naway from fossil fuels and toward renewable energy-based systems, \r\nfor instance, will require significant land-use decisions to avoid \r\nnegative trade-offs with biodiversity and food security (Capellan\u0002Perez et al. 2017). Previous sections underline a related need to move \r\nbeyond focusing on 'rational' assessments of the costs and benefits \r\nof policies and technologies to involve people at all levels in order \r\nto overcome these multiple barriers. A transition to a lower carbon \r\nsystem is unlikely to happen even if models find it technically feasible \r\nand cost-effective. Rather, achieving a  transition requires breaking \r\nlocked-in high-carbon technological trajectories, path dependencies \r\nand resistance to change from the industries and actors that are \r\nbenefiting from the current system (Rogge et al. 2017)"


id: 6c3869cd035152c8584adcd04049c90d
created_at: 2023-03-16 14:02:55
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" Lock-in effects \r\nmay be weaker in sectors and policy areas where fewer technologies \r\nexist, potentially opening the door to innovations that embed the \r\nclimate in broader sustainability objectives (e.g.,  technologies and \r\ninnovations that support the integration of food, water and energy \r\ngoals). Such effects may still happen when there are significant \r\ninformation asymmetries and high-cost barriers to action, as can \r\noccur when working across multiple climate and development\u0002related sectors (Kemp and Never 2017)."


id: 4e522f52de95f050ddd51dfeac7be856
created_at: 2023-03-16 14:02:55
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"However, the same conditions that may serve to impede a transition \r\n(i.e.,  organisational structure, behaviour, technological lock-in) can \r\nalso be 'flipped' to enable it (Burch 2010; Lee et al. 2017), while the \r\nframing of policies that are relevant to the sustainable development \r\nagenda can also create a stronger basis and stronger policy support. \r\nThe technological developments and broader cultural changes that \r\nmay generate new social demands on infrastructure to contribute to \r\nsustainable development will involve a process of social learning and \r\nawareness building (Naber et al. 2017; Sengers et al. 2019). However, \r\nit is also important to note that strong shocks to these systems, \r\nincluding accelerated climate change impacts, economic crises and \r\npolitical changes, may provide crucial openings for accelerated \r\ntransitions to sustainable systems through fundamental institutional \r\nchanges (Broto et al. 2014)"


id: 5fdab36cdf1bc53ae7b46225b7451858
created_at: 2023-03-16 14:02:55
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" The global COVID-19 pandemic is \r\none such shock that has sparked widespread conversations about \r\nrecovery that is fundamentally more sustainable, equitable and \r\nresilient (McNeely and Munasinghe 2021). Key enabling conditions \r\nappear to be individual and collective actions, including leadership \r\nand education; financial, material, social and technical drivers that \r\nfoster innovation; robust national and regional innovation systems \r\nthat enhance technological diffusion (Wieczorek 2018); supportive \r\npolicy and governance dynamics at multiple levels that permit both \r\nagility and coherence (Gopel et al. 2016); measures to recognise and \r\naddress the challenges to equality inherent in the transition; and long\u0002range, holistic planning that explicitly seeks synergies between \r\nclimate change and sustainable development while avoiding trade\u0002offs"


id: f9875fb58f5c3a218bb572ad1ae5f6bf
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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content:
" The sections that follow seek to assess and integrate these key \r\ncategories of the barriers to and enablers of an accelerated transition \r\nto sustainable development pathways."


id: 5d5c503ec16d2803d917c4ce7f718db7
created_at: 2023-03-16 14:02:55
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content:
"The results from these research communities reveal how \r\nexperiential learning takes place and how it encourages bonding \r\nbetween people, society and nature. This can be achieved by going \r\njointly and consciously into nature (Gioacchino 2019), by creating \r\nspaces for intensive-dialogue sessions with colleagues (Goldman\u0002Schuyler et al. 2017) and forming, for example, a very practical u.lab \r\nhub, which involves following the MIT-u.lab course with a  local \r\ncommunity and is accompanied scientifically (Pomeroy and Oliver \r\n2018). Others have pointed to social networks such as the 'transition \r\ninitiative' (Hopkins 2010), eco-village networks (Barani et al. 2018), \r\ncivil-society movements (Seyfang and Smith 2007) and intentional \r\ncommunities (Grinde et al. 2018; Veciana and Ottmar 2018) as ways \r\nof generating the shared understandings that are central to inner \r\nand outer transitions, as well as the broader development of social \r\nmovements"


id: e986c7691d19c76df352c698822c4c90
created_at: 2023-03-16 14:02:55
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vector_id: 34249
content:
" In some cases, these networks build on principles \r\nlike permaculture to encourage people to 'observe and interact', \r\n'produce no waste' and 'design from patterns to details', not only \r\nin agriculture and gardening, but also in sustainable businesses \r\nand technologies to reduce CO2 emissions (Ferguson and Lovell \r\n2014; Lessem 2018)."


id: 150b139cede7b98b490debb59cbdbe6
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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content:
"Sustainable development and deep decarbonisation will involve \r\npeople and communities being connected locally through various \r\nmeans - including globally via the internet and digital technologies \r\n(Bradbury 2015; Scharmer 2018; Scharmer, C, Kaufer 2015) - in ways \r\nthat form social fields that allow sustainability to unfold (Gillard \r\net al. 2016), and that prompt other shifts in thinking and behaviour \r\nthat are consistent with the 1.5degC goal (O'Brien 2018; Veciana and \r\nOttmar 2018). Indeed, social movements serve to develop collective \r\nidentities, foster collective learning and accelerate collective action \r\nranging from energy justice (Campos and Marin-Gonzalez 2020) \r\n(Section 17.4.5) to restricting fossil fuel extraction and supply (Piggot \r\n2018). This does not apply only to adults: as seen in the 'Fridays for \r\nFuture' marches, the young are also involving themselves politically \r\n(Peterson et al. 2019)"


id: 4f174c8c2f61f1de9816c84bc4c8916c
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vector_id: 9886
content:
" Many initiatives have started with these \r\nmarches, including 'science for future' and new forms of sustainability \r\nscience (Shrivastava et al. 2020)."


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content:
"Private-sector entrepreneurs also play an important role in fostering \r\nand accelerating transitions to sustainable development (Burch et al. \r\n2016; Ehnert et al. 2018a; Dale et al. 2017). Sustainable entrepreneurs \r\n(SEs), for instance, are described as those who participate in the \r\ndevelopment of an innovation while simultaneously being rooted \r\nin the incumbent energy-intensive system. SE actors who have \r\ndeveloped longer-term relationships, both formal and informal, with \r\nthe public authorities can have considerable influence on developing \r\nnovel renewable-energy technologies (Gasbarro et al. 2017). \r\nInstitutions and policies that nurture the activities of sustainable \r\nentrepreneurs, in particular small- and medium-sized enterprises \r\n(Burch et al"


id: 894a4f7ff369690c19c257f13f2de192
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 19137
content:
" 2016), can facilitate and strengthen transitions toward \r\nmore sustainable development pathways, as can more fundamental \r\nadjustments to underlying business models, rather than relying only \r\non incremental adjustments in the efficiency with which resources \r\nare used (Burch and Di Bella 2021)."


id: 464455c5887448cc6dc390d8069d2154
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 8441
content:
"In Norway, many incumbent energy firms have already expanded \r\ntheir operations into the alternative-energy sector as both \r\nproducers and suppliers (who often follow the lead of producers). \r\nProducers are responding to perceptions of larger-scale changes \r\nin the energy landscape (e.g.,  the green shift), along with \r\nuncertainties in their own sectors, and innovation can spill across \r\nactors in multiple sectors (Koasidis et al. 2020). While these firms \r\nare expanding out of self-interest, the expansion provides more \r\nlegitimacy to new forms of technology and enables transfers of \r\nknowledge and resources to be introduced within this developing \r\nniche (Steen and Weaver 2017). Many large, well-established firms \r\nare pursuing sustainability agendas and opting for transparency \r\nwith regard to their greenhouse gas emissions (Kolk et al. 2008; \r\nGuenther et al"


id: 1b9ff0be786c87ef4dbd5174de4b59e4
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 1804
content:
" 2016), supply-chain management (Formentini and \r\nTaticchi 2016) and sustainable technology or service development \r\n(Dangelico et al. 2016)."


id: b940441567b3df5681fbf6ce91df139
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
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vector_id: 26681
content:
"Many forms of transformational change to energy systems are \r\nnot possible when financial systems still privilege investing in \r\nunsustainable, carbon-intensive sectors. One of the root causes of the \r\nfailure of traditional financial systems is the undervaluation of natural \r\ncapital and unsettled property-right issues that are associated with \r\nit. The exclusion of proper rents for scarcities or for global and local \r\nexternalities, including climate change, can undermine larger-scale \r\nchanges to energy systems (Clark et al. 2018). But even smaller\u0002scale low-carbon energy and infrastructure projects can fail to get \r\noff the ground if uncertainty and investment risk discourage project \r\nplanning and bank-lending programmes (Bolton et al. 2016). The EU's \r\nprevious actions regarding the 'shareholder maximisation norm' and \r\nnon-binding measures have created path dependencies, limiting its \r\nflexibility in creating sustainable financial legislation"


id: 3d834caa244d5a54dc8a5e4949713b71
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 7036
content:
" However, the \r\nSustainable Finance Initiative and the Single Market may prove to be \r\n'policy hotspots' in encouraging sustainable finance (Ahlstrom 2019). \r\nTaking advantage of these hotspots may be crucial in overcoming \r\npath dependencies and setting new ones in motion."


id: 2af22c68c44cc8b1bb1bc85aaa1aa4d7
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 4185
content:
"Some studies have focused on factors that can break institutional \r\nand ideational lock-ins (Arranz 2017), while others have found \r\nthat intentional higher-level (or, in the language of socio-technical \r\ntransitions, 'landscape') pressures can be the destabilising force \r\nneeded to move transitions forward (Falcone and Sica 2015). Often \r\nthe state or national government (as the sovereign that determines \r\nhow resources are used and allocated) can play a  key role in \r\ndestabilising incumbent energy regimes, a role that is significantly \r\nstrengthened by public support (Arranz 2017; Avelino et al. 2016). \r\nHowever, this role is not limited to government insiders. In some \r\ncontexts, regime outsiders have also played a  pivotal role in \r\ndestabilising regimes by combining persuasive narratives that gain \r\nmarket influence (Arranz 2017)"


id: 825ed8233bf4331893bbfce7e8b63904
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 18033
content:
" Carbon-intensive luxury goods and \r\nservices for wealthy consumers, for instance, especially if applied \r\nat the 'acceleration' phase of a transition, can help transform long\u0002term social practices and behaviour and dissolve the 'structural \r\nimperative for growth' (Wiedmann et al. 2020). In a similar fashion, \r\nenvironmental taxes can remove 'locked-in' technology and place \r\npressure on dominant regimes to become more sustainable (Bachus \r\nand Vanswijgenhoven 2018)."


id: ea07f20f6eaf20faa7a9d6b30bc6e3a3
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 34316
content:
"Various strategies and processes have been explored that might \r\nfacilitate the translation of barriers into enablers, thus accelerating \r\ntransitions to sustainable development. Common themes include \r\nfrequent monitoring and system evaluation to reveal the barriers \r\nin the first place, the collaborative co-creation and envisioning of \r\npathways toward sustainable development, ambitious goal-setting, \r\nthe strategic tackling of sources of path dependence or inertia, \r\niterative evaluations of progress and risk management, adaptive \r\nmanagement and building in opportunities for agile course-correction \r\nat multiple levels of governance (Burch et al. 2014; Halbe et al. \r\n2015). Given the political infeasibility of stable, long-term climate \r\npolicies, the better choice may be to embrace uncertainty in specific \r\npolicies but entrench the low-carbon transition as the overarching \r\ngoal"


id: a5ddd95914e0f05563835aa86e9dce15
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 23654
content:
" Framing climate policy too narrowly, rather than taking a more \r\nholistic, sustainable development-oriented approach, may tie success \r\nto single policies, rather than allowing for system-wide change."


id: 732bdd4d92dcf1bcba73fccc26d5a502
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 15736
content:
"Even with this diversity, prominent lines of economic, institutional, \r\npsychological and systems thinking have reflected on interventions \r\nthat can enable transitions. Because these disciplines often focus on \r\ndifferent levels of analysis and draw upon diverse analytical methods \r\nand empirical evidence, the recommended interventions also tend \r\nto vary. For instance, economic arguments often point to the need \r\nfor targeted regulation or investments, institutional claims centre \r\non multi-level governance reforms, and psychology encourages \r\nparticipation to change mindsets and social norms. Systems-level \r\nperspectives offer a useful frame for bringing together these views, but \r\nmay not capture the richness and details of them treated separately. \r\nGreater inter- and transdisciplinary research is needed to integrate \r\nthe more focused interventions and show how they work together in \r\na system"


id: f48bd185c028c0555fe827d3087427c7
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 35979
content:
" Such research will be particularly important for working on \r\nthe concern running through these studies: strengthening synergies \r\nbetween climate and the broader sustainable development agenda."


id: 8ef8593f0e6171d93fbbfdf5df7ffc85
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 20030
content:
"From a cross-sectoral perspective, it can be concluded that the AFOLU \r\nsector offers many low-cost mitigation options with synergetic SDG \r\nimpacts, which, however, can also create trade-offs between land use \r\nfor food, energy, forest and biodiversity. Some options can help to \r\nmitigate such trade-offs, like agricultural practices, forest conservation \r\nand soil carbon sequestration. Lifestyle changes, including dietary \r\nchanges and reduced food waste, could jointly support the SDGs and \r\nmitigation. Industry also offers several mitigation options with SDG \r\nsynergies, for example, related to energy efficiency and the circular \r\neconomy. Some of the renewable-energy options in industry could \r\nindicate some trade-offs in relation to land use, with implications \r\nfor food- and water security and costs"


id: ca75304427c4ddf8eb6b05034e51ac5e
created_at: 2023-03-16 14:02:55
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vector_id: 29412
content:
" Cities provide a  promising \r\nbasis for implementing mitigation with SDG synergies, particularly \r\nif urban planning, transportation, infrastructure and settlements are \r\ncoordinated jointly. Similarly, studies of the building sector have \r\nidentified many synergies between the SDGs and mitigation, but \r\nthere are issues related to the costs of new technologies. Also, in \r\nrelation to households and buildings, important equity issues emerge \r\ndue to the ability of low-income groups to afford the introduction of \r\nnew technologies. Altogether these cross-sectoral conclusions create \r\na need for policies to address both synergies and trade-offs, as well as \r\nfor coordination between different sectoral domains. Context-specific \r\nassessments of synergies and trade-offs are here important, as is \r\nsharing the benefits and costs associated with mitigation policies."


id: ef97e02aa419387518adda0d8ad0df8
created_at: 2023-03-16 14:02:55
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vector_id: 35194
content:
"Several opportunities for creating SDG synergies and avoiding trade\u0002offs have also been identified in relation to integrated adaptation \r\nand mitigation policies. The AFOLU sector has a  large potential for \r\nintegrating adaptation and mitigation policies related to agriculture, \r\nbioenergy crops, forestry and water use. As was concluded for \r\nmitigation options, integrated adaptation and mitigation policies also \r\nentail the risks of creating trade-offs in relation to food, water, energy \r\naccess and biodiversity. There are several potentially strong links \r\nbetween climate change adaptation in industry and climate change \r\nadaptation more generally. Various supply chains can be affected by \r\nclimate change, and mitigation options related to energy and water \r\nsupply can be disrupted by climate events, implying that great benefits \r\nmay come from integrating adaptation in industrial planning efforts"


id: e37af4f9adddfedae01d43fe6a0e9b65
created_at: 2023-03-16 14:02:55
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vector_id: 33268
content:
" \r\nAdaptation options in industry can imply increasing the demand for \r\npackaging materials such as plastics and for access to refrigeration, \r\nwhich are also major sources of GHG emissions, which then would \r\nrequire further mitigation options. Mitigation and the co-benefits \r\nof adaptation in urban areas in relation to air quality, health, green \r\njobs and equality issues can in most cases be synergetic and can also \r\nsupport the SDGs. One exception are compact cities, with their trade\u0002offs between mitigation and adaptation because decreasing urban \r\nsprawl can increase the risks of flooding and heat stress. Detailed \r\nmapping of mitigation and adaptation in urban areas shows that there \r\nare many, very close interactions between the two policy domains and \r\nthat coordinated governance across sectors is therefore called for."


