Editing IPCC:AR6/WGII/Chapter-16 (section)

===== 16.5.2.3.4 Risk to living standards (RKR-D) =====

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This RKR includes risks to (i) aggregate economic output at the global and national levels, (ii) poverty and (iii) livelihoods, and their implications for economic inequality. It is informed by key risks identified by regional and sectoral chapters. Risks are potentially severe as measured by the magnitude of impacts in comparison with historical events or as inferred from the number of people currently vulnerable.

# Risks to aggregate economic output would become severe at the global scale with high warming and minimal adaptation ( ''medium confidence'' ), with severity defined as the potential for persistent annual economic losses due to climate change to match or exceed losses during the world’s worst historical economic recessions. With historically observed levels of adaptation, warming of ~4°C may cause a 10–23% decline in annual global GDP by 2100 relative to global GDP without warming, due to temperature impacts alone ( [[#Burke--2015|Burke et al., 2015]] ; [[#Kahn--2019|Kahn et al., 2019]] ; [[#Kalkuhl--2020|Kalkuhl and Wenz, 2020]] ). These magnitudes exceed economic losses during the Great Recession (2008–2009, ~5% decline in global GDP, up to 15–18% in some countries) and the COVID-19 pandemic (2020, ~3% decline globally, up to 10% in some countries) ( [[#IMF--2020|IMF, 2020]] ; [[#IMF--2021|IMF, 2021]] ). Unlike past recessions, climate change impacts would occur continuously every year. However, smaller effects (1–8%) are found when using alternative methodologies ( [[#Diaz--2017|Diaz and Moore, 2017]] ; [[#Nordhaus--2017|Nordhaus and Moffat, 2017]] ; [[#Kompas--2018|Kompas et al., 2018]] ; [[#Kalkuhl--2020|Kalkuhl and Wenz, 2020]] ), assuming less warming ( [[#Kahn--2019|Kahn et al., 2019]] ; Takakura et al., 2019), and assuming lower vulnerability and/or more adaptation ( [[#Diaz--2017|Diaz and Moore, 2017]] ); this literature is comprehensively summarised in Cross-Working Group Chapter Box ECONOMIC. Impacts at high levels of warming are particularly uncertain, as all methodologies require extrapolation and insufficiently incorporate possible tipping elements in the climate system ( [[#Kopp--2016|Kopp et al., 2016]] ).

Annual economic output losses in developing countries could exceed the worst country-level losses during historical economic recessions ( ''medium confidence'' ). Assuming global warming of ~4°C by 2100, historical adaptation levels and high vulnerability, losses across Sub-Saharan Africa may reach 12% of GDP by 2050 ( [[#Baarsch--2020|Baarsch et al., 2020]] ) and 80% by 2100 ( [[#Burke--2015|Burke et al., 2015]] ), and ~9% on average across developing countries by 2100 ( [[#Acevedo--2017|Acevedo et al., 2017]] ). The largest estimates are debated and depend on assumptions about development trends, adaptive capacity, and whether temperature impacts the level or growth rate of economic activity ( [[#Kalkuhl--2020|Kalkuhl and Wenz, 2020]] ). Severe risks are more likely in (typically hotter) developing countries because of nonlinearities in the relationship between economic damages and temperature ( [[#Burke--2015|Burke et al., 2015]] ; [[#Acevedo--2017|Acevedo et al., 2017]] ). These risks are highest in scenarios and countries with: a large portion of the workforce employed in highly exposed industries ( [[#Acevedo--2017|Acevedo et al., 2017]] ); a high concentration of population and economic activity on coastlines ( [[#Hsiang--2014|Hsiang and Jina, 2014]] ; [[#Acevedo--2017|Acevedo et al., 2017]] ); and an increase in the frequency or intensity of disasters triggered by natural hazards ( [[#Berlemann--2018|Berlemann and Wenzel, 2018]] ; [[#Botzen--2019|Botzen et al., 2019]] ). Whether baseline economic growth may help avoid severe future risks is highly uncertain ( [[#Dell--2012|Dell et al., 2012]] ; [[#Burke--2015|Burke et al., 2015]] ; [[#Acevedo--2017|Acevedo et al., 2017]] ; [[#Deryugina--2017|Deryugina and Hsiang, 2017]] ).

