This document is made possible by the generous support of the American people through the United States 
Agency for International Development (USAID) under the terms of contract no. 7200AA18C00057, which 
supports the Research Technical Assistance Center (RTAC). The document was produced by the Frederick S. 
Pardee Center for International Futures at the University of Denver under the RTAC contract. The contents of 
this document are the sole responsibility of RTAC and NORC at the University of Chicago, and do not necessarily 
reflect the views of USAID or the United States Government.
Climate Change, Economic 
Production, Inequality, and 
Poverty
Jonathan D. Moyer, Audrey Pirzadeh, Barbara Stone
January 2023
RESEARCH TECHNICAL ASSISTANCE CENTER
Climate Change, Economic Production, Inequality, and Poverty 1
Research Technical Assistance Center
The Research Technical Assistance Center is a network of academic researchers generating timely 
research for USAID to promote evidence-based policies and programs. The project is led by NORC at 
the University of Chicago in partnership with Arizona State University, Centro de Investigación de la 
Universidad del Pacifico (Lima, Peru), Davis Management Group, the Duke Center for International 
Development at Duke University, Forum One, the Institute of International Education, the Pulte 
Institute for Global Development at the University of Notre Dame, Population Reference Bureau, the 
Resilient Africa Network at Makerere University (Kampala, Uganda), the United Negro College Fund, 
the University of Chicago, and the University of Illinois at Chicago.
Cover photo: Morgana Wingard, USAID.
Suggested Citation
Moyer et al. 2023. Climate Change, Economic Production, Inequality, and Poverty. Working Paper.
Research Technical Assistance Center: Washington, DC.
Acknowledgments
The authors would like to recognize the following people for their contributions to this project: José
Solórzano, Willem Verhagen, Emily Slusser, Becca Stephan, Gabriela Alcaraz Velasco, Alexandre
Monnard, Brooke Jardine, Taylor Hanna, Yutang Xiong, Mohammod Irfan, Graham Everett, and Barry B.
Hughes.
Working Paper | January 2023 2
Abstract
Climate change is widely understood to be a significant driver of future patterns of human well-being, 
with numerous studies concluding that climate change will jeopardize the food sources and livelihoods of 
the poorest and most vulnerable people around the world, reduce economic growth, and increase 
inequality and poverty. While these relationships are generally accepted in the academic literature, few 
have quantified the impact of climate change on future levels of poverty across different income 
thresholds and scenarios. This paper seeks to fill that gap, modeling the impact of climate change on 
future patterns of human poverty across various thresholds and scenarios by drawing on representative 
concentration pathway (RCP) scenarios that model alternative patterns of temperature change at a 
national level using established damage functions operationalized within the International Futures (IFs) 
tool. We draw on established long-term forecasts of economic growth, inequality, and population 
change from the shared socioeconomic pathways (SSPs) project, adding them to the IFs integrated 
assessment model. We use these pathways and the RCP scenarios to build a relationship between 
changing patterns of country-year temperature change and productivity through “damage functions” 
established in the literature. In addition to representing the economic growth pathways for these 
alternative scenarios, we reduce uncertainty by exploring alternative patterns of inequality, 
operationalized through scenario analysis. Finally, we attempt to incorporate the economic effect of 
tipping points in addition to RCP scenarios based on model estimates from recent literature. 
The climate scenarios estimate that the number of people pushed into extreme poverty—defined by the 
World Bank as having a purchasing power parity of less than $1.90 per day in 2011 U.S. dollars—ranges 
from 478,000 to 37 million by 2050 but by 2100 will reduce by 600,000 to an increase of 72 million. 
Tipping point scenarios further widen the range of impact to 50 to 73 million by 2050 and 31 to 84 
million by 2100. The population projected to be pushed into poverty—defined by the World Bank as 
having a purchasing power parity of less than $3.20 per day in 2011 U.S. dollars—ranges from 4 to 72 
million by 2050 but by 2100 will reduce by 650,000 to an increase of 145 million. Tipping point scenarios 
further widen the range of impact to 110 to 153 million by 2050 and 113 to 260 million by 2100. The 
number of people projected to be pushed into moderate poverty—defined by the World Bank as having 
a purchasing power parity of less than $5.50 per day in 2011 U.S. dollars—ranges from 15 to 80 million 
by 2050 but by 2100 will reduce by 65,000 to an increase of 264 million. Tipping point scenarios further 
widen the range of impact to 164 to 210 million by 2050 and 353 to 669 million by 2100. As the world’s 
climate continues to change, the share of the population living under each of these poverty thresholds 
will increase. An RCP6.0 scenario operationalized in IFs projects that by 2100, more than 31 percent of 
the world population will be living in extreme poverty due to climate change. Another 28 percent are 
projected to be living in poverty and another 22 percent in moderate poverty.
Climate Change, Economic Production, Inequality, and Poverty 3
Table of Contents
Abstract................................................................................................................................................................................2
1. Introduction...............................................................................................................................................................9
2. Background ..............................................................................................................................................................12
3. Methodology............................................................................................................................................................16
4. The Effect of Climate Change on Productivity and Poverty ........................................................................20
Climate Effects on Poverty .......................................................................................................................................27
Effects of Tipping Points............................................................................................................................................31
5. Discussion.................................................................................................................................................................35
6. Conclusion ...............................................................................................................................................................37
Bibliography.......................................................................................................................................................................38
Appendix............................................................................................................................................................................47
Working Paper | January 2023 4
List of Tables
Table 1: Scenarios used in this analysis. .....................................................................................................................16
Table 2: The effect of climate on global economic growth. Measured in trillions of 2011 US$ at market 
exchange rates, all tables measure the cumulative differences between RCP scenarios and No-Climate 
scenarios. ...........................................................................................................................................................................25
Table 3: The effect of climate on global economic growth as percent of GDP measured in trillions of 
2011 US$ at market exchange rates...........................................................................................................................25
Table 4: The effect of climate on the global Gini Index. Measures the absolute difference between RCP 
scenarios and No-Climate scenarios. .........................................................................................................................26
Table 5: The effect of climate on the global Gini Index measured as percent difference between RCP 
scenarios and No-Climate scenarios. .........................................................................................................................26
Table 6. The effect of climate tipping points on global economic growth as percent of GDP measured in 
trillions of 2011 US$ at market exchange rates.......................................................................................................32
Table 7. The effect of climate tipping points on global poverty ($3.20 and $5.50) measured in millions as 
the difference between climate tipping point scenarios and no-climate scenarios..........................................34
Table 8: Table for all scenarios of GDP per capita by decade..............................................................................48
Table 9: Regional table with GDP percentage difference. .....................................................................................49
Table 10: All scenarios for the Gini coefficient by decade. ...................................................................................51
Table 11: All scenarios for $1.90 headcount by decade.........................................................................................53
Table 12: All scenarios marginal effect of climate change on poverty at $1.90 headcount measured in 
millions. ..............................................................................................................................................................................56
Table 13: The cumulative effect of climate on economic growth by region. Measured in trillions of 2011 
US$ at market exchange rates between RCP scenario and No-Climate scenario..........................................57
Table 14: The cumulative effect of climate on economic growth by World Bank income group. Measured 
in trillions of 2011 US$ at the difference in market exchange rates between RCP scenario and No￾Climate scenario. .............................................................................................................................................................58
Table 15: The effect of climate on the regional Gini Index. Measures the absolute difference between 
RCP scenarios and No-Climate scenarios.................................................................................................................58
Table 16: The effect of climate on the Gini Index of World Bank income groups. Measures the absolute 
difference between RCP scenarios and No-Climate scenarios............................................................................59
Climate Change, Economic Production, Inequality, and Poverty 5
Table 17: Marginal effect of climate change on poverty $3.20 headcount measured in millions by decade.
..............................................................................................................................................................................................59
Table 18: The effect of climate on regional population living on less than $1.90 per day measured in 
millions. Measures the absolute difference between RCP scenarios and No-Climate scenarios. ...............60
Table 19: The effect of climate on the population of World Bank income groups living on less than $1.90 
per day measured in millions. Measures the absolute difference between RCP scenarios and No-Climate 
scenarios. ...........................................................................................................................................................................61
Table 20. The effect of climate tipping points on the global population living at each poverty threshold 
($1.90, $3.20, $5.50). Measures the absolute difference between RCP Tipping Point scenarios and No￾Climate scenarios in millions. .......................................................................................................................................61
Table 21: Marginal effect of climate change on poverty $5.50 headcount measured in millions. ................62
Table 22: Base case GDP per capita measured in thousands of 2011 US$.......................................................63
Table 23: Base Case Gini Coefficient..........................................................................................................................68
Table 24: Base Case poverty $1.90 headcount measured in millions.................................................................73
Working Paper | January 2023 6
List of Figures
Figure 1: Modeling approach used in this manuscript.............................................................................................17
Figure 2: DICE, FUND, and PAGE relationships between temperature change and reduced GDP (from
Diaz)....................................................................................................................................................................................18
Figure 3: Dasgupta et al. (2020) relationship between average temperature and the Gini coefficient.......18
Figure 4: RCP scenarios in IFs (2.6, 4.0, 6.5, 8.0) for average global temperature from 2017 to 2100 using 
a 10-year moving average. .............................................................................................................................................21
Figure 5: SSP scenarios in IFs without a climate effect for global GDP per capita at a constant 2011 US$ 
from 2017 to 2100. .........................................................................................................................................................22
Figure 6: SSP scenarios in IFs without a climate effect for global Gini Index from 2017 to 2100. ..............22
Figure 7: SSP scenarios in IFs without a climate Population..................................................................................23
Figure 8: SSP scenarios in IFs without a climate effect for global population living on less than $1.90 per 
day from 2017 to 2100...................................................................................................................................................24
Figure 9: Climate-attributable global population living on less than $1.90 per day for SSPs by RCPs........27
Figure 10: Climate-attributable global population living on less than $1.90 per day for IFs by RCPs.........28
Figure 11: Climate-attributable global population living on less than $3.20 per day for SSPs by RCPs......29
Figure 12: Climate-attributable global population living on less than $3.20 per day for IFs by RCPs.........29
Figure 13: Climate-attributable global population living on less than $5.50 per day for SSPs by RCPs......30
Figure 14: Climate-attributable global population living on less than $5.50 per day for IFs by RCPs.........30
Figure 15: Climate-attributable percent of impoverished people ($1.90, $3.20, $5.50 levels) for SSP3 
RCP6.0................................................................................................................................................................................31
Figure 16: Climate-attributable percent of impoverished people ($1.90, $3.20, $5.50 levels) for IFs 
RCP6.0................................................................................................................................................................................31
Figure 17. Climate tipping points attributable global population living on less than $1.90 per day for SSPs 
by RCPs..............................................................................................................................................................................33
Figure 18. Climate tipping points attributable global population living on less than $1.90 per day for IFs 
RCPs. ..................................................................................................................................................................................33
Figure 19: GDP per capita for all scenarios...............................................................................................................47
Climate Change, Economic Production, Inequality, and Poverty 7
Figure 20: All scenarios for the Gini coefficient.......................................................................................................50
Figure 21: All scenarios for $1.90 headcount. ..........................................................................................................53
Figure 22: All scenarios marginal effect of climate change on poverty $1.90 headcount measured in 
millions. ..............................................................................................................................................................................55
Working Paper | January 2023 8
List of Acronyms
GDP Gross domestic product
International Futures
RCP Representative concertation pathways
SDG Sustainable Development Goal
SSP Shared Socioeconomic Pathways
UNPD United Nations Population Division
IFs
Climate Change, Economic Production, Inequality, and Poverty 9
1. Introduction
Climate change will negatively impact future human well-being in multiple ways, with established 
research highlighting its effects on conflict (Hendrix 2018), nutrition (Jägermeyr et al. 2021; Leisner 
2020), economic activity (Bretschger and Pattakou 2019; Alagidede, Adu, and Frimpong 2016; Tol 2009; 
Kahn et al. 2021; Newell, Prest, and Sexton 2021; Auffhammer 2018; Waldhoff et al. 2014; Dell, Jones, 
and Olken 2014), inequality (Burzyński et al. 2019; Dasgupta and Robinson 2022; Espagne, Marx, and 
Ngo-Duc 2019), mortality (Anderson et al. 2018; Chen et al. 2018; Fang et al. 2013), and poverty 
(Hallegatte and Rozenberg 2017; Jafino et al. 2020; Ahmed, Diffenbaugh, and Hertel 2009; Azzarri and 
Signorelli 2020; Thurlow, Zhu, and Diao 2008). While many dimensions of climate change and 
socioeconomic development have been operationalized, others remain characterized by uncertainty. 
Understanding how future patterns of climate change interact with future patterns of socioeconomic 
development to set the stage for human vulnerability and adaptation remains a crucial area of research.
Future challenges to adaptation to climate change are driven both by the future patterns of 
socioeconomic development and the future severity of climate impacts on socioeconomic development. 
This manuscript furthers our understanding of future states of socioeconomic development with and 
without climate effects on human development as measured by the average income of individuals at 
various thresholds. We forecast future levels of poverty for 183 countries with and without climate 
effects across various scenarios, highlighting how future adaptive capacity will rest on an understanding 
of both changing socioeconomic development and climate severity.
Because of the centrality of poverty to global development, this research focuses on future levels and 
rates of poverty across various income thresholds at the country, regional, and global levels. Extreme 
poverty—measured as the number of people living on less than $1.90 per day in purchasing power 
parity in 2011 USD—is the first United Nations Sustainable Development Goal (SDG) and the focus of 
much academic and policy research (Chandy, Ledlie, and Penciakova 2013; IHME 2019; Moyer et al. 
2022). Other poverty thresholds are also important for understanding patterns of human development 
with $3.20 and $5.50 per day representing two higher levels of income-per-day that also represent 
people living with acute material needs.
Measures of income or consumption-based poverty are not a panacea for understanding development 
trends and have previously been criticized. Purchasing power parity measures have been criticized for 
their inability to comprehensively measure consumption across distinct development contexts (Pogge 
and Reddy 2005; Ravallion 2008; Allen 2017) and for high levels of measurement uncertainty (Moatsos 
and Lazopoulos 2021; Moatsos 2016). Various approaches have been proposed to mitigate these 
measurement challenges, with many researchers focusing on poverty as a multidimensional concept 
(Alkire and Santos 2013; Bourguignon and Chakravarty 2019; Robles Aguilar and Sumner 2020; 
Thorbecke 2013) requiring the measurement of consumption power but also factors related to food 
security, education, health, and inclusive politics. These dimensions of development are highlighted in the 
work of Sen and Nussbaum (Sen 1999; Nussbaum, Glover, and World Institute for Development 
Economics Research 1995).
While measures of income or consumption-based poverty are limited, they do provide a useful 
benchmark for understanding the future adaptive capacity of populations. Recent analysis shows that 
rising temperatures and increased weather pattern volatility are likely outcomes of current and 
expected future levels of anthropogenic climate change (Allan et al. 2021). Research also shows that 
Working Paper | January 2023 10
populations most vulnerable to these disruptive changes to current and future climate patterns are 
those with the fewest resources, a phenomenon that occurs across different dimensions of 
development. Poverty is a multiplier of the effects of future climate change on human well-being.
Future levels of poverty will be shaped by two broad and interacting trends. First, poverty will be 
directly impacted by the trajectory of socioeconomic development, particularly by factors related to 
patterns of economic growth, income distribution, and population change. In a world of greater growth 
and income distribution and less population growth, future patterns of poverty will be lower relative to 
alternative scenarios with distinct development outcomes across these variables. Second, climate change 
will affect the number of people living in poverty across income thresholds. As the planet warms, 
precipitation patterns will change, food security issues will emerge, weather systems will become more 
volatile, and productivity will be reduced. That will lead to considerable growth in the number of people 
living with extreme material want.
Evidence abounds that climate change will have deleterious effects of human poverty, with both ground￾up approaches (Hallegatte and Rozenberg 2017) and model-based approaches (Jafino et al. 2020; Ahmed, 
Diffenbaugh, and Hertel 2009; Thurlow, Zhu, and Diao 2008) reaching similar conclusions. But we have 
a poor understanding of how climate change is impacting development more broadly (Dickerson, 
Cannon, and O’Neill 2021). This gap in understanding between micro-level studies that highlight the 
importance of poverty as a measure of adaptive capacity and the highly likely negative effect of climate 
change on future poverty broadly motivates this analysis.
This paper implements representative concertation pathways (RCPs) scenarios measuring country-level 
temperature change within the International Futures (IFs) model. We do this by using RCPs 
operationalized at a country level to drive changing patterns of economic production through 
established “damage functions” (Tol 2009; Waldhoff et al. 2014; Diaz and Moore 2017; W. Nordhaus 
2018) and by contextualizing these model results with other approaches that use different 
methodologies to capture dynamics related to temperature change and economic growth (Kahn et al. 
2021; Newell, Prest, and Sexton 2021; Burke, Hsiang, and Miguel 2015). We also operationalize the 
relationship between changing temperature and inequality (Espagne, Marx, and Ngo-Duc 2019; 
Dasgupta, Emmerling, and Shayegh 2020). The relative dearth of research into temperature effects on 
future patterns of inequality forms the basis of our uncertainty and scenario framing.
Of the four RCP scenarios operationalized within the IFs system, the range of people projected to be 
pushed into extreme poverty is 15 to 37 million by 2050 and 2 to 34 million by 2100. The population 
projected to be pushed into poverty ranges from 30 to 72 million by 2050 and 8 to 103 million by 2100. 
The number of people pushed into moderate poverty ranges from 33 to 80 million by 2050 and 23 to 
264 million by 2100. Advancing climate change will push an increasingly large share of the population 
under each of these poverty thresholds, with an RCP6.0 scenario operationalized in IFs showing more 
than 30 percent of the world population will be extremely poor by 2100 because of climate change, 25 
percent will be poor, and 18 percent will be moderately poor. The shared socioeconomic pathways
(SSP)-RCP scenario combinations also largely support these findings. The four SSP-RCP scenarios 
conclude that the number of people projected to be pushed into extreme poverty ranges from 478,000 
to 23 million by 2050 but by 2100 will reduce by 600,000 to an increase of 72 million. The population 
projected to be pushed into poverty ranges from 4 to 41 million by 2050 but by 2100 will reduce by 
650,000 to an increase of 145 million by 2100. The population projected to be pushed into moderate 
poverty ranges from 15 to 48 million by 2050 but will reduce by 65,000 to an increase of over 219 
million by 2100.
Climate Change, Economic Production, Inequality, and Poverty 11
These conclusions raise several important questions. First, when contextualized within other drivers of 
poverty, they show climate change will be a more limited driver of increases in poverty relative to other 
pressing challenges, like the COVID-19 pandemic or civil wars. Recent research has shown that the 
global pandemic increased extreme poverty by 73.9 million in 2020, while the range across all scenarios 
is 43.5 to 155.0 million (Moyer et al. 2022). Other research projects that between 2022 and 2030 the 
baseline scenario for the impact of civil wars is an additional 148.2 million, with a projected range of 50.7 
to 186.0 million, people living in extreme poverty (Moyer, n.d.). Under that scenario, the effect of 
climate change on poverty is similar to the effect of COVID-19 on poverty, though it occurs eight 
decades later. And the effect of civil wars on poverty over the next eight years, under this projection, 
will be significantly greater than the long-term effect of climate change.
The findings in this report are lower than the only other significant research in this area, a World Bank 
study that estimates that the number of people pushed into extreme poverty by climate change will be 
122 million by 2030 in a worst-case scenario (Hallegatte 2016). The difference in findings is driven by 
alternative methodologies, with the World Bank report using a micro-simulation approach that shows 
significant impacts on incomes in the agricultural sector. 
This disparity between our findings and Hallegate leads to a number of challenging conclusions. First, the 
methodology used in this manuscript relies upon well established “damage functions” to drive the impact 
of changing temperature on economic output. Damage functions have been widely used in literature to 
understand the economic effects of climate change, though they have met criticism. Some contend that 
damage function approaches do not adequately capture the effects of changing temperatures on 
economic output. Others contend that damage function approaches actually overestimate the effects of 
climate change by neglecting the impact of human adaptation. The inequality relationship used here may 
also be criticized for not adequately capturing the effects of changing temperature or a broader change 
in climactic systems and inequality. It may be the case that the study we rely on underestimates or fails 
to specify how changing temperature will affect the distribution of resources.
More broadly, the methodology used in this report may be inappropriate for evaluating these 
relationships. Additional research efforts may be needed to better understand how climate change will 
impact future poverty levels that focus on a) extreme measures of the effect of climate change via heat 
waves, storms, and other significant disruptions instead of smoothed average effects; and b) pathways in 
which climate change impacts human well-being that fall outside of temperature change, economic 
growth, and resource distribution.
Working Paper | January 2023 12
2. Background
Temperature change has begun to be used as a gauge for changes in economic outcomes. Increased 
global temperatures have been linked to effects on economic growth (Dell, Jones, and Olken 2012; 
Burke, Hsiang, and Miguel 2015; Newell, Prest, and Sexton 2021, 20); annual income (Deryugina and 
Hsiang 2014); labor productivity and supply (Graff Zivin, Hsiang, and Neidell 2018; Antonelli et al. 2021); 
human capital (Graff Zivin, Hsiang, and Neidell 2018); demography (Casey et al. 2019); food security 
(Deschênes and Greenstone 2007; Antonelli et al. 2021); and energy consumption (Isaac and van Vuuren 
2009; De Cian and Sue Wing 2019). 
Historically, Dell et al. (2012) investigated evidence of temperature shocks on economic growth in the 
last half century through historical temperature fluctuations within countries to determine their effects 
on aggregate economic outcomes. Higher temperatures were found to reduce agricultural and industrial 
output and growth rates and to contribute to political instability (Dell et al. 2012). More specifically, 
through panel data of 174 countries from 1960 to 2014, Kahn et al. (2021) found that per capita gross 
domestic product (GDP) growth is adversely affected by persistent changes in temperature, above or 
below historical norms. The study also found these adverse effects vary with regional temperature, 
climate conditions, and income levels. Dell et al. (2012) concluded that poorer countries will sustain 
considerably higher reductions in growth rates. Globally, Kahn et al. (2021) concluded that a persistent 
increase in average global temperature by 0.04 degrees Celsius per year, in the absence of climate 
mitigation policies, will reduce world real GDP per capita by more than 7 percent by 2100. If each 
country’s temperature were to rise by 0.04 degrees Celsius annually, GDP is predicted to fall by 13 
percent globally by 2100 (Kahn et al. 2021). 