id: 7f97bb0ec1f7be4155754c2763fba552
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 17621
content:
"Meeting the ambitions of the Paris Agreement will require phasing \r\nout fossil fuels from energy systems, which is technically possible and \r\nis estimated to be relatively low in cost. However, studies also show \r\nthat replacing fossil fuels with renewables can have major synergies \r\nand trade-offs with a  broader agenda of sustainable development \r\nif a balance is established in relation to land use, food security and \r\njob creation (McCollum et al. 2018). Furthermore, the transition to \r\nlow-emission pathways will require policy efforts that also address \r\nthe emissions locked-in to existing infrastructure, like power plants, \r\nfactories, cargo ships and other infrastructure already in use: for \r\nexample, today coal-fired power plants account for 30% of all energy\u0002related emissions"


id: 7cb2742e02312ebd5c357174d9656c1b
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 17215
content:
" Thus, even though the transition away from fossil \r\nfuels is desirable and technically feasible, it is still largely constrained \r\nby existing fossil fuel-based infrastructure and the existence of \r\nstranded investments. The 'committed' emissions from existing fossil \r\nfuel infrastructure may consume all the remaining carbon budget in the \r\n1.5degC scenario or two thirds of the carbon budget in the 2degC scenario."


id: b24b9be426725ac9b5cb87a8585b233a
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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content:
"Ultimately, the evidence demonstrates that there is rarely any one \r\nsingle factor promoting or preventing transitions. A  constellation \r\nof elements come into play, including technological innovations, \r\nshifts in markets, social and behavioural dynamics, and governance \r\narrangements. Indeed, transitions require an examination of the role \r\nof values, attitudes, beliefs and the structures that shape behaviour, as \r\nwell as the dynamics of social movements and education at multiple \r\nlevels. Likewise, technological and social innovation both play an \r\nimportant role in enabling transitions, highlighting the importance of \r\nmulti-institutional and multi-stakeholder actors building institutional \r\nsupport networks, facilitating collaboration between sectors and \r\nactors, and promoting learning and social change"


id: e1b09fa370b0f593631248bed42b4ad2
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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content:
" Financial tools \r\nand economic instruments are crucial enablers, since many forms \r\nof transformational change to energy systems are not possible \r\nwhen financial systems still privilege investing in unsustainable, \r\ncarbon-intensive sectors. These instruments are deployed within \r\nthe context of the multi-level governance of climate change, which \r\nsuggests the importance of complementary policies and institutions \r\nthat simultaneously integrate across multiple sectors and scales to \r\naddress the multiple sources of lock-in that are shaping the current \r\ncarbon-intensive energy system. Systems-oriented approaches, \r\nwhich holistically address the intersections among climate, water \r\nand energy (for instance), have significant potential to reveal and \r\nhelp avoid trade-offs, foster experimentation, and deliver a range of \r\nco-benefits on the path towards sustainable development."


id: 97d2f103182801231f12a1de705f52bc
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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content:
"Negative economic and social impacts in some regions could emerge as a consequence of ambitious climate change mitigation \r\npolicies if these are not aligned with key sustainable development aspirations such as those represented by the Sustainable \r\nDevelopment Goals (SDGs) on 'no poverty, energy-, water- and food access', and so on, which could in turn slow down the transition \r\nprocess. Nonetheless, many climate change mitigation policies could generate incomes, new jobs and other benefits. Capturing \r\nthese benefits could require specific policies and investments to be targeted directly towards including all parts of society in the \r\nnew activities and industries created by the climate change mitigation policies, and that activities that are reduced in the context of \r\ntransitions to a low-carbon future, including industries and geographical areas, are seeing new opportunities"


id: a34c578bad1ab88f9726157c4edb593a
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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content:
" Poor understanding \r\nof how governance at multiple levels can meet these challenges to the transition may fail to make significant progress in relation \r\nto national policies and a  global climate agreement. It may therefore either support or weaken the climate architecture, thus \r\nconstituting a limiting factor."


id: 1962c445f411f739bafae4dd8d814604
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 1479
content:
"Institutions are critical in accelerating the transition towards sustainable development: they can help to shape climate change \r\nresponse strategies in terms of both adaptation and mitigation. Local institutions are the custodians of critical adaptation services, \r\nranging from the mobilisation of resources, skills development and capacity-building to the dissemination of critical strategies. \r\nTransitions towards sustainable development are mediated by actors within particular institutions, the governance mechanisms they \r\nuse as implementing tools and the political coalitions they form to enable action. Patterns of production and consumption have \r\nimplications for a low-carbon development, and many of these patterns can act as barriers or opportunities towards sustainable \r\ndevelopment"


id: cad470a13568f182258b03ed4ede26d6
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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content:
" Trade policies, international economic issues and international financial flows can positively support the speed and \r\nscale of the transition; alternatively, they can have negative impacts on policies that may inhibit the process. Nonetheless, contextual \r\nfactors are a fundamental part of the change process, and institutions and their governance systems provide pathways that can \r\ninfluence contextual realities on the ground. For instance, politically vested interests may lead powerful lobby groups or coalition \r\nnetworks to influence the direction of the transition, or they could put pressure on a given political elite through the imposition of \r\nregulatory standards, taxation, incentives and policies that may speed or delay the transition process"


id: 5f7fd9d9844857d269b8d05ed0895139
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updated_at: 2023-03-16 14:02:55
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content:
" Civil-society institutions, such \r\nas NGOs or research centres, can act as effective governance 'watchdogs' in the transition process, particularly when they exercise \r\na challenge function and question government actions in respect of transitions related to sustainable development."


id: 97b483937822eaf2a03dabf71b3adbe3
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 21393
content:
" Atmospheric aerosols may be either emitted as primary \r\nparticulate matter or formed within the atmosphere from gaseous \r\nprecursors (secondary production). Aerosols may be composed of \r\nsea salt, organic carbon, black carbon (BC), mineral species (mainly \r\ndesert dust), sulphate, nitrate and ammonium or their mixtures. See \r\nalso Short-lived climate forcers (SLCFs)."


id: 12d138c0fa5f171b52c30ff7f2edc1a2
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 463
content:
" The UNFCCC thus makes a distinction between climate \r\nchange attributable to human activities altering the atmospheric \r\ncomposition and climate variability attributable to natural causes."


id: 792cf04246e6003fe8011fa2e4cec36c
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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content:
"Developed/developing countries (Industrialised/developed/\r\ndeveloping countries) There is a  diversity of approaches for \r\ncategorising countries on the basis of their level of development, \r\nand for defining terms such as 'industrialised', 'developed', or \r\n'developing'. Several categorisations are used in this report: (i) In \r\nthe United Nations (UN) system, there is no established convention \r\nfor the designation of developed and developing countries or areas. \r\n(ii) The UN Statistics Division specifies developed and developing \r\nregions based on common practice. In addition, specific countries \r\nare designated as least developed countries, landlocked developing \r\ncountries, Small Island Developing States (SIDS), and transition \r\neconomies. Many countries appear in more than one of these \r\ncategories. (iii) The World Bank uses income as the main criterion for \r\nclassifying countries as low, lower middle, upper middle, and high \r\nincome"


id: b4c0a15318f568b2933f3423a94c4cf6
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content:
" (iv) The UN Development Programme (UNDP) aggregates \r\nindicators for life expectancy, educational attainment, and income \r\ninto a single composite Human Development Index (HDI) to classify \r\ncountries as low, medium, high, or very high human development."


id: 6d56e2ca4c8052101dc375f8b9c45c4
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updated_at: 2023-03-16 14:02:55
content_type: text
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vector_id: 14818
content:
"Greenhouse gas emission metric A simplified relationship used \r\nto quantify the effect of emitting a unit mass of a given greenhouse \r\ngas (GHG) on a specified key measure of climate change. A relative \r\nGHG emission metric expresses the effect from one gas relative to \r\nthe effect of emitting a unit mass of a reference GHG on the same \r\nmeasure of climate change. There are multiple emission metrics, \r\nand the most appropriate metric depends on the application"


id: a97f52b98019c00f978b8a08f72de568
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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content:
" GHG \r\nemission metrics may differ with respect to: (i) the key measure of \r\nclimate change they consider; (ii) whether they consider climate \r\noutcomes for a specified point in time or integrated over a specified \r\ntime horizon; (iii) the time horizon over which the metric is applied; \r\n(iv) whether they apply to a  single emission pulse, emissions \r\nsustained over a period of time, or a combination of both; and (v) \r\nwhether they consider the climate effect from an emission compared \r\nto the absence of that emission or compared to a reference emissions \r\nlevel or climate state."


id: ec508f1fcb2f22a444832c17e0935e6e
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 34670
content:
" These can be used to \r\nassess impacts and mitigation in a cost-benefit framework and have \r\nbeen used to estimate the social cost of carbon. See also Integrated \r\nAssessment Scenario Ensemble."


id: f545ea7702c6df023866dead445d0a43
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 36106
content:
"Just transitions A set of principles, processes and practices that \r\naim to ensure that no people, workers, places, sectors, countries or \r\nregions are left behind in the transition from a high-carbon to a low\u0002carbon economy. It stresses the need for targeted and proactive \r\nmeasures from governments, agencies, and authorities to ensure that \r\nany negative social, environmental or economic impacts of economy\u0002wide transitions are minimised, whilst benefits are maximised for \r\nthose disproportionally affected. Key principles of just transitions \r\ninclude: respect and dignity for vulnerable groups; fairness in energy \r\naccess and use, social dialogue and democratic consultation with \r\nrelevant stakeholders; the creation of decent jobs; social protection; \r\nand rights at work"


id: 5d5e5fd22a4a78ab980827a51ebdd88f
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 12214
content:
" Just transitions could include fairness in energy, \r\nland use and climate planning and decision-making processes; \r\neconomic diversification based on low-carbon investments; realistic \r\ntraining/retraining programmes that lead to decent work; gender\u0002specific policies that promote equitable outcomes; the fostering \r\nof international cooperation and coordinated multilateral actions; \r\nand the eradication of poverty. Lastly, just transitions may embody \r\nthe redressing of past harms and perceived injustices (ILO 2015; \r\nUNFCCC 2016)."


id: 40c88bf07e149215a4a6351d135ac5ae
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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index: climate_gpt_only_giec
vector_id: 7575
content:
"lifetimes (from hours to about two decades) but characterised by \r\ndifferent physiochemical properties and environmental effects. Their \r\nemission or formation has a  significant effect on radiative forcing \r\nover a period determined by their respective atmospheric lifetimes. \r\nChanges in their emissions can also induce long-term climate effects \r\nvia, in particular, their interactions with some biogeochemical cycles. \r\nSLCFs are classified as direct or indirect, with direct SLCFs exerting \r\nclimate effects through their radiative forcing and indirect SLCFs being \r\nthe precursors of other direct climate forcers. Direct SLCFs include \r\nmethane (CH4 ), ozone (O3 ), primary aerosols and some halogenated \r\nspecies. Indirect SLCFs are precursors of ozone or secondary aerosols. \r\nSLCFs can be cooling or warming through interactions with radiation \r\nand clouds. They are also referred to as near-term climate forcers. \r\nMany SLCFs are also air pollutants"


id: 8d6a7c324fbcbadfd69b97c69176b578
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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content:
" A subset of exclusively warming \r\nSLCFs is also referred to as short-lived climate pollutants (SLCPs), \r\nincluding methane, ozone, and black carbon (BC)."


id: 2c66e16ee591d1c65e7c1be28f242dd9
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 4417
content:
"Urban The categorisation of areas as 'urban' by government \r\nstatistical departments is generally based either on population \r\nsize, population density, economic base, provision of services, or \r\nsome combination of the above. Urban systems are networks and \r\nnodes of intensive interaction and exchange including capital, \r\nculture, and material objects. Urban areas exist on a continuum with \r\nrural areas and tend to exhibit higher levels of complexity, higher \r\npopulations and population density, intensity of capital investment, \r\nand a preponderance of secondary (processing) and tertiary (service) \r\nsector industries. The extent and intensity of these features varies \r\nsignificantly within and between urban areas. Urban places and \r\nsystems are open with much movement and exchange between \r\nmore rural areas as well as other urban regions"


id: ffb6e4987317280aad26ab7703d58f8d
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 37775
content:
" Urban areas can \r\nbe globally interconnected facilitating rapid flows between them - \r\nof capital investment, of ideas and culture, human migration, and \r\ndisease. See also Cities, Peri-urban areas, and Urbanisation."


id: 8f4226516f2d242a28d5138165104330
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 20085
content:
"Across this report, a number of different metrics to characterise cost \r\nof climate change mitigation are employed. To facilitate a meaningful \r\neconomic comparison across diverse options at the technology level, \r\nthe metric of 'levelised costs' is used throughout several chapters of \r\nthis report in various forms. The most used metrics are the levelised \r\ncost of energy (LCOE), the levelised cost of conserved energy (LCCE), \r\nand the levelised cost of conserved carbon (LCCC). These metrics are \r\nused throughout the AR6 WGIII to provide a benchmark for comparing \r\ndifferent technologies or practices of achieving the respective output. \r\nEach comes with a set of context-specific caveats that need to be \r\ntaken into account for correct interpretation. Various literature \r\nsources caution against drawing too strong conclusions from these \r\nmetrics. Annex II in AR5, namely Section A.II.3.1, includes a detailed \r\ndiscussion on interpretations and caveats"


id: b59bc2de28e75627611c92f60f0876ba
created_at: 2023-03-16 14:02:55
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vector_id: 26103
content:
"Wherever emissions, removals and mitigation potentials are \r\nexpressed as CO2-eq in this report, efforts have been made to \r\nrecalculate those values consistently in terms of GWP100 values from \r\nAR6 WGI. However, in some cases it was not possible or feasible to \r\ndisentangle conclusions from the existing literature into individual \r\ngases and then re-aggregate those emissions using updated GWP100 \r\nvalues. The existing literature assessed by WGIII uses a range of \r\nGWP100 values from previous IPCC reports; for CH4, these values \r\nvary between 21 (based on the IPCC Second Assessment Report) \r\nto 28 or even 34 (based on the IPCC Fifth Assessment Report and \r\ndepending on whether the study included or excluded climate-carbon \r\ncycle feedbacks). Consistent application of any metric is challenging \r\nas individual GHG emission species are not always provided in the \r\nliterature assessed by WGIII"


id: 24784f51299e7ae0a01a1cc4a8414fb8
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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content:
" Where a full recalculation of CO2-eq \r\nemissions or mitigation potentials into GWP100 AR6 values was not \r\npossible or feasible, and especially if non-CO2 emissions constitute \r\nonly a minor fraction of total emissions or abatement, individual \r\nchapters note this inconsistency and provide an indication of the \r\npotential magnitude of inconsistency."


