# document

id: aa59faf697372e9f8d8354faeb27b892
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 24338
content:
"Mitigation measures In climate policy, mitigation measures are \r\ntechnologies, processes or practices that contribute to mitigation, \r\nfor example, renewable energy technologies, waste minimisation \r\nprocesses, and public transport commuting practices."


id: df98ec85dcb92cd992daa4f1ea90d44f
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 32680
content:
"Mitigation option A technology or practice that reduces greenhouse \r\ngas emissions or enhances sinks."


id: ca3da8cc862732cc663127732813ee43
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 29378
content:
"Mitigation potential The quantity of net greenhouse gas emission \r\nreductions that can be achieved by a given mitigation option relative \r\nto specified emission baselines."


id: 9c721c7289916805d2860ffd47d757cf
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 22170
content:
"Material substitution Replacement of one material (including an \r\nenergy carrier used as a feedstock) by another, due to scarcity, price, \r\ntechnological change, or because of lower environmental impacts or \r\ngreenhouse gas emissions."


id: 1891c3773472c8da0957ae36c2323f48
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 1358
content:
"[Note: Net greenhouse gas emissions reduction is the sum of reduced emissions \r\nand/or enhanced sinks.]"


id: b038837f1f8132a94f4329ce21f74771
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 25248
content:
"'Processes of data collection over time, providing basic datasets, \r\nincluding associated accuracy and precision, for the range of \r\nrelevant variables. Possible data sources are field measurements, \r\nfield observations, detection through remote sensing and interviews' \r\n(UN-REDD 2009)."


id: 70a7aa782451b3a328be9cebe79dcb48
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 15344
content:
"'The process of formal reporting of assessment results to the UNFCCC, \r\naccording to predetermined formats and established standards, \r\nespecially the Intergovernmental Panel on Climate Change (IPCC) \r\nGuidelines and GPG (Good Practice Guidance)' (UN-REDD 2009)."


id: 3f726e5cb15c7bef2f91b8e4064fdbe9
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 7368
content:
"'The process of formal verification of reports, for example, the \r\nestablished approach to verify national communications and national \r\ninventory reports to the UNFCCC' (UN-REDD 2009)."


id: 3b962ad924ebb6e77b0367110fae24bf
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 6727
content:
"Methane (CH4) The greenhouse gas (GHG) methane is the major \r\ncomponent of natural gas and associated with all hydrocarbon fuels. \r\nSignificant anthropogenic emissions also occur as a result of animal \r\nhusbandry and paddy rice production. Methane is also produced \r\nnaturally where organic matter decays under anaerobic conditions, \r\nsuch as in wetlands. Under future global warming, there is potential \r\nfor increased methane emissions from thawing permafrost, wetlands \r\nand sub-sea gas hydrates. See also Short-lived climate forcers (SLCFs)."


id: aa7eeb3426a6eed52a66ef675d9a6074
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 24357
content:
"Nature's contributions to people (NCP) All the contributions, \r\nboth positive and negative, of living nature (i.e.,  diversity of \r\norganisms, ecosystems, and their associated ecological and \r\nevolutionary processes) to the quality of life for people. Beneficial \r\ncontributions from nature include such things as food provision, "


id: 797ad501bb4d53106acb51eb954ea993
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 16685
content:
"water purification, flood control, and artistic inspiration, whereas \r\ndetrimental contributions include disease transmission and predation \r\nthat damages people or their assets. Many NCP may be perceived as \r\nbenefits or detriments depending on the cultural, temporal or spatial \r\ncontext (Diaz et al. 2018). See also Ecosystem services."


id: 79aeec549652fb76a0fc0a4d8395862d
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 16711
content:
"Note 2: Under the Paris Rulebook (Decision 18/CMA.1, annex, paragraph 37), \r\nparties have agreed to use GWP100 values from the IPCC AR5 or GWP100 \r\nvalues from a  subsequent IPCC Assessment Report to report aggregate \r\nemissions and removals of GHGs. In addition, parties may use other metrics \r\nto report supplemental information on aggregate emissions and removals \r\nof GHGs.]"


id: 4fc4e7491e1983b286cc007fe1d14a63
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 9990
content:
"Nature-based solutions Actions to protect, sustainably manage \r\nand restore natural or modified ecosystems that address societal \r\nchallenges effectively and adaptively, simultaneously providing \r\nhuman well-being and biodiversity benefits (IUCN 2016). See also \r\nBiodiversity and Ecosystem."


id: 3a38aab3e6c4b611c9ddb745a82855ba
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 6528
content:
"See also Greenhouse gas neutrality, Net-zero CO2 emissions, and \r\nLand use, land-use change and forestry (LULUCF)."


id: 71a0f425d9d93cb4745709684817ec2a
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 15512
content:
"Nitrous oxide (N2O) The main anthropogenic source of N2O, \r\na  greenhouse gas (GHG), is agriculture (soil and animal manure \r\nmanagement), but important contributions also come from sewage \r\ntreatment, fossil fuel combustion, and chemical industrial processes. \r\nN2O is also produced naturally from a wide variety of biological sources \r\nin soil and water, particularly microbial action in wet tropical forests."


id: 87dd9c3b08f4f5dcb71f03f08da52698
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 18900
content:
"Net negative greenhouse gas emissions A situation of net \r\nnegative greenhouse gas emissions is achieved when metric\u0002weighted anthropogenic greenhouse gas (GHG) removals exceed \r\nmetric-weighted anthropogenic GHG emissions. Where multiple \r\nGHG are involved, the quantification of net emissions depends on \r\nthe metric chosen to compare emissions of different gases (such \r\nas global warming potential, global temperature change potential, \r\nand others, as well as the chosen time horizon). See also Carbon \r\ndioxide removal (CDR), Greenhouse gas emission metric, Net-zero \r\nCO2 emissions; Net-zero greenhouse gas emissions, and Negative \r\ngreenhouse gas emissions."


id: fb9c7926d8ac2a206132adaa5dbc5001
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 37088
content:
"Ocean alkalinisation/Ocean alkalinity enhancement A proposed \r\ncarbon dioxide removal (CDR) method that involves deposition of \r\nalkaline minerals or their dissociation products at the ocean surface. \r\nThis increases surface total alkalinity, and may thus increase ocean \r\ncarbon dioxide (CO2 ) uptake and ameliorate surface ocean acidification. \r\nSee also Anthropogenic removals."


id: bc16bfa1a8f3c1be10df95e6e751e1fc
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 27159
content:
"Net-zero CO2 emissions Condition in which anthropogenic\r\ncarbon dioxide (CO2 ) emissions are balanced by anthropogenic CO2\r\nremovals over a specified period."


id: 911f8c0ef029eee9c62627f0e2e73246
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 20368
content:
"Ocean fertilisation A proposed carbon dioxide removal (CDR)\r\nmethod that relies on the deliberate increase of nutrient supply to \r\nthe near-surface ocean with the aim of sequestering additional CO2\r\nfrom the atmosphere through biological production. Methods include \r\ndirect addition of micro-nutrients or macro-nutrients. To be successful, \r\nthe additional carbon needs to reach the deep ocean where it has \r\nthe potential to be sequestered on climatically relevant time scales. \r\nSee also Anthropogenic removals."


id: fa8186e4cead346e0e57dc6d838adc7
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 36931
content:
"[Note: Carbon neutrality and net-zero CO2 emissions are overlapping concepts. \r\nThe concepts can be applied at global or sub-global scales (e.g.,  regional, \r\nnational and sub-national). At a global scale, the terms carbon neutrality and \r\nnet-zero CO2 emissions are equivalent. At sub-global scales, net-zero CO2\r\nemissions is generally applied to emissions and removals under direct control \r\nor territorial responsibility of the reporting entity, while carbon neutrality\r\ngenerally includes emissions and removals within and beyond the direct \r\ncontrol or territorial responsibility of the reporting entity. Accounting rules \r\nspecified by GHG programmes or schemes can have a significant influence on \r\nthe quantification of relevant CO2 emissions and removals.]"


id: 63f2ba96bd430e13064b3f62aad56290
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 13293
content:
"Offset (in climate policy) The reduction, avoidance or removal of \r\na unit of greenhouse gas (GHG) emissions by one entity, purchased \r\nby another entity to counterbalance a unit of GHG emissions by that \r\nother entity. Offsets are commonly subject to rules and environmental \r\nintegrity criteria intended to ensure that offsets achieve their stated \r\nmitigation outcome. Relevant criteria include, but are not limited to, \r\nthe avoidance of double counting and leakage, use of appropriate \r\nbaselines, additionality, and permanence or measures to address \r\nimpermanence. See also Greenhouse gas emission metric and \r\nCarbon neutrality."


id: 28cbd6de257625d0397bd2d16af08236
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 3850
content:
"See also Carbon neutrality, Land use, land-use change and forestry \r\n(LULUCF) and Net-zero greenhouse gas emissions."


id: 5109d58e5c1220dd3d5974c2295b85f8
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 10200
content:
"Net-zero greenhouse gas emissions Condition in which metric\u0002weighted anthropogenicgreenhouse gas (GHG) emissions are balanced \r\nby metric-weighted anthropogenic GHG removals over a  specified \r\nperiod. The quantification of net-zero GHG emissions depends on the \r\nGHG emission metric chosen to compare emissions and removals of \r\ndifferent gases, as well as the time horizon chosen for that metric."


id: f4a4ec039a2f37541e080717642080d8
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 35994
content:
"Organic farming An agricultural production system that aims \r\nto utilise natural processes and cycles to limit off-farm and notably \r\nsynthetic inputs, while also aiming to enhance agroecosystems and \r\nsociety. Organic farming is often legally defined and governed by \r\nstandards, typically guided by principles outlined by the International \r\nFederation of Organic Agriculture Movements (IFOAM  - Organics \r\nInternational) (IFOAM - Organics International 2014)."


id: 7c7057017fbae407300c4e160de5666c
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 17178
content:
"[Note 1: Greenhouse gas neutrality and net-zero GHG emissions are \r\noverlapping concepts. The concept of net-zero GHG emissions can be applied \r\nat global or sub-global scales (e.g.,  regional, national and sub-national). \r\nAt a global scale, the terms GHG neutrality and net-zero GHG emissions are \r\nequivalent. At sub-global scales, net-zero GHG emissions is generally applied \r\nto emissions and removals under direct control or territorial responsibility of \r\nthe reporting entity, while GHG neutrality generally includes anthropogenic \r\nemissions and anthropogenic removals within and beyond the direct control \r\nor territorial responsibility of the reporting entity. Accounting rules specified \r\nby GHG programmes or schemes can have a  significant influence on the \r\nquantification of relevant emissions and removals."


id: ac752106a2570e4dc09b8198dda7680d
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 24671
content:
"Ozone (O3) The triatomic form of oxygen, and a  gaseous \r\natmospheric constituent. In the troposphere, O3 is created both \r\nnaturally and by photochemical reactions involving gases resulting \r\nfrom human activities (e.g.,  smog). Tropospheric O3 acts as \r\na greenhouse gas (GHG). In the stratosphere, O3 is created by the \r\ninteraction between solar ultraviolet radiation and molecular oxygen "


id: eb2a521b0eabe52e49daa303a9cd7e14
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 34500
content:
"(O2). Stratospheric O3 plays a  dominant role in the stratospheric \r\nradiative balance. Its concentration is highest in the ozone layer."


id: 46558765b162cadcb57d4c5428dd1215
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 8452
content:
"Pareto optimum A state in which no one's welfare can be \r\nincreased without reducing someone else's welfare."


id: 931b255c9f29674de693f6ed0965578f
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 20659
content:
"Pathways that first exceed a  specified concentration, forcing, or \r\nglobal warming level, and then return to or below that level again \r\nbefore the end of a  specified period of time (e.g.,  before 2100). \r\nSometimes the magnitude and likelihood of the overshoot is also \r\ncharacterised. The overshoot duration can vary from one pathway \r\nto the next, but in most overshoot pathways in the literature and \r\nreferred to as overshoot pathways in the AR6, the overshoot occurs \r\nover a period of at least one decade and up to several decades."


id: 90b10ceba1beedc78f9e5cf7138bd627
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 20302
content:
"Particulate matter (PM) Atmospheric aerosols involved in \r\nair pollution issues. Of greatest concern for health are particles of \r\naerodynamic diameter less than or equal to 10 micrometers, usually \r\ndesignated as PM10 and particles of diameter less than or equal to \r\n2.5 micrometers, usually designated as PM2.5."


id: 483bc45f6591e5229354375b66d177f2
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 8735
content:
"Path dependence The generic situation where decisions, events, \r\nor outcomes at one point in time constrain adaptation, mitigation, \r\nor other actions or options at a later point in time. See also Lock-in."


id: 75e05ece7b321a38d37b22bdb561b1d5
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 16138
content:
"Scenarios that include time series of emissions and concentrations of \r\nthe full suite of greenhouse gases (GHGs) and aerosols and chemically \r\nactive gases, as well as land use/land cover (Moss et al. 2010). The \r\nword representative signifies that each RCP provides only one of \r\nmany possible scenarios that would lead to the specific radiative \r\nforcing characteristics. The term pathway emphasises that not only \r\nthe long-term concentration levels are of interest, but also the \r\ntrajectory taken over time to reach that outcome (Moss et al. 2010)."


id: 281efad965aba4bd46bc9beace7a6e31
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 3717
content:
"Pathways The temporal evolution of natural and/or human \r\nsystems towards a future state. Pathway concepts range from sets \r\nof quantitative and qualitative scenarios or narratives of potential \r\nfutures to solution-oriented decision-making processes to achieve \r\ndesirable societal goals. Pathway approaches typically focus on \r\nbiophysical, techno-economic, and/or socio-behavioural trajectories \r\nand involve various dynamics, goals, and actors across different \r\nscales. See also Scenario and Storyline."


id: 9845765a9acb0c3a86246bf24c90b12f
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 21471
content:
"RCPs usually refer to the portion of the concentration pathway \r\nextending up to 2100, for which integrated assessment models\r\nproduced corresponding emission scenarios. Extended concentration \r\npathways describe extensions of the RCPs from 2100 to 2300 \r\nthat were calculated using simple rules generated by stakeholder \r\nconsultations, and do not represent fully consistent scenarios. \r\nFour  RCPs produced from integrated assessment models were \r\nselected from the published literature and used in the Fifth IPCC \r\nAssessment, and are also used in this Assessment for comparison, \r\nspanning the range from approximately below 2degC warming to \r\nhigh (>4degC) warming best-estimates by the end of the 21st century: \r\nRCP2.6, RCP4.5 and RCP6.0 and RCP8.5."


id: 822e0241939f27f2e7e4a5c2b2da7b
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 18009
content:
"A series of adaptation choices involving trade-offs between short\u0002term and long-term goals and values. These are processes of \r\ndeliberation to identify solutions that are meaningful to people in \r\nthe context of their daily lives and to avoid potential maladaptation."


id: e767baddb5d36497e5c86b3391764669
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 33898
content:
"Iterative processes for managing change within complex systems in \r\norder to reduce disruptions and enhance opportunities associated \r\nwith climate change."


id: bb13348a822b397772a8e5c47bdc9bff
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 26975
content:
"Development pathways evolve as the result of the countless \r\ndecisions being made and actions being taken at all levels of societal \r\nstructure, as well due to the emergent dynamics within and between \r\ninstitutions, cultural norms, technological systems and other drivers \r\nof behavioural change. "


id: 629e4e8ed0c385d1c8ac6463914db142
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 13086
content:
"See also Shifting development pathways (SDPs) and Shifting \r\ndevelopment pathways to sustainability (SDPS)."


id: d9034070cdb40810162c599cedf8d0a5
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 31651
content:
"SSPs have been developed to complement the Representative \r\nConcentration Pathways (RCPs). By design, the RCP emission and \r\nconcentration pathways were stripped of their association with \r\na certain socio-economic development. Different levels of emissions\r\nand climate change along the dimension of the RCPs can hence \r\nbe explored against the backdrop of different socio-economic \r\ndevelopment pathways (SSPs) on the other dimension in a matrix. \r\nThis integrative SSP-RCP framework is now widely used in the climate \r\nimpact and policy analysis literature, where climate projections"


id: 5e0977c8d48303288e252837a233a341
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 12325
content:
"A temporal evolution of a set of mitigation scenario features, such as \r\ngreenhouse gas emissions and socio-economic development."


id: 36bf8bf964dd246afcaa000956a9e9d5
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 5996
content:
"obtained under the RCP scenarios are analysed against the backdrop \r\nof various SSPs. As several emission updates were due, a  new set \r\nof emission scenarios was developed in conjunction with the SSPs. \r\nHence, the abbreviation SSP is now used for two things: On the one \r\nhand SSP1, SSP2, ..., SSP5 are used to denote the five socio-economic \r\nscenario families. On the other hand, the abbreviations SSP1-1.9, \r\nSSP1-2.6, ..., SSP5-8.5 are used to denote the newly developed \r\nemission scenarios that are the result of an SSP implementation \r\nwithin an integrated assessment model. Those SSP  scenarios are \r\nbare of climate policy assumption, but in combination with so-called \r\nshared policy assumptions (SPAs), various approximate radiative \r\nforcing levels of 1.9, 2.6, ..., or 8.5 W m-2 are reached by the end of \r\nthe century, respectively."


id: eaa02f597cbeb9920ad65919aca78aac
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 34422
content:
"Primary energy The energy that is embodied in resources as they \r\nexist in nature (e.g.,  coal, biomass uranium, solar radiation, wind, \r\nocean currents) (Grubler et al. 2012). "


id: 1ead05806729cc6e335e68ddaca7b59a
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 2268
content:
"[Note: Primary energy is defined in several alternative ways. The method \r\nused in this report is the direct equivalent method, which counts one unit \r\nof secondary energy provided from non-combustible sources as one unit of \r\nprimary energy. For more details on the methodology, see Section 7 in Working \r\nGroup III Annex II.]"


id: c884165fbb5f826d96eafc05b8227db9
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 29112
content:
"Trajectories describing consistent sets of possible futures of \r\ngreenhouse gas (GHG) emissions, atmospheric concentrations, \r\nor global mean surface temperatures implied from mitigation and \r\nadaptation actions associated with a  set of broad and irreversible \r\neconomic, technological, societal, and behavioural changes. This can \r\nencompass changes in the way energy and infrastructure are used \r\nand produced, natural resources are managed and institutions are set \r\nup, and in the pace and direction of technological change."


id: d346fe5130f0eb1229c12ae3704e6cdc
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 30767
content:
"Primary production The synthesis of organic compounds by plants \r\nand microbes, on land or in the ocean, primarily by photosynthesis \r\nusing light and carbon dioxide (CO2 ) as sources of energy and \r\ncarbon respectively. It can also occur through chemosynthesis, using \r\nchemical energy, for example, in deep sea vents."


id: daf8b3a69184b7d427c3929bc7e8d658
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 31972
content:
"Private costs Costs carried by individuals, companies or other \r\nprivate entities that undertake an action, whereas social costs include \r\nadditionally the external costs on the environment and on society as \r\na whole. Quantitative estimates of both private and social costs may \r\nbe incomplete, because of difficulties in measuring all relevant effects."


id: 882ae1f08e9b6757c00f594c7378f5d2
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 18944
content:
"Peri-urban areas Dynamic transition zones that have intense \r\ninteraction between rural and urban economies, activities, households, \r\nand lifestyles. Neither fully rural or urban (Seto et al. 2010)."


id: b6f0fa762c231834155b346ec635b1a
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 26309
content:
"Production-based emissions Emissions released to the atmosphere\r\nfor the production of goods and services by a certain entity (e.g., a person, \r\nfirm, country, or region). See also Consumption-based emissions."


id: 2d1a31c5e5ff7f7e1d64b1d76b9972e
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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index: climate_gpt_only_giec
vector_id: 4536
content:
"Policies (for climate change mitigation and adaptation)\r\nStrategies that enable actions to be undertaken to accelerate \r\nadaptation and mitigation. Policies include those developed by \r\nnational and subnational public agencies, and with the private \r\nsector. Policies for adaptation and mitigation often take the form of \r\neconomic incentives, regulatory instruments, and decision-making \r\nand engagement processes."


id: 67aa0e381b2c091045109e0bd9e68861
created_at: 2023-03-16 14:01:53
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index: climate_gpt_only_giec
vector_id: 13902
content:
"Projection A potential future evolution of a  quantity or set of \r\nquantities, often computed with the aid of a model. Unlike predictions, \r\nprojections are conditional on assumptions concerning, for example, \r\nfuture socio-economic and technological developments that may or may \r\nnot be realised. See also Climate projection, Pathways and Scenario."


id: d53254ab6bcab34af59087c6db589e16
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 31072
content:
"Political economy The set of interlinked relationships between \r\npeople, the state, society and markets as defined by law, politics, \r\neconomics, customs and power that determine the outcome of trade \r\nand transactions and the distribution of wealth in a country or economy."


id: ac1f1236a4f00696d97fe611c3cb950d
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 24609
content:
"Prosumers A consumer that also produces energy and inputs \r\nenergy to the system, for which it is an active agent in the energy \r\nsystem and market."


id: 443799f46f971caf4f6891a3b9ac76d5
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 8155
content:
"Radiative forcing The change in the net, downward minus \r\nupward, radiative flux (expressed in W m-2) due to a  change in \r\nan external driver of climate change, such as a  change in the \r\nconcentration of carbon dioxide (CO2 ), the concentration of volcanic \r\naerosols or in the output of the Sun. The stratospherically adjusted \r\nradiative forcing is computed with all tropospheric properties held \r\nfixed at their unperturbed values, and after allowing for stratospheric \r\ntemperatures, if perturbed, to readjust to radiative-dynamical \r\nequilibrium. Radiative forcing is called instantaneous if no change in \r\nstratospheric temperature is accounted for. The radiative forcing once \r\nboth stratospheric and tropospheric adjustments are accounted for is \r\ntermed the 'effective radiative forcing'."


id: 8e51c08bb7116fadf46c7ec91e352d87
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 19916
content:
"Pool, carbon and nitrogen A reservoir in the Earth System where \r\nelements, such as carbon and nitrogen, reside in various chemical forms \r\nfor a period of time. See also Sequestration, Sink, Source and Uptake."


id: d2763097b3ca3dd0e237c416c78ad0ee
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 30641
content:
"Poverty A complex concept with several definitions stemming from \r\ndifferent schools of thought. It can refer to material circumstances \r\n(such as need, pattern of deprivation or limited resources), economic \r\nconditions (such as standard of living, inequality or economic position) \r\nand/or social relationships (such as social class, dependency, exclusion, \r\nlack of basic security or lack of entitlement)."


id: 4aaced51a92be34bada3f6c810de0d80
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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index: climate_gpt_only_giec
vector_id: 9156
content:
"Poverty eradication A set of measures to end poverty in all its \r\nforms everywhere. See also Sustainable Development Goals (SDGs)."


id: 3a4dba328279586b9ee9045cb493f30c
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 6541
content:
"Rebound effect Phenomena whereby the reduction in energy \r\nconsumption or emissions (relative to a baseline) associated with the \r\nimplementation of mitigation measures in a jurisdiction is offset to \r\nsome degree through induced changes in consumption, production, \r\nand prices within the same jurisdiction. The rebound effect is most \r\ntypically ascribed to technological energy efficiency improvements."


id: 70f481d8aa705f1e7c09bedd0c079a39
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 15404
content:
"Resource cascade Tracking resource use (materials, energy, water, \r\netc.), efficiency and losses through all conversion steps from primary \r\nresource extraction to various conversion steps, all the way to final \r\nservice delivery."


id: 8f7ef867cd7dad4fe554c7c1fe8ab956
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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index: climate_gpt_only_giec
vector_id: 20124
content:
"Reducing Emissions from Deforestation and Forest Degradation \r\n(REDD+) REDD+ refers to reducing emissions from deforestation; \r\nreducing emissions from forest degradation; conservation of forest \r\ncarbon stocks; sustainable management of forests; and enhancement \r\nof forest carbon stocks (see UNFCCC decision 1/CP.16, para. 70)."


id: ac054edc1ebf23580fffbbf920b67e60
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 24584
content:
"Risk The potential for adverse consequences for human or \r\necological systems, recognising the diversity of values and objectives \r\nassociated with such systems. In the context of climate change, \r\nrisks can arise from potential impacts of climate change as well as \r\nhuman responses to climate change. Relevant adverse consequences \r\ninclude those on lives, livelihoods, health and well-being, economic, \r\nsocial and cultural assets and investments, infrastructure, services \r\n(including ecosystem services), ecosystems and species."


id: 671fafec207637bb33ff2bb4c0dc10d6
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 13818
content:
"Reference period A time period of interest, or a period over which \r\nsome relevant statistics are calculated. A reference period can be \r\nused as a baseline period or as a comparison to a baseline period."


id: 296c5b2eabebcd71e846a51f8ef44118
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 3956
content:
"A time period against which differences are calculated (e.g., expressed \r\nas anomalies relative to a baseline)."


id: 8225dc5e4636a3228e35e6eb1e71dadb
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 18000
content:
"In the context of climate change impacts, risks result from dynamic \r\ninteractions between climate-related hazards with the exposure\r\nand vulnerability of the affected human or ecological system to the \r\nhazards. Hazards, exposure and vulnerability may each be subject \r\nto uncertainty in terms of magnitude and likelihood of occurrence, \r\nand each may change over time and space due to socio-economic \r\nchanges and human decision-making (see also risk management, \r\nadaptation and mitigation)."


id: e98545182759242e9b7cd66999321e6e
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 34248
content:
"Reforestation Conversion to forest of land that has previously \r\ncontained forests but that has been converted to some other use. "


id: 63e4515ee4d7644580b7a5598bc52904
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 13282
content:
"In the context of climate change responses, risks result from the \r\npotential for such responses not achieving the intended objective(s), \r\nor from potential trade-offs with, or negative side-effects on, other \r\nsocietal objectives, such as the Sustainable Development Goals \r\n(SDGs) (see also risk trade-off). Risks can arise, for example, from \r\nuncertainty in implementation, effectiveness or outcomes of climate \r\npolicy, climate-related investments, technology development or \r\nadoption, and system transitions."


id: 85101e52bd8b803017d57b9123bf9436
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 18459
content:
"See also Anthropogenic removals, Carbon dioxide removal (CDR) \r\nand Reducing Emissions from Deforestation and Forest Degradation \r\n(REDD+)."


id: a0c38ddc36c77d4f74f23483baaba57f
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 22841
content:
"Regenerative agriculture A universally agreed definition of \r\nthis relatively new farming approach has yet to be established, \r\nbut regenerative agriculture broadly refers to the implementation \r\nof varying combinations of agricultural management practices, to \r\nensure the continued restoration and enhancement of soil health, \r\nbiodiversity and ecosystem functioning, in conjunction with profitable \r\nagricultural production."


id: afc63f177666e9cfd7f5e2645eaba918
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 25174
content:
"Region Land and/or ocean area characterised by specific \r\ngeographical and/or climatological features. The climate of a region \r\nemerges from a multi-scale combination of its own features, remote \r\ninfluences from other regions, and global climate conditions."


id: 5f157127bc8e92e8d64107c21e3f7a97
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 12507
content:
"Risk management Plans, actions, strategies or policies to reduce \r\nthe likelihood and/or magnitude of adverse potential consequences, \r\nbased on assessed or perceived risks. See also Risk assessment, and \r\nRisk perception."


id: 5e7cfd9b56a643cf060f131081349c21
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 12403
content:
"Risk perception The subjective judgement that people make about \r\nthe characteristics and severity of a risk. See also Risk assessment,\r\nand Risk management."


id: da54720e4222f48d81b8eeabba626ab7
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 31874
content:
"Renewable energy (RE) Any form of energy that is replenished \r\nby natural processes at a rate that equals or exceeds its rate of use."


id: 34e9b04d08f9481159cc2b79cc2e3bc2
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 5732
content:
"Risk trade-off The change in the portfolio of risks that occurs when \r\na countervailing risk is generated (knowingly or inadvertently) by an \r\nintervention to reduce the target risk (Wiener and Graham 2009)."


id: c9204e850635840ac5a436a37d575b32
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 29206
content:
"Renewable energy sources such as wind and solar energy whose \r\noutput is determined by weather, in contrast to 'dispatchable' \r\ngenerators that adjust their output as a  reaction to economic \r\nincentives. Variable renewables have also been termed intermittent, \r\nfluctuating, or non-dispatchable (Hirth 2013)."


id: ad1feec2980210eedc02ac232b079113
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 24782
content:
"Sea surface temperature (SST) The subsurface bulk temperature \r\nin the top few metres of the ocean, measured by ships, buoys and \r\ndrifters. From ships, measurements of water samples in buckets \r\nwere mostly switched in the 1940s to samples from engine intake \r\nwater. Satellite measurements of skin temperature (uppermost layer; \r\na fraction of a millimetre thick) in the infrared or the top centimetre \r\nor so in the microwave are also used, but must be adjusted to be \r\ncompatible with the bulk temperature."


id: 9683c48975f4f6e10107073a8a846463
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 21201
content:
"Resilience The capacity of interconnected social, economic and \r\necological systems to cope with a hazardous event, trend or disturbance, \r\nresponding or reorganising in ways that maintain their essential \r\nfunction, identity and structure. Resilience is a positive attribute when \r\nit maintains capacity for adaptation, learning and/or transformation \r\n(Arctic Council 2016). See also Hazard, Risk and Vulnerability."


id: efd7ed91f0658002595c6888f6a72e75
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 35240
content:
"Scenario A plausible description of how the future may develop \r\nbased on a  coherent and internally consistent set of assumptions \r\nabout key driving forces (e.g., rate of technological change, prices) "


id: 7a41952cee503c87fe52bd0da87c42ed
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 16801
content:
"and relationships. Note that scenarios are neither predictions nor \r\nforecasts, but are used to provide a  view of the implications of \r\ndevelopments and actions."


id: 7b614b813e1f723f383cccfa44e4706e
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 16998
content:
"Sequestration The process of storing carbon in a carbon pool. See \r\nalso Pool, carbon and nitrogen, Sequestration potential, Sink, Soil \r\ncarbon sequestration (SCS), Source, and Uptake."


id: 18cddf917b0fa69f49cd4fc1be50ab01
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 1405
content:
"A plausible representation of the future development of atmospheric \r\nconcentrations of substances that are radiatively active (e.g., greenhouse \r\ngases, aerosols, tropospheric ozone), plus human-induced land cover \r\nchanges that can be radiatively active via albedo changes, and often \r\nused as input to a climate model to compute climate projections."


id: ccea15bab5a6eeb92222129483d6b215
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 29782
content:
"Sequestration potential The quantity of greenhouse gases that \r\ncan be removed from the atmosphere by anthropogenic enhancement \r\nof sinks and stored in a pool. See Mitigation potential for different \r\nsubcategories of sequestration potential. See also Pool, carbon and \r\nnitrogen, Sequestration, Sink, Source, and Uptake."


id: 1e6cb8444039cd12131b6069abb8a4dd
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 2228
content:
"A plausible representation of the future development of emissions \r\nof substances that are radiatively active (e.g.,  greenhouse gases \r\nor aerosols), plus human-induced land-cover changes that can \r\nbe radiatively active via albedo changes, based on a coherent and \r\ninternally consistent set of assumptions about driving forces (such \r\nas demographic and socio-economic development, technological \r\nchange, energy and land use) and their key relationships. \r\nConcentration scenarios, derived from emission scenarios, are often \r\nused as input to a climate model to compute climate projections."


id: a5515127347c4dfd557f3a88ca5369bb
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 23577
content:
"Service provisioning Various services (such as illumination and \r\nmobility) can be provided by 'systems' through the use of energy, \r\nmaterials, and other resources comprising: (i) Resource flows \r\n(e.g., energy); (ii) Technologies for resource use and energy conversion \r\n(e.g., vehicles and their engines); and (iii) Social/organisational forms \r\nof service delivery (e.g.,  publicly owned companies, or privately \r\nowned companies, e-commerce)."


id: 7baf72a4360cdd3b127f0a537d7ba334
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 17060
content:
"Services Activities that help satisfy human wants or needs. While \r\nthey usually involve relationships between producers and consumers, \r\nservices are less tangible and less storable than goods since they \r\nrepresent flows not stocks, and when their regeneration conditions \r\nare protected they may be reused over time."


id: 8d11aa1c3a690b9651c2d3a241c15e24
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 19723
content:
"A plausible description of the future that describes how the (studied) \r\nsystem responds to the implementation of mitigation policies \r\nand measures."


id: 9a250c975284ec1a2e13e4f26f30d100
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 21776
content:
"Settlements Places of concentrated human habitation. Settlements \r\ncan range from isolated rural villages to urban regions with significant \r\nglobal influence. They can include formally planned and informal or \r\nillegal habitation and related infrastructure. See also Cities and Urban."


id: 6bfbf4b8f5318ffe7963fa423f184ba0
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 14611
content:
"[Note 1: In many types of climate change research, reference scenarios \r\nreflect specific assumptions about patterns of socio-economic development \r\nand may represent futures that assume no climate policies or specified \r\nclimate policies, for example, those in place or planned at the time a study \r\nis carried out. Reference scenarios may also represent futures with limited \r\nor no climate impacts or adaptation, to serve as a point of comparison for \r\nfutures with impacts and adaptation. These are also referred to as 'baseline \r\nscenarios' in the literature. "


id: f9fe5c20cd50c71f0cf8420800faa2b1
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 36843
content:
"Sharing economy A system which allows people to share goods \r\nand services by enabling collaborative use, access or ownership."


id: a153d42e09a94b3cf186a7f8d57d980a
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 22940
content:
"Shifting development pathways (SDPs) In this report, shifting \r\ndevelopment pathways describes transitions aimed at re-directing \r\nexisting developmental trends. Societies may put in place enabling \r\nconditions to influence their future development pathways, when \r\nthey endeavour to achieve certain outcomes. Some outcomes may \r\nbe common, while others may be context-specific, given different \r\nstarting points. See also Development pathways (under Pathways), \r\nand Shifting development pathways to sustainability."


id: 55745ad6ebf4627818cfa934342f7150
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 10919
content:
"Note 2: Reference scenarios can also be climate policy or impact scenarios, \r\nwhich in that case are taken as a  point of comparison to explore the \r\nimplications of other features, for example, of delay, technological options, \r\npolicy design and strategy or to explore the effects of additional impacts and \r\nadaptation beyond those represented in the reference scenario. "


id: b2d293d21e7fde60116f0e080a062b0e
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 25650
content:
"Note 3: The term business as usual scenario has been used to describe \r\na  scenario that assumes no additional policies beyond those currently in \r\nplace, and where patterns of socio-economic development are consistent \r\nwith recent trends. The term is now used less frequently than in the past. "


id: 96d826382cf5592a5dcf8078f341703f
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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index: climate_gpt_only_giec
vector_id: 21247
content:
"Shifting development pathways to sustainability Shifting \r\ndevelopment pathways to sustainability involves transitions aligned \r\nwith a  shared aspiration in the Sustainable Development Goals \r\n(SDGs) agreed globally, though sustainability may be interpreted \r\ndifferently in various contexts as societies pursue a  variety of \r\nsustainable development objectives. See also Development pathways \r\n(under Pathways), and Shifting development pathways (SDPs)."


id: 7de603a1cc60473a88a3d57b8f666232
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 17382
content:
"Note 4: In climate change attribution or impact attribution research reference \r\nscenarios may refer to counterfactual historical scenarios assuming no \r\nanthropogenic greenhouse gas (GHG) emissions (climate change attribution) \r\nor no climate change (impact attribution).]"


id: 556b87d5590dd3460e076d2f4c42a3b4
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 10911
content:
"Social costs The full costs of an action in terms of social welfare \r\nlosses, including external costs associated with the impacts of this \r\naction on the environment, the economy (GDP, employment) and on \r\nthe society as a whole."


id: 6c2211350fa0892df9b76d8196c20c6e
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 14643
content:
"Social group A collective of people who share similar characteristics \r\nand collectively may have a sense of unity (Forsyth 2010)."


id: 3d2ac5dea7ee5823fbd8e97885dcd873
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 6988
content:
"Social identity The portion of an individual's self-concept derived \r\nfrom perceived membership in a  relevant social group (Tajfel and \r\nTurner 1986)."


id: 7167fe03dffa09cfba2512e4f1081192
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 15479
content:
"Social inclusion A process of improving the terms of participation \r\nin society, particularly for people who are disadvantaged, through \r\nenhancing opportunities, access to resources, and respect for rights \r\n(UNDESA 2018)."


id: a77c8ee662c89ccb98139373dc2b0240
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 23857
content:
"Simple climate model (SCM) A broad class of lower-dimensional \r\nmodels of the energy balance, radiative transfer, carbon cycle, or \r\na combination of such physical components. SCMs are also suitable \r\nfor performing emulations of climate-mean variables of Earth System \r\nModels (ESMs), given that their structural flexibility can capture both \r\nthe parametric and structural uncertainties across process-oriented \r\nESM responses. They can also be used to test consistency across \r\nmultiple lines of evidence with regard to climate sensitivity ranges, \r\ntransient climate responses (TCRs), transient climate response to \r\ncumulative CO2 emissions (TCREs) and carbon cycle feedbacks. \r\nSee also Emulators."


id: 55431cb9d00740a652cd8602d4921cf5
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 10880
content:
"Social learning A process of social interaction through which \r\npeople learn new behaviours, capacities, values, and attitudes."


id: 10b22e94d9e5a0d75f90f94327eaf2ff
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 101
content:
"Social-ecological system An integrated system that includes \r\nhuman societies and ecosystems, in which humans are part of \r\nnature. The functions of such a system arise from the interactions and \r\ninterdependence of the social and ecological subsystems. The system's \r\nstructure is characterised by reciprocal feedbacks, emphasising that \r\nhumans must be seen as a part of, not apart from, nature (Berkes and \r\nFolke 1998; Arctic Council 2016)."


id: 9ef6c331a85bb1bf78dcce162b25b809
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 22554
content:
"Sink Any process, activity or mechanism which removes \r\na  greenhouse gas, an aerosol or a  precursor of a  greenhouse gas\r\nfrom the atmosphere (UNFCCC Article 1.8 (UNFCCC 1992)). See also \r\nPool, carbon and nitrogen, Sequestration, Source and Uptake."


id: f77cba620dd4a9630bad92ea4a2ebf57
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 36448
content:
"Socio-technical transitions Where technological change is \r\nassociated with social systems and the two are inextricably linked."


id: 4f1e32ad55e4a09c34a106545d1037ed
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 9890
content:
"Soil carbon sequestration (SCS) Land management changes \r\nwhich increase the soil organic carbon content, resulting in a  net \r\nremoval of carbon dioxide (CO2 ) from the atmosphere. See also \r\nAnthropogenic removals and Carbon dioxide removal (CDR)."


id: b1d47303cf343dc0a0d172ddd4bf330b
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 25486
content:
"Small Island Developing States (SIDS) SIDS, as recognised by \r\nthe United Nations OHRLLS (UN Office of the High Representative for \r\nthe Least Developed Countries, Landlocked Developing Countries and \r\nSmall Island Developing States), are a distinct group of developing \r\ncountries facing specific social, economic and environmental \r\nvulnerabilities (UN-OHRLLS 2011). They were recognised as a special \r\ncase, both for their environment and development, at the Rio Earth \r\nSummit in Brazil in 1992. Fifty-eight countries and territories are \r\npresently classified as SIDS by the UN OHRLLS, with 38 being UN \r\nmember states and 20 being non-UN members or associate members \r\nof the Regional Commissions (UN-OHRLLS 2018)."