Developing countries are more vulnerable to the negative effects of climate change, as rising food and 
energy prices decrease the ability of poor families to shield against economic and climate shocks. Most 
families living below or near the poverty line spend much of their household income on food and 
energy, limiting the amount of money they can set aside for the future (Hallegatte et al. 2014). Policies 
put in place in response to climate change can deepen these vulnerabilities by restricting land use and 
increasing the costs of productivity. It is important to study climate changes’ effects on poverty because 
families and regions in developing countries most affected are usually too materially poor to reflect in 
aggregate figures, such as GDP (Hallegatte et al. 2014). 
Dietz and Millner (2015) identify three reasons developing countries are more vulnerable to climate 
change compared to developed countries: geography, sensitivity, and a lack of adaptative capacity. The 
geography of developing countries makes them more subject to variability in the availability of water, 
rising temperatures, and changes in rainfall caused by climate change. That makes developing countries 
less favorable to economic activity, stunting capital availability and investor confidence and engendering 
an atmosphere of price volatility. Cevik and Jalles (2020; 2022) found that yields on government bonds 
are significantly affected by climate change, increasing the probability that developing countries will 
default on sovereign debt. Sensitivity to climate change stems from the dependance of developing 
countries on sectors that are reliant on and sensitive to climatic conditions. Dietz and Millner (2015) 
characterize this to mean that because many families in developing countries are pursuing marginal 
livelihoods, climate change impacts their ability to save and invest over the long-term and can reduce 
their labor productivity. Lastly, developing countries are more vulnerable to the negative effects of 
climate change because of their lack of adaptive capacity, in part attributable to the aforementioned 
factors, and exacerbated by a lack of savings and access to credit, good governance, infrastructure and 
Climate Change, Economic Production, Inequality, and Poverty 13
information (Dietz and Millner 2015). Consequently, there is interconnectivity between climate change￾induced poverty and prices, assets, productivity and opportunities, all of which affect the ability of 
families and governments to adapt to an increasing number of climate shocks (Hallegatte et al. 2014). 
Climate change impacts household consumption patterns and availability, which in turn can influence 
whether families fall into poverty. Climate change reduces future incomes by depressing productivity and 
raising costs due to disruptions in economic growth (Taconet, Méjean, and Guivarch 2020). 
Consumption patterns are altered by the effects of climate changes on the living conditions, purchasing 
power, and access to public goods and services of families, and their need to redirect their efforts to 
mitigation and adaptation strategies. Living conditions of the poor can make them additionally vulnerable 
to the impacts of climate change and its policy shocks through natural disasters, a reduction in 
ecosystem goods, decreased social protections, and rising energy prices (Hallegatte et al. 2014). Over a 
25-year period in the Andhra Pradesh community in India, 14 percent of households in 36 communities 
escaped poverty, but during that same time, 12 percent of households fell into poverty, leading to a net 
poverty reduction rate of 2 percent (Hallegatte et al. 2014). 
Natural disasters increase the probability of families dropping below poverty thresholds (Hallegatte et al. 
2014; Walker et al. 2007; Barbier 2010; Dercon and Christiaensen 2011; 2011). Households’ capital 
accumulation is jeopardized by the destruction of assets through natural disasters such as flooding. With 
increased asset losses and expenditures, families are prone to saving and investing less, as they are 
forced to devote more of their resources to everyday survival (Hallegatte et al. 2014). A decrease in 
investment reduces income growth overall (Haushofer and Fehr 2014). Climate change is also linked to 
reduced investment in the education, nutrition, and health of children, impacting their future job 
prospects and overall well-being. The unfortunate conclusion is that climate change impacts the well￾being of entire families and can trap families in multi-generational cycles of poverty. 
In a poverty rate and determinants study of 379 rural Bangladeshi households, interviewed between 
1987–1988 and 2000, when asked to report the causes of their poverty dynamics, approximately 15 
percent cited natural disasters and 18 percent cited a loss of natural assets as main reasons for losses in 
income (Hallegatte et al. 2014). For these reasons, low-income households are often risk averse, 
pursuing lower-risk, lower-return strategies that perpetuate poverty (Hallegatte et al. 2014). 
A study by Ayoo (2022) about poverty reduction strategies in developing countries emphasized the 
necessity of risk-taking and investment in productive, income-generating activities, but climate change—
and the uncertainty it brings—affects how households handle and pursue risk. Natural disasters are a 
major factor in this uncertainty. In a study of poverty rates and determinants over a 25-year period, 
1981–2006, in 36 villages of three districts in the Andhra Pradesh community in India, drought proved an 
important factor: “All else being the same, the odds of a non-poor household falling into poverty 
increase more than 15 times when drought is a significant feature of its event history” (Krishna 2006). 
Among the 12 percent of families in 36 communities in the Andhra Pradesh community in India who fell 
into poverty, 44 percent cited “drought, irrigation failure, or crop disease” as reasons for their loss in 
income (Hallegatte et al. 2014). Even if families can increase diversification and reduce risk-taking, the 
increased volatility and uncertainty brought on by climate change decreases average incomes and income 
growth (Hallegatte et al. 2014). In addition, most household investments in poor communities are in 
inflexible assets, creating irreversibility or non-transferability in investment and adding to inability to 
respond to climate shocks through adaptation and mitigation. 
Working Paper | January 2023 14
Growing evidence points to the effects of climate change on agricultural productivity, undermining 
advances in poverty reduction. Poorer families have limited budgets and increasing food prices put them 
at high risk of falling into poverty. Fluctuations in agricultural production are also linked to poverty. Such 
fluctuations lower price stability, demand, and supply, which in turn affects household consumption, 
savings, and labor productivity (Hertel, Burke, and Lobell 2010). A changing climate can reduce crop 
yields, and climate policies to mitigate greenhouse gas emissions can increase food prices, further 
harming the poor (Porter et al. 2014). Changes to agricultural productivity also alter global supply chains 
and create ripple effects on food security, prices, and poverty. Price volatility, while it can benefit sellers, 
is unpredictable and therefore, also a negative effect of climate change on long-term economic growth 
and agricultural productivity. 
Using disaggregated data on country’s household activity within a global trade model (Global Trade 
Analysis Project), Hertel, Burke, and Lobell (2010) present three scenarios on climate change’s effects 
on agriculture and the resulting impacts on global commodity prices, national economic welfare, and the 
incidence of poverty. They consider 15 developing countries, analyzing low, medium, and high 
productivity economic scenarios. They project that in the low productivity scenario—depicting, high 
crop sensitivity to warming, rapid temperature change, and a lower carbon dioxide (CO2) fertilization 
affect— prices for major staples will rise from 10 to 60 percent by 2030, with poverty rates in African 
and Asia for non-agricultural households rising by 20 to 50 percent and falling by similar rates among 
agricultural households in Asia and Latin America. The medium productivity economic scenario shows 
little effect on poverty rates, commodity prices, or cost of living, on average. While the high productivity 
scenario—depicting, low sensitivity of crops to climate change, relatively slow warming, and high lower 
CO2 fertilization affect—lowers commodity prices. Compared with the low productivity scenario, the 
high productivity scenario poverty rates rose in all the countries in which poverty had fallen and 
decreased poverty rates in the remaining four countries where poverty had risen, with global poverty 
rising slightly. This mirroring is primarily dependent upon the composition of poverty populations since 
higher prices can help agricultural workers, while hurting the urban poor and vis-versa (Hertel, Burke, 
and Lobell 2010).
The World Bank’s Aftershocks report of 2018 analyzes how historical natural disasters can inform 
current and future mitigation and adaptation strategies. Aftershocks’ chapter on the 2017 Tropical 
Cyclone Enawo in Madagascar showcases the vulnerability of depending upon volatile agricultural prices 
to escape poverty and the effects of climate change on productivity. From years of consistently rising 
vanilla prices, farmers had become dependent on the crop, putting all of their resources into its 
production. In the major growing region of Sava, Madagascar, initial reports of Enawo’s destruction 
reported 90-100 percent of production as lost. The cyclone had completely devastated vanilla 
production and disrupted the industry globally. To respond, the Malagasy government modeled losses to 
estimate the economic impact of the cyclone, which allowed the government and farmers to understand 
the risk of future cyclones to then inform mitigating crop vulnerabilities and diversifying livelihoods. 
Enawo demonstrated how increased demand for an agricultural crop, and the corresponding high prices, 
can lead to stunted economic growth rates (Phillips et al. 2018). 
Climate change affects financial and fiscal stability, decreases long-term growth, and widens income 
distribution disparities. Effects are dependent on the size and composition of economies and the 
resilience of physical infrastructure and institutions, all of which contribute to the capacity for mitigation 
and adaptation to climate change (Cevik and Jalles 2022). Within countries, Islam and Winkel (2017) 
found that disadvantaged households experience a disproportionate burden of the adverse effects of 
Climate Change, Economic Production, Inequality, and Poverty 15
climate change, which creates greater, subsequent income distribution inequalities. Tol et al. (2004) and 
Diffenbaugh and Burke (2019) found that low income countries tend to become poorer in the face of 
climate change, due to geographical and institutional constraints to adapt. King and Harrington (2018) 
found that tropical regions, with less developed infrastructure and higher base temperatures, are most 
affected economically by global warming. Colder countries, or higher-latitude regions, can benefit from 
increased global temperatures, while tropical countries are further disadvantaged (Diffenbaugh and 
Burke 2019). 
That is not to say that higher-latitude countries are not also impacted by rising temperatures. In China, 
for example, H. Deng et al. (2020) found evidence that rising temperatures have increased household 
income inequality in urban areas of the country. As temperatures have risen, so have energy costs. 
Poorer households cannot afford to compensate for rising temperatures by using additional air 
conditioning. Kjellstrom et al. (a.2009) also studied the effects of increasing temperatures, indoors and 
out, on working conditions in South African Gold mines, sugar cane agricultural workers in El Salvador 
and Nicaragua, South India assembly workers in car and truck factories, Northern Vietnam shoe factory 
workers, and offices and factories without air conditioning in the Southern U.S. states. Albeit 
situationally specific, workers in these overheated and humid environments are more likely to 
experience heat stroke and strain, compromising their health, well-being, and labor productivity. Climate 
change will exacerbate these already existing risks, as Kjellstrom et al. (b.2009) find that many jobs in 
low and middle income countries necessitate working outdoors, or indoors without air conditioning, 
with laborers reducing productivity in order to avoid serious heat stroke, exhaustion, and thus, incurring 
loss of income. Therefore, H. Deng et al. (2020) and Kjellstrom et al. (a. & b. 2009) highlight the 
different exposure risks associated with climate changes within and among countries, finding the 
consequences vary in severity between high and low latitude countries. Newell et al.’s (2021) study on 
historical fluctuations in temperature found that while a rise in temperatures caused statistically 
significant losses in GDP and agricultural production in poor countries, in rich countries historical 
fluctuations in temperature did not result in statistically significant effects on the growth of GDP. 
In addition to impacting productivity, climate change also has uneven effects on the distribution of 
resources. The link between the determinates of income inequality and the macroeconomic impacts of 
climate change has been studied by Harrington, et al., (2016); Mahlstein et al., (2011), and Schleussner et 
al., (2016). Datt and Ravallion (1998) and Ferreira et al. (2015) both find extensive evidence that 
macroeconomic instability depresses income growth among the poor, leading to greater income 
inequality. Studies have also shown inequality to be both positively and negatively affected by fiscal 
policies and financial development, political regimes and institutional factors, demographic and social 
characteristics, human capital accumulation and access to entrepreneurial activity, and globalizations' 
impact on trade openness and investment. There is also literature related to the financial effects of 
climate change, with Nordhaus (1991;1992) and Cline (1992)’s aggregate damage functions being the 
principal manner of analyzing climate-economy intersects. 
To further investigation into the empirical relationship between climate change and inequality, Cevik and 
Jalles (2022) found that, between 1955 and 2019, increases in climate change vulnerabilities were 
positively associated with rising income inequality. The coefficient on climate change vulnerability’s effect 
on income distribution contrasts between developing and developed countries. Climate change 
vulnerability in developed countries has no statistically significant effect on income distribution, while 
developing countries’ coefficient on climate change vulnerability is seven times greater than that of 
developed countries. The greater effect of climate change vulnerability on developing countries is partly 
Working Paper | January 2023 16
due to a weaker capacity for climate change adaptation and mitigation, geography and sectoral 
distribution of economic activity, and the tendency for climate shocks to induce higher wealth and 
income losses in low income countries. Although subject to a higher degree of uncertainty, Cevik and 
Jalles (2022) also found that increased climate resilience is associated with lower income inequality.
3. Methodology
To study the future effect of climate change on poverty we model future patterns of economic 
production, income distribution, and population change for 186 countries from 2017 to 2100. We do 
this using multiple existing modeling tools and outputs that include SSPs (O’Neill et al. 2017), RCPs (van 
Vuuren et al. 2011a), and International Futures (IFs) (B. B. Hughes 2019a) integrated assessment model. 
Two main pathways in this analysis cut across the modeling approaches described here. The first is from 
changing patterns of temperature to economic growth through a damage function. The second is from 
changing patterns of temperature change to inequality. The final output of this analysis is 27 scenarios 
forecasting national levels and rates of poverty for 186 countries from 2017-2100. Further uncertainty is 
framed in the Appendix.
Table 1: Scenarios used in this analysis.
Scenario 
Name
Damage 
Function
Economic 
Forecast
Inequality 
Forecast
Demographic 
Forecast
Poverty 
Module
IFs No-Climate 
Change
None IFs Model 
(Hughes 2019)
IFs Model 
(Hughes 2019)
IFs Model 
(Hughes 2019)
IFs Model 
(Hughes 2019)
IFs Climate 
Change (RCP 
2.6, 4.5, 6.0, 
8.0)
Nordhaus 
DICE* Model
IFs Model 
(Hughes 2019)
IFs Model 
(Hughes 2019)
IFs Model 
(Hughes 2019)
IFs Model 
(Hughes 2019)
SSP1-5 No￾Climate 
Change
None Dellink et al. 
(2017)
Rao et al. (2016) KC and Lutz 
(2017)
IFs Model 
(Hughes 2019)
SSP1-5 
Climate 
Change (RCP 
2.6, 4.5, 6.0, 
8.0)
Nordhaus 
DICE Model
Dellink et al. 
(2017)
Rao et al. (2016) KC and Lutz 
(2017)
IFs Model 
(Hughes 2019)
*DICE = Dynamic Integrated Climate-Economy Model
The scenarios used in this analysis are described in Table 1. We use a modeling strategy that begins with 
the IFs modeling platform because it dynamically represents development as an integrated system (key 
model assumptions are described below), connecting systems of economic growth, inequality, 
demographics, and poverty. Two scenarios from the IFs model form the backbone of this analysis by 
comparing a future global poverty scenario with and without the effects of climate change (more on the 
operationalization of those assumptions below). 
To more broadly frame uncertainty associated with this analysis we rely upon three SSP series. The SSPs 
are a set of socioeconomic scenario narratives that frame five alternative futures characterized by 
alternative assumptions about future challenges to mitigation and adaptation of climate change. The 
optimistic scenario has few challenges to mitigation and adaptation and is characterized by sustainable 
and inclusive development (SSP1), while the pessimistic scenario has high challenges to mitigation and 
Climate Change, Economic Production, Inequality, and Poverty 17
adaptation and is characterized by conflict and scarcity (SSP3). The scenario with significant challenges to 
adaptation but few challenges to mitigation is characterized by high within and across-country inequality 
(SSP4) while the future path with high challenges to mitigation and few challenges to adaptation is 
characterized by massive fossil fuel-led growth (SSP5). SSP2 is characterized by moderate challenges 
framed as a “middle-of-the-road” scenario. We use three SSP series in this analysis focused on economic 
growth (Dellink et al. 2017), inequality (Rao et al. 2019), and demographic change (Kc and Lutz 2017).
Figure 1: Modeling approach used in this manuscript.
Figure 1 presents another perspective on the modeling relationships used in this paper. Both the IFs and 
SSP scenarios are created by first using RCP forecasts of average country-level temperature change 
relative to 1990 values across four scenarios that represent different values for radiative forcing (van 
Vuuren et al. 2011a). These alternative scenarios form the starting point of this analysis and are 
represented on the left. The RCP temperature forecasts drive two alternative relationships, one that 
captures the effects of changing temperatures on overall levels of productivity and another that captures 
the relationship between changing temperature and the Gini coefficient for income inequality. 
Damage functions establish a relationship between changing patterns of temperature relative to 1990 
levels and overall economic output operationalized on a country basis. Three main models have 
established damage functions: the Dynamic Integrated Climate-Economy Model (DICE), Policy Analysis 
of the Greenhouse Effect (PAGE), and Climate Framework for Uncertainty, Negotiation and 
Distribution (FUND) (Diaz and Moore 2017). These functions are intended to capture a broad range of 
climate effects in a single relationship and have been widely used to assess climate financing and the 
economic cost of future climate change. These relationships are developed from micro studies on the 
effects of multiple pathways on economic production (Tol 2009). Figure 2 highlights the relationship 
between changing patterns of temperature and economic production using various damage function 
approaches. For this working paper we take the quadratic relationships outlined in the DICE model (W. 
Nordhaus 2018). This relationship shows a non-linear increase in climate effects driven by changing 
temperature. The effect of this damage function in IFs is to proportionally reduce the annual flow of 
value-add measured across six sectors. More information on the IFs model is included below.
Working Paper | January 2023 18
Figure 2: DICE, FUND, and PAGE relationships between temperature change and reduced GDP (from 
Diaz).
We add these inequality effects in our base model by establishing a relationship between changing 
temperature and the Gini coefficient for household inequality in the IFs system. Dasgupta et al. (2020) 
estimated a non-linear relationship between mean temperatures and inequality (Dasgupta et al. 2020). 
The model measured the relationship on a district, household, and individual level for South Africa, 
based on data from 1990 to 2016. Dasgupta et al. (2020) found that cold-country temperature countries 
experience productivity increases and inequality decreases until the Gini optimum of 11 degrees Celsius 
is reached, at which point productivity continues to increase at the expense of rising inequality until 
reaching a threshold of 13 degrees Celsius. Temperatures above 13 degrees Celsius yield declining 
growth rates and inequality rising at faster rates (Dasgupta et al. 2020). Since South Africa is hotter than 
the global average, its optimal temperature is estimated to be approximately 15.6 degrees Celsius, and 
the individual optimal temperature for household income per capita growth is estimated to be 16.8 
degrees Celsius.
Figure 3: Dasgupta et al. (2020) relationship between average temperature and the Gini coefficient.
-2
0
2
4
6
8
10
12
14
16
18
0 1 2 3 4 5 6 7 8
Percent change to GDP
Temperature Change relative to 1990
DICE PAGE FUND
y = 0.0005x2
- 0.0167x + 0.1307
R² = 0.9998
-0.015
-0.01
-0.005
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0 5 10 15 20 25 30
Change to Gini
Average National Temperature (C)
Climate Change, Economic Production, Inequality, and Poverty 19
In addition to the steps outlined above, we rely on the SSP scenarios forecasting long-term patterns of 
economic growth, inequality, and demographic change to provide a context for broadly framing 
uncertainty associated with these forecasts and demographic headcounts by country and region. These 
scenarios are exogenously imposed on the IFs system and marginal effects from changing patterns of 
temperature through productivity can override these scenarios to isolate the climate-to-poverty effects. 
These relationships, SSP, and RCP projections are operationalized within the IFs system, a fully 
integrated model that connects systems representing agriculture, climate, conflict, demographics, 
education, energy, gender, governance, health, infrastructure, international relations, and technology 
related sub-modules and has been developed over many decades by a team led by Barry Hughes 
(2019b). Descriptions of the key model elements included in this manuscript have been previously 
published and document representation of the probability of civil war onset (B. B. Hughes et al. 2014; 
Joshi, Hughes, and Sisk 2015), economic growth (B. B. Hughes and Narayan 2021; Burgess et al. 2022; B. 
B. Hughes 2019b), inequality (B. B. Hughes 2019b), demographics (B. B. Hughes 2019b), and levels of 
poverty (B. B. Hughes et al. 2009; Moyer et al. 2022). 
IFs includes a recursive dynamic computable general equilibrium model structure to forecast long-term 
patterns of economic growth (B. B. Hughes 2019b; B. B. Hughes and Narayan 2021; Moyer et al. 2022; 
Burgess et al. 2022). It is structured with a Cobb-Douglas production function (1928), Solow residual 
(1956), and six capital sectors along with labor by skill level both initialized using GTAP data (Global 
Trade Analysis Project 2018). All financial flows are tracked following a social accounting matrix (SAM) 
(Keuning and de Ruuter 1988) which includes an input-output table (Adam 1995).
𝐺𝐷𝑃𝑐,𝑡 = ∑[𝐶𝐷𝐴𝑐,𝑠,𝑡=1 ∗ 𝑇𝐸𝐹𝐹𝑐,𝑠 ∗ 𝐶𝐴𝑃𝑈𝑇𝑐,𝑠 ∗ 𝐾𝑆𝑐,𝑠
𝐴𝑙𝑝ℎ𝑎𝑆𝑐,𝑠 ∗ 𝐿𝐴𝐵𝑆𝑐,𝑠
(1−𝐴𝑙𝑝ℎ𝑎𝑆𝑐,𝑠)
]
𝑆
Where TEFF, KS, LABS and CAPUT are sector specific values of total factor productivity, capital, labor and 
capacity utilization. CDA is a scaling factor computed in the base year to make model computations consistent 
with historical data. Subscript c is country, s is economic sector, t is time (Moyer et al. 2022).
IFs forecasts the Gini coefficient for income inequality to capture trends in long-term patterns of the 
distribution of resources within countries. The approach computes the area under the equality curve 
(Gastwirth 1972) where the x axis measures the portion of population and y the portion of income. It 
uses two categories of labor broken down by skill and initialized using GTAP data (Global Trade 
Analysis Project 2018). The household earned income levels are calculated in the SAM and are driven by 
the labor share of value added across the six sectors represented in the production function. The 
formulation modifies earned income to calculate disposable income by augmenting or decrementing the 
former by government transfers for welfare and pensions, taxation income (consumption tax as well as 
pension tax), remittances, and returns on household investment.