id: ec5767a2a74e634936beb601aacf48e4
created_at: 2023-03-16 14:02:55
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vector_id: 34672
content:
"In order to estimate the indirect emissions of sectors and subsectors, \r\nthe CO2 Emissions from Fuel Combustion dataset of the International \r\nEnergy Agency (IEA 2020a) is used. This database reports direct and \r\nindirect CO2 emissions for IEA sectors, which are related to the IPCC \r\n(IPCC 2019) classification of emissions sources. The IEA adopted \r\na new methodology in 2020 that is in line with the methodology used \r\nin Annex II of the WGIII contribution to AR5 (Krey et al. 2014), namely \r\nSection A.II.4. The IEA now estimates individual electricity and heat \r\nspecific emission factors and allocates indirect emissions related \r\nto electricity and heat in the sectors where these forms of energy \r\nare used respectively (IEA 2020b)"


id: fd6de154def5ced395bba4326847fc84
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 37386
content:
" In order to estimate the share of \r\nenergy input that results in the production of heat from the share that \r\nresults in the production of electricity in Combined heat and Power \r\nplants, the IEA fixes the efficiency for heat production equal to 90%, \r\nwhich is the typical efficiency of a heat boiler and then allocates the \r\nremaining inputs to electricity production (IEA 2020b). "


id: 446ecca471336e722022892341bce13b
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 8189
content:
"Adopting climate mitigation options can generate multiple positive \r\n(synergies) and negative (trade-offs) interactions with sustainable \r\ndevelopment. Understanding these are crucial for selecting \r\nmitigation options and policy choices that maximise the synergies, \r\nminimise trade-offs, and potentially offset trade-offs (Roy et  al. \r\n2018). Chapter  5 in the IPCC's Special Report on Global Warming \r\nof 1.5degC examines the synergies and trade-offs of adaptation and \r\nmitigation measures with sustainable development and UN's \r\nSustainable Development Goals (SDGs). Building on this, the sectoral \r\nchapters in the WGIII contribution to the AR6 include a qualitative \r\nassessment of the synergies and trade-offs between mitigation \r\noptions in different sectors and the SDGs based on existing literature. \r\nAll these assessments are collated and presented in Chapter  17 \r\nwith a supplementary table including the details of the synergies \r\nand trade-offs with a line of sight (Section  17.3.3"


id: b9ad009ca584315eab5b69603a470fcb
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vector_id: 26755
content:
"7, Figure  17.1 \r\nand Supplementary Material Table  17.1). The assessment also \r\nrecognises that interactions of mitigation options with the SDGs are "


id: fa080a90872e6ecc387517fc48974d0a
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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content:
"One of the most basic aspects of a  modelling tool is how it \r\napproaches the system modelled from a  solution perspective. \r\nA broad interpretation of mathematical algorithm concepts classifies \r\nmodels as simulation and optimisation models. Simulation models\r\nare based on the evaluation of the dynamic behaviour of a system \r\n(Lund et al. 2017). They can be used to determine the performance of \r\na system under alternative options of key parameters in a plausible \r\nmanner. Most often, simulation models require comprehensive \r\nknowledge of each parameter, in order to choose a  specific path \r\nunder several alternatives. On the other hand, optimisation models\r\nseek to maximise or minimise a  mathematical objective function \r\nunder a set of constraints (Banos et al. 2011; Iqbal et al. 2014). Most \r\noften, the objective function represents the total cost or revenue of \r\na  given system or the total welfare of a  given society"


id: 23397e95b9f5014a729c86613a088f5f
created_at: 2023-03-16 14:02:55
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content:
" One major \r\naspect of optimisation models is that the solution is achieved by \r\nsimultaneously binding a  set of constraints, which can be used to \r\nrepresent real-life limitations on the system, such as: constraints \r\non flows, resource and technology availability, labour and financial \r\nlimitations, environmental aspects, and many other characteristics \r\nthat the model may require (Fazlollahi et al. 2012; Pfenninger et al. \r\n2014; Cedillos Alvarado et al. 2016). Specifically, when modelling \r\nclimate mitigation responses, limiting carbon budgets is often used \r\nto represent future temperature level pathways (Rogelj et al. 2016; \r\nMillar et al. 2017; Peters 2018; Gidden et al. 2019)."


id: 34edd34d8ac12cd450bc5916d8f28cf5
created_at: 2023-03-16 14:02:55
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content:
"Another major distinction among modelling tools is related to the \r\nsolution methodology from a  temporal perspective. They can have \r\na perfect foresight intertemporal assumption or a recursive-dynamic \r\nassumption. Intertemporal optimisation with perfect foresight is \r\nan optimisation method for achieving an overall optimal solution \r\nover time. It is based on perfect information on all future states of \r\na system and assumptions (such as technology availability and prices) \r\nand, as such, today's and future decisions are made simultaneously, \r\nresulting in a single path of optimal actions that lead to the overall \r\noptimal solution (Keppo and Strubegger 2010; Gerbaulet et al. 2019). \r\nSuch a modelling approach can present an optimal trajectory of the \r\nset of actions and policies that would lead to the overall first-best \r\nsolution"


id: c8ff1b32a5816b8698b85a2962fc3b94
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content:
" However, real-life decisions are not always based on optimal \r\nsolutions (Ellenbeck and Lilliestam 2019) and, therefore, solutions \r\nfrom perfect foresight models can be challenging to be implemented \r\nby policymakers (Pindyck 2013, 2017). For instance, perfect foresight \r\nimplies perfect knowledge of the future states of the system, such as \r\nfuture demand for goods and products and availability of production \r\nfactors and technology."


id: 3d6eae202e707dbe082b84a54b034fd5
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content:
"Commonly climate mitigation models are referred to as bottom-up \r\nand top-down depending upon their degree of detail (van Vuuren \r\net al. 2009). Generally, bottom-up approaches present more \r\nsystematic individual technological details about a reduced number \r\nof mitigation strategies of a specific sector or sub-sector. These models \r\ntend to disregard relations between specific sectors/technologies and \r\nmiss evaluating interactions with the whole system. On the other \r\nhand, top-down approaches present a more aggregated and global \r\nanalysis, in detriment of less detailed technological heterogeneity. \r\nThey tend to focus on interactions within the whole system, such as \r\nmarket and policy instrument interactions within the global economy \r\nsystems. Studies using top-down models are more capable of \r\nrepresenting economic structural change than adopting technology\u0002explicit decarbonisation strategies (van Vuuren et al. 2009; Kriegler \r\net al. 2015a)"


id: 8ec2c4a177b63af18108af6062836ca8
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content:
" Integrated assessment models (IAMs) typically use \r\na top-down approach to model sectoral mitigation strategies."


id: 6c185fc102313964de3594453518f7b0
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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content:
"itself, its utilisation, and end-life, either via reuse, recycling or \r\nfinal disposal (Rebitzer et al. 2004; Finnveden et al. 2009; Guinee \r\net al. 2011; Curran 2013; Hellweg and Mila i Canals 2014). \r\nThe  environmental impacts covered include all types of loads \r\non  the environment through the extraction of natural resources \r\nand emission of hazardous substances. For this reason, LCA has \r\nthe flexibility to evaluate an entire product system, hence avoiding \r\nsub-optimisation in a  single process and identifying the products \r\nand processes that result in the least environmental impact. Thus, \r\nit allows for the quantification of possible trade-offs between \r\ndifferent environmental impacts (e.g.,  eliminating air emissions by \r\nincreasing non-renewable energy resources) (Hawkins et al. 2013; \r\nNordelof et al. 2014; Gibon et al. 2017) and/or from one stage to \r\nother (e.g"


id: debc6ad22ef76e906e5ab4e755c2c3e6
created_at: 2023-03-16 14:02:55
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index: climate_gpt_only_giec
vector_id: 32538
content:
",  reuse or recycling a  product to bring it back in at the \r\nraw material acquisition phase) (Hertwich and Hammitt 2001a,b). It \r\ngives a holistic view of complex systems and reduces the number of \r\nparameters for which decisions have to be taken, while not glossing \r\nover technical and economical details. In recent years, LCA has been \r\nwidely used in both retrospective and prospective analysis of product \r\nchains in various climate mitigation fields, namely comparing existing \r\nenergy technologies with planned alternatives (Cetinkaya et al. 2012; \r\nPortugal-Pereira et al. 2015), product innovation and development \r\n(Wender et al. 2014; Portugal-Pereira et al. 2015; Sharp and Miller \r\n2016), certification schemes (Prussi et al. 2021), or supply chain \r\nmanagement (Hagelaar 2001; Blass and Corbett 2018)."


id: bb06fd66f09f898a5d27a262a5b6bdf4
created_at: 2023-03-16 14:02:55
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content:
"time steps. Therefore, the solution path is a series of solutions in short \r\ntrajectories that, ultimately, is very unlikely to achieve the overall \r\noptimal solution over the whole time period considered (Fuso Nerini \r\net al. 2017). Nonetheless, the solution represents a set of possible and \r\nplausible policies and behavioural choices of the agents that could be \r\ntaken in short-term cycles, without perfect information (Heuberger \r\net al. 2018; Hanna and Gross 2020). In between, some models \r\nconsider imperfect or adaptive expectations, where economic \r\ndecisions are based on past, current and imperfectly anticipated \r\nfuture information (Keppo and Strubegger 2010; Kriegler et al. 2015a; \r\nLoffler et al. 2019)"


id: 75fbf888fb66b18ad0286a2fc62fb10
created_at: 2023-03-16 14:02:55
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content:
" Modelling tools can also be differentiated by \r\ntheir level of representation of economic agents and sectors: they \r\ncan have a  full representation of all agents of the economy and \r\ntheir interactions with each other (general equilibrium) or focus \r\non a more detailed representation of a subset of economic sectors \r\nand  agents (partial equilibrium) (Cheng et al. 2015; Babatunde \r\net al. 2017; Hanes and Carpenter 2017; Sanchez et al. 2018; \r\nGuedes et al. 2019; Pastor et al. 2019) (Annex III.I.2)."


id: eb38a7b6ed27c550bb6990aa38fecec2
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content:
"Modelling exercises vary considerably in terms of key characteristics, \r\nincluding geographical scales, time coverage, environmental \r\nvariables, technologies portfolios, and socio-economic assumptions. \r\nA detailed comparison of key characteristics of global and national \r\nmodels used in this report is presented in Annex III.I.9. Geographical \r\ncoverage ranges from sub-national (Cheng et al. 2015; Feijoo et al. \r\n2018; Rajao et al. 2020), national (Li et al. 2019; Sugiyama et al. 2019; \r\nVishwanathan et al. 2019; Schaeffer et al. 2020), regional (Vrontisi \r\net al. 2016; Hanaoka and Masui 2020) and global (Gidden et al. \r\n2018; Kriegler et al. 2018a; McCollum et al. 2018; Rogelj et al. 2019b; \r\nDrouet et al. 2021) models. Even models with the same geographical \r\ncoverage can still be significantly different from each other, for \r\ninstance, due to the number of regions within the model"


id: f2a4365f368b443b886475525f275dde
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content:
" Models \r\ncan also have spatially implicit and explicit formulations, which in \r\nturn can have different spatial resolution. This distinction is especially \r\nimportant for land-use models, which account for changes in land \r\nuse and agricultural practices (Annex III.I.7: Land-use modelling). \r\nThe time horizon, time steps and time resolution are major aspects \r\nthat differ across models. Model horizon can range from short- to \r\nlong-term, typically reaching from a few years to up until the end \r\nof the century (Fujimori et al. 2019b; Gidden et al. 2019; Rogelj \r\net al. 2019a; Ringkjob et al. 2020). Time resolution is particularly \r\nrelevant for specific applications, such as power sector models, which \r\nhave detailed representation of power technologies dispatch and \r\noperation (Soria et al. 2016; Abujarad et al. 2017; Guan et al. 2020)."


id: 4636653679c760148d1de76fb7faedd8
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content:
"The disaggregation of economic actors and sectors and the \r\nrepresentation of their interaction differ across full-economy \r\nframeworks. A  main distinction is between models based on full \r\nSocial Accounting Matrix (SAM) and aggregated growth approaches. \r\nOn the one hand, SAM-based frameworks - CGE and macro\u0002econometric - follow a  multi-sectoral approach distinguishing \r\nfrom several to a hundred different economic sectors or production \r\ngoods and represent sector-specific value-added, final consumption \r\nand interindustry intermediary consumption (Robinson 1989). \r\nThey also represent economic agents (firms, households, public \r\nadministration, etc.) with specific behaviours and budget constraints. \r\nOn the other hand, macro-economic growth frameworks are reduced \r\nto a single macro-economic agent producing, consuming and investing \r\na  single macroeconomic good without considering interindustry \r\nrelationships"


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content:
" In some detailed process IAMs, the aggregated growth \r\napproach is combined with a detailed representation of energy supply \r\nand  demand systems that surmises different economic actors and \r\nsubsectors. However, the energy system is driven by an aggregated \r\ngrowth engine (Bauer et al. 2008)."


id: 42b2a8ec5273e785e558cd0b33cd34fe
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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content:
"Several types of 'full-economy' frameworks are used in integrated \r\nassessment models. The general equilibrium framework - often \r\nreferred to as Computable General Equilibrium (CGE) - represents the \r\neconomic interdependencies between multiple sectors and agents, \r\nand the interaction between supply and demand on multiple markets \r\n(Robinson et al. 1999). It captures the full circularity of economic \r\nflows through income and demand relationships and feedbacks \r\nincluding the overall balance of payments. Most CGE approaches \r\nused are neoclassical supply-led models with market clearing \r\nbased on price adjustment. Representative agents usually minimise \r\nproduction costs or maximise utility under given production and \r\nutility function, although optimal behaviours are not a precondition \r\nper se. Most CGE models also include assumptions of perfect markets \r\nwith full employment of factors although market imperfections and \r\nunderemployment of factors (e.g"


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content:
", unemployment) can be assumed \r\n(Babiker and Eckaus 2007; Guivarch et al. 2011). CGE frameworks can \r\neither be static or dynamic and represent pathways as a sequence of \r\nequilibria in the second case."


id: e6e624553ed4218dcaa3a3d62c320229
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updated_at: 2023-03-16 14:02:55
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vector_id: 33813
content:
"In most models the treatment of economic growth follows Solow \r\nor Ramsey growth approach based on the evolution through time \r\nof production factors, endowment and productivity. Classically, \r\nlabour endowment and demography are exogenous, and capital \r\naccumulates through investment. Partial equilibrium frameworks do \r\nnot model economic growth but use exogenous growth assumptions \r\nderived from growth models. Factors' productivity evolution \r\nis assumed exogenous in most cases that is, general technical \r\nprogress is assumed to be an autonomous process. A few models \r\nfeature endogenous growth aspects where factor productivity \r\nincreases with cumulated macroeconomic investment. Models \r\nalso differ in the content of technical progress and alternatively \r\nconsider unbiased total factor productivity improvement or \r\nlabour-specific factor-augmenting productivity"


id: a53b76b77771bd06f2c408bc044de15b
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vector_id: 23560
content:
" In multi-sectoral \r\nmacroeconomic models, economic growth comes with endogenous \r\nchanges of the sectoral composition of GDP known as structural \r\nchange. Structural change results from the interplay between \r\ndifferentiated changes of productivity between sectors and of \r\nthe structure of final demand as income grows (Herrendorf et al. \r\n2014). If general technical progress is mostly assumed exogenous \r\nand autonomous at an aggregated level, innovation in relation \r\nto energy demand and technical systems follow more detailed \r\nspecifications in models. Energy efficiency can be assumed an \r\nautonomous process at different levels - macroeconomic, sector \r\nor technology - or energy technical change can be endogenous "


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updated_at: 2023-03-16 14:02:55
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content:
"Multi-regional models consider interactions between regions through \r\ntrade in energy goods, non-energy goods and services - depending on \r\nmodel scope - and emission permits in the context of climate policy. \r\nFor each type of goods, trade is usually represented as a  common \r\npool where regions interact with the pool through supply (exports) or \r\ndemand (imports). A few models consider bilateral trade flows between \r\nregions. Traded goods can be assumed as perfectly substitutable \r\nbetween regions of origin (Heckscher-Ohlin assumption), such as is \r\noften the case for energy commodities, or as imperfectly substitutable \r\n(e.g., Armington goods) for non-energy goods"


id: c9987af0a0cc2a1ff853d9910bf6641
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updated_at: 2023-03-16 14:02:55
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index: climate_gpt_only_giec
vector_id: 29273
content:
" The representation of \r\ntrade and capital imbalances at the regional level and their evolution \r\nthrough time vary across models and imbalances are either not \r\nconsidered (regional current accounts are balanced at each point in \r\ntime), or a constraint for intertemporal balance is included (an export \r\nsurplus today will be balanced by an import surplus in the future), \r\nor else trade imbalances follow other rules such as a  convergence \r\ntowards zero in the long run (Foure et al. 2020)."


id: cab0b0c26e0cef2d8f59d322870d1e2f
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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index: climate_gpt_only_giec
vector_id: 29450
content:
"where  is the consumption discount rate (also known as the social \r\ndiscount rate),  is the utility discount rate (also known as the \r\npure time discount rate, or time preferences rate) which is a value \r\njudgement that determines the present value of a  change in the \r\nutility experienced in the future and hence it is an ethical parameter, \r\ngg! is the growth rate of consumption per capita over time, and  is \r\nthe elasticity of marginal utility of consumption, which is also a value \r\njudgement and hence an ethical parameter. The parameter  is also \r\na  measure of risk aversion and of society's aversion to inequality \r\nwithin and across generations. The pure time preference rate is an \r\nexogenous parameter, but the social discount rate is endogenously \r\ncomputed by the model itself and depends on the growth rate \r\nof consumption per capita over time"


id: b01d69f344687a79a602f1d81b5c6157
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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index: climate_gpt_only_giec
vector_id: 25231
content:
" Note that more complex \r\nframeworks disentangle inequality aversion from risk aversion, and \r\nintroduce uncertainty, leading to extensions of the social discount \r\nrate equation (see, for instance, Gollier 2013)."