id: 8f5189e1f162ee95d1bcfc8a0dd3a391
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 20096
content:
"Soil organic matter The organic component of soil, comprising \r\nplant and animal residue at various stages of decomposition, and \r\nsoil organisms."


id: bf73489c22c4d246f3d283831d05a972
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 27677
content:
"Solar energy Energy from the Sun. Often the phrase is used to \r\nmean energy that is captured from solar radiation either as heat, as \r\nlight that is converted into chemical energy by natural or artificial \r\nphotosynthesis, or by photovoltaic panels and converted directly into \r\nelectricity. See also Renewable energy."


id: 9d7f026c73d5b32211d5c45164db31ef
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 22311
content:
"Smart grids A smart grid uses information and communications \r\ntechnology to gather data on the behaviours of suppliers and \r\nconsumers in the production, distribution, and use of electricity. \r\nThrough automated responses or the provision of price signals, this \r\ninformation can then be used to improve the efficiency, reliability, \r\neconomics, and sustainability of the electricity network."


id: 94a80b48d318586a246839415ab17e45
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 20898
content:
"Solar radiation modification (SRM) Refers to a  range of \r\nradiation modification measures not related to greenhouse gas \r\n(GHG) mitigation that seek to limit global warming. Most methods \r\ninvolve reducing the amount of incoming solar radiation reaching \r\nthe surface, but others also act on the longwave radiation budget by \r\nreducing optical thickness and cloud lifetime."


id: d23b3a352c5c72f9ec45968acb3ec0c1
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 30604
content:
"economic and social functions, at local, national, and global levels, and \r\nthat does not cause damage to other ecosystems (Forest Europe 1993)."


id: 4b8b7ed97a56c9761c465539ca19b912
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 9308
content:
"(UNFCCC Article 1.9 (UNFCCC 1992)). See also Sink, Pool, carbon and \r\nnitrogen, Sequestration, Sequestration Potential and Uptake."


id: 9823aaaea965740920eb117bf52c06f6
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 21446
content:
"Sustainable intensification (of agriculture) Increasing yields \r\nfrom the same area of land while decreasing negative environmental \r\nimpacts of agricultural production and increasing the provision of \r\nenvironmental services (CGIAR 2019). "


id: 4a131f9ee2e675f6d45b4cce856e12b9
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 9058
content:
"Spill-over effect The effects of domestic or sector mitigation \r\nmeasures on other countries or sectors. Spill-over effects can be \r\npositive or negative and include effects on trade, (carbon) leakage, \r\ntransfer of innovations, and diffusion of environmentally sound \r\ntechnology and other issues."


id: f1ce56b5a3a3a1df26a1b9d80544dbc0
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 35556
content:
"[Note: This definition is based on the concept of meeting demand from a finite \r\nland area, but it is scale-dependent. Sustainable intensification at a  given \r\nscale (e.g., global or national) may require a decrease in production intensity \r\nat smaller scales and, in particular, places (often associated with previous, \r\nunsustainable, intensification) to achieve sustainability (Garnett et al. 2013).]"


id: 188b0c34588164d3a33f462c14c0536f
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 1356
content:
"Storyline A way of making sense of a situation or a series of events \r\nthrough the construction of a set of explanatory elements. Usually, \r\nit is built on logical or causal reasoning. In climate research, the \r\nterm storyline is used both in connection to scenarios as related to \r\na future trajectory of the climate and human systems or to a weather \r\nor climate event. In this context, storylines can be used to describe \r\nplural, conditional possible futures or explanations of a  current \r\nsituation, in contrast to single, definitive futures or explanations."


id: bfb7b876c4b88d58e3cf5bf10d784b2b
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 27722
content:
"Sustainable land management The stewardship and use of \r\nland resources, including soils, water, animals and plants, to meet \r\nchanging human needs, while simultaneously ensuring the long-term \r\nproductive potential of these resources and the maintenance of their \r\nenvironmental functions (WOCAT, no date)."


id: 90c2d906e0eb7c4de74a3500deb8a762
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 20318
content:
"A narrative description of a  scenario (or family of scenarios), \r\nhighlighting the main scenario characteristics, relationships between \r\nkey driving forces and the dynamics of their evolution."


id: 7da6f52e1be2d67aaeb79abc930a1285
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 17343
content:
"Systems of Innovation (SI) The set of public and private \r\nsector organisations (i.e.,  formally organised entities such as firms \r\nand universities; 'actors') and institutions, whose activities and \r\ninteractions generate, modify and deploy new technologies. The SI \r\napproach has been used to understand and analyse innovation at \r\nthe national, regional, and technological levels, and in transnational \r\ncontexts (Lundvall 1992, 1988)."


id: 64e9ea56a910da1561823fbf184055f7
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 13445
content:
"Stranded assets Assets exposed to devaluations or conversion to \r\n'liabilities' because of unanticipated changes in their initially expected \r\nrevenues due to innovations and/or evolutions of the business \r\ncontext, including changes in public regulations at the domestic and \r\ninternational levels."


id: 4ced37754ad990592724dc7334e27cb0
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 9525
content:
"Technology deployment The act of bringing technology into effective \r\napplication, involving a set of actors and activities to initiate, facilitate \r\nand/or support its implementation. See also Technology diffusion."


id: ed2feb2116b230270ccdb881324d52e9
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 34794
content:
"Subnational actors State/provincial, regional, metropolitan and \r\nlocal/municipal governments as well as non-party stakeholders, \r\nsuch as civil society, the private sector, cities and other subnational \r\nauthorities, local communities and indigenous peoples."


id: 4bb965eabacead85e73ddbe1117b76ea
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 9328
content:
"Technology diffusion The spread of a technology across different \r\ngroups users/markets over time. See also Technology deployment and \r\nTechnology transfer."


id: 351e996b127afbeb991276f2dee99b64
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 5766
content:
"Sufficiency A set of measures and daily practices that avoid \r\ndemand for energy, materials, land and water while delivering human \r\nwell-being for all within planetary boundaries."


id: d89fd224ed27451030cfbfcb8273d583
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 31595
content:
"Technology transfer The exchange of knowledge, hardware and \r\nassociated software, money and goods among stakeholders, which \r\nleads to the spread of technology for adaptation or mitigation. The \r\nterm encompasses both diffusion of technologies and technological \r\ncooperation across and within countries. See also Technology diffusion."


id: f0219ac2f6d12e7034c1ea03c89f40f8
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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index: climate_gpt_only_giec
vector_id: 35284
content:
"Sustainability A dynamic process that guarantees the persistence \r\nof natural and human systems in an equitable manner."


id: eea53b2a57051934475360d7ba4a39ee
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 35019
content:
"Sustainable development (SD) Development that meets the \r\nneeds of the present without compromising the ability of future \r\ngenerations to meet their own needs (WCED 1987) and balances \r\nsocial, economic and environmental concerns. See also Development \r\npathways and Sustainable Development Goals (SDGs)."


id: 45ea31bd95215e7411c280200066656f
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 8385
content:
"Teleconnection Association between climate variables at widely \r\nseparated, geographically fixed locations related to each other through \r\nphysical processes and oceanic and/or atmospheric dynamical pathways. \r\nTeleconnections can be caused by several climate phenomena, such as \r\nRossby wave-trains, mid-latitude jet and storm track displacements, \r\nfluctuations of the Atlantic Meridional Overturning Circulation (AMOC),\r\nfluctuations of the Walker circulation, etc. They can be initiated by \r\nmodes of climate variability, thus providing the development of remote \r\nclimate anomalies at various temporal lags."


id: 9af4440ee0b5a72c76e2273aac5fc26f
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 21915
content:
"Sustainable Development Goals (SDGs) The 17 global goals \r\nfor development for all countries established by the United Nations \r\nthrough a participatory process and elaborated in the 2030 Agenda \r\nfor Sustainable Development, including ending poverty and hunger; \r\nensuring health and well-being, education, gender equality, clean \r\nwater and energy, and decent work; building and ensuring resilient \r\nand sustainable infrastructure, cities and consumption; reducing \r\ninequalities; protecting land and water ecosystems; promoting peace, \r\njustice and partnerships; and taking urgent action on climate change. \r\nSee also Sustainable development."


id: ed2b700eabc3fd98cce50a8c92aeb362
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 34790
content:
"Temperature overshoot Exceedance of a specified global warming \r\nlevel, followed by a decline to or below that level during a specified \r\nperiod of time (e.g.,  before 2100). Sometimes the magnitude and \r\nlikelihood of the overshoot is also characterised. The overshoot duration \r\ncan vary from one pathway to the next, but in most overshoot pathways\r\nin the literature and as referred to as overshoot pathways in the AR6, \r\nthe overshoot occurs over a period of at least one decade and up to \r\nseveral decades."


id: 2285d16c7d94b617f0050aa320ed95f3
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 2883
content:
"example, in the Earth System from the atmosphere to the ocean \r\nor to the land. See also Pool, carbon and nitrogen, Sequestration,\r\nSequestration potential, Sink and Source."


id: 7958a79a592597380a87fee385b0b58a
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 16661
content:
"Tipping point A critical threshold beyond which a  system \r\nreorganises, often abruptly and/or irreversibly. See also Irreversibility."


id: 8a701e82a23ae22272c40b1de3977f21
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 19315
content:
"Trade-off A competition between different objectives within \r\na  decision situation, where pursuing one objective will diminish \r\nachievement of other objective(s). A trade-off exists when a policy \r\nor measure aimed at one objective (e.g., reducing greenhouse gas\r\nemissions) reduces outcomes for other objective(s) (e.g., biodiversity\r\nconservation, energy security) due to adverse side effects, thereby \r\npotentially reducing the net benefit to society or the environment. \r\nSee also Co-benefit."


id: 68fbf9f5d0ff4ad15e32539b7e453b95
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 14111
content:
"Urban heat island The relative warmth of a city compared with \r\nsurrounding rural areas, associated with heat trapping due to land \r\nuse, the configuration and design of the built environment, including \r\nstreet layout and building size, the heat-absorbing properties of urban \r\nbuilding materials, reduced ventilation, reduced greenery and water \r\nfeatures, and domestic and industrial heat emissions generated directly \r\nfrom human activities. See also City region, Urban, and Urban System."


id: 38d4174e9d9b81d69d3b9683687c89c8
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 6311
content:
"Transition The process of changing from one state or condition to \r\nanother in a given period of time. Transition can occur in individuals, \r\nfirms, cities, regions and nations, and can be based on incremental or \r\ntransformative change."


id: 6c2a6323f5cd57c85cbb1fd045f6c422
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 14647
content:
"Uncertainty A state of incomplete knowledge that can result from \r\na lack of information or from disagreement about what is known or \r\neven knowable. It may have many types of sources, from imprecision \r\nin the data to ambiguously defined concepts or terminology, \r\nincomplete understanding of critical processes, or  uncertain \r\nprojections of human behaviour. Uncertainty can therefore be \r\nrepresented by quantitative measures (e.g.,  a  probability density \r\nfunction) or by qualitative statements (e.g., reflecting the judgement \r\nof a team of experts) (Moss and Schneider 2000; Mastrandrea et al. \r\n2010). See also Confidence and Likelihood."


id: e14a1f5aab1dd6e61e170ffda9eadb2a
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 32938
content:
"Urban Systems Urban systems refer to two interconnected systems: \r\nfirst, the comprehensive collections of city elements with multiple \r\ndimensions and characteristics: a) encompass physical, built, socio\u0002economic-technical, political, and ecological subsystems; b) integrate \r\nsocial agent/constituency/processes with physical structure \r\nand processes; and c) exist within broader spatial and temporal scales \r\nand governance and institutional contexts; and second, the global \r\nsystem of cities and towns. See also City region, and Urban."


id: 11911d67221499cdc8ad263ec6460fb7
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 255
content:
"United Nations Convention to Combat Desertification (UNCCD)\r\nA legally binding international agreement linking environment and \r\ndevelopment to sustainable land management, established in 1994. \r\nThe Convention's objective is 'to combat desertification and mitigate \r\nthe effects of drought in countries experiencing drought and/or \r\ndesertification'. The Convention specifically addresses the arid, \r\nsemi-arid and dry sub-humid areas, known as the drylands, and has \r\na particular focus on Africa. As of September 2020, the UNCCD had \r\n197 Parties. See also Desertification, Drought and Land degradation."


id: a46f87101c25610866c6eddd600385ca
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 23430
content:
"Urbanisation Urbanisation is a  multi-dimensional process that \r\ninvolves at least three simultaneous changes: (i) land-use change: \r\ntransformation of formerly rural settlements or natural land into urban \r\nsettlements; (ii) demographic change: a shift in the spatial distribution \r\nof a population from rural to urban areas; and (iii) infrastructure change: \r\nan increase in provision of infrastructure services including electricity, \r\nsanitation, etc. Urbanisation often includes changes in lifestyle, culture, \r\nand behaviour, and thus alters the demographic, economic, and social \r\nstructure of both urban and rural areas (Stokes and Seto 2019; Seto \r\net al. 2014; UNDESA 2018). See also Urban, and Urban Systems."


id: 72374e0d4848a1bd3085b0f00da18971
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 15592
content:
"United Nations Framework Convention on Climate Change \r\n(UNFCCC) The UNFCCC was adopted in May 1992 and opened for \r\nsignature at the 1992 Earth Summit in Rio de Janeiro. It entered into \r\nforce in March 1994 and, as of September 2020, had 197 Parties \r\n(196 States and the European Union). The Convention's ultimate \r\nobjective is the 'stabilisation of greenhouse gas concentrations in the \r\natmosphere at a level that would prevent dangerous anthropogenic \r\ninterference with the climate system' (UNFCCC 1992). The provisions \r\nof the Convention are pursued and implemented by two further \r\ntreaties: the Kyoto Protocol and the Paris Agreement."


id: 15b9f2a2af61e90d2b7055ee4851d78e
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 938
content:
"Vulnerability The propensity or predisposition to be adversely \r\naffected. Vulnerability encompasses a  variety of concepts and \r\nelements including sensitivity or susceptibility to harm and lack of \r\ncapacity to cope and adapt. See also Exposure, Hazard and Risk."


id: f755739944f867ee5abf8f1dc8d2fee4
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 36431
content:
"Well-being A state of existence that fulfils various human needs, \r\nincluding material living conditions, meaningful social and community \r\nrelationships and quality of life, as well as the ability to pursue one's \r\ngoals, to thrive, and feel satisfied with one's life. Ecosystem well-being \r\nrefers to the ability of ecosystems to maintain their diversity and quality."


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created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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index: climate_gpt_only_giec
vector_id: 16747
content:
"Uptake The transfer of substances (such as carbon) or energy \r\n(e.g.,  heat) from one compartment of a  system to another; for "


id: a2902f007eba1f366710b12256d35d7f
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 23138
content:
"Relational well-being concept based on the premise that experiencing \r\nlife purpose, challenges and growth leads to flourishing, self-realisation, \r\npersonal expression, and full functioning (Niemiec 2014; Lamb and \r\nSteinberger 2017)."


id: c0a3fa145cfe7c4ced8b8261f2beb6d5
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 27873
content:
"Subjective well-being concept based on the idea that attaining \r\npleasure and avoiding pain leads to happiness (Ryan and Deci 2001)."


id: ddc87dbdc221d82f1166479926ea62c
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 32390
content:
"Wind energy Kinetic energy from airflow arising from the uneven \r\nheating of the Earth's surface. The wind's kinetic energy is converted \r\nto mechanical shaft energy and electricity by a  wind turbine, \r\na rotating machine. A wind farm, wind project, wind park, or wind \r\npower plant is a group of wind turbines interconnected to a common \r\nutility system through a system of transformers, distribution lines, and \r\n(usually) one substation. See also Renewable energy."


id: aba25b71227a7997db8d22d8597af123
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 24526
content:
"Wiener, J.B. and J.D. Graham, 2009: Risk vs Risk: Tradeoffs in Protecting \r\nHealth and the Environment. [Wiener, J.B. and J.D. Graham (eds.)]. Harvard \r\nUniversity Press, Cambridge, MA, USA, 352 pp."


id: badff907fb70888eb7a4691fb1cb340c
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 26940
content:
"WOCAT, [undated]: Glossary. World Overview of Conservation Approaches \r\nand Technologies (WOCAT). https://www.wocat.net/en/glossary."


id: 5c10ba72aafef54cb69e5c884bc3771f
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 11994
content:
"Renee van Diemen (the Netherlands/United Kingdom), William F. Lamb (Germany/United \r\nKingdom), Minal Pathak (India), Andy Reisinger (New Zealand), Stephane de la Rue du Can \r\n(the United States of America), Jim Skea (United Kingdom), Raphael Slade (United Kingdom), \r\nShreya Some (India), Linda Steg (the Netherlands)"


id: 9b4210b02f58655439951c22853f917
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 21969
content:
"IPCC, 2022: Annex II: Definitions, Units and Conventions [Al Khourdajie, A., R. van Diemen, W.F. Lamb, M. Pathak, \r\nA. Reisinger, S. de la Rue du Can, J. Skea, R. Slade, S. Some, L. Steg (eds)]. In IPCC, 2022: Climate Change 2022: Mitigation \r\nof Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on \r\nClimate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, \r\nR. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)]. Cambridge University Press, Cambridge, UK and \r\nNew York, NY, USA. doi: 10.1017/9781009157926.021."


id: 49ee97162f6aed0b7864ce300e4e14a9
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 9036
content:
"in Eastern Europe which are not members of the European Union \r\nare allocated to Eastern Europe and West-Central Asia (EEA). In AR5, \r\nthese formed part of the Economies in Transition group. The remainder \r\nof Western Asia (non-Annex I) is allocated to the Middle East."


id: de99238542d595f0c64d70396b82f6eb
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 32521
content:
"This annex on Definitions, Units and Conventions provides background \r\ninformation on material used in the Working Group III contribution \r\nto the Intergovernmental Panel on Climate Change (IPCC) Sixth \r\nAssessment Report (AR6 WGIII). The material presented in this annex \r\ndocuments metrics and common datasets that are typically used \r\nacross multiple chapters of the report. In a few instances there are \r\nno updates to what was adopted by WGIII during the production of \r\nthe Fifth Assessment Report (AR5), in which case this annex refers to \r\nAnnex II of AR5 (Krey et al. 2014)."


id: 81935913db1c151ce70d944bf5c2f381
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 17920
content:
"Following the practice of the UN Statistics Division, we note that the \r\ndesignations employed and the presentation of material in this report \r\ndo not imply the expression of any opinion by the United Nations, the \r\nIPCC or the authors of this report concerning the legal status of any \r\ncountry, territory, city or area or of its authorities, or concerning the \r\ndelimitation of its frontiers or boundaries. The term 'country' as used \r\nin this material also refers, as appropriate, to territories or areas. "


id: d413fa54d0ac63a5a687c31eadb59598
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 30884
content:
"The annex comprises four parts: Part I introduces standards, metrics \r\nand common definitions adopted in the report; Part II presents \r\nmethods to derive or calculate certain quantities and identities used \r\nin the report; Part III provides more detailed background information \r\nabout common data sources; and Part IV presents integrative \r\nmethodologies used in the assessment. While this structure may help \r\nreaders to navigate through the annex, it is not possible in all cases to \r\nunambiguously assign a certain topic to one of these parts, naturally \r\nleading to some overlap between the parts."


id: 6e318ea51e3c7ed7f179ee2838cf5cf5
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 14955
content:
"Africa: Algeria, Angola, Benin, Botswana, Burkina Faso, Burundi, \r\nCote d'Ivoire, Cabo Verde, Cameroon, the Central African Republic, \r\nChad, the Comoros, the Congo, the Democratic Republic of the Congo, \r\nDjibouti, Egypt, Equatorial Guinea, Eritrea, Eswatini, Ethiopia, Gabon, \r\nthe Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, \r\nLibya, Madagascar, Malawi, Mali, Mauritania, Mauritius, Morocco, \r\nMozambique, Namibia, the Niger, Nigeria, Rwanda, Sao Tome and \r\nPrincipe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, the \r\nSouth Sudan, the Sudan, Togo, Tunisia, Uganda, the United Republic \r\nof Tanzania, Zambia, Zimbabwe."


id: b338c196b3780d3e63505e15976ef930
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 25712
content:
"In this report, two different levels of classification are used as a standard \r\nto present the results of analysis. The basis for the classification is the \r\nUN Statistics Division Standard Country or Area Codes for Statistical Use, \r\nalso known as the M49 Standard (UNSD 1999). This covers geographical \r\nregions and, at the time of the literature cut-off date, identified \r\ndeveloped regions, developing regions and least developed countries."


id: 735f0a7b561bf97fbe06e54c52990c82
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 15776
content:
"Middle East: Bahrain, Islamic Republic of Iran, Iraq, Israel, Jordan, \r\nKuwait, Lebanon, Oman, Qatar, Saudi Arabia, the State of Palestine, \r\nthe Syrian Arab Republic, the United Arab Emirates, Yemen."


id: e3d2b023560871484146eb4472838c5c
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 33320
content:
"Latin America and Caribbean: Antigua and Barbuda, Argentina, \r\nthe Bahamas, Barbados, Belize, Plurinational State of Bolivia, Brazil, \r\nChile, Colombia, Costa Rica, Cuba, Dominica, the Dominican Republic, \r\nEcuador, El Salvador, Grenada, Guatemala, Guyana, Haiti, Honduras, \r\nJamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Saint Kitts \r\nand Nevis, Saint Lucia, Saint Vincent and the Grenadines, Suriname, \r\nTrinidad and Tobago, Uruguay, Bolivarian Republic of Venezuela."


id: aea8f6715f21e56e2851f95565572c4e
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 25009
content:
"The high-level classification has six categories (Table 1): one covering \r\nNorth America, Europe, and Australia, Japan and New Zealand, \r\nlabelled 'developed countries', and five covering other countries, \r\nall classified as developing using the M49 standard at the cut\u0002off date. The high-level classification is an expansion of the RC5 \r\n(Regional Categorisation 5) adopted in AR5 WGIII, with Africa and \r\nthe Middle East now identified separately. The low-level classification \r\n(ten categories) divides developed countries into three geographical \r\nregions, and Asia and Pacific into three sub-regions. "


id: 234c4f4ade4edfa8b34e71f984fa48cd
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 3005
content:
"Eastern Asia: China, the Republic of Korea, the Democratic People's \r\nRepublic of Korea, Mongolia."


id: b38149156db294389bd4f92b20ffd0a
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 25751
content:
"The high- and low-level classification schemes reflect schemes used \r\nin many global models and statistical sources. Where the report \r\nsynthesises data, only these standard classification schemes have been \r\nused. On occasions, the underlying literature may deviate from the \r\nstandard classification scheme and direct citations may unavoidably \r\nrefer to alternative classifications. This is dealt with on a case-by-case \r\nbasis and does not imply any endorsement of the scheme used in the \r\nunderlying literature by the IPCC or the authors of this report. "


id: 631c704628f49fbcd21904eedb928171
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 13161
content:
"South-East Asia and Pacific: Brunei Darussalam, Cambodia, \r\nCook  Islands, Fiji, Indonesia, Kiribati, the Lao People's Democratic \r\nRepublic, Malaysia, the Marshall Islands, Federated States of \r\nMicronesia, Myanmar, Nauru, Niue, Palau, Papua New Guinea, the \r\nPhilippines, Samoa, Singapore, Solomon Islands, Thailand, Timor-Leste, \r\nTonga, Tuvalu, Vanuatu, Viet Nam."


id: b7b0b8508b9ce9f7e084201ca24f82b0
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 26431
content:
"The detailed allocation of countries and areas to the low-level \r\nclassification is shown in Section 1.1. Following AR5, the classification \r\nscheme deviates from the UN regional classification with the result \r\nthat Annex I, Annex II and non-Annex I countries as defined under \r\nthe UN Framework Convention on Climate Change (UNFCCC) are \r\ndistinguished. Some Annex I countries in Western Asia and countries "


id: d5704efbc7aba85ea42557a446e1d263
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 31103
content:
"North Macedonia, Norway, Poland, Portugal, Romania, San Marino, \r\nSerbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, the \r\nUnited Kingdom of Great Britain and Northern Ireland."


id: 1af7a4267b23e6a78eb00979ff700579
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 1715
content:
"Eastern Europe and West-Central Asia: Armenia, Azerbaijan, \r\nBelarus, Georgia, Kazakhstan, Kyrgyzstan, the Republic of Moldova, \r\nthe Russian Federation, Tajikistan, Turkmenistan, Ukraine, Uzbekistan."


id: 708e94368b843f7891069145a5910398
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 15327
content:
"The following sections introduce standard units and unit conversions \r\nused throughout this report."


id: b0beaf788a17175bf690a88ce8083a02
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 25327
content:
"Standard units of measurements include Systeme International (SI) \r\nunits, SI-derived units, and other non-SI units as well the standard \r\nprefixes for basic physical units. "


id: fe4105ce4d3addabab8729813931bf7b
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 37533
content:
"Note that all monetary and monetary-related units are expressed in constant US \r\nDollar 2015 (USD2015) or constant International Dollar 2015 (Int$2015)."


id: d9ee651902af9c73833f98f19dff6670
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 31807
content:
"In addition to the above physical units, datasets often report carbon \r\nemissions in either units of carbon (C) or carbon dioxide (CO2). In this \r\nreport we report carbon dioxide (CO2) emissions where possible, \r\nusing the conversion factor (44/12) to convert from units of C into \r\nCO2. Finally, we note that the conversion from GJ to kWh is as \r\nfollows: 1 GJ = ~277.78 kWh."


id: 48ce36ce4fa247d083c427b372df8616
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 8832
content:
"Where aggregate greenhouse gas emissions are reported, this report \r\nuses the Global Warming Potential with a time horizon of 100 years \r\n(GWP100); for details see Section 8."


id: e426aa550c618761a2e9f92e5d640f42
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 33375
content:
"To achieve comparability across cost und price information from \r\ndifferent regions, where possible monetary quantities reported in the \r\nAR6 WGIII have been expressed in constant US Dollar 2015 (USD2015) \r\nor constant International Dollar 2015 (Int$2015), as suitable. "


id: d30363aac671aa12ca0940fb72d9466e
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 30721
content:
"To facilitate a consistent monetary unit conversion process, a simple \r\nand transparent procedure to convert different monetary units from \r\nthe literature to USD2015 is established and described below."


id: cefd138b1b810ea6b3fc3052f403643d
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 30096
content:
"In practice, the order of applying these two steps will lead to different \r\nresults. In this report, the conversion route adopted is LCUx -\r\nLCU2015 - USD2015, i.e., national or regional deflators are used \r\nto measure country- or region-specific inflation between year X and \r\n2015 in local currency, then current (2015) exchange rates are used \r\nto convert to USD2015. The reason for adopting this route is when \r\nthe economy's GDP deflator is used to convert to a common base \r\nyear, that is, 2015, it captures the changes in prices of all goods \r\nand services that the economy produces. To convert from LCU2015 \r\nto USD2015, the official 2015 exchange rates are used. Note that \r\nexchange rates often fluctuate significantly in the short term."


id: 291f7cf787f8052522e5cb7b775451a0
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 3902
content:
"The levelised cost of energy (LCOE) can be defined as the unique \r\nbreak-even cost-price where discounted revenues (price x quantities) \r\nare equal to the discounted net expenses (Moomaw et  al. 2011), \r\nwhich is expressed as follows:"


id: 41d34aa4f71227da7df1d502d30ce61b
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 7760
content:
"In order to be consistent with the choice of the World Bank databases \r\nas the primary source for gross domestic product (GDP) and other \r\nfinancial data throughout the report, deflators and exchange rates \r\nfrom the World Bank Development Indicators are used.3"


id: 97374410daf900d5dc134ec5f05cb8d1
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 21305
content:
"where Et is the energy delivered in year t (might vary from year to \r\nyear), expenses cover all (net) expenses in the year t, i is the discount \r\nrate and n the lifetime of the project."


id: 34fced6fe8dff07c59d9dd497a82ebab
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 5747
content:
"The lifetime expenses comprise investment costs I, operation and \r\nmaintenance cost O&M (including waste management costs), fuel \r\ncosts F, carbon costs C, and decommissioning costs D. In this case, \r\nlevelised cost can be determined by (IEA 2010):"


id: e40d5b75dfc00df7cd36897bd95fa8a9
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 33361
content:
"GDP2015 (in LCU2015 prices) = GDP2010 (in LCU2010 prices) \r\n* \"#$!\"#\" %&' ()*+,-./\r\n\"#$!\"#$ %&' ()*+,-./"


id: 30059b90791e46803f0555535588326e
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 4983
content:
"to the usage of the baseline technology; and CRF is the capital \r\nrecovery factor depending on the discount rate and the lifetime of \r\nthe measure in years as defined above. It should be stressed once \r\nmore that this equation is only valid if DO&M and DE are constant \r\nover the project lifetime. As LCCE are designed to be compared with \r\ncomplementary levelised cost of energy supply, they do not include \r\nthe annual fuel cost difference. Any additional monetary benefits that \r\nare associated with the energy-saving measure must be taken into \r\naccount as part of the O&M difference. "


id: cc16278aa8747f6e25a86835d947f1ad
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 29652
content:
"where !\"# = \r\n1 - (1 - %)!\" is the capital recovery factor and \r\nNPV the net present value of all lifetime expenditures (Suerkemper \r\net al. 2012). "


id: a2f2e35e913b0466b671143eea38c9c9
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 23203
content:
"The levelised cost of conserved carbon can be used for comparing \r\nmitigation costs per  unit of avoided carbon emissions and \r\ncomparing  these specific emission reduction costs for different \r\noptions. This concept can be applied to other pollutants."


id: 59b341727da152a8a1ba342525ffc92b
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 11612
content:
"Where I is the upfront investment, O&M are the annual operation \r\nand maintenance costs, F are the annual fuel costs, and E is the \r\nannual energy provision. The investment I should be interpreted \r\nas the sum of all capital expenditures needed to make the \r\ninvestment fully operational discounted to t = 0. These might \r\ninclude discounted retrofit payments during the project lifetime and \r\ndiscounted decommissioning costs at the end of the lifetime. Where \r\napplicable, annual O&M costs have to take into account revenues for \r\nby-products and existing carbon costs must be added or treated as \r\npart of the annual fuel costs.\r\n!\"\"\" = \"$%. '()(+!,-"


id: 3e2f4647a18fb556e4ce2d0d146fdd
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 7153
content:
"The levelised cost of conserved energy (LCCE) annualises the \r\ninvestment and operation and maintenance cost differences between \r\na baseline technology and the energy-efficient alternative and divides \r\nthis quantity by the annual energy savings. "


id: 3fbf4e5acc30b87e5612a04ba2bebf78
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 7415
content:
"/0-1/ #34/56/6)\r\n,#\r\nwhere DI is the difference in investment costs of a mitigation \r\nmeasure (e.g., in USD) as compared to a baseline investment; DO&M\r\nis the difference in annual operation and maintenance costs (e.g., in \r\nUSD) and DB denotes the annual benefits, all compared to a baseline \r\nfor which the option is not implemented. Note that annual benefits \r\ninclude reduced expenditures for fuels, if the investment project \r\nreduces emissions via a reduction in fuel use. As such LCCC depend \r\non energy prices. An important characteristic of this equation is that \r\nLCCC can become negative if DB is bigger than the sum of the other \r\ntwo terms in the numerator."


id: b089e6b517e2681246dbbf84930d7463
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 25295
content:
"The conceptual formula for LCCE is essentially the same as Equation \r\n(4) above, with DE measuring in this context the amount of energy \r\nsaved annually (Suerkemper et al. 2012):"


id: bf3322290d15a517913884187675366e
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 27633
content:
"In order to ensure consistency throughout the reported growth rates \r\nfor emissions in AR6 WGIII, this section establishes the convention for \r\ncalculating these rates."


id: faf4c0aaf1b13e609dbe5a1ad1b47cd5
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 37002
content:
"where DI is the difference in investment costs of an energy saving \r\nmeasure (e.g., in USD) as compared to a baseline investment; DO&M\r\nis the difference in annual operation and maintenance costs of an \r\nenergy saving measure (e.g., in USD) as compared to the baseline \r\nin which the energy-saving measure is not implemented; DE is the \r\nannual energy conserved by the measure (e.g., in kWh) as compared "


id: a21e9038825b8f7376567fb4e534c82c
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 23068
content:
"In order to undertake a timeseries calculation over a decade, the \r\n10-year period should be defined as follows: from 1st of January \r\n2001 to 31st of December 2010, that is 2001-2010."


id: 524192b4bff8905129964a30a9458b1b
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 10393
content:
"When relevant a leap-year adjustment is required in order to \r\nensure valid interpretation of annual growth rates in the case \r\nof adjacent years. A leap-year affects adjacent years growth rate by \r\napproximately 0.3% yr-1 ! 1\r\n365& which causes growth rates to go up \r\napproximately 0.3% if the first year is a leap year, and down 0.3% \r\nif the second year is a leap year (Friedlingstein et al. 2019)."


id: 56ac5beff61e1d0188d39dd1472b86d4
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 11116
content:
"Primary energy accounting methods are used to report primary \r\nenergy from non-combustible energy sources, in other words, nuclear \r\nenergy and all renewable energy sources except biomass. Annex II of \r\nAR5, namely Section A.II.4, includes a detailed discussion of the three \r\nmain methods dominant in the literature. The method adopted in AR6 \r\nis the direct equivalent method which counts one unit of secondary \r\nenergy provided from non-combustible sources as one unit of \r\nprimary energy, that is, 1 kWh of electricity or heat is accounted for \r\nas 1 kWh = 3.6 MJ of primary energy. This method is mostly used \r\nin the long-term scenarios literature, including multiple IPCC reports \r\n(IPCC 1995, Morita et al. 2001, Fisher et al. 2007, Fischedick et al. \r\n2011), because it deals with fundamental transitions of energy \r\nsystems that rely to a large extent on low-carbon, non-combustible \r\nenergy sources."


id: eccd84f5f42f41d1b24e88722237d718
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 34736
content:
"The relative growth rate of EFF over time periods of greater than one \r\nyear is derived as follows. "


id: ebb97a81edb264df56a3f0e5e6136e6d
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 34590
content:
"The concept of risk is a key aspect of how the IPCC assesses and \r\ncommunicates to decision-makers the potential adverse impacts of, \r\nand response options to, climate change. For the AR6 cycle, the definition \r\nof risk was revised (see below). Authors and IPCC Bureau members \r\nfrom all three Working Groups produced a Guidance (Reisinger et al. \r\n2020) for authors on the concept of risk in order to ensure a consistent \r\nand transparent application across Working Groups. "


id: b01b458eff338ac99091bc486724c3b
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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index: climate_gpt_only_giec
vector_id: 25230
content:
"A number of different methods exist for calculating economic growth \r\nrates (e.g.,  GDP), all of which lead to slightly different numerical \r\nresults. If not stated otherwise, the annual growth rates shown in the \r\nreport are derived using the Log Difference Regression technique or \r\nGeometric Average techniques which can be shown to be equivalent."


id: 974628c15a14f24ad8ddeba86f586b5d
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 21320
content:
"This section summarises this Guidance briefly with a focus on issues \r\nrelated to WGIII, in other words, with focus on mitigation. "


id: 5121e3d4e82becd3536ab2826eb0406a
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 10218
content:
"Risk is the potential for adverse consequences for human or ecological \r\nsystems, recognising the diversity of values and objectives associated \r\nwith such systems. In the context of climate change, risks can arise \r\nfrom potential impacts of climate change as well as human responses \r\nto climate change. Relevant adverse consequences include those on \r\nlives, livelihoods, health and well-being, economic, social and cultural \r\nassets and investments, infrastructure, services (including ecosystem \r\nservices), ecosystems and species."


id: bfd744431a3315ffcdb53685cbc53b6d
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 27737
content:
"Other methods that are used to calculate annual growth rates include \r\nthe Ordinary Least Square technique and the Average Annual Growth \r\nRate technique."


id: 10ef579242cd1f9abdbefb1e82145d3f
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
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vector_id: 140
content:
"In order to compare or contrast trends between two different years, \r\nfor instance comparing 2000 and 2010 cumulative CO2 emissions, the \r\nyear 2000 runs from 1st of January to 31st of December and similarly \r\nthe year 2010 runs from 1st of January to 31st of December. "


id: b5baa1ce982c7215578046119f70fd46
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 26121
content:
"assumed to remain constant. Such assumptions are important for \r\nanalytical robustness and are stated where relevant."


id: 111c64a2d9d49c1d4fefa50f7deef82f
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 171
content:
"The investment and finance literature and practitioner community \r\nbroadly distinguish between 'physical risk' and 'transition risk'. The \r\nterm 'physical risk' generally refers to risks arising from climate \r\nchange impacts and climate-related hazards, while the term \r\n'transition risk' typically refers to risks associated with the transition \r\nto a low carbon economy. These two types of risk may interact and \r\ncreate cascading or compounding risks."


id: ea81fdd6ccf02923093db2b744248e57
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 34404
content:
"Plans, actions, strategies or policies to reduce the likelihood and/\r\nor magnitude of adverse potential consequences, based on assessed or \r\nperceived risks (see also risk assessment, risk perception, risk transfer)."


id: 9adbf4925d77ecce292926131c92fc94
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 21897
content:
"In much of the business and financial literature, the term 'physical \r\nrisk' relates to those derived from the hazard x exposure x \r\nvulnerability framework. Physical risks arise from the potential for \r\nclimate change impacts on the financial value of assets such as \r\nindustrial plants or real estate, risks to facilities and infrastructure, \r\nimpact on operations, water and raw material availability and supply \r\nchain disruptions. Physical risks have direct financial consequences \r\nfor organisations where those risks are realised, as well as up-front \r\ninsurance and investment related costs and downstream effects for \r\nusers of relevant goods and services. "


id: 8f860eaf13a30422c24428d65f4a91c8
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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content:
"In this report, with the aim of improving the ability of decision-makers \r\nto understand and manage risk, the term is used when considering \r\nthe potential for adverse outcomes and the uncertainty relating to \r\nthese outcomes. "


id: 285c9ee9a0c56b1421c7875cce75682e
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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index: climate_gpt_only_giec
vector_id: 3766
content:
"The term risk is not used as a simple substitute for probability or \r\nchance, to describe physical hazards, or as generic term for 'anything \r\nbad that may happen in future'. While the probability of an adverse \r\noutcome does not necessarily have to be quantified, it needs to be \r\ncharacterised in some way to allow a risk assessment to inform \r\nresponses via risk management."


id: 8f38e929ff28c9dae428fe92a8286df9
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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index: climate_gpt_only_giec
vector_id: 20074
content:
"Transition risks typically refer to risks associated with transition \r\nto a low carbon economy, which can entail extensive policy, legal, \r\ntechnology, and market changes to address mitigation and adaptation \r\nrequirements related to climate change. Depending on the nature, \r\nspeed, and focus of these changes, transition risks may pose varying \r\nlevels of financial and reputational risk to organisations. Transition \r\nrisks, if realised, can result in stranded assets, loss of markets, reduced \r\nreturns on investment, and financial penalties, as well as adverse \r\noutcomes for governance and reputation. "


id: 31c8d9a0a57ca89b01c3067d2a3b8b4
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
content_type: text
index: climate_gpt_only_giec
vector_id: 5243
content:
"In the AR6, risk refers to the potential for adverse consequences \r\nonly. The term hazard is used where climatic events or trends has \r\nan identified potential for having adverse consequences to specific \r\nelements of an affected system. The contribution of Working Group I \r\nto the AR6 uses the more general term 'climatic impact driver' \r\nwhere a specific change in climate could have positive or negative \r\nconsequences, and where a given climatic change may therefore act \r\nas a driver of risk or of an opportunity."