𝐺𝐼𝑁𝐼𝐷𝑂𝑀𝑐,𝑡 = 𝐹(𝐻𝐻𝐼𝑁𝐶𝐷𝐼𝑆𝑐,𝑡,𝑙𝑠, 𝐻𝐻𝑃𝑂𝑃𝑐,𝑡,𝑙𝑠)
Where GINIDOM is the domestic Gini coefficient for income inequality, HHINCDIS is household disposable 
income and HHPOP is the household size. Subscript c is country, t is time, and ls is labor by skill level.
The poverty module (Moyer et al. 2022; B. B. Hughes et al. 2009) uses inputs from the economic 
growth and inequality modules. IFs forecasts poverty by first taking disposable income, described above, 
and allocating it to either consumption or savings driven by three factors. First, the model adjusts the 
consumption/savings ratio based on long-term changing patterns of country development. As GDP per 
Working Paper | January 2023 20
capita of countries rise countries slowly increase their share of savings relative to consumption. Next, 
the model adjusts consumption and savings shares in response to changing demographic age structure 
because both young and old consume a larger share of disposable income than the working-age 
population. Finally, the model adjusts patterns of consumption/savings based on signals sent from 
sectoral prices and interest rates. Both signals are driven by equilibrating mechanisms connected to 
underlying inventory stocks. 
The demographic module in IFs is a standard cohort component model using data from the United 
Nations Population Division (UNPD) (2019). Fertility is driven by levels of infant mortality, 
contraception use, GDP per capita, and average levels of education (B. B. Hughes 2019b). Forecasts 
include 21 mortality categories by age and sex driven by both multiple and cause specific distal and 
proximate drivers (B. Hughes et al. 2011; B. B. Hughes et al. 2011; Sellers 2020). Migration assumptions 
are exogenously imposed from UNPD medium variant projections.
Finally, the IFs model uses a structural approach to estimate poverty rates at different per capita 
household consumption levels (represented in inflation and purchasing power-adjusted currency) that 
draw on a log-normal distribution of household income, (Bourguignon 2004; Shorrocks and Wan 2008). 
Base poverty rate values are initialized using data from PovcalNet (World Bank 2021), which are 
originally survey based. Changes in the Gini coefficient for household income affect the horizontal shape 
of the commonly log-normal distribution of household income (B. B. Hughes et al. 2009 see Chapter 4). 
Levels of household income and consumption are changed by dynamics of the economic module and are 
described above.
We represent the effect of changing temperatures on future patterns of economic production through 
co-called “damage functions” (Bretschger and Pattakou 2019; Tol 2009; Diaz and Moore 2017; W. 
Nordhaus 2018; Neumann et al. 2020; Wouter Botzen and van den Bergh 2012).
4. The Effect of Climate Change on 
Productivity and Poverty
This section of the report introduces the baseline behavior of the scenarios used in this analysis. It 
begins by introducing the RCP scenarios as they are operationalized within the IFs model. Next, it 
introduces the core scenario behavior for different SSPs as well as the IFs model, all excluding the effect 
of climate change. The core scenarios used in this report are GDP per capita (Dellink et al. 2017), the 
Gini coefficient for income inequality (Rao et al. 2019), and population (Kc and Lutz 2017). Finally, the 
baseline poverty scenarios (Moyer et al. 2022) are introduced.
Baseline Behavior
Figure 4 shows the baseline behavior for temperature change taken from RCP modeling efforts (van 
Vuuren et al. 2011b). RCP2.6 shows the slowest increase in global temperature from an average global 
temperature of around 15 degrees centigrade to 15.6 by mid-century, remaining flat through the end of 
the time horizon. Next, RCP4.5 shows a more rapid increase in average global temperature, growing 
from 15 degrees to 16 degrees by mid-century and then increasing to 16.9 degrees by the end of the 
century. RCP6.0 also shows growth through mid-century though less rapid than RCP4.5 in the first half, 
reaching 15.8 degrees by 2050. This scenario shows an increase in global average temperature to 17.6 
Climate Change, Economic Production, Inequality, and Poverty 21
degrees by the end of the century. RCP8.5 is the most pessimistic model, showing an increase of global 
temperature to 16.4 degrees by 2050, growing to 19.5 degrees by the end of the century—a 30.7 
percent increase in global average temperature over that time horizon.
Figure 4: RCP scenarios in IFs (2.6, 4.0, 6.5, 8.0) for average global temperature from 2017 to 2100 using 
a 10-year moving average.
14
15
16
17
18
19
20
2017
2020
2023
2026
2029
2032
2035
2038
2041
2044
2047
2050
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2059
2062
2065
2068
2071
2074
2077
2080
2083
2086
2089
2092
2095
2098
Degrees Celsius (10-year moving average)
RCP 2.6 RCP 4.5 RCP 6.0 RCP 8.5
The core SSP and IFs base case scenarios are introduced in this section, starting with GDP per capita 
measured at purchasing power parity (Figure 5). Scenarios with greater challenges to adaptation show 
much lower long-term development trajectories, with SSP3 and SSP4 seeing increases in GDP per capita 
starting at $16,000 in 2017 and growing to between $32,000 and $54,000 by the end of the century. The 
“middle-of-the-road” scenario (SSP2) is more optimistic in its growth trajectory, increasing to $90,000 
per capita by the end of the century. Scenarios with fewer challenges to adaptation reflect much more 
optimistic development pathways, with SSP1 showing economic development of $124,000 per capita by 
the end of the century and SSP5 showing growth to $205,000 by the end of the time horizon. 
The IFs base case scenario is less optimistic, reflecting long-term growth patterns closer to SSP 
scenarios that have high challenges to adaptation in the face of climate change. The IFs scenario grows 
GDP per capita to the level of SSP3 by the middle of the time horizon and SSP4 by the end of the time 
horizon. This reflects assumptions about long-term low growth in high income countries that reduces 
the convergence ceiling of economic growth potential among low income countries (Burgess et al. 
2022).
Working Paper | January 2023 22
Figure 5: SSP scenarios in IFs without a climate effect for global GDP per capita at a constant 2011 US$ 
from 2017 to 2100.
0
50
100
150
200
250
2017
2020
2023
2026
2029
2032
2035
2038
2041
2044
2047
2050
2053
2056
2059
2062
2065
2068
2071
2074
2077
2080
2083
2086
2089
2092
2095
2098
GDP per Capita at PPP (2011$)
IFs No Climate SSP1 SSP2 SSP3 SSP4 SSP5
Figure 6 shows the baseline behavior of the Gini coefficient for income inequality across scenarios used 
in this analysis. SSPs with greater challenges to adaptation show the most significant increases in income 
inequality, growing across time. The “middle-of-the-road” scenario (SSP2) begins with a similar 
distribution to IFs base case scenario but then diverges and becomes more equal. SSP scenarios with 
fewer challenges to adaptation show a decrease in future income inequality and the IFs base case 
scenario models a world of changing income inequality that remains flat across time. 
Figure 6: SSP scenarios in IFs without a climate effect for global Gini Index from 2017 to 2100. 
0.25
0.3
0.35
0.4
0.45
0.5
2017
2020
2023
2026
2029
2032
2035
2038
2041
2044
2047
2050
2053
2056
2059
2062
2065
2068
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2077
2080
2083
2086
2089
2092
2095
2098
Gini Index for Income Inequality
IFs No Climate SSP1 SSP2 SSP3 SSP4 SSP5
Climate Change, Economic Production, Inequality, and Poverty 23
Population is another important driver of the future level of poverty, with scenarios showing higher 
population levels increasing the likelihood of larger impoverished populations driven by various factors 
(Figure 7). SSP3, which models a world of high challenges to both adaptation and mitigation, shows the 
largest increase in population overtime, growing to 12.7 billion by 2100. SSP2 and SSP4, which model a 
world characterized by “middle-of-the-road” development and unequal development, show population 
growth that peaks in mid-century at 9.3 billon and falls to 9.1 (SSP2) and 9.3 (SSP4) billion by the end of 
the century. Scenarios with low challenges to adaptation project lower population distributions, with a 
peak of 8.7 billion by mid-century and a decline in overall population to 7.5 (SSP5) and 7.0 billion (SSP1).
The IFs base case scenario projections on future population growth fall between the most pessimistic 
SSP and more middle-of-the road projections. The IFs population projection grows throughout the 
century, though at a diminishing rate, to reach 10.2 billion by 2100.
Figure 7: SSP scenarios in IFs without a climate Population.
6,000
7,000
8,000
9,000
10,000
11,000
12,000
13,000
14,000
2017
2020
2023
2026
2029
2032
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2098
Millions of People
IFs No Climate SSP1 SSP2 SSP3 SSP4 SSP5
The global no-climate poverty projections are shown in Figure 8. Here, SSP scenarios with greater 
challenges to adaptation show higher levels of overall poverty than scenarios with fewer challenges to 
adaptation, declining from 710 million in 2017 to 448 million (SSP3) and 482 million (SSP4) by mid￾century and then further to 270 million (SSP3) and 146 million (SSP4) by the end of the century. SSP2, 
reflecting middle-of-the-road development assumptions, shows a significant reduction in poverty across 
time, declining to 104 million by mid-century and 15 million by 2100. Scenarios with few challenges to 
adaptation show even more rapid declines in the population living in extreme poverty, decreasing to 29 
million (SSP1) and 19 million (SSP5) by mid-century and 1.5 million (SSP1) and 3.4 million (SSP5) by the 
end of the time horizon.
The IFs base case scenario shows significantly different behavior, reflecting different assumptions in the 
above drivers. First, the IFs scenario includes the effect of COVID-19 in its base case, increasing the 
number of people in poverty from 710 million to 737 million in the early years of the pandemic. Next, 
the IFs base case is driven by more pessimistic assumptions about short-term GDP growth as well as 
more pessimistic assumptions about short-term population growth relative to other SSP scenarios. The 
IFs projection shows persistently higher poverty than other SSPs for the first half of this century, 
eventually passing SSP3 and SSP4 projections, but reduces to approximately 376 million people living on 
Working Paper | January 2023 24
less than $1.90 per day by mid-century. This scenario declines through the end of the century and 
projects 27 million people in extreme poverty by the end of the time horizon.
Figure 8: SSP scenarios in IFs without a climate effect for global population living on less than $1.90 per 
day from 2017 to 2100.
0
100
200
300
400
500
600
700
800
2017
2020
2023
2026
2029
2032
2035
2038
2041
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2059
2062
2065
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Millions of People
IFs No Climate SSP1 SSP2 SSP3 SSP4 SSP5
Climate Effects on Development
This section evaluates the outcomes of our model runs across different key drivers of poverty as well as 
poverty itself. We begin by comparing how climate scenarios are impacting the long-term development 
of economic production/consumption, the Gini coefficient for inequality, and poverty by region and 
income group.
Table 2 shows the cumulative impact of climate change across SSPs in GDP measured at market 
exchange rates (in 2011 US$). In a world of low radiative forcing and sustainable development (SSP1 
RCP2.6) climate change will reduce economic output by $23.4 trillion by mid-century and US$187.6 
trillion by 2100. In a “middle-of-the-road” scenario, coupling SSP2 assumptions and RCP4.5 assumptions, 
the economic cost of climate change is $26.3 trillion by mid-century and $429.9 trillion by the end of the 
century. In worlds of increased challenges to climate change the effect is less, reflecting more pessimistic 
assumptions about no-climate GDP growth. SSP3, a world of high challenges to both adaptation and 
mitigation in the face of climate change, sees a reduction in total GDP by the end of the century of 
$276.5 trillion when coupled with RCP6.0. SSP4 sees an even more significant reduction when compared 
with RCP6.0 with a cumulative reduction in GDP of $343.2 trillion. The greatest reduction occurs in the 
SSP with the largest economic production, SSP5. When coupled with RCP8.5, SSP5 sees a reduction of 
$1,853.0 trillion through the end of the century. The IFs model results show less overall impact on GDP 
because the long-term growth forecast is more pessimistic than most SSP scenarios. In RCP2.6, IFs 
projects a reduction in total economic activity of $92.2 trillion, in RCP4.5 a reduction of $291.9 trillion, 
in RCP6.0 a reduction of $338.9 trillion, and in RCP8.5 a reduction of $804.0 trillion.
Climate Change, Economic Production, Inequality, and Poverty 25
Table 2: The effect of climate on global economic growth. Measured in trillions of 2011 US$ at market 
exchange rates, all tables measure the cumulative differences between RCP scenarios and No-Climate 
scenarios.
World 2030 2050 2070 2100
SSP1 RCP2.6 -$2.2 -$23.4 -$75.2 -$187.6
SSP2 RCP4.5 -$2.1 -$26.3 -$109.1 -$429.9
SSP3 RCP6.0 -$1.6 -$15.9 -$61.4 -$276.5
SSP4 RCP6.0 -$1.6 -$18.2 -$75.8 -$343.2
SSP5 RCP8.5 -$2.3 -$42.3 -$272.2 -$1,853.0
IFs RCP2.6 -$0.4 -$10.3 -$35.5 -$92.2
IFs RCP4.5 -$0.5 -$15.7 -$72.8 -$291.9
IFs RCP6.0 -$0.4 -$10.5 -$54.9 -$338.9
IFs RCP8.5 -$0.7 -$21.1 -$128.6 -$804.0
As a share of GDP, the various SSP and RCP combinations paint a broad range of possible economic 
futures depending on assumptions about future temperature change and economic growth (Table 3). In 
the highest temperature change scenario—RCP8.5—total economic production/consumption is reduced 
by 6.3 percent by 2100 compared with the most optimistic scenario where economic 
production/consumption is reduced by approximately 0.5 percent from mid-century to 2100.1 The IFs 
base case results show a spread of impact that ranges from –0.42 percent of GDP by 2100 in the lowest 
carbon emission projection (RCP2.6) to –7.37 percent impact in a worst-case scenario (RCP8.5). 
Regionally, in the “middle-of-the-road” scenario SSP2-RCP4.5 the largest reductions in economic activity 
by the end of the century occur in Southern Asia, followed by sub-Saharan Africa and the Middle East 
and North Africa, a reflection of assumptions about each region’s economic potential (see Appendix). 
The largest economic reduction for the same scenario by economic grouping occurs in lower-middle 
income countries followed by upper-middle income and high income countries (see Appendix). 
Table 3: The effect of climate on global economic growth as percent of GDP measured in trillions of 2011 
US$ at market exchange rates.
World 2030 2050 2070 2100
SSP1 RCP2.6 -0.25% -0.54% -0.58% -0.51%
SSP2 RCP4.5 -0.26% -0.87% -1.45% -1.90%
SSP3 RCP6.0 -0.20% -0.65% -1.35% -3.10%
SSP4 RCP6.0 -0.20% -0.63% -1.23% -2.96%
SSP5 RCP8.5 -0.27% -1.05% -2.64% -6.36%
IFs RCP2.6 -0.12% -0.50% -0.55% -0.42%
IFs RCP4.5 -0.15% -0.82% -1.55% -1.88%
IFs RCP6.0 -0.11% -0.61% -1.31% -2.99%
1 A recent white paper released by the White House examined the macroeconomic risks associated with climate forecasts at 
different RCP levels (site). These studies project a decrease in global GDP in 2100 for an RCP8.5 scenario ranging from 1 
percent to 23 percent (Newell et al 2021; Burke et al 2015). Our forecast for RCP8.5 scenarios projects a 6.3 percent to 7.6 
percent decrease in global GDP, well within the range of other studies in this area
Working Paper | January 2023 26
World 2030 2050 2070 2100
IFs RCP8.5 -0.21% -1.24% -3.31% -7.37%
The impact of the SSP RCPs on the future distribution of income inequality, with the range of outcomes 
in 2050 spanning from an increase of 0.0011 points on a 0–1 point scale for SSP1-RCP2.6 to a high of 
0.002 for all other scenarios, represent an increase of 0.316 percent to 0.697 percent globally. By 2100 
this further increases from a low of 0.0022 in SSP1-RCP2.6 to a high of 0.014 in SSP5-RCP8.5, 
representing an increase of 0.774 percent to 4.625 percent. In the IFs, the Gini coefficient increases by 
0.0013 by 2050 driven by RCP2.6 and up to 0.004 driven by RCP8.5, representing 0.341 percent to 1.05 
percent. By 2100, the increase in Gini is 0.0023 in RCP2.6 and 0.025 in RCP8.5, representing an increase 
of 0.601 percent to 6.527 percent. The regions with the greatest increase in the Gini driven by change in 
temperature by the end of the century in the “middle-of-the-road” scenario SSP2-RCP6.0 are sub￾Saharan Africa and Southern Asia followed by Southeast Asia, and then Latin America and the Caribbean 
(see Appendix). The largest increase in Gini for the same scenario by economic groupings occurs in low￾middle income countries followed by low income countries (see Appendix).
Table 4: The effect of climate on the global Gini Index. Measures the absolute difference between RCP 
scenarios and No-Climate scenarios.
World 2030 2050 2070 2100
SSP1 RCP2.6 0.0002 0.0011 0.0018 0.0022
SSP2 RCP4.5 0 0.002 0.004 0.006
SSP3 RCP6.0 0 0.002 0.004 0.011
SSP4 RCP6.0 0 0.002 0.004 0.01
SSP5 RCP8.5 0 0.002 0.006 0.014
IFs RCP2.6 0.0002 0.0013 0.0021 0.0023
IFs RCP4.5 0 0.002 0.005 0.007
IFs RCP6.0 0.001 0.002 0.005 0.011
IFs RCP8.5 0.001 0.004 0.011 0.025
Table 5: The effect of climate on the global Gini Index measured as percent difference between RCP 
scenarios and No-Climate scenarios.
World 2030 2050 2070 2100
SSP1 RCP2.6 0.06% 0.32% 0.58% 0.77%
SSP2 RCP4.5 0.09% 0.49% 1.13% 1.79%
SSP3 RCP6.0 0.12% 0.43% 0.99% 2.38%
SSP4 RCP6.0 0.11% 0.37% 0.91% 2.53%
SSP5 RCP8.5 0.10% 0.70% 1.93% 4.63%
IFs RCP2.6 0.05% 0.34% 0.55% 0.60%
IFs RCP4.5 0.00% 0.52% 1.31% 1.83%
IFs RCP6.0 0.26% 0.52% 1.31% 2.87%
IFs RCP8.5 0.26% 1.05% 2.88% 6.53%
Climate Change, Economic Production, Inequality, and Poverty 27
Climate Effects on Poverty
Based on the methodology, baseline and scenario model results described above, the effect of changing 
temperature on productivity and inequality is to increase poverty at $1.90, $3.20, and $5.50 income 
thresholds for all tested scenarios through 2050 and for scenarios with less socioeconomic development 
and more significant climate change through 2100. The greatest increases in poverty for SSP scenarios 
for the population living on less than $1.90 is the combination of SSP3—a world of significant challenges 
to adaptation and mitigation—and RCP6.0, with an additional 72.3 million people pushed into poverty by 
the end of the century. This scenario is followed closely by SSP4—a world characterized by inequality—
coupled with RCP6.0, pushing an additional 32.1 million people into poverty by 2100 measured as less 
than $1.90 per day.
Figure 9: Climate-attributable global population living on less than $1.90 per day for SSPs by RCPs.
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The SSP/RCP combination from our core set that pushes the greatest number of people into poverty is 
SSP3-RCP6.0. On a regional basis, this scenario pushes an additional 4.3 million people into extreme 
poverty by 2030 in sub-Saharan Africa, the region that experiences the largest increases from 2030-
2070. Over this period and following the logic of this scenario, an additional 13.6 million people are 
pushed into poverty by 2050, 19.5 million by 2070, and 19 million by 2100. The region hit second 
hardest in terms of the number of people pushed into extreme poverty is Southern Asia, which sees an 
increase in extreme poverty through 2030 of 3.2 million people followed by 7.7 million by 2050, 15 
million by 2070, and 46.9 million by 2100. Other regions that experience growth in poverty driven by 
climate change in this scenario include the Latin America and the Caribbean which sees an increase of 
6.5 million people by 2100, Southeast Asia which sees an increase of 1.1 million by 2100, and the Middle 
East and North Africa which sees an increase of 1.6 million by 2100.
The greatest increase in extreme poverty driven by SSP3-RCP6.0 is among lower-middle income 
countries, which see an increase of 5.6 million by 2030, 15.5 million by 2050, 26.9 million by 2070, and 
59.3 million by 2100. The next largest increase occurs in low income countries, which show an increase 
Working Paper | January 2023 28
of 2.2 million people by 2030, 6.9 million by 2050, 9.7 million by 2070, and 8.7 million by 2100. Upper￾middle income countries also see an increase, though at lower levels, with the extreme poverty 
population increasing by 1.7 million by 2050 and 6.8 million by 2100. High income countries see a 
reduction of extremely poor people in this scenario.
The IFs scenario results show the greatest increase in the number of people living on less than $1.90 per 
day in RCP8.5, with an additional 37.4 million people living on less than $1.90 per day by 2050 and 60 
million by 2100. IFs-RCP6.0 leads to an additional 21.6 million pushed into poverty, while IFs-RCP4.5 
pushes an additional 12 million people into extreme poverty by 2100. IFs-RCP2.5 pushes an additional 
15.4 million into poverty by mid-century and 2.3 million into poverty by 2100.
Figure 10: Climate-attributable global population living on less than $1.90 per day for IFs by RCPs.
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At higher poverty thresholds the number of people pushed into poverty also increases. SSP3-RCP6.0 
leads to an additional 145.7 million people pushed into poverty as measured as those living on less than 
$3.20 per day. SSP4-RCP6.0 leads to an additional 88.6 million people pushed into poverty by 2100. 