id: 4337d2b598bf7b677994ccf288dc672
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 7977
content:
"Furthermore, advanced modelling tools have been developed for the \r\npurpose of providing estimations of system-wide inertial frequency \r\nresponse that would assist system operators in maintaining adequate \r\nsystem inertia (Sharma et al. 2011; Teng and Strbac 2017). These \r\ninnovative models also provide fundamental evidence regarding \r\nthe role and value of advanced technologies and control systems in \r\nsupporting cost-effective operation of future electricity systems \r\nwith very high penetration of renewable generation. In particular, \r\nthe importance of enhancing the control capabilities of renewable \r\ngeneration and applying flexible technologies, such as energy \r\nstorage (Hall and Bain 2008; Obi et al. 2017; Arbabzadeh et al. 2019), \r\ndemand-side response, interconnection (Aghajani et al. 2017) and \r\ntransmission grid extensions (Schaber et al. 2012) to provide system \r\nstability control, is demonstrated through novel system integration \r\nmodels (Lund et al. 2015; Sinsel et al"


id: 9ec2f05d51e111dcfd402b95696a2b17
created_at: 2023-03-16 14:02:55
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vector_id: 22516
content:
"Outside social discounting for intertemporal optimisation, \r\ndiscounting is used in simulation models to compute the life cycle \r\ncosts of investment decisions (e.g., energy efficiency choices, choices \r\nbetween different types of technologies based on their levelised \r\ncosts). In this case, the discount rate can be interpreted as the \r\ncost of capital faced by investors. The cost of capital influences the \r\nmerit order of technologies and lower capital cost favours capital\u0002intensive technologies over technologies with higher variable costs. \r\nModels can reflect regional, sectoral or technology-specific cost of \r\ncapital - through heterogeneous discount rates for life cycle cost \r\nestimates in simulation models (Iyer et al. 2015) or as hurdle rates \r\nin energy optimisation models (Ameli et al. 2021). In some cases, \r\nsimulation models may also produce mitigation pathways following \r\nthe Hotelling principle and assuming that the carbon price rises at the \r\nsocial discount rate (e.g"


id: 588a3277d3b9d5d51356e513196e398
created_at: 2023-03-16 14:02:55
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content:
", Global Change Assessment Model (GCAM) \r\nscenarios in the Shared Socio-economic Pathways (SSP) study with \r\ncarbon prices increasing at 5% yearly (Guivarch and Rogelj 2017))."


id: 173e386da05bb483c31468252a3189d6
created_at: 2023-03-16 14:02:55
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vector_id: 1167
content:
"A novel modelling framework is proposed to deliver inertia and \r\nsupport primary frequency control through variable-speed wind \r\nturbines (Morren et al. 2006) and PVs (Waffenschmidt and Hui 2016; \r\nLiu et al. 2017), including quantification of the value of this technology \r\nin future renewable generation-dominated power grids (Chu et al. \r\n2020). Advanced models for controlling distributed energy storage \r\nsystems to provide an effective virtual inertia have been developed, \r\ndemonstrating the provision of virtual synchronous machine \r\ncapabilities for storage devices with power electronic converters, \r\nwhich can support system frequency management following \r\ndisturbances (Hammad et al. 2019; Markovic et al. 2019)"


id: 3239b67e4ae1b460b22b9638f858f7a
created_at: 2023-03-16 14:02:55
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vector_id: 5315
content:
" Regarding \r\nthe application of interconnection for exchange of balancing services \r\nbetween neighbouring power grids, alternative control schemes for \r\nhigh-voltage direct current (HVDC) converters have been proposed, \r\nin order to demonstrate that this would reduce the cost of balancing \r\n(Tosatto et al. 2020)."


id: ede0ca2ef4972c7a8df45677ce901bbc
created_at: 2023-03-16 14:02:55
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content:
"GHG emissions from buildings are usually modelled on the basis of \r\nthe estimated energy demand per energy carrier and appropriate \r\nemissions factors. The purpose of most building models is to assess \r\nthe impact of mitigation measures on energy demand in the use phase \r\nof buildings and for a given assumption on the per-capita floor area \r\nand technological improvement (IEA 2021; Pauliuk et al. 2021b). After \r\ndecades of ignoring material cycles and embodied emissions (Pauliuk \r\net al. 2017), a few IAMs are now including material stocks and flows \r\n(Deetman et al. 2020; IEA 2021; Zhong et al. 2021). However, the \r\ntop-down nature of these models and the modelling methodology of \r\nembodied emissions, which are added onto the emissions estimated \r\nin the use phase, questions the policy relevance of these estimates. As \r\nof today, the resource efficiency and climate change (RECC) scenario \r\n(IRP 2020; Fishman et al. 2021; Pauliuk and Heeren 2021; Pauliuk et al"


id: bf8caef58d306d2ce4438ee75762b8f8
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vector_id: 27695
content:
" \r\n2021b; ) is the only global scenario identified which includes measures \r\nto limit, in the first place, embodied emissions from buildings. The \r\nscenario is modelled using the bottom-up ODYM-RECC model."


id: 46d84b3b73e1baf1aed6b4ccf199400
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content:
"how much movement occurs, or the travel demand associated \r\nwith each mode. Models commonly quantify demand through \r\ntransportation mode (e.g.,  active transit, passenger vehicles, \r\ntrucks, boats, planes, etc.) or how movement occurs (e.g., passenger \r\ntravel distance pkm and freight distance tkm). Higher fidelity models \r\nprovide more nuanced breakdowns of demand by trips of various \r\nlengths such as short-, medium-, and long-distance trips or by \r\nregion (e.g., kilometres or tkm per region). The scope of the model \r\noften determines how much information it provides on where and \r\nwhen movement occurs. While larger scale models typically provide \r\naggregate travel demand, higher resolution travel demand models \r\ncan be integrated into transportation system models and provide \r\nmuch more information on origin and destination of trips, when and \r\nwhere trips occur, and the route of travel taken"


id: 51066fb3b3eeb5ea3e4aadf45e3230ce
created_at: 2023-03-16 14:02:55
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content:
" This level of detail \r\nis not often characterised in the output of system models but can be \r\nemployed as a 'base' model to determine how travel occurs before \r\naggregation (Edelenbosch et al. 2017a; Yeh et al. 2017)."


id: 73d754db47938130a83d3133df11c6ac
created_at: 2023-03-16 14:02:55
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content:
"IAMs (Krey et al. 2016; Edelenbosch et al. 2017a) are typically \r\nglobal  in scope and seek to solve for feasible pathways meeting \r\na  global temperature target (Annex III.I.9). This implies finding \r\nleast-cost mitigation options within and across sectors. In contrast, \r\nglobal and national transport energy system models (GTEMs/NTEMs) \r\ntypically only assess feasible pathways within the transport sector \r\n(Yeh et al. 2017). The range of feasible pathways can be determined \r\nthrough optimisation, simulation, accounting and exploratory \r\nmethods, as outlined in Table 2. Some GTEMs are linked to an IAMs \r\nmodel (Krey et al. 2016; Edelenbosch et al. 2017a; Roelfsema et al. \r\n2020). The key difference between IAMs and GTEMs or NTEMs is \r\nwhether the transportation system is integrated with the rest of the \r\nenergy systems, specifically regarding energy and fuel production and \r\nuse, fuel prices, economic drivers such as GDP, and mitigation options \r\ngiven a policy goal"


id: c8dbf56554b9e2646d873f7020670310
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content:
" IAMs can endogenously determine these factors \r\nbecause the transport sector is just one of many sectors captured by \r\nthe IAM. While this gives IAMs certain advantages, IAMs sacrifice \r\nresolution and complexity for this broader scope. For example, \r\nmost IAMs lack a sophisticated travel demand model that reflects \r\nthe heterogeneity of demands and consumer preferences, whereas \r\nGTEM/NTEMs can incorporate greater levels of details regarding \r\ntravel demands, consumer choices, and the details of transport \r\npolicies. Consequently, what GTEMs/NTEMs lack in integration with \r\nother sectors they make up through more detailed analyses of travel \r\npatterns, policies, and impacts (Yeh et al. 2017)."


id: bea2ee8e727a5ace9734aad1304e63f7
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content:
"Several noteworthy recent active research areas in long-term \r\ntransportation energy systems modelling involves the consideration \r\nof infrastructure investment and consumer acceptance for non-fossil \r\nfuel vehicles including charging for electric vehicles (Jochem et al. \r\n2019; Statharas et al. 2021) and refuelling stations for hydrogen \r\nvehicles (Rose and Neumann 2020); and the greater integration \r\nof the electric, transport, residential, and industrial sectors in fuel \r\nproduction, storage, and utilisation (Bellocchi et al. 2020; Lester et al. \r\n2020; Olovsson et al. 2021; Rottoli et al. 2021). While national and \r\nregional transport energy models have the advantage of exploring \r\nthese relationships in greater spatial, temporal, and policy details for \r\nspecific countries or regions (Jochem et al. 2019; Bellocchi et al. 2020; \r\nLester et al. 2020; Rottoli et al. 2021; Statharas et al"


id: e2eafac77b29c3be7a46d36d40f6c2f3
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vector_id: 33200
content:
" 2021), the IAMs \r\nhave the advantage of examining these interactions across the entire \r\neconomy at the global level (Brear et al. 2020; Rottoli et al. 2021)."


id: e0dfc0b2858e04857c1ba161474a3b19
created_at: 2023-03-16 14:02:55
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content:
"LUMs represent different land use activities for managed land \r\n(agriculture including cropland and pastures, managed forests, and \r\ndedicated energy crops) while natural lands (primary forests, natural \r\ngrasslands, shrubland, savannahs, etc.) act as land reserves that \r\ncan be converted to management depending on other constraints \r\n(Popp et al. 2014a; Schmitz et al. 2014). Typically, the agricultural \r\nsector has the greatest level of detail across land use sectors. LUMs \r\ninclude different crop and livestock production activities, some even \r\nat the spatially explicit level and differentiated by production system \r\n(Havlik et al. 2014; Weindl et al. 2015). Forestry is covered with varying \r\ndegrees of complexity across LUMs. While some models represent only \r\nafforestation/deforestation activities dynamically, others have detailed \r\nrepresentation of forest management activities and/or forest industries \r\n(Lauri et al. 2017)"


id: e30c87335fff79e0bfa418e12baf85ef
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content:
" The models endogenously determine the land \r\nallocation of different land use activities as well as land-use changes \r\naccording to different economic principles (land rent, substitution \r\nelasticities, etc.) and/or considering biophysical characteristics such as \r\nland suitability (Schmitz et al. 2014; Weindl et al. 2017)."


id: f41e1fc993c7c2907cd53564bd84d03
created_at: 2023-03-16 14:02:55
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content:
"Two broad categories of land-based mitigation options are represented \r\nin LUMs: (i) reduction of GHG (CO2, CH4 and N2O) emissions from \r\nland use, and (ii) carbon sink enhancement options including biomass \r\nsupply for bioenergy. Each of these categories is underpinned by \r\na portfolio of mitigation options with varying degrees of complexity \r\nand parameterisation across LUMs. The representation of mitigation \r\nmeasures is influenced on the one hand by the availability of data \r\nfor its techno-economic characteristics and future prospects as well \r\nas the computational challenge, for example in terms of spatial and \r\nprocess detail, to represent the measure, and on the other hand, by \r\nstructural differences and general focus of the different LUMs, and \r\nprioritisation of different mitigation options by the modelling teams. \r\nWhile GHG emission reduction and CO2 sequestration options such as \r\nafforestation are typically covered directly in LUMs (Hasegawa et al"


id: bdf9592719298d026f393081530915a2
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content:
" \r\n2021), carbon sequestration from biomass supplied for bioenergy \r\nwith carbon capture and storage (BECCS) is usually not accounted for \r\nin LUMs but in the energy sector and hence is taken care of directly \r\nin the IAMs. Yet, LUMs provide estimates of available biomass for \r\nenergy production and the impacts of its production."


id: 83db9492d98c229361c49801afd24481
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content:
"Agricultural non-CO2 emissions covered in LUMs include CH4 from \r\nenteric fermentation, manure management and cultivation of \r\nrice paddies, and N2O emissions from soils (fertiliser and manure \r\napplication, crop residues) and manure management and are based \r\non IPCC accounting guidelines (IPCC 2019a). For each of those \r\nsources, LUMs typically represent a (sub)set of technical, structural \r\nand demand-side mitigation options. Technical options refer to \r\ntechnologies such as anaerobic digesters, feed supplements or \r\nnitrogen inhibitors that are either explicitly represented (Frank et al. \r\n2018) or implicitly via the use of marginal abatement cost curves \r\n(MACC) (Lucas et al. 2007; Beach et al. 2015; Harmsen et al. 2019). \r\nEmission savings from structural changes refer to more fundamental \r\nchanges in the agricultural sector, for example through international \r\ntrade, production system changes or reallocation and substitution \r\neffects (Havlik et al. 2014)"


id: e32cda59994defa981c2f227d61fa3a2
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content:
" Demand-side options include dietary \r\nchanges and reduction of food waste (Springmann et al. 2016; \r\nCreutzig et al. 2018; Ritchie et al. 2018; Frank et al. 2019; Mbow \r\net al. 2019; Clark et al. 2020; Ivanova et al. 2020; Popp et al. 2010; \r\nRosenzweig et al. 2020). For the forest sector, emission reduction \r\noptions are mainly targeting CO2 from deforestation (Overmars et al. \r\n2014; Hasegawa et al. 2017; Rochedo et al. 2018; Bos et al. 2020; \r\nDoelman et al. 2020; Eriksson 2020). Mitigation/restoration options \r\nfor wetlands to reduce emissions from drained organic soils are \r\ntypically not represented in LUMs (Humpenoder et al. 2020)."


id: d78b1f6d55b871645438eecd74fba819
created_at: 2023-03-16 14:02:55
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content:
"Typically, IAMs consider multi-level systems of global, regional, national \r\nand local constraints and balance equations for different categories \r\nsuch as emissions, material and energy flows, financial flows, and \r\nland availability that are solved simultaneously. Intertemporal IAMs \r\ncan fully incorporate not only flow constraints that are satisfied in \r\neach period, but also stock constraints that are aggregated over time \r\nand require to balance activities over time. Changes of activities, for \r\nexample induced by policies to reduce emissions, are connected to \r\na  variety of balance equations and constraints and therefore such \r\npolicies lead to system-wide changes that can be analysed with \r\nIAMs. Many IAMs also contain gridded components to capture, for \r\nexample, land-use and climate change processes where the spatial \r\ndistribution matters greatly for the dynamics of the system"


id: 1df72b832bbb58888276c7ac97236be0
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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content:
" Processes \r\nthat operate on smaller spatial and temporal scales than resolved \r\nby IAMs, such as temporal variability of renewables, are included by \r\nparameterisation and statistical modelling approaches that capture \r\nthe impact of these subscale processes on the system dynamics at \r\nthe macro level (Pietzcker et al. 2017)."


id: 4a9895a36e8f7ba373ef2fd8e5b89e0
created_at: 2023-03-16 14:02:55
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content:
"Previous IPCC Assessment Reports relied either on the climate \r\noutput from each individual IAM (IPCC 2000) or a more streamlined \r\napproach, where one consistent emulator set-up was used to assess \r\nall scenarios. For instance, in AR5 and the Special Report on Global \r\nWarming of 1.5degC (SR1.5), MAGICC was used for scenario classification \r\n(Clarke et al. 2014; Rogelj et al. 2018a). In recent years, numerous \r\nother emulators have been developed and increased confidence \r\nand understanding can thus be gained by combining insights from \r\nmore than one emulator. For example, SR1.5 used MAGICC for its \r\nscenario classification, with additional insights provided by the FaIR \r\nmodel (Smith et al. 2018). The SR1.5 experience highlighted that \r\nthe veracity of emulators 'is a  substantial knowledge gap in the \r\noverall assessment of pathways and their temperature thresholds' \r\n(Rogelj et al. 2018a). Since SR1"


id: d6c0cb4342ac1c449f3a24354b5478fd
created_at: 2023-03-16 14:02:55
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content:
"5, international research efforts have \r\ndemonstrated tractable ways to compare emulator performance \r\n(Nicholls et al. 2020b), as well as their ability to accurately represent \r\na  set of uncertainty ranges in physical parameters (Nicholls et al. \r\n2021b), such as those reported by AR6 WGI (Forster et al. 2021)."