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'''Figure 16.9 |''' '''Illustrative examples from individual studies of risks to living standards and the conditions under which they could become severe.''' Selected studies are not representative of the literature, but provide examples of potentially severe risks to aggregate economic output, poverty and livelihoods. High, medium and low levels of warming, exposure/vulnerability and adaptation are defined as in Figure 16.10.

# Under medium warming pathways, climate change risks to poverty would become severe if vulnerability is high and adaptation is low ( ''limited evidence'' , ''high agreement'' ). We define poverty in terms of absolute consumption levels and define severity as tens to hundreds of millions of additional people in poverty relative to the number without climate change (globally) or an absolute increase in the number of people living in poverty compared with today (nationally or locally). This global impact is comparable to the effect of the 2007 food price shock ( [[#De%20Hoyos--2009|De Hoyos and Medvedev, 2009]] ) and the 2020 COVID-19 pandemic ( [[#World%20Bank--2020|World Bank, 2020]] ) and can be compared to about 700 million in poverty in 2017, down from 1.9 billion in 1990 ( [[#World%20Bank--2020|World Bank, 2020]] ).

In a high-vulnerability development pathway, climate change in 2030 could push 35–132 million people into extreme poverty, in addition to the people already in poverty assuming climate is unchanged (disregarding impacts from natural variability; [[#Hallegatte--2017|Hallegatte and Rozenberg, 2017]] ; [[#Jafino--2020|Jafino et al., 2020]] ). In a low-warming pathway, risks from mitigation costs could also be severe if no progressive redistribution from carbon pricing revenues is applied (Soergel et al., 2021). At the national level, there is ''limited evidence'' of climate change causing an absolute increase in poverty (e.g., absolute increase of ~1–2% yr −1 through 2040, [[#Montaud--2017|Montaud et al., 2017]] ). Potentially severe risks to poverty are also supported by (1) the observed impacts of past disasters ( [[#Winsemius--2018|Winsemius et al., 2018]] ; [[#Hallegatte--2020|Hallegatte et al., 2020]] ; [[#Rentschler--2020|Rentschler and Melda, 2020]] ) and previous crises such as food price shocks ( [[#Ivanic--2008|Ivanic and Martin, 2008]] ) or current diseases ( [[#WHO--2018|WHO, 2018]] ) on poor people and on poverty; (2) the expectation that these events will become more intense or frequent in some regions (WGI Chapter 12, [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ); and (3) population growth and the low adaptive and coping capacities of the poor ( [[#Leichenko--2014|Leichenko and Silva, 2014]] ; [[#Huynh--2018|Huynh and Stringer, 2018]] ; [[#Thomas--2020|Thomas et al., 2020]] ). This literature provides indirect evidence that climate change will keep many people poor and may cause more than tens of millions to fall into poverty ( ''limited evidence'' , ''high agreement'' ).