id: 7692a6c1f32d7d2d87b2a04ed320194e
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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vector_id: 16239
content:
"A key issue is the stranding of assets that may not provide the \r\nexpected financial returns and may end up as large financial liabilities. "


id: 564eb3042010893d50cc54782405ec91
created_at: 2023-03-16 14:01:53
updated_at: 2023-03-16 14:01:53
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index: climate_gpt_only_giec
vector_id: 11062
content:
"Climate-related risk to food security arises from multiple drivers that \r\ninclude both climate change impacts, responses to climate change \r\nand other stressors."


id: 4c0f05c3d5db104dec3a017881a16ddd
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 9392
content:
"In the context of responses to climate change, drivers of risk include \r\nthe demand for land from climate change responses (both adaptation \r\nand mitigation), the role of markets (e.g.,  price spikes related to \r\nbiofuel demand in other countries), governance (how are conflicts \r\nabout access to land and water resolved) and human behaviour more \r\ngenerally (e.g., trade barriers, dietary preferences)."


id: 2d6bee29a2f4526033cece104515718
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 4590
content:
"to account for future nationally determined contributions (NDCs) in \r\naccordance with this approach. Parties may also report supplemental \r\ninformation on aggregate emissions and removals, expressed as \r\nCO2-eq, using other GHG emission metrics assessed by the IPCC \r\n(4/CMA.1 and 18/CMA.1: UNFCCC 2019)."


id: a80613f82b7c105084fe834ca85db611
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 23950
content:
"Scientific reasons for the use of GWP100 as default GHG emission \r\nmetric in WGIII are that GWP100 approximates the relative \r\ndamages caused by the two most important anthropogenic GHGs \r\nCO2 and CH4 for social discount rates around 3%. In addition, \r\nfor pathways that limit warming to 2degC (>67%) or lower, using \r\nGWP100 to inform cost-effective abatement choices between gases \r\nwould achieve these long-term temperature goals at close to least \r\nglobal cost within a  few percent (high confidence) (see Cross\u0002Chapter Box 2 in Chapter 2)."


id: 150d4413ca9d105ef5258d51be4710d1
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 815
content:
"Comprehensive mitigation policy relies on consideration of all \r\nanthropogenic forcing agents, which differ widely in their atmospheric \r\nlifetimes and impacts on the climate system. GHG emission metrics4\r\nprovide simplified information about the effect that emissions \r\nof different GHGs have on global temperature or other aspects \r\nof climate, usually expressed relative to the effect of emitting CO2. \r\nAn assessment of different GHG emission metrics from a mitigation \r\nperspective is provided in Cross-Chapter Box  2 and Chapter  2 \r\nSupplementary Material, building on the assessment of GHG emission \r\nmetrics from a physical science perspective in AR6 WGI (Forster et al., \r\n2021, Section 7.6)."


id: 2e88a54ca068a03d8bf6ed6db59452b2
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 4768
content:
"However, all emission metrics have limitations and uncertainties, \r\ngiven that they simplify the complexity of the physical climate \r\nsystem and its response to past and future GHG emissions. The most \r\nsuitable metric for any given climate policy application, depends on \r\njudgements about the specific context, policy objectives and the way \r\nin which a metric would be used."


id: 6cb9e43be1a75598e7bc693f0e468448
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 14720
content:
"The WGIII contribution to the AR6 reports aggregate emissions and \r\nremovals using updated values for the Global Warming Potential \r\nwith a time horizon of 100 years (GWP100) from AR6 WGI unless \r\nstated otherwise. These updated GWP100 values reflect updated \r\nscientific understanding of the response of the climate system to \r\nemissions of different gases, and include a methodological update \r\nto incorporate climate-carbon cycle feedbacks associated with the \r\nemission of non-CO2 gases (Forster et al. 2021). For the second-most \r\nimportant anthropogenic greenhouse gas, methane, the updated \r\nGWP100 value of 27 is similar but slightly lower than the value of 28 \r\nreported in the AR5 without climate-carbon cycle feedbacks. A full \r\nset of GWP100 values used in this report, based on the assessment \r\nof WGI (Forster et al. 2021, Section 7.6 and Table 7.SM.7), is provided \r\nin Table 9."


id: 2b02eff8cf9d28e23c9ac28b007bec57
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 4197
content:
"To further reduce ambiguity regarding actual climate outcomes over \r\ntime from any given set of emissions, the WGIII contribution to the \r\nAR6 reports emissions and mitigation options for individual gases \r\nwhere possible based on the available literature, and reports CO2-eq \r\nemissions where this is judged to be policy relevant by author teams \r\nin addition to, not instead of individual gases."


id: e48a7110a9c23804149058d1d66340d0
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 33434
content:
"GWP100 was chosen in the WGIII contribution to the AR6 as the default \r\nGHG emissions metric for both procedural and scientific reasons."


id: cc7e498c4f97ac887778898b55be482f
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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content:
"Procedural reasons are to provide continuity with the use of GWP100 \r\nin past IPCC reports and the dominant use of GWP100 in the literature \r\nassessed by WGIII, and to match decisions made by Governments as \r\npart of the Paris Agreement Rulebook. Parties to the Paris Agreement \r\ndecided to report aggregated emissions and removals (expressed \r\nas CO2-eq) based on the Global Warming Potential with a time \r\nhorizon of 100 years (GWP100), using values from IPCC AR5 or \r\nfrom a subsequent IPCC report as agreed upon by the CMA,5\r\n and "


id: 7bf8df37954eb3fcf6ed1ffa6e7b7da1
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 17108
content:
"Table 9 | GWP100 values and atmospheric lifetimes for a range of GHGs, \r\nbased on AR6 WGI (Forster et al. 2021)."


id: b08f06704dd2d2d8fd86fa524ea4b15c
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 25298
content:
"Our primary data source is the Emissions Database for Global \r\nAtmospheric Research (EDGAR) (Crippa et al. 2021, Minx et al. 2021). \r\nThis dataset provides annual CO2, CH4, N2O and F-gas emissions on \r\na country and emissions source level for the time span 1970 to 2019. \r\nThe fossil fuel combustion component of EDGAR is closely linked to \r\nand sourced from International Energy Agency (IEA 2021) energy \r\nand emissions estimates. Section  2.2.1 in Chapter  2 of this report \r\ndescribes the differences between and coverage of different global \r\nemissions datasets."


id: 6a692e1ce64930fec095618cbac3b142
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 14346
content:
"In addition to EDGAR, land-use CO2 emissions are sourced as the \r\nmean of three bookkeeping models, in a convention established by \r\nthe Global Carbon Project (Friedlingstein et al. 2020) and consistent \r\nwith the Working Group I approach. The bookkeeping models are \r\nBLUE (Bookkeeping of Land Use Emissions), Hansis et  al. (2015), \r\nHoughton and Nassikas (2017) and OSCAR (Gasser et al. 2020)."


id: e74c02b2583e9d66bdf00069526dd185
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 33883
content:
"Global total greenhouse gas emissions reported throughout AR6 \r\nare the sum of EDGAR and land-use CO2 emissions. Significant \r\nuncertainties are associated with each gas and emissions source. \r\nThese uncertainties are comprehensively treated in Section 2.2.1 of \r\nChapter 2."


id: df90b9551a4e088e34ab34767c997a9
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 32676
content:
"The list below shows how emission sources in EDGAR are mapped to \r\nsectors throughout the AR6 WGIII. This defines unambiguous system \r\nboundaries for the sectors as represented in Chapters 6, 7 and 9-11 \r\nin the report and enables a discussion and representation of emission \r\nsources without double-counting."


id: a4a2fcbedc277d90377af4eb5e065d79
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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content:
"Emission sources follows the definitions by the IPCC Task Force on \r\nNational Greenhouse Gas Inventories (TFI) (IPCC 2019). EDGARv6 \r\nidentifies each source as either 'Fossil' or 'Bio'. The 'Bio' label \r\nindicates the biomass component of fuel combustion, while 'Fossil' \r\nis the default label for all other emissions sources (including, for \r\nexample, agricultural GHG emissions)."


id: 254d98be2c474384c168d9671a2f4e91
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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content:
"In this section we report on the historical emissions data used in \r\nthe report (Section  9), the sectoral mapping on emissions sources \r\n(Section  9.1), the methane emissions sources (Section  9.2), and \r\nindirect emissions (Section 10)."


id: 22083bd677b2e8218f349cbe8a381b2d
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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content:
"Historic emissions data for countries, regions and sectors are \r\npresented throughout the report, but especially in Chapters 2, \r\n6-7, 9-11, the Technical Summary and Summary for Policymakers. \r\nTo ensure consistency and transparency we use the same emissions \r\ndata across these chapters, with a single methodology, division \r\nof emissions sources, and following the classification scheme \r\nof countries and areas in Section 1 above."


id: c45de33b579c777363d150dc527bcfcb
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
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content:
"In order to identify emission trends and mitigation opportunities \r\nby sector WGIII allocates each emission source to a sector and \r\nsubsequently a subsector (check Section  9 above). These trends \r\nand mitigation opportunities are, in most cases and whenever possible, \r\nreported in the native unit of gases as well as in CO2-eq using IPCC \r\nAR6 GWP100 values (Section 8). In the case of methane (CH4), it has \r\ntwo different GWP100 values according to its source. The relevant \r\nsources of methane are: biogenic methane, fossil methane (source: \r\ncombustion) and fossil methane (source: fugitive and process)."


id: b61b54ebcc081daeb1c530f50bd3c733
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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content:
"For fossil methane (combustion), despite it being fossil, the correct \r\nGWP100 value is always the low one, for the dataset reported here. \r\nThis is due to the fact that the emissions data provider EDGAR \r\n(Section 9) considers a complete oxidation to CO2 of all the carbon \r\ncontained in the fossil fuel upon combustion, which is then reflected \r\nin the CO2 emissions factors for the different sources based on the \r\ncarbon content of fuels. In other words, IPCC (IPCC 2019) methods \r\nand defaults (Tier 1 IPCC CO2 emissions factors) have been used \r\nwhere the associated CO2 emissions are estimated on the basis of \r\ncomplete (100%) oxidation to CO2 of carbon contained in combusted \r\nmass, which includes not only CO2 directly released to the atmosphere \r\nbut also CO2 generated in the atmosphere from the carbon released \r\nas methane and converted to CO2 only subsequently."


id: ed30a8aa9e45f0d15af116160d2ae58b
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 34795
content:
"The majority of biogenic methane emissions result from the AFOLU \r\nsector due to livestock and other agricultural practices, but also \r\nfrom the energy systems, building, transport and industry (waste) \r\nsectors. Meanwhile, fossil methane (combustion) emissions result \r\nfrom electricity and heat generation in the energy systems sector \r\nas well as various combustion activities in all other sectors. Finally, \r\nfossil methane (fugitive and process) is emitted from the extraction \r\nand transportation of fossil fuels (fugitive methane), in addition to \r\nsome activities in the industry sector (fugitive and process methane). \r\nSee Table 12 below for a comprehensive list."


id: a91a303bb98251a9e76309627ad59b1d
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 24127
content:
"There are two exceptions applied to the above categorisation, both \r\nbelong to the industry sector, sector codes 6Cb1 (Waste incineration - \r\nuncontrolled municipal solid waste (MSW) burning) and 6D (other \r\nwaste). Uncontrolled MSW burning (6Cb1) includes both biogenic \r\nand fossil material, with incomplete oxidation for this source even \r\nwhen the IPCC Tier 1 default emission/oxidation factor is used. The \r\nGWP100 value adopted for this source is the low one, given that \r\nthe fossil-origin methane component is unlikely to be very large. \r\nThe 'other waste' (6D) source may also include both biogenic and \r\nfossil methane. However, it is unclear what type of waste handling \r\nis included here. Furthermore, the associated CO2 emissions are not \r\nestimated. Therefore, the high GWP100 value is used."


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content:
"There are two GWP100 values assigned to methane depending \r\non its source: a GWP100 value of 27 for biogenic methane and \r\nfossil methane  (combustion), and a higher GWP100 value of 29.8 \r\nfor fossil  methane (fugitive and process), see Table  11 below. The \r\ndifference between these two GWP100 values arises from treatment \r\nof the effect of methane conversion into CO2 during its chemical \r\ndecay in the atmosphere. The higher GWP100 value takes account of \r\nthe warming caused by CO2 that methane decays into, which adds \r\nto the warming caused by methane itself, while the lower GWP100 \r\nvalue does not."


id: 922b296f2f56c244598a65ec41e50c2d
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 20516
content:
"In total, the estimation of EDGAR methane emissions in 2019 \r\nusing a  GWP100 value of 27 across all related sources results in \r\n10.2 GtCO2-eq, compared to 10.6 GtCO2-eq using the higher GWP100 \r\nvalue as described. This is primarily driven by the readjustment of \r\nmethane emissions from hard coal mining, gas production, and \r\nventing and flaring (sectors 1B1a1, 1B2b1 and 1B2c)."


id: 6f591d08ab4ed2910cb3258bc26b6d85
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 15147
content:
"In the case of biogenic methane, the correct GWP100 value is always \r\nthe low value irrespective of the specific source. This is because \r\nall CO2 originated from biomass is either already estimated and \r\nreported as CO2 emissions from AFOLU sector, or in the case of short\u0002rotation biomass, the original removal of CO2 from the atmosphere is \r\nnot reported and hence neither does the release of CO2 back into the \r\natmosphere need to be reported."


id: 5762795b98fcf347e6b081f9654a145f
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 11240
content:
"For fossil methane, the correct GWP100 value depends on the source, \r\nin other words, combustion source vs fugitive and process sources. \r\nFossil methane (fugitive and process) should use the higher GWP100 "


id: 52ba79fe036f3681e5871f192ee5f494
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
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vector_id: 10476
content:
"indirect emissions in EDGAR using the IEA data. First, we match the \r\nsectors in EDGAR and IEA. Second, for each country and emissions \r\nsource available in the IEA database, we take the IEA indirect \r\nemissions value and divide it by the total IEA value for electricity and \r\nheat. Third, we multiply these values through by the EDGAR value for \r\nelectricity and heat. This procedure ensures that indirect emissions, \r\nin principle, sum to the correct total (EDGAR) value of electricity and \r\nheat that we use elsewhere in the reporting. However, total indirect \r\nemissions still do not sum to the total electricity and heat sector. This \r\nis due to an incomplete allocation of electricity and heat emissions \r\nin the IEA dataset, equal to 0.008 GtCO2 in 2018, or about 0.06% of \r\nthe total electricity and heat generation."


id: 5a627e817607346622ecfe701d85fd20
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
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vector_id: 11731
content:
"Carbon dioxide emissions resulting from fuel combusted to produce \r\nelectricity and heat are traditionally reported in the energy sector. \r\nAn indirect emissions accounting principle allocates these emissions \r\nto the end-use sectors (industry, buildings, transport, and agriculture) \r\nwhere the electricity and heat are ultimately consumed. Attributing \r\nindirect emissions to consuming sectors makes it possible to assess \r\nthe full potential impact of demand-side mitigation actions that \r\nreduce electricity and heat consumption (de la Rue du Can et al. 2015)."


id: fc710b65a952b79f5061a7ee7752f86b
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 37226
content:
"Additionally, a couple of adjustments were made to allocate \r\nemissions from IEA sector categories to IPCC categories from IPCC \r\nTask force definition as described in IPCC (2019) Guidelines (see \r\nSection 9). These include:"


id: d532fb367af9fdb13bdded3033353066
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
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vector_id: 31073
content:
"The base data for total global, regional and sectoral emissions in this \r\nreport is the EDGAR database (see Section 9). Since there are some \r\ndiscrepancies between the electricity and heat emissions totals in \r\nEDGAR and IEA, we make some adjustments in order to estimate "


id: 5ac74f7e98a18d3e21104244f2e742a1
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 11795
content:
"Table 13 | Feasibility dimensions and indicators to assess the barriers and enablers of implementing mitigation options."


id: 7841d14a7dcc8c009937f292d210734c
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 16487
content:
"other category was allocated to the other end-use sectors based \r\non the share of energy allocated to each of these sub-sectors for \r\neach region."


id: 5b6671cabafc6574a5f267222ca0bbac
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 11881
content:
"The sectoral chapters in AR6 WGIII assess six dimensions of feasibility, \r\nwith each dimension comprising a key set of indicators that can be \r\nevaluated by combining various strands of literature (see Table 13). \r\nThe feasibility of systems-level changes is addressed in Chapter 3 of \r\nthis report. "


id: 3559eb041f7105dcccd02deac38927c6
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 5807
content:
"The sectoral chapters in this report assess to what extent the indicators \r\nin Table 13 would be enablers or barriers to implementation using the \r\nfollowing scores (Nilsson et al. 2016):"


id: e05f1e4fc1fd5fb7429b5147d5eecc75
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
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vector_id: 32801
content:
"Finally, it is also worth noting that indirect emissions only cover CO2\r\nemissions and that a small portion of non-CO2 are not included in \r\nthe IEA dataset and therefore have not been allocated to the end use \r\nsectors. Non-CO2 emissions from total electricity and heat generation \r\nrepresents 0.55% of all GHG emissions from that sector."


id: ab3a173b69e42881227b05b9404ad43e
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 24454
content:
"In this section we report on assessment methods adopted in the \r\nreport. Section 11 describes the methodology adopted for assessing \r\nthe feasibility of mitigation response options. Section 12 describes \r\nthe methodology adopted for assessing synergies and trade-offs \r\nbetween mitigation options and the SDGs."


id: fa5c4c8971b76c0163b7e0f09205ba4c
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 36906
content:
"The feasibility assessment aims to identify barriers and enablers of \r\nthe deployment of mitigation options and pathways. The assessment \r\norganises evidence to support decision making on actions and policies "


id: 575f41556a25186d4c3375a2829559b0
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 11239
content:
"context-specific and therefore provides a detailed explanation in the \r\nsupplementary table of Chapter 17. "


id: 2e6e700f5693ff95d6990f8bfdf221d7
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 4751
content:
"Each sectoral chapter assesses to what extent the indicators listed \r\nabove would be an enabler or barrier to the implementation of \r\nselected mitigation options, by using the above scores. Then the total \r\nnumber of minus and plus points were computed, relative to the \r\nmaximum possible number of points, per feasibility dimensions, for \r\neach option; a + counts as two plus points, a - as two minus points, \r\nand a +- as one plus and one minus point. The resulting scores reveal \r\nthe extent to which each feasibility dimension enables or inhibits \r\nthe deployment of the relevant option, and indicates which type of \r\nadditional effort would be needed to reduce or remove barriers as to \r\nimprove the feasibility of relevant options."


id: c9c12bd7d825799434727c55c011dd6
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 29299
content:
"For the assessment, the mitigation options were shortlisted from \r\neach of the sectoral chapters. The sectoral chapters assessed the \r\nliterature in terms of the impacts of each of these mitigation options \r\non the 17 SDGs. The assessment uses three signs: "


id: 4dcbea5492895c2e65fd220516aed911
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 9670
content:
"In some cases, where there is gap in literature, these are left \r\nblank denoting that these impacts have not been assessed in the \r\nliterature included in the sectoral chapters. To support these signs, \r\nbrief statements are provided followed by uncertainty qualifiers in \r\nthe supplementary table of Chapter 17. These uncertainty qualifiers \r\ndenote the confidence levels (low, medium and high)."


id: bea55c92c19bd4aa05b2f6c7fb67c35a
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 27549
content:
"The assessment is based on the literature, which is reflected in a line \r\nof sight. When appropriate, it is indicated whether the feasibility \r\nof an option varies across context (e.g.,  region), scale (e.g.,  small, \r\nmedium, full scale), time (e.g., implementation in 2030 versus 2050) \r\nand warming level (e.g., 1.5degC versus 2degC). "


id: a440c8ef5a247d580000853fd219fc0
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 23396
content:
"Synergies and trade-offs may occur between the feasibility \r\ndimensions, and between specific mitigation options. Therefore, \r\nChapters 3 and 4 employ a systems perspective and discuss the \r\nfeasibility of mitigation scenarios and pathways in the long term \r\nand near to mid-term, respectively, on the basis of the feasibility \r\nassessments in the sectoral chapters taking into account such \r\nsynergies and trade-offs. Chapter  5 (demand, services and social \r\naspects of mitigation), Chapter 13 (national and sub-national policies \r\nand institutions), Chapter 14 (international cooperation), Chapter 15 \r\n(investment and finance) and Chapter  16 (innovation, technology \r\ndevelopment and transfer) address technological, economic, socio\u0002cultural and institutional enabling conditions that can enhance the \r\nfeasibility of options and remove relevant barriers."


id: 59e3e2212c9e66c6a6451da3d5d53f32
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 11650
content:
"Press, Cambridge, UK and New York, NY, USA, pp. 445-538. https://doi.\r\norg/10.1017/9781009157940.007."


id: 4f3d25fc9ddda7a2ee05469ad76b668d
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 9907
content:
"UNSD, 1999: Standard Country or Area Codes for Statistical Use, Revision 4. \r\nhttps://unstats.un.org/unsd/methodology/m49/."


id: 569f9d02c9b32407bbfea3984083e4bd
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 11104
content:
"Valentina Bosetti (Italy), James Edmonds (the United States of America), Manfred Fischedick \r\n(Germany), Petr Havlik (Austria/Czech Republic), Paulina Jaramillo (the United States of \r\nAmerica), Volker Krey (Germany/Austria), Franck Lecocq (France), Andre F.P. Lucena (Brazil), \r\nMalte Meinshausen (Australia/Germany), Sebastian Mirasgedis (Greece), Brian O'Neill (the United \r\nStates of America), Glen P. Peters (Norway/Australia), Joeri Rogelj (Belgium/United Kingdom), \r\nSteven Rose (the United States of America), Yamina Saheb (France/Algeria), Goran Strbac (Serbia/\r\nUnited Kingdom), Anders Hammer Stromman (Norway), Detlef P. van Vuuren (the Netherlands), \r\nNan Zhou (the United States of America)."


id: cdbc1b666a09a20a5651f81295bc4386
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 29893
content:
"Alaa Al Khourdajie (United Kingdom/Syria), Hossein Ameli (Germany), Cornelia Auer \r\n(Germany), Nico Bauer (Germany), Edward Byers (Austria/Ireland), Michael Craig (the United \r\nStates of America), Bruno Cunha (Brazil), Stefan Frank (Austria), Jan S. Fuglestvedt (Norway), \r\nMathijs  Harmsen (the  Netherlands), Alan Jenn (the United States of America), Jarmo Kikstra \r\n(Austria/the Netherlands), Paul Kishimoto (Canada), Robin Lamboll (United Kingdom/the \r\nUnited States of America), Julien Lefevre (France), Eric Masanet (the United States of America), \r\nDavid McCollum (the United States of America), Zebedee Nicholls (Australia), Aleksandra \r\nNovikova (Germany/the Russian Federation), Simon Parkinson (Canada), Pedro Rochedo (Brazil), \r\nSasha Samadi (Germany), David Verez (Cuba/Spain), Sonia Yeh (Sweden)."


id: 649758edde0ca13a41a35196221dca31
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 13393
content:
"IPCC, 2022: Annex III: Scenarios and modelling methods [Guivarch, C., E. Kriegler, J. Portugal-Pereira, V. Bosetti, \r\nJ. Edmonds, M. Fischedick, P. Havlik, P. Jaramillo, V. Krey, F. Lecocq, A. Lucena, M. Meinshausen, S. Mirasgedis, B. O'Neill, \r\nG.P. Peters, J. Rogelj, S. Rose, Y. Saheb, G. Strbac, A. Hammer Stromman, D.P. van Vuuren, N. Zhou (eds)]. In IPCC, 2022: \r\nClimate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report \r\nof the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, \r\nD.  McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)]. \r\nCambridge University Press, Cambridge, UK and New York, NY, USA. doi: 10.1017/9781009157926.022"


id: 7c7d5de8ad4f4b6912f56b08f0989058
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 17188
content:
"Although this dichotomic classification has been mentioned in \r\nthe literature, since the IPCC's Fifth Assessment Report (AR5), \r\nclimate mitigation models have evolved towards a  more hybrid\r\napproach incorporating attributes of both bottom-up and top-down\r\napproaches. This is partly due to different modelling communities \r\nhaving different understandings of these two approaches' principles, \r\nwhich can be misleading."


id: c52a7fc972d9cfb7dfca930497a65303
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
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content:
"The use of scenarios and modelling methods are pillars in IPCC Working \r\nGroup III (WGIII) Assessment Reports. Past WGIII assessment report \r\ncycles identified knowledge gaps about the integration of modelling \r\nacross scales and disciplines, mainly between global integrated \r\nassessment modelling methods and bottom-up modelling insights of \r\nmitigation responses. The need to improve the transparency of model \r\nassumptions and enhance the communication of scenario results was \r\nalso recognised."


id: 6cf1daa1666b102585e4931ed316fae7
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 14763
content:
"This annex on Scenarios and Modelling Methods aims to address some \r\nof these gaps by detailing the modelling frameworks applied in the \r\nWGIII Sixth Assessment Report (AR6) chapters and disclose scenario \r\nassumptions and its key parameters. It has been explicitly included in \r\nthe Scoping Meeting Report of the WGIII contribution to the AR6 and \r\napproved by the IPCC Panel at the 46th Session of the Panel."


id: 4175a275c81cd598b2f7ecf578e28dfb
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
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content:
"The annex includes two parts: Part I on Modelling Methods summarises \r\nmethods and tools available to evaluate sectoral, technological and \r\nbehavioural mitigation responses as well as integrated assessment \r\nmodels (IAMs) for the analysis of 'whole system' transformation \r\npathways; Part II on Scenarios sets out the portfolio of climate \r\nchange scenarios and mitigation pathways assessed in the AR6 WGIII \r\nchapters, its underlying principles and interactions with scenario \r\nassessments by WGI and WGII."


id: da6d70347549a0585d2ad4c30069e23c
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 31893
content:
"Modelling frameworks vary vastly among themselves, and several \r\nkey characteristics can be used as basis for model classification \r\n(Scrieciu et al. 2013; Dodds et al. 2015; Hardt and O'Neill 2017; \r\nCapellan-Perez et al. 2020). Broadly, literature characterises models \r\nalong three dimensions: (i) level of detail and heterogeneity, \r\n(ii) mathematical algorithm concepts, and (iii) temporal and spatial \r\nsystem boundaries (Krey 2014)."


id: a6a3981c4c2b973c61057c5b1981bb44
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 23742
content:
"Recursive-dynamic models, also known as myopic or limited \r\nforesight models, make decisions over sequential periods of time. For \r\neach time step, the solution is achieved without information on future "


id: 402e219723a693f5a61b357c3e7f3fea
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
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content:
"The most basic aspect to differentiate models is their main objective \r\nfunction, which includes the detail at which they represent key \r\nsectors, systems and agents. This affects the decision on methodology \r\nand other coverage aspects. Several models have been developed for \r\ndifferent sectoral representation, such as the energy (Annex III.I.3), \r\nbuildings (Annex III.I.4), transport (Annex III.I.5), industry \r\n(Annex III.I.6) and land-use (Annex III.I.7) models."


id: 99c2c9a1a29ae3b0d5eab45c2cd045c7
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 21714
content:
"Two different types of LCA approaches can be distinguished: \r\nAttributional Life Cycle Assessment (ALCA) and Consequential \r\nLife Cycle Assessment (CLCA). ALCA aims at describing the direct \r\nenvironmental impacts of a  product. It typically uses average \r\nand historical data to quantify the environmental burden during \r\na product's life cycle, and it tends to exclude market effects or other \r\nindirect effects of the production and consumption of products \r\n(Baitz 2017). CLCA, on the other hand, focuses on the effects of \r\nchanges due to product life cycle, including both consequences \r\ninside and outside the product life cycle (Earles and Halog 2011). \r\nThus, the system boundaries are generally expanded to represent \r\ndirect and indirect effects of products' outputs. CLCA tends to \r\ndescribe more complex systems than ALCA, which are highly \r\nsensitive to data assumptions (Plevin et al. 2014; Weidema et al. \r\n2018; Bamber et al. 2020)."


id: ef1aed1b326f4843eea7eedd7905277a
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 35090
content:
"Integrated assessment models (IAMs) are simplified \r\nrepresentations of complex physical and social systems, focusing \r\non the interaction between economy, society and the environment \r\n(Annex  III.I.9). They represent the coupled energy-economy-land\u0002climate system to varying degrees. In a  way, IAMs differ among \r\nthemselves on all the topics discussed in this section: significant \r\nvariation in geographical, sectoral, spatial and time resolution; they \r\nrely greatly on socio-economic assumptions; different technological \r\nrepresentation; partial or general equilibrium assumptions; \r\ndifferentiated between perfect foresight or recursive-dynamic \r\nmethodology. The difficulty in fully representing the extent of \r\nclimate damages in monetary terms may be the most important \r\nand challenging limitation of IAMs and it is mostly directed to cost\u0002benefit IAMs. However, all categories of IAMs present important \r\nlimitations (Annex III.I.9)."


id: f2c6ce43d5598771b93dd8cf7c23dd42
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 35721
content:
"Life Cycle Assessment (LCA) is an integrated technique to evaluate \r\nthe sustainability of a product throughout its life cycle. It quantifies \r\nthe environmental burdens associated with all stages from the \r\nextraction of raw materials, through the production of the product "


id: 4d7c4e849455d8a0c5a90df4c3b2f60d
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 9623
content:
"Following this brief synopsis of modelling taxonomies, Section I.2 \r\ndetails key aspects of economic frameworks and principles used to \r\nmodel climate mitigation responses and estimate their costs. Sections \r\nI.3, I.4, I.5, I.6, and I.7 present key aspects of sectoral modelling \r\napproaches in energy systems, buildings, transport, industry, and \r\nland use, respectively. Interactions between WGI climate emulators \r\nand WGIII mitigation models are described in Section I.8. A review \r\nof integrated assessment model approaches, their components \r\nand limitations, is presented in Section I.9. Sections I.10 and I.11 \r\npresent comparative tables of key characteristics and measures \r\nof national and global models that contributed to the AR6 WGIII \r\nscenario database."


id: 14751cc0800a47fe8dad51cbac7c400b
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 726
content:
"Partial equilibrium frameworks do not cover the full economy \r\nbut only represent a  subset of economic sectors and markets \r\ndisconnected from the rest of the economy. They basically represent \r\nmarket balance and adjustments for a subset of sectors under ceteris \r\nparibus assumptions about other markets (labour, capital, etc.), \r\nincome, and so on, ignoring possible feedbacks. Partial equilibrium \r\nframeworks are used in sectoral models, as well as to model several \r\nsectors and markets at the same time - for example, energy and \r\nagriculture markets - in energy system models and some detailed \r\nprocess IAMs but still without covering the full economy."


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vector_id: 17515
content:
"Macro-econometric frameworks represent similar sectoral \r\ninterdependence with balance of payments as general equilibrium, \r\nand are sometimes considered a subset of the general equilibrium \r\nframework. They differ from standard neoclassical CGE models in \r\nthe main aspect that economic behaviours are not micro-founded \r\noptimising behaviours but are represented by macroeconomic and \r\nsectoral functions estimated through econometric techniques (Barker \r\nand Scrieciu 2010). In addition, they usually adopt a  demand\u0002led post-Keynesian approach where final demand and investment \r\ndetermine supply and not the other way around. Prices also do not \r\ninstantaneously clear markets and adjust with lag."


id: 2489104d6601c500be914c0908b8c724
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 3192
content:
"Macro-economic growth frameworks are also full-economy \r\napproaches derived from aggregated growth models. They are \r\nbased on a  single macroeconomic production function combining \r\ncapital, labour and sometimes energy to produce a generic good for \r\nconsumption and investment. They are used as the macroeconomic \r\ncomponent of cost-benefit IAMs (Nordhaus 1993) and some \r\ndetailed-process IAMs."


id: c6342508611e2771169e85c175f4ba
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
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vector_id: 28758
content:
"in AR6, including the uses of discounting. In cost-benefit analysis \r\n(CBA), discounting enables the comparison of mitigation costs and \r\nclimate change damage. In the context of mitigation and in cost\u0002effectiveness analysis (CEA), discounting allows the comparison of \r\nmitigation costs over time."


id: c473d574300df1a6b3e38f6a66499e36
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 28481
content:
"and induced as a learning by doing process or as a result of R&D \r\ninvestments (learning-by-searching) (Loschel 2002)."


id: 491fd07f9f035ff7603708e8534a7122
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 8895
content:
"In optimisation models a social discount rate is used to compare costs \r\nand benefits over time. In the case of partial equilibrium optimisation \r\nmodels, the objective is typically to minimise total discounted system \r\ncost. The social discount rate is then an exogenous parameter, \r\nwhich can be assumed constant or changing (generally decreasing) \r\nover time (e.g., Gambhir et al. (2017), where a 5% discount rate is \r\nused). In the case of intertemporal welfare optimisation models, \r\na Ramsey intertemporal optimisation framework is generally used, \r\nconsidering a representative agent who decides how to allocate her \r\nconsumption, and hence saving, over time, subject to a  resource \r\nconstraint. Ramsey (1928) shows that the solution must always \r\nsatisfy the Ramsey Equation, which provides the determinants of the \r\nsocial discount rate. The Ramsey Equation is given as follows:"


id: d31cd752399f11aa45d583ad4cb2a4b6
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 30736
content:
"Strategic interaction can also occur between regions, especially in \r\nthe presence of externalities such as climate change, energy prices \r\nor technology spillovers. Intertemporal models can include several \r\ntypes of strategic interaction: (i) a  cooperative Pareto optimal \r\nsolution where all externalities are internalised and based on the \r\nmaximisation of a global discounted welfare with weighted regional \r\nwelfare (Negishi weights), (ii) a  non-cooperative solution that is \r\nstrategically optimal for each region (Nash equilibrium) (Leimbach \r\net al. 2017b), and (iii) partially cooperative solutions (Eyckmans and \r\nTulkens 2003; Yang 2008; Brechet et al. 2011; Tulkens 2019), akin to \r\nclimate clubs (Nordhaus 2015).\r\n =  + "


id: 5ff91fd56d8f7a0fb4ca22c1f0d3cb5f
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 12678
content:
"Models cover different investment flows depending on the \r\neconomic framework used. Partial equilibrium models compute \r\nenergy system and/or sectoral (transport, building, industry, etc.) \r\ntechnology-specific investment flows associated with productive \r\ncapacities and equipment. Full-economy models compute both \r\nenergy system and macroeconomic investment, the second being \r\nused to increase macroeconomic capital stock. Full-economy multi\u0002sectoral models compute sector-specific (energy and non-energy \r\nsectors) investment and capital flows with some details about the \r\ninvestments goods involved."


id: 92b149a222972ad68c9cccae0448e48e
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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content:
"Discounting is also used for ex post comparison of mitigation cost \r\npathways across models and scenarios. Values typically used for such \r\nex post comparison are 2-5% (e.g., Admiraal et al. 2016). Across this \r\nreport, whenever discounting is used for ex post comparisons, the \r\ndiscount rate applied is stated explicitly."


id: b0bb8ce4f062e666ed9990ba3abee789
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
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vector_id: 25324
content:
"Full-economy models differ in the representation of macro-finance. \r\nIn most CGE and macro-economic growth frameworks financial \r\nmechanisms are only implicit and total financial capacity and \r\ninvestment are constrained by savings. Consequently, investment in \r\na given sector (e.g., low-carbon energy) fully crowds out investment \r\nin other sectors. In macro-econometric frameworks, macro-finance is \r\nsometimes explicit, and investments can be financed by credit on top \r\nof savings, which implies more limited crowding out of investments \r\n(Mercure et al. 2019). Macro-financial constraints are usually not \r\naccounted for in partial equilibrium models."


id: 11481598159b74482a1972f8ed2c8880
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 199
content:
"The choice of the appropriate social discount rate (and the appropriate \r\nrate of pure time preference when applicable) is highly debated \r\n(e.g., Arrow et al. 2013; Gollier and Hammitt 2014; Polasky and Dampha \r\n2021) and two general approaches are commonly used. Based on \r\nethical principles, the prescriptive approach states that the discount \r\nrate should reflect how costs and benefits supported by different \r\ngenerations should be weighted. The descriptive approach identifies \r\nthe social discount rate to the risk-free rate of return to capital as \r\nobserved in the real economy, which generally yields higher values."


id: b9f67b993b7bc8048177fe26239992a6
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 26799
content:
"Models compare economic flows over time through discounting. \r\nTable 5 summarises key characteristics of different models assessed "


id: c11a834a6acbe6068d7a4cb532530245
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 27957
content:
"Dispatch model (EUCAD) (Despres 2015), SWITCH (Fripp 2012), GenX\r\n(TNO 2021), and Python for Power System Analysis (PyPSA) (Brown \r\net al. 2018) investigated these issues. SWITCH is a stochastic model, \r\nin which investments in renewable and conventional power plants \r\nare optimised over a  multi-year period (Fripp 2012). In GenX the \r\noperational flexibility as well as capacity planning is optimised from \r\na  system-wide perspective (TNO 2021). PyPSA is an optimisation \r\nmodel for modern electricity systems, including unit commitment of \r\ngeneration plants, renewable sources, storage, and interaction with \r\nother energy vectors (Brown et al. 2018)."


id: c5fe1803abe5fc69c06f51dcbe41b9a6
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 28730
content:
"and a lower discount rate yields more abatement effort and lower \r\nglobal temperature increases (Stern 2006; Hansel et al. 2020). \r\nIn CEA, the choice of social discount rate influences the timing of \r\nemission reductions to limit warming to a given temperature level. \r\nA  lower discount rate increases short-term emissions reductions, \r\nlowers temperature overshoot, favours currently available mitigation \r\noptions (energy efficiency, renewable energy, etc.) over future \r\ndeployment of net negative emission options and distributes \r\nmitigation effort more evenly between generations (Emmerling et al. \r\n2019; Strefler et al. 2021b)."


id: aa182260f4d046c66b1339ce6956ba2f
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 24305
content:
"In the literature, the energy system models are categorised based on \r\ndifferent criteria, such as (i) energy sectors covered, (ii) geographical \r\ncoverage, (iii) time resolution, (iv) methodology, and (v) programming \r\ntechniques. In the following sections, examples on different types of \r\nenergy system models applied in Chapter 6 are presented."