id: eb485c952f9b4482b8bf0b08a15d747b
created_at: 2023-03-16 14:02:55
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vector_id: 34514
content:
"IAMs include a variety of model types that can be distinguished into \r\ntwo broad classes (Weyant 2017). The first class comprises cost\u0002benefit IAMs that fully integrate a  stylised socio-economic model \r\nwith a  reduced-form climate model to simultaneously account for \r\nthe costs of mitigation and the damages of global warming using \r\nhighly aggregate cost functions derived from more detailed models. \r\nIn the model context, these functions do not explicitly represent the \r\nunderlying processes, but map mitigation efforts and temperature to \r\ncosts. This closed-loop approach between climate and socio-economic \r\nsystems enables cost-benefit analysis by balancing the cost of \r\nmitigation and the benefits of avoided climate damages. This can be \r\ndone in a globally cooperative setting to derive the globally optimal \r\nclimate policy where no region can further improve its welfare without \r\nreducing the welfare of another region (Pareto optimum)"


id: 44653f39ab0efc8241cc1190ba0823f
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content:
" Alternatively, \r\nit can be assumed that nations do not engage in emission mitigation at \r\nall or mitigate in a non-cooperative way, only considering the marginal \r\nbenefit of their own action (Nash equilibrium). Also, differing degrees \r\nof partial cooperation are possible."


id: ba075db6ec85d456be0c5ca4fb115703
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 26809
content:
"Typically, IAMs comprise a  model of energy flows, emissions and \r\nthe associated costs (Krey 2014). The demand for exploring the \r\nParis Agreement climate goals led to model developments to make \r\nthe challenges and opportunities of the associated transformation \r\npathways more transparent. Since AR5 much progress has been \r\nachieved to improve the representation of mitigation options in the \r\nenergy supply sector (e.g., renewable energy integration (Pietzcker \r\net al. 2017), energy trade (Bauer et al. 2016; McCollum et al. 2016; \r\nBauer et al. 2017; Jewell et al. 2018), capacity inertia, carbon \r\nremovals, and decarbonisation bottlenecks (Luderer et al. 2018)) and \r\ntechnological and behavioural change measures in energy demand \r\nsectors such as transport (van Sluisveld et al. 2016; Edelenbosch et al. \r\n2017a; McCollum et al. 2017). An energy sector model can be run as \r\na partial equilibrium model using exogenous demand drivers for final \r\nenergy and energy services"


id: d152de535ff0a0c9c416a62da285ce
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updated_at: 2023-03-16 14:02:55
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vector_id: 30469
content:
" These models derive mitigation policy \r\ncosts in terms of additional energy sector costs and area under the \r\nmarginal abatement costs curve."


id: c43b40d4a0b3b09527a93b7924e22839
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 28440
content:
"The second class of IAMs, called process-based IAMs, focuses on \r\nthe analysis of transformation processes depending on a broad set \r\nof activities that induce emissions as side effects. They describe the \r\ninterlinkages between economic activity, energy use, land use, and \r\nemissions with emission reductions and removals as well as broader \r\nsustainable development targets. GHGs and other climate pollutants \r\nare caused by a broad range of activities that are driven by socio\u0002economic developments (Riahi et al. 2017) and also induce broader \r\nenvironmental consequences such as land-use change (Popp et al. \r\n2017) and air pollution (Rao et al. 2017b). With few exceptions, these \r\nmodels typically do not close the loop with climate change and \r\ndamages that affect the economy, but focus on emission scenarios \r\nand climate change mitigation pathways"


id: 88e4fe107632fe2722a93ffe21aa0a6a
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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index: climate_gpt_only_giec
vector_id: 19072
content:
" Due to the process-based \r\nrepresentations of emission sources and alternatives, it is not only \r\npossible to investigate the implications of policies on GHG emissions, \r\nbut also the trade-offs and synergies with social and environmental \r\nsustainability criteria (von Stechow et al. 2015) (Annex III.I.9.3). \r\nThe analysis of different cross-sectoral synergies and trade-offs is \r\nfrequently termed a nexus analysis, such as the energy-water-land \r\nnexus. The analysis can also address socio-economic sustainability \r\ncriteria such as energy access and human health. Process-based IAMs \r\nare also used to explore the synergies and trade-offs of 'common, but \r\ndifferentiated responsibilities' by analysing issues of burden sharing, \r\nequity, international cooperation, policy differentiation and transfer \r\nmeasures (Tavoni et al. 2015; Fujimori et al. 2016; Leimbach and \r\nGiannousakis 2019; Bauer et al. 2020b)."


id: 3cecdc2e47d4275e0e3339e9cd0b0d46
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updated_at: 2023-03-16 14:02:55
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vector_id: 6947
content:
"et al. 2020) and some sources of CH4 and N2O constitute serious \r\nmitigation bottlenecks. Second, bioenergy is identified as a  crucial \r\nprimary energy source for low-emission energy  supply and carbon \r\nremoval (Bauer et al. 2020a; Butnar et al. 2020; Calvin et al. 2021). \r\nThird, land use-based mitigation measures such as afforestation \r\nand reduced deforestation have substantial mitigation potentials. \r\nFinally, land-cover changes alter the Earth surface albedo, which \r\nhas implications for regional and global climate. Pursuing the Paris \r\nAgreement climate goals requires the inclusion of a  broad set of \r\noptions regarding GHG emissions and removals, which will intensify \r\nthe interaction between the energy sector, the economy and the land \r\nuse sector. Consequently, intersectoral policy coordination becomes \r\nmore important and the land-related synergies and trade-offs with \r\nsustainable development targets will intensify (Calvin et al. 2014b; \r\nKreidenweis et al"


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" 2016; Frank et al. 2017; van Vuuren et al. 2017a; \r\nHumpenoder et al. 2018; Bauer et al. 2020d). IAMs used by the IPCC \r\nin the AR6 have continuously improved the integration of land-use \r\nmodels with energy models to explore climate mitigation scenarios \r\nunder varying policy and technology conditions (Rogelj et al. 2018a; \r\nSmith et al. 2019). However, feedbacks from changes in climate \r\nvariables are not included in the land-use sector models, or are only \r\nincluded to a limited degree."


id: 3c2cceaab5501fc4b4c62b8fd0c6b4f6
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content:
"Sustainable development impacts extending beyond climate \r\noutcomes have been assessed by the IAMs that contributed to the \r\nassessment, particularly in the context of the targets and indicators \r\nconsistent with the Sustainable Development Goals (SDGs). The \r\nrepresentation of individual SDGs is diverse (Figure  3), and recent \r\nmodel development has focused mainly on improving capabilities \r\nto assess climate change mitigation policy combined with indicators \r\nfor economic growth, resource access, air pollution and land use \r\n(van Soest et al. 2019). Synergies and trade-offs across sustainable \r\ndevelopment objectives can be quantified by analysing multi\u0002sector impacts across ensembles of IAM scenarios generated from \r\nsingle or multiple models (McCollum et al. 2013; Mouratiadou \r\net al. 2016). Modules have also been developed for IAMs with \r\nthe specific purpose of incorporating policies that address non\u0002climatic sustainability outcomes (Cameron et al. 2016; Fujimori et al"


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content:
" \r\n2018; Parkinson et al. 2019). Similar features have been utilised to \r\nincorporate explicit adaptation measures and targeted policies that \r\nbalance mitigation goals with other sustainability criteria (Bertram \r\net al. 2018; McCollum et al. 2018)."


id: 425b667f3bef7f4f9f2cd6d3f08c0ee2
created_at: 2023-03-16 14:02:55
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content:
"Reduced complexity climate models (often called simple climate \r\nmodels or emulators) are used for communicating WGI physical \r\nclimate science knowledge to the research communities associated \r\nwith other IPCC working groups (Annex III.I.8). They are used by IAMs \r\nto model the climate outcome of the multi-gas emissions trajectories \r\nthat IAMs produce (van Vuuren et al. 2011a). A main application of \r\nsuch models is related to scenario classifications in WGIII (Clarke \r\net al. 2014; Rogelj et al. 2018a). Since WGIII assesses a large number \r\nof scenarios, it must rely on the use of these simple climate models; \r\nmore computationally demanding models (as used by WGI) will \r\nnot be feasible to apply. For consistency across the AR6 reports, it \r\nis important that these reduced-complexity models are up to date \r\nwith the latest assessments from WGI. This relies on calibrating these \r\nmodels so that they match, as closely as possible, the assessments \r\nmade by WGI (Annex III.II.2.5.1)"


id: af4f72c06c48ebe8dacfc9d0d7726a52
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content:
"marginal abatement costs across countries and sectors vs regionally \r\nand sectorally fragmented action (Blanford et al. 2014a; Bertram et al. \r\n2015; Kriegler et al. 2015b, 2018b; Bauer et al. 2020b; Roelfsema \r\net al. 2020), (iv) pathways with different emphasis on supply-side vs \r\ndemand-side mitigation measures (Grubler et al. 2018; van Vuuren \r\net al. 2018) or more broadly different sustainable development \r\nstrategies (Riahi et al. 2012; van Vuuren et al. 2015; Soergel et al. \r\n2021), and (v) pathways with different preferences about technology \r\ndeployment, in particular with regard to carbon capture and storage \r\n(CCS) and carbon dioxide removal (CDR) (Krey 2014; Kriegler et al. \r\n2014a; Riahi et al. 2015; Strefler et al. 2018; Rose et al. 2020; Luderer \r\net al. 2021; Strefler et al. 2021b)"


id: e970a00ff748673e0956736f7ff80c37
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content:
" Key uncertainties that were explored \r\nin the IAM literature are between (i) different socio-economic futures \r\nas, for example, represented by the Shared Socio-economic Pathways \r\n(SSPs) (Bauer et al. 2017; Popp et al. 2017; Riahi et al. 2017), \r\n(ii)  different technological developments (Bosetti et al. 2015) and \r\n(iii) different resource potentials (Kriegler et al. 2016)."


id: 7917021b922a6c9e3e9d6116fabe8325
created_at: 2023-03-16 14:02:55
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content:
"Policy analysis with IAMs follows the approach that a  baseline \r\nscenario is augmented by some kind of policy intervention. To \r\naddress the uncertainties in baseline projections, the scientific \r\ncommunity has developed the Shared Socio-economic Pathways \r\n(SSPs) that provide a set of vastly different future developments as \r\nreference cases (Annex III.II.1.3,2). Most scenarios used in AR6 are \r\nbased on the middle-of-the-road reference system (SSP2). Depending \r\non the research interest, the baseline can be defined as a no-policy \r\nbaseline or it can include policies that either address GHG emissions \r\nlike the nationally determined contributions (NDCs) or other pre\u0002existing policies such as energy subsidies and taxes. There is no \r\nstandard definition for baseline scenarios regarding the inclusion of \r\npolicies. The baseline scenario is augmented by additional policies \r\nlike a carbon tax aiming towards a long-term climate goal"


id: 7ab59a4f514267f85be7e90df003f325
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content:
" Hence, \r\nthe IAM-based policy analysis assumes a reference system like SSP2 \r\nwithin which policy scenarios are compared with a baseline scenario."


id: 95200ff5f560ae34e73e13a36805ac75
created_at: 2023-03-16 14:02:55
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content:
"These concerns spur continuous model development and \r\nimprovements in scenario design (Keppo et al. 2021), particularly \r\nwith regard to improved representations of energy demand, \r\nrenewable energy, carbon dioxide removal technologies, and land \r\nmanagement. IAMs are aiming to keep pace with the development \r\nof sector-specific models, including latest advances in estimating and \r\nmodelling climate damages (Piontek et al. 2019). In places, where \r\ndynamic modelling approaches are lacking, scenarios are being used \r\nto explore relevant futures (Grubler et al. 2018). Moreover, sector\u0002specific model comparison studies have brought together domain \r\nexperts and modellers to improve model representations in these \r\nareas (Edelenbosch et al. 2017a; Pietzcker et al. 2017; Bauer et al. \r\n2020a; Harmsen et al. 2020; Rose et al. 2020)"


id: 16515f48f620811159ee999df7038db4
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content:
" Although most models \r\nare still relying on the concept of a single representative household \r\nrepresenting entire regions, efforts are under way to better represent \r\nagent heterogeneity and distributional impacts of climate change \r\nand climate mitigation policies (Rao et al. 2017a; Peng et al. 2021)."


id: 2deb2330ec30ff98930478e270928583
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content:
"Second, concerns have been raised that IAMs are non-transparent and \r\nthus make it difficult to grasp the context and meaning of their results \r\n(Skea et al. 2021). These concerns have facilitated a substantial increase \r\nin model documentation (see the common IAM documentation at \r\nwww.iamcdocumentation.eu as an entry point) and open-source \r\nmodels. Nonetheless, more communication tools and co-production of \r\nknowledge formats will be needed to contextualise IAM results for users \r\n(Auer et al. 2021). When projecting over a century, uncertainties are \r\nlarge and cannot be ignored. Efforts have been undertaken (Marangoni \r\net al. 2017; Gillingham et al. 2018; Harmsen et al. 2021; Wilson et al"


id: bef0c0021e6e22d595badd9f9592913e
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vector_id: 27595
content:
" \r\n2021) to diagnose key similarities and differences between models and \r\nbetter gauge robust findings from these models and how much they \r\ndepend on key assumptions (such as, for example, long-term growth \r\nof the economy, the monetary implication of climate damages, or the \r\ndiffusion and cost of key mitigation technologies)."


id: 8bc1e46f083a636c68e8c5a5c04c86d2
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"Third, there are concerns that IAMs are describing transformative \r\nchange on the level of energy and land use, but are largely silent \r\nabout the underlying socio-cultural transitions that could imply \r\nrestructuring of society and institutions. Weyant (2017) notes the \r\ninability of IAMs to mimic extreme and discontinuous outcomes \r\nrelated to these underlying drivers as one of their major limitations. \r\nThis is relevant when modelling extreme climate damages as well \r\nas when modelling disruptive changes. Dialogues and collaborative \r\nwork between IAM researchers and social scientists have explored \r\nways to bridge insights from the various communities to provide \r\na  more complete picture of high-impact climate change scenarios \r\nand, on the other end, deep transformation pathways (Turnheim \r\net al. 2015; Geels et al. 2016; Trutnevyte et al. 2019)"


id: 94f8dae31a6bf4d7fc039e3e29c96793
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content:
" The extension \r\nof IAM research to sustainable development pathways is giving rise \r\nto further inter-disciplinary research on underlying transformations \r\ntowards the Paris climate goals and other sustainable development \r\ngoals (Kriegler et al. 2018c; Sachs et al. 2019b)."


id: 81041d64fcb7e1d7bbe7808329463c89
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content:
"Third, climate change scenarios are used to integrate knowledge \r\nand analysis between the three different climate change research \r\ncommunities working on the climate system and its response to \r\nhuman interference (linked to WG I of the IPCC), climate change \r\nimpacts, adaptation and vulnerability (linked to WGII) and climate \r\nchange mitigation (linked to WGIII) (O'Neill et al. 2016; IPCC \r\n2000; van Vuuren et al. 2011b) (Annex III.II.1.3). This involves the \r\nadoption of common scenario frameworks that allow the consistent \r\nuse of, for example, shared emissions scenarios, socio-economic \r\ndevelopment scenarios and climate change projections (Moss et al. \r\n2010; Kriegler et al. 2012; van Vuuren et al. 2012; O'Neill et al. 2014; \r\nvan Vuuren et al. 2014). The integrative power of scenarios extends \r\nbeyond the climate change research community into neighbouring \r\nfields such as the social sciences and ecology (Pereira et al. 2020; \r\nRosa et al. 2020)"


id: 31e876dd969fa0376b5bcc732b6517c2
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content:
" To foster such integration, underlying scenario \r\nnarratives have proven extremely useful as they allow researchers to \r\ndevelop and link quantitative scenario expressions in very different \r\ndomains of knowledge (O'Neill et al. 2020)."