SSP2-RCP4.5—a “middle-of-the-road” scenario leads to an additional 26 million people pushed under 
the $3.20 per day threshold by 2050 and an additional 15.7 million by 2100. The IFs results show 72.4 
million people living on less than $3.20 per day by 2050 in an RCP8.5 world and 46.7 million in an 
RCP4.5 world, 39.8 million in an RCP6.0 world, and 30.3 million in an RCP2.6 world. By 2100, RCP8.5 
operationalized in IFs shows over 103 million pushed into poverty followed by RCP6.0 (41.6 million), 
RCP4.5 (26.5 million) and RCP2.6 (8 million).
Climate Change, Economic Production, Inequality, and Poverty 29
Figure 11: Climate-attributable global population living on less than $3.20 per day for SSPs by RCPs.
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Figure 12: Climate-attributable global population living on less than $3.20 per day for IFs by RCPs.
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SSP3-RCP6.0 pushes an additional 219.2 million people into poverty as measured as those living on less 
than $5.50 per day by 2100 followed by SSP4-RCP6.0, which leads to an additional 160.9 million people 
under this threshold by 2100. SSP2-RCP4.5 leads to an additional 15.9 million people in extreme poverty 
by the middle of the century and 2.3 million more people living on less than $5.50 per day by 2100. The 
number of people pushed into more moderate poverty using the IFs model results grows to 80.4 million 
by 2050 in RCP8.5. This is followed by RCP4.5 (52 million), RCP6.0 (43 million) and RCP2.6 (32 million). 
By 2100 the number of people pushed into poverty measured by the under $5.50 per day income 
threshold grows to 264.4 million in RCP8.5 followed by 113.8 million in RCP6.0, 74.3 million in RCP4.5, 
and 23 million in RCP2.6.
Working Paper | January 2023 30
Figure 13: Climate-attributable global population living on less than $5.50 per day for SSPs by RCPs.
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Figure 14: Climate-attributable global population living on less than $5.50 per day for IFs by RCPs.
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While the number of impoverished people grows as we climb the poverty-income ladder, the 
proportion of the population in poverty due to climate change as a share of total poverty tells a different 
story. The graphs below show SSP3-RCP6.0 and the IFs-RCP6.0 scenarios for the population living on 
less than $1.90, $3.20, and $5.50 per day as a share of the total populations living under those income 
thresholds from 2017–2100. In the SSP-RCP combination scenarios, the percentage of the population 
living on less than $1.90 per day attributable to climate change grows from 5 percent in 2050 to 21 
percent by 2100. In the IFs simulation, that share grows from 5 percent in 2050 to over 31 percent by 
2100. The climate-attributable percent of the population living on less than $3.20 per day also grows 
across time and reaches 15 percent by 2100 in SSP3-RCP6.0 and 28 percent in the IFs 
operationalization. The climate-attributable share of the population living on less than $5.50 per day 
increases to 10 percent by 2100 in SSP3-RCP6.0 and 22 percent in the IFs simulation.
Climate Change, Economic Production, Inequality, and Poverty 31
Figure 15: Climate-attributable percent of impoverished people ($1.90, $3.20, $5.50 levels) for SSP3 
RCP6.0.
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Figure 16: Climate-attributable percent of impoverished people ($1.90, $3.20, $5.50 levels) for IFs RCP6.0.
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Effects of Tipping Points
While climate change is forecasted to impact development through economic losses and heightened 
poverty, the effect may escalate even further if climate tipping points are surpassed. The science behind 
tipping points is marked with uncertainty, however, model estimations of the economic impact of tipping 
points have been conducted (Dietz et al. 2021). In this section we present climate tipping point 
scenarios that forecasts the increased potential of climate to affect global development. 
The tipping point scenarios for SSP/RCP combinations depict a large range of impact on global GDP 
(Table 6). In the “middle-of-the-road” scenario SSP2-RCP4.5 global GDP is reduced 26.5 percent by the 
end of the century. In SSP3-RCP6.0, a world of significant challenges to adaptation and mitigation, 
Working Paper | January 2023 32
surpassing tipping points reduces global GDP 30.7 percent by the end of the century. Tipping points in 
IFs RCP scenarios show similar effects with reductions in global GDP across all scenarios by the end of 
the century. The greatest reduction in global GDP occurs in a world with high temperature change, IFs 
RCP8.5, with a 30.2 percent reduction by 2100. This scenario is followed by IFs RCP6.0 (-26.9 percent), 
IFs RCP4.5 (-26 percent), and IFs RCP2.6 (-24.9 percent). The impact of tipping points increases with 
higher temperatures highlighting the significance of mitigation on future economic development. Relative 
to climate scenarios, the effect of tipping points further reduces GDP by 25 percent on average across 
all SSP/IFs RCP combinations.
Table 6. The effect of climate tipping points on global economic growth as percent of GDP measured in 
trillions of 2011 US$ at market exchange rates.
World 2030 2050 2070 2100
SSP1 RCP2.6 -4.3% -10.6% -16.5% -25.4%
SSP2 RCP4.5 -4.5% -11.7% -17.8% -26.5%
SSP3 RCP6.0 -4.6% -12.0% -19.6% -30.7%
SSP4 RCP6.0 -4.5% -11.4% -17.9% -28.2%
SSP5 RCP8.5 -4.3% -11.0% -18.2% -29.8%
IFs RCP2.6 -0.8% -5.7% -12.9% -24.9%
IFs RCP4.5 -0.8% -6.0% -13.7% -26.0%
IFs RCP6.0 -0.8% -5.8% -13.5% -26.9%
IFs RCP8.5 -0.9% -6.4% -15.3% -30.2%
Climate tipping points are projected to have a significant impact on extreme poverty for all SSP/RCP 
combinations (Figure 17). The largest impact occurs in SSP3-RCP6.0 where 284 million people are 
pushed into extreme poverty by the end of the century. Second to this scenario is SSP4-RCP6.0 which 
sees an additional 180.6 million people pushed in extreme poverty followed by SSP2-RCP4.5 (22.1 
million), SSP1-RCP2.6 (3.5 million), and SSP5-RCP8.5 (2.6 million). These forecasts show a significant 
impact on extreme poverty if tipping points are surpassed in a world with high challenges to adaptation 
(SSP3 and SSP4). Relative to SSP/RCP climate scenarios, tipping points are forecasted to push an 
additional 3 to 211 million people into extreme poverty by the end of the century.
Climate Change, Economic Production, Inequality, and Poverty 33
Figure 17. Climate tipping points attributable global population living on less than $1.90 per day for SSPs 
by RCPs.
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A similar story is true for IFs RCP combinations with tipping points considered (Figure 18). Across all 
scenarios extreme poverty is projected to increase with a peak around mid-century followed by a 
decline out to 2100. The largest impact is seen in a world of high temperature change, IFs RCP8.5, as 84 
million people are pushed into extreme poverty by the end of the century. This scenario is followed by 
IFs RCP6.0 (49.5 million), IFs RCP4.5 (41 million), and IFs RCP2.6 (31 million). Relative to IFs RCP 
climate scenarios, tipping points are projected to push an additional 28 to 50 million people into 
extreme poverty. 
Figure 18. Climate tipping points attributable global population living on less than $1.90 per day for IFs 
RCPs.
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Working Paper | January 2023 34
Operationalized within the IFs RCP and SSP/RCP scenarios, the impact of tipping points increases across 
each poverty level (Table 7). Out of all SSP/RCP combinations, the “middle-of-the-road” scenario SSP3-
RCP6.0 shows the highest impact on poverty at the $3.20 threshold pushing 614.9 million people into 
poverty by the end of the century. This scenario is followed by SSP4-RCP6.0 (456.8 million), SSP2-
RCP4.5 (67 million), SSP1-RCP2.6 (6.7 million), and SSP5-RCP8.5 (4.6 million). Relative to SSP/RCP 
climate scenarios, tipping points are projected to further increase poverty by 4 to 469 million people. In 
the IFs tipping point scenarios, RCPs with greater temperature increases result in a higher effect on 
poverty. IFs RCP8.5 shows the greatest impact of tipping points as 260.6 million are pushed into poverty 
by the end of the century. The impact of tipping points reduces as RCPs decrease, IFs RCP6.0 pushes 
167.4 million into poverty by 2100 and is followed by IFs RCP4.5 (143.3 million) and IFs RCP2.6 (113.2
million). Relative to IFs RCP climate scenarios, tipping points are projected to further increase poverty 
by 105 to 157 million people.
The population of people living under moderate poverty ($5.50 per day) also increases with tipping 
points operationalized (Table 7). The range of impact for SSP/RCP combinations shows that surpassing 
tipping points pushes 13.5 million to over 1 billion people into poverty by the end of the century. 
Relative to SSP/RCP climate scenarios, tipping points push an additional 9 to 841 million people into 
moderate poverty. The range of impact in IFs RCP combinations with tipping points projects that 353.4 
to 669.2 million people are pushed into moderate poverty by 2100. Relative to IFs RCP climate 
scenarios, tipping points push an additional 330 to 404 million people into moderate poverty by the end 
of the century.
Table 7. The effect of climate tipping points on global poverty ($3.20 and $5.50) measured in millions as 
the difference between climate tipping point scenarios and no-climate scenarios.
Empty cell $3.20 $5.50 
Empty cell 2050 2100 2050 2100
SSP1 RCP2.6 28.4 6.7 94.1 13.5
SSP2 RCP4.5 119.1 67.0 252.1 141.3
SSP3 RCP6.0 267.4 614.9 362.9 1061.0
SSP4 RCP6.0 221.9 456.8 274.8 876.4
SSP5 RCP8.5 20.1 4.6 72.3 9.1
IFs RCP2.6 110.1 113.2 164.7 353.4
IFs RCP4.5 126.9 143.3 183.1 423.7
IFs RCP6.0 119.8 167.4 174.9 475.8
IFs RCP8.5 152.9 260.6 210.2 669.2
Tipping point scenarios have shown an even wider range of potential impact that climate could have on 
global development. Although tipping points are mostly misunderstood, our scenarios highlight the 
significance of furthering research in this area as the potential effects of tipping points could be the 
difference between eradicating extreme poverty by 2100 or perpetuating extreme forms of poverty into 
the next century. With reductions in GDP of up to 30 percent and increases in extreme poverty of up 
to 284 million people, tipping points have the potential to considerably derail progress on global 
development if they are surpassed. As more research is conducted to better understand the relationship 
of tipping points and how they interact with each other, future development analyses must consider the 
potential impact of surpassing these critical climate limits.
Climate Change, Economic Production, Inequality, and Poverty 35
5. Discussion
The model results presented here tell a complicated story about the relationship between temperature 
change, economic production, and income distribution. While the effect of climate change is indeed 
significant on the future distribution of poverty, our research shows that socioeconomic development 
will have a significant impact on poverty in addition to the impact of temperature change alone. The 
methodology used here shows that the impact of climate change on the distribution of poverty will build 
slowly over time. In the worst-case climate scenarios for the most extreme measure of poverty (under 
$1.90 per day), 72 million people are pushed into poverty relative to a world of no-climate change by 
the end of the century. This number grows to 284 million people when tipping points are accounted for. 
The long-term effect of socioeconomic change is significant in shaping future poverty thresholds as 
extremely high growth scenarios, even those coupled with extremely high temperature change (SSP5-
RCP8.5), show the elimination of extreme poverty before mid-century. 
To contextualize these findings, these scenarios show a similar level of poverty increases to research 
analyzing the effect of COVID-19 on extreme poverty in 2020–21 (Moyer et al. 2022) and are 
significantly dwarfed when compared with the effect of intrastate conflict on poverty over the next 
decade (Moyer n.d.). Contextualizing the long-term effect of climate change on poverty produces results 
that show that climate change will increase the number of people living in extreme poverty by 2100 at a 
similar level to recent estimates for increases to extreme poverty due to COVID-19. Furthermore, 
studies of the future impact of civil wars on extreme poverty show that, over the next eight years, they 
will account—even in very pessimistic scenarios—for a greater number of extremely poor than climate 
change will by the end of the century (Moyer 2023).
These results may be surprising considering the significant research focused on raising awareness of the 
importance of climate change mitigation to reduce the effects of climate change. Numerous studies 
demonstrate that dramatic changes to how energy and economic systems operate are needed to put the 
earth on a more sustainable path. The cost of these climate mitigation efforts will bring additional costs, 
including increases in the price of food and energy, that will impact the poor and most vulnerable. 
This report has significant implications for methodologies attempting to study the relationship between 
climate change and human development. First, the approach used here relies upon smoothed effects—
we start with average temperature change and damage functions that use smoothed relationships 
between average temperature and GDP reductions as a share of GDP, and then filter them through a 
large integrated assessment model focused on average annual drivers and outputs. But climate change 
has been shown to have significant effects on extreme events—effects that may be much more 
significant than their average effects. Significant heat waves may not move average annual temperature 
values very much, but they have particularly strong effects on populations subjected to temporarily 
extreme conditions. Recent flooding in Pakistan is another example of how average effects as modeled 
through general integrated assessment models may be poor at capturing tipping points and other 
threshold changing outcomes. The effect of climate change on extreme events is very broad and includes 
known events (oceanic heatwaves, heatwaves, mega storms, etc.) as well as theorized effects that are 
difficult to model (changing oceanic currents, atmospheric patterns, etc.). 
Another important implication for the study of climate change impacts on human well-being relates to 
the causal pathways by which climate change will likely affect future outcomes. The pathway explored in 
this report focuses on how average changing temperatures will impact economic production and 
Working Paper | January 2023 36
inequality. While damage functions are intended to be comprehensive in their representation of the 
effect of climate change on economic production, they are a single relationship that is, at best, a 
significant abstraction of the impacts of changing temperature on economies suited for specific purposes. 
Instead of relying upon abstract relationships, modeling efforts that explore the effect of climate change 
on human development should focus on causal pathways established to build up a broad foundation of 
knowledge about how future climate change is likely to impact human well-being.
A final implication for future studies relates to the use of income-based poverty measures of purchasing 
power parity as an adequate measure of human development or well-being. Income-based measures 
suffer from both conceptual and empirical limitations. Conceptually, income-based poverty is an 
incomplete measure of human well-being. To get a wholistic sense of how populations are faring in their 
daily lives, studies must capture a broad range of measures of health, education, safety, inclusion, and 
community. Empirically, purchasing power parity measures of income have also been criticized for what 
they miss and how they capture temporal trends. 
Climate Change, Economic Production, Inequality, and Poverty 37
6. Conclusion
This working paper has taken a logical approach to estimating the future effect of climate change on 
poverty over longer time horizons than had previously been studied. To do so, the study used scenarios, 
model relationships, and models already in existence, relying on average temperature change to estimate 
the impact of climate on the economy and inequality. The findings suggest that the effects of climate 
change on poverty may be more muted than other global challenges and that if the goal is to reduce 
poverty, high-cost policy interventions to mitigate the effects of climate change may create more harm 
than good. Our findings suggest that policy interventions should instead target socioeconomic 
development, especially in regions already experiencing high levels of poverty and inequality and sub￾Saharan Africa and South Asia. 
But while that conclusion is a worthy starting point for further research, the method used to arrive at 
this conclusion is incomplete. It smooths variability in temperature and does not account for the 
dynamic range of impact that climate change could have on future development. Further research is 
needed to arrive at a more reasonable estimate of the impact of climate change on poverty. Suggested 
areas for future investigation include the influence of extreme weather events, carbon dioxide 
fertilization, and tipping points. While our research has attempted to incorporate potential effects of 
tipping points on the economy (Dietz et al. 2021), this field of research is defined by uncertainty and 
therefore requires further research to understand how surpassing these critical limits could impact 
future development. Alternative pathways of climate change-to-poverty should be explored using tools 
that represent more climate extremes and a broader set of impacts stemming from changing 
temperature, precipitation, storm surge, and other factors intended to be included in damage functions, 
like those used in DICE. However, these factors may be too stylized and simplistic for use, a finding that 
is supported by other critiques of the damage function literature (Pindyck 2013).
Working Paper | January 2023 38
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Climate Change, Economic Production, Inequality, and Poverty 47
Appendix
Figure 19: GDP per capita for all scenarios.
Working Paper | January 2023 48
Table 8: Table for all scenarios of GDP per capita by decade.
Scenario 2017 2030 2040 2050 2060 2070 2080 2090 2100
2611SSP $13.89 $21.79 $29.79 $38.18 $47.05 $56.92 $67.50 $79.34 $92.52
2612SSP $13.81 $19.68 $23.99 $28.54 $33.80 $40.28 $47.92 $56.96 $67.54
2613SSP $13.70 $17.94 $19.52 $20.35 $20.97 $21.83 $22.78 $23.82 $25.07
2614SSP $13.78 $19.67 $23.87 $27.40 $30.66 $33.97 $37.05 $40.10 $43.23
2615SSP $13.93 $23.67 $35.08 $47.84 $62.43 $79.98 $100.60 $125.50 $155.40
261IFsU $16.19 $18.45 $20.96 $24.02 $27.85 $33.05 $39.23 $46.81 $56.02
2621SSP $16.19 $25.74 $35.79 $46.83 $58.95 $72.73 $87.77 $104.70 $123.60
2622SSP $16.19 $23.34 $28.83 $34.83 $41.95 $50.86 $61.55 $74.37 $89.51
2623SSP $16.19 $21.42 $23.55 $24.85 $25.92 $27.34 $28.94 $30.71 $32.78
2624SSP $16.19 $23.33 $28.53 $33.06 $37.32 $41.72 $45.93 $50.19 $54.63
2625SSP $16.19 $27.89 $42.13 $58.68 $78.14 $101.90 $129.90 $163.90 $204.50
4511SSP $13.89 $21.79 $29.76 $38.08 $46.81 $56.43 $66.75 $78.28 $91.21
4512SSP $13.81 $19.68 $23.97 $28.47 $33.63 $39.94 $47.38 $56.19 $66.58
4513SSP $13.70 $17.94 $19.50 $20.30 $20.86 $21.64 $22.52 $23.49 $24.71
4514SSP $13.78 $19.67 $23.84 $27.33 $30.50 $33.67 $36.62 $39.54 $42.59
4515SSP $13.93 $23.67 $35.05 $47.71 $62.11 $79.30 $99.49 $123.80 $153.10
451IFsU $16.19 $18.45 $20.93 $23.95 $27.70 $32.76 $38.79 $46.19 $55.24
4521SSP $16.19 $25.74 $35.76 $46.71 $58.66 $72.13 $86.82 $103.30 $121.90
4522SSP $16.19 $23.34 $28.80 $34.74 $41.74 $50.44 $60.88 $73.40 $88.26
4523SSP $16.19 $21.42 $23.53 $24.78 $25.79 $27.11 $28.62 $30.30 $32.32
4524SSP $16.19 $23.33 $28.51 $32.97 $37.13 $41.36 $45.41 $49.51 $53.83
4525SSP $16.19 $27.89 $42.09 $58.53 $77.75 $101.00 $128.50 $161.70 $201.70
6011SSP $13.89 $21.81 $29.80 $38.17 $46.93 $56.60 $66.78 $77.97 $90.46
6012SSP $13.81 $19.69 $24.01 $28.53 $33.71 $40.05 $47.40 $55.97 $66.02
6013SSP $13.70 $17.95 $19.53 $20.34 $20.91 $21.70 $22.52 $23.40 $24.49
6014SSP $13.78 $19.68 $23.88 $27.39 $30.57 $33.77 $36.63 $39.38 $42.21
6015SSP $13.93 $23.69 $35.10 $47.82 $62.26 $79.53 $99.53 $123.30 $151.80
601IFsU $16.19 $18.45 $20.97 $23.99 $27.76 $32.83 $38.72 $45.89 $54.63
6021SSP $16.19 $25.75 $35.81 $46.81 $58.80 $72.33 $86.86 $103.00 $120.90
6022SSP $16.19 $23.36 $28.84 $34.82 $41.84 $50.58 $60.90 $73.12 $87.55
6023SSP $16.19 $21.43 $23.57 $24.84 $25.85 $27.19 $28.63 $30.18 $32.05
6024SSP $16.19 $23.34 $28.55 $33.05 $37.22 $41.48 $45.42 $49.30 $53.36
6025SSP $16.19 $27.91 $42.15 $58.66 $77.94 $101.30 $128.60 $161.10 $200.00
8511SSP $13.89 $21.79 $29.73 $37.98 $46.50 $55.71 $65.32 $75.75 $87.00
8512SSP $13.81 $19.69 $23.98 $28.47 $33.59 $39.82 $47.03 $55.42 $65.19
8513SSP $13.70 $17.94 $19.49 $20.24 $20.72 $21.36 $22.02 $22.71 $23.54
8514SSP $13.78 $19.67 $23.82 $27.26 $30.29 $33.23 $35.81 $38.22 $40.54
8515SSP $13.93 $23.67 $35.02 $47.59 $61.69 $78.28 $97.35 $119.80 $146.00
Climate Change, Economic Production, Inequality, and Poverty 49
Scenario 2017 2030 2040 2050 2060 2070 2080 2090 2100
851IFsU $16.19 $18.44 $20.92 $23.86 $27.45 $32.25 $37.79 $44.42 $52.27
8521SSP $16.19 $25.73 $35.73 $46.59 $58.28 $71.22 $85.01 $100.10 $116.40
8522SSP $16.19 $23.35 $28.82 $34.75 $41.70 $50.30 $60.46 $72.44 $86.51
8523SSP $16.19 $21.42 $23.51 $24.72 $25.62 $26.76 $28.00 $29.32 $30.82
8524SSP $16.19 $23.32 $28.48 $32.89 $36.88 $40.83 $44.42 $47.87 $51.28
8525SSP $16.19 $27.88 $42.05 $58.38 $77.24 $99.74 $125.80 $156.60 $192.50
IFsNoClimate $16.19 $18.47 $21.02 $24.12 $27.99 $33.22 $39.41 $47.01 $56.25
SSP1 $13.92 $21.90 $29.98 $38.48 $47.44 $57.39 $68.04 $79.94 $93.23
SSP12 $16.19 $25.80 $35.93 $47.07 $59.29 $73.14 $88.24 $105.20 $124.20
SSP2 $13.85 $19.78 $24.15 $28.76 $34.08 $40.61 $48.30 $57.39 $68.06
SSP22 $16.19 $23.40 $28.94 $35.01 $42.20 $51.15 $61.89 $74.75 $89.96
SSP3 $13.74 $18.02 $19.65 $20.51 $21.14 $22.01 $22.96 $24.00 $25.27
SSP32 $16.19 $21.47 $23.65 $24.98 $26.07 $27.50 $29.10 $30.87 $32.96
SSP4 $13.81 $19.76 $24.02 $27.62 $30.92 $34.26 $37.35 $40.42 $43.58
SSP42 $16.19 $23.38 $28.65 $33.23 $37.54 $41.97 $46.19 $50.46 $54.92
SSP5 $13.97 $23.79 $35.31 $48.21 $62.95 $80.64 $101.40 $126.50 $156.60
SSP52 $16.19 $27.96 $42.30 $58.99 $78.59 $102.40 $130.60 $164.70 $205.60
Table 9: Regional table with GDP percentage difference.