id: 57558e3795e5ee7ded7a1c10eed7f192
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 11231
content:
"A.III.I.3.1.1 Modelling Electricity System Operation and Planning \r\nwith Large-scale Penetration of Renewables"


id: ae79aa872013df89dbcb9b93b9c86193
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 24980
content:
"A number of advanced grid modelling approaches have been \r\ndeveloped (Sani Hassan et al. 2018), such as robust optimisation \r\n(Jiang et al. 2012), interval optimisation (Dvorkin et al. 2015), or \r\nstochastic optimisation (Meibom et al. 2011; Monforti et al. 2014) to \r\noptimally schedule the operation of the future low-carbon systems \r\nwith high penetration of variable renewable energies. Advanced \r\nstochastic models demonstrated that this would not only lead to \r\nsignificantly higher cost of system management but may eventually \r\nlimit the ability of the system to accommodate renewable generation \r\n(Bistline and Young 2019; Hansen et al. 2019; Perez et al. 2019; \r\nBadesa et al. 2020). Modelling tools such as European Model for \r\nPower System Investment with Renewable Energy (EMPIRE) (Skar \r\net al. 2016), Renewable Energy Mix for Sustainable Electricity \r\nSupply (REMix) (Scholz et al. 2017), European Unit Commitment And "


id: b7cda55dfa609b30af6164d3841597e0
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 26452
content:
"Several integrated models have been developed in order to study \r\nthe interaction between different energy vectors and whole-system \r\napproaches, such as Integrated Energy System Simulation model\r\n(IESM) (NREL 2020), Integrated Whole-Energy System (IWES) (Strbac \r\net al. 2018), UK TIMES (Daly and Fais 2014), and Calliope (Pfenninger \r\nand Pickering 2018)."


id: bbd805899cd9fedef386e3cc3420f649
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 27114
content:
"IESM is an approach in which the multi-system energy challenge is \r\ninvestigated holistically rather than looking at each of the systems in \r\nisolation. IESM capabilities include co-optimisation across multiple "


id: db95bcf46851428667413a624882abe3
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
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vector_id: 32066
content:
"as phasing out fossil fuel subsidies, and climate change mitigation \r\n(Burniaux and Chateau 2010)."


id: ea324081a8900c753fb49c12842e3578
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 34350
content:
"energy systems, including electricity, natural gas, hydrogen, and water \r\nsystems. These provide the opportunity to perform hydro, thermal, \r\nand gas infrastructure investment and resource use coordination for \r\ntime horizons ranging from sub-hourly (markets and operations) to \r\nmulti-year (planning) (NREL 2020)."


id: f6d01ee429fa25184c004842d4e0093f
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
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vector_id: 36345
content:
"In the EPPA model, different processes (e.g.,  economic and \r\ntechnological) which have impacts on the environment from regional \r\nto global at multiple scales are simulated. The outputs of this \r\nmodelling (e.g., greenhouse gas emissions, air and water pollutants) \r\nare provided to the MIT Earth System (MESM), which investigates the \r\ninteraction between sub-models of physical, dynamical and chemical \r\nprocesses in different systems (Chen et al. 2016)."


id: 7b86c7e591cc8f09cb61e1f17d095af3
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 17028
content:
"The IWES model incorporates detailed modelling of electricity, gas, \r\ntransport, hydrogen, and heat systems and captures the complex \r\ninteractions across those energy vectors. The IWES model also considers \r\nthe short-term operation and long-term investment timescales (from \r\nseconds to years) simultaneously, while coordinating operation \r\nof and investment in local district and national/international level \r\nenergy infrastructures (Strbac et al. 2018)."


id: bc3e2ff9c16c0a56aa25f2cc793ccdca
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 27196
content:
"Hybrid models are a combination of macro-economic models (i.e., top\u0002down) with at least one energy sector model (i.e., bottom-up) that \r\ncould benefit from the advantages of both mentioned approaches. \r\nIn this regard, linking these two models can be carried out either \r\nmanually through transferring the data from one model to the other \r\n(soft linking), or automatically (hard linking) (Prina et al. 2020). In \r\nthis section, some of these models are presented including World \r\nEnergy Model (WEM) (IEA 2020a) and the National Energy Modelling \r\nSystem (NEMS) (Fattahi et al. 2020)."


id: b2576bfbee879d9283a2637dcab113f9
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 25570
content:
"The UK TIMES Model ('The Integrated MARKAL-EFOM System') \r\nuses linear programming to produce a  least-cost energy \r\nsystem, optimised according to a number of user constraints, over \r\nmedium- to long-term time horizons. It portrays the UK energy \r\nsystem, from fuel extraction and trading to fuel processing and \r\ntransport, electricity generation and all final energy demands \r\n(Taylor et al. 2014; Daly and Fais 2014). The model generates \r\nscenarios for the evolution of the energy system based on different \r\nassumptions around the evolution of demand and future technology \r\ncosts, measuring energy system costs and all greenhouse gases \r\n(GHGs) associated with the scenario. UKTM is built using the \r\nTIMES model generator: as a partial equilibrium energy system and \r\ntechnologically detailed model, it is well suited to investigate the \r\neconomic, social, and technological trade-offs between long-term \r\ndivergent energy scenarios."


id: ef917ef8f449bf269ca918ec34b69211
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 35188
content:
"The WEM is a  simulation model covering energy supply, energy \r\ntransformation and energy demand. The majority of the end-use sectors \r\nuse stock models to characterise the energy infrastructure. In addition, \r\nenergy-related CO2 emissions and investments related to energy \r\ndevelopments are specified. The model is focused on determining the \r\nshare of alternative technologies in satisfying energy service demand. \r\nThis includes investment costs, operating and maintenance costs, fuel \r\ncosts and in some cases costs for emitting CO2 (IEA 2020a)."


id: 374605ba40d30b38169c563f754d9ab3
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 6083
content:
"Calliope is an open source Python-based toolchain for developing \r\nenergy system models, focusing on flexibility, and high temporal and \r\nspatial granularities. This model has the ability to execute many runs \r\non the same base model, with clear separation of model (data) and \r\nframework (code) (Pfenninger and Pickering 2018)."


id: ff4e061f08e4c59e2ea3a8952244ee89
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 37715
content:
"The NEMS is an energy-economy modelling system applied for \r\nthe USA through 2030. NEMS projects consider the production, \r\nimport, conversion, consumption, and prices of energy, subject \r\nto assumptions on macroeconomic and financial factors, world \r\nenergy markets, resource availability and costs, behavioural and \r\ntechnological choice criteria, cost and performance characteristics \r\nof energy technologies, and demographics. NEMS was designed and \r\nimplemented by the Energy Information Administration (EIA) of the \r\nUS Department of Energy. NEMS is used by EIA to project the energy, \r\neconomic, environmental, and security impacts on the United States \r\nconsidering alternative energy policies and assumptions related to \r\nenergy markets (Fattahi et al. 2020)."


id: 5a7778528fbab07658b9773b7a67bd46
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 11745
content:
"To study the impact of low-carbon energy systems on the economy, \r\nnumerous integrated assessment modelling tools (top-down models) \r\nare applied, such as: General Equilibrium Model for Economy-Energy\u0002Environment (GEM-E3) (Capros et al. 2013), ENV-Linkages (Burniaux \r\nand Chateau 2010), and Emissions Prediction and Policy Analysis \r\n(EPPA) (Chen et al. 2016)."


id: f55972f2834da707e7f4dd9ef0442b8
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 36125
content:
"GEM-E3 is a  recursive dynamic computable general equilibrium \r\nmodel that covers the interactions between the economy, \r\nthe  energy system and the environment. It is specially designed \r\nto evaluate energy, climate, and environmental policies. GEM-E3 can \r\nevaluate consistently the distributional and macro-economic effects \r\nof policies for the various economic sectors and agents across the \r\ncountries/regions (Capros et al. 2013)."


id: 6f7104e088f131d2a82acad7268a2f3a
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 15162
content:
"GHG emissions and mitigation potentials in the building sector \r\nare modelled using either a  top-down, a  bottom-up or a  hybrid \r\napproach (Figure 1)."


id: 1fcaaea4fba67baac77805a6df834ca2
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 2437
content:
"The top-down models are used for assessing economy-wide \r\nresponses of building policies. These models are either economic or \r\ntechnological and have low granularity."


id: 6311bfa9f73d715ef47a58fc81c7a8bf
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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content:
"The bottom-up models are data intensive and based on microscopic \r\ndata of individual end uses and the characteristics of each component \r\nof buildings. Bottom-up models can be either physics-based, also \r\nknown as engineering models; data-driven, also known as statistical \r\nmodels; or a  combination of both, also known as hybrid bottom\u0002up models. Bottom-up models are useful to assess the technico\u0002economic potentials of the overall building stock by extrapolating \r\nthe estimated energy consumption of a representative set of invidual \r\nbuildings (Duerinck et al. 2008; Hall and Buckley 2016; Bourdeau \r\net al. 2019)."


id: 3263f64970f2d7cfeb5a7c32832f2b08
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 5336
content:
"former are included in top-down, hybrid and bottom-up models while \r\nthe latter is usually included in bottom-up and agent-based models \r\n(Niamir et al. 2020; IEA 2021). In non-residential buildings, value \r\nadded is considered among the drivers."


id: f27b49c88550fce66f7db9011b3c6c63
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 35675
content:
"Hybrid models used for buildings can be either optimisation or \r\nsimulation models (Duerinck et al. 2008; Hall and Buckley 2016; \r\nBourdeau et al. 2019) (Figure 1). The latter can also be agent-based \r\nmodels and could be combined with building performance models \r\nto allow for an assessment of occupants' behaviour (Papadopoulos \r\nand Azar 2016; Sachs et al. 2019a; Niamir et al. 2020). Hybrid models \r\nare used for exploring the impacts of resource constraints and \r\nfor  investigating the role of specific technological choices as well \r\nas for analysing the impact of specific building policies."


id: dc5887754a45975e6f29d736a75a7cfb
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 32172
content:
"The use of geographical information systems (GIS) layers (Reinhart \r\nand Cerezo Davila 2016) combined with machine learning techniques \r\n(Bourdeau et al. 2019) allows the creation of detailed datasets of \r\nbuilding characteristics while optimising the computing time, thus, \r\nleading to a better representation of energy demand of buildings and \r\na more accurate assessment of GHG mitigation potential."


id: 8a9b3a634dee8f6b47ef9071278ea23b
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 19348
content:
"The assessment conducted in Chapter  9 was based on the SER \r\n(Sufficiency, Efficiency, Renewable), framework with sufficiency \r\nbeing all the measures and daily practices which avoid, in the first \r\nplace, the demand for energy, materials, water, land and other \r\nnatural resources over the life cycle of buildings and appliances/\r\nequipment, while providing decent living standards for all within the \r\nplanetary boundaries. By contrast to efficiency, sufficiency measures \r\ndo not consume energy in the use phase. Efficiency improvement \r\nof the building envelope and appliances/equipment are the main \r\nmitigation options considered in the existing models and scenarios. \r\nThey are, usually, combined with market-based and information \r\ninstruments and to some extent with behaviour change. As of today, "


id: 6e71ab5c8d518c910a5404dce8b1097
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 15001
content:
"Comprehensive models represent energy demand per energy carrier \r\nand end use for both residential and non-residential buildings, for \r\ndifferent countries or sets of countries, further disaggregated across \r\nurban/rural and income groups. Drivers of energy demand considered \r\ninclude population, the floor area per capita, appliances ownership \r\nand to some extent occupants' behaviour in residential buildings. The "


id: 8f52e6a17221107fe26668c36cb1327
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 20099
content:
"current policies scenario, to increase by 56% by 2030, followed by \r\na decrease of 75% by 2050. Such variations in emissions over two \r\ndecades in the developing world raise questions about the policy \r\nrelevance of these scenarios. In developed countries, emissions are \r\nprojected to go down in all regions across all scenarios, except in \r\nSSP2 scenario in Asia-Pacific, where emissions are projected to \r\nincrease by 18% by 2030 followed by a decrease of 25% by 2050, \r\ncompared to current policies scenario. It is worth noting that, across \r\nall scenarios, Eastern Asia is the region with the lowest estimated \r\nmitigation potential compared to the current policies (Figure 2-b)."


id: 6e46f7d790dcd4b7155d65b2bf99a34
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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content:
"Grubler et al. (2018), Pauliuk et al. (2021b), Kuhnhenn et al. (2020), \r\nMillward-Hopkins et al. (2020), Kikstra et al. (2021), and van Vuuren \r\net al. (2021) are the only six global models/scenarios to include \r\nsufficiency measures, out of which detailed data were available only \r\nfor two scenarios (Pauliuk et al. 2021b; van Vuuren et al. 2021)."


id: ca15edb82318686ff42db1d5768d3b1
created_at: 2023-03-16 14:01:54
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"In total, 931 scenarios were submitted to the AR6 scenario database, \r\nout of which only two scenarios provided detailed data allowing for \r\nan assessment of emissions reductions based on the SER framework \r\nconsidered in the building chapter. An additional 78 bottom-up \r\nmodels/scenarios were gathered (Table 1). Mitigation potentials from \r\nthese scenarios are assessed using either a decomposition analysis \r\n(Section 9.3) or an aggregation of bottom-up potential estimates for \r\ndifferent countries into regional and then global figures (Section 9.6)."


id: e1dc438b7b2dc5339150f30f85b4ae45
created_at: 2023-03-16 14:01:54
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"The link to the Sustainable Development Goals (SDGs) is not always \r\nexplicit in buildings models/scenarios. However, some models include \r\nrequirements to ensure access to decent living standards for all \r\n(Grubler et al. 2018); Millward-Hopkins et al. 2020; Kikstra et al. \r\n2021) or to specifically meet the 2030 SDG 7 goal (IEA 2020a, 2021)."


id: a844acb9d73a1cf5ac72272a56a83cdc
created_at: 2023-03-16 14:01:54
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content:
"Scenarios considered in the illustrative mitigation pathways included \r\nin Chapter 3 were assessed, compared to current policy scenarios. \r\nThe assessment was possible for only the combined direct CO2\r\nemissions for both residential and non-residential buildings due to \r\nlack of data on other gases as well as on indirect and embodied \r\nemissions. The assessment shows mitigation potentials, compared to \r\ncurrent policies scenarios, at a global level ranging from 9% to 13% \r\nby 2030 and from 58% to 89% in 2050 (Figure 2-b)."


id: 159e04ee1e8812beefadfe824ca3aac8
created_at: 2023-03-16 14:01:54
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content:
"The AR6 scenario database received 101 models with a  building \r\ncomponent, out of which 96 were IAM models and five \r\nbuilding  specific models. This is equivalent to 931 scenarios. After \r\nan initial screening, quality control and further vetting to assess \r\nif they sufficiently represented historical trends and climate goals, \r\n43  models (42 IAMs and 1 building-specific model) were kept for \r\nthe assessment, thus reducing the number of scenarios to assess \r\nto 554. The unvetted scenarios are still available in the database. "


id: 5827826caac78a7695ca9900a05d657a
created_at: 2023-03-16 14:01:54
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content:
"There are great discrepancies in the projected potentials by the IAMs \r\nacross regions and scenarios. In the deep electrification and high \r\nrenewable scenario, emissions in Africa are projected to increase by \r\n88% by 2030, followed by a decrease of 97% by 2050, compared to \r\ncurrent policies scenario. Similarly, in the sustainable development \r\nscenario, emissions in developing Asia are projected, compared to "


id: 4755c4ded647d605b8a73faf424d0b0b
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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content:
"*The percentages correspond to the increase or decrease in relation to the same year with the Current Policies Scenario as a baseline. "


id: 9af3d5e5d8cd21ad0427d0b263d91317
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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content:
"Figure 2 | GHG emissions reductions in the building sector (direct emissions) in scenarios considered as illustrative mitigation pathways in Chapter 3."


id: 9df13e2f00587ea5b82a83b4a67b2431
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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content:
"After a  final screening based on the SER (Sufficiency, Efficiency, \r\nRenewable) framework, only two IAMs were kept. Given the top\u0002down nature of IAMs and their weaknesses in assessing mitigation \r\nmeasures, especially sufficiency measures, 78 bottom-up models with \r\ntechnological representation have been included in the assessment \r\n(Table  1). These additional bottom-up models were not submitted \r\nto the AR6 scenario database. However, scenario owners supplied \r\nChapter 9 with the underlying assumptions and data."


id: 666c0079b3bf4ad0ba7cb63d23446b94
created_at: 2023-03-16 14:01:54
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vector_id: 13705
content:
"GHG emissions from transport are largely a  function of travel \r\ndemand, transport mode, and transport technology and fuel. \r\nThe purpose of transportation system models is to describe how \r\nfuture demand for transport can be fulfilled through different \r\nmodes and technologies under different climate change mitigation \r\ntargets or policies. Within a given transport mode, technologies differ \r\nby efficiency and fuel use."


id: 495de70827f1c440eca504ca11574521
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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content:
"A key distinguishing feature between different model types is how \r\nthey control the above components. Our review of the transport \r\nenergy system models can be broadly divided into three main \r\ncategories: (i) optimisation models, (ii) simulation models, and \r\n(iii) accounting and exploratory models."


id: 9163d24ef9f6d5032cd1af7da08a1280
created_at: 2023-03-16 14:01:54
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vector_id: 20405
content:
"Common components of transportation energy systems models mirror \r\nthese main drivers of GHG emissions. Most models will also quantify "


id: e8554088a9eb00e3299fbdc519895af2
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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content:
"preferences given the characteristics of mode or technology) (Daly \r\net al. 2014; Schafer 2017), or other feedback loops (Linton et al. 2015)."


id: 2a32e23a71cc2a27a6b1fa216bc0cc43
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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content:
"Due to the model types' relative strengths and weaknesses, they are \r\ncommonly applied to certain problem types (Table 2). Models can do \r\nforecasting, which makes projections of how futures may evolve, \r\nor backcasting, which makes projections of a  future that meets \r\na predefined goal such as a policy target of 80% reduction in GHG \r\nemissions from a historical level by a certain year. Models are often \r\nalso used to explore 'what-if' questions, to confirm the feasibility\r\nof certain assumptions/outcomes, and to quantify the  impacts of \r\na change such as a policy under different conditions. Enhancing fuel \r\nefficiency standards, banning internal combustion engines, setting \r\nfuel quality standards, and the impacts of new technologies are \r\ntypical examples of problem types analysed in energy system models."


id: b244edaea2775da8996d69ae385ed5ba
created_at: 2023-03-16 14:01:54
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content:
"While these four model types drive the component dynamics in \r\ndifferent ways, they commonly include modules that include: \r\nlearning and diffusion (via exogenous, e.g.,  autonomous learning, \r\nor endogenous learning regarding costs and efficiency: i.e.,  cost \r\ndecreases and/or efficiency increases as a  function of adoption, \r\nand increased diffusion due to lower costs) (Jochem et al. 2018), \r\nstock turnover (the performance and characteristics of vehicle \r\nfleets including survival ages, mileages, fuel economies and loads/\r\noccupancy rates are tracked for each new sales/vehicle stocks), \r\nconsumer choice (theories of how people invest in new technology \r\nand utilise different modes of transport based on their individual "


id: 9f9b0c3e024d74183262b1351719df5c
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 22651
content:
"The global/national transport energy system models included in the \r\ntransportation chapter (Chapter 10) are listed below in Table 3."


id: 80f11d44fbea928f0f22ba02285a8b70
created_at: 2023-03-16 14:01:54
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"representation of demand-side strategies such as material recycling, \r\nlonger use of products or sharing of products."


id: bc2916ac8480ea2229b42b68cfc97b7a
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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content:
"Particularly, demand for industrial products is often considered in \r\nmore detail in bottom-up models of the industry sector than in top\u0002down models by taking more drivers into account. These drivers can \r\nbe, inter alia, population, gross value added, construction activity, \r\ntransport activity, but also changes in material efficiency, recycling \r\nrates and scrap rates as well as product use efficiency (e.g., through \r\nlonger use of products or sharing of products) (Fleiter et al. 2018; \r\nMaterial Economics 2019; IEA 2020b)."


id: 4c8b92de6a02672d6a0b83800d7074a
created_at: 2023-03-16 14:01:54
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content:
"Industry sector modelling approaches can vary considerably from \r\none another. As with other types of models, a key characteristic of \r\nindustry sector models is related to their geographical scope. While \r\nIAMs are often global in scope, many bottom-up sector models \r\nare limited to individual countries or regions. The models' system \r\nboundaries also differ, with some models fully considering the use of \r\nenergy for feedstock purposes and other models focusing only on the \r\nuse of energy for energetic purposes. Differences between models \r\nalso exist in regard to differentiation between the industry sector and \r\nthe energy transformation sector, concerning, for example, refineries \r\nand industrial power plants."


id: 1fccd1d5a2f27dd6b5820a9681a8e1ba
created_at: 2023-03-16 14:01:54
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content:
"In most top-down IAMs, some energy-intensive sectors, such as iron \r\nand steel or cement, are included separately, at least in a generalised \r\nmanner, but typically few if any sector-specific technologies are \r\nexplicitly represented. Instead, energy efficiency improvements in \r\nthe industry sector and its subsectors are often either determined by \r\nexogenous assumptions or are a function of energy prices. Likewise, \r\nfuel switching occurs primarily as a  result of changes in relative \r\nfuel prices, which in turn are influenced by CO2 price developments. \r\nIn IAMs that include specific technologies, fuel switching can be \r\nconstrained based on the characteristics of those technologies, \r\nwhile in IAMs with no technological detail, more generic "


id: 7b8a062cbbc3d1ecb32172841bfbdfd
created_at: 2023-03-16 14:01:54
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content:
"Industry sector models vary in regard to their representation of demand \r\nfor industrial goods or products. A more detailed representation of \r\ndemand in a model allows for a more explicit discussion of different \r\ntypes of drivers of industrial demand and therefore a more detailed "


id: 21c1f6bc5d0f01d01df5bb2640bb6e57
created_at: 2023-03-16 14:01:54
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content:
"Land use related IAM modelling results as presented in Chapter 7 \r\nare based on comprehensive land-use models (LUMs) that are \r\neither integrated directly, or through emulators, into the integrated \r\nassessment framework. Given the increasing awareness of the \r\nimportance of the land use sector to achieve ambitious climate \r\nmitigation targets, LUMs and their integration into IAM systems was \r\none of the key innovations to the integrated assessment over the \r\npast decade to allow for an economy-wide quantification of climate \r\nstabilisation pathways."


id: cd3b91e1dce3acbb442e4f7cdf5c2865
created_at: 2023-03-16 14:01:54
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content:
"In bottom-up models, individual technological mitigation options are \r\nrepresented in detail, especially for energy-intensive sectors such as \r\niron and steel, cement and chemicals. Typically, for each considered \r\ntechnology, not only specific energy demand but also investment \r\nand operating costs are included in these models. Investment costs \r\ncan change over time, either based on an exogenous assumption or \r\non an endogenised process such as a learning rate. While bottom\u0002up models often consider technology-specific learning, IAMs cover \r\ntechnological progress in a more general way associated to industry \r\nbranches. The uptake of new technologies is typically restricted in \r\nbottom-up models, for example by assuming a minimum lifetime for \r\nexisting stock or by assuming S-shaped diffusion curves (Fleiter et al. \r\n2018). The industrial sector models included in the industry chapter \r\n(Chapter 11) are listed in Table 4."


id: 1d3f01d00e30429d7dc6064b47890b7f
created_at: 2023-03-16 14:01:54
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content:
"LUMs allow developments in the land-use sector to be projected over \r\ntime and the impacts of mitigation policies on different economic \r\n(markets, trade, prices, demand, supply, etc.) and environmental \r\n(land use, emissions, fertiliser, irrigation water use, etc.) indicators \r\nto be assessed. The following models submitted scenarios to the AR6 \r\ndatabase: AIM (Fujimori et al. 2014, 2017; Hasegawa et al. 2017), \r\nEPPA (Chen et al. 2016), GCAM (Calvin et al. 2019), IMAGE (Stehfest \r\net al. 2014), MERGE, MESSAGE-GLOBIOM (Havlik et al. 2014; Fricko \r\net al. 2017; Huppmann et al. 2019), POLES (Keramidas et al. 2017), \r\nREMIND-MAgPIE (Kriegler et al. 2017; Dietrich et al. 2019), WITCH \r\n(Emmerling et al. 2016)."


id: 28abb81889839b4b8ca17f722ef71e09
created_at: 2023-03-16 14:01:54
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content:
"Aggregated, top-down models of the industry sector, as used in \r\nmost IAMs, are typically calibrated based on long-term historical \r\ndata, for example on the diffusion of new technologies or on new \r\nfuels. These models are therefore able to implicitly consider real-life \r\nrestrictions of the whole sector that bottom-up models (with their \r\nfocus on individual technologies) may not fully take into account. \r\nThese restrictions may arise, inter alia, from delays in the construction \r\nof infrastructure or market actors possessing incomplete information \r\nabout new technologies. Furthermore, as IAMs also model the climate \r\nsystem, these models can principally take into account potential \r\nrepercussions of climate change impacts on the growth rate and \r\nstructure of economies."


id: 3a36d808e572ac60d3537fab2f0fef45
created_at: 2023-03-16 14:01:54
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content:
"However, a downside of top-down models is that they are typically \r\nlimited in their representation of individual technologies and \r\nprocesses in the industry sector and particularly of technology\u0002driven structural change. This lack of technological detail limits the \r\nusefulness of these models to analyse technology-specific and sector\u0002specific mitigation measures and related policies. Top-down models \r\nalso tend to have a relatively aggregated representation of industrial \r\nenergy demand, meaning demand-side mitigations strategies such as \r\nrecycling, product-service efficiency and demand reduction options \r\nare difficult to assess with these models (Pauliuk et al. 2017)."


id: 1682833e6e98a2409d60ddf2f0ab8237
created_at: 2023-03-16 14:01:54
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content:
"In contrast, technology-rich bottom-up models allow detailed \r\nanalysis of the potential of new technologies, processes and fuels \r\nin individual industrial sectors to reduce GHG emissions. Their often\u0002detailed analysis of the demand side allows demand-side mitigation \r\nstrategies to be evaluated. Furthermore, radical future changes in \r\ntechnology, climate policy or social norms can more easily be reflected \r\nin bottom-up models than in top-down models which are calibrated \r\non past observations. Both types of models are typically not able to \r\naccount for product substitution (e.g., steel vs plastics) arising from \r\nchanging production cost differentials or changing product quality \r\ndue to new production processes. In principle, technology-rich input\u0002output models could fill this gap."


id: bcba5cf0d47a73b0ab85c80713d5b3cd
created_at: 2023-03-16 14:01:54
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content:
"LUMs project demand for food, feed, other industrial or energy uses \r\nfor different agriculture and forestry commodities over time. While \r\npartial equilibrium models typically use reduced-form demand \r\nfunctions with greater level of detail at the commodity level, however \r\nlimited agriculture and forestry, Computable General Equilibrium \r\n(CGE) models represent demand starting from utility functions from \r\nwhich it is possible to derive demand functions, and functional forms \r\nfor income and price elasticities, however for a more limited set of \r\nagricultural and forestry commodities but with full coverage of all \r\neconomic sectors (Valin et al. 2014; von  Lampe et al. 2014). Over "


id: e0808ae495f06d130f3f1cfc64a5482f
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content:
"time, demand for food, feed, and other industrial uses is projected \r\nconditional on population and income growth while bioenergy \r\ndemand is typically informed in partial equilibrium (PE) models by \r\nlinking with IAMs/energy systems models, and is usually endogenous \r\nin CGE/IAMs (Hasegawa et al. 2020). Depending on the model, \r\ndemand projections are sensitive to price changes (Valin et al. \r\n2014). International trade is often represented in LUMs using either \r\nArmington or spatial equilibrium approaches (von Lampe et al. 2014)."


id: a43eb366785c4935e8fe4de783361032
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 23395
content:
"There are significant differences between UNFCCC nationally reported \r\nGHG inventories and analytical global land use models. According \r\nto Grassi et al. (2017), this discrepancy results in a  3GtCO2-eq \r\ndifference in estimates between country reports and global models. \r\nThe difference relies on different methods to classify and assess \r\nmanaged forests and forest management fluxes (Houghton et al. \r\n2012; Pongratz et al. 2014; Smith et al. 2014; Tubiello et al. 2015; \r\nGrassi et al. 2017, 2021). While global models account for GHG \r\nemissions from indirect human-induced effects and natural effects in \r\nunmanaged land, countries only consider fluxes of land use and land\u0002use change in managed land. In order to produce policy-relevant \r\nland-use model exercises, reconciling these differences is needed by \r\nharmonising definitions and approaches of anthropogenic land and \r\nthe treatment of indirect environmental change (Grassi et al. 2017)."


id: 27ae42f0d2a3c85dcdc41e6e88dfb803
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vector_id: 3650
content:
"A.III.I.7.3.2 Treatment of Terrestrial Carbon Dioxide Removal \r\nOptions including Biomass Supply for Bioenergy"


id: 617826ed71e05253fbb2beee430532b2
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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content:
"Terrestrial carbon dioxide removal options are only partially included \r\nin LUMs and mostly rely on afforestation and bioenergy with carbon \r\ncapture and storage (BECCS) (Fuss et al. 2014, 2018; Minx et al. 2018; \r\nSmith et al. 2019; Butnar et al. 2020). Especially some nature-based \r\nsolutions (Griscom et al. 2017) such as soil carbon management \r\n(Paustian et al. 2016), which have the potential to alter the \r\ncontribution of land-based mitigation in terms of timing, potential and \r\nsustainability consequences, are only recently being implemented in \r\nLUMs (Frank et al. 2017; Humpenoder et al. 2020). The representation \r\nof bioenergy feedstocks varies across models but typically LUMs have \r\ncomprehensive representation of a series of crops (starch, sugar, oil, \r\nwood/lignocellulosic feedstocks) or residues/byproducts that can be \r\nused for liquid and solid bioenergy production (Hanssen et al. 2019)."


id: 887c683e22f65affda600668f212a46e
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
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content:
"Aside reporting the implications on agriculture, forestry and other \r\nland use (AFOLU) GHG emissions, LUMs can provide a  set of \r\nenvironmental and socio-economic impact indicators to assess the \r\nquantified climate stabilisation pathways in a  broader sustainable \r\ndevelopment agenda (van Vuuren et al. 2015; Obersteiner et al. 2016; \r\nvan Vuuren et al. 2019; Frank et al. 2021; Soergel et al. 2021). These \r\nindicators typically span from land use area developments (Popp et al. \r\n2017; Stehfest et al. 2019), fertiliser use, irrigation water use and \r\nenvironmental flows (Bonsch et al. 2015; Pastor et al. 2019; Chang \r\net al. 2021; de Vos et al. 2021), and on biodiversity (Leclere et al. \r\n2020; Marquardt et al. 2021), to market impacts on commodity prices \r\nand food consumption, or impact on undernourishment (Hasegawa \r\net al. 2018; Doelman et al. 2019; Fujimori et al. 2019a; Hasegawa et al. \r\n2020; Soergel et al. 2021)."


id: 271ec5d4747df918b63beabb568d5b50
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vector_id: 3549
content:
"Climate model emulators (often referred to as reduced complexity or \r\nsimple climate models) are used to integrate the WGI knowledge of \r\nphysical climate science into the WGIII assessment. Hence, emulators \r\nare used to assess the climate implications of the GHG and other "


id: bc112ad161ca93e189db6f6d41f15882
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 27153
content:
"emissions trajectories that IAMs produce (van Vuuren et al. 2008; \r\nRogelj et al. 2011; Clarke et al. 2014; Schaeffer et al. 2015; Rogelj \r\net al. 2018a). The IAM literature typically uses one of two approaches: \r\ncomprehensive emulators such as MAGICC (Meinshausen et al. 2011) \r\nor Hector (Hartin et al. 2015) or minimal complexity representations \r\nsuch as the representation used in DICE (Nordhaus 2018), PAGE \r\n(Yumashev et al. 2019; Kikstra et al. 2021c) and Fund (Waldhoff \r\net al. 2014). In physical science research, a wider range of different \r\nemulators are used (Nicholls et al. 2020b, 2021a)."


id: 3a901c6f5da4fc1a147a9e52ec842d34
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 6579
content:
"Process-based integrated assessment models (IAMs) describe the \r\ncoupled energy-land-economy-climate system (Weyant 2009; Krey \r\n2014; Weyant 2017). They typically capture all greenhouse gas \r\n(GHG) emissions induced by human activities and, in many cases, \r\nemissions of other climate forcers like sulphate aerosols. Process\u0002based IAMs represent most GHG and climate pollutant emissions by \r\nmodelling the underlying processes in energy and land use. Those \r\nmodels are able to endogenously describe the change in emissions \r\ndue to changes in energy and land use activities, particularly in \r\nresponse to climate action. But IAMs differ in the extent to which \r\nall emissions and the corresponding sources, processes and activities \r\nare represented endogenously and, thus, can be subjected to policy \r\nanalysis.1\r\n IAMs also differ regarding the scope of representing carbon \r\nremoval options and their interlinkage with other vital systems such \r\nas the energy and land-use sectors."


id: 8864a580b27d8487f2c1cea7fd82d5e9
created_at: 2023-03-16 14:01:54
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"A key application of emulators within IPCC WGIII is the classification \r\nof emission scenarios with respect to their global mean temperature \r\noutcomes (Clarke et al. 2014; Rogelj et al. 2018a). WGIII relies on \r\nemulators to assess the full range of carbon-cycle and climate \r\nresponse uncertainty of thousands of scenarios, as assessed by AR6 \r\nWGI. An exercise of such amplitude is currently infeasible with more \r\ncomputationally demanding state-of-the-art Earth system models. \r\nCross-Chapter Box 7.1 in AR6 WGI documents how emulators used \r\nin WGIII are consistent with the physical science assessment of WGI \r\n(Forster et al. 2021)."


id: 3835b17524c9b276d24d5053b50b4a5b
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"Global IAMs are used to analyse global emissions scenarios \r\nextrapolating current trends under a  variety of assumptions and \r\nclimate change action pathways under a variety of global goals. In \r\nrecent years, a  class of national and regional IAMs have emerged \r\nthat describe the coupled energy-land-economy system in a  given \r\ngeography. They typically have higher sectoral, policy and technology \r\nresolution than global models and make assumptions about boundary \r\nconditions set by global markets and international policy regimes. \r\nThese IAMs are used to study trends and transformation pathways \r\nfor a given region (Shukla and Chaturvedi 2011; Capros et al. 2014; \r\nLucena et al. 2016)."


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"Finally, the recently developed OpenSCM-Runner package (Nicholls \r\net al. 2020a) provides users with the ability to run multiple emulators \r\nfrom a  single interface. OpenSCM-Runner has been built in \r\ncollaboration with the WGIII research community and forms part of \r\nthe WGIII assessment (Annex III.II.2.5.1)."


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"including sensitivity analysis (McJeon et al. 2011; Luderer et al. \r\n2013; Rogelj et al. 2013a; Bosetti et al. 2015; Marangoni et al. 2017; \r\nGiannousakis et al. 2021), model comparisons (Clarke et al. 2009; \r\nKriegler et al. 2014a, 2015a; Riahi et al. 2015; Tavoni et al. 2015; \r\nKriegler et al. 2016; Riahi et al. 2017; Luderer et al. 2018; Roelfsema \r\net al. 2020; Riahi et al. 2021; van Soest et al. 2021), model diagnostics \r\n(Kriegler et al. 2015a; Wilkerson et al. 2015; Harmsen et al. 2021), and \r\ncomparison with historical patterns (Wilson et al. 2013; van Sluisveld \r\net al. 2015; Napp et al. 2017)."


id: 1df9a31ff2df1c5b8c8fb6843afbbded
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"Energy models can be also embedded into a  broader, long-term \r\nmacroeconomic context in a general equilibrium model (Bauer et al. \r\n2008; Messner and Schrattenholzer 2000). The demands for final \r\nenergy and energy services are endogenously driven by an economic \r\ngrowth model that also endogenises the economic allocation problem \r\nof macroeconomic resources for the energy sector that crowd out \r\nwith alternatives. This allows impact analysis of climate policies on \r\neconomic growth and structural change, investment financing and \r\ncrowding out as well as income distribution and tax revenue recycling \r\n(Guivarch et al. 2011). Moreover, general equilibrium models also \r\nderive mitigation costs in terms of GDP losses and consumption \r\nlosses, which comprise the full macroeconomic impacts rather than \r\nonly the narrow energy-related costs (Paltsev and Capros 2013)."


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"In recent years, substantial efforts have been devoted to improve \r\nand integrate land-use sector models in IAMs (Popp et al. 2014b, \r\n2017). This acknowledges the importance of land-use GHG \r\nemissions of the agricultural and forestry sectors as well as the \r\nrole of bioenergy, afforestation and other land-based mitigation \r\nmeasures. The integration is particularly important in light of the \r\nlong-term climate goals of the Paris Agreement, for four reasons \r\n(IPCC 2019b). First, the GHG emissions from land-use change \r\naccount for more than 10% of global GHG emissions (Kuramochi "


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"There exists a broad range of process-based IAMs that differ regarding \r\nthe economic modelling approaches (Annex III.I.2) as well as the \r\nmethodology and detail of sector representation (Annex  III.I.3-7) \r\nand how they are interlinked with each other."


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"This leads to differences in model results regarding global aggregates \r\nas well as sectoral and regional outputs. Several approaches have \r\nbeen used to evaluate the performance of IAMs and understand \r\ndifferences in IAM behaviour (Schwanitz 2013; Wilson et al. 2021), "


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"Arvesen et al. 2018; Tokimatsu et al. 2020). This holistic perspective \r\nensures mitigation pathways do not exacerbate challenges for other \r\nsectors or environmental indicators. At the same time, pathways \r\nare leveraging potential synergies along the way towards achieving \r\nmultiple goals."


id: 9f5f9bef4cceccf38600e5f06cb93013
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"IAMs rely on biophysical models with a relatively high degree of spatial \r\nand temporal resolution to inform coarser-scale economic models of \r\nthe potentials and costs for land, water and energy systems (Johnson \r\net al. 2019). IAMs leverage population, GDP and  urbanisation \r\nprojections to generate consistent water, energy and crop demand \r\nprojections across multiple sectors (e.g.,  agriculture, livestock, \r\ndomestic, manufacturing and electricity generation) (Mouratiadou \r\net al. 2016). The highly distributed nature of decisions and impacts \r\nacross sectors, particularly for land and water, has been addressed \r\nusing multi-scale frameworks that embed regional and sub-regional \r\nmodels within global IAMs (Mosnier et al. 2014; Hejazi et al. 2015; \r\nBijl et al. 2018; Portugal-Pereira et al. 2018). These analyses have \r\ndemonstrated how local constraints and policies interact with \r\nnational and international strategies aimed at reducing emissions."


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"A key purpose of IAMs is to provide orientation knowledge for the \r\ndeliberation of future climate action strategies by policymakers, civil \r\nsociety and the private sector. This is done by presenting different \r\ncourses of actions (climate change and climate action pathways) \r\ntowards a  variety of long-term climate outcomes under a  broad \r\nrange of assumptions about future socio-economic, institutional \r\nand technological developments. The resulting climate change and \r\nclimate action pathways can be analysed in terms of their outcomes \r\ntowards a set of societal goals (such as the SDGs) and the resulting \r\ntrade-offs between different pathways. Key trade-offs that have \r\nbeen investigated in the IAM literature are between (i) no, moderate, \r\nand ambitious mitigation pathways (Riahi et al. 2017), (ii) early vs \r\ndelayed mitigation action (Riahi et al. 2015; Luderer et al. 2018), \r\n(iii) global action with a  focus on economic efficiency equalising "


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"An energy-water-land nexus approach integrates the analysis of \r\nlinked resources and infrastructure systems to provide a consistent \r\nplatform for multi-sector decision-making (Howells et al. 2013). Many \r\nof the IAMs that contributed to the assessment incorporate a nexus \r\napproach that considers simultaneous constraints on land, water and \r\nenergy, as well as important mutual dependencies (Fricko et al. 2017; \r\nFujimori et al. 2017; Calvin et al. 2019; Dietrich et al. 2019; van Vuuren \r\net al. 2019). Recently IAMs have also been integrated with life cycle \r\nassessment tools in assessing climate mitigation policies to better \r\nunderstand the relevance of life cycle GHG emissions in cost-optimal \r\nmitigation scenarios (Portugal-Pereira et al. 2016; Pehl et al. 2017; "


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"Figure 3 | The representation of Sustainable Development Goals by Integrated Assessment Models. (a) Individual target coverage from a multi-model survey; \r\nand (b) SDG interactions and coverage by IAM models according to a combination of expert and model surveys. The strength dimension of SDG interactions is indicated by grey \r\nshading: darker shades represent strong interactions while white represents no interactions. Orange cells indicate where there is the highest agreement between the importance \r\nof interactions and model representation, while blue coloured cells show the most important interactions without model representation. Source: van Soest et al. (2019)."