id: 817f6eaa2f82c24b9a11108f5fac3513
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content:
"Second, climate change scenarios are developed to explore pathways \r\ntowards long-term climate goals. Goal-oriented scenarios often carry \r\nthe word 'pathway' in their name, such as climate change mitigation \r\npathway, climate change adaptation pathway, or more generally \r\nclimate change transition or transformation pathway. They are \r\nsometimes called 'backcasting'4\r\n scenarios, or 'short backcasts', in \r\nthe literature, particularly when contrasted with forecasts (Robinson \r\n1982). Goal-oriented/backcasting scenarios are inherently normative \r\nand intricately linked to human intervention. They can be used to \r\ncompare and contrast different courses of actions. For example, \r\nthey are applied in climate change mitigation analysis by comparing \r\nreference scenarios without or with only moderate climate policy \r\nintervention, sometimes called baseline scenarios, with mitigation \r\npathways that achieve certain climate goals (Grant et al. 2020)"


id: c36c74e7244031a34651a65cd25ebe48
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content:
" \r\nTransformation pathways to climate goals are examples of \r\nbackcasting scenarios. Among other things, they can be used to learn \r\nabout the multi-dimensional trade-offs between raising or lowering "


id: f8c7e9bc834a5c77809bb7df44a3d03e
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"Global mitigation scenarios are mostly derived from global integrated \r\nassessment models (Annex III.I.9) and have been developed in \r\nsingle model studies as well as multi-model comparison studies. \r\nThe research questions of these studies have evolved together with \r\nthe climate policy debate and the knowledge about climate change, \r\ndrivers, and response measures. The assessment of global mitigation \r\npathways in the Fifth Assessment Report (AR5) (Clarke et al. 2014) \r\nwas informed, inter alia, by a  number of large-scale multi-model \r\nstudies comparing overshoot and not-to-exceed scenarios for a range \r\nof concentration stabilisation targets (Energy Modelling Forum (EMF) \r\nstudy 22: EMF22) (Clarke et al. 2009), exploring the economics of \r\ndifferent decarbonisation strategies and robust characteristics of the \r\nenergy transition in global mitigation pathways (EMF27, RECIPE) \r\n(Luderer et al. 2012; Krey and Riahi 2013; Kriegler et al"


id: 6829caab565a6149e202b97fce057e63
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" 2014a), and \r\nanalysing co-benefits and trade-offs of mitigation strategies with \r\nenergy security, energy access, and air quality objectives (Global \r\nEnergy Assessment: GEA) (McCollum et al. 2011; Riahi et al. 2012; \r\nMcCollum et al. 2013; Rao et al. 2013; Rogelj et al. 2013b). They also \r\ninvestigated the importance of international cooperation for reaching \r\nambitious climate goals (EMF22, EMF27, AMPERE) (Clarke et al. \r\n2009; Blanford et al. 2014b; Kriegler et al. 2015b), the implications of \r\ncollective action towards the 2degC goal from 2020 onwards vs delayed \r\nmitigation action (AMPERE, LIMITS) (Kriegler et al. 2014b; Riahi et al. \r\n2015), and the distribution of mitigation costs and burden-sharing \r\nschemes in global mitigation pathways (LIMITS) (Tavoni et al. 2014, \r\n2015). Scenarios from these and other studies were collected in \r\na  scenario database supporting the AR5 assessment (Krey et al. \r\n2014)"


id: 5c2210ed3d2f08ec8b65f38ae25f67f9
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content:
" With a shelf life of 8 to 14 years, they are now outdated and \r\nno longer part of this assessment."


id: 15915987a957b2e921c3a79d9d2f58ae
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content:
"A large variety of recent modelling studies, mostly based on individual \r\nmodels, deepened research on a diverse set of questions (Annex III.\r\nII.3.2). Selected examples are the impact of peak vs end-of-century \r\ntargets on the timing of action in mitigation pathways (Rogelj et al. \r\n2019a; Strefler et al. 2021a); demand-side driven deep mitigation \r\npathways with sustainable development co-benefits (Bertram et al. \r\n2018; Grubler et al. 2018; van Vuuren et al. 2018); synergies and \r\ntrade-offs between mitigation and sustainable development goals \r\n(Fujimori et al. 2020; Soergel et al. 2021); and the integration of \r\nclimate impacts into mitigation pathways (Schultes et al. 2021). \r\nThere have also been a number of recent sectoral studies with global \r\nintegrated assessment models and other global models across all \r\nsectors, for example the energy sector (IRENA 2020; Kober et al. \r\n2020; IEA 2021) and transport sector (Edelenbosch et al. 2017a; \r\nMercure et al. 2018; Zhang et al"


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content:
" 2018; Fisch-Romito and Guivarch \r\n2019; Rottoli et al. 2021; Lam and Mercure 2021; Paltsev et al. \r\n2022). Very recent work investigated the impact of COVID-19 on \r\nmitigation pathways (Kikstra et al. 2021a) and co-designed global \r\nscenarios for users in the financial sector (NGFS 2021). In addition \r\nto these policy-, technology- and sector-oriented studies, a  few \r\ndiagnostic studies developed mitigation scenarios to diagnose model \r\nbehaviour (Harmsen et al. 2021) and explore model harmonisation \r\n(Giarola et al. 2021)."


id: 3e469a2469341fa28b5be7812edfffc
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content:
"Since AR5, many new studies published global mitigation pathways \r\nand associated emissions projections. After the adoption of the \r\nParis Agreement, several large-scale multi-model studies newly \r\ninvestigated pathway limiting warming to 1.5degC (ADVANCE: Luderer \r\net al. (2018); CD-LINKS: McCollum et al. (2018a); ENGAGE: Riahi et al. \r\n(2021); SSPs: Rogelj et al. (2018b)), allowing this report to conduct \r\na  robust assessment of 1.5degC pathways. Most scenario studies \r\ntook the hybrid climate policy architecture of the Paris Agreement \r\nwith global goals, nationally determined contributions (NDCs) and \r\nan increasing number of implemented national climate policies \r\nas a  starting point, including hybrid studies with participation of \r\nglobal and national modelling teams to inform the global stocktake \r\n(ENGAGE: Fujimori et al. (2021); COMMIT: van Soest et al. (2021); CD\u0002LINKS: Schaeffer et al. (2020), Roelfsema et al. (2020))"


id: fc14d96f5e8468d7bdb66cc5beae9f4b
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content:
" Multi-model \r\nstudies covered a range of scenarios from extrapolating current policy \r\ntrends and the implementation of NDCs, respectively, to limiting \r\nwarming to 1.5degC-2degC with immediate global action and after passing \r\nthrough the NDCs in 2030, respectively. These scenarios are used to \r\ninvestigate, among others, the end-of-century warming implications \r\nof extrapolating current policy trends and NDCs (Perdana et al. 2020); \r\nthe ability of the NDCs to keep limiting warming to 1.5oC-2degC in reach \r\n(Luderer et al. 2018; Vrontisi et al. 2018; Roelfsema et al. 2020), the \r\nscope for global accelerated action to go beyond the NDCs in 2030 \r\n(van Soest et al. 2021), and the benefits of early action vs the risk of \r\novershoot and the use of net negative CO2 emissions in the long\u0002"


id: 8ed18281fc2a0595a8d69277b46a9519
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content:
"thermal comfort (expressed as degree-days) (Creutzig et al. 2018). \r\nService transition pathways seek to meet adequate levels of such \r\nservices with minimal carbon emissions, using combinations of \r\ndemand- and supply-side options. Ideally this is done by improving \r\nthe efficiency of service provision systems to minimise overall \r\nfinal energy and resource demand, thereby reducing pressure on \r\nsupply-side and carbon dioxide removal technologies (Grubler \r\net al. 2018). Specifically, this includes providing convenient access \r\nto end-use services (health care, education, communication, etc.), \r\nwhile minimising both primary and end-use energy required. \r\nService transition pathways provide a compelling scenario narrative \r\nfocused on well-being, resulting in technology and policy pathways \r\nthat give explicit priority to decent living standards"


id: c23aac64909d20f2fbbf2b6111be873e
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content:
" Furthermore, \r\nmore efficient service provision often involves combinations of \r\nbehavioural, infrastructural and technological change, expanding \r\nthe options available to policymakers for achieving mitigation goals \r\n(van Sluisveld et al. 2016, 2018). These dimensions are synergistic, \r\nin particular in that behavioural and lifestyle changes often require \r\ninfrastructures adequately matching lifestyles. Service transition \r\nscenarios are primarily assessed in Chapter 5 of the report."


id: 154ae0e5373482c4f6d76ba92498407f
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content:
"There are also a  range of sector transition scenarios, both on the \r\nglobal and the country level. These include scenarios for the \r\ntransition of the electricity, buildings, industry, transport and AFOLU \r\nsectors until 2050. Due to the accelerated electrification in mitigation \r\npathways, sector coupling plays an increasingly important role to \r\novercome decarbonisation bottlenecks, complicating a  separate \r\nsector-by-sector scenario assessment. Likewise, the energy-water\u0002land nexus limits the scope of a separate assessment of the energy \r\nand agricultural sectors. Nevertheless, sector transition scenarios \r\nplay an important role for this assessment as they can usually offer \r\nmuch more technology, policy and behaviour detail than integrated \r\nassessment models. They are primarily assessed in the sector chapters \r\nof the report"


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content:
" Their projections of emissions reductions in the sectors \r\nin the near to medium term is used to check the sector dynamics of \r\nglobal models in Chapter 3 of the report."


id: af80db35d7bb53dc89102f91f20ea4ca
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vector_id: 25129
content:
"For the first three assessment reports, the IPCC directly commissioned \r\nemission scenarios with social, economic, energy and partially \r\npolicy aspects as drivers of projected GHG emissions. The first set \r\nof scenarios, the 'SA90' of the IPCC First Assessment Report (IPCC \r\n1990), had four distinct scenarios, 'business-as-usual' and three \r\npolicy scenarios of increasing ambition. The set of 'IS92' scenarios \r\nused in the Second Assessment Report investigated variations of \r\nbusiness-as-usual scenarios with respect to uncertainties about the \r\nkey drivers of economic growth, technology and population (Leggett \r\net al. 1992). The SRES scenarios from the IPCC Special Report on \r\nEmission Scenarios (SRES) (IPCC 2000) were produced by multiple \r\nmodelling organisations and were used in the Third and Fourth \r\nAssessment reports"


id: cc5173d6bdcd176a7f5bcb854c2ef1a7
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vector_id: 29685
content:
" Four distinct scenario families were characterised \r\nby narratives and projections of key drivers like population \r\ndevelopment and economic growth (but no policy measures) \r\nto examine their influence on a  range of GHG and air pollutant \r\nemissions. Until the Fourth Assessment Report, the IPCC organised \r\nthe scenario development process centrally. Since then, scenarios are \r\ndeveloped by the research community and the IPCC limited its role \r\nto catalysing and assessing scenarios. To shorten development times, \r\na parallel approach was chosen (Moss et al. 2010) and representative \r\nconcentration pathways (RCPs) were developed (van Vuuren et al. \r\n2011b) to inform the next generation of climate modelling for the \r\nFifth Assessment Report. RCPs explored four different emissions and \r\natmospheric composition pathways structured to result in different \r\nlevels of radiative forcing in 2100: 2.6, 4.5, 6.0 and 8.5 W m-2"


id: 49b75245fc9672cc3bcc1c79d1551ec6
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updated_at: 2023-03-16 14:02:55
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vector_id: 8991
content:
" They \r\nwere used as an input to the Climate Model Intercomparison Project \r\nPhase 5 (CMIP5) (Taylor et al. 2011) and its results were assessed in \r\nAR5 (Collins et al. 2013)."


id: 790d2e661ab555c66322023420bb37a7
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 16599
content:
"Recent transition scenarios considered overarching accelerated \r\nclimate mitigation strategies across multiple sectors, including \r\ndemand reduction, energy efficiency improvement, electrification \r\nand switching to low-carbon fuels. The sectoral strategies considered \r\nare often specific to national resource availability, political, economic, \r\nclimate, and technological conditions. Many sectoral transition \r\nstrategies have focused on the energy supply sectors, particularly \r\nthe power sector, and the role for renewable and bio-based fuels in \r\ndecarbonising energy supply and carbon capture and sequestration \r\n(CCS). Some studies present comprehensive scenarios for both supply\u0002side and demand-side sectors, including sector-specific technologies, \r\nstrategies, and policies"


id: 52a21b98388abeafb289dec8654d2a40
created_at: 2023-03-16 14:02:55
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vector_id: 10462
content:
" Nearly all demand sector scenarios have \r\nemphasised the need for energy efficiency, conservation and \r\nreduction through technological changes, with a limited number of \r\nmodels also exploring possible behavioural changes enabled by new \r\ntechnological and societal innovations."


id: 4f3fa0d5df785c827d5cd39b3eeb4629
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 9908
content:
"The current scenario framework for climate change research (Kriegler \r\net al. 2014c; O'Neill et al. 2014; van Vuuren et al. 2014) is based on \r\nthe concept of Shared Socio-economic Pathways (SSPs) (Kriegler et al. \r\n2012; O'Neill et al. 2014). Unlike their predecessor scenarios from \r\nthe SRES (IPCC 2000), their underlying narratives are motivated by \r\nthe purpose of using the framework for mitigation and adaptation \r\npolicy analysis. Hence the narratives are structured to cover the space \r\nof socio-economic challenges to both adaptation and mitigation. \r\nThey tell five stories of sustainability (SSP1), middle of the road \r\ndevelopment (SSP2), regional rivalry (SSP3), inequality (SSP4) and \r\nfossil-fuelled development (SSP5) (O'Neill et al. 2017). SSP1, SSP2, \r\nand SSP3 were structured to explore futures with socio-economic \r\nchallenges to adaptation and mitigation increasing from low to high \r\nwith increasing number of SSP"


id: 7f87978a32eceb7e3eb6e66ae7f52e61
created_at: 2023-03-16 14:02:55
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content:
" SSP4 was structured to explore a world \r\nwith high socio-economic challenges to adaptation but low socio\u0002economic challenges to mitigation, while SSP5 explored a world with \r\nlow challenges to adaptation but high challenges to mitigation. The \r\nfive narratives have been translated into population and education \r\n(Kc and Lutz 2017), economic growth (Crespo Cuaresma 2017; Dellink \r\net al. 2017; Leimbach et al. 2017a), and urbanisation projections \r\n(Jiang and O'Neill 2017) for each of the SSPs."


id: f54859e9002d410997bf9a84819899ae
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 36108
content:
"The new SSP-based emissions and concentrations pathways provided \r\nthe input for CMIP6 (Eyring et al. 2015; O'Neill et al. 2016) and its \r\nclimate change projections are assessed in AR6 (WGI Cross-chapter \r\nBox 1.2, WGI Chapter 4). From the original set of more than 100 SSP\u0002based energy-land use-emissions scenarios produced by six IAMs \r\n(Figure  4), five Tier 1 scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, \r\nSSP3-7.0, SSP5-8.5), and four Tier 2 scenarios (SSP4-3.4, SSP4-6.0, \r\nvariants of SSP7-3.0, SSP5-3.4) were selected7\r\n (O'Neill et al. 2016), \r\nfurther processed and harmonised with historic emissions and \r\nland-use change estimates (Gidden et al. 2019; Hurtt et al. 2020), \r\nand then taken up by CMIP6 models. WGI focuses its assessment \r\nof CMIP6 climate change projections on the five Tier 1 scenarios \r\n(WGI Chapter 4), but also uses the Tier 2 scenarios where they allow \r\nassessment of specific aspects like air pollution"


id: fc5cdb0a806b161fc6ce3d929bc47501
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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content:
" All SSP-based IAM \r\nscenarios from the original studies are included in the AR6 emissions \r\nscenario database and are part of the assessment of global mitigation \r\npathways in Chapter 3."


id: 423123f7cb4424d4a3297366ac5613f0
created_at: 2023-03-16 14:02:55
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content:
"The SSP narratives and associated projections of socio-economic \r\ndrivers provide the core components for building SSP-based scenario \r\nfamilies. These basic SSPs are not scenarios or goal-oriented pathways \r\nthemselves (despite carrying 'pathway' in the name), but building \r\nblocks from which to develop full-fledged scenarios. In particular, \r\ntheir basic elements do not make quantitative assumptions about \r\nenergy and land use, emissions, climate change, climate impacts \r\nand climate policy. Even though including these aspects in the \r\nscenario-building process may alter some of the basic elements, such \r\nas projections of economic growth, the resulting scenario remains \r\nassociated with its underlying SSP. To improve the ability of SSPs \r\nto capture socio-economic environments, basic SSPs have been \r\nextended in various ways, including the addition of quantitative \r\nprojections on further key socio-economic dimensions like inequality \r\n(Rao et al. 2019), governance (Andrijevic et al"


id: 2b0481d826a8aa0baaa65d8feda84c50
created_at: 2023-03-16 14:02:55
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content:
" 2020b), and gender \r\nequality (Andrijevic et al. 2020a). Extensions also included spatially \r\ndownscaled projections of, for example, population developments \r\n(Jones and O'Neill 2016). By now, the SSPs have been widely used in \r\nclimate change research ranging from projections of future climate \r\nchange to mitigation, impact, adaptation and vulnerability analysis \r\n(O'Neill et al. 2020)."