Empty cell Empty 
cell
SSP1 RCP2.6 SSP2 RCP4.5 SSP3 RCP6.0 SSP4 RCP6.0 SSP5 RCP8.5
Middle East 
& Northern 
Africa
2030 -0.299% -0.274% -0.258% -0.255% -0.295%
2050 -0.629% -0.956% -0.77% -0.743% -1.15%
2070 -0.593% -1.523% -1.661% -1.433% -3.022%
2100 -0.521% -2.069% -3.347% -3.251% -7.421%
Oceania 2030 -0.205% -0.205% -0.119% -0.118% -0.204%
2050 -0.397% -0.582% -0.41% -0.407% -0.802%
2070 -0.469% -1.005% -0.926% -0.93% -2.031%
2100 -0.464% -1.459% -2.012% -2.117% -5.142%
Southeast 
Asia
2030 -0.163% -0.194% -0.167% -0.154% -0.2%
2050 -0.308% -0.56% -0.492% -0.375% -0.7%
2070 -0.35% -0.937% -0.944% -0.743% -1.711%
2100 -0.314% -1.258% -1.776% -1.777% -4.112%
Southern 
Asia 
(India+)
2030 -0.214% -0.294% -0.292% -0.293% -0.24%
2050 -0.442% -0.991% -0.774% -0.778% -0.958%
2070 -0.53% -1.47% -1.485% -1.189% -2.519%
2100 -0.486% -1.986% -3.8% -2.744% -6.319%
Latin 
America & 
Caribbean 
2030 -0.253% -0.251% -0.25% -0.212% -0.274%
2050 -0.466% -0.755% -0.704% -0.578% -0.913%
2070 -0.521% -1.22% -1.377% -1.046% -2.307%
2100 -0.485% -1.671% -2.598% -2.463% -5.846%
Working Paper | January 2023 50
Empty cell Empty 
cell
SSP1 RCP2.6 SSP2 RCP4.5 SSP3 RCP6.0 SSP4 RCP6.0 SSP5 RCP8.5
sub￾Saharan 
Africa
2030 -0.217% -0.285% -0.257% -0.259% -0.261%
2050 -0.417% -0.796% -0.699% -0.702% -0.93%
2070 -0.457% -1.243% -1.422% -1.396% -2.332%
2100 -0.433% -1.648% -2.823% -3.198% -5.714%
Figure 20: All scenarios for the Gini coefficient.
Climate Change, Economic Production, Inequality, and Poverty 51
Table 10: All scenarios for the Gini coefficient by decade.
Scenario 2017 2030 2040 2050 2060 2070 2080 2090 2100
2611SSP 0.413 0.406 0.391 0.368 0.349 0.332 0.32 0.312 0.305
2612SSP 0.421 0.428 0.426 0.421 0.412 0.404 0.398 0.393 0.389
2613SSP 0.428 0.449 0.459 0.47 0.48 0.491 0.501 0.51 0.519
2614SSP 0.43 0.459 0.474 0.483 0.488 0.488 0.483 0.478 0.473
2615SSP 0.413 0.403 0.388 0.362 0.346 0.335 0.326 0.318 0.313
261IFsU 0.379 0.38 0.381 0.382 0.383 0.384 0.385 0.385 0.385
2621SSP 0.379 0.373 0.36 0.34 0.323 0.308 0.297 0.289 0.283
2622SSP 0.379 0.385 0.384 0.38 0.373 0.366 0.36 0.356 0.353
2623SSP 0.379 0.397 0.406 0.414 0.423 0.432 0.441 0.448 0.455
2624SSP 0.379 0.404 0.417 0.425 0.43 0.429 0.425 0.42 0.415
2625SSP 0.379 0.372 0.358 0.336 0.322 0.312 0.304 0.298 0.293
4511SSP 0.413 0.406 0.391 0.369 0.35 0.335 0.324 0.316 0.309
4512SSP 0.421 0.428 0.426 0.421 0.414 0.407 0.401 0.398 0.394
4513SSP 0.428 0.449 0.46 0.471 0.482 0.494 0.506 0.516 0.525
4514SSP 0.43 0.459 0.474 0.484 0.49 0.491 0.488 0.484 0.479
4515SSP 0.413 0.403 0.388 0.363 0.348 0.337 0.328 0.322 0.316
451IFsU 0.379 0.38 0.382 0.384 0.385 0.387 0.388 0.39 0.39
4521SSP 0.379 0.374 0.36 0.34 0.324 0.31 0.3 0.293 0.286
4522SSP 0.379 0.386 0.384 0.38 0.374 0.368 0.363 0.36 0.357
4523SSP 0.379 0.397 0.406 0.415 0.425 0.435 0.444 0.453 0.46
4524SSP 0.379 0.404 0.417 0.426 0.431 0.432 0.429 0.425 0.42
4525SSP 0.379 0.372 0.359 0.336 0.323 0.314 0.307 0.301 0.296
6011SSP 0.413 0.406 0.391 0.368 0.35 0.334 0.323 0.317 0.311
6012SSP 0.421 0.428 0.426 0.421 0.414 0.406 0.401 0.399 0.397
6013SSP 0.428 0.449 0.459 0.47 0.482 0.493 0.505 0.517 0.528
6014SSP 0.43 0.459 0.474 0.484 0.489 0.49 0.488 0.485 0.482
6015SSP 0.413 0.403 0.388 0.362 0.347 0.336 0.328 0.322 0.317
601IFsU 0.379 0.381 0.382 0.383 0.385 0.387 0.389 0.392 0.394
6021SSP 0.379 0.374 0.36 0.34 0.324 0.309 0.3 0.293 0.288
6022SSP 0.379 0.386 0.384 0.38 0.374 0.367 0.363 0.361 0.359
6023SSP 0.379 0.398 0.406 0.415 0.424 0.434 0.444 0.454 0.463
6024SSP 0.379 0.404 0.417 0.426 0.431 0.431 0.429 0.426 0.423
6025SSP 0.379 0.372 0.359 0.336 0.323 0.313 0.306 0.301 0.297
8511SSP 0.413 0.406 0.391 0.37 0.352 0.338 0.329 0.325 0.322
8512SSP 0.421 0.428 0.426 0.422 0.416 0.411 0.408 0.408 0.409
8513SSP 0.428 0.449 0.46 0.472 0.485 0.499 0.514 0.528 0.542
8514SSP 0.43 0.459 0.475 0.485 0.492 0.496 0.495 0.495 0.496
8515SSP 0.413 0.403 0.388 0.364 0.349 0.34 0.333 0.329 0.327
Working Paper | January 2023 52
Scenario 2017 2030 2040 2050 2060 2070 2080 2090 2100
851IFsU 0.379 0.381 0.383 0.385 0.389 0.393 0.397 0.402 0.408
8521SSP 0.379 0.374 0.361 0.341 0.326 0.313 0.305 0.3 0.298
8522SSP 0.379 0.386 0.384 0.381 0.376 0.371 0.369 0.369 0.37
8523SSP 0.379 0.397 0.406 0.416 0.427 0.439 0.451 0.463 0.475
8524SSP 0.379 0.404 0.418 0.427 0.433 0.436 0.435 0.435 0.435
8525SSP 0.379 0.372 0.359 0.337 0.325 0.317 0.311 0.308 0.306
IFsNoClimate 0.379 0.38 0.381 0.381 0.382 0.382 0.382 0.383 0.383
SSP1 0.413 0.406 0.39 0.367 0.348 0.331 0.319 0.31 0.303
SSP12 0.379 0.373 0.36 0.339 0.321 0.306 0.295 0.287 0.281
SSP2 0.421 0.428 0.425 0.42 0.411 0.402 0.396 0.391 0.387
SSP22 0.379 0.385 0.383 0.378 0.371 0.364 0.358 0.354 0.351
SSP3 0.428 0.449 0.459 0.468 0.478 0.488 0.498 0.507 0.516
SSP32 0.379 0.397 0.405 0.413 0.421 0.43 0.438 0.445 0.452
SSP4 0.43 0.459 0.473 0.482 0.486 0.485 0.481 0.475 0.469
SSP42 0.379 0.404 0.417 0.424 0.428 0.427 0.423 0.418 0.412
SSP5 0.413 0.403 0.387 0.361 0.345 0.334 0.324 0.317 0.312
SSP52 0.379 0.371 0.358 0.335 0.321 0.311 0.303 0.297 0.292
Climate Change, Economic Production, Inequality, and Poverty 53
Figure 21: All scenarios for $1.90 headcount.
Table 11: All scenarios for $1.90 headcount by decade.
Scenario 2017 2030 2040 2050 2060 2070 2080 2090 2100
2611SSP 710.5 485.5 213.6 84.91 34.64 16.14 9.722 6.386 3.825
2612SSP 710.5 681.6 482.7 311.9 165.6 94.9 59.14 45.99 50.09
2613SSP 710.5 920.7 951.6 1,052 993.8 911.5 861 818.3 849
2614SSP 710.5 907 958.5 1,057 1,014 917.8 797.2 702.2 621.1
2615SSP 710.5 418.2 146.2 48.21 20.81 12.02 9.297 7.117 5.555
261IFsU 710.5 609.4 511 391.4 251.7 139.8 79.96 46.81 29.97
2621SSP 710.5 225.9 85.79 29.47 13.47 7.218 4.666 2.834 1.572
2622SSP 710.5 339.3 209.8 110.4 51.28 27.15 17.6 13.57 16.81
2623SSP 710.5 488.4 483.9 469 407.5 332.6 296.4 280.8 288.7
2624SSP 710.5 493.3 505.7 501.1 435.3 333.7 259.3 207.2 152.8
Working Paper | January 2023 54
Scenario 2017 2030 2040 2050 2060 2070 2080 2090 2100
2625SSP 710.5 187.5 59.7 19.78 10.48 7.187 6.125 4.893 2.956
4511SSP 710.5 486.1 215.6 86.56 35.76 16.76 9.776 6.267 3.674
4512SSP 710.5 682.4 486.6 318.2 172.9 102.7 66.27 53.54 58.99
4513SSP 710.5 921.7 957.7 1,067 1,023 959.7 925.3 896.6 933.5
4514SSP 710.5 907.9 964 1,069 1,038 956.7 843.8 755.5 673.3
4515SSP 710.5 418.8 147.6 49.08 21.17 11.97 8.907 6.609 5.007
451IFsU 710.5 610.2 515.9 399.4 264.1 152.9 90.41 55.29 35.58
4521SSP 710.5 226.3 86.63 29.91 13.66 7.207 4.464 2.593 1.421
4522SSP 710.5 339.9 211.9 112.9 53.58 29.24 19.37 15.51 19.78
4523SSP 710.5 489 488 477.9 423.8 356.9 325.8 315.9 324.5
4524SSP 710.5 493.9 509.6 509.4 450 354.3 280.7 228.6 169.3
4525SSP 710.5 187.9 60.25 19.92 10.44 6.957 5.628 4.362 2.858
6011SSP 710.5 485.5 213.5 85.46 35.36 16.62 9.761 6.252 3.646
6012SSP 710.5 681.7 482.4 313.9 170.4 100.3 65.98 55.36 63.49
6013SSP 710.5 920.9 951.2 1,056 1,012 944.6 924.1 917.3 978.2
6014SSP 710.5 907.2 958.3 1,061 1,029 945.4 843.7 771.2 701.8
6015SSP 710.5 418.3 146.1 48.52 21.05 11.99 8.873 6.507 4.811
601IFsU 710.5 610.6 511.5 396.6 261.2 151.6 92.88 59.14 40.32
6021SSP 710.5 225.9 85.8 29.6 13.58 7.219 4.444 2.548 1.379
6022SSP 710.5 339.4 209.8 111.2 52.76 28.55 19.22 15.99 21.38
6023SSP 710.5 488.4 483.7 471.9 418.2 349.3 325.2 324.6 342.9
6024SSP 710.5 493.3 505.5 503.7 444.8 348.8 281 235.1 178.9
6025SSP 710.5 187.6 59.76 19.83 10.46 7.027 5.604 4.234 2.82
8511SSP 710.5 487.2 217.7 89.07 37.61 18 10.39 6.648 3.948
8512SSP 710.5 683.4 489.2 324.6 182.1 113.3 78.25 69.15 83.71
8513SSP 710.5 923.2 963.4 1,085 1,064 1,033 1,043 1,068 1,185
8514SSP 710.5 909.3 969.1 1,085 1,071 1,016 931.9 876.5 834.5
8515SSP 710.5 419.8 149.3 50.47 21.88 12.18 8.772 6.388 4.732
851IFsU 710.5 613.2 522.3 413.3 285.2 176.8 116.7 80.02 61.67
8521SSP 710.5 227 87.51 30.57 14.04 7.38 4.466 2.547 1.452
8522SSP 710.5 340.6 213.4 115.4 56.42 32.23 22.78 20.34 29.12
8523SSP 710.5 490.3 492.1 489.3 447.2 394.9 382.3 396.5 437.1
8524SSP 710.5 495.1 513.4 520 471.2 387.4 324.7 282.8 230.2
8525SSP 710.5 188.5 60.85 20.19 10.51 6.854 5.31 4.039 2.832
IFsNoClimate 710.5 602.3 498.9 376 237.9 130.3 73.98 43.33 27.63
SSP1 710.5 475.8 205.4 80.81 33.03 15.18 9.071 5.475 2.985
SSP12 710.5 221.2 83.08 28.99 13.44 6.947 4.376 2.669 1.57
SSP2 710.5 669.3 466.9 296.3 154.5 87.53 54.23 41.87 45.41
SSP22 710.5 333 202.5 104.7 48.36 25.53 16.76 12.92 15.7
SSP3 710.5 906.1 928.2 1,013 947.1 862 811.3 770.2 800.4
Climate Change, Economic Production, Inequality, and Poverty 55
Scenario 2017 2030 2040 2050 2060 2070 2080 2090 2100
SSP32 710.5 480 469.4 448.7 384.9 310.6 276.3 262.4 270.6
SSP4 710.5 892.7 936.5 1,025 976.1 878.7 761.1 670.7 591.8
SSP42 710.5 485.4 492.2 482.5 414.9 315.6 245.3 197.1 146.7
SSP5 710.5 409.7 140.5 46.13 20.39 12.19 9.707 7.598 6.144
SSP52 710.5 183.6 58.14 19.6 10.61 7.574 6.72 5.571 3.452
Figure 22: All scenarios marginal effect of climate change on poverty $1.90 headcount measured in millions. 
Working Paper | January 2023 56
Table 12: All scenarios marginal effect of climate change on poverty at $1.90 headcount measured in 
millions.
Scenario 2017 2030 2040 2050 2060 2070 2080 2090 2100
2611SSP 0 9.7 8.2 4.1 1.61 0.96 0.651 0.911 0.84
2612SSP 0 12.3 15.8 15.6 11.1 7.37 4.91 4.12 4.68
2613SSP 0 14.6 23.4 39 46.7 49.5 49.7 48.1 48.6
2614SSP 0 14.3 22 32 37.9 39.1 36.1 31.5 29.3
2615SSP 0 8.5 5.7 2.08 0.42 -0.17 -0.41 -0.481 -0.589
261IFsU 0 7.1 12.1 15.4 13.8 9.5 5.98 3.48 2.34
2621SSP 0 4.7 2.71 0.48 0.03 0.271 0.29 0.165 0.002
2622SSP 0 6.3 7.3 5.7 2.92 1.62 0.84 0.65 1.11
2623SSP 0 8.4 14.5 20.3 22.6 22 20.1 18.4 18.1
2624SSP 0 7.9 13.5 18.6 20.4 18.1 14 10.1 6.1
2625SSP 0 3.9 1.56 0.18 -0.13 -0.387 -0.595 -0.678 -0.496
4511SSP 0 10.3 10.2 5.75 2.73 1.58 0.705 0.792 0.689
4512SSP 0 13.1 19.7 21.9 18.4 15.17 12.04 11.67 13.58
4513SSP 0 15.6 29.5 54 75.9 97.7 114 126.4 133.1
4514SSP 0 15.2 27.5 44 61.9 78 82.7 84.8 81.5
4515SSP 0 9.1 7.1 2.95 0.78 -0.22 -0.8 -0.989 -1.137
451IFsU 0 7.9 17 23.4 26.2 22.6 16.43 11.96 7.95
4521SSP 0 5.1 3.55 0.92 0.22 0.26 0.088 -0.076 -0.149
4522SSP 0 6.9 9.4 8.2 5.22 3.71 2.61 2.59 4.08
4523SSP 0 9 18.6 29.2 38.9 46.3 49.5 53.5 53.9
4524SSP 0 8.5 17.4 26.9 35.1 38.7 35.4 31.5 22.6
4525SSP 0 4.3 2.11 0.32 -0.17 -0.617 -1.092 -1.209 -0.594
6011SSP 0 9.7 8.1 4.65 2.33 1.44 0.69 0.777 0.661
6012SSP 0 12.4 15.5 17.6 15.9 12.77 11.75 13.49 18.08
6013SSP 0 14.8 23 43 64.9 82.6 112.8 147.1 177.8
6014SSP 0 14.5 21.8 36 52.9 66.7 82.6 100.5 110
6015SSP 0 8.6 5.6 2.39 0.66 -0.2 -0.834 -1.091 -1.333
601IFsU 0 8.3 12.6 20.6 23.3 21.3 18.9 15.81 12.69
6021SSP 0 4.7 2.72 0.61 0.14 0.272 0.068 -0.121 -0.191
6022SSP 0 6.4 7.3 6.5 4.4 3.02 2.46 3.07 5.68
6023SSP 0 8.4 14.3 23.2 33.3 38.7 48.9 62.2 72.3
6024SSP 0 7.9 13.3 21.2 29.9 33.2 35.7 38 32.2
6025SSP 0 4 1.62 0.23 -0.15 -0.547 -1.116 -1.337 -0.632
8511SSP 0 11.4 12.3 8.26 4.58 2.82 1.319 1.173 0.963
8512SSP 0 14.1 22.3 28.3 27.6 25.77 24.02 27.28 38.3
8513SSP 0 17.1 35.2 72 116.9 171 231.7 297.8 384.6
8514SSP 0 16.6 32.6 60 94.9 137.3 170.8 205.8 242.7
Climate Change, Economic Production, Inequality, and Poverty 57
Scenario 2017 2030 2040 2050 2060 2070 2080 2090 2100
8515SSP 0 10.1 8.8 4.34 1.49 -0.01 -0.935 -1.21 -1.412
851IFsU 0 10.9 23.4 37.3 47.3 46.5 42.72 36.69 34.04
8521SSP 0 5.8 4.43 1.58 0.6 0.433 0.09 -0.122 -0.118
8522SSP 0 7.6 10.9 10.7 8.06 6.7 6.02 7.42 13.42
8523SSP 0 10.3 22.7 40.6 62.3 84.3 106 134.1 166.5
8524SSP 0 9.7 21.2 37.5 56.3 71.8 79.4 85.7 83.5
8525SSP 0 4.9 2.71 0.59 -0.1 -0.72 -1.41 -1.532 -0.62
Table 13: The cumulative effect of climate on economic growth by region. Measured in trillions of 2011 
US$ at market exchange rates between RCP scenario and No-Climate scenario.
Empty cell Empty 
cell
SSP1 RCP2.6 SSP2 RCP4.5 SSP3 RCP6.0 SSP4 RCP6.0 SSP5 RCP8.5
Middle 
East & 
Northern 
Africa
2030 -$0.16 -$0.15 -$0.12 -$0.13 -$0.16
2050 -$1.72 -$1.97 -$1.47 -$1.59 -$3.06
2070 -$5.42 -$8.77 -$6.27 -$7.37 -$20.88
2100 -$13.25 -$36.90 -$31.53 -$39.64 -$143.00
Oceania 2030 -$0.03 -$0.03 -$0.02 -$0.02 -$0.03
2050 -$0.22 -$0.25 -$0.14 -$0.18 -$0.42
2070 -$0.66 -$0.90 -$0.50 -$0.79 -$2.51
2100 -$1.59 -$3.30 -$1.81 -$3.53 -$18.08
Southeast 
Asia
2030 -$0.07 -$0.08 -$0.06 -$0.06 -$0.09
2050 -$1.00 -$1.21 -$0.81 -$0.76 -$2.11
2070 -$3.73 -$5.74 -$3.49 -$3.50 -$15.35
2100 -$9.94 -$24.10 -$15.39 -$17.22 -$101.20
Southern 
Asia 
(India +)
2030 -$0.13 -$0.14 -$0.13 -$0.13 -$0.13
2050 -$2.17 -$3.15 -$1.65 -$2.10 -$4.57
2070 -$9.92 -$16.85 -$7.64 -$10.29 -$42.67
2100 -$31.62 -$83.68 -$46.50 -$50.00 -$345.40
Latin 
America 
& 
Caribbean 
2030 -$0.17 -$0.16 -$0.14 -$0.13 -$0.17
2050 -$1.57 -$1.83 -$1.37 -$1.32 -$2.76
2070 -$4.90 -$7.26 -$5.40 -$5.22 -$16.71
2100 -$12.18 -$28.15 -$22.74 -$23.80 -$102.70
sub￾Saharan 
Africa
2030 -$0.06 -$0.07 -$0.05 -$0.05 -$0.07
2050 -$1.04 -$1.32 -$0.73 -$0.68 -$2.40
2070 -$6.01 -$8.76 -$4.38 -$3.49 -$28.46
2100 -$26.02 -$62.44 -$34.37 -$24.06 -$332.00
Working Paper | January 2023 58
Table 14: The cumulative effect of climate on economic growth by World Bank income group. Measured 
in trillions of 2011 US$ at the difference in market exchange rates between RCP scenario and No-Climate 
scenario.