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"or fossil fuel subsidy removal. If certain technologies or activities are \r\nrelated to spill-overs such as technology learning, carbon pricing \r\nmight be complemented by technology support (Schultes et al. 2018). \r\nIf carbon-pricing policies are fragmented or delayed, additional and \r\nearly sector policies can help reduce distortions and carbon leakage \r\neffects (Bauer et al. 2020b). All these variations to the policy regime \r\ncan lead to very different transformation pathways and policy costs, \r\nwhich is a core result of the IAM analysis."


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"By applying sensitivity analysis, IAMs can be used to assess the \r\nimportance of strategically developing new technologies and \r\noptions for mitigation and identifying sticking points in climate \r\npolicy frameworks. The sensitivity analysis evaluates differences \r\nin outcomes subject to changes in assumptions. For instance, the \r\nassumption about the timing and costs of CCS and CDR availability \r\ncan be varied (Bauer et al. 2020a). The differences in mitigation \r\ncosts and the transformation pathways support the assessment of \r\npolicy prioritisation by identifying and quantifying crucial levers for \r\nachieving long-term climate mitigation targets such as R&D efforts \r\nand timing of policies."


id: 5cb582bb2b3c4d12be9ebb072330069
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"The application of IAMs and their results for providing knowledge \r\non climate change response strategies have been criticised based on \r\nfour arguments (Gambhir et al. 2019; Keppo et al. 2021). First, there \r\nare concerns that IAMs are missing important dynamics, for example \r\nwith regard to climate damages and economic co-benefits of \r\nmitigation (Stern 2016), demand-side responses (Wilson et al. 2012), \r\nbioenergy, land degradation and management (Creutzig et al. 2015; \r\nIPCC 2019b), carbon dioxide removal (Smith et al. 2016), rapid \r\ntechnological progress in the renewable energy sector (Creutzig et al. \r\n2017), actor heterogeneity, and distributional impacts of climate \r\nchange and climate policy. This has given rise to criticism that \r\nIAMs lack credibility in a set of crucial assumptions, among which \r\nstands out the critique on the availability of carbon dioxide removal \r\ntechnologies (Anderson and Peters 2016; Bednar et al. 2019)."


id: dcf8f4c21f19fec0af4337516c08d76d
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"Most policy analysis with process-based IAMs apply a mix of short\u0002term policy evaluation and long-term policy optimisation. Policy \r\nevaluation applies an exogenous set of policies such as the stated \r\nNDCs and evaluates the emission outcomes. Policy optimisation is \r\nmostly implemented as a  cost-effectiveness analysis: a  long-term \r\nclimate stabilisation target is set to derive the optimal mitigation \r\nstrategy that equalises marginal abatement cost across sectors, GHGs \r\nand countries. This optimal mitigation strategy can be implemented \r\nby a  broad set of well-coordinated sector-specific policies or by \r\ncomprehensive carbon-pricing policies."


id: de2e1283f0c7f53eb2bd3229ae81fab8
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"Most commonly, the baseline scenario is either a  no-policy \r\nbaseline or based on the NDCs applying an extrapolation beyond \r\n2030 (Grant et al. 2020; Roelfsema et al. 2020). The climate policy \r\nregimes most commonly applied include a  long-term target to be \r\nreached. The optimal climate strategy can be phased in gradually \r\nor applied immediately after 2020. It can focus on a global carbon \r\nprice equalising marginal abatement costs across countries or policy \r\nintensities can vary across countries and sectors in the near to medium \r\nterm. The climate policy regime can include or exclude effort-sharing \r\nmechanisms and transfers between regions. Also, it can be extended \r\nto include additional sector policies such as improved forest protection "


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"Finally, there are concerns that IAM analysis could focus on only \r\na subset of relevant futures and thus push society in certain directions \r\nwithout sufficient scrutiny (Beck and Mahony 2017). IAMs aim to \r\nexplore a  wide range of socio-economic, technology and policy \r\nassumptions (Riahi et al. 2017), but it remains a constant challenge to \r\ncapture all relevant perspectives (O'Neill et al. 2020). These concerns \r\ncan be addressed by adopting an iterative approach between \r\nresearchers and societal actors in shaping research questions and \r\nIAM applications (Edenhofer and Kowarsch 2015). IAM research is \r\nconstantly taking up concerns about research gaps and fills it with \r\nnew pathway research, as, for example, occurred for low energy \r\ndemand and limited bioenergy with CCS scenarios (Grubler et al. \r\n2018; van Vuuren et al. 2018)."


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"ambition (Clarke et al. 2014; Schleussner et al. 2016). In addition, \r\ndifferent transformation pathways to the same goal are often used \r\nto analyse trade-offs between different routes towards this goal \r\n(Rogelj et al. 2018a). These scenarios need to be looked at as a set to \r\nunderstand attainable outcomes and the trade-offs between them. \r\nWith scenarios, context matters."


id: 83271560ad505ae0ad93ffbcecafc41b
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"Scenarios are descriptions of alternative future developments. They \r\nare used to explore the potential implications of possible future \r\ndevelopments and how they might depend on alternative courses of \r\naction. They are particularly useful in the context of deep uncertainty. \r\nScenarios are conditional on the realisation of external assumptions \r\nand can be used to explore possible outcomes under a  variety \r\nof assumptions."


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"Future climate change is a  prime example for the application of \r\nscenarios. It is driven by human activities across the world and \r\nthus can be altered by human agency. It affects all regions over \r\nmany centuries to come. Humankind's response to climate change \r\ntouches not only on the way we use energy and land, but also on \r\nsocio-economic and institutional layers of societal development. \r\nClimate change scenarios provide a  central tool to analyse this \r\nwicked problem."


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"Climate change scenarios are developed for a number of purposes \r\n(O'Neill et al. 2020). First, they are constructed to explore possible \r\nclimate change futures covering the causal chain from (i) socio\u0002economic developments to (ii) energy and land use to (iii) greenhouse \r\ngas emissions to (iv) changes in the atmospheric composition of \r\ngreenhouse gases and short-lived climate forcers and the associated \r\nradiative forcing to (v) changes in temperature and precipitation \r\npatterns to (vi) bio-physical impacts of climate change and finally to \r\n(vii) impacts on socio-economic developments, thus closing the loop. \r\nQuantitative scenarios exploring possible climate change futures \r\nare often called climate change projections and climate change \r\nimpact projections."


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"Fourth, climate change scenarios and their assessment aim to inform \r\nsociety (Kowarsch et al. 2017; Weber et al. 2018; Auer et al. 2021). \r\nTo achieve this, it is important to connect climate change scenarios \r\nto broader societal development goals (Riahi et al. 2012; van Vuuren \r\net al. 2015; Kriegler et al. 2018c; Soergel et al. 2021) and relate them \r\nto social, sectoral and regional contexts (Absar and Preston 2015; \r\nFrame et al. 2018; Kok et al. 2019; Aguiar et al. 2020). To this end, \r\nscenarios can be seen as tools for societal discourse and decision\u0002making to coordinate perceptions about possible and desirable \r\nfutures between societal actors (Edenhofer and Kowarsch 2015; Beck \r\nand Mahony 2017)."


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"Different types of climate change scenarios are linked to different \r\npurposes and knowledge domains and different models are used \r\nto construct them (Annex III.I). Global reference and mitigation \r\nscenarios and their associated emissions projections, which are often \r\ncalled emission scenarios, and national, sector and service transition \r\nscenarios are key types of scenarios assessed in the Working Group III \r\nreport. They are briefly summarised below.5"


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"A brief description of the common climate change scenario \r\nframework with relevance for all three IPCC Working Groups is \r\nprovided in Annex III.II.1.3, and a discussion how the WGI and WGII \r\nassessments relate to the WGIII scenario assessment is given in \r\nAnnex III.II.2.5."


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"term (Bertram et al. 2021; Hasegawa et al. 2021; Riahi et al. 2021). \r\nOther large-scale multi-model studies looked into specific topics: the \r\ninternational economic implications of the NDCs in 2030 (EMF36) \r\n(Bohringer et al. 2021), the impact of mitigating short-lived climate \r\nforcers on warming and health co-benefits in mitigation pathways \r\n(EMF30) (Harmsen et al. 2020; Smith et al. 2020b) and the role and \r\nimplications of large-scale bioenergy deployment in global mitigation \r\npathways (EMF33) (Bauer et al. 2020a; Rose et al. 2020)."


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"The scenarios from most of these and many other studies were \r\ncollected in the AR6 scenario database (Annex III.II.3.2) and are \r\nprimarily assessed in Chapter  3 of the report. However sectoral \r\nchapters have also used the scenarios, including their climate \r\nmitigation categorisations, to ensure consistent cross-chapter \r\ntreatment. Only a  small fraction of these scenarios were already \r\navailable to the assessment of global mitigation pathways in \r\nthe Special Report on Global Warming of 1.5degC (SR1.5) (Rogelj \r\net al. 2018a) and were included in the supporting SR1.5 database \r\n(Huppmann et al. 2018)."


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"A large number of transition scenarios is developed on a national/\r\nregional level by national integrated assessment, energy-economy \r\nor computable general equilibrium models, among others. These \r\naim to analyse the implications of current climate plans of countries \r\nand regions, as well as long-term strategies until 2050 investigating \r\ndifferent degrees of low-carbon development. National/regional \r\ntransition scenarios are assessed in Chapter 4 of the report."


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"Recent research has focused on several different types of national \r\ntransition scenarios that focus on accelerated climate mitigation \r\npathways in the near term to 2050. These include scenarios considered "


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"by the authors as tied to meeting specific global climate goals6 and \r\nscenarios tied to specific policy targets (e.g.,  carbon neutrality or \r\n80-95% reduction from a  certain baseline year). A  majority of the \r\naccelerated national transition modelling studies up to 2050 evaluate \r\npathways that the authors consider compatible with a  2degC global \r\nwarming limit, with fewer scenarios defined as compatible with 1.5degC \r\nglobal pathways. Regionally, national transition scenarios have centred \r\non countries in Asia (particularly in China, India, Japan), in the European \r\nUnion, and in North America, with fewer and more narrowly focused \r\nscenario studies in Latin America and Africa (Lepault and Lecocq 2021)."


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"A central feature of service transition pathways is a  focus on the \r\nprovision of adequate energy services to provide decent standards \r\nof living for all as the main scenario objective. Energy services are \r\nproxies for well-being, with common examples being provision \r\nof shelter (expressed as m2\r\n per capita), mobility (expressed as \r\npassenger-kilometres), nutrition (expressed as kCal per capita), and "


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content:
"et al. 2014) (Figure 4). It is structured along two dimensions: socio\u0002economic assumptions varied along the SSPs, and climate (forcing) \r\noutcomes varied along the Representative Concentration Pathways \r\n(RCPs) (van Vuuren et al. 2011b). To distinguish resulting emission \r\nscenarios from the original four RCPs (RCP2.6, RCP4.5, RCP6.0, and \r\nRCP8.5), they are typically named SSPx-y with x = {1,...,5} the SSP \r\nlabel and y = {1.9, 2.6, 3.4, 4.5, 6.0, 7.0, 8.5} W m-2 the nominal \r\nforcing level in 2100. The four forcing levels that were already \r\ncovered by the original RCPs are bolded here."


id: 60c88af911d2c71e3fca72f90dff2da5
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"The integrated assessment modelling community has used the SSPs to \r\nprovide a set of global integrated energy-land use-emissions scenarios \r\n(Bauer et al. 2017; Calvin et al. 2017; Fricko et al. 2017; Fujimori \r\net al. 2017; Kriegler et al. 2017; Popp et al. 2017; Rao et al. 2017b; \r\nRiahi et al. 2017; van Vuuren et al. 2017b; Rogelj et al. 2018b) in line \r\nwith the matrix architecture of the scenario framework (van Vuuren "


id: 51429b100dba4d9fbcb3cfd00bd1c706
created_at: 2023-03-16 14:01:54
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content:
"The development of a scenario involves design choices, in addition to \r\nthe selection of the model. This section will focus on key choices related \r\nto scenario design, and the respective socio-economic, technical, and "


id: 49e9f9964ac7310a00e4640d16a6f110
created_at: 2023-03-16 14:01:54
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"scenarios based on the SSP-RCP framework also applied radiative \r\nforcing levels in 2100 (Riahi et al. 2017). Temperature targets were \r\noften implemented by imposing end-of-century carbon budgets, that \r\nis, cumulative emissions up until 2100. In the case of 2degC pathways, \r\nthose budgets were usually chosen such that the 2degC limit was not \r\novershoot with some pre-defined probability (Luderer et al. 2018). \r\nArguably, the availability of net negative CO2 emissions has led to \r\nhigh levels of carbon dioxide removal (CDR) in the second half of \r\nthe century, although CDR deployment is often already substantial to \r\ncompensate residual emissions (Rogelj et al. 2018a)."


id: 32e0e481aacf1098f69099a817a203f0
created_at: 2023-03-16 14:01:54
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content:
"policy assumptions. Model selection cannot be separated from these \r\nchoices. The various advantages and disadvantages of models are \r\ndescribed in Annex III, Part I (Modelling Methods)."


id: 236b15cdda706ef934f0b93c84cade92
created_at: 2023-03-16 14:01:54
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content:
"Recent literature has increasingly focused on alternative approaches \r\nsuch as peak warming or peak CO2 budget constraints to implement \r\ntargets (Rogelj et al. 2019b; Johansson et al. 2020; Riahi et al. 2021). \r\nNevertheless, due to the availability of net negative CO2 emissions \r\nand the assumption of standard (exponentially increasing) emissions\u0002pricing profiles from economic theory, peak and decline temperature \r\nprofiles still occurred in a large number of mitigation pathways in \r\nthe literature even in the presence of peak warming and carbon \r\nbudget targets (Strefler et al. 2021b). This has led to proposals to \r\ncombine peak targets with additional assumptions affecting the \r\ntiming of emissions reductions like a constraint on net negative CO2"


id: b62e81b1d107800bfff680819a522d55
created_at: 2023-03-16 14:01:54
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content:
"emissions (Obersteiner et al. 2018; Rogelj et al. 2019a; Riahi et al. \r\n2021) and different carbon pricing profiles (Strefler et al. 2021b). \r\nThese proposals are aiming at a stabilisation rather than a peak and \r\ndecline of warming under a given warming limit. However, arguments \r\nin support of peak and decline warming profiles also exist: the goal of \r\nhedging against positive feedback loops in the Earth system (Lenton \r\net al. 2019) and the aim of increasing the likelihood of staying below \r\na  temperature limit towards the end of the century (Schleussner \r\net al. 2016). It is also noteworthy that peak and decline temperature \r\npathways are connected to achieving net-zero GHG emissions (with \r\nCO2-eq emissions calculated using GWP100) in the second half of the \r\ncentury (Rogelj et al. 2021)."


id: 1d62df040b020acabaf74192d1c83092
created_at: 2023-03-16 14:01:54
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content:
"Efficiency considerations: Process-based IAMs typically calculate \r\ncost-effective mitigation pathways towards a  given target as \r\na  benchmark case (Clarke et al. 2014). In these pathways, global \r\nmitigation costs are minimised by exploiting the abatement options \r\nwith the least marginal costs across all sectors and regions at any \r\ntime, implicitly assuming a  globally integrated and harmonised \r\nmitigation regime. This idealised benchmark is typically compared \r\nacross different climate targets or with reference scenarios \r\nextrapolating current emissions trends (UNEP 2019). It naturally \r\nevolves over time as the onset of cost-effective action is being set \r\nto the immediate future of respective studies. This onset was pushed \r\nback from 2010-2015 in studies assessed by AR5 (Clarke et al. 2014) \r\nto the first modelling time step after 2020 in studies assessed by AR6."


id: 49612c1dbc8dbabc0f0740b0ceb7dec9
created_at: 2023-03-16 14:01:54
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content:
"Scenarios limiting warming to below 2degC phase in climate policies in \r\nall regions and sectors. Almost all converge to a harmonised global \r\nmitigation regime before the end of century (with the exception \r\nof Bauer et al. (2020b)). In practice, policies are often a  mix of \r\nregulations, standards, or subsidies. Implementing these real-world \r\npolicies can give different outcomes to optimal uniform carbon \r\npricing (Mercure et al. 2019). Modelled carbon prices will generally \r\nbe lower when other policies are implemented (Calvin et al. 2014a; \r\nBertram et al. 2015). As countries implement more and a diverse set \r\nof policies, the need to further develop the policy assumptions in \r\nmodels is becoming apparent (Grant et al. 2020; O'Neill et al. 2020; \r\nKeppo et al. 2021)."


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"The notion of cost-effectiveness is sensitive to economic assumptions in \r\nthe underlying models, particularly concerning the assumptions on pre\u0002existing market distortions (Guivarch et al. 2011; Clarke et al. 2014; Krey \r\net al. 2014) and the discount rate on future values. Those assumptions \r\nare often not clearly expressed. Most models have a discount rate of \r\n3-5%, though the range of alternatives is larger. Cost-benefit IAMs \r\nhave had a tradition of exploring the importance of discount rates, but \r\nprocess-based IAMs have generally not. A lower discount rate brings \r\nmitigation forward in time and uses less net negative CO2 emissions \r\nin cases where target overshoot is allowed (Emmerling et al. 2019; \r\nRealmonte et al. 2019). While most models report discount rates in \r\ndocumentation, there is arguably too little sensitivity analysis of how \r\nthe discount rate affects modelled outcomes."


id: 8ba61e847b0189d84a9e25ff2b74481c
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"Cost-effective pathways typically do not account for climate impacts \r\nbelow the temperature limit, although recent updates to climate \r\ndamage estimates suggest a  strengthening of near-term action in \r\ncost-effective mitigation pathways (Schultes et al. 2021). Recently, \r\nthe research community has begun to combine mitigation pathway \r\nanalysis with ex post analysis of associated climate impacts and the \r\nbenefits of mitigation (Drouet et al. 2021). Cost-effective pathways \r\nthat tap into least cost abatement options globally without considering \r\ncompensation schemes to equalise the mitigation burden between \r\ncountries are not compatible with equity considerations. There is \r\na  large body of literature exploring international burden-sharing \r\nregimes to accompany globally cost-effective mitigation pathways \r\n(Tavoni et al. 2015; Pan et al. 2017; van den Berg et al. 2020)."


id: b4bca839393a95feba37c337cf605853
created_at: 2023-03-16 14:01:54
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content:
"Policy assumptions: Cost-effective mitigation scenarios assume that \r\nclimate policies are globally uniform. There is a substantial literature "


id: 88cf9d541d54630cb835e927fde6f92c
created_at: 2023-03-16 14:01:54
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"2018) proved its value for the assessment as well as for broad use of \r\nthe scenario information by researchers and stakeholders. This is now \r\nbeing continued for AR6."


id: d70a9856e518d4dc4bccefed82868d59
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"Within these key scenario design choices, model choice cannot be \r\nignored. Not all models can implement aspects of a scenario in the \r\nsame way. Alternative target implementations are difficult for some \r\nmodel frameworks, and implementation issues also arise around \r\ntechnological change and policy implementation. Certain scenario \r\ndesigns may lock out certain modelling frameworks. These issues \r\nindicate the need for a diversity of scenario designs (Johansson et al. \r\n2020) to ensure that model diversity can be fully exploited."


id: 7b63cf1e383c5870ba40206c1603c786
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content:
"It is possible for many assumptions to be harmonised, depending on \r\nthe research question. The SSPs were one project aimed at increasing \r\nharmonisation and comparability. It is also possible to harmonise \r\nemission data, technology assumptions, and policies (Giarola et al. \r\n2021). While harmonisation facilitates greater comparability between \r\nstudies, it also limits scenario and model diversity. The advantages \r\nand disadvantages of harmonisation need to be discussed for \r\neach model study."


id: ebe7642b069643fc31fe04da75956890
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content:
"In order to develop feasibility metrics of mitigation scenarios \r\n(Chapter 3, Section 3.8), the assessment relied on the multidimensional \r\nfeasibility framework developed in Brutschin et al. (2021), considering \r\nfive feasibility dimensions: (i) geophysical, (ii) technological, \r\n(iii)  economic, (iv) institutional and (v) socio-cultural. For each \r\ndimension, a set of indicators was developed, capturing not only the \r\nscale but also the timing and the disruptiveness of transformative \r\nchange (Kriegler et al. 2018b). All AR6 scenarios (C1-C3 climate \r\ncategories) were categorised through this framework to quantify \r\nfeasibility challenges by climate category, time, policy architecture \r\nand by feasibility dimension, summarised in Figure 3.43 (Chapter 3)."


id: bafa4742f08d5b5ad34cdca473417283
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content:
"Table 8 | Feasibility dimensions, associated indicators and thresholds for the onset of medium and high concerns about feasibility (Chapter 3.8)."


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"concern that might be challenging but within reach, given certain \r\nenablers, (iii) high levels of concern representing unprecedented \r\nlevels of transformation, attainable only under consistent enabling \r\nconditions. Indicator thresholds defining these three levels of \r\nconcern were obtained from the available literature and developed \r\nwith additional empirical literature. Table  8 summarises the main \r\nindicators used and the associated thresholds for medium and high \r\nlevels of concern. Finally, we aggregated feasibility concerns for each \r\ndimension and each decade, employing the geometric mean, a non\u0002compensatory method which limits the degree of substitutability \r\nbetween indicators, and used for example by the United Nations for \r\nthe Human Development Index (HDI). Alternative aggregation scores \r\nsuch as the counting of scenarios exceeding the thresholds were \r\nalso implemented."


id: 75e60abb18f7733ec54e22c79d3c1b7f
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"In the IPCC Special Report on Global Warming of 1.5degC (SR1.5), \r\nillustrative pathways (IPs) were used in addition to descriptions of \r\nthe key characteristics of the full set of scenarios in the database \r\nto assess and communicate the results from the scenario literature. \r\nWhile the latter express the spread in scenario outcomes highlighting \r\nuncertain vs robust outcomes, IPs can be used to contrast different \r\nstories of mitigating climate change (Rogelj et al. 2018a)."


id: b80fcba4c6cadd46f1407584012fa70f
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"Following the example of the SR1.5, IPs have also been selected for \r\nthe AR6 of WGIII. In contrast to SR1.5, the selection needed to cover \r\na  larger range of climate outcomes while keeping the number of \r\nIPs limited. The selection focused on a range of critical themes that "


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"current policies as formulated around 2018 (Current Policies, CurPol) \r\nthrough to 2030 and then continues to follow a similar mitigation \r\neffort to 2100. The associated quantitative scenario (NGFS 2021) \r\nselected by Chapter 3 leads to about 3degC-4degC warming at the end of \r\nthe century. The second reference pathway follows emission pledges \r\nto 2030 (NDCs) and then continues with moderate climate action \r\nover time (Moderate Action, ModAct)."


id: bfbdf63aa3167574a15e1c3791ae80c
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"emerged from the AR6 assessment: (i) the level of ambition of climate \r\npolicy, (ii) the different mitigation strategies, (iii) timing of mitigation \r\nactions, and (iv) the combination of climate policy with sustainable \r\ndevelopment policies. The IPs consist of narratives (Table  9) as \r\nwell as possible quantifications. The IPs are illustrative and denote \r\nimplications of different societal choices for the development of future \r\nemissions and associated transformations of main GHG-emitting \r\nsectors. For Chapter  3, for each of the IPs a  quantitative scenario \r\nwas selected from the AR6 scenario database to have particular \r\ncharacteristics and from diverse modelling frameworks (Table 10)."


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"The five IMPs are deep mitigation pathways with warming in the \r\n1.5degC-2degC range. The first IMP pursues gradual strengthening beyond \r\nNDC ambition levels until 2030 and then acts to likely limit warming \r\nto 2degC (Climate Category C3) (IMP-GS) (van Soest et al. 2021) \r\n(Chapter  3.5.3). Three others follow different mitigation strategies \r\nfocusing on low energy demand (IMP-LD) (Grubler et al. 2018), "


id: f7096c4bd43f60987e3cbf337a8fedae
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content:
"In total, two reference pathways with warming above 2degC and five \r\nIllustrative Mitigation Pathways (IMPs) limiting warming in the \r\n1.5-2degC range were selected. The first reference pathway follows "


id: dd2b102ad43de0376c481c7e1981fc3f
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content:
"Table 10 | Quantitative scenario selection to represent the two reference pathways and five Illustrative Mitigation Pathways warming to 1.5degC-2degC \r\nfor the assessment in Chapter 3. These quantitative representations of the IMPs have also been taken up by a few other chapters where suitable. The warming profile of \r\nIMP-Neg peaks around 2060 and declines to below 1.5degC (50% likelihood) shortly after 2100. While technically classified as a C3, it exhibits the characteristics of C2 high \r\novershoot pathways."


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"renewable electricity (IMP-Ren) (Luderer et al. 2021) and large-scale \r\ndeployment of carbon dioxide removal measures resulting in net \r\nnegative CO2 emissions in the second half of the century (IMP-Neg). \r\nThe fifth IMP explicitly pursues a  broad sustainable development \r\nagenda and follows SSP1 socio-economic assumptions (IMP-SP) \r\n(Soergel et al. 2021). IMP-LD, IMP-Ren and IMP-SP limit warming to \r\n1.5degC (>50%) with no or limited overshoot (C1), while IMP-Neg has \r\na higher overshoot and only returns to nearly 1.5degC (50% chance) \r\nby 2100 (close to C2). In addition, two sensitivity cases for IMP-Ren \r\nand IMP-Neg are considered that limit warming to 2degC (>67%) (C3) \r\nrather than pursuing limiting warming to 1.5degC."


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"(Annex III.I.8) calibrated to the WGI assessment. These three steps \r\nensure a  consistent and comparable assessment of the climate \r\nresponse across emission scenarios from the literature."


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"The IMPs are used in different parts of the report. We just mention \r\nsome examples here. In Chapter  3, they are used to illustrate key \r\ndifferences between the mitigation strategies, for instance in terms \r\nof timing and sectoral action. In Chapter  6, Box  6.9 discusses \r\nthe  consequences for energy systems. Chapter  7 discusses some \r\nof the land-use consequences. In Chapter 8, the implications of the \r\nIMPs are further explored for urban systems where the elements of \r\nenergy, innovation, policy, land use and lifestyle interact (Chapter 8, \r\nSections 8.3 and 8.4). In Chapter 10, the consequences of different \r\nmitigation strategies for mobility are highlighted in different figures. \r\nThe IMPs are discussed further in Chapter 1, Section 1.3; Chapter 3, \r\nSection 3.2; and the respective sector chapters."


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"A transparent assessment pipeline has been set up across WGI and \r\nWGIII to ensure integration of the WGI assessment in the climate \r\nassessment of emission scenarios in WGIII. This pipeline consists of \r\na  step where emissions scenarios are harmonised with historical \r\nemissions (harmonisation), a  step in which species not reported \r\nby an IAM are filled in (infilling), and a step in which the emission \r\nevolutions are assessed with three climate model emulators "


id: f58398a6271bab8802c529b1b5acefec
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"Infilling missing species: Infilling ensures that scenarios include all \r\nrelevant anthropogenic emissions. This reduces the risk of a biased \r\nclimate assessment and is important because not all IAMs report \r\nall climatically active emission species. Infilling was only performed \r\nfor scenarios where models provided native reporting of energy "


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"year of peak warming, ocean heat uptake, atmospheric CO2, CH4 and \r\nN2O concentrations and effective radiative forcing from a range of \r\nspecies including CO2, CH4, N2O and aerosols for each emissions \r\nscenario, as well as an estimate of CO2 and non-CO2 contributions \r\nto the temperature increase. The climate emulator's GSAT projections \r\nare normalised to match the WGI Chapter 2 assessed total warming \r\nbetween 1850-1900 and 1995-2014 of 0.85degC."


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"Figure 5 | Comparison of non-CO2 warming relevant for the derivation of cumulative carbon budgets - and its sensitivity to (a) assumptions on historical \r\nemissions and (b) the set of investigated scenarios (right). Panel (c) shows how the relationship across scenarios between peak surface temperature and non-CO2\r\nwarming and peak cumulative CO2 is different for modelling frameworks. All dashed regression lines are at the 5th and 95th percentiles, solid lines are a regression at the median."


id: 498b557f7196ad644280c3265da16d6d
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"All panels depict non-CO2 warming in relation to 2010-2019 at the time of peak cumulative CO2, using MAGICC7. Scenarios that reach net-zero CO2 this century are coloured, \r\nwith dots in grey indicating scenarios that do not reach net-zero CO2 but still remain below 2degC median peak warming relative to 2010-2019 levels in this century. The scenario \r\nset 'AR6 database' in (b) includes only scenarios of those model frameworks that are shown in panel (c)) which have a detailed land-use model and enough scenarios to imply \r\na relationship."


id: d796250783c51a5ddd4d565375170e4f
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"Panel (a) The WGI remaining carbon budget takes into account the non-CO2 warming in dependence of peak surface temperatures via a regression line approach (lighter \r\nblue-coloured solid line). For the same scenario set, with historical emissions assumptions as used in Cross-Chapter Box 7.1 (darker blue-coloured solid line), a relationship is \r\nfound with a difference of approximately 0.05degC."


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"Panel (b) The WGIII database of scenarios tends to imply very similar non-CO2 warming at peak cumulative CO2 to the SR15 scenario database, especially around 1.5degC above \r\npre-industrial (0.43degC above 2010-2019 levels), though with slightly lower non-CO2 warming for higher peak temperatures."


id: 59e2780360a90419759ab9bb9a04aa45
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"Panel (c) Regressions at the 5th, 50th, and 95th percentiles indicate a model framework footprint affecting the relationship between peak warming and non-CO2 warming at \r\npeak cumulative CO2."


id: 8855404899ceb13eaeb497014442ecbe
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"offset by the difference arising from using either the SR1.5 or AR6 \r\nscenario databases (see panel (b) in Figure 5)."


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"Estimates of the remaining carbon budget that take into account non\u0002CO2 uncertainty are not only dependent on historical assumptions, \r\nbut also on future non-CO2 scenario characteristics, which are \r\ndifferent across the various scenarios in the AR6 database. In panel \r\n(b) of Figure 5, we show how the SR15 database of scenarios, which \r\nwas used to inform the WGI remaining carbon budget, differs from \r\nthe larger set considered in the WGIII report (both using MAGICC7 \r\nusing input files in line with Nicholls et al. (2021)). Overall, there \r\nis limited difference in the covered range of non-CO2 warming at \r\ndifferent peak surface temperature levels, leading to no clear change \r\nin estimated carbon budgets compared to SR1.5 based on the full \r\nscenario database. However, as discussed in Cross-Working Group \r\nBox 1 in Chapter 3, and shown in panel (c) of Figure 5, mitigation \r\nstrategies expressed by both the IAM footprint and scenario design "


id: 435771e1f2332510005c5d504c13b63f
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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content:
"WGII sets out common climate dimensions to help contextualise \r\nand facilitate consistent communication of impacts and synthesis \r\nacross WGII, as well as to facilitate WGI and WGII integration, with \r\nthe dimensions adopted when helpful and possible across WGII (AR6 \r\nWGII Cross-Chapter Box  1.1). 'Common climate dimensions' are \r\ndefined as common global warming levels (GWLs), time periods, and \r\nlevels of other variables as needed by WGII authors (see below for \r\na list of variables associated with these dimensions). Projected ranges \r\nfor associated climate variables were derived from the AR6 WGI \r\nreport and supporting resources and help contextualise and inform \r\nthe projection of potential future climate impacts and key risks. The \r\ninformation enables the mapping of climate variable levels to climate "


id: d7aa122b58998c3d53fb30c815452805
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 31458
content:
"and WGIII's global warming scenario classes are relatable but differ \r\nin several important ways. While GWLs represent temperature change \r\nthat occurs at some point in time, emissions scenarios in a temperature \r\nclass result in an evolving warming response over time. The emissions \r\nscenario warming also has a likelihood attached to the warming level \r\nat any point in time, that is, actual warming outcomes can be lower \r\nor higher than median warming projections within the range of the \r\nestimated uncertainty. Thus, multiple WGII results across GWLs will be \r\nrelevant to any particular WGIII emissions pathway, including at the \r\npeak temperature level."


id: dd2c88757cf20f8bb6d34719955473d1
created_at: 2023-03-16 14:01:54
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content:
"[Note: The scenario numbers documented in this section refer to all \r\nscenarios that were submitted and not retracted by the literature \r\nacceptance deadline of 11 October 2021, and that fulfilled the \r\nrequirement of being supported by an eligible literature source. Not \r\nall those scenarios were used in the assessment, for example some \r\ndid not pass the vetting process as documented in II.3.1.]"


id: e244b1f0d1c7b107b27de98ab14c6320
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 33099
content:
"As for previous IPCC reports of Working Group III, including the \r\nSpecial Report on Global Warming of 1.5degC (SR1.5) (Huppmann \r\net al. 2018; Rogelj et al. 2018a) and the Fifth Assessment Report \r\n(AR5) (Clarke et al. 2014; Krey et al. 2014), quantitative information \r\non mitigation pathways is collected in a  dedicated AR6 scenario \r\ndatabase8\r\n to underpin the assessment."


id: 93a445d384843788a883d796d99402ff
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 20746
content:
"For facilitating integration with WGIII, GWLs need to be related to \r\nWGIII's classification of mitigation efforts by temperature outcome. \r\nWGII's Chapter 3 groups full century emissions projections resulting from \r\na large set of assessed mitigation scenarios into temperature classes \r\n(Chapter 3, Sections 3.2 and 3.3, Annex III.II.2.5.1, and Annex III.II.3.2.1). \r\nScenarios are classified by median peak global mean temperature \r\nincrease since 1850-1900 in the bands <2degC, 2degC-2.5degC, 2.5degC-3degC, \r\n3degC-4degC, and >4degC, with the range below 2degC broken out in greater \r\ndetail using estimates of warming levels at peak and in 2100 for which \r\nthe warming response is projected to be likely higher (33th percentile), \r\nas likely higher as lower (median), and likely lower (67th percentile) \r\n(Chapter 3, Section 3.2 and Annex III.II.3.2.1). WGII's common GWLs "


id: 3c537e13b3d9e1ff9abee4fec7efedee
created_at: 2023-03-16 14:01:54
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vector_id: 6856
content:
"(global mitigation pathways), Table  18 (national and regional \r\nmitigation pathways) and Table 19 (sector transition pathways)."


id: 597a403163f2c22ea59f1284bf99afc8
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 11574
content:
"were open for a period of 22 months (September 2019 to July 2021), \r\nwith updates possible until October 2021 in line with the literature \r\nacceptance deadline. The data submission process included various \r\nquality control procedures to increase accuracy and consistency in \r\nreporting. Additional categorisation and processing of metadata over \r\nthe full database provided a wide range of indicators and categories \r\nthat were made centrally available to authors of the report to \r\nenhance consistency of the assessment, such as: climate, policy and \r\ntechnology categories; characteristics about emissions, energy, socio\u0002economics and carbon sequestration; metadata such as literature \r\nreferences, model documentation and related projects."


id: 27dde895b09ade7a91b0d26f9ee7a28a
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 3683
content:
"To facilitate the AR6 assessment, modelling teams were invited to \r\nsubmit their available emissions scenarios to a web-based database \r\nhosted by the International Institute for Applied Systems Analysis \r\n(IIASA).9\r\n The co-chairs of Working Group III as well as a  range of \r\nscientific institutions, including the Integrated Assessment Modelling \r\nConsortium (IAMC), University of Cape Town and the Centre \r\nInternational de Recherche sur l'Environnement et le Developpement \r\n(CIRED), supported the open call for scenarios which was subdivided \r\ninto four dedicated calls:"


id: 9f3638dea1d7d5c4d123a97eed47c41b
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
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vector_id: 22584
content:
"A common data reporting template with a defined variable structure \r\nwas used and all teams were required to register and submit detailed \r\nmodel and scenario metadata. Scenarios were required to come from \r\na formal quantitative model and the scenarios must be published in \r\naccordance with IPCC literature requirements. The calls for scenarios "


id: 2ffe4904963f871d1e99f1fa07ddeb0c
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 4978
content:
"Table  11 | Summary of the vetting criteria and ranges applied to the global scenarios for the climate assessment and preliminary screening for \r\nIllustrative Mitigation Pathways."


id: 14610c8d18e262c945259076188180f9
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 717
content:
"e total of scenarios as not all scenarios reported all variables. EIP stands for energy and industrial pro"


id: 415b89bb7bec6944b3a8e0b5eab70536
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
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vector_id: 7680
content:
"criteria for inclusion in the Chapter  3 climate assessment are \r\ndescribed in Annex III.II.3.2.1."


id: d049acec83c5053533ab6697936d0017
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 30302
content:
"models report only at five- to 10-year intervals. 2020 as reported in \r\nmost scenarios collected in the database does not include the impact \r\nof the COVID-19 pandemic."


id: 110d75fddeac23e880c8122fa46306fe
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 164
content:
"Almost three-quarters of submitted global scenarios passed the \r\nvetting. The remaining quarter comprised a  fraction of scenarios \r\nthat were rolled over from the SR1.5 database, and were no longer \r\nup to date with recent developments (excluding the COVID-19 \r\nshock). This included scenarios that started stringent mitigation \r\naction already in 2015. Other scenarios were expected to deviate \r\nfrom historical trends due to their diagnostic design. All historical \r\ncriteria for reported variables needed to be met in order to \r\npass the vetting."


id: 66c1df0f063dea4347d1454a405eba7d
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 13760
content:
"Scenarios were submitted by both individual studies and model inter\u0002comparisons. The main model inter-comparisons submitting scenarios \r\nare shown in Table  12. Model inter-comparisons have a  shared \r\nexperimental design and assess research questions across different \r\nmodelling platforms to enable more structured and systematic \r\nassessments. The model comparison projects thus help to understand \r\nthe robustness of the insights."


id: e0ad4f5ab86fba91199a11231df9e80e
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 32852
content:
"The number of submitted scenarios varies considerably by study, \r\nfor example from 10 to almost 600 scenarios for the model inter\u0002comparison studies (Table 12). The number of scenarios also varies \r\nsubstantially by model (Table 15), highlighting the fact that the global \r\nscenario set collected in the AR6 scenario database is not a statistical \r\nsample (Section II.2.2)."