id: 506848c7538253a19ed026e582701db5
created_at: 2023-03-16 14:02:55
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vector_id: 10096
content:
"IAMs could not identify SSP-based emissions scenarios for all \r\ncombinations of SSPs and RCPs (Riahi et al. 2017; Rogelj et al. \r\n2018b) (Figure 4). The highest emission scenarios leading to forcing \r\nlevels similar to RCP8.5 could only be obtained in a  baseline \r\nwithout climate policy in SSP5 (SSP5-8.5). Since by now climate \r\npolicies are implemented in many countries around the world, \r\nthe likelihood of future emission levels as high as in SSP5-8.5 has \r\nbecome small (Ho et al. 2019). Baselines without climate policies \r\nfor SSP1 and SSP4 reach up to 6.0-7.0 W m-2, with baselines for \r\nSSP2 and SSP3 coming in higher at around 7.0 W m-2. On the lower \r\nend, no 1.5degC (RCP1.9) and likely 2degC scenarios (RCP2.6) could be \r\nidentified for SSP3 due to the lack of cooperative action in this \r\nworld of regional rivalry. 1.5degC scenarios (RCP1.9) could only be \r\nreached by all models under SSP1 assumptions. Models struggled \r\nto limit warming to 1"


id: c366155d3d27ee3981d8f56dcd710de6
created_at: 2023-03-16 14:02:55
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vector_id: 28312
content:
"5degC under SSP4 assumptions due to limited \r\nability to sustainably manage land, and under SSP5 assumptions \r\ndue to their high dependence on ample fossil fuel resources in the \r\nbaseline (Rogelj et al. 2018b)."


id: 75fb7b87e5245811ec30f081a2e93aa
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 16158
content:
"Target setting: Goal-oriented scenarios in the climate scenario \r\nliterature initially focused on concentration stabilisation but have \r\nnow shifted towards temperature limits and associated carbon \r\nbudgets. In early model intercomparisons, climate targets were \r\noften specified as a CO2-equivalent concentration level, for example, \r\n450ppm CO2-eq or 550ppm CO2-eq (Clarke et al. 2009). These \r\ntargets were either applied as not-to-exceed or overshoot targets. \r\nIn the latter case, concentration levels could be returned to the \r\ntarget level by 2100. Overshoot was particularly allowed for low \r\nconcentration and temperature targets as many models could not \r\nfind a solution otherwise (Clarke et al. 2009; Blanford et al. 2014b; \r\nKriegler et al. 2014a; Rogelj et al. 2018b). Bioenergy with carbon \r\ncapture and storage (BECCS) was an important technology that \r\nfacilitated aggressive targets to be met in 2100"


id: 2fe1ff501f5735daab62a15ad756196f
created_at: 2023-03-16 14:02:55
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vector_id: 4957
content:
" Due to its ability \r\nto remove CO2 from the atmosphere and produce net negative CO2\r\nemissions, it enabled overshoot of the target, leading to a distinctive \r\npeak-and-decline behaviour in concentration, radiative forcing, and \r\ntemperature (Clarke et al. 2014; Fuss et al. 2014). The mitigation "


id: 95a40a34c6705412bf95cd23bb29342f
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 21042
content:
"contrasting these benchmark cases with pathways derived under the \r\nassumption of regionally fragmented and heterogeneous mitigation \r\npolicy regimes (Blanford et al. 2014b; Kriegler et al. 2015b, 2018b; \r\nRoelfsema et al. 2020; van Soest et al. 2021; Bauer et al. 2020b). \r\nFor example, the Shared Policy Assumptions (Kriegler et al. 2014c) \r\nused in the SSP-RCP framework allow for some fragmentation of \r\npolicy implementation, and many scenarios follow current policies or \r\nemission pledges until 2030 before implementing stringent policies \r\n(Riahi et al. 2015; Vrontisi et al. 2018; Roelfsema et al. 2020). Other \r\nstudies assume a  gradual strengthening of emissions pledges and \r\nregulatory measures converging to a globally harmonised mitigation \r\nregime slowly over time (Kriegler et al. 2018b; van Soest et al. 2021)"


id: 4dc4cf2fe2602d084451f1e79f6959d4
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vector_id: 9665
content:
" \r\nWith increasing announcements of mid-century strategies and the \r\nrise of net-zero CO2 or GHG targets, global mitigation scenario \r\nanalysis has begun to build in nationally-specific policy targets until \r\nmid-century (NGFS 2021)."


id: 3b82dc368ed866b4c42e817ee4e9f222
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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content:
"Socio-economic drivers: Key socio-economic drivers of emission \r\nscenarios are assumptions on population and economic activity. \r\nThere are other socio-economic assumptions, often included in \r\nunderlying narratives (O'Neill et al. 2017), that strongly affect \r\nenergy demand per capita or unit of GDP and dietary choices (Bauer \r\net al. 2017; Popp et al. 2017; Grubler et al. 2018; van Vuuren et al. \r\n2018). The SSPs are often used to help harmonise socio-economic \r\nassumptions, and further explore the scenario space. Many studies \r\nfocus on the middle-of-the-road SSP2 as their default assumption, \r\nand many use SSP variations to explore the sensitivity of their results \r\nto socio-economic drivers (Marangoni et al. 2017; Riahi et al. 2017; \r\nRogelj et al. 2017). While the SSPs help harmonisation, they are not \r\nunique and do not fully explore the scenario space (O'Neill et al. \r\n2020). A  wider range of narratives describing alternative worlds \r\nis also conceivable"


id: 3809da79e021ba368e082ac2f5fd4077
created_at: 2023-03-16 14:02:55
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content:
" The sustainability world (SSP1), for example, is \r\na world with strong economic growth, but sustainability worlds with \r\nlow growth or even elements of degrowth in developed countries \r\ncould also be explored. Thus, standardisation of scenario narratives \r\nand drivers has advantages, but can also risk narrowing the scenario \r\nspace that is explored by the literature. Consequently, many studies \r\nin the literature have adopted other socio-economic assumptions, \r\nfor example with regard to population and GDP (Kriegler et al. \r\n2016; Gillingham et al. 2018) and sustainable development trends \r\n(Soergel et al. 2021)."


id: f59a8d0721cfd5906b86ea721903366c
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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content:
"or thousands of technologies. Despite the importance of technology \r\ncosts (Creutzig et al. 2017), there has been limited comparison of \r\ntechnology assumptions across models (Kriegler et al. 2015b; Krey \r\net al. 2019). There is, however, a substantial literature on the sensitivity \r\nof mitigation scenarios to technology assumptions, including \r\nmodel comparisons (Kriegler et al. 2014a; Riahi et al. 2015), single\u0002model sensitivity studies (McJeon et al. 2011; Krey and Riahi 2013; \r\nGiannousakis et al. 2021) and multi-model sensitivity studies (Bosetti \r\net al. 2015). Not only are the initial technology costs important, \r\nbut also how these costs evolve over time either exogenously or \r\nendogenously. Since IAMs have so many interacting technologies, \r\nassumptions on one technology can affect the deployment of another. \r\nFor example, limits on solar energy expansion rates, or integration, \r\nmay lead to higher levels of deployment for alternative technologies"


id: 8ba03bc395c4d08e36035b20b5c07128
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vector_id: 19482
content:
" \r\nBecause of these interactions, it can be difficult to determine what \r\nfactors affect deployment across a range of models."


id: f44c07e9c306388d70e23699d720c4aa
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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vector_id: 35945
content:
"The calls for scenarios issued in preparation of this assessment report \r\nallowed the collection of a  large ensemble of scenarios, coming \r\nfrom many modelling teams using various modelling frameworks \r\nin many different studies. Although a  large ensemble of scenarios \r\nwas gathered, it should be acknowledged that only a portion of the \r\nfull uncertainty space is investigated, and that the distribution of \r\nthe scenarios within the ensemble reflects the context of the studies \r\nthe scenarios were developed in. This introduces 'biases' in the \r\nensemble, for example, (i) the topics of the scenario studies collected \r\nin the database determine coverage of the scenario space, with large \r\nmodel-comparison studies putting large weight on selected topics \r\nover less explored topics explored by individual models, (ii)  some \r\nmodels are more represented than others, (iii) only 'optimistic' \r\nmodels (i.e"


id: 5df313314022cf7e582fe562c42b3ab0
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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content:
",  models finding lower mitigation costs) reach the \r\nlowest mitigation targets (Tavoni and Tol 2010). Where appropriate, \r\nsampling bias was recognised in the assessment, but formal methods \r\nto reduce bias were not employed due to conceptual limitations."


id: f4078deebb601b9772ee64899b52c81e
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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index: climate_gpt_only_giec
vector_id: 35903
content:
"Furthermore, although attempts have been made to elicit scenario \r\nlikelihoods from expert knowledge (Christensen et al. 2018), \r\nscenarios are difficult to associate with probabilities as they typically \r\ndescribe a  situation of deep uncertainty (Grubler and Nakicenovic \r\n2001). This and the non-statistical nature of the scenario ensemble \r\ncollected in the database do not allow a probabilistic interpretation \r\nof the distribution of output variables in the scenario database. \r\nThroughout the report, descriptive statistics are used to describe \r\nthe spread of scenario outcomes across the scenarios ensemble. The \r\nranges of results and the position of scenarios outcomes relative to \r\nsome thresholds of interest are analysed"


id: 5ba423f7f35409af4538d5d072e7233c
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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content:
" In some figures, the median \r\nof the distribution of results is plotted together with the interquartile \r\nrange and possibly other percentiles (5th-10th-90th-95th) to facilitate \r\nthe assessment of results, but these should not be interpreted in \r\nterms of likelihood of outcomes."


id: 7aa354ae15053c7314884b12ca71d496
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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index: climate_gpt_only_giec
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content:
"The WGIII assessment draws on the full literature on mitigation \r\nscenarios. To support the assessment, as many mitigation scenarios \r\nin the literature as possible were collected in a scenario database \r\nwith harmonised output reporting (Annex III.II.3). The collection of \r\nmitigation pathways in a common database is motivated by a number \r\nof reasons: First, to establish comparability of quantitative scenario \r\ninformation in the literature which is often only sporadically available \r\nfrom tables and figures in peer-reviewed publications, reports and \r\nelectronic supplementary information. Moreover, this information \r\nis often reported using different output variables and definitions \r\nrequiring harmonisation. Second, to increase latitude of the \r\nassessment by establishing direct access to quantitative information \r\nunderlying the scenario literature"


id: 4d9697377f9e594915b1712b1edb5187
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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index: climate_gpt_only_giec
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content:
" Third, to improve transparency \r\nand reproducibility of the assessment by making the quantitative \r\ninformation underlying the scenario figures and tables shown in \r\nthe report available to the readers of AR6. The use of such scenario \r\ndatabases in AR5 WGIII (Krey et al. 2014) and SR1.5 (Huppmann et al. "


id: b2241ee94fa70e19c8b565ae6815e140
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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content:
"Harmonisation: IAMs may use different historical datasets, and \r\nemission scenarios submitted to the AR6 WGIII scenario database \r\n(Annex III.II.3) are therefore harmonised against a common source \r\nof historical emissions. To be consistent with WGI, we use the \r\nsame historical emissions that were used for CMIP6 and RCMIP \r\n(Gidden et al. 2018; Nicholls et al. 2020b). This dataset comprises \r\nmany different emission harmonisation sources (Velders et al. 2015; \r\nGutschow et al. 2016; Le Quere et al. 2016; van Marle et al. 2017; \r\nMeinshausen et al. 2017; Hoesly et al. 2018), including estimates \r\nof CO2 emissions from agriculture, forestry, and land-use change \r\n(mainly CEDS, (Hoesly et al. 2018)) which are on the lower end of \r\nhistorical observation uncertainty as assessed in Chapter  2"


id: 7e13c457ddecadb9ccc93457ed35ce9c
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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index: climate_gpt_only_giec
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content:
" The \r\nharmonisation is performed so that different climate futures resulting \r\nfrom two different scenarios are a result of different future emission \r\nevolutions within the scenarios, not due to different historical \r\ndefinitions and starting points. Sectoral CO2 emissions from energy \r\nand industrial processes and CO2 from agriculture, forestry, and \r\nland-use change were harmonised separately. All other emissions \r\nspecies are harmonised based on the total reported emissions per \r\nspecies. For CO2 from energy and industrial processes we use a ratio\u0002based method with convergence in 2080, in line with CMIP6 (Gidden \r\net al. 2018, 2019). For CO2 from agriculture, forestry, and land-use \r\nchange and other emissions species with high historical interannual \r\nvariability, we use an offset method with convergence target 2150, \r\nto avoid strong harmonisation effects resulting from uncertainties in \r\nhistorical observations"


id: 22d7860b4d5a48ea69b1725c79a36593
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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index: climate_gpt_only_giec
vector_id: 2934
content:
" For all remaining fluorinated gases (F-gases), \r\nconstant ratio harmonisation is used. For all other emissions species, \r\nwe use the default settings of Gidden et al. (2018, 2019a)."


id: f204da08f48058f7c97d2938800b6c37
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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index: climate_gpt_only_giec
vector_id: 35606
content:
"and industrial process CO2, land use CO2, CH4, and N2O emissions \r\nto avoid infilling gases that have large individual radiative forcing \r\ncontributions and cannot be infilled with high confidence. Models \r\nthat did not meet this minimum reporting requirement were not \r\nincluded in the climate assessment. Infilling is performed following \r\nthe methods and guidelines in Lamboll et al. (2020). Missing species \r\nare infilled based on the relationship with CO2 from energy and \r\nindustrial processes as found in the harmonised set of all scenarios \r\nreported to the WGIII scenario database that pass the vetting \r\nrequirements. To ensure high stability to small changes, we apply \r\na Quantile Rolling Window method (Lamboll et al. 2020) for aerosol \r\nprecursor emissions, volatile organic compounds and greenhouse \r\ngases other than F-gases, based on the quantile of the reported CO2\r\nfrom energy and industrial processes in the database at each time \r\npoint"


id: 6d7755d1ef7eae79828ec1c471d6917f
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
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index: climate_gpt_only_giec
vector_id: 14832
content:
" F-fases and other gases with small radiative forcing are infilled \r\nbased on a  pathway with lowest root mean squared difference, \r\nallowing for consistency in spite of limited independently modelled \r\npathways in the database."


id: fbc4c52d510d490eaa6a65528603cb38
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 37110
content:
"The GSAT projections from the emulator runs are used for classifying \r\nthose emissions scenarios in the AR6 database that passed the initial \r\nvetting and allowed a  robust climate assessment. MAGICC (v7) \r\nwas selected as emulator for the climate classification of scenarios, \r\nas it happens to be slightly warmer than the other considered \r\nclimate emulator, particularly for the higher and long-term warming \r\nscenarios - reflecting long-term warming in line with Earth system \r\nmodels (ESMs) (WGI Cross-Chapter Box  7.1). This means that \r\nscenarios identified to stay below a given warming limit with a given \r\nprobability by MAGICC will in general be identified to have this \r\nproperty by the other emulator as well. There is the possibility that \r\nthe other emulator would classify a  scenario in a  lower warming \r\nclass based on their slightly cooler emulation of the temperature \r\nresponse. Unlike during the assessment of the SR1.5 database in the \r\nIPCC SR1"


id: 7efbf579a0e56522e4cf64e8174ed6d3
created_at: 2023-03-16 14:02:55
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index: climate_gpt_only_giec
vector_id: 17533
content:
"5 report, the updated versions of FaIR and MAGICC are \r\nhowever very close, providing robustness to the climate assessment. \r\nMAgiCC and FaIR were both used to assess the overall uncertainty \r\nin the warming response for a single scenario or a set of scenarios, \r\nincluding both parametric and model uncertainty. Specifically, the \r\n5th to 95th percentile range across the two emulators is calculated, \r\ncharacterising the joint climate uncertainty range of the two models."


id: bdfe702d4227a9d9b3942089c28c1201
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vector_id: 27448
content:
"WGI-calibrated emulators: Using expert judgement, emulators that \r\nreproduce the best estimates and uncertainties of the majority of AR6 \r\nWGI assessed metrics are recommended for scenario classification \r\nuse by WGIII (see WGI Cross-Chapter Box  7.1). MAGICC (v7) was \r\nused for the main scenario classification, with FaIR (v1.6.2) being \r\nused to provide additional uncertainty ranges on reported statistics to \r\ncapture climate model uncertainty. The WGI emulators' probabilistic \r\nparameter ensembles are derived such that they match a range of key \r\nclimate metrics assessed by WGI and the extent to which agreement \r\nis achieved is evaluated (WGI Cross-Chapter Box 7.1). Of particular \r\nimportance to this evaluation is the verification against the WGI \r\ntemperature assessment of the five scenarios assessed in Chapter 4 \r\nof WGI (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5)"


id: b46b7d19f8eaa3ba1fa017640feaec47
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 25906
content:
" \r\nThe inclusion of the temperature assessment as a benchmark for the \r\nemulators provides the strongest verification that WGIII's scenario \r\nclassification reflects the WGI assessment. The comprehensive nature \r\nof the evaluation is a  clear improvement on previous reports and \r\nensures that multiple components of the emulators, from their climate \r\nresponse to effective radiative forcing through to their carbon cycles, \r\nhave been examined before they are deemed fit for use by WGIII."