Empty cell Empty cell SSP1 RCP2.6 SSP2 RCP4.5 SSP3 RCP6.0 SSP4 RCP6.0 SSP5 RCP8.5
Low 
Income
2030 -$0.02 -$0.02 -$0.02 -$0.02 -$0.02
2050 -$0.45 -$0.45 -$0.25 -$0.21 -$0.95
2070 -$2.95 -$3.65 -$1.63 -$1.14 -$12.97
2100 -$13.81 -$29.08 -$14.39 -$8.00 -$163.10
Low￾Middle 
Income
2030 -$0.21 -$0.23 -$0.20 -$0.20 -$0.23
2050 -$3.65 -$4.99 -$2.68 -$3.17 -$7.49
2070 -$16.48 -$27.22 -$13.50 -$15.83 -$70.37
2100 -$53.85 -$141.60 -$85.72 -$83.98 -$605.60
Upper￾Middle 
Income
2030 -$0.88 -$0.80 -$0.64 -$0.63 -$0.88
2050 -$10.76 -$10.50 -$6.94 -$7.63 -$18.59
2070 -$32.57 -$41.73 -$25.28 -$30.08 -$105.20
2100 -$68.73 -$141.50 -$101.90 -$125.50 -$541.80
High 
Income
2030 -$1.11 -$1.02 -$0.73 -$0.76 -$1.14
2050 -$8.55 -$10.33 -$5.99 -$7.17 -$15.32
2070 -$23.17 -$36.49 -$20.96 -$28.78 -$83.75
2100 -$51.23 -$117.80 -$74.48 -$125.70 -$543.00
Table 15: The effect of climate on the regional Gini Index. Measures the absolute difference between RCP 
scenarios and No-Climate scenarios.
Empty cell Empty 
cell
SSP1 RCP2.6 SSP2 RCP4.5 SSP3 RCP6.0 SSP4 RCP6.0 SSP5 RCP8.5
Middle 
East & 
Northern 
Africa
2030 0.001 0.001 0.001 0.001 0.001
2050 0.003 0.004 0.003 0.003 0.005
2070 0.004 0.007 0.006 0.007 0.013
2100 0.003 0.009 0.012 0.014 0.027
Oceania 2030 0.002 0.002 0.001 0.001 0.002
2050 0.003 0.004 0.003 0.003 0.006
2070 0.004 0.007 0.007 0.007 0.012
2100 0.004 0.01 0.013 0.013 0.025
Southeast 
Asia
2030 0.003 0.002 0.002 0.002 0.003
2050 0.005 0.006 0.005 0.005 0.008
2070 0.006 0.01 0.008 0.008 0.017
2100 0.007 0.013 0.016 0.016 0.032
Southern 
Asia (India 
+)
2030 0.003 0.002 0.002 0.002 0.003
2050 0.005 0.007 0.005 0.005 0.01
2070 0.006 0.011 0.009 0.008 0.02
2100 0.006 0.014 0.017 0.016 0.039
2030 0.002 0.002 0.002 0.002 0.003
Climate Change, Economic Production, Inequality, and Poverty 59
Empty cell Empty 
cell
SSP1 RCP2.6 SSP2 RCP4.5 SSP3 RCP6.0 SSP4 RCP6.0 SSP5 RCP8.5
Latin 
America & 
Caribbean 
2050 0.005 0.006 0.005 0.005 0.008
2070 0.005 0.01 0.008 0.008 0.016
2100 0.005 0.012 0.016 0.016 0.033
sub￾Saharan 
Africa
2030 0.002 0.003 0.002 0.002 0.003
2050 0.005 0.007 0.006 0.006 0.009
2070 0.006 0.011 0.01 0.01 0.019
2100 0.007 0.014 0.019 0.019 0.037
Table 16: The effect of climate on the Gini Index of World Bank income groups. Measures the absolute 
difference between RCP scenarios and No-Climate scenarios.
Empty cell Empty 
cell
SSP1 RCP2.6 SSP2 RCP4.5 SSP3 RCP6.0 SSP4 RCP6.0 SSP5 RCP8.5
Low 
Income
2030 0.002 0.002 0.002 0.002 0.002
2050 0.004 0.006 0.005 0.005 0.008
2070 0.006 0.01 0.008 0.008 0.017
2100 0.008 0.012 0.016 0.016 0.034
Low￾Middle 
Income
2030 0.002 0.002 0.002 0.002 0.003
2050 0.005 0.007 0.005 0.005 0.009
2070 0.006 0.011 0.009 0.009 0.019
2100 0.006 0.014 0.018 0.017 0.037
Upper￾Middle 
Income
2030 -0.0014 -0.0012 -0.001 -0.001 -0.001
2050 -0.0025 -0.0026 -0.0018 -0.002 -0.003
2070 -0.0022 -0.003 -0.0019 -0.0022 -0.004
2100 -0.0018 -0.0024 -0.0012 -0.001 -0.001
High 
Income
2030 -0.002 -0.002 -0.002 -0.002 -0.003
2050 -0.004 -0.005 -0.004 -0.004 -0.006
2070 -0.005 -0.008 -0.006 -0.007 -0.011
2100 -0.005 -0.009 -0.008 -0.009 -0.017
Table 17: Marginal effect of climate change on poverty $3.20 headcount measured in millions by decade.
Empty cell Empty 
cell
IFs RCP4.5 SSP1 
RCP2.6
SSP2 
RCP4.5
SSP3 
RCP6.0
SSP4 
RCP6.0
SSP5 
RCP8.5
Middle 
East and 
Northern 
Africa
2030 0.553 0.398 0.478 0.455 0.382 0.389
2070 2.685 0.057 0.52 2.543 1.948 0.057
2100 1.272 0 0.016 4.553 3.798 0
Oceania 2030 0.044 0.035 0.042 0.039 0.039 0.038
2070 0.156 0.005 0.03 0.144 0.159 0.005
2100 0.06 0.001 0.011 0.135 0.225 0.001
Southeast 
Asia
2030 1.476 0.886 1.057 1.1 0.987 0.728
2070 4.05 0.52 0.181 3.641 2.995 0.126
2100 1.622 0.05 0.008 5.153 5.95 0.004
Working Paper | January 2023 60
Empty cell Empty 
cell
IFs RCP4.5 SSP1 
RCP2.6
SSP2 
RCP4.5
SSP3 
RCP6.0
SSP4 
RCP6.0
SSP5 
RCP8.5
Southern 
Asia 
(India +)
2030 7.49 5.438 7.116 7.677 6.978 5.744
2070 13.09 0.036 11.04 34.64 17.88 0.206
2100 1.564 0 15.69 80.78 16.78 0.003
Latin 
America 
and 
Caribbea
n 
2030 1.25 0.906 1.071 1.181 1.065 0.924
2070 5.381 0.05 1.188 5.86 2.544 0.034
2100 3.769 0.001 0.476 11.36 2.915 0.003
sub￾Saharan 
Africa
2030 2.261 4.144 4.635 3.759 3.633 5.304
2070 34.08 0.889 6.012 36 40.02 1.108
2100 18.37 1.068 0.976 47.88 65.99 0.219
World 2030 11.62 11.17 13.58 13.36 12.29 12.48
2070 58.91 1.009 17.68 80.36 62.42 0.141
2100 26.49 0.729 15.74 145.7 88.65 -0.657
Table 18: The effect of climate on regional population living on less than $1.90 per day measured in 
millions. Measures the absolute difference between RCP scenarios and No-Climate scenarios.
Empty cell Empty 
cell
SSP1 RCP2.6 SSP2 RCP4.5 SSP3 RCP6.0 SSP4 RCP6.0 SSP5 RCP8.5
Middle 
East & 
Northern 
Africa
2030 0.172 0.221 0.22 0.206 0.188
2050 0.071 0.319 0.612 0.566 0.055
2070 0.003 0.057 1.403 1.239 0.002
2100 0 0 1.591 0.752 0
Oceania 2030 0.022 0.026 0.026 0.027 0.02
2050 0.007 0.027 0.058 0.067 0.006
2070 0.002 0.014 0.066 0.074 0.003
2100 0 0.007 0.059 0.091 0
Southeast 
Asia
2030 0.404 0.249 0.343 0.337 0.116
2050 0.263 0.118 0.672 0.764 0.039
2070 0.255 0.049 0.967 1.327 0.049
2100 0.005 0.001 1.125 2.409 0
Southern 
Asia (India 
+)
2030 0.912 1.942 3.194 2.814 0.957
2050 0.004 1.746 7.703 5.342 0.044
2070 0 1.976 15.04 5.682 0.003
2100 0 4.803 46.93 5.154 0
Latin 
America & 
Caribbean 
2030 0.446 0.567 0.68 0.625 0.448
2050 0.123 0.682 1.659 1.11 0.093
2070 0.011 0.493 3.291 1.4 0.006
2100 0 0.171 6.498 1.472 0
2030 3.184 4.403 4.312 4.309 3.661
2050 0.526 6.16 13.59 14.65 1.106
Climate Change, Economic Production, Inequality, and Poverty 61
Empty cell Empty 
cell
SSP1 RCP2.6 SSP2 RCP4.5 SSP3 RCP6.0 SSP4 RCP6.0 SSP5 RCP8.5
sub￾Saharan 
Africa
2070 0.415 2.117 19.52 26.04 0.325
2100 0.291 0.239 19.04 28.65 0.043
Table 19: The effect of climate on the population of World Bank income groups living on less than $1.90 
per day measured in millions. Measures the absolute difference between RCP scenarios and No-Climate 
scenarios.
Empty cell Empty 
cell
SSP1 RCP2.6 SSP2 RCP4.5 SSP3 RCP6.0 SSP4 RCP6.0 SSP5 RCP8.5
Low 
Income
2030 1.931 2.319 2.287 2.286 1.903
2050 0.13 3.057 6.963 7.49 0.46
2070 0.567 0.832 9.738 12.71 0.214
2100 0.289 0.054 8.702 12.75 0.033
Low￾Middle 
Income
2030 2.695 4.416 5.687 5.311 2.959
2050 0.671 5.178 15.51 13.88 0.672
2070 0.07 3.203 26.96 21.66 0.078
2100 0.001 4.894 59.37 24.38 0
Upper￾Middle 
Income
2030 0.484 0.624 0.729 0.661 0.5
2050 0.185 0.783 1.727 1.081 0.202
2070 0.047 0.644 3.436 1.323 0.091
2100 0.006 0.254 6.871 1.324 0.01
High 
Income
2030 -0.397 -0.411 -0.333 -0.353 -0.423
2050 -0.508 -0.884 -0.925 -1.19 -0.739
2070 -0.412 -0.969 -1.454 -2.53 -1.104
2100 -0.294 -1.126 -2.627 -6.323 -0.663
Table 20. The effect of climate tipping points on the global population living at each poverty threshold 
($1.90, $3.20, $5.50). Measures the absolute difference between RCP Tipping Point scenarios and No￾Climate scenarios in millions. 
Empty cell Empty cell $1.90 $3.20 $5.50 
SSP1 RCP2.6
2030 24.32 61.29 101.10
2050 9.11 28.42 94.07
2070 4.95 9.62 26.00
2100 3.54 6.72 13.45
SSP2 RCP4.5
2030 36.26 82.46 116.60
2050 39.71 119.10 252.10
2070 20.33 73.29 195.40
2100 22.08 67.02 141.30
SSP3 RCP6.0
2030 47.44 93.94 117.00
2050 129.30 267.40 362.90
2070 184.60 425.70 667.20
2100 284.00 614.90 1061.00
Working Paper | January 2023 62
Empty cell Empty cell $1.90 $3.20 $5.50 
SSP4 RCP6.0
2030 45.60 86.24 107.30
2050 123.90 221.90 274.80
2070 175.00 359.90 508.90
2100 180.60 456.80 876.40
SSP5 RCP8.5
2030 21.98 58.01 99.54
2050 6.56 20.13 72.27
2070 3.51 6.83 20.40
2100 2.66 4.61 9.13
IFs RCP2.6
2030 11.01 18.46 12.72
2050 50.24 110.10 164.70
2070 52.29 147.30 319.50
2100 31.15 113.20 353.40
IFs RCP4.5
2030 11.82 19.73 13.29
2050 58.82 126.90 183.10
2070 68.14 185.70 382.30
2100 41.11 143.30 423.70
IFs RCP6.0
2030 12.26 20.25 14.27
2050 55.73 119.80 174.90
2070 66.47 179.10 368.00
2100 49.48 167.40 475.80
IFs RCP8.5
2030 14.88 23.79 14.66
2050 73.30 152.90 210.20
2070 96.96 251.60 485.60
2100 84.47 260.60 669.20
Table 21: Marginal effect of climate change on poverty $5.50 headcount measured in millions. 
Empty cell Empty 
cell
IFs 
RCP4.5
SSP1 
RCP2.6
SSP2 
RCP4.5
SSP3 
RCP6.0
SSP4 
RCP6.0
SSP5 
RCP8.5
Middle East 
and Northern 
Africa
2030 0.637 0.864 0.886 0.732 0.696 0.833
2070 5.925 0.282 1.562 5.199 1.677 0.429
2100 4.522 0.01 0.232 10.18 5.749 0.007
Oceania 2030 0.029 0.035 0.04 0.033 0.032 0.043
2070 0.253 0.008 0.076 0.235 0.23 0.007
2100 0.183 0.002 0.012 0.3 0.442 0.001
Southeast Asia 2030 1.613 1.526 1.867 1.62 1.465 1.735
2070 9.097 0.549 1.033 7.158 3.77 0.199
2100 6.015 0.279 0.045 12.28 7.855 0.024
Southern Asia 
(India +)
2030 3.271 7.561 7.397 5.917 6.033 8.836
2070 43.06 0.863 26.57 43.09 31.74 2.852
2100 14.6 0.01 29.77 97.04 43.95 0.107
Climate Change, Economic Production, Inequality, and Poverty 63
Empty cell Empty 
cell
IFs 
RCP4.5
SSP1 
RCP2.6
SSP2 
RCP4.5
SSP3 
RCP6.0
SSP4 
RCP6.0
SSP5 
RCP8.5
Latin America 
and Caribbean 
2030 1.711 1.721 1.837 1.822 1.645 1.829
2070 9.133 0.273 3.029 9.9 4.302 0.26
2100 8.176 0.008 1.476 19.88 5.191 0.021
sub-Saharan 
Africa
2030 -0.012 2.573 2.284 1.41 1.293 3.834
2070 41.38 1.812 15.47 40.57 34.75 3.123
2100 41.23 2.597 2.951 88.65 105.6 0.844
World 2030 2.451 12.56 11.8 8.936 8.858 15.66
2070 106.7 3.031 45.84 99.9 72.09 5.05
2100 74.3 2.38 32.59 219.2 160.9 -0.065
Table 22: Base case GDP per capita measured in thousands of 2011 US$.
Country 2017 2030 2040 2050 2060 2070 2080 2090 2100
Afghanistan $2.06 $2.40 $2.99 $3.97 $5.53 $7.84 $10.83 $14.17 $17.72
Albania $12.77 $15.73 $18.87 $23.91 $30.18 $37.72 $51.06 $70.40 $89.44
Algeria $11.74 $11.29 $11.79 $12.52 $13.79 $14.82 $15.50 $16.64 $18.78
Angola $7.31 $6.85 $8.23 $10.26 $12.67 $15.76 $19.78 $24.94 $30.04
Argentina $23.60 $23.66 $25.78 $28.08 $30.40 $32.89 $35.86 $39.87 $45.03
Armenia $12.12 $15.33 $19.64 $24.70 $29.54 $37.70 $49.46 $63.76 $81.33
Australia $48.40 $56.21 $64.53 $74.19 $84.23 $96.93 $110.9 $126.8 $144.1
Austria $54.17 $56.70 $59.38 $64.38 $71.23 $81.50 $92.96 $104.4 $117.1
Azerbaijan $14.12 $16.04 $19.68 $23.11 $26.45 $31.22 $36.06 $44.59 $57.88
Bahamas $35.91 $35.16 $36.08 $39.16 $43.55 $49.19 $58.16 $70.85 $86.17
Bahrain $47.71 $44.37 $44.32 $42.54 $41.76 $45.78 $51.83 $58.62 $67.56
Bangladesh $4.90 $7.37 $9.63 $12.36 $15.38 $18.80 $23.21 $29.20 $36.03
Barbados $15.80 $17.28 $19.74 $23.69 $30.35 $42.20 $59.04 $80.64 $105.4
Belarus $18.36 $18.74 $21.00 $23.76 $27.93 $34.37 $43.11 $56.59 $73.81
Belgium $50.44 $56.74 $63.09 $71.46 $80.76 $91.59 $102.9 $116.0 $129.9
Belize $7.14 $7.38 $8.96 $11.35 $14.47 $18.71 $25.20 $36.15 $52.88
Benin $3.05 $4.18 $5.75 $8.43 $12.57 $18.80 $27.34 $39.20 $53.71
Bhutan $11.14 $11.19 $11.84 $12.78 $14.02 $16.12 $19.34 $25.35 $35.92
Bolivia $8.42 $9.13 $10.95 $13.56 $16.82 $21.19 $27.92 $38.61 $52.95
Bosnia and Herzegovina $13.75 $17.69 $23.44 $30.47 $40.69 $54.76 $73.08 $94.19 $116.9
Botswana $15.95 $18.24 $22.66 $28.84 $37.72 $50.43 $66.26 $83.67 $102.8
Brazil $14.52 $15.75 $17.53 $19.51 $21.41 $23.30 $25.57 $28.92 $33.49
Brunei Darussalam $60.99 $58.31 $55.55 $52.84 $50.20 $49.37 $49.68 $51.00 $53.52
Bulgaria $21.47 $26.28 $29.48 $32.01 $35.40 $40.43 $47.40 $54.26 $63.37
Burkina Faso $2.04 $2.73 $3.67 $5.14 $7.41 $10.55 $14.25 $18.34 $22.72
Burundi $0.77 $0.88 $1.06 $1.40 $2.06 $3.23 $5.11 $7.95 $11.56
Cabo Verde $6.64 $7.61 $9.03 $10.89 $13.24 $16.33 $20.65 $27.64 $38.97
Working Paper | January 2023 64
Country 2017 2030 2040 2050 2060 2070 2080 2090 2100
Cambodia $3.93 $5.36 $7.38 $10.34 $14.10 $18.78 $24.92 $33.13 $43.41
Cameroon $3.67 $4.21 $4.95 $6.11 $7.77 $9.98 $12.59 $15.50 $18.61
Canada $48.32 $52.49 $57.92 $65.24 $72.83 $81.17 $90.26 $101.7 $114.3
Central African Republic $0.91 $1.19 $1.49 $2.02 $3.02 $4.60 $6.98 $10.38 $14.67
Chad $1.59 $1.77 $2.25 $3.11 $4.67 $7.13 $10.48 $14.82 $19.73
Chile $24.41 $26.75 $29.19 $32.06 $35.21 $41.46 $49.33 $58.92 $70.25
China $14.24 $21.23 $28.45 $36.77 $46.62 $60.76 $76.67 $93.16 $110.8
Colombia $14.17 $16.06 $17.72 $19.52 $21.48 $23.82 $26.82 $31.56 $38.13
Comoros $3.03 $3.38 $3.98 $5.03 $6.60 $8.82 $11.69 $15.28 $19.73
Congo $4.24 $4.48 $6.15 $8.58 $12.41 $18.50 $26.71 $37.93 $53.24
Congo, Dem. Republic of 
the $1.06 $1.60 $2.67 $4.72 $8.35 $13.52 $18.61 $23.36 $27.63
Costa Rica $20.35 $23.74 $28.52 $35.19 $43.30 $53.38 $65.95 $81.65 $98.40
Cote D'Ivoire $4.83 $6.61 $8.62 $11.62 $15.72 $21.03 $27.77 $36.55 $46.21
Croatia $27.15 $34.21 $39.72 $47.09 $56.69 $68.75 $81.63 $95.53 $110.4
Cuba $21.60 $19.92 $21.35 $23.97 $27.11 $31.49 $37.96 $48.66 $63.22
Cyprus $38.29 $42.21 $45.98 $52.04 $59.89 $73.79 $89.32 $105.0 $121.5
Czech Republic $33.11 $35.67 $37.54 $39.44 $44.55 $52.19 $59.71 $68.10 $79.51
Denmark $55.36 $66.21 $76.91 $92.12 $108.5 $127.2 $147.2 $168.2 $190.5
Djibouti $4.91 $6.56 $8.81 $11.33 $14.02 $16.33 $18.45 $21.56 $25.30
Dominican Republic $16.74 $21.76 $27.22 $33.48 $41.67 $50.33 $59.25 $70.36 $83.45
Ecuador $11.62 $11.51 $12.55 $13.76 $14.84 $16.21 $18.13 $20.97 $25.59
Egypt $11.01 $13.28 $16.03 $19.44 $23.50 $27.97 $32.19 $36.91 $43.99
El Salvador $8.45 $9.89 $11.99 $14.23 $16.27 $18.27 $21.11 $25.67 $32.60
Equatorial Guinea $22.55 $14.94 $17.65 $21.81 $26.03 $30.70 $35.07 $40.89 $47.26
Eritrea $1.11 $1.69 $2.91 $5.93 $12.71 $21.41 $31.61 $44.89 $59.38
Estonia $33.82 $42.42 $56.19 $70.43 $86.01 $104.6 $121.0 $138.2 $157.7
Eswatini $8.41 $10.46 $14.15 $19.61 $27.98 $39.78 $51.23 $64.31 $81.18
Ethiopia $2.02 $2.98 $4.35 $6.67 $10.05 $14.60 $19.79 $25.23 $29.59
Fiji $13.43 $14.54 $18.13 $23.17 $29.88 $39.47 $50.05 $64.11 $81.34
Finland $47.57 $52.10 $56.50 $63.09 $71.67 $82.94 $96.22 $113.0 $131.3
France $44.58 $47.82 $50.17 $54.28 $61.22 $70.19 $80.38 $92.74 $107.3
Gabon $15.01 $15.02 $16.54 $18.76 $22.35 $28.10 $35.54 $44.57 $54.42
Gambia $2.07 $2.73 $3.48 $4.89 $6.85 $9.61 $12.86 $16.54 $20.84
Georgia $13.59 $18.14 $22.50 $27.17 $32.96 $41.64 $53.66 $66.39 $81.46
Germany $53.07 $54.17 $58.29 $64.83 $72.69 $84.04 $97.44 $112.0 $128.9
Ghana $5.12 $6.45 $8.64 $12.06 $17.23 $25.00 $35.18 $47.31 $59.59
Greece $28.60 $32.40 $35.02 $39.46 $48.08 $58.60 $70.72 $84.75 $100.4
Grenada $16.22 $17.97 $22.48 $27.80 $33.59 $42.97 $54.56 $71.51 $93.79
Guatemala $8.32 $9.85 $11.77 $14.10 $16.35 $18.62 $21.24 $24.45 $28.61
Climate Change, Economic Production, Inequality, and Poverty 65
Country 2017 2030 2040 2050 2060 2070 2080 2090 2100
Guinea $2.42 $3.04 $3.68 $4.68 $5.99 $7.64 $9.61 $11.81 $13.96
Guinea Bissau $1.93 $2.21 $2.71 $3.58 $5.08 $7.69 $11.42 $16.19 $23.58
Guyana $12.01 $75.11 $96.70 $108.1 $117.5 $126.6 $135.8 $145.7 $155.7
Haiti $3.15 $3.09 $3.55 $4.34 $5.54 $7.24 $9.51 $12.38 $15.50
Honduras $5.56 $6.54 $8.13 $10.29 $12.99 $16.35 $21.01 $28.63 $40.41
Hong Kong $59.84 $71.35 $89.02 $101.9 $114.6 $130.9 $144.0 $154.8 $168.9
Hungary $29.50 $36.91 $42.07 $49.13 $57.61 $68.46 $78.58 $87.40 $97.30
Iceland $55.64 $72.67 $89.70 $108.7 $127.8 $149.0 $170.0 $193.2 $218.1
India $6.18 $8.36 $11.00 $14.29 $18.09 $22.67 $28.93 $38.04 $50.18
Indonesia $10.94 $12.97 $14.38 $15.86 $17.67 $20.06 $22.91 $26.21 $31.00
Iran $15.88 $16.15 $17.60 $17.92 $18.53 $20.63 $23.26 $26.48 $32.67
Iraq $10.53 $11.82 $14.82 $18.93 $23.76 $29.01 $36.19 $47.06 $61.70
Ireland $77.75
$134.7
0
$156.3
0
$173.7
0
$190.9
0
$204.2
0
$214.5
0
$223.9
0
$230.6
0
Israel $39.12 $52.97 $66.53 $82.29 $99.89 $118.2 $135.4 $154.5 $176.6
Italy $41.58 $44.17 $47.10 $52.47 $60.88 $72.24 $84.54 $98.71 $114.1
Jamaica $9.60 $9.80 $10.79 $12.29 $14.04 $16.44 $20.36 $26.44 $35.32
Japan $41.44 $45.56 $48.06 $51.72 $57.57 $66.88 $78.58 $92.27 $108.8
Jordan $10.00 $10.93 $11.93 $13.05 $14.62 $16.51 $18.31 $20.94 $24.88
Kazakhstan $24.86 $28.88 $35.01 $40.79 $46.50 $55.57 $61.92 $68.78 $76.82
Kenya $4.20 $5.44 $6.95 $9.32 $12.81 $16.95 $21.59 $28.24 $36.37
Korea, Dem. People's 
Republic $2.17 $2.34 $2.62 $3.22 $4.14 $5.58 $7.97 $11.85 $18.02
Korea, Republic of $40.96 $51.77 $61.19 $74.96 $91.35 $110.6 $131.9 $155.4 $181.0
Kosovo $10.44 $12.92 $15.89 $19.65 $24.49 $31.50 $42.32 $56.98 $75.64
Kuwait $50.86 $45.63 $41.03 $36.16 $33.00 $30.88 $28.36 $26.21 $25.53
Kyrgyzstan $5.05 $5.19 $5.95 $7.05 $8.47 $10.46 $12.85 $15.85 $20.10
Lao People's Dem. 