id: b57567b3050f7c5f6d2f6809cb6d3f1
created_at: 2023-03-16 14:01:54
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vector_id: 26081
content:
"2266 global scenarios were submitted to the scenario database that \r\nfulfilled a  minimum requirement of reporting at least one global \r\nemission or energy variable covering multiple sectors. 1686 global \r\nscenarios passed the vetting criteria described in Table  11. These \r\nscenarios were subsequently flagged as meeting minimum quality \r\nstandards for use in long-term scenarios assessment. Additional "


id: 767ec0b28f79682b67a59325efb9f5af
created_at: 2023-03-16 14:01:54
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vector_id: 16234
content:
"Table 12 | Model inter-comparison studies that submitted global scenarios to the AR6 scenario database and for which at least one scenario passed the \r\nvetting. Scenario counts refer to all scenarios submitted by a study (in brackets), those that passed vetting (centre) and those that passed the vetting and received a climate \r\nassessment (left)."


id: e694f329d3cd233af004b8eecdddbc6a
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 33759
content:
"Table 13 | Single-model studies that submitted global scenarios to the AR6 scenario database and for which at least one scenario passed the vetting.\r\nScenario counts refer to all scenarios submitted by a  study (in brackets), those that passed vetting (centre) and those that passed the vetting and received a  climate \r\nassessment (left)."


id: 26ef0632c3789da960923be155da61f6
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 3517
content:
"For inclusion in the climate assessment, in addition to passing the \r\nvetting (Section II.3.1), scenarios needed to run until the end of \r\ncentury and report as a  minimum CO2 (total and for energy and \r\nindustrial processes (EIP)), CH4 and N2O emissions to 2100. Where \r\nCO2 for AFOLU was not reported, the difference between total and \r\nEIP in 2020 must be greater than 500 MtCO2. Of the total 2266 \r\nglobal scenarios submitted, 1574 could be assessed in terms of \r\ntheir associated climate response, and 1202 of those passed the \r\nvetting process."


id: 16724bacbfab249636ffab843b5f4c3c
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 1058
content:
"The global scenarios underpinning the assessment in Chapter 3 have \r\nbeen classified, to the degree possible, by their warming outcome. \r\nThe definition of the climate categories and the distribution of \r\nscenarios in the database across these categories is shown in \r\nTable 14 (Chapter 3, Section 3.2). The first four of these categories \r\ncorrespond to the ones used in the IPCC SR1.5 (Rogelj et al. 2018a) \r\nwhile the latter four have been added as part of the AR6 to capture \r\na broader range of warming outcomes."


id: 17fa909b585b47a52435462be352367f
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 1260
content:
"Table 14 | Classification of global pathways into warming levels using MAGICC (Chapter 3, Section 3.2)."


id: b7df199e0a38d83fe96ce8f84d64a773
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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content:
"Table 15 | Global scenarios by modelling framework and climate category. Table includes number of scenarios that passed all vetting checks and number of all \r\nscenarios that received a climate categorisation (in brackets, including those not passing vetting). Unique model versions have been grouped into modelling frameworks for \r\npresentation in this table.12 For a full list of unique model versions, please see the AR6 scenario database."


id: 11110c997062f740e0be6830e40142e7
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 166
content:
"Table 16 | Global scenarios by modelling framework that were not included in the climate assessment due to a time horizon shorter than 2100 or \r\na limited reporting of emissions species that did not include CO2 (total emissions or emissions from energy and industry), CH4 or N2O. Unique model \r\nversions have been grouped into modelling frameworks for presentation in this table.13 For a full list of unique model versions, please see the AR6 scenario database."


id: f7b1a8d2bb5dff1a519fe63ab15e3793
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 36487
content:
"from 1.55degC in SR1.5 (1.52degC if reassessed with AR6 assessment \r\npipeline) to 1.58degC in AR6."


id: 2fc71acc0920d5b86e32697a0a8fda52
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 4942
content:
"Global scenarios were also classified based on their assumptions \r\nregarding climate policy. This information can be deduced from \r\nstudy protocols or the description of scenario designs in the \r\npublished literature. It has also been elicited as meta-information \r\nfor scenarios that were submitted to the AR6 database. There are \r\nmultiple purposes for a policy classification, including controlling for \r\nthe level of near-term action (Chapter 3, Section 3.5) and estimating \r\ncosts and other differences between two policy classes (Chapter 3, \r\nSection  3.6). Policy classes can be combined with climate classes, \r\nfor example to identify scenarios that follow the NDC until 2030 and \r\nlimit warming to 2degC (>67%)."


id: 6f8ba5d5e1a13c627f0372cad2895486
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 15181
content:
"Comparison between SR1.5 and AR6 scenarios and associated \r\nclimate responses are shown in Figure 6, bottom panel. We show that \r\nchanges in the climate assessment pipeline are minor compared to \r\nclimate model uncertainty ranges in WGI (in the order of 0.1degC), but \r\nshow considerable variation due to different scenario characteristics. \r\nThe updated harmonisation and infilling together have a  small \r\ncooling effect compared to raw modelled emissions for the subset of \r\n95 scenarios in C1, C2, and C3 that also were assessed in SR15 (SR1.5 \r\nChapter 2, Table 2.4). This is due to both applying more advanced \r\nharmonisation methods consistent with the CMIP6 harmonisation \r\nused for WGI, and changing the historical harmonisation year from \r\n2010 to 2015. Together with the update in the climate emulator, \r\nwe find that the total AR6 assessment is remarkably consistent \r\nwith SR1.5, albeit slightly cooler (in the order of 0.05degC at peak \r\ntemperature, 0.1degC in 2100)."


id: de3d55009d83fc84892f55c20c59f64d
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 32453
content:
"Middle row: Characteristics of CO2 emissions pathways and the distribution of median peak temperature assessments for C1 and C3. From left to right: (i) \r\nChange in CO2 emissions levels and reductions in 2030, 2040 and 2050 between the AR6 (n=408), AR6 and SR1.5 overlap (n=60) and SR15 sets (n=91). (ii) distribution of \r\nscenarios with different median peak temperature scenario outcomes for C1 and C3 for AR6 and SR1.5 (both with AR6 temperature assessment as a solid line and with SR1.5 \r\ntemperature assessment as a dashed line, with median in yellow). (iii) Year of net-zero CO2 for C1 and C3 for AR6 and SR1.5. Within C3, 27 AR6 scenarios and 2 SR1.5 scenarios \r\nwith no net-zero year before 2100 have not been visualised. The violin area is proportional to the number of scenarios. Coloured lines indicate the 25th and 75th percentiles, \r\nwhile the dashed black line indicates the median."


id: 7edd34ac02f2427442f4cfa97e36fb66
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 17521
content:
"Bottom row: Change in median global mean surface air temperature (GSAT) between the AR6 and SR1.5 climate assessments for both 2100 values and \r\npeak temperature values during the 21st century. Positive values indicate that the temperature assessment is higher for the same scenario than the SR1.5 climate \r\nassessment. From left to right: (i) the effect of using MAGICCv7 calibrated to the WGI assessment compared with MAGICC6 as used in SR1.5; (ii) the effect of more advanced \r\nemissions harmonisation methods; (iii) the effect of more advanced emissions infilling methods; (iv) the total effect which is the sum of the three components. Boxplots show \r\nthe median and interquartile range, with the whiskers indicating the 95% range."


id: fa44643d79448a72978a68478f9f2a76
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 36893
content:
"Table  17 | Policy classification of global scenarios. If the total for a  class exceeds the sum of the subclasses, there are scenarios in the class that could not be \r\nassigned to a subclass."


id: e08f661b98c9f9a2b66f81cb97043408
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 32830
content:
"from a  set of larger model intercomparison projects, JMIP/EMF35 \r\n(Sugiyama et al. 2020a) focusing on Japan, CD-LINKS (Roelfsema et al. \r\n2020; Schaeffer et al. 2020), COMMIT (van Soest et al. 2021), ENGAGE \r\n(Fujimori et al. 2021), and Paris Reinforce (Perdana et al. 2020; Nikas \r\net al. 2021) each covering several countries/regions from the following: \r\nAustralia, Brazil, China, EU, India, Indonesia, Japan, Korea, Russia, \r\nThailand, USA, Vietnam. The remaining pathways stem from individual \r\nmodelling studies that submitted scenarios to the database (Table 18)."


id: dd30334b36fd7dee4df131581d92fc18
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 32299
content:
"National and regional pathways have been collected in the AR6 \r\nscenario database to support the Chapter 4 assessment. In total more \r\nthan 500 pathways for 24 countries/regions have been submitted to \r\nthe AR6 scenario database by integrated assessment, energy-economic \r\nand computable general equilibrium modelling research teams. This \r\nrepresents a  limited sample of the overall literature on mitigation \r\npathways at the national level. The majority of these pathways originate "


id: f368883c0f15eefd67411f5bd84d9ca5
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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vector_id: 35803
content:
"a\r\n Countries are abbreviated by their ISO 3166-1 alpha-3 letter codes. EU denotes the European Union.\r\nNotes: CP = current policies, NDC = implementation of Nationally Determined Contributions by 2025/30, Other = all other scenarios."


id: ae83cdf15bbf6909f35c7e18d17551e5
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 24989
content:
"of perspectives, including long-term global and macro-region \r\ntrends for the sectors, sectoral analysis of the Illustrative Pathways, \r\nand comparison of the scenarios between full-economy IAMs and \r\nsector-specific models on shorter time horizons. These perspectives \r\nhave a  bi-directional utility - to understand how well IAMs are \r\nrepresenting sectoral trends from more granular models, and to \r\nposition sectoral models in the context of full-economy transitions to \r\nverify consistency with different climate outcomes."


id: 5c5eaebb102d5763a5ccd735104e9fd1
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 12045
content:
"Sectoral transition pathways based on the AR6 scenario database \r\nare addressed in a number of Chapters, primarily Chapter 6 (Energy \r\nsystems), Chapter  7 (AFOLU), Chapter  9 (Buildings), Chapter  10 \r\n(Transport) and Chapter  11 (Industry). These analyses cover both \r\ncontributions from global IAMs and from sector-specific models \r\nwith regional or global coverage. The assessments cover a  variety "


id: 8c81226c91948d3301befb802bed96a
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 19629
content:
"Table 19 | Overview of how models and scenarios were used in sectoral chapters. All scenario and model counts listed in the table are contained in the AR6 \r\nscenario database, with the exception of Chapter 9 (Buildings), which supplemented its dataset with a large number of scenarios separately pulled from the sectoral literature. \r\nScenario counts represents unique model-scenario combinations in the database."


id: 7df5acac9a03b18476d12a14d5360d2a
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 17391
content:
"Note 1: The number of models and scenarios reported in the table cannot be summed across chapters, as there is considerable overlap in selected model-scenario combinations \r\nacross chapters, depending on the filtering processes used for relevant analyses. Moreover, the numbers in the table - and certainly not their sum - are not intended to match \r\nthose reported for the global pathways assessed by Chapter 3 in Section II.3.2."


id: d4ee41d12a997ca0e04b2d6e13ac91a3
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
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index: climate_gpt_only_giec
vector_id: 31031
content:
"Note 2: Numbers shown in the model-count column are arrived at through the authors' best judgement. This has to do with the overlapping nature of unique model versions \r\n(within a given model family) as models evolve over time. In this case, model versions with substantial overlap were considered the same model, whereas model versions \r\nthat differ significantly were counted as unique. For example, MESSAGEix-GLOBIOM 1.0 and MESSAGEix-GLOBIOM_1.1 are counted as the same model, while MESSAGEix\u0002GLOBIOM 1.0 and MESSAGE are counted as different. If instead counting all model versions uniquely, then the following counts would apply to each chapter: Energy systems \r\n(30/38/29), AFOLU (18/27/25/4), Buildings (80), Transport (50), Industry (32)."


id: 2a5216bcc7be5bb56be4623f06354afe
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
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content:
"Note 3: The Transport chapter figures in Chapter 10, Section 10.7, are produced from the final AR6 scenario database by the code accompanying this report. The set of model and \r\nscenario names appearing in each plot or figure of Section 10.7 varies, depending on whether particular submissions to the database included the specific variables appearing \r\nin that plot. Authors advise inspecting the data files accompanying each figure for the set of models/scenarios specific to that figure, or running the code against the final \r\ndatabase snapshot to reproduce the figures in question."


id: 60e2bddcfb9bdc02084e02a774e91664
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 12812
content:
"IPCC, 2022: Annex IV: Contributors to the IPCC Working Group III Sixth Assessment Report. In Climate Change 2022: \r\nMitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental \r\nPanel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, \r\nS. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)]. Cambridge University Press, \r\nCambridge, UK and New York, NY, USA. "


id: 74dffa303585d6d86b4d5cb9bea611d0
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
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vector_id: 15981
content:
"ANDREW, Robbie M.\r\nCentre for International Climate \r\nand Environmental Research (CICERO)\r\nNorway"


id: 47f8f422db75ae18a0c3672f8d96717d
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 8701
content:
"CULLEN, Jonathan M.\r\nDepartment of Engineering, \r\nUniversity of Cambridge\r\nNew Zealand/United Kingdom"


id: 1dc68ab80a02009ce217b414eb8c660a
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 2122
content:
"FRANK, Robert H.\r\nClimate One/Cornell SC Johnson \r\nCollege of Business\r\nthe United States of America"


id: a466d4b599bc3345488ab1469e41362d
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 23428
content:
"FUGLESTVEDT, Jan S.\r\nCentre for International Climate \r\nand Environmental Research (CICERO)\r\nNorway"


id: 163609f0c9dc46efd2fc789dfd8d50f1
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 1020
content:
"HAHMANN, Andrea N.\r\nDepartment of Wind Energy, \r\nTechnical University of Denmark\r\nChile/Denmark"


id: 72eb74332c9bfee18a05ebba2c0aa92
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 15692
content:
"GEELS, Frank W.\r\nManchester Institute of Innovation \r\nResearch, University of Manchester\r\nUnited Kingdom/the Netherlands"


id: 9bbbc15f30bf19a75b4bc77979ed36b6
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 22064
content:
"GERBER, James S.\r\nInstitute on the Environment, \r\nUniversity of Minnesota\r\nthe United States of America"


id: 629bcbef9c71acb2117e54f9e66bfb94
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 13080
content:
"JONCKHEERE, Inge G.C.\r\nFood and Agriculture Organisation \r\nof the United Nations (FAO)\r\nItaly/Belgium"


id: c78b1582c054e19fa2f2779ecf9685c3
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 28980
content:
"REISCH, Lucia A.\r\nCambridge Judge Business School, \r\nUniversity of Cambridge\r\nUnited Kingdom"


id: 57571b49b64d4d3d8e7b49aba2c9761c
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
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vector_id: 11234
content:
"SMITH, Stephen M.\r\nSmith School of Enterprise and the \r\nEnvironment, University of Oxford\r\nUnited Kingdom"


id: ff1c0a7128ebd27972f1f49dc5d002e9
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 37689
content:
"SIMS, Ralph E.H.\r\nCentre for Energy Research, School \r\nof Engineering and Advanced Technology, \r\nMassey University\r\nNew Zealand"


id: f2193455971b7d0899586136cffe27e2
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 35618
content:
"TOTH, Ferenc L.\r\nInternational Institute for Applied \r\nSystems Analysis (IIASA)\r\nAustria/Hungary"


id: 7b7288eac754c453b5e97cdd5cb6f100
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 17017
content:
"TAN, Xianchun C.\r\nInstitute of Science and Development, \r\nChinese Academy of Sciences\r\nChina"


id: 6903933e407b4f089c1f3e1518fa142f
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 14116
content:
"TUBIELLO, Francesco N.\r\nFood and Agriculture Organisation \r\nof the United Nations (FAO)\r\nItaly"


id: 31496efea433f476124cfec34e4a432f
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 5175
content:
"IPCC, 2022: Annex V: Expert Reviewers of the IPCC Working Group III Sixth Assessment Report. In Climate Change 2022: \r\nMitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental \r\nPanel on Climate Change [P.R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, \r\nS. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, J. Malley, (eds.)]. Cambridge University Press, \r\nCambridge, UK and New York, NY, USA."


id: c87eeda977e3be624f69f49c88a3fb8a
created_at: 2023-03-16 14:01:54
updated_at: 2023-03-16 14:01:54
content_type: text
index: climate_gpt_only_giec
vector_id: 29110
content:
"BALBUS, John M.\r\nNational Institute of Environmental \r\nHealth Sciences\r\nThe United States of America"


id: 64e20e222ea8b51b59a5cc0b69cbf200
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 13437
content:
"BERGER, Mitchell\r\nU.S. Department of Health and \r\nHuman Services\r\nThe United States of America"


id: 921c9c09c169a568365deac9360ba2cb
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 20512
content:
"HERNANDEZ, Michelle D.\r\nYOUNGO Cities Working Group \r\n(UNFCCC Official Youth Constituency)\r\nThe United States of America"


id: 4f4f3de3f15749928f83990740fe9709
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 9915
content:
"ISLAM, Md. Sirajul\r\nDepartment of Civil and Environmental \r\nEngineering, North South University\r\nBangladesh"


id: ce74db9b0be677d8d4ce31809ba6b983
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 30015
content:
"IPCC, 2022: Annex VI: Acronyms. In Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group \r\nIII to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [P.R. Shukla, J. Skea, R. Slade, \r\nA. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, \r\nS. Luz, J. Malley, (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA."


id: d7643fdd5efd6a2c56e6872d81963ffc
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 31407
content:
"This annex, which was not part of the approved outline of the Working Group III contribution \r\nto the IPCC Sixth Assessment Report, provides a list of acronyms used in the report."


id: 87d1d9113be330ff54e6bbd6e18af987
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 18896
content:
"The journey of this report reflects extraordinary efforts by all \r\ncontributors under exceptional circumstances. The COVID-19 \r\npandemic, which started during the review of the Second Order \r\nDraft, disrupted the process. However, the early response of Working \r\nGroup I, through the hard work of the authors, review editors, chapter \r\nscientists, the Technical Support Unit, and Bureau members, made \r\nit possible to deliver a report that meets the most stringent science \r\nquality standards, thanks to the in-depth, broad review process, with \r\nan exhaustive, robust, rigorous and transparent assessment of the \r\nlatest state of climate science knowledge. We commend the additional \r\nefforts taken, including by all reviewers from the research community \r\nand government representatives who adapted to a new way of working \r\nto complete this vital report"


id: 8f840929b93c971dba463ce76a01ab78
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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vector_id: 20128
content:
" Our particular appreciation goes to the \r\nWorking Group I Co-Chairs Valerie Masson-Delmotte and Panmao Zhai \r\nfor their leadership throughout the process. Their extraordinary efforts \r\nensured that this robust and clear report is available to the world today."


id: 4370e4662f1893bb5251ea1a27c3cd91
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 8020
content:
"The introduction (Chapter 1) of the Report frames the WGI \r\nassessment within the broader AR6 and global climate policy \r\ncontext and introduces the key concepts, lines of evidence, and \r\nmajor developments. The remainder of the Report is structured in \r\nthree segments. The first segment focuses on large-scale climate \r\nchange (Chapters 2-4). These chapters provide an updated and \r\ncomprehensive assessment of knowledge about the current state \r\nof the climate system, human influence and projections of future \r\nchange on key large-scale indicators of the climate system. Chapter 2 \r\nassesses observed large-scale changes in the climate system in the \r\nlong-term paleoclimate context. Chapter 3 updates the assessment \r\nof human influence on the climate system, considering natural \r\nvariability, model performance and detection and attribution"


id: 42a1057a7643b2811253a209f1974e36
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 7875
content:
" \r\nChapter 4 covers global climate projections spanning time horizons \r\nfrom the near term (2021-2040) to the mid-term (2041-2060), long \r\nterm (2081-2100) and beyond."


id: 82e60387413e008dfa61a003b4685be7
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 18120
content:
"oxide and the assessment of carbon and other biogeochemical \r\nfeedbacks. Chapter  6 assesses changes in the emissions and \r\nabundances of individual SLCFs, how these changes affect \r\nEarth's energy balance through radiative forcing and feedback in \r\nthe climate system, the implications of changing climate on air \r\nquality, and the implications of SLCF mitigation for climate and \r\nfor air quality. Chapter 7 addresses Earth's energy budget through \r\nadvances in observations, understanding and quantification of \r\neffective radiative forcing, and the assessment of feedbacks and \r\nclimate sensitivity. Chapter 8 assesses observed and projected \r\nchanges in the global water cycle, the physical understanding of \r\nthe complexity of its response to multiple drivers, and implications \r\nfor water availability"


id: 7a445151e4379ca685edc5ae458a49a2
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 16803
content:
" The physical processes underlying global \r\nand regional changes in the ocean, cryosphere and sea level and \r\nthe understanding of observed and projected future changes since \r\nthe AR5 and Special Report on the Ocean and Cryosphere in a \r\nChanging Climate are assessed in Chapter 9."


id: ba8bc800cd89a44ad3e9264dec913d2a
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 26883
content:
"The final four chapters of the Report (Chapters 10-12 and the Atlas) \r\nare dedicated to the assessment and distillation of regional climate \r\ninformation from multiple lines of evidence at sub-continental to \r\nlocal scales (including urban climate), building on information from \r\nprevious chapters on large-scale climate and process understanding, \r\nwith a focus on recent and projected regional changes in mean \r\nclimate, extremes, and climatic impact-drivers. Chapter 10 assesses \r\nthe foundations of how regional climate information is distilled from \r\nmultiple lines of evidence and the interplay between anthropogenic \r\ncauses and internal variability at regional scales. Chapter 11 addresses \r\nchanges in weather and climate extremes on global and regional \r\nscales, including observed changes and their attribution, as well as \r\nprojected changes. Chapter 12 assesses the climatic conditions that \r\nmay lead to impacts and risks across the world's regions"


id: b09f769176a593ed6b623100af726fdb
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 25309
content:
" Changes \r\nin mean climate at regional scales, in particular observed trends and \r\ntheir attribution and projected future changes in temperature \r\nand precipitation, are assessed in the Atlas chapter."


id: 2f8c1e2cf0c8f6010076548e93753b33
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 4899
content:
"The preparation of the WGI AR6 report was also informed by \r\nrecommendations from several IPCC expert meetings. The first \r\nmeeting focused on assessing climate information for regions \r\n(in 2018, co-organized by WGI and WGII, and hosted at ICTP, Trieste, \r\nItaly), which provided a scoping of the Interactive Atlas. A  second \r\nmeeting focused on short-lived climate forcers (in 2018, co-organized \r\nby TFI and WGI, and hosted by the World Meteorological Organization \r\nin Geneva, Switzerland), which identified science advances in the \r\nunderstanding of emissions and climate effects of SLCFs and needs \r\nfor improvements in emission inventories and methodologies. A \r\nthird meeting on mitigation, sustainability, and climate stabilization \r\nscenarios (in 2019, organized by WGIII in Addis Ababa, Ethiopia), led \r\nto cross-WG coordination related to scenarios"


id: 3e3eb972a3c377675aed16a823ed59cd
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 7164
content:
" Recommendations \r\nfor clarity and readability from the 2016 IPCC Expert Meeting \r\non Communication (organized by the IPCC Secretariat in Oslo, \r\nNorway) were taken into account in developing technical guidance, \r\ntraining and resources provided to authors and in particular for the \r\npreparation of the text and figures of the Frequently Asked Questions \r\nand the Summary for Policymakers."


id: 4ee4dbe25e7f51c5fcc672f9b86aa3d3
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 9850
content:
"Richard P. Allan (United Kingdom), Paola A. Arias (Colombia), Sophie Berger (France/Belgium), Josep G. \r\nCanadell (Australia), Christophe Cassou (France), Deliang Chen (Sweden), Annalisa Cherchi (Italy), Sarah \r\nL. Connors (France/United Kingdom), Erika Coppola (Italy), Faye Abigail Cruz (Philippines), Aida Diongue\u0002Niang (Senegal), Francisco J. Doblas-Reyes (Spain), Herve Douville (France), Fatima Driouech (Morocco), \r\nTamsin L. Edwards (United Kingdom), Francois Engelbrecht (South Africa), Veronika Eyring (Germany), \r\nErich Fischer (Switzerland), Gregory M. Flato (Canada), Piers Forster (United Kingdom), Baylor Fox\u0002Kemper (United States of America), Jan S. Fuglestvedt (Norway), John C. Fyfe (Canada), Nathan P. Gillett \r\n(Canada), Melissa I. Gomis (France/Switzerland), Sergey K. Gulev (Russian Federation), Jose Manuel \r\nGutierrez (Spain), Rafiq Hamdi (Belgium), Jordan Harold (United Kingdom), Mathias Hauser (Switzerland), \r\nEd Hawkins (United Kingdom), Helene T"


id: 23529d2ea0462b6d116f2e439d43c0be
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 3010
content:
" Hewitt (United Kingdom), Tom Gabriel Johansen (Norway), \r\nChristopher Jones (United Kingdom), Richard G. Jones (United Kingdom), Darrell S. Kaufman (United \r\nStates of America), Zbigniew Klimont (Austria/Poland), Robert E. Kopp (United States of America), Charles \r\nKoven (United States of America), Gerhard Krinner (France/Germany, France), June-Yi Lee (Republic of \r\nKorea), Irene Lorenzoni (United Kingdom/Italy), Jochem Marotzke (Germany), Valerie Masson-Delmotte \r\n(France), Thomas K. Maycock (United States of America), Malte Meinshausen (Australia/Germany), Pedro \r\nM.S. Monteiro (South Africa), Angela Morelli (Norway/Italy), Vaishali Naik (United States of America), \r\nDirk Notz (Germany), Friederike Otto (United Kingdom/Germany), Matthew D"


id: ad25065a4fb67aa409b9da77d5e51cc5
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 24786
content:
" Palmer (United Kingdom), \r\nIzidine Pinto (South Africa/Mozambique), Anna Pirani (Italy), Gian-Kasper Plattner (Switzerland), \r\nKrishnan Raghavan (India), Roshanka Ranasinghe (The Netherlands/Sri Lanka, Australia), Joeri Rogelj \r\n(United Kingdom/Belgium), Maisa Rojas (Chile), Alex C. Ruane (United States of America), Jean-Baptiste \r\nSallee (France), Bjorn H. Samset (Norway), Sonia I. Seneviratne (Switzerland), Jana Sillmann (Norway/\r\nGermany), Anna A. Sorensson (Argentina), Tannecia S. Stephenson (Jamaica), Trude Storelvmo (Norway), \r\nSophie Szopa (France), Peter W. Thorne (Ireland/United Kingdom), Blair Trewin (Australia), Robert Vautard \r\n(France), Carolina Vera (Argentina), Noureddine Yassaa (Algeria), Sonke Zaehle (Germany), Panmao Zhai \r\n(China), Xuebin Zhang (Canada), Kirsten Zickfeld (Canada/Germany)"


id: c21163e544113b2ce194310712a4e33e
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 28112
content:
"Krishna M. AchutaRao (India), Bhupesh Adhikary (Nepal), Edvin Aldrian (Indonesia), Kyle Armour (United \r\nStates of America), Govindasamy Bala (India/United States of America), Rondrotiana Barimalala (South \r\nAfrica/Madagascar), Nicolas Bellouin (United Kingdom/France), William Collins (United Kingdom), \r\nWilliam D. Collins (United States of America), Susanna Corti (Italy), Peter M. Cox (United Kingdom), \r\nFrank J. Dentener (EU/The Netherlands), Claudine Dereczynski (Brazil), Alejandro Di Luca (Australia, \r\nCanada/Argentina), Alessandro Dosio (Italy), Leah Goldfarb (France/United States of America), Irina \r\nV. Gorodetskaya (Portugal/Belgium, Russian Federation), Pandora Hope (Australia), Mark Howden \r\n(Australia), A.K"


id: 8e09da9dfac2636c33851deb323530dd
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 19877
content:
"M Saiful Islam (Bangladesh), Yu Kosaka (Japan), James Kossin (United States of America), \r\nSvitlana Krakovska (Ukraine), Chao Li (China), Jian Li (China), Thorsten Mauritsen (Germany/Denmark), \r\nSebastian Milinski (Germany), Seung-Ki Min (Republic of Korea), Thanh Ngo Duc (Vietnam), Andy \r\nReisinger (New Zealand), Lucas Ruiz (Argentina), Shubha Sathyendranath (United Kingdom/Canada, \r\nOverseas Citizen of India), Aimee B. A. Slangen (The Netherlands), Chris Smith (United Kingdom), Izuru \r\nTakayabu (Japan), Muhammad Irfan Tariq (Pakistan), Anne-Marie Treguier (France), Bart van den Hurk \r\n(The Netherlands), Karina von Schuckmann (France/Germany), Cunde Xiao (China)"


id: 12da112e330166484d20f0e4bd081891
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 470
content:
"Panel (a) Observed global warming (increase in global surface temperature). Whiskers show the very likely range.\r\nPanel (b) Evidence from attribution studies, which synthesize information from climate models and observations. The panel shows temperature \r\nchange attributed to: total human influence; changes in well-mixed greenhouse gas concentrations; other human drivers due to aerosols, ozone and land-use \r\nchange (land-use reflectance); solar and volcanic drivers; and internal climate variability. Whiskers show likely ranges.\r\nPanel (c) Evidence from the assessment of radiative forcing and climate sensitivity. The panel shows temperature changes from individual components \r\nof human influence: emissions of greenhouse gases, aerosols and their precursors; land-use changes (land-use reflectance and irrigation); and aviation contrails. \r\nWhiskers show very likely ranges. Estimates account for both direct emissions into the atmosphere and their effect, if any, on other climate drivers"


id: 6e451a52bf09cc12efa79c28973eb3cf
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 14970
content:
" For aerosols, \r\nboth direct effects (through radiation) and indirect effects (through interactions with clouds) are considered. \r\n{Cross-Chapter Box 2.3, 3.3.1, 6.4.2, 7.3}"


id: 3c4233f6288bbe6bfa054b397d7c7874
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
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vector_id: 6847
content:
"The IPCC AR6 WGI inhabited regions are displayed as hexagons with identical size in their approximate geographical location (see legend for regional acronyms). \r\nAll assessments are made for each region as a whole and for the 1950s to the present. Assessments made on different time scales or more local spatial scales might \r\ndiffer from what is shown in the figure. The colours in each panel represent the four outcomes of the assessment on observed changes. Striped hexagons (white \r\nand light-grey) are used where there is low agreement in the type of change for the region as a whole, and grey hexagons are used when there is limited data and/\r\nor literature that prevents an assessment of the region as a whole. Other colours indicate at least medium confidence in the observed change"


id: ed1098bd024379441a3e6e42baf92c5
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" The confidence \r\nlevel for the human influence on these observed changes is based on assessing trend detection and attribution and event attribution literature, and it is indicated \r\nby the number of dots: three dots for high confidence, two dots for medium confidence and one dot for low confidence (single, filled dot: limited agreement; single, \r\nempty dot: limited evidence)."


id: 221a4706f42ec5bf0e5a52e8af126cf7
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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"Box SPM.1.1: This Report assesses the climate response to five illustrative scenarios that cover the range of possible future \r\ndevelopment of anthropogenic drivers of climate change found in the literature. They start in 2015, and include scenarios22\r\nwith high and very high GHG emissions (SSP3-7.0 and SSP5-8.5) and CO2 emissions that roughly double from current \r\nlevels by 2100 and 2050, respectively, scenarios with intermediate GHG emissions (SSP2-4.5) and CO2 emissions remaining \r\naround current levels until the middle of the century, and scenarios with very low and low GHG emissions and CO2 emissions \r\ndeclining to net zero around or after 2050, followed by varying levels of net negative CO2 emissions23 (SSP1-1.9 and \r\nSSP1-2.6), as illustrated in Figure SPM.4. Emissions vary between scenarios depending on socio-economic assumptions, \r\nlevels of climate change mitigation and, for aerosols and non-methane ozone precursors, air pollution controls"


id: f3361475653fe37eef6529d3c8c304ff
created_at: 2023-03-16 14:01:55
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" Alternative \r\nassumptions may result in similar emissions and climate responses, but the socio-economic assumptions and the feasibility \r\nor likelihood of individual scenarios are not part of the assessment. \r\n{1.6, Cross-Chapter Box 1.4, TS.1.3} (Figure SPM.4)"


id: e7c3716b7f2f048ff4eca17e7e79948f
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"Box SPM.1.2: This Report assesses results from climate models participating in the Coupled Model Intercomparison Project \r\nPhase 6 (CMIP6) of the World Climate Research Programme. These models include new and better representations of \r\nphysical, chemical and biological processes, as well as higher resolution, compared to climate models considered in previous \r\nIPCC assessment reports. This has improved the simulation of the recent mean state of most large-scale indicators of climate \r\nchange and many other aspects across the climate system. Some differences from observations remain, for example in \r\nregional precipitation patterns. The CMIP6 historical simulations assessed in this Report have an ensemble mean global \r\nsurface temperature change within 0.2degC of the observations over most of the historical period, and observed warming is \r\nwithin the very likely range of the CMIP6 ensemble"


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created_at: 2023-03-16 14:01:55
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content:
" However, some CMIP6 models simulate a warming that is either above \r\nor below the assessed very likely range of observed warming. "


id: ec92baea29f505f602c827e8c43bfc60
created_at: 2023-03-16 14:01:55
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"Panel (b) Warming contributions by groups of anthropogenic drivers and by scenario are shown as the change in global surface \r\ntemperature (degC) in 2081-2100 relative to 1850-1900, with indication of the observed warming to date. Bars and whiskers represent median values \r\nand the very likely range, respectively. Within each scenario bar plot, the bars represent: total global warming (degC; 'total' bar) (see Table SPM.1); warming \r\ncontributions (degC) from changes in CO2 ('CO2' bar) and from non-CO2 greenhouse gases (GHGs; 'non-CO2 GHGs' bar: comprising well-mixed greenhouse \r\ngases and ozone); and net cooling from other anthropogenic drivers ('aerosols and land use' bar: anthropogenic aerosols, changes in reflectance due to \r\nland-use and irrigation changes, and contrails from aviation) (see Figure SPM.2, panel c, for the warming contributions to date for individual drivers). The \r\nbest estimate for observed warming in 2010-2019 relative to 1850-1900 (see Figure SPM"


id: c440f71684e49083a5e4cf922316839c
created_at: 2023-03-16 14:01:55
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content:
"2, panel a) is indicated in the darker column in the 'total' bar. \r\nWarming contributions in panel (b) are calculated as explained in Table SPM.1 for the total bar. For the other bars, the contribution by groups of drivers is \r\ncalculated with a physical climate emulator of global surface temperature that relies on climate sensitivity and radiative forcing assessments.\r\n{Cross-Chapter Box 1.4; 4.6; Figure 4.35; 6.7; Figures 6.18, 6.22 and 6.24; 7.3; Cross-Chapter Box 7.1; Figure 7.7; Box TS.7; Figures TS.4 and TS.15}"


id: b227e0c25db4b2d86d6e010ac6c04d2a
created_at: 2023-03-16 14:01:55
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"Panel (a) Comparison of observed and simulated annual mean surface temperature change. The left map shows the observed changes in annual \r\nmean surface temperature in the period 1850-2020 per degC of global warming (degC). The local (i.e., grid point) observed annual mean surface temperature changes \r\nare linearly regressed against the global surface temperature in the period 1850-2020. Observed temperature data are from Berkeley Earth, the dataset with \r\nthe largest coverage and highest horizontal resolution. Linear regression is applied to all years for which data at the corresponding grid point is available. The \r\nregression method was used to take into account the complete observational time series and thereby reduce the role of internal variability at the grid point level. \r\nWhite indicates areas where time coverage was 100 years or less and thereby too short to calculate a reliable linear regression"


id: 8d3f8d8d36af514131d8cd5f0ff8f4ba
created_at: 2023-03-16 14:01:55
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" The right map is based on model \r\nsimulations and shows change in annual multi-model mean simulated temperatures at a global warming level of 1degC (20-year mean global surface temperature \r\nchange relative to 1850-1900). The triangles at each end of the colour bar indicate out-of-bound values, that is, values above or below the given limits."


id: f8cbe1d25ca2b3d70bd884ab9f9e8ade
created_at: 2023-03-16 14:01:55
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content:
"In panel (c), high positive percentage changes in dry regions may correspond to small absolute changes. In panel (d), the unit is the standard deviation \r\nof interannual variability in soil moisture during 1850-1900. Standard deviation is a widely used metric in characterizing drought severity. A projected \r\nreduction in mean soil moisture by one standard deviation corresponds to soil moisture conditions typical of droughts that occurred about once every six years \r\nduring 1850-1900. In panel (d), large changes in dry regions with little interannual variability in the baseline conditions can correspond to small absolute \r\nchange. The triangles at each end of the colour bars indicate out-of-bound values, that is, values above or below the given limits. Results from all models \r\nreaching the corresponding warming level in any of the five illustrative scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) are averaged"


id: 1b13dce4c53cf7ef489a8fdbfc543cec
created_at: 2023-03-16 14:01:55
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" \r\nMaps of annual mean temperature and precipitation changes at a global warming level of 3degC are available in Figure 4.31 and Figure 4.32 in Section 4.6.\r\nCorresponding maps of panels (b), (c) and (d), including hatching to indicate the level of model agreement at grid-cell level, are found in Figures 4.31, 4.32 and \r\n11.19, respectively; as highlighted in Cross-Chapter Box Atlas.1, grid-cell level hatching is not informative for larger spatial scales (e.g., over AR6 reference regions) \r\nwhere the aggregated signals are less affected by small-scale variability, leading to an increase in robustness."


id: 80894cd8843a6459409ce13b1f970906
created_at: 2023-03-16 14:01:55
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content:
"was exceeded on average once in a decade during the 1850-1900 reference period. Agricultural and ecological drought events are defined as the annual \r\naverage of total column soil moisture below the 10th percentile of the 1850-1900 base period. These extremes are defined on model grid box scale. For hot \r\ntemperature extremes and extreme precipitation, results are shown for the global land. For agricultural and ecological drought, results are shown for drying regions \r\nonly, which correspond to the AR6 regions in which there is at least medium confidence in a projected increase in agricultural and ecological droughts at the 2degC \r\nwarming level compared to the 1850-1900 base period in the Coupled Model Intercomparison Project Phase 6 (CMIP6)"


id: a50661e0d46dec14aa24c03522dfd72b
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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content:
" These regions include Western North \r\nAmerica, Central North America, Northern Central America, Southern Central America, Caribbean, Northern South America, North-Eastern South America, South \r\nAmerican Monsoon, South-Western South America, Southern South America, Western and Central Europe, Mediterranean, West Southern Africa, East Southern \r\nAfrica, Madagascar, Eastern Australia, and Southern Australia (Caribbean is not included in the calculation of the figure because of the too-small number of full land \r\ngrid cells). The non-drying regions do not show an overall increase or decrease in drought severity. Projections of changes in agricultural and ecological droughts \r\nin the CMIP Phase 5 (CMIP5) multi-model ensemble differ from those in CMIP6 in some regions, including in parts of Africa and Asia. Assessments of projected \r\nchanges in meteorological and hydrological droughts are provided in Chapter 11. \r\nIn the 'frequency' section, each year is represented by a dot"


id: 6c1f5c4fb01da029c15bb97cfb1bee16
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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content:
" The dark dots indicate years in which the extreme threshold is exceeded, while light dots are years \r\nwhen the threshold is not exceeded. Values correspond to the medians (in bold) and their respective 5-95% range based on the multi-model ensemble from \r\nsimulations of CMIP6 under different Shared Socio-economic Pathway scenarios. For consistency, the number of dark dots is based on the rounded-up median. \r\nIn the 'intensity' section, medians and their 5-95% range, also based on the multi-model ensemble from simulations of CMIP6, are displayed as dark and \r\nlight bars, respectively. Changes in the intensity of hot temperature extremes and extreme precipitation are expressed as degree Celsius and percentage. As for \r\nagricultural and ecological drought, intensity changes are expressed as fractions of standard deviation of annual soil moisture. \r\n{11.1; 11.3; 11.4; 11.6; 11.9; Figures 11.12, 11.15, 11.6, 11.7, and 11.18}"


id: b61e87210865ff7c17d29fe40be938c3
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content:
"The bar chart illustrates the projected amount of cumulative anthropogenic CO2 emissions (GtCO2) between 1850 and 2100 remaining in the atmosphere (grey \r\npart) and taken up by the land and ocean (coloured part) in the year 2100. The doughnut chart illustrates the proportion of the cumulative anthropogenic \r\nCO2 emissions taken up by the land and ocean sinks and remaining in the atmosphere in the year 2100. Values in % indicate the proportion of the cumulative \r\nanthropogenic CO2 emissions taken up by the combined land and ocean sinks in the year 2100. The overall anthropogenic carbon emissions are calculated by \r\nadding the net global land-use emissions from the CMIP6 scenario database to the other sectoral emissions calculated from climate model runs with prescribed CO2\r\nconcentrations"


id: 55dbf913cca8e6702ce86a7fb98914c0
created_at: 2023-03-16 14:01:55
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vector_id: 10989
content:
"33 Land and ocean CO2 uptake since 1850 is calculated from the net biome productivity on land, corrected for CO2 losses due to land-use change by \r\nadding the land-use change emissions, and net ocean CO2 flux."