id: 67ff3622727a84b96eb370d63b33e90f
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 13949
content:
"Carbon budgets in WGI and WGIII: The remaining carbon budget \r\ncorresponding to a  certain level of future warming depends on \r\nnon-CO2 emissions of modelled pathways. Box  3.4 in Chapter  3 \r\nhighlighted this key uncertainty in estimating carbon budgets. In this \r\nsection (Figure 5), we put this into the context of the dependence \r\nof carbon budgets on two aspects of the non-CO2 warming \r\ncontribution: (i) assumptions on historical non-CO2 emissions and \r\nhow they can impact future non-CO2 warming estimates relative to \r\na recent reference period (2010-2019) (Panel a) and (ii) the scenario \r\nset underlying estimates of non-CO2 warming at the time of reaching \r\nnet zero CO2 (Panel b). Both aspects affect the estimated remaining \r\ncarbon budget by changing the non-CO2 warming contribution from \r\nthe base year to the time of reaching net zero CO2. MAGICC7 is \r\nused in WGI in conjunction with different input files for the historical \r\nwarming"


id: 8d0dd7eb29a9f9ee5efd4eff6aeaebc2
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 19719
content:
" For the reported remaining carbon budget estimates (WGI \r\nCB) WGI used the non-CO2 warming contributions from MAGICC7 \r\nin line with Meinshausen et al. (2020) and in line with the CMIP6 \r\nGHG concentration projections, while the WGI emulator setup \r\nin line with WGI Cross-Chapter Box  7.1 was used for the WGIII \r\nclimate assessment. The WGIII assessment uses MAGICC7 in line \r\nwith Nicholls et al. (2021) in line with the emission harmonisation \r\nprocess employed in WGIII (see above). The difference in historical \r\nassumptions changes the estimated non-CO2 contribution by up to \r\nabout 0.05degC for the lower temperature levels, or slightly more than \r\n10% of the warming until 1.5degC relative to 2010-2019. For peak \r\nwarming around 2degC relative to pre-industrial levels (about 0.97degC \r\nwarming relative to 2010-2019 in Figure 5 plots), the difference is "


id: 6a60cd4a339e9cc10e3e282e78cf0c08
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 14337
content:
"Scenario climate assessment: For the WGIII scenario climate \r\nassessment, emulators are run hundreds to thousands of times per \r\nscenario, sampling from an emulator-specific probabilistic parameter \r\nset, which incorporates carbon cycle and climate system uncertainty in \r\nline with the WGI assessment (WGI Cross-Chapter Box 7.1). Percentiles \r\nfor different output variables provide information about the spread \r\nin individual variables for a given scenario, but the set of variables \r\nfor a given percentile do not form an internally consistent climate \r\nchange projection. Instead, joint distributions of these parameter sets \r\nare employed by the calibrated emulators. Consistent climate change \r\nprojections are represented by individual ensemble member runs and \r\nthe whole ensemble of these individual member runs"


id: 585b8e65b66279120df420e32ab0ea50
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 11407
content:
" To facilitate \r\nanalysis, multiple percentiles of these large (hundred to thousand \r\nmember) ensemble distributions of projected climate variables are \r\nprovided in the AR6 scenario database. The emulators provide an \r\nassessment of global surface air temperature (GSAT) response to \r\nemission scenarios and its key characteristics like peak warming and "


id: 80e325fe464a15e81d03f03d76aff5f6
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 17802
content:
"projections by WGI (AR6 WGI Table SPM.1) and vice versa, with \r\nranges of results provided to characterise the physical uncertainties \r\nrelevant to assessing climate impacts risk. Common socio-economic \r\ndimensions are not adopted in WGII due to a  desire to draw on \r\nthe full literature, inform the broad ranges of relevant possibilities \r\n(climate, development, adaptation, mitigation), and be flexible. \r\nThe impacts literature is wide-ranging and diverse, with a fraction \r\nbased on global socio-economic scenarios. WGII's approach allows \r\nchapters and cross-chapter boxes to assess how impacts and ranges \r\ndepend on socio-economic factors affecting exposure, vulnerability, \r\nand adaptation independently, as appropriate for their literature. For \r\nexample, WGII Chapter  16 assesses how Representative Key Risks \r\nvary under low vs high exposure/vulnerability conditions by drawing \r\non impact literature based on Shared Socio-economic Pathways \r\n(SSPs)"


id: 343d85f220aff1eedfcd10368b839a31
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 5630
content:
" In general, WGII chapters, when possible and conducive \r\nwith their literature, used GWLs or climate projections based on \r\nRepresentative Concentration Pathways or SSPs to communicate \r\ninformation and facilitate integration and synthesis, with impacts \r\nresults characterised according to other drivers when possible and \r\nrelevant, such as socio-economic conditions."


id: 855174e6212351d57c5c8a5362154cfd
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 18503
content:
"However, socio-economic conditions are an important factor \r\ndefining both impacts exposure, vulnerability, and adaptation, \r\nas well as mitigation opportunity and costs, that needs special \r\nconsiderations. The WGIII scenario assessment is using additional \r\nclassifications relating to, inter alia, near-term policy developments, \r\ntechnology availability, energy demand, population and economic \r\ngrowth (Chapter 3, Section 3.3 and Annex III.II.3.2.2), and a set of \r\nillustrative mitigation pathways with varying socio-techno-economic \r\nassumptions (Annex III.II.2.4, Chapter  3, Section  3.2). Synthesising \r\nWGII assessments of climate change impacts and WGIII assessments \r\nof climate change mitigation efforts for similar GWLs/global warming \r\nscenario classes would have to address how socio-techno-economic \r\nconditions affect impacts, adaptation, and mitigation outcomes"


id: 5117c47899733e953d896fb34eef437b
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 10208
content:
" \r\nFurthermore, a synthesis of mitigation costs and mitigation benefits in \r\nterms of avoided climate change impacts would require a framework \r\nthat ensures consistency in socio-economic development assumptions \r\nand emissions and adaptation dynamics and allows for consideration \r\nof benefits and costs along the entire pathway (O'Neill et al. 2020) \r\n(Cross-Working Group Box 1 in Chapter 3)."


id: 853f810989df3ec350224f954ad8284b
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 18491
content:
"In the context of common climate dimensions, WGII considers common \r\nprojected GWL ranges by time period, the timing for when GWLs \r\nmight be reached, and projected continental-level result ranges for \r\nselect temperature and precipitation variables by GWL (average \r\nand  extremes), as well as sea surface temperature changes by \r\nGWL and ocean biome. Where available, WGII considers the assessed \r\nWGI ranges as well as the raw CMIP5 and CMIP6 climate change \r\nprojections (ranges and individual projections) from Earth system \r\nmodels (Hauser et al. 2019). With WGII's climate impacts literature \r\nbased primarily on climate projections available at the time of AR5 \r\n(CMIP5) and earlier, or assumed temperature levels, it was important to \r\nbe able to map climate variable levels to climate projections of different \r\nvintages and vice versa. WGII's common GWLs are based on AR6 WGI's \r\nproposed 'Tier 1' dimensions of integration range - 1.5, 2.0, 3.0, and \r\n4"


id: cde9253f2079a5afbb6371584fe28984
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 29919
content:
"0@C (relative to the 1850 to 1900 period), which are simply proposed \r\ncommon GWLs to facilitate integration across and within WGs (WGI \r\nChapter 1). Within WGII, GWLs facilitate comparison of climate states \r\nacross climate change projections, assessment of the full impacts \r\nliterature, and cross-chapter comparison. Across AR6, GWLs facilitate \r\nintegration across Working Groups of climate change projections, \r\nclimate change risks, adaptation opportunities, and mitigation."


id: bf599374bb6f40fc5cc2bacf1876146
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 27656
content:
"For all scenarios reporting global data, a  vetting process was \r\nundertaken to ensure that key indicators were within reasonable \r\nranges for the baseline period - primarily for indicators relating to \r\nemissions and the energy sector (Table 11). As part of the submission \r\nprocess, model teams were contacted individually with information \r\non the vetting outcome with regard to their submitted scenarios, \r\ngiving them the opportunity to verify the reporting of their data. \r\nChecks on technology-specific variables for nuclear, solar and wind \r\nenergy, and CCS, screen not only for accuracy with respect to recent \r\ndevelopments, but also indicate reporting errors relating to different \r\nprimary energy accounting methods. While the criteria ranges appear \r\nto be large, the focus of these scenarios is the medium to long term \r\nand there is also uncertainty in the historical values"


id: 726c313b523aab9b2b84d7847298c095
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 15617
content:
" For vetting of the \r\nIllustrative Mitigation Pathways, the same criteria were used, albeit \r\nwith narrower ranges (Table  11). Selected future values were also \r\nvetted and the result of the vetting reported to authors, but not used \r\nas exclusion criterion. Where possible the latest values available were \r\nused, generally 2019, and if necessary extrapolated to 2020 as most "


id: d185cb93c61b6a9f5a9d1534f2db05e1
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 30499
content:
"Changes in climate classification of scenarios since SR1.5: Since \r\nthe definition of warming classes was unchanged from SR1.5 for the \r\nlower range of scenarios limiting warming to 2degC or lower, changes \r\nin overall emissions characteristics of scenarios in these classes \r\nfrom SR1.5 to AR6 would need to come from the substantially \r\nlarger ensemble of deep mitigation scenarios collected in the AR6 \r\ndatabase compared to the SR1.5 database and from updates in the \r\nmethodology of the climate assessment. Updates since SR1.5 include \r\nthe methodology for infilling and harmonisation and the use of \r\nan updated climate emulator (MAGICC v7) to provide consistency \r\nwith AR6 WGI assessment (Annex III.II.2.5.1). Out of the full set of \r\nSR1.5, 57% of the 411 scenarios that were represented with global \r\ntemperature assessments in SR1.5 also have been assessed in AR6. \r\nSome SR1"


id: 934c69729728a687c76bcb99ed7d5d77
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 20688
content:
"5 scenarios could not be taken on board since they are \r\noutdated (too early emissions reductions) and failed the vetting or do \r\nnot provide sufficient information/data to be included in AR6."


id: 66abc5d6370543ccd142d071783915be
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 13744
content:
"Table  17 presents the policy classification that was chosen for \r\nthis assessment and the distribution of scenarios across the policy \r\nclasses. There is a top-level distinction between diagnostic scenarios, \r\nscenarios from cost-benefit analyses, scenarios without globally \r\ncoordinated action, scenarios with immediate such action, and hybrid \r\nscenarios that move to globally coordinated action after a period of \r\ndiverse and uncoordinated national action. On the second hierarchy \r\nlevel, scenarios are classified along distinctive features of scenarios \r\nin each class. Scenarios without globally coordinated action are \r\noften used as reference scenarios and come as baselines without \r\nclimate policy efforts, as an extrapolation of current policy trends \r\nor as implementation and extrapolation of NDCs (Grant et al. 2020)"


id: b412fcfe5617df17fff3e208069b314
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 25846
content:
" \r\nScenarios that act immediately to limit warming to some level can \r\nbe distinguished by whether or not they include transfers to reflect \r\nequity considerations (Tavoni et al. 2015; Bauer et al. 2020c; van den \r\nBerg et al. 2020) or by whether or not they assume additional policies \r\naugmenting a  global carbon price (Soergel et al. 2021). Scenarios \r\nthat delay globally coordinated action until 2030 can differ in their \r\nassumptions about the level of near-term action (Roelfsema et al. \r\n2020; van Soest et al. 2021)."


id: e8d95bd1be080e21dadcd7d69f664987
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 34151
content:
"The lowest temperature category (C1, limiting warming to 1.5degC with \r\nno or low overshoot) used for classifying the most ambitious climate \r\nmitigation pathways in the literature, indicates that emissions are on \r\naverage higher in AR6 in the near term (e.g., 2030) and the time of \r\nnet-zero CO2 is later by about five years compared to SR1.5 (Figure 6, \r\nmiddle panel). These differences can in part be ascribed to the fact \r\nthat historical emissions in scenarios, especially among those that \r\npassed the vetting, have risen since SR1.5 in line with inventories. \r\nThis increase has moved the attainable near-term emissions \r\nreductions upwards. As a result, the scenarios in the lowest category \r\nhave also a lower probability of staying below 1.5degC peak warming. \r\nUsing the WGI emulators, we find that the median probability of \r\nstaying below 1.5degC in the lowest category (C1) has dropped from \r\nabout 46% in the SR1.5 scenarios to 38% among the AR6 scenarios"


id: 76730ef1eb3da79c3f234c30c048f1c5
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 16227
content:
" \r\nNote that the likelihood of the SR1.5 scenarios limiting warming to \r\n1.5degC with no or limited overshoot has changed from 41% in SR1.5 \r\nto 46% in AR6 due to the updated climate assessment using the WGI \r\nAR6 climate emulator. Within C1, the vast majority of scenarios that \r\nwere submitted to AR6 but were not assessed in SR1.5 have median \r\npeak temperatures close to 1.6degC. The AR6 scenarios in the lowest \r\ncategory show higher emissions and have a lower chance of keeping \r\nwarming below 1.5degC, as indicated by the panels showing the \r\ndistribution of peak warming and exceedance probability in AR6 vs \r\nSR1.5, with for instance C1 median peak temperature warming going "


id: 7bae3dfe9366501b1e1105ee0917504c
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 17059
content:
"To identify the policy classification of each global scenario in the \r\nAR6 database, classes are first assigned via text pattern matching \r\non all the metadata collected when submitting the scenarios to the \r\ndatabase. The algorithm first looks for keywords and text patterns \r\nto establish whether a  scenario represents a  global, fragmented, \r\ndiagnostic or CBA policy setup. Then it looks for evidence on \r\nthe presence of specific regional policies, delayed actions and \r\ntransfers of permits. Eventually the different pieces of evidence \r\nare harmonised into a  single policy categorisation decision. The \r\nprocess has been calibrated on the best-known scenarios belonging \r\nto the larger model intercomparison projects, and fine-tuned \r\non the other scenarios via further validation against the related \r\nliterature, consistency checks on reported emission and carbon \r\nprice trajectories, exchanges with modellers and supervision \r\nby the involved IPCC authors"


id: d01830584611598d3261557b88048338
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 30271
content:
" If the information available is \r\nenough to identify a policy category number but not sufficient for \r\na subcategory, then only the number is retained (e.g., P2 instead \r\nof P2a/b/c). A suffix added after P0 further qualifies a diagnostic \r\nscenario as one of the other policy categories."


id: f2d1301343aa766610a38afc778c9834
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 35726
content:
"Top row: The probability of exceeding 1.5C for scenarios using the AR6 climate assessment pipeline for C1, C2, and C3. All scenarios in AR6 that pass vetting \r\nrequirements and get climate classification C1, C2, or C3, are labelled as 'AR6' (n=541). The scenarios that are both in the AR6 database (passing the vetting) and were used \r\nfor SR1.5 Table 2.4, and are classified as C1, C2 and C3 using the AR6 assessment, are labelled as 'AR6 and SR1.5 overlap' (n=95). 'SR1.5 (n=127)' shows all SR1.5 scenarios \r\n(except five that were not resubmitted for the AR6 report), including those that failed AR6 vetting, that are classified C1, C2, C3 with the updated AR6 temperature assessment. \r\nDashed lines indicate cut-off temperature exceedance probabilities that align with AR6 category definitions. The violin area is proportional to the number of scenarios. Coloured \r\nlines indicate the 25th and 75th percentiles, while the dashed black line indicates the median"


id: 65264f5bc7d66c3023d3970a34246999
created_at: 2023-03-16 14:02:55
updated_at: 2023-03-16 14:02:55
content_type: text
index: climate_gpt_only_giec
vector_id: 13479
content:
" The insets in each figure show how the temperature category classifications have \r\nchanged from SR1.5 to AR6 for those scenarios that are in both databases."