# Climate change poses severe risks to livelihoods at low levels of warming, high exposure/vulnerability and low adaptation in climate-sensitive regions, ecosystems and economic sectors ( ''high confidence'' ), where severity refers to the disruption of livelihoods for tens to hundreds of millions of additional people ( [[#Arnell--2014|Arnell and Lloyd-Hughes, 2014]] ; [[#Liu--2018|Liu et al., 2018]] ). More widespread severe risks would occur at high levels of warming (with high exposure/vulnerability and low adaptation) where there is additional potential for one or more social or ecological tipping points to be triggered ( [[#Cai--2015|Cai et al., 2015]] ; [[#Cai--2016b|Cai et al., 2016b]] ; [[#Kopp--2016|Kopp et al., 2016]] ; [[#Steffen--2018|Steffen et al., 2018]] ; [[#Lenton--2019|Lenton et al., 2019]] ), and for severe impacts on livelihoods to cascade from relatively more climate-sensitive to relatively less climate-sensitive sectors and regions ( ''medium confidence'' ) ( [[#Lawrence--2020|Lawrence et al., 2020]] ). Severity assessment is based on the current magnitude of exposure and vulnerability across multiple social and ecological systems, projected future exposure and vulnerability, and the rate at which hazard frequency or intensity is expected to increase ( [[#Otto--2017|Otto et al., 2017]] ; [[#Roy--2018|Roy et al., 2018]] ; [[#Li--2019|Li et al., 2019]] , [[IPCC:Wg2:Chapter:Chapter-8#8.5|Section 8.5]] ). Without effective adaptation measures, regions with high dependence on climate-sensitive livelihoods—particularly agriculture and fisheries in the tropics and coastal regions—would be severely impacted even at low levels of warming ( ''high confidence'' ) ( [[#Hoegh-Guldberg--2018b|Hoegh-Guldberg et al., 2018b]] ; [[#Roy--2018|Roy et al., 2018]] ). For example, it is estimated that 330–396 million people could be exposed to lower agricultural yields and associated livelihood impacts at warming between 1.5°C and 2°C ( [[#Byers--2018|Byers et al., 2018]] ). Risks to the 200 million people with livelihoods derived from small-scale fisheries would also be severe, given sensitivity to ocean warming, acidification and coral reef loss occurring beyond 1.5°C ( [[#Cheung--2018b|Cheung et al., 2018b]] ; [[#Froehlich--2018|Froehlich et al., 2018]] ; [[#Free--2019|Free et al., 2019]] ; [[#Barnard--2021|Barnard et al., 2021]] ). Livelihoods in highly exposed locations, such as Small Island Developing States, low-lying coastal areas, arid or semiarid regions, the Arctic, and urban informal settlements or slums, are particularly vulnerable ( [[#Ford--2015c|Ford et al., 2015c]] ; [[#Hagenlocher--2018|Hagenlocher et al., 2018]] ; [[#Ahmadalipour--2019|Ahmadalipour et al., 2019]] ; [[#Tamura--2019|Tamura et al., 2019]] ). Within populations, the poor, women, children, the elderly and Indigenous populations are especially vulnerable due to a combination of factors, including gendered divisions of paid and/or unpaid labour, as well as barriers in access to information, skills, services or resources ( [[#Bose--2017|Bose, 2017]] ; [[#Thomas--2019b|Thomas et al., 2019b]] ; [[#Anderson--2020|Anderson and Singh, 2020]] ; [[#Adzawla--2021|Adzawla and Baumüller, 2021]] ) ( ''high confidence'' ). Future structural transformation could moderate risk severity by improving adaptive capacity, creating livelihoods in less climate-sensitive sectors, or by enabling sustainable migration to less climate-sensitive locations ( [[#Henderson--2017|Henderson et al., 2017]] ; [[#Roy--2018|Roy et al., 2018]] ). However, successful risk moderation would depend upon simultaneous avoidance of both climate-change-related and mitigation-related ( [[#Doelman--2019|Doelman et al., 2019]] ; [[#Fujimori--2019|Fujimori et al., 2019]] ; [[#Doelman--2020|Doelman et al., 2020]] ) or maladaptation-related risks ( [[#Magnan--2016|Magnan et al., 2016]] ; [[#Benveniste--2020|Benveniste et al., 2020]] ; [[#Schipper--2020|Schipper, 2020]] ).

Climate change also could increase income inequality between countries ( ''high confidence'' ) as well as within them ( ''medium evidence'' , ''high agreement'' ) resulting from and exacerbating impacts on aggregate economic activity, poverty and livelihoods. Increasing inequality implies larger impacts on the least well-off, threatens their ability to respond to climate hazards, compromises basic principles of fairness and established global development goals, and potentially threatens the functioning of society and long-term progress ( [[#Roe--2011|Roe and Siegel, 2011]] ; [[#Cingano--2014|Cingano, 2014]] ; [[#van%20der%20Weide--2018|van der Weide and Milanovic, 2018]] ). There is evidence that warming has slowed down the convergence in between-country income in recent decades ( [[#Diffenbaugh--2019|Diffenbaugh and Burke, 2019]] ). Future impacts may halt or even reverse this trend during this century owing to high sensitivity of developing economies ( [[#Burke--2015|Burke et al., 2015]] ; [[#Pretis--2018|Pretis et al., 2018]] ; [[#Baarsch--2020|Baarsch et al., 2020]] ), although projections depend as much or more on future socioeconomic development pathways and mitigation policies as on warming levels (Takakura et al., 2019; [[#Harding--2020|Harding et al., 2020]] ; [[#Taconet--2020|Taconet et al., 2020]] ). Within countries, studies that find adverse impacts on low-income groups imply an increase in inequality ( [[#Hallegatte--2017|Hallegatte and Rozenberg, 2017]] ; [[#Hsiang--2017|Hsiang et al., 2017]] ), although evidence for long-term climate impacts on within-country inequality at global scale remains limited.

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