id: 3f6d552b6f375b979a1088a1b051cfe5
created_at: 2023-03-16 14:01:55
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content:
"Panel (c) Global ocean surface pH (a measure of acidity) based on CMIP6 model simulations. Very likely ranges are shown for SSP1-2.6 and SSP3-7.0.\r\nPanel (d) Global mean sea level change in metres, relative to 1900. The historical changes are observed (from tide gauges before 1992 and altimeters \r\nafterwards), and the future changes are assessed consistently with observational constraints based on emulation of CMIP, ice-sheet, and glacier models. Likely\r\nranges are shown for SSP1-2.6 and SSP3-7.0. Only likely ranges are assessed for sea level changes due to difficulties in estimating the distribution of deeply \r\nuncertain processes. The dashed curve indicates the potential impact of these deeply uncertain processes. It shows the 83rd percentile of SSP5-8.5 projections that \r\ninclude low-likelihood, high-impact ice-sheet processes that cannot be ruled out; because of low confidence in projections of these processes, this curve does not \r\nconstitute part of a likely range"


id: 5bcca9c963ce173d08c5395cec2d241d
created_at: 2023-03-16 14:01:55
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index: climate_gpt_only_giec
vector_id: 11958
content:
" Changes relative to 1900 are calculated by adding 0.158 m (observed global mean sea level rise from 1900 to 1995-2014) to \r\nsimulated and observed changes relative to 1995-2014."


id: f20edd8cb6efdff96c9e4301c3884216
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 35615
content:
"Top panel: Historical data (thin black line) shows observed global surface temperature increase in degC since 1850-1900 as a function of historical cumulative carbon \r\ndioxide (CO2) emissions in GtCO2 from 1850 to 2019. The grey range with its central line shows a corresponding estimate of the historical human-caused surface \r\nwarming (see Figure SPM.2). Coloured areas show the assessed very likely range of global surface temperature projections, and thick coloured central lines show the \r\nmedian estimate as a function of cumulative CO2 emissions from 2020 until year 2050 for the set of illustrative scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and \r\nSSP5-8.5; see Figure SPM.4). Projections use the cumulative CO2 emissions of each respective scenario, and the projected global warming includes the contribution \r\nfrom all anthropogenic forcers"


id: b74db2da5dc5187cf35d3420ca71cbc7
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 26369
content:
" The relationship is illustrated over the domain of cumulative CO2 emissions for which there is high confidence that the transient climate \r\nresponse to cumulative CO2 emissions (TCRE) remains constant, and for the time period from 1850 to 2050 over which global CO2 emissions remain net positive under \r\nall illustrative scenarios, as there is limited evidence supporting the quantitative application of TCRE to estimate temperature evolution under net negative CO2 emissions.\r\nBottom panel: Historical and projected cumulative CO2 emissions in GtCO2 for the respective scenarios.\r\n{Section 5.5, Figure 5.31, Figure TS.18}"


id: 3161ef3dacc536fd5709cb3dd9b20438
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vector_id: 5187
content:
"Krishna Achuta Rao (India), Bhupesh Adhikary (Nepal), Richard P. Allan (United Kingdom), \r\nKyle Armour (United States of America), Govindasamy Bala (India/United States of America), \r\nRondrotiana Barimalala (South Africa/Madagascar), Sophie Berger (France/Belgium), \r\nJosep G. Canadell (Australia), Christophe Cassou (France), Annalisa Cherchi (Italy), William Collins \r\n(United Kingdom), William D. Collins (United States of America), Sarah L. Connors (France/United \r\nKingdom), Susanna Corti (Italy), Faye Cruz (Philippines), Frank J. Dentener (EU/The Netherlands), \r\nClaudine Dereczynski (Brazil), Alejandro Di Luca (Australia, Canada/Argentina), Aida Diongue \r\nNiang (Senegal), Francisco J. Doblas-Reyes (Spain), Alessandro Dosio (Italy), Herve Douville (France), \r\nFrancois Engelbrecht (South Africa), Veronika Eyring (Germany), Erich Fischer (Switzerland), Piers \r\nForster (United Kingdom), Baylor Fox-Kemper (United States of America), Jan  S. Fuglestvedt \r\n(Norway), John C"


id: 65b3cf2335c42ab877dc1c1f54bd0ada
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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vector_id: 13580
content:
" Fyfe (Canada), Nathan P. Gillett (Canada), Leah Goldfarb (France/United States \r\nof America), Irina Gorodetskaya (Portugal/Russian Federation, Belgium), Jose Manuel Gutierrez \r\n(Spain), Rafiq Hamdi (Belgium), Ed Hawkins (United Kingdom), Helene T. Hewitt (United Kingdom), \r\nPandora Hope (Australia), AKM Saiful Islam (Bangladesh), Christopher Jones (United Kingdom), \r\nDarrell S.  Kaufman (United States of America), Robert E.  Kopp (United States of America), \r\nYu  Kosaka (Japan), James Kossin (United States of America), Svitlana  Krakovska (Ukraine), \r\nJune-Yi Lee (Republic of Korea), Jian Li (China), Thorsten Mauritsen (Sweden, Denmark), Thomas \r\nK. Maycock (United States of America), Malte Meinshausen (Australia/Germany), Seung-Ki Min \r\n(Republic of Korea), Pedro M. S"


id: bd8e6348da5817daff503a63e839be43
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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vector_id: 27393
content:
" Monteiro (South Africa), Thanh Ngo-Duc (Vietnam), Friederike \r\nOtto (United Kingdom/Germany), Izidine Pinto (South Africa/Mozambique), Anna Pirani (Italy), \r\nKrishnan Raghavan (India), Roshanka Ranasinghe (The Netherlands/Sri Lanka, Australia), Alex \r\nC. Ruane (United States of America), Lucas Ruiz (Argentina), Jean-Baptiste Sallee (France), Bjorn \r\nH. Samset (Norway), Shubha Sathyendranath (UK/Canada, United Kingdom, Overseas Citizen of \r\nIndia), Sonia I. Seneviratne (Switzerland), Anna A. Sorensson (Argentina), Sophie Szopa (France), \r\nIzuru Takayabu (Japan), Anne-Marie Treguier (France), Bart van den Hurk (The Netherlands), "


id: c5044a523af42f51b06659f66008b892
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content:
"Gudfinna Adalgeirsdottir (Iceland), Lincoln M. Alves (Brazil), Terje Berntsen (Norway), \r\nSara M. Blichner (Norway), Lisa Bock (Germany), Gregory G. Garner (United States of America), \r\nJoelle Gergis (Australia), Sergey K. Gulev (Russian Federation), Mathias Hauser (Switzerland), \r\nFlavio Lehner (United States of America/Switzerland), Chao Li (China), Marianne T. Lund \r\n(Norway), Daniel J. Lunt (United Kingdom), Sebastian Milinski (Germany), Gemma Teresa \r\nNarisma (Philippines), Zebedee R. J. Nicholls (Australia), Dirk Notz (Germany), Sophie Nowicki \r\n(United States of America/France, United States of America), Bette Otto-Bliesner (United States \r\nof America), Brodie Pearson (United States of America/United Kingdom), Adam S. Phillips \r\n(United States of America), James Renwick (New Zealand), Stephane Senesi (France), Lucas Silva \r\n(Portugal/Switzerland), Aimee B. A. Slangen (The Netherlands), Thomas F"


id: 943f95b45b95f0d878733edcd2a7f941
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 20833
content:
"Arias, P.A., N. Bellouin, E. Coppola, R.G. Jones, G. Krinner, J. Marotzke, V. Naik, M.D. Palmer, G.-K. Plattner, J. Rogelj, \r\nM. Rojas, J. Sillmann, T. Storelvmo, P.W. Thorne, B. Trewin, K. Achuta Rao, B. Adhikary, R.P. Allan, K. Armour, G. Bala, \r\nR. Barimalala, S. Berger, J.G. Canadell, C. Cassou, A. Cherchi, W. Collins, W.D. Collins, S.L. Connors, S. Corti, F. Cruz, \r\nF.J. Dentener, C. Dereczynski, A. Di Luca, A. Diongue Niang, F.J. Doblas-Reyes, A. Dosio, H. Douville, F. Engelbrecht, \r\nV.  Eyring, E. Fischer, P. Forster, B. Fox-Kemper, J.S. Fuglestvedt, J.C. Fyfe, N.P. Gillett, L. Goldfarb, I. Gorodetskaya, \r\nJ.M. Gutierrez, R. Hamdi, E. Hawkins, H.T. Hewitt, P. Hope, A.S. Islam, C. Jones, D.S. Kaufman, R.E. Kopp, Y. Kosaka, \r\nJ.  Kossin, S.  Krakovska, J.-Y. Lee, J. Li, T. Mauritsen, T.K. Maycock, M. Meinshausen, S.-K. Min, P.M.S. Monteiro, \r\nT.  Ngo-Duc, F.  Otto, I. Pinto, A. Pirani, K. Raghavan, R. Ranasinghe, A.C. Ruane, L. Ruiz, J.-B. Sallee, B.H. Samset, \r\nS"


id: 38dd3b69fbb843768273f9109ad6e559
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 6319
content:
" Sathyendranath, S.I. Seneviratne, A.A. Sorensson, S. Szopa, I. Takayabu, A.-M. Treguier, B. van den Hurk, R. Vautard, \r\nK. von Schuckmann, S. Zaehle, X. Zhang, and K. Zickfeld, 2021: Technical Summary. In Climate Change 2021: The \r\nPhysical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental \r\nPanel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Pean, S. Berger, N. Caud, Y. Chen, \r\nL. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekci, R. Yu, \r\nand B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33-144. \r\ndoi:10.1017/9781009157896.002."


id: 503ff0d6f29852974b083af230a700cf
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 10068
content:
"The Report has been peer-reviewed by the scientific community \r\nand governments (Annex X provides the Expert Reviewer list). The \r\nsubstantive introduction provided by Chapter 1 is followed by a first \r\nset of chapters dedicated to large-scale climate knowledge (Chapters \r\n2-4), which encompasses observations and paleoclimate evidence, \r\ncauses of observed changes, and projections; these are complemented \r\nby Chapter 11 for large-scale changes in extremes. The second set of \r\nchapters (Chapters 5-9) is orientated around the understanding of \r\nkey climate system components and processes, including the global \r\ncycles of carbon, energy and water; short-lived climate forcers and their \r\nlink to air quality; and the ocean, cryosphere and sea level change"


id: 463ebde145ff196e905d25b9a512f7a1
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 8439
content:
" \r\nThe last set of chapters (Chapters 10-12 and the Atlas) is dedicated to \r\nthe assessment and distillation of regional climate information from \r\nmultiple lines of evidence at sub-continental to local scales (including \r\nurban climate), with a focus on recent and projected regional changes \r\nin mean climate, extremes, and climatic impact-drivers. The new online "


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vector_id: 1710
content:
"This Report assesses new scientific evidence relevant \r\nfor a world whose climate system is rapidly changing, \r\noverwhelmingly due to human influence. The five IPCC \r\nassessment cycles since 1990 have comprehensively and \r\nconsistently laid out the rapidly accumulating evidence \r\nof a changing climate system, with the Fourth Assessment \r\nReport in 2007 being the first to conclude that warming \r\nof the climate system is unequivocal. Sustained changes \r\nhave been documented in all major elements of the climate \r\nsystem: the atmosphere, land, cryosphere, biosphere and \r\nocean (Section TS.2). Multiple lines of evidence indicate \r\nthe recent large-scale climatic changes are unprecedented \r\nin a multi-millennial context and that they represent \r\na  millennial-scale commitment for the slow-responding \r\nelements of the climate system, resulting in continued \r\nworldwide loss of ice, increase in ocean heat content, sea \r\nlevel rise and deep ocean acidification (Box TS.2; Section \r\nTS.2). {1.2"


id: 325ff8e810248a606351465fb0789f2d
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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content:
"Paleoclimate evidence is integrated within multiple lines of evidence across the WGI Report to more fully understand \r\nthe climate system. Paleo evidence extends instrument-based observations of climate variables and climate drivers \r\nback in time, providing the long-term context needed to gauge the extent to which recent and potential future \r\nchanges are unusual (Section TS.2, Figure TS.1). Pre-industrial climate states complement evidence from climate model \r\nprojections by providing real-world examples of climate characteristics for past global warming levels, with empirical \r\nevidence for how the slow-responding components of the climate system operate over centuries to millennia - the \r\ntime scale for committed climate change (Core Concepts Box, Box TS.4, Box TS.9)"


id: 61bee0965b33f627a017d76206674a4
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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content:
" Information about the state of \r\nthe climate system during well-described paleoclimate reference periods helps narrow the uncertainty range in the \r\noverall assessment of Earth's sensitivity to climate forcing (Section TS.3.2.1). {Cross-Chapter Box 2.1, FAQ 1.3, FAQ 2.1}"


id: 1671e567096985ced9eb831c4961fe6f
created_at: 2023-03-16 14:01:55
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content:
"Sea level and ice sheets. Although past and future global warming differ in their forcings, evidence from paleoclimate \r\nrecords and modelling show that ice-sheet mass and global mean sea level (GMSL) responded dynamically over multiple millennia \r\n(high confidence). This evidence helps to constrain estimates of the committed GMSL response to global warming (Box TS.4). For \r\nexample, under a past global warming levels of around [2.5degC to 4degC] relative to 1850-1900, like during the mid-Pliocene Warm \r\nPeriod, sea level was [5 to 25 m] higher than 1900 (medium confidence); under past global warming levels of [10degC to 18degC], like \r\nduring the Early Eocene, the planet was essentially ice free (high confidence)"


id: 4c38ca9987a200e98671db29884f6b3e
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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vector_id: 9418
content:
" Constraints from these past warm periods, combined \r\nwith physical understanding, glaciology and modelling, indicate a committed long-term GMSL rise over 10,000 years, reaching about \r\n8 to 13 m for sustained peak global warming of 2degC and up to 28 to 37 m for 5degC, which exceeds the AR5 estimate. {2.3.3.3, 9.4.1.4, \r\n9.4.2.6, 9.6.2, 9.6.3.5}"


id: f191e0aa53674b46ba95d14cd2df97eb
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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vector_id: 35509
content:
"Ocean. Since AR5, better integration of paleo-oceanographic data with modelling along with higher-resolution analyses of \r\ntransient changes have improved understanding of long-term ocean processes. Low-latitude sea surface temperatures at the Last \r\nGlacial Maximum cooled more than previously inferred, resolving some inconsistencies noted in AR5. This paleo context supports \r\nthe assessment that ongoing increase in ocean heat content (OHC) represents a long-term commitment (see Core Concepts Box), \r\nessentially irreversible on human time scales (high confidence). Estimates of past global OHC variations generally track those of \r\nsea surface temperatures around Antarctica, underscoring the importance of Southern Ocean processes in regulating deep-ocean \r\ntemperatures"


id: 81c133e6ffbecf9f4f65a52881c10198
created_at: 2023-03-16 14:01:55
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vector_id: 17943
content:
" Paleoclimate data, along with other evidence of glacial-interglacial changes, show that Antarctic Circumpolar flow \r\nstrengthened and that ventilation of Antarctic Bottom Water accelerated during warming intervals, facilitating release of CO2 stored in \r\nthe deep ocean to the atmosphere. Paleo evidence suggests significant reduction of deep-ocean ventilation associated with meltwater \r\ninput during times of peak warmth. {2.3.1.1, 2.3.3.1, 9.2.2, 9.2.3.2}"


id: 1002a1537fe843cad4b8479de22df256
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
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content:
"satellite measurement techniques are now long enough to be \r\nrelevant for climate assessments. For example, globally distributed, \r\nhigh-vertical-resolution profiles of temperature and humidity in the \r\nupper troposphere and stratosphere can be obtained from the early \r\n2000s using global navigation satellite systems, leading to updated \r\nestimates of recent atmospheric warming. Improved measurements \r\nof ocean heat content, warming of the land surface, ice-sheet mass \r\nloss and sea level changes allow a better closure of the global \r\nenergy and sea level budgets relative to AR5. For surface and \r\nballoon-based networks, apparent regional data reductions result \r\nfrom a combination of data policy issues, data curation/provision \r\nchallenges, and real cessation of observations, and are to an extent \r\ncounter-balanced by improvements elsewhere. Limited observational \r\nrecords of extreme events and spatial data gaps currently limit the \r\nassessment of some observed regional climate change"


id: ff0d7a99d282ce678a02ffb2576fbe84
created_at: 2023-03-16 14:01:55
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vector_id: 37677
content:
"Reanalyses combine observations and models (e.g., a numerical \r\nweather prediction model) using data assimilation techniques to \r\nprovide a spatially complete, dynamically consistent estimate of \r\nmultiple variables describing the evolving climate state. Since AR5, \r\nnew reanalyses have been developed for the atmosphere and the \r\nocean with various combinations of increased resolution, extended \r\nrecords, more consistent data assimilation and larger availability \r\nof uncertainty estimates. Limitations remain, for example, in how \r\nreanalyses represent global-scale changes to the water cycle. Regional \r\nreanalyses use high-resolution, limited-area models constrained by \r\nregional observations and with boundary conditions from global \r\nreanalyses"


id: 4ef1b3fd56d6c8923b06698e4bb5c52e
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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content:
" There is high confidence that regional reanalyses better \r\nrepresent the frequencies of extremes and variability in precipitation, \r\nsurface air temperature and surface wind than global reanalyses \r\nand provide estimates that are more consistent with independent \r\nobservations than dynamical downscaling approaches. {1.5.2, \r\n10.2.1.2, Annex I}"


id: b8ba701232ae7601911457434cb67d1f
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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content:
"Climate model simulations coordinated and collected as part of the \r\nWorld Climate Research Programme's Coupled Model Intercomparison \r\nProject Phase 6 (CMIP6), complemented by a range of results from \r\nthe previous phase (CMIP5), constitute a key line of evidence \r\nsupporting this Report. The latest generation of CMIP6 models have \r\nan improved representation of physical processes relative to previous \r\ngenerations, and a wider range of Earth system models now represent \r\nbiogeochemical cycles. Higher-resolution models that better capture \r\nsmaller-scale processes are also increasingly becoming available \r\nfor climate change research (Figure TS.2, Panels a and b). Results \r\nfrom coordinated regional climate modelling initiatives, such as the \r\nCoordinated Regional Climate Downscaling Experiment (CORDEX) \r\ncomplement and add value to the CMIP global models, particularly in \r\ncomplex topography zones, coastal areas and small islands, as well \r\nas for extremes. {1.5.3, 1.5.4, 2.8"


id: d1a46ba441bdb541f4d841e6e8736b21
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content:
"This report assesses results from climate models \r\nparticipating in the Coupled Model Intercomparison Project \r\nPhase 6 (CMIP6) of the World Climate Research Programme. \r\nThese models include new and better representation of \r\nphysical, chemical and biological processes, as well as \r\nhigher resolution, compared to climate models considered \r\nin previous IPCC Assessment Reports. This has improved \r\nthe simulation of the recent mean state of most large-scale \r\nindicators of climate change and many other aspects across \r\nthe climate system. Some differences from observations \r\nremain, for example in regional precipitation patterns"


id: d348b7fafa3c7f47241b4a2b312f4804
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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vector_id: 30769
content:
" \r\nProjections of the increase in global surface temperature, \r\nthe pattern of warming, and global mean sea level rise \r\nfrom previous IPCC Assessment Reports and other studies \r\nare broadly consistent with subsequent observations, \r\nespecially when accounting for the difference in radiative \r\nforcing scenarios used for making projections and the \r\nradiative forcings that actually occurred. "


id: d5fbb4e3f5c92d7005352ff01bb66870
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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vector_id: 31181
content:
"Some CMIP6 models demonstrate an improvement in how clouds \r\nare represented. CMIP5 models commonly displayed a negative \r\nshortwave cloud radiative effect that was too weak in the present \r\nclimate. These errors have been reduced, especially over the \r\nSouthern Ocean, due to a more realistic simulation of supercooled \r\nliquid droplets with sufficient numbers and an associated increase \r\nin the cloud optical depth. Because a negative cloud optical depth \r\nfeedback in response to surface warming results from 'brightening' \r\nof clouds via active phase change from ice to liquid cloud particles \r\n(increasing their shortwave cloud radiative effect), the extratropical \r\ncloud shortwave feedback in CMIP6 models tends to be less negative, \r\nleading to a better agreement with observational estimates (medium \r\nconfidence)"


id: 293b63e4e8096197f32642a090b6a780
created_at: 2023-03-16 14:01:55
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vector_id: 3922
content:
" CMIP6 models generally represent more processes \r\nthat drive aerosol-cloud interactions than the previous generation \r\nof climate models, but there is only medium confidence that those \r\nenhancements improve their fitness-for-purpose of simulating \r\nradiative forcing of aerosol-cloud interactions. {6.4, 7.4.2, FAQ 7.2}"


id: 7e7c0b66826b202b2d5973bba7a9c933
created_at: 2023-03-16 14:01:55
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vector_id: 17462
content:
"CMIP6 models are able to reproduce most aspects of the spatial \r\nstructure and variance of the El Nino-Southern Oscillation (ENSO) \r\nand Indian Ocean Basin and Dipole modes of variability (medium \r\nconfidence). However, despite a slight improvement in CMIP6, some \r\nunderlying processes are still poorly represented. Models reproduce \r\nobserved spatial features and variance of the Southern Annular \r\nMode (SAM) and Northern Annular Mode (NAM) very well (high \r\nconfidence). The summertime SAM trend is well captured, with \r\nCMIP6 models outperforming CMIP5 models (medium confidence). \r\nBy contrast, the cause of the NAM trend towards its positive phase \r\nis not well understood. In the Tropical Atlantic basin, which contains \r\nthe Atlantic Zonal and Meridional modes, major biases in modelled \r\nmean state and variability remain"


id: a00054e4cdf24f7a08e99757427a0ea8
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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content:
" Model performance is limited in \r\nreproducing sea surface temperature anomalies for decadal modes \r\nof variability, despite improvements from CMIP5 to CMIP6 (medium \r\nconfidence) (see also Section TS.1.4.2.2, Table TS.4). {3.7.3-3.7.7}"


id: 79e57e2c2ee1866e8db9ef527fce19a8
created_at: 2023-03-16 14:01:55
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content:
"Two important quantities used to estimate how the climate system \r\nresponds to changes in greenhouse gas (GHG) concentrations are the \r\nequilibrium climate sensitivity (ECS) and transient climate response \r\n(TCR16). The CMIP6 ensemble has broader ranges of ECS and TCR \r\nvalues than CMIP5 (see Section TS.3.2 for the assessed range). These \r\nhigher sensitivity values can, in some models, be traced to changes \r\nin extratropical cloud feedbacks (medium confidence). To combine \r\nevidence from CMIP6 models and independent assessments of ECS \r\nand TCR, various emulators are used throughout the report. Emulators \r\nare a broad class of simple climate models or statistical methods \r\nthat reproduce the behaviour of complex ESMs to represent key \r\ncharacteristics of the climate system, such as global surface temperature \r\nand sea level projections"


id: 46b97eb85f7691b00c6e01cc7fa9b3e7
created_at: 2023-03-16 14:01:55
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content:
" The main application of emulators in AR6 \r\nis to extrapolate insights from ESMs and observational constraints to \r\nproduce projections from a larger set of emissions scenarios, which \r\nis achieved due to their computational efficiency. These emulated \r\nprojections are also used for scenario classification in WGIII. {Box 4.1, \r\n4.3.4, 7.4.2, 7.5.6, Cross-Chapter Box 7.1, FAQ 7.2}"


id: a449dbc55c47a1bfa7f21135f58f845b
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vector_id: 23404
content:
"Observational datasets have been extended and improved since \r\nAR5, providing stronger evidence that the climate is changing and \r\nallowing better estimates of natural climate variability on decadal \r\ntime scales. There is very high confidence that the slower rate of global \r\nsurface temperature change observed over 1998-2012 compared to \r\n1951-2012 was temporary, and was, with high confidence, induced \r\nby internal variability (particularly Pacific Decadal Variability) and \r\nvariations in solar irradiance and volcanic forcing that partly offset the \r\nanthropogenic warming over this period. Global ocean heat content \r\ncontinued to increase throughout this period, indicating continuous \r\nwarming of the entire climate system (very high confidence). Hot \r\nextremes also continued to increase during this period over land \r\n(high confidence)"


id: 70962930d34892160d09209cbfd9e3fe
created_at: 2023-03-16 14:01:55
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vector_id: 15331
content:
" Even in a continually warming climate, periods \r\nof reduced and increased trends in global surface temperature at \r\ndecadal time scales will continue to occur in the 21st century (very \r\nhigh confidence). {Cross-Chapter Box 3.1, 3.3.1, 3.5.1, 4.6.2, 11.3.2}"


id: f98cd00273883633187fdfa8409ddc8f
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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vector_id: 36783
content:
"properties, resulting in warming of the atmosphere, ocean \r\nand land components of the climate system. Other human \r\nactivities influencing climate include the emission of \r\naerosols and other short-lived climate forcers, and land-use \r\nchange such as urbanization. Progress in our understanding \r\nof human influence is gained from longer observational \r\ndatasets, improved paleoclimate information, a stronger \r\nwarming signal since AR5, and improvements in climate \r\nmodels, physical understanding and attribution techniques \r\n(see Core Concepts Box). Since AR5, the attribution to \r\nhuman influence has become possible across a wider range \r\nof climate variables and climatic impact-drivers (CIDs, \r\nsee Core Concepts Box). New techniques and analyses \r\ndrawing on several lines of evidence have provided greater \r\nconfidence in attributing changes in regional weather and \r\nclimate extremes to human influence (high confidence). \r\n{1.3, 1.5.1, Appendix 1.A, 3.1-3.8, 5.2, 6.4.2, 7.3.5, 7.4.4, \r\n8"


id: 16311e030d1ee5182a2caad5dfbaea
created_at: 2023-03-16 14:01:55
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vector_id: 1016
content:
"Regional climate changes can be moderated or amplified by regional \r\nforcing from land-use and land-cover changes or from aerosol \r\nconcentrations and other short-lived climate forcers (SLCFs). For \r\nexample, the difference in observed warming trends between cities \r\nand their surroundings can partly be attributed to urbanization \r\n(very high confidence). While established attribution techniques \r\nprovide confidence in our assessment of human influence on \r\nlarge-scale climate changes (as described in Section TS.2), new \r\ntechniques developed since AR5, including attribution of individual \r\nevents, have provided greater confidence in attributing changes in \r\nclimate extremes to climate change (Box TS.10). Multiple attribution \r\napproaches support the contribution of human influence to several \r\nregional multi-decadal mean precipitation changes (high confidence)"


id: 9080b3049712387f70ac31ba32075a6f
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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vector_id: 20269
content:
"SSP1-1.9 represents the low end of future emissions pathways, \r\nleading to warming below 1.5degC in 2100 and limited temperature \r\novershoot of 1.5degC over the course of the 21st century (see \r\nFigure TS.6). At the opposite end of the range, SSP5-8.5 represents \r\nthe very high warming end of future emissions pathways from the \r\nliterature. SSP3-7.0 has overall lower GHG emissions than SSP5-8.5 \r\nbut, for example, CO2 emissions still almost double by 2100 compared \r\nto today's levels. SSP2-4.5 and SSP1-2.6 represent scenarios with \r\nstronger climate change mitigation and thus lower GHG emissions. \r\nSSP1-2.6 was designed to limit warming to below 2degC. Infographic \r\nTS.1 presents a narrative depiction of SSP-related climate futures. \r\nNo likelihood is attached to the scenarios assessed in this Report, \r\nand the feasibility of specific scenarios in relation to current trends \r\nis best informed by the WGIII contribution to AR6"


id: ee951056268455fcfb2a552190e4acc5
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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vector_id: 35007
content:
" In the scenario \r\nliterature, the plausibility of some scenarios with high CO2 emissions, \r\nsuch as RCP8.5 or SSP5-8.5, has been debated in light of recent \r\ndevelopments in the energy sector. However, climate projections from \r\nthese scenarios can still be valuable because the concentration levels \r\nreached in RCP8.5 or SSP5-8.5 and corresponding simulated climate \r\nfutures cannot be ruled out. That is because of uncertainty in carbon\u0002cycle feedbacks which, in nominally lower emissions trajectories, can \r\nresult in projected concentrations that are higher than the central \r\nconcentration levels typically used to drive model projections. {1.6.1; \r\nCross-Chapter Box 1.4; 4.2.2, 5.4; SROCC; Chapter 3 in WGIII}"


id: b83d50afc6b07c29960e77e6b177ebe8
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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index: climate_gpt_only_giec
vector_id: 26519
content:
"Scenarios in AR6 cover a broader range of emissions futures than \r\nconsidered in AR5, including high CO2 emissions scenarios without \r\nclimate change mitigation as well as a low CO2 emissions scenario \r\nreaching net zero CO2 emissions (see Core Concepts Box) around mid\u0002century. In this Report, a core set of five illustrative scenarios is used \r\nto explore climate change over the 21st century and beyond (Section \r\nTS.2). They are labelled SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and \r\nSSP5-8.517 and span a wide range of radiative forcing levels in 2100. \r\nThey start in 2015 and include scenarios with high and very high \r\nGHG emissions and CO2 emissions that roughly double from current \r\nlevels by 2100 and 2050, respectively (SSP3-7.0 and SSP5-8.5); \r\nscenarios with intermediate GHG emissions and CO2 emissions \r\nremaining around current levels until the middle of the century \r\n(SSP2-4"


id: ac49ab8b3ebf5bcc4cf628d461c5b08f
created_at: 2023-03-16 14:01:55
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vector_id: 24645
content:
"5); and scenarios with very low and low GHG emissions and \r\nCO2 emissions declining to net zero around or after 2050, followed by \r\nvarying levels of net negative CO2 emissions (SSP1-1.9 and SSP1-2.6). \r\nThese SSP scenarios offer unprecedented detail of input data for ESM \r\nsimulations and allow for a more comprehensive assessment of \r\nclimate drivers and responses, in particular because some aspects, \r\nsuch as the temporal evolution of pollutants, emissions or changes in \r\nland use and land cover, span a broader range in the SSP scenarios \r\nthan in the RCPs used in AR5. Modelling studies utilizing the RCPs \r\ncomplement the assessment based on SSP scenarios, for example, "


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content:
"The socio-economic narratives underlying SSP-based scenarios \r\ndiffer in their assumed level of air pollution control. Together with \r\nvariations in climate change mitigation stringency, this difference \r\nstrongly affects anthropogenic emissions trajectories of SLCFs, \r\nsome of which are also air pollutants. SSP1 and SSP5 assume strong \r\npollution control, projecting a decline of global emissions of ozone \r\nprecursors (except methane; CH4) and of aerosols and most of \r\ntheir precursors in the mid- to long term. The reductions due to air \r\npollution controls are further strengthened in scenarios that assume \r\na marked decarbonization, such as SSP1-1.9 or SSP1-2.6. SSP2-4.5 \r\nis a medium pollution-control scenario with air pollutant emissions \r\nfollowing current trends, and SSP3-7.0 is a weak pollution-control \r\nscenario with strong increases in emissions of air pollutants over the \r\n21st century"


id: fcb1c1c5c1f07a947e2c8ef5df18ec82
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vector_id: 37268
content:
" Methane emissions in SSP-based scenarios vary with \r\nthe overall climate change mitigation stringency, declining rapidly \r\nin SSP1-1.9 and SSP1-2.6 but declining only after 2070 in SSP5-8.5. \r\nSSP trajectories span a wider range of air pollutant emissions than \r\nconsidered in the RCP scenarios (see Figure TS.4), reflecting the \r\npotential for large regional differences in their assumed pollution "


id: 1620366594e6312c5e77db3f5e93338c
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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index: climate_gpt_only_giec
vector_id: 1003
content:
"Quantifying geographical response patterns of climate \r\nchange at various global warming levels (GWLs), such as \r\n1.5degC or 2degC above the 1850-1900 period, is useful for \r\ncharacterizing changes in mean climate, extremes and \r\nclimatic impact-drivers. Global warming levels are used \r\nin this Report as a dimension of integration independent \r\nof the timing when the warming level is reached and of \r\nthe emissions scenario that led to the warming. For many \r\nclimate variables the response pattern for a given GWL is \r\nconsistent across different scenarios. However, this is not \r\nthe case for slowly responding processes, such as ice-sheet \r\nand glacier mass loss, deep ocean warming, and the related \r\nsea level rise"