Republic $7.26 $9.41 $13.53 $19.57 $28.36 $39.71 $55.28 $74.09 $94.95
Latvia $28.67 $36.38 $47.11 $63.90 $84.02 $107.8 $128.9 $148.1 $168.9
Lebanon $15.95 $9.31 $9.58 $11.59 $14.91 $17.62 $20.17 $23.02 $27.11
Lesotho $2.67 $2.88 $3.53 $4.55 $6.08 $8.18 $10.73 $13.71 $17.47
Liberia $1.56 $1.72 $2.12 $2.98 $4.58 $7.22 $10.69 $14.83 $19.20
Libya $23.47 $21.50 $22.34 $23.90 $26.67 $29.79 $33.59 $41.21 $53.74
Lithuania $33.76 $45.22 $60.44 $79.13 $97.27 $117.0 $133.3 $147.8 $163.1
Luxembourg
$115.0
0
$117.6
0
$116.4
0
$116.7
0
$118.4
0
$121.6
0
$122.5
0
$122.8
0
$122.1
0
Macedonia, North $15.71 $18.07 $21.36 $25.53 $30.79 $38.58 $51.48 $68.49 $87.46
Madagascar $1.58 $1.83 $2.40 $3.33 $4.81 $6.74 $8.65 $10.75 $12.79
Malawi $1.47 $1.77 $2.35 $3.37 $5.13 $7.70 $10.77 $14.34 $18.12
Malaysia $26.66 $29.98 $33.74 $37.51 $41.49 $48.29 $58.27 $70.46 $83.92
Maldives $18.06 $18.93 $20.09 $20.53 $20.53 $22.36 $25.12 $28.92 $34.91
Working Paper | January 2023 66
Country 2017 2030 2040 2050 2060 2070 2080 2090 2100
Mali $2.25 $2.55 $2.99 $3.79 $5.22 $7.42 $10.29 $13.86 $17.89
Malta $43.56 $64.32 $84.75 $98.35 $108.7 $124.8 $138.1 $152.2 $169.0
Mauritania $5.11 $5.56 $6.05 $6.81 $8.23 $10.35 $13.20 $16.60 $21.13
Mauritius $21.42 $22.98 $25.08 $28.92 $34.13 $41.45 $54.63 $71.86 $89.98
Mexico $19.72 $18.88 $19.66 $20.74 $22.00 $23.47 $25.21 $27.48 $31.18
Micronesia $3.49 $3.65 $4.62 $6.23 $8.40 $11.37 $15.81 $23.05 $34.19
Moldova, Republic of $11.65 $14.46 $17.95 $21.37 $25.10 $30.27 $35.65 $44.15 $57.88
Mongolia $11.37 $13.95 $18.13 $23.37 $29.45 $39.38 $53.53 $72.27 $92.27
Montenegro $19.68 $22.70 $26.46 $31.77 $38.94 $52.11 $69.59 $90.45 $113.5
Morocco $7.31 $8.00 $9.07 $10.36 $12.07 $14.42 $17.28 $21.10 $27.59
Mozambique $1.28 $1.57 $2.13 $3.23 $5.02 $7.59 $10.38 $13.55 $16.94
Myanmar $4.23 $4.39 $4.94 $5.48 $6.10 $6.96 $8.14 $9.57 $11.13
Namibia $10.17 $10.65 $14.08 $20.18 $29.57 $42.07 $55.26 $71.97 $91.98
Nepal $3.57 $4.50 $5.41 $6.70 $8.47 $10.73 $13.94 $18.41 $24.93
Netherlands $55.09 $63.83 $71.34 $82.77 $94.50 $106.4 $118.9 $132.3 $146.0
New Zealand $42.22 $50.79 $58.20 $68.42 $79.64 $93.08 $107.2 $123.6 $140.3
Nicaragua $6.00 $6.30 $6.95 $7.59 $8.42 $9.60 $11.04 $12.84 $15.09
Niger $1.16 $1.65 $2.25 $3.28 $4.98 $7.49 $10.71 $14.36 $17.97
Nigeria $5.19 $5.52 $6.74 $8.66 $11.66 $15.58 $19.93 $25.67 $32.21
Norway $64.05 $74.91 $82.31 $93.08
$106.7
0
$123.0
0
$140.5
0
$160.0
0
$180.0
0
Oman $33.31 $30.52 $31.07 $29.91 $29.25 $31.99 $36.21 $44.13 $55.71
Pakistan $5.09 $6.03 $7.21 $8.98 $11.33 $14.01 $16.64 $19.69 $23.32
Palestine $6.40 $6.84 $8.59 $11.54 $15.30 $19.74 $25.49 $34.05 $45.48
Panama $30.45 $33.00 $37.59 $42.07 $48.81 $57.00 $67.20 $79.28 $91.22
Papua New Guinea $4.29 $4.91 $5.95 $7.47 $9.42 $11.75 $14.39 $17.33 $20.58
Paraguay $12.59 $13.64 $15.55 $17.73 $19.97 $22.66 $25.91 $30.73 $38.39
Peru $12.51 $13.96 $15.80 $17.77 $20.10 $22.88 $26.27 $31.91 $40.62
Philippines $8.12 $9.97 $12.33 $15.35 $18.82 $22.98 $28.51 $36.96 $48.18
Poland $30.06 $37.00 $43.85 $52.56 $63.51 $77.79 $91.25 $103.9 $117.6
Portugal $33.04 $36.56 $39.16 $44.99 $54.01 $65.67 $79.29 $94.61 $111.3
Puerto Rico $34.36 $49.89 $67.77 $87.82
$106.4
0
$121.7
0
$138.1
0
$156.0
0
$171.9
0
Qatar $91.74 $91.16 $98.16 $93.90 $85.03 $89.20 $92.22 $94.40 $92.84
Romania $27.14 $34.22 $39.25 $44.34 $52.45 $62.37 $71.50 $79.84 $88.97
Russian Federation $25.93 $26.93 $30.04 $32.75 $34.92 $38.63 $44.56 $52.34 $63.24
Rwanda $1.98 $2.98 $4.42 $6.84 $10.36 $14.75 $20.26 $28.00 $37.62
Samoa $6.49 $6.75 $8.13 $10.12 $12.96 $16.52 $20.71 $27.34 $37.77
Sao Tome and Principe $3.95 $4.78 $6.24 $8.48 $12.05 $16.17 $19.93 $24.80 $31.38
Saudi Arabia $47.31 $47.89 $47.86 $47.71 $49.52 $53.59 $56.26 $59.44 $63.78
Senegal $3.20 $4.53 $6.25 $8.78 $12.47 $17.61 $24.03 $32.90 $44.79
Climate Change, Economic Production, Inequality, and Poverty 67
Country 2017 2030 2040 2050 2060 2070 2080 2090 2100
Serbia $16.61 $21.55 $26.32 $32.05 $39.18 $52.22 $70.86 $89.08 $108.1
Seychelles $28.12 $32.38 $36.15 $40.84 $49.23 $59.95 $69.62 $81.11 $96.12
Sierra Leone $1.63 $1.86 $2.22 $2.88 $4.01 $5.81 $8.20 $11.10 $14.39
Singapore $95.31
$112.5
0
$123.9
0
$133.0
0
$140.7
0
$151.9
0
$160.7
0
$165.0
0
$168.2
0
Slovakia $30.06 $33.41 $36.36 $38.59 $43.00 $49.96 $56.79 $64.32 $73.57
Slovenia $36.51 $44.49 $52.51 $61.59 $75.49 $93.28 $110.4 $128.4 $148.4
Solomon Islands $2.66 $2.80 $3.70 $4.92 $6.79 $9.54 $13.03 $16.95 $21.50
Somalia $1.08 $1.40 $1.93 $3.26 $5.87 $10.19 $15.94 $22.73 $30.88
South Africa $13.86 $13.76 $15.00 $16.54 $18.23 $20.71 $23.49 $27.26 $33.82
Spain $39.53 $41.07 $42.44 $44.62 $50.01 $58.42 $68.09 $79.68 $94.32
Sri Lanka $12.58 $12.16 $13.75 $15.90 $19.00 $23.06 $28.22 $36.58 $49.47
St. Lucia $14.93 $16.02 $17.88 $19.77 $21.59 $23.74 $27.22 $32.77 $40.07
St. Vincent and the 
Grenadines $13.10 $15.55 $18.54 $22.13 $26.94 $33.34 $41.13 $52.42 $67.69
Sudan $4.60 $4.40 $5.01 $5.99 $7.35 $9.09 $10.89 $13.18 $15.83
Sudan South $1.54 $1.95 $2.41 $3.12 $4.28 $6.28 $9.26 $13.81 $20.24
Suriname $18.28 $16.05 $17.03 $19.11 $22.12 $25.96 $30.70 $38.21 $50.94
Sweden $51.95 $61.54 $72.05 $84.01 $97.14
$114.1
0
$131.8
0
$151.4
0
$173.1
0
Switzerland $69.10 $76.29 $82.73 $91.08
$101.9
0
$116.3
0
$130.6
0
$145.7
0
$163.1
0
Syrian Arab Republic $4.44 $4.28 $5.08 $6.18 $7.90 $9.20 $10.22 $11.89 $13.93
Taiwan $37.21 $48.02 $57.05 $65.85 $75.67 $87.90 $101.7 $114.3 $128.4
Tajikistan $3.25 $4.48 $6.17 $8.52 $11.65 $15.91 $21.82 $30.99 $45.49
Tanzania $2.53 $3.30 $4.48 $6.35 $9.27 $13.55 $19.12 $26.50 $35.58
Thailand $17.42 $18.71 $20.16 $22.11 $24.78 $28.46 $33.54 $40.74 $49.39
Timor-Leste $3.18 $4.56 $6.05 $8.30 $12.15 $17.20 $23.46 $32.37 $44.56
Togo $2.01 $2.77 $3.89 $5.80 $8.90 $13.60 $19.44 $26.90 $36.19
Tonga $6.47 $6.81 $7.66 $8.93 $10.88 $13.35 $16.38 $21.02 $29.18
Trinidad and Tobago $27.28 $27.99 $32.42 $37.53 $46.35 $58.21 $71.74 $86.31 $102.7
Tunisia $11.23 $10.98 $12.21 $13.95 $16.25 $19.74 $24.34 $31.02 $42.09
Turkey $25.20 $31.41 $37.27 $44.66 $53.42 $62.32 $71.96 $82.50 $92.97
Turkmenistan $14.21 $16.66 $21.48 $27.55 $35.07 $45.65 $57.35 $70.62 $86.53
Uganda $2.08 $2.77 $4.08 $5.99 $9.39 $14.00 $18.81 $24.76 $31.88
Ukraine $11.86 $8.70 $9.12 $9.52 $9.91 $11.19 $12.18 $13.29 $14.62
United Arab Emirates $67.18 $73.44 $73.53 $64.54 $52.73 $53.40 $58.51 $64.84 $70.03
United Kingdom $46.37 $48.15 $51.05 $55.67 $62.16 $71.24 $81.47 $93.87 $107.8
United States of America $59.91 $69.36 $78.55 $89.06 $99.90
$111.3
0
$123.6
0
$137.2
0
$152.8
0
Uruguay $23.01 $27.64 $36.95 $51.12 $66.51 $84.08 $101.3 $120.3 $140.7
Uzbekistan $6.84 $9.01 $11.60 $14.72 $18.53 $24.26 $32.90 $45.90 $64.45
Working Paper | January 2023 68
Country 2017 2030 2040 2050 2060 2070 2080 2090 2100
Vanuatu $3.08 $3.40 $4.54 $6.35 $8.75 $11.92 $16.18 $22.44 $31.91
Venezuela, Bolivarian 
Republic $11.63 $5.00 $5.08 $5.93 $7.39 $8.84 $9.93 $10.65 $10.87
Viet Nam $9.00 $12.70 $16.23 $20.23 $25.21 $32.97 $44.01 $58.41 $77.89
Yemen $2.31 $2.34 $2.84 $3.62 $4.76 $6.40 $8.61 $11.71 $15.51
Zambia $3.49 $3.97 $4.89 $5.98 $7.72 $10.41 $13.33 $16.66 $20.80
Zimbabwe $2.42 $2.80 $3.84 $5.40 $7.82 $11.12 $14.65 $18.45 $24.72
Table 23: Base Case Gini Coefficient.