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content:
" The response of these variables depends on \r\nthe time it takes to reach the GWL, differs if the warming is \r\nreached in a transient warming state or after a temporary \r\novershoot of the warming level, and will continue to evolve, \r\nover centuries to millennia, even after global warming \r\nhas stabilized. Different GWLs correspond closely to \r\nspecific cumulative CO2 emissions due to their near-linear \r\nrelationship with global surface temperature. This Report \r\nuses 1.0degC, 1.5degC, 2.0degC, 3.0degC and 4.0degC above 1850-1900 \r\nconditions as a primary set of GWLs. {1.6.2, 4.2.4, 4.6.1, 5.5, \r\nCross-Chapter Box 11.1, Cross-chapter Box 12.1}"


id: 654474670f49928e744b1f0e9c12e19
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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vector_id: 13511
content:
"Since the RCPs are also labelled by the level of radiative forcing \r\nthey reach in 2100, they can in principle be related to the core set \r\nof AR6 scenarios (Figure TS.4). However, the RCPs and SSP-based \r\nscenarios are not directly comparable. First, the gas-to-gas \r\ncompositions differ; for example, the SSP5-8.5 scenario has higher \r\nCO2 but lower CH4 concentrations compared to RCP8.5. Second, the \r\nprojected 21st-century trajectories may differ, even if they result \r\nin the same radiative forcing by 2100. Third, the overall effective \r\nradiative forcing (see Core Concepts Box) may differ, and tends \r\nto be higher for the SSPs compared to RCPs that share the same \r\nnominal stratospheric-temperature-adjusted radiative forcing label. \r\nComparing the differences between CMIP5 and CMIP6 projections \r\n(Cross-Section Box TS"


id: 86e20ec5829d18f4618cdb71e9d8ca58
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 18755
content:
"1) that were driven by RCPs and SSP-based \r\nscenarios, respectively, indicates that about half of the difference in \r\nsimulated warming arises because of higher climate sensitivity being \r\nmore prevalent in CMIP6 model versions; the remainder arises from \r\nhigher ERF in nominally corresponding scenarios (e.g., RCP8.5 and \r\nSSP5-8.5; medium confidence) (see Section TS.1.2.2). In SSP1-2.6 and \r\nSSP2-4.5, changes in ERF also explain about half of the changes in the \r\nrange of warming (medium confidence). For SSP5-8.5, higher climate \r\nsensitivity is the primary reason behind the upper end of the CMIP6-\r\nprojected warming being higher than for RCP8.5 in CMIP5 (medium \r\nconfidence). Note that AR6 uses multiple lines of evidence beyond \r\nCMIP6 results to assess global surface temperature under various \r\nscenarios (see Cross-Section Box TS.1 for the detailed assessment). \r\n{1.6, 4.2.2, 4.6.2.2, Cross-Chapter Box 7.1}"


id: 6873fcfa1b948fa48f07bdb6c70fc2e5
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 14019
content:
"Earth system models can be driven by anthropogenic CO2\r\nemissions ('emissions-driven' runs), in which case atmospheric \r\nCO2 concentration is a projected variable; or by prescribed time\u0002varying atmospheric concentrations ('concentration-driven' runs). In \r\nemissions-driven runs, changes in climate feed back on the carbon \r\ncycle and interactively modify the projected CO2 concentration \r\nin each ESM, thus adding the uncertainty in the carbon cycle \r\nresponse to climate change to the projections. Concentration\u0002driven simulations are based on a central estimate of carbon cycle \r\nfeedbacks, while emissions-driven simulations help quantify the role \r\nof feedback uncertainty. The differences in the few ESMs for which \r\nboth emissions and concentration-driven runs were available for the \r\nsame scenario are small and do not affect the assessment of global \r\nsurface temperature projections discussed in Cross-Section Box TS.1 \r\nand Section TS.2 (high confidence)"


id: e1a151f6e5e9a786b03c5e4ce6568e46
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 32991
content:
" By the end of the 21st century, \r\nemissions-driven simulations are on average around 0.1degC cooler \r\nthan concentration-driven runs, reflecting the generally lower CO2\r\nconcentrations simulated by the emissions-driven ESMs, and have a \r\nspread about 0.1degC greater, reflecting the range of simulated CO2\r\nconcentrations. However, these carbon cycle-climate feedbacks do \r\naffect the transient climate response to cumulative CO2 emissions \r\n(TCRE18), and their quantification is crucial for the assessment of \r\nremaining carbon budgets consistent with global warming levels \r\nsimulated by ESMs (see Section TS.3). {1.6.1, Cross-Chapter Box 1.4, \r\n4.2, 4.3.1, 5.4.5, Cross-Chapter Box 7.1}"


id: 601b372e8242280cecf72caba2d397b4
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 12696
content:
"resulting increase in global surface temperature (Section TS.3.2). This \r\nimplies that continued CO2 emissions will cause further warming \r\nand associated changes in all components of the climate system. For \r\ndeclining cumulative CO2 emissions (i.e., if negative net emissions \r\nare achieved), the relationship is less strong for some components, \r\nsuch as the hydrological cycle. The WGI report uses cumulative CO2\r\nemissions to compare climate response across scenarios and provides \r\na link to the emissions pathways assessment in WGIII. The advantage \r\nof using cumulative CO2 emissions is that it is an inherent emissions \r\nscenario characteristic rather than an outcome of the scenario-based \r\nprojections, where uncertainties in the cause-effect chain from \r\nemissions to temperature change are important (Figure TS.4), for \r\nexample, the uncertainty in ERF and TCR. Cumulative CO2 emissions \r\ncan also provide a link to the assessments of mitigation options"


id: 471569464cd757afbad869becb3a954d
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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vector_id: 8563
content:
" \r\nCumulative CO2 emissions do not carry information about non-CO2\r\nemissions, although these can be included with specific emissions \r\nmetrics to estimate CO2-equivalent emissions. (Section TS.3.3) {1.3.2, \r\n1.6, 4.6.2, 5.5, 7.6}"


id: cce0486a845aed12821afb2eb059fb35
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 29771
content:
"Multiple lines of evidence derived from observations, model simulations \r\nand other approaches can be used to construct climate information \r\non a regional scale as described in detail in Sections TS.4.1.1 and \r\nTS.4.1.2. Depending on the phenomena and specific context, these \r\nsources and methodologies include theoretical understanding of the \r\nrelevant processes, drivers and feedbacks of climate at regional scale; \r\ntrends in observed data from multiple datasets; and the attribution \r\nof these trends to specific drivers. Furthermore, simulations from \r\ndifferent model types (including global and regional climate models, \r\nemulators, statistical downscaling methods, etc.) and experiments \r\n(e.g., CMIP, CORDEX, and large ensembles of single-model simulations \r\nwith different initial conditions), attribution methodologies and other \r\nrelevant local knowledge (e.g., indigenous knowledge) are utilized \r\n(see Box TS.11). {1.5.3, 1.5.4, Cross-Chapter Box 7.1, 10.2-10.6, 11.2, \r\nAtlas.1"


id: a682e70e97946724cc072cfd53dac7ce
created_at: 2023-03-16 14:01:55
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content_type: text
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vector_id: 23727
content:
"This box synthesizes the outcomes of the assessment of past, current and future global surface temperature. Global mean surface \r\ntemperature (GMST) and global surface air temperature (GSAT) are the two primary metrics of global surface temperature used to \r\nestimate global warming in IPCC reports. GMST merges sea surface temperature (SST) over the ocean and 2 m air temperature over \r\nland and sea ice areas and is used in most paleo, historical and present-day observational estimates. The GSAT metric is 2 m air \r\ntemperature over all surfaces and is the diagnostic generally used from climate models. Changes in GMST and GSAT over time differ \r\nby at most 10% in either direction (high confidence), but conflicting lines of evidence from models and direct observations, combined \r\nwith limitations in theoretical understanding, lead to low confidence in the sign of any difference in long-term trend"


id: a9aa348b7b7938de19c43ad4b1cd5ac5
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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index: climate_gpt_only_giec
vector_id: 24224
content:
" Therefore, \r\nlong-term changes in GMST/GSAT are presently assessed to be identical, with expanded uncertainty in GSAT estimates. Hence the term \r\nglobal surface temperature is used in reference to both quantities in the text of the TS and SPM. {Cross-Chapter Box 2.3}"


id: 75993124d0c6eb0f6cdb1e605bd8eedb
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 16092
content:
"Global surface temperature has increased by 0.99 [0.84 to 1.10] degC from 1850-1900 to the first two decades of the \r\n21st century (2001-2020) and by 1.09 [0.95 to 1.20] degC from 1850-1900 to 2011-2020. Temperatures as high as \r\nduring the most recent decade (2011-2020) exceed the warmest centennial-scale range reconstructed for the present \r\ninterglacial, around 6500 years ago [0.2degC to 1degC] (medium confidence). The next most recent warm period was \r\nabout 125,000 years ago during the last interglacial when the multi-centennial temperature range [0.5degC to 1.5degC] \r\nencompasses the 2011-2020 values (medium confidence). The likely range of human-induced change in global surface \r\ntemperature in 2010-2019 relative to 1850-1900 is 0.8degC to 1.3degC, with a central estimate of 1.07degC, encompassing \r\nthe best estimate of observed warming for that period, which is 1.06degC with a very likely range of [0.88degC to 1"


id: 812560e9acb9648fdb91d9b40e60a95f
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 17841
content:
"21degC], \r\nwhile the likely range of the change attributable to natural forcing is only -0.1degC to +0.1degC. "


id: 8c53cfe216c4fc0e763e0e23b406f39d
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
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vector_id: 19588
content:
"Compared to 1850-1900, average global surface temperature over the period 2081-2100 is very likely to be higher by \r\n[1.0degC to 1.8degC] in the low CO2 emissions scenario SSP1-1.9 and by [3.3degC to 5.7degC] in the high CO2 emissions scenario \r\nSSP5-8.5. In all scenarios assessed here except SSP5-8.5, the central estimate of 20-year averaged global surface \r\nwarming crossing the 1.5degC level lies in the early 2030s, which is in the early part of the likely range (2030-2052) \r\nassessed in SR1.5. It is more likely than not that under SSP1-1.9, global surface temperature relative to 1850-1900 will \r\nremain below 1.6degC throughout the 21st century, implying a potential temporary overshoot of 1.5degC global warming \r\nof no more than 0.1degC. Global surface temperature in any individual year could exceed 1.5degC relative to 1850-1900 \r\nby 2030 with a likelihood between 40% and 60% across the scenarios considered here (medium confidence)"


id: 32ad79828d3c3e2933ceb41f368a63d2
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 5392
content:
" A 2degC \r\nincrease in global surface temperature relative to 1850-1900 will be crossed under SSP5-8.5 but is extremely unlikely \r\nto be crossed under SSP1-1.9. Periods of reduced and increased global surface temperature trends at decadal time \r\nscales will continue to occur in the 21st century (very high confidence). The effect of strong mitigation on 20-year \r\nglobal surface temperature trends would be likely to emerge during the near term (2021-2040), assuming no major \r\nvolcanic eruptions occur. (Figure TS.8, Cross-Section Box TS.1, Figure 1) {2.3, 3.3, 4.3, 4.4, 4.5, 4.6, 7.3}"


id: 6554f12c73b34bd5684c23c3709f2ae6
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 13520
content:
"Global surface temperature has evolved over geological time (Figure TS.1, Box TS.2). Beginning approximately 6500 years ago, global \r\nsurface temperature generally decreased, culminating in the coldest multi-century interval of the post-glacial period (since roughly \r\n7000 years ago), which occurred between around 1450 and 1850 (high confidence). Over the last 50 years, global surface temperature \r\nhas increased at an observed rate unprecedented in at least the last two thousand years (high confidence). Temperatures as high as \r\nduring the most recent decade (2011-2020) exceed the warmest centennial-scale range reconstructed for the present interglacial, \r\naround 6500 years ago [0.2degC to 1degC] (medium confidence). The next most recent warm period was about 125,000 years ago during \r\nthe Last Interglacial when the multi-centennial temperature range [0.5degC to 1.5degC] encompasses the 2011-2020 values (medium \r\nconfidence) (Cross-Section Box TS.1, Figure 1)"


id: 4e261e0e5e3e13a2428920d778cc0af2
created_at: 2023-03-16 14:01:55
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vector_id: 9718
content:
" During the mid-Pliocene Warm Period, around 3.3-3.0 million years ago, global surface \r\ntemperature was 2.5degC to 4degC warmer (medium confidence). {2.3.1, Cross-Chapter Box 2.1 and 2.4}"


id: f1dc369e03470d5cb03237c96d6e85b7
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 35570
content:
"The likely range of human-induced change in global surface temperature in 2010-2019 relative to 1850-1900 is 0.8degC to 1.3degC, \r\nwith a central estimate of 1.07degC (Figure Cross-Section Box TS.1, Figure 1), encompassing the best estimate of observed warming \r\nfor that period, which is 1.06degC with a very likely range of [0.88degC to 1.21degC], while the likely range of the change attributable \r\nto natural forcing is only -0.1degC to +0.1degC. This assessment is consistent with an estimate of the human-induced global surface \r\ntemperature rise based on assessed ranges of perturbations to the top of the atmosphere (effective radiative forcing) and with metrics \r\nof feedbacks of the climate response (equilibrium climate sensitivity and the transient climate response). Over the same period, well\u0002mixed greenhouse gas forcing likely warmed global surface temperature by 1.0degC to 2.0degC, while aerosols and other anthropogenic \r\nforcings likely cooled global surface temperature by 0"


id: b385a0832c89e1631e88c5f9ee6d7826
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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index: climate_gpt_only_giec
vector_id: 25755
content:
"The observed slower increase in global surface temperature (relative to preceding and following periods) in the 1998-2012 period, \r\nsometimes referred to as 'the hiatus', was temporary (very high confidence). The increase in global surface temperature during the \r\n1998-2012 period is also greater in the data sets used in the AR6 assessment than in those available at the time of AR5. Using these \r\nupdated observational data sets and a like-for-like consistent comparison of simulated and observed global surface temperature, all \r\nobserved estimates of the 1998-2012 trend lie within the very likely range of CMIP6 trends. Furthermore, the heating of the climate \r\nsystem continued during this period, as reflected in the continued warming of the global ocean (very high confidence) and in the \r\ncontinued rise of hot extremes over land (medium confidence)"


id: d0306750cf65b0ad375c190ed71e3cc9
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 30286
content:
" Since 2012, global surface temperature has risen strongly, with the \r\npast five years (2016-2020) being the hottest five-year period between 1850 and 2020 (high confidence). {2.3.1, 3.3.1, 3.5.1, Cross\u0002Chapter Box 3.1}"


id: e9d9b1afc85d6aefe0554eb8a8f5a77b
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 34286
content:
"Cross-Section Box TS.1, Figure 1 (continued): The intent of this figure is to show global surface temperature observed changes from the Holocene to now, \r\nand projected changes. (a) Global surface temperature over the Holocene divided into three time scales: (i) 12,000 to 1000 years ago (10,000 BCE to 1000 CE) \r\nin 100-year time steps, (ii) 1000 to 1900 CE, 10-year smooth, and (iii) 1900 to 2020 CE (mean of four datasets in panel c). Bold lines show the median of the \r\nmulti-method reconstruction, with 5% and 95% percentiles of the ensemble members (thin lines). Vertical bars are 5-95th percentile ranges of estimated global \r\nsurface temperature for the Last Interglacial and mid-Holocene (medium confidence) (Section 2.3.1.1). All temperatures are relative to 1850-1900. (b) Spatially \r\nresolved trends (degC per decade) for (upper map) HadCRUTv5 over 1981-2020, and (lower map, total change) multi-model mean projected changes from 1995-2014 \r\nto 2081-2100 in the SST3-7.0 scenario"


id: 4915179a53e0ac962e57e2087ce12987
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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index: climate_gpt_only_giec
vector_id: 8881
content:
" Observed trends have been calculated where data are present in both the first and last decade and for at least 70% of all \r\nyears within the period using ordinary least squares. Significance is assessed with autoregressive AR(1) model correction and denoted by stippling. Hatched areas \r\nin the lower map show areas of conflicting model evidence on significance of changes. (c) Temperature from instrumental data for 1850-2020, including annually \r\nresolved averages for the four global surface temperature datasets assessed in Section 2.3.1.1.3 (see text for references). The grey shading shows the uncertainty \r\nassociated with the HadCRUTv5 estimate. All temperatures are relative to the 1850-1900 reference period"


id: cc2ade628b032526f0215a3cc0b6ecee
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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vector_id: 29665
content:
" (d) Recent past and 2015-2050 evolution of annual mean \r\nglobal surface temperature change relative to 1850-1900, from HadCRUTv5 (black), Coupled Model Intercomparison Project Phase 6 (CMIP6) historical simulations \r\n(up to 2014, in grey, ensemble mean solid, 5% and 95% percentiles dashed, individual models thin), and CMIP6 projections under scenario SSP2-4.5, from four \r\nmodels that have an equilibrium climate sensitivity near the assessed central value (thick yellow). Solid thin coloured lines show the assessed central estimate of \r\n20-year change in global surface temperature for 2015-2050 under three scenarios, and dashed thin coloured lines the corresponding 5% and 95% quantiles. (e)\r\nAssessed projected change in 20-year running mean global surface temperature for five scenarios (central estimate solid, very likely range shaded for SSP1-2.6 and \r\nSSP3-7.0), relative to 1995-2014 (left y-axis) and 1850-1900 (right y-axis). The y-axis on the right-hand side is shifted upward by 0"


id: 74768d07e366f665cb0b8da23f805e44
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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index: climate_gpt_only_giec
vector_id: 15917
content:
"85degC, the central estimate of the \r\nobserved warming for 1995-2014, relative to 1850-1900. The right y-axis in (e) is the same as the y-axis in (d). {2.3, 4.3, 4.4}"


id: d329c636474833d7cf98b90c1179fb10
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 30748
content:
"During the near term (2021-2040), a 1.5degC increase in global surface temperature, relative to 1850-1900, is very likely to occur \r\nin scenario SSP5-8.5, likely to occur in scenarios SSP2-4.5 and SSP3-7.0, and more likely than not to occur in scenarios SSP1-1.9 and \r\nSSP1-2.6. The time of crossing a warming level is defined here as the midpoint of the first 20-year period during which the average \r\nglobal surface temperature exceeds the level. In all scenarios assessed here except SSP5-8.5, the central estimate of crossing the 1.5degC \r\nlevel lies in the early 2030s. This is in the early part of the likely range (2030-2052) assessed in SR1.5, which assumed continuation of \r\nthe then-current warming rate; this rate has been confirmed in the AR6. Roughly half of this difference arises from a larger historical \r\nwarming diagnosed in AR6"


id: 36723565d0028afe50dba45b3eb95d73
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 5958
content:
" The other half arises because for central estimates of climate sensitivity, most scenarios show stronger \r\nwarming over the near term than was estimated as 'current' in SR1.5 (medium confidence). When considering scenarios similar to SSP1-\r\n1.9 instead of linear extrapolation, the SR1.5 estimate of when 1.5degC global warming is crossed is close to the central estimate reported \r\nhere. (Cross-Section Box TS.1, Table 1) {2.3.1, Cross-Chapter Box 2.3, 3.3.1, 4.3.4, Box 4.1}"


id: d3b3a81e65ff0737672e7489c67c10ff
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 30827
content:
"Table TS.2 | Summary table on observed changes in extremes, their attribution since 1950 (except where stated otherwise), and projected changes at \r\n+1.5degC, +2degC and +4degC of global warming, on global and continental scales. An increase in warm/hot extremes refers to warmer and/or more frequent hot days and \r\nnights and warm spells/heatwaves, over most land areas. A decrease in cold extremes refers to warmer and/or fewer cold days and nights and cold spells/cold waves, over most \r\nland areas. Drought events are relative to a predominant fraction of land area. For tropical cyclones, observed changes and attribution refer to Categories 3-5, while projected \r\nchanges refer to Categories 4-5. Tables 11.1 and 11.2 are more detailed versions of this table, containing, in particular, information on regional scales"


id: ae7c6dfecfbd8b5e9f42970fb85a42ec
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 24983
content:
" In general, higher \r\nwarming levels also imply stronger projected changes for indicators where the confidence level does not depend on the warming level and the table does not explicitly quantify \r\nthe global sensitivity. See also Box TS.10. {9.6, Box 9.2, 11.3, 11.7}"


id: 903bd74506b9254c06efabd1a2c2c3ad
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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index: climate_gpt_only_giec
vector_id: 20226
content:
"There is high confidence that tropospheric ozone has been increasing \r\nfrom 1750 in response to anthropogenic changes in ozone precursor \r\nemissions (nitrogen oxides, carbon monoxide, non-methane volatile \r\norganic compounds, and methane), but with medium confidence in \r\nthe magnitude of this change, due to limited observational evidence \r\nand knowledge gaps. Since the mid-20th century, tropospheric \r\nozone surface concentrations have increased by 30-70% across the \r\nNorthern Hemisphere (medium confidence); since the mid-1990s, \r\nfree tropospheric ozone has increased by 2-7% per decade in most \r\nnorthern mid-latitude regions and 2-12% per decade in sampled \r\ntropical regions. Future changes in surface ozone concentrations will \r\nbe primarily driven by changes in precursor emissions rather than \r\nclimate change (high confidence). Stratospheric ozone has declined \r\nbetween 60degS-60degN by 2.2% from 1964-1980 to 2014-2017 \r\n(high confidence), with the largest declines during 1980-1995"


id: ee97167ec811f7a910245c95f76e5026
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
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index: climate_gpt_only_giec
vector_id: 35009
content:
" \r\nThe strongest loss of stratospheric ozone continues to occur in \r\naustral spring over Antarctica (ozone hole), with emergent signs of \r\nrecovery after 2000. The 1750-2019 ERF for total (stratospheric and \r\ntropospheric) ozone is 0.47 [0.24 to 0.71] W m-2, which is dominated \r\nby tropospheric ozone changes. {2.2.5, 6.3.2, 7.3.2, 7.3.5}"


id: 7f35dddaa045a747201dad55196629f1
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vector_id: 17567
content:
"Tropospheric aerosols mainly act to cool the climate system, \r\ndirectly by reflecting solar radiation, and indirectly by enhancing \r\ncloud reflectance. Ice cores show increases in aerosols across the \r\nNorthern Hemisphere mid-latitudes since 1700 and reductions \r\nsince the late 20th century (high confidence). Aerosol optical depth \r\n(AOD), derived from satellite- and ground-based radiometers, \r\nhas decreased since 2000 over the mid-latitude continents of \r\nboth hemispheres, but increased over South Asia and East Africa \r\n(high confidence). Trends in AOD are more pronounced from sub\u0002micrometre aerosols for which the anthropogenic contribution is \r\nparticularly large. Global carbonaceous aerosol budgets and trends \r\nremain poorly characterized due to limited observations, but black \r\ncarbon (BC), a warming aerosol component, is declining in several \r\nregions of the Northern Hemisphere (low confidence). Total aerosol \r\nERF in 2019, relative to 1750, is -1.1 [-1.7 to -0"


id: 8235f856267de15098a49eac601f1bcd
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 18014
content:
"It is likely that the proportion of major (Category 3-5) tropical \r\ncyclones (TCs) and the frequency of rapid TC intensification events \r\nhave increased over the past four decades. The average location \r\nof peak TC wind-intensity has very likely migrated poleward in \r\nthe western North Pacific Ocean since the 1940s, and TC forward \r\ntranslation speed has likely slowed over the contiguous USA since \r\n1900. It is likely that the poleward migration of TCs in the western \r\nNorth Pacific and the global increase in TC intensity rates cannot be \r\nexplained entirely by natural variability. There is high confidence\r\nthat average peak TC wind speeds and the proportion of Category \r\n4-5 TCs will increase with warming and that peak winds of the \r\nmost intense TCs will increase"


id: d99e05880e91654fa70db0f67bed2a6
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 31749
content:
" There is medium confidence that the \r\naverage location where TCs reach their maximum wind-intensity will \r\nmigrate poleward in the western North Pacific Ocean, while the total \r\nglobal frequency of TC formation will decrease or remain unchanged \r\nwith increasing global warming. {11.7.1}"


id: 3ecde802ddd3bdbbc3c5cfa361e2c5f
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 7263
content:
"Since the 1970s, near-surface average winds have likely weakened \r\nover land. Over the ocean, near-surface average winds likely\r\nstrengthened over 1980-2000, but divergent estimates lead to \r\nlow confidence thereafter. Extratropical storm tracks have likely\r\nshifted poleward since the 1980s. There is low confidence in \r\nprojected poleward shifts of the Northern Hemisphere mid-latitude \r\njet and storm tracks due to large internal variability and structural \r\nuncertainty in model simulations. There is medium confidence in \r\na projected decrease in the frequency of atmospheric blocking over \r\nGreenland and the North Pacific in boreal winter in 2081-2100 under \r\nthe SSP3-7.0 and SSP5-8.5 scenarios. There is high confidence that \r\nSouthern Hemisphere storm tracks and associated precipitation have \r\nmigrated polewards over recent decades, especially in the austral \r\nsummer and autumn, associated with a trend towards more positive \r\nphases of the Southern Annular Mode (SAM) (Section TS.4.2"


id: d403445b6d3757cb08e5e364a4ef14f6
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 30871
content:
"2) and \r\nthe strengthening and southward shift of the Southern Hemisphere \r\nextratropical jet in austral summer. In the long term (2081-2100), "


id: c0b7557d718547c7bf83c56cfea2863e
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 27883
content:
"There is low confidence in observed recent changes in the total \r\nnumber of extratropical cyclones over both hemispheres. There is also \r\nlow confidence in past-century trends in the number and intensity of \r\nthe strongest extratropical cyclones over the Northern Hemisphere \r\ndue to the large interannual-to-decadal variability and temporal and \r\nspatial heterogeneities in the volume and type of assimilated data \r\nin atmospheric reanalyses, particularly before the satellite era. Over \r\nthe Southern Hemisphere, it is likely that the number of extratropical \r\ncyclones with low central pressures (<980 hPa) has increased since \r\n1979. The frequency of intense extratropical cyclones is projected to \r\ndecrease (medium confidence). Projected changes in the intensity \r\ndepend on the resolution of climate models (medium confidence). \r\nThere is medium confidence that wind speeds associated with \r\nextratropical cyclones will change following changes in the storm \r\ntracks. {2.3.1, 3.3.3, 4.5.1, 4.5"


id: 784f5b34a2ed0683cf51af901cc8beac
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 16493
content:
"Future global warming exceeding the assessed very likely range cannot be ruled out and is potentially associated \r\nwith the highest risks for society and ecosystems. Such low-likelihood, high-warming storylines tend to exhibit \r\nsubstantially greater changes in the intensity of regional drying and wetting than the multi-model mean. Even at \r\nlevels of warming within the very likely range, global and regional low-likelihood outcomes might occur, such as large \r\nprecipitation changes, additional sea level rise associated with collapsing ice sheets (see Box TS.4), or abrupt ocean \r\ncirculation changes. While there is medium confidence that the Atlantic Meridional Overturning Circulation (AMOC) \r\nwill not experience an abrupt collapse before 2100, if it were to occur, it would very likely cause abrupt shifts in \r\nregional weather patterns and water cycle. The probability of these low-likelihood outcomes increases with higher \r\nglobal warming levels"


id: f4e1a6a96d85f813f84d4ae0ad0500d6
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 36036
content:
" If the real-world climate sensitivity lies at the high end of the assessed range, then global \r\nand regional changes substantially outside the very likely range projections occur for a given emissions scenario. \r\nWith increasing global warming, some very rare extremes and some compound events (multivariate or concurrent \r\nextremes) with low likelihood in past and current climate will become more frequent, and there is a higher chance \r\nthat events unprecedented in the observational record occur (high confidence). Finally, low-likelihood, high-impact \r\noutcomes may also arise from a series of very large volcanic eruptions that could substantially alter the 21st century \r\nclimate trajectory compared to SSP-based Earth system model (ESM) projections. {Cross-Chapter Box 4.1, 4.3, 4.4, 4.8, \r\n7.3, 7.4, 7.5, 8.6, 9.2, 9.6, Box 9.4, Box 11.2, Cross-Chapter Box 12.1}"


id: 938e2e207f695d72b4cfcd4d60bd407f
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 20728
content:
"Uncertainty in the true values of equilibrium climate sensitivity (ECS) and transient climate response (TCR) dominate uncertainty \r\nin projections of future warming under moderate to strong emissions scenarios (Section TS.3.2). A real-world ECS higher than the \r\nassessed very likely range (2degC-5degC) would require a strong historical aerosol cooling and/or a trend towards stronger warming from \r\npositive feedbacks linked to changes in SST patterns (pattern effects), combined with a strong positive cloud feedback and substantial \r\nbiases in paleoclimate reconstructions - each of which is assessed as either unlikely or very unlikely, but not ruled out. Since CMIP6 \r\ncontains several ESMs that exceed the upper bound of the assessed very likely range in future surface warming, these models can \r\nbe used to develop low-likelihood, high warming storylines to explore risks and vulnerabilities, even in the absence of a quantitative \r\nassessment of likelihood. {4.3.4, 4.8, 7.3.2, 7.4.4, 7.5"


id: d0018a9c0604ce8317ad12a8679367dd
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 30254
content:
"CMIP6 models with surface warming outside, or close to, the upper bound of the very likely range exhibit patterns of large widespread \r\ntemperature and precipitation changes that differ substantially from the multi-model mean in all scenarios. For SSP5-8.5, the \r\nhigh-warming models exhibit widespread warming of more than 6degC over most extratropical land regions and parts of the Amazon. \r\nIn the Arctic, annual mean temperatures increase by more than 10degC relative to present-day, corresponding to about 30% more than \r\nthe best estimate of warming. Even for SSP1-2.6, high-warming models show on average 2degC-3degC warming relative to present-day \r\nconditions over much of Eurasia and North America (about 40% more than the best estimate of warming) and more than 4degC warming \r\nrelative to the present over the Arctic in 2081-2100 (Box TS.3, Figure 1)"


id: 1c80f338e0187516a37a9ec469bb468
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 1939
content:
" Such a high global warming storyline would imply that the \r\nremaining carbon budget consistent with a 2degC warming is smaller than the assessed very likely range. Put another way, even if \r\na carbon budget that likely limits warming to 2degC is met, a low-likelihood, high-warming storyline would result in warming of 2.5degC \r\nor more. {4.8}"


id: 8e3a06bffb28e5cfa75b38473b05a0de
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 19907
content:
"CMIP6 models with global warming close to the upper bound of the assessed very likely warming range tend to exhibit greater \r\nchanges in the intensity of regional drying and wetting than the multi-model mean. Furthermore, these model projections show \r\na larger area of drying and tend to show a larger fraction of strong precipitation increases than the multi-model mean. However, \r\nregional precipitation changes arise from both thermodynamic and dynamic processes so that the most pronounced global warming \r\nlevels are not necessarily associated with the strongest precipitation response. Abrupt human-caused changes to the water cycle \r\ncannot be ruled out. Positive land surface feedbacks, involving vegetation and dust, can contribute to abrupt changes in aridity, \r\nbut there is only low confidence that such changes will occur during the 21st century"


id: 1ebaec701bef7de79b28c0c699b4adb0
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 2281
content:
" Continued Amazon deforestation, combined \r\nwith a warming climate, raises the probability that this ecosystem will cross a tipping point into a dry state during the 21st century \r\n(low confidence). (See also Box TS.9). {4.8, 8.6.2}"


id: c113d16144c182d403b85548ffa1539f
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 27953
content:
"Observations, models and paleo-evidence indicate that \r\nrecently observed changes in the ocean are unprecedented \r\nfor centuries to millennia (high confidence). Over the past \r\nfour to six decades, it is virtually certain that the global \r\nocean has warmed, with human influence extremely \r\nlikely the main driver since the 1970s, making climate \r\nchange irreversible over centuries to millennia (medium \r\nconfidence). It is virtually certain that upper ocean salinity \r\ncontrasts have increased since the 1950s and extremely \r\nlikely that human influence has contributed. It is virtually \r\ncertain that upper ocean stratification has increased since \r\n1970 and that sea water pH has declined globally over the \r\nlast 40 years, with human influence being the main driver \r\nof the observed surface open ocean acidification (virtually \r\ncertain)"


id: 8a351775cdcc45bce583f78ba8d76d6
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 19278
content:
" A long-term increase in surface open ocean pH \r\noccurred over the past 50 million years (high confidence), \r\nand surface ocean pH as low as recent times is uncommon \r\nin the last 2 million years (medium confidence). There is \r\nhigh confidence that marine heatwaves have become more \r\nfrequent in the 20th century, and most of those since 2006 \r\nhave been attributed to anthropogenic warming (very \r\nlikely). There is high confidence that oxygen levels have \r\ndropped in many regions since the mid 20th century and \r\nthat the geographic range of many marine organisms has \r\nchanged over the last two decades. "


id: f39c997e04c16fc279b36e77c655a506
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 35839
content:
"It is virtually certain that the global ocean has warmed since at least \r\n1971, representing about 90% of the increase in the global energy \r\ninventory (Section TS.3.1). The ocean is currently warming faster than \r\nat any other time since at least the last deglacial transition (medium \r\nconfidence), with warming extending to depths well below 2000 m \r\n(very high confidence). It is extremely likely that human influence \r\nwas the main driver of this recent ocean warming. Ocean warming \r\nwill continue over the 21st century (virtually certain), and will likely\r\ncontinue until at least to 2300 even for low CO2 emissions scenarios. \r\nOcean warming is irreversible over centuries to millennia (medium \r\nconfidence), but the magnitude of warming is scenario-dependent from \r\nabout the mid-21st century (medium confidence). The warming will \r\nnot be globally uniform, with heat primarily stored in Southern Ocean \r\nwater-masses and weaker warming in the subpolar North Atlantic (high \r\nconfidence)"


id: eeb104b307d9a59e6842a4a23610de4e
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 35028
content:
" Limitations in the understanding of feedback mechanisms \r\nlimit our confidence in future ocean warming close to Antarctica and \r\nhow this will affect sea ice and ice shelves. {2.3.3, 3.5.1, 4.7.2, 7.2.2, \r\n9.2.2, 9.2.3, 9.2.4, 9.3.2, 9.6.1, Cross-Chapter Box 9.1}"


id: b84c6fc52326e8b71faef6f8b4744a27
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 26525
content:
"It is virtually certain that surface pH has declined globally over the \r\nlast 40 years and that the main driver is uptake of anthropogenic \r\nCO2. Ocean acidification and associated reductions in the saturation \r\nstate of calcium carbonate - a constituent of skeletons or shells of \r\na variety of marine organisms - is expected to increase in the 21st \r\ncentury under all emissions scenarios (high confidence). A long-term \r\nincrease in surface open ocean pH occurred over the past 50 million \r\nyears (high confidence), and surface ocean pH as low as recent \r\ntimes is uncommon in the last 2 million years (medium confidence). \r\nThere is very high confidence that present-day surface pH values \r\nare unprecedented for at least 26,000 years and current rates of pH \r\nchange are unprecedented since at least that time. Over the past 2-3 \r\ndecades, a pH decline in the ocean interior has been observed in all \r\nocean basins (high confidence) (Figure TS.11d). {2.3.3, 2.3.4, 3.6.2, \r\n4.3.2, 5.3.2, 5.3"


id: 1a0d84a2fc4633802483866b94a159a0
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 1565
content:
"Current Arctic sea ice coverage levels (both annual and late summer) \r\nare at their lowest since at least 1850 (high confidence), and for \r\nlate summer for the past 1000 years (medium confidence). Since the \r\nlate 1970s, Arctic sea ice area and thickness have decreased in both \r\nsummer and winter, with sea ice becoming younger, thinner and more \r\ndynamic (very high confidence). It is very likely that anthropogenic \r\nforcing, mainly due to greenhouse gas increases, was the main driver \r\nof this loss, although new evidence suggests that anthropogenic \r\naerosol forcing has offset part of the greenhouse gas-induced losses \r\nsince the 1950s (medium confidence). The annual Arctic sea ice area \r\nminimum will likely fall below 1 million km2 at least once before \r\n2050 under all assessed SSP scenarios. This practically sea ice-free \r\nstate will become the norm for late summer by the end of the 21st \r\ncentury in high CO2 emissions scenarios (high confidence)"


id: 7eb33053cb8e23f2ded14e718fcb4b3
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 17500
content:
" Arctic \r\nsummer sea ice varies approximately linearly with global surface \r\ntemperature, implying that there is no tipping point and observed/\r\nprojected losses are potentially reversible (high confidence). {2.3.2, \r\n3.4.1, 4.3.2, 9.3.1, 12.4.9}"


id: 77aed4cf374f269fe84334ba6e9a433d
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 16384
content:
"It is likely that the Antarctic Ice Sheet has lost 2670 +- 530 Gt, \r\ncontributing 7.4 +- 1.5 mm to GMSL rise over 1992-2020. The total \r\nAntarctic ice mass losses were dominated by the West Antarctic Ice \r\nSheet, with combined West Antarctic and Peninsula annual loss rates \r\nincreasing since about 2000 (very high confidence). Furthermore, it \r\nis very likely that parts of the East Antarctic Ice Sheet have lost mass \r\nsince 1979. Since the 1970s, snowfall has likely increased over the \r\nwestern Antarctic Peninsula and eastern West Antarctica, with large \r\nspatial and interannual variability over the rest of Antarctica. Mass \r\nlosses from West Antarctic outlet glaciers, mainly induced by ice shelf \r\nbasal melt (high confidence), outpace mass gain from increased \r\nsnow accumulation on the continent (very high confidence)"


id: 8a34bf36259fda1e0a9099f7bf60abcb
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 19276
content:
" \r\nHowever, there is only limited evidence, with medium agreement, \r\nof anthropogenic forcing of the observed Antarctic mass loss since \r\n1992 (with low confidence in process attribution). Increasing mass \r\nloss from ice shelves and inland discharge will likely continue to \r\noutpace increasing snowfall over the 21st century (Figure TS.11f). \r\nDeep uncertainty persists with respect to the possible evolution of \r\nthe Antarctic Ice Sheet along high-end mass-loss storylines within \r\nthe 21st century and beyond, primarily related to the abrupt and \r\nwidespread onset of marine ice sheet instability and marine ice cliff \r\ninstability. (See also Boxes TS.3 and TS.4). {2.3.2, 3.4.3, 9.4.2, 9.6.3, \r\nBox 9.4, Atlas.11.1}"


id: 1ba5de2d36b2498ee73b5ca1609b6185
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 1809
content:
"The Greenland Ice Sheet was smaller than at present during the \r\nLast Interglacial period (roughly 125,000 years ago) and the mid\u0002Holocene (roughly 6,000 years ago) (high confidence). After reaching \r\na recent maximum ice mass at some point between 1450 and 1850, \r\nthe ice sheet retreated overall, with some decades likely close to \r\nequilibrium (i.e., mass loss approximately equalling mass gained). It \r\nis virtually certain that the Greenland Ice Sheet has lost mass since \r\nthe 1990s, with human influence a contributing factor (medium \r\nconfidence). There is high confidence that annual mass changes have \r\nbeen consistently negative since the early 2000s. Over the period \r\n1992-2020, Greenland likely lost 4890 +- 460 Gt of ice, contributing \r\n13.5 +- 1.3 mm to GMSL rise. There is high confidence that Greenland \r\nice mass losses are increasingly dominated by surface melting and \r\nrunoff, with large interannual variability arising from changes in \r\nsurface mass balance"


id: a1c69f8d74f76b4f5e3aacb14df80497
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 23013
content:
" Projections of future Greenland ice-mass loss \r\n(Box TS.4, Table 1; Figure TS.11e) are dominated by increased surface \r\nmelt under all emissions scenarios (high confidence). Potential \r\nirreversible long-term loss of the Greenland Ice Sheet, and of parts "


id: a0235c91ccc9f9b657d379f4f2ec0136
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 22731
content:
"Global mean sea level (GMSL) increased by 0.20 [0.15 to 0.25] m over the period 1901 to 2018, with a rate of rise \r\nthat has accelerated since the 1960s to 3.7 [3.2 to 4.2] mm yr -1 for the period 2006-2018 (high confidence). Human \r\nactivities were very likely the main driver of observed GMSL rise since 1971, and new observational evidence leads \r\nto an assessed sea level rise over the period 1901 to 2018 that is consistent with the sum of individual components \r\ncontributing to sea level rise, including expansion due to ocean warming and melting of glaciers and ice sheets (high \r\nconfidence). It is virtually certain that GMSL will continue to rise over the 21st century in response to continued \r\nwarming of the climate system (Box TS.4, Figure 1). Sea level responds to greenhouse gas (GHG) emissions more \r\nslowly than global surface temperature, leading to weaker scenario dependence over the 21st century than for global \r\nsurface temperature (high confidence)"


id: 73b7fb5426033b905edd28fa3bfa2a49
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 15823
content:
" This slow response also leads to long-term committed sea level rise, associated \r\nwith ongoing ocean heat uptake and the slow adjustment of the ice sheets, that will continue over the centuries and \r\nmillennia following cessation of emissions (high confidence) (Box TS.9). By 2100, GMSL is projected to rise by 0.28-\r\n0.55 m (likely range) under SSP1-1.9 and 0.63-1.01 m (likely range) under SSP5-8.5 relative to the 1995-2014 average \r\n(medium confidence). Under the higher CO2 emissions scenarios, there is deep uncertainty in sea level projections for \r\n2100 and beyond associated with the ice-sheet responses to warming. In a low-likelihood, high-impact storyline and a \r\nhigh CO2 emissions scenario, ice-sheet processes characterized by deep uncertainty could drive GMSL rise up to about \r\n5 m by 2150. Given the long-term commitment, uncertainty in the timing of reaching different GMSL rise levels is an \r\nimportant consideration for adaptation planning. {2.3, 3.4, 3.5, 9.6, Box 9"


id: 5f958af9216be8db7dc1a083c911b3be
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 12614
content:
"Beyond 2100, GMSL will continue to rise for centuries to millennia due to continuing deep ocean heat uptake and mass loss from \r\nice sheets, and will remain elevated for thousands of years (high confidence). By 2150, considering only those processes in whose \r\nprojections we have at least medium confidence and assuming no acceleration in ice-mass flux after 2100, GMSL is projected to rise \r\nbetween 0.6 m (0.4-0.9 m, likely range, SSP1-1.9) and 1.3 m (1.0-1.9 m, likely range) (SSP5-8.5), relative to the period 1995-2014 \r\nbased on the SSP scenario extensions. Under high CO2 emissions, processes in which there is low confidence, such as MICI, could drive \r\nGMSL rise up to about 5 m by 2150 (Box TS.4, Figure 1a). By 2300, GMSL will rise 0.3-3.1 m under low CO2 emissions (SSP1-2.6) (low \r\nconfidence). Under high CO2 emissions (SSP5-8.5), projected GMSL rise is between 1.7 and 6.8 m by 2300 in the absence of MICI and \r\nby up to 16 m considering MICI (low confidence)"


id: 9cf4e893362df043f73bec70f1536dbb
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 22233
content:
" Over 2000 years, there is medium agreement and limited evidence that committed \r\nGMSL rise is projected to be about 2-3 m with 1.5degC peak warming, 2-6 m with 2degC of peak warming, 4-10 m with 3degC of peak \r\nwarming, 12-16 m with 4degC of peak warming, and 19-22 m with 5degC of peak warming. {9.6.3}"


id: c006700faf7ef7f07ae89316272491a6
created_at: 2023-03-16 14:01:55
updated_at: 2023-03-16 14:01:55
content_type: text
index: climate_gpt_only_giec
vector_id: 27766
content:
"Based on multiple lines of evidence using interhemispheric gradients of CO2 concentrations, isotopes, and inventory data, it is \r\nunequivocal that the growth in CO2 in the atmosphere since 1750 (see Section TS.2.2) is due to the direct emissions from human \r\nactivities. The combustion of fossil fuels and land-use change for the period 1750-2019 resulted in the release of 700 +- 75 PgC (likely\r\nrange, 1 PgC = 1015 g of carbon) to the atmosphere, of which about 41% +- 11% remains in the atmosphere today (high confidence). \r\nOf the total anthropogenic CO2 emissions, the combustion of fossil fuels was responsible for about 64% +- 15%, growing to an 86% +- \r\n14% contribution over the past 10 years. The remainder resulted from land-use change. During the last decade (2010-2019), average \r\nannual anthropogenic CO2 emissions reached the highest levels in human history at 10.9 +- 0.9 PgC yr -1 (high confidence). Of these \r\nemissions, 46% accumulated in the atmosphere (5.1 +- 0"