Country 2017 2030 2040 2050 2060 2070 2080 2090 2100
Afghanistan 0.454 0.454 0.454 0.454 0.454 0.454 0.454 0.454 0.454
Albania 0.332 0.332 0.332 0.332 0.332 0.332 0.332 0.332 0.332
Algeria 0.276 0.276 0.276 0.276 0.276 0.276 0.276 0.276 0.276
Angola 0.513 0.513 0.513 0.513 0.513 0.513 0.513 0.513 0.513
Argentina 0.411 0.411 0.411 0.411 0.411 0.411 0.411 0.411 0.411
Armenia 0.336 0.336 0.336 0.336 0.336 0.336 0.336 0.336 0.336
Australia 0.344 0.344 0.344 0.344 0.344 0.344 0.344 0.344 0.344
Austria 0.297 0.297 0.297 0.297 0.297 0.297 0.297 0.297 0.297
Azerbaijan 0.266 0.266 0.266 0.266 0.266 0.266 0.266 0.266 0.266
Bahamas 0.4013 0.4013 0.4013 0.4013 0.4013 0.4013 0.4013 0.4013 0.4013
Bahrain 0.3642 0.3642 0.3642 0.3642 0.3642 0.3642 0.3642 0.3642 0.3642
Bangladesh 0.324 0.324 0.324 0.324 0.324 0.324 0.324 0.324 0.324
Barbados 0.3655 0.3655 0.3655 0.3655 0.3655 0.3655 0.3655 0.3655 0.3655
Belarus 0.254 0.254 0.254 0.254 0.254 0.254 0.254 0.254 0.254
Belgium 0.274 0.274 0.274 0.274 0.274 0.274 0.274 0.274 0.274
Belize 0.533 0.533 0.533 0.533 0.533 0.533 0.533 0.533 0.533
Benin 0.478 0.478 0.478 0.478 0.478 0.478 0.478 0.478 0.478
Bhutan 0.374 0.374 0.374 0.374 0.374 0.374 0.374 0.374 0.374
Bolivia 0.446 0.446 0.446 0.446 0.446 0.446 0.446 0.446 0.446
Bosnia and Herzegovina 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33
Botswana 0.533 0.533 0.533 0.533 0.533 0.533 0.533 0.533 0.533
Brazil 0.533 0.533 0.533 0.533 0.533 0.533 0.533 0.533 0.533
Brunei Darussalam 0.4192 0.4192 0.4192 0.4192 0.4192 0.4192 0.4192 0.4192 0.4192
Bulgaria 0.404 0.404 0.404 0.404 0.404 0.404 0.404 0.404 0.404
Burkina Faso 0.353 0.353 0.353 0.353 0.353 0.353 0.353 0.353 0.353
Burundi 0.386 0.386 0.386 0.386 0.386 0.386 0.386 0.386 0.386
Cabo Verde 0.424 0.424 0.424 0.424 0.424 0.424 0.424 0.424 0.424
Cambodia 0.4566 0.4566 0.4566 0.4566 0.4566 0.4566 0.4566 0.4566 0.4566
Cameroon 0.466 0.466 0.466 0.466 0.466 0.466 0.466 0.466 0.466
Canada 0.333 0.333 0.333 0.333 0.333 0.333 0.333 0.333 0.333
Climate Change, Economic Production, Inequality, and Poverty 69
Country 2017 2030 2040 2050 2060 2070 2080 2090 2100
Central African Republic 0.562 0.562 0.562 0.562 0.562 0.562 0.562 0.562 0.562
Chad 0.433 0.433 0.433 0.433 0.433 0.433 0.433 0.433 0.433
Chile 0.444 0.444 0.444 0.444 0.444 0.444 0.444 0.444 0.444
China 0.385 0.385 0.385 0.385 0.385 0.385 0.385 0.385 0.385
Colombia 0.497 0.497 0.497 0.497 0.497 0.497 0.497 0.497 0.497
Comoros 0.453 0.453 0.453 0.453 0.453 0.453 0.453 0.453 0.453
Congo 0.489 0.489 0.489 0.489 0.489 0.489 0.489 0.489 0.489
Congo, Dem. Republic of 
the 0.421 0.421 0.421 0.421 0.421 0.421 0.421 0.421 0.421
Costa Rica 0.483 0.483 0.483 0.483 0.483 0.483 0.483 0.483 0.483
Cote D'Ivoire 0.415 0.415 0.415 0.415 0.415 0.415 0.415 0.415 0.415
Croatia 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304
Cuba 0.4238 0.4238 0.4238 0.4238 0.4238 0.4238 0.4238 0.4238 0.4238
Cyprus 0.314 0.314 0.314 0.314 0.314 0.314 0.314 0.314 0.314
Czech Republic 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25
Denmark 0.287 0.287 0.287 0.287 0.287 0.287 0.287 0.287 0.287
Djibouti 0.416 0.416 0.416 0.416 0.416 0.416 0.416 0.416 0.416
Dominican Republic 0.422 0.422 0.422 0.422 0.422 0.422 0.422 0.422 0.422
Ecuador 0.447 0.447 0.447 0.447 0.447 0.447 0.447 0.447 0.447
Egypt 0.315 0.315 0.315 0.315 0.315 0.315 0.315 0.315 0.315
El Salvador 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38
Equatorial Guinea 0.5105 0.5105 0.5105 0.5105 0.5105 0.5105 0.5105 0.5105 0.5105
Eritrea 0.4519 0.4519 0.4519 0.4519 0.4519 0.4519 0.4519 0.4519 0.4519
Estonia 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304 0.304
Eswatini 0.546 0.546 0.546 0.546 0.546 0.546 0.546 0.546 0.546
Ethiopia 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35
Fiji 0.367 0.367 0.367 0.367 0.367 0.367 0.367 0.367 0.367
Finland 0.274 0.274 0.274 0.274 0.274 0.274 0.274 0.274 0.274
France 0.316 0.316 0.316 0.316 0.316 0.316 0.316 0.316 0.316
Gabon 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38
Gambia 0.359 0.359 0.359 0.359 0.359 0.359 0.359 0.359 0.359
Georgia 0.379 0.379 0.379 0.379 0.379 0.379 0.379 0.379 0.379
Germany 0.319 0.319 0.319 0.319 0.319 0.319 0.319 0.319 0.319
Ghana 0.435 0.435 0.435 0.435 0.435 0.435 0.435 0.435 0.435
Greece 0.344 0.344 0.344 0.344 0.344 0.344 0.344 0.344 0.344
Grenada 0.4205 0.4205 0.4205 0.4205 0.4205 0.4205 0.4205 0.4205 0.4205
Guatemala 0.483 0.483 0.483 0.483 0.483 0.483 0.483 0.483 0.483
Guinea 0.337 0.337 0.337 0.337 0.337 0.337 0.337 0.337 0.337
Guinea Bissau 0.507 0.507 0.507 0.507 0.507 0.507 0.507 0.507 0.507
Guyana 0.451 0.451 0.451 0.451 0.451 0.451 0.451 0.451 0.451
Working Paper | January 2023 70
Country 2017 2030 2040 2050 2060 2070 2080 2090 2100
Haiti 0.411 0.411 0.411 0.411 0.411 0.411 0.411 0.411 0.411
Honduras 0.494 0.494 0.494 0.494 0.494 0.494 0.494 0.494 0.494
Hong Kong 0.4153 0.4152 0.4152 0.4152 0.4152 0.4152 0.4153 0.4153 0.4153
Hungary 0.306 0.306 0.306 0.306 0.306 0.306 0.306 0.306 0.306
Iceland 0.261 0.261 0.261 0.261 0.261 0.261 0.261 0.261 0.261
India 0.357 0.357 0.357 0.357 0.357 0.357 0.357 0.357 0.357
Indonesia 0.381 0.381 0.381 0.381 0.381 0.381 0.381 0.381 0.381
Iran 0.408 0.408 0.408 0.408 0.408 0.408 0.408 0.408 0.408
Iraq 0.295 0.295 0.295 0.295 0.295 0.295 0.295 0.295 0.295
Ireland 0.314 0.314 0.314 0.314 0.314 0.314 0.314 0.314 0.314
Israel 0.39 0.39 0.39 0.39 0.39 0.39 0.39 0.39 0.39
Italy 0.359 0.359 0.359 0.359 0.359 0.359 0.359 0.359 0.359
Jamaica 0.455 0.455 0.455 0.455 0.455 0.455 0.455 0.455 0.455
Japan 0.329 0.329 0.329 0.329 0.329 0.329 0.329 0.329 0.329
Jordan 0.337 0.337 0.337 0.337 0.337 0.337 0.337 0.337 0.337
Kazakhstan 0.275 0.275 0.275 0.275 0.275 0.275 0.275 0.275 0.275
Kenya 0.408 0.408 0.408 0.408 0.408 0.408 0.408 0.408 0.408
Korea, Dem. People's 
Republic 0.3904 0.3904 0.3904 0.3904 0.3904 0.3904 0.3904 0.3904 0.3904
Korea, Republic of 0.314 0.314 0.314 0.314 0.314 0.314 0.314 0.314 0.314
Kosovo 0.29 0.2898 0.2898 0.2898 0.2899 0.2899 0.2899 0.2899 0.29
Kuwait 0.3662 0.3662 0.3662 0.3662 0.3662 0.3662 0.3662 0.3662 0.3662
Kyrgyzstan 0.273 0.273 0.273 0.273 0.273 0.273 0.273 0.273 0.273
Lao People's Dem. Republic 0.388 0.388 0.388 0.388 0.388 0.388 0.388 0.388 0.388
Latvia 0.356 0.356 0.356 0.356 0.356 0.356 0.356 0.356 0.356
Lebanon 0.318 0.318 0.318 0.318 0.318 0.318 0.318 0.318 0.318
Lesotho 0.449 0.449 0.449 0.449 0.449 0.449 0.449 0.449 0.449
Liberia 0.353 0.353 0.353 0.353 0.353 0.353 0.353 0.353 0.353
Libya 0.3736 0.3736 0.3736 0.3736 0.3736 0.3736 0.3736 0.3736 0.3736
Lithuania 0.373 0.373 0.373 0.373 0.373 0.373 0.373 0.373 0.373
Luxembourg 0.345 0.345 0.345 0.345 0.345 0.345 0.345 0.345 0.345
Macedonia, North 0.342 0.342 0.342 0.342 0.342 0.342 0.342 0.342 0.342
Madagascar 0.426 0.426 0.426 0.426 0.426 0.426 0.426 0.426 0.426
Malawi 0.447 0.447 0.447 0.447 0.447 0.447 0.447 0.447 0.447
Malaysia 0.411 0.411 0.411 0.411 0.411 0.411 0.411 0.411 0.411
Maldives 0.313 0.313 0.313 0.313 0.313 0.313 0.313 0.313 0.313
Mali 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33
Malta 0.292 0.292 0.292 0.292 0.292 0.292 0.292 0.292 0.292
Mauritania 0.326 0.326 0.326 0.326 0.326 0.326 0.326 0.326 0.326
Mauritius 0.368 0.368 0.368 0.368 0.368 0.368 0.368 0.368 0.368
Climate Change, Economic Production, Inequality, and Poverty 71
Country 2017 2030 2040 2050 2060 2070 2080 2090 2100
Mexico 0.454 0.454 0.454 0.454 0.454 0.454 0.454 0.454 0.454
Micronesia 0.401 0.401 0.401 0.401 0.401 0.401 0.401 0.401 0.401
Moldova, Republic of 0.259 0.259 0.259 0.259 0.259 0.259 0.259 0.259 0.259
Mongolia 0.327 0.327 0.327 0.327 0.327 0.327 0.327 0.327 0.327
Montenegro 0.385 0.385 0.385 0.385 0.385 0.385 0.385 0.385 0.385
Morocco 0.395 0.395 0.395 0.395 0.395 0.395 0.395 0.395 0.395
Mozambique 0.54 0.54 0.54 0.54 0.54 0.54 0.54 0.54 0.54
Myanmar 0.307 0.307 0.307 0.307 0.307 0.307 0.307 0.307 0.307
Namibia 0.591 0.591 0.591 0.591 0.591 0.591 0.591 0.591 0.591
Nepal 0.328 0.328 0.328 0.328 0.328 0.328 0.328 0.328 0.328
Netherlands 0.285 0.285 0.285 0.285 0.285 0.285 0.285 0.285 0.285
New Zealand 0.3605 0.3605 0.3605 0.3605 0.3605 0.3605 0.3605 0.3605 0.3605
Nicaragua 0.462 0.462 0.462 0.462 0.462 0.462 0.462 0.462 0.462
Niger 0.343 0.343 0.343 0.343 0.343 0.343 0.343 0.343 0.343
Nigeria 0.351 0.351 0.351 0.351 0.351 0.351 0.351 0.351 0.351
Norway 0.27 0.27 0.27 0.27 0.27 0.27 0.27 0.27 0.27
Oman 0.3677 0.3677 0.3677 0.3677 0.3677 0.3677 0.3677 0.3677 0.3677
Pakistan 0.316 0.316 0.316 0.316 0.316 0.316 0.316 0.316 0.316
Palestine 0.337 0.337 0.337 0.337 0.337 0.337 0.337 0.337 0.337
Panama 0.499 0.499 0.499 0.499 0.499 0.499 0.499 0.499 0.499
Papua New Guinea 0.419 0.419 0.419 0.419 0.419 0.419 0.419 0.419 0.419
Paraguay 0.485 0.485 0.485 0.485 0.485 0.485 0.485 0.485 0.485
Peru 0.433 0.433 0.433 0.433 0.433 0.433 0.433 0.433 0.433
Philippines 0.423 0.423 0.423 0.423 0.423 0.423 0.423 0.423 0.423
Poland 0.297 0.297 0.297 0.297 0.297 0.297 0.297 0.297 0.297
Portugal 0.338 0.338 0.338 0.338 0.338 0.338 0.338 0.338 0.338
Puerto Rico 0.3595 0.3595 0.3595 0.3595 0.3595 0.3595 0.3595 0.3595 0.3595
Qatar 0.3917 0.3917 0.3917 0.3917 0.3917 0.3917 0.3917 0.3917 0.3917
Romania 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36 0.36
Russian Federation 0.372 0.372 0.372 0.372 0.372 0.372 0.372 0.372 0.372
Rwanda 0.437 0.437 0.437 0.437 0.437 0.437 0.437 0.437 0.437
Samoa 0.387 0.387 0.387 0.387 0.387 0.387 0.387 0.387 0.387
Sao Tome and Principe 0.563 0.563 0.563 0.563 0.563 0.563 0.563 0.563 0.563
Saudi Arabia 0.3678 0.3678 0.3678 0.3678 0.3678 0.3678 0.3678 0.3678 0.3678
Senegal 0.403 0.403 0.403 0.403 0.403 0.403 0.403 0.403 0.403
Serbia 0.362 0.362 0.362 0.362 0.362 0.362 0.362 0.362 0.362
Seychelles 0.321 0.321 0.321 0.321 0.321 0.321 0.321 0.321 0.321
Sierra Leone 0.357 0.357 0.357 0.357 0.357 0.357 0.357 0.357 0.357
Singapore 0.3853 0.3853 0.3853 0.3853 0.3853 0.3853 0.3853 0.3853 0.3853
Slovakia 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25
Working Paper | January 2023 72
Country 2017 2030 2040 2050 2060 2070 2080 2090 2100
Slovenia 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25
Solomon Islands 0.371 0.371 0.371 0.371 0.371 0.371 0.371 0.371 0.371
Somalia 0.368 0.368 0.368 0.368 0.368 0.368 0.368 0.368 0.368
South Africa 0.63 0.63 0.63 0.63 0.63 0.63 0.63 0.63 0.63
Spain 0.347 0.347 0.347 0.347 0.347 0.347 0.347 0.347 0.347
Sri Lanka 0.393 0.393 0.393 0.393 0.393 0.393 0.393 0.393 0.393
St. Lucia 0.512 0.512 0.512 0.512 0.512 0.512 0.512 0.512 0.512
St. Vincent and the 
Grenadines 0.4354 0.4354 0.4354 0.4354 0.4354 0.4354 0.4354 0.4354 0.4354
Sudan 0.342 0.342 0.342 0.342 0.342 0.342 0.342 0.342 0.342
Sudan South 0.441 0.441 0.441 0.441 0.441 0.441 0.441 0.441 0.441
Suriname 0.579 0.579 0.579 0.579 0.579 0.579 0.579 0.579 0.579
Sweden 0.288 0.288 0.288 0.288 0.288 0.288 0.288 0.288 0.288
Switzerland 0.327 0.327 0.327 0.327 0.327 0.327 0.327 0.327 0.327
Syrian Arab Republic 0.375 0.375 0.375 0.375 0.375 0.375 0.375 0.375 0.375
Taiwan 0.2662 0.2661 0.2661 0.2661 0.2661 0.2661 0.2661 0.2661 0.2662
Tajikistan 0.34 0.34 0.34 0.34 0.34 0.34 0.34 0.34 0.34
Tanzania 0.405 0.405 0.405 0.405 0.405 0.405 0.405 0.405 0.405
Thailand 0.365 0.365 0.365 0.365 0.365 0.365 0.365 0.365 0.365
Timor-Leste 0.287 0.287 0.287 0.287 0.287 0.287 0.287 0.287 0.287
Togo 0.431 0.431 0.431 0.431 0.431 0.431 0.431 0.431 0.431
Tonga 0.376 0.376 0.376 0.376 0.376 0.376 0.376 0.376 0.376
Trinidad and Tobago 0.403 0.403 0.403 0.403 0.403 0.403 0.403 0.403 0.403
Tunisia 0.328 0.328 0.328 0.328 0.328 0.328 0.328 0.328 0.328
Turkey 0.414 0.414 0.414 0.414 0.414 0.414 0.414 0.414 0.414
Turkmenistan 0.408 0.408 0.408 0.408 0.408 0.408 0.408 0.408 0.408
Uganda 0.428 0.428 0.428 0.428 0.428 0.428 0.428 0.428 0.428
Ukraine 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26
United Arab Emirates 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26
United Kingdom 0.351 0.351 0.351 0.351 0.351 0.351 0.351 0.351 0.351
United States of America 0.412 0.412 0.412 0.412 0.412 0.412 0.412 0.412 0.412
Uruguay 0.395 0.395 0.395 0.395 0.395 0.395 0.395 0.395 0.395
Uzbekistan 0.353 0.353 0.353 0.353 0.353 0.353 0.353 0.353 0.353
Vanuatu 0.376 0.376 0.376 0.376 0.376 0.376 0.376 0.376 0.376
Venezuela, Bolivarian 
Republic 0.448 0.448 0.448 0.448 0.448 0.448 0.448 0.448 0.448
Viet Nam 0.357 0.357 0.357 0.357 0.357 0.357 0.357 0.357 0.357
Yemen 0.367 0.367 0.367 0.367 0.367 0.367 0.367 0.367 0.367
Zambia 0.571 0.571 0.571 0.571 0.571 0.571 0.571 0.571 0.571
Zimbabwe 0.443 0.443 0.443 0.443 0.443 0.443 0.443 0.443 0.443
Climate Change, Economic Production, Inequality, and Poverty 73
Table 24: Base Case poverty $1.90 headcount measured in millions.
Country 2017 2030 2040 2050 2060 2070 2080 2090 2100
Afghanistan 11.92 14.65 14.96 13.34 8.765 3.775 1.801 1.035 0.612
Albania 0.037 0.019 0.012 0.003 0.001 0 0 0 0
Algeria 0.136 0.207 0.124 0.029 0.006 0.003 0.002 0.001 0.001
Angola 13.61 17.12 17.55 19.9 21.95 16.94 10.14 7.027 6.093
Argentina 0.195 0.208 0.135 0.087 0.066 0.053 0.041 0.023 0.012
Armenia 0.026 0.013 0.004 0.001 0 0 0 0 0
Australia 0.123 0.083 0.047 0.028 0.018 0.01 0.006 0.003 0.001
Austria 0.028 0.019 0.012 0.006 0.004 0.003 0.002 0.001 0.001
Azerbaijan 0.001 0 0 0 0 0 0 0 0
Bahamas 0 0.0009 0.0012 0.0006 0.0003 0.0002 0.0001 0 0
Bahrain 0 0.0013 0.0004 0.0002 0.0002 0.0002 0.0002 0.0001 0.0001
Bangladesh 18.38 5.004 2.366 0.826 0.257 0.087 0.026 0.006 0.001
Barbados 0.017 0.014 0.011 0.006 0.002 0.001 0 0 0
Belarus 0.003 0.006 0.002 0 0 0 0 0 0
Belgium 0.015 0.008 0.005 0.002 0.001 0.001 0 0 0
Belize 0.051 0.076 0.101 0.063 0.032 0.021 0.013 0.005 0.001
Benin 5.34 5.146 4.383 2.909 1.474 0.613 0.253 0.08 0.026
Bhutan 0.012 0.031 0.028 0.021 0.015 0.008 0.003 0.001 0
Bolivia 0.646 0.604 0.402 0.283 0.179 0.09 0.038 0.012 0.004
Bosnia and Herzegovina 0.002 0.007 0.001 0 0 0 0 0 0
Botswana 0.308 0.41 0.345 0.217 0.118 0.07 0.036 0.014 0.006
Brazil 9.183 7.186 4.881 3.457 2.915 2.631 2.098 1.407 0.926
Brunei Darussalam 0 0.0001 0 0.0001 0.0001 0.0001 0.0001 0 0
Bulgaria 0.097 0.037 0.018 0.013 0.008 0.005 0.003 0.002 0.001
Burkina Faso 7.37 4.974 3.449 1.824 0.463 0.104 0.03 0.006 0.002
Burundi 8.476 10.58 12 13.76 11.12 4.346 1.087 0.218 0.056
Cabo Verde 0.015 0.015 0.009 0.006 0.003 0.001 0 0 0
Cambodia 3.883 2.254 1.395 0.764 0.365 0.169 0.071 0.025 0.008
Cameroon 5.814 6.065 6.112 5.897 4.26 2.446 1.466 0.871 0.496
Canada 0.091 0.086 0.083 0.057 0.035 0.022 0.014 0.008 0.004
Central African Republic 3.326 3.914 4.585 4.651 3.551 2.295 1.359 0.745 0.406
Chad 6.134 6.793 6.875 5.796 2.655 1.045 0.361 0.098 0.029
Chile 0.053 0.048 0.035 0.024 0.017 0.01 0.006 0.003 0.001
China 5.595 0.367 0.044 0.012 0.004 0.001 0 0 0
Colombia 1.97 1.389 1.227 1.008 0.729 0.516 0.369 0.247 0.149
Comoros 0.152 0.183 0.193 0.167 0.103 0.054 0.028 0.013 0.005
Congo 2.073 2.281 1.529 1.131 0.721 0.297 0.086 0.023 0.006
Congo, Dem. Republic of 
the 59.14 59.36 36.49 13.95 3.197 0.744 0.219 0.078 0.034
Working Paper | January 2023 74
Country 2017 2030 2040 2050 2060 2070 2080 2090 2100
Costa Rica 0.052 0.031 0.019 0.011 0.005 0.003 0.001 0 0
Cote D'Ivoire 6.09 3.847 2.781 1.699 0.892 0.33 0.104 0.029 0.01
Croatia 0.022 0.003 0.001 0.001 0 0 0 0 0
Cuba 0.183 0.123 0.112 0.055 0.035 0.023 0.012 0.005 0.002
Cyprus 0.0004 0.0019 0.0026 0.0013 0.0005 0.0001 0 0 0
Czech Republic 0 0.006 0.003 0.001 0 0 0 0 0
Denmark 0.008 0.002 0.001 0 0 0 0 0 0
Djibouti 0.161 0.08 0.037 0.019 0.01 0.006 0.004 0.001 0.001
Dominican Republic 0.073 0.029 0.014 0.007 0.003 0.002 0.001 0 0
Ecuador 0.541 0.467 0.369 0.32 0.282 0.247 0.178 0.089 0.041
Egypt 2.866 1.747 0.912 0.371 0.132 0.056 0.025 0.009 0.003
El Salvador 0.122 0.088 0.055 0.031 0.017 0.01 0.004 0.001 0
Equatorial Guinea 0.013 0.029 0.02 0.015 0.012 0.008 0.005 0.003 0.002
Eritrea 1.403 1.464 0.933 0.164 0.012 0.002 0.001 0 0
Estonia 0.004 0.002 0 0 0 0 0 0 0
Eswatini 0.322 0.274 0.222 0.16 0.095 0.051 0.03 0.017 0.008
Ethiopia 27.93 13.34 6.12 1.645 0.231 0.03 0.005 0.001 0
Fiji 0.002 0.002 0.001 0 0 0 0 0 0
Finland 0.004 0.004 0.003 0.002 0.001 0 0 0 0
France 0.009 0.055 0.047 0.033 0.019 0.01 0.005 0.002 0.001
Gabon 0.07 0.02 0.015 0.02 0.007 0.002 0.001 0 0
Gambia 0.224 0.124 0.07 0.029 0.008 0.002 0 0 0
Georgia 0.199 0.08 0.034 0.019 0.007 0.002 0.001 0 0
Germany 0 0.065 0.044 0.024 0.016 0.009 0.005 0.002 0.001
Ghana 3.708 2.581 1.787 0.886 0.293 0.09 0.025 0.006 0.002
Greece 0.094 0.064 0.04 0.019 0.01 0.006 0.002 0.001 0
Grenada 0.006 0.003 0.002 0.001 0.001 0 0 0 0
Guatemala 1.253 1.154 0.984 0.724 0.523 0.391 0.284 0.181 0.109
Guinea 2.936 1.786 1.283 0.673 0.361 0.162 0.055 0.016 0.005
Guinea Bissau 1.174 1.383 1.49 1.503 1.194 0.688 0.379 0.202 0.089
Guyana 0.038 0 0 0 0 0 0 0 0
Haiti 2.808 3.529 3.658 3.398 2.218 1.318 0.784 0.394 0.198
Honduras 1.661 1.593 1.39 1.086 0.765 0.505 0.3 0.14 0.054
Hong Kong 0 0.015 0.006 0.003 0.002 0.001 0.001 0 0
Hungary 0.058 0.016 0.008 0.002 0.001 0 0 0 0
Iceland 0.0001 0.0002 0 0 0 0 0 0 0
India 139.1 48.57 19.68 7.063 2.459 0.792 0.211 0.039 0.007
Indonesia 11.79 5.313 3.69 2.845 2.035 1.19 0.599 0.304 0.152
Iran 0.274 0.309 0.194 0.17 0.131 0.067 0.027 0.011 0.003
Iraq 0.464 0.135 0.021 0.002 0 0 0 0 0
Climate Change, Economic Production, Inequality, and Poverty 75
Country 2017 2030 2040 2050 2060 2070 2080 2090 2100
Ireland 0.006 0 0 0 0 0 0 0 0
Israel 0.02 0.008 0.003 0.001 0 0 0 0 0
Italy 0.86 0.642 0.445 0.271 0.152 0.09 0.054 0.029 0.015
Jamaica 0.054 0.08 0.077 0.053 0.033 0.02 0.009 0.003 0.001
Japan 0.928 0.765 0.68 0.431 0.268 0.099 0.043 0.017 0.006
Jordan 0.021 0.007 0.004 0.004 0.002 0.001 0.001 0 0
Kazakhstan 0.004 0.003 0 0 0 0 0 0 0
Kenya 17.6 14.08 11.57 7.297 3.226 1.465 0.729 0.291 0.103
Korea, Dem. People's 
Republic 8.165 7.537 8.696 3.26 2.151 1.13 0.309 0.065 0.01
Korea, Republic of 0.127 0.034 0.007 0.001 0 0 0 0 0
Kosovo 0.008 0.024 0.008 0.001 0 0 0 0 0
Kuwait 0 0.002 0.001 0.002 0.003 0.004 0.006 0.01 0.012
Kyrgyzstan 0.067 0.109 0.094 0.042 0.01 0.002 0 0 0
Lao People's Dem. Republic 0.661 0.346 0.138 0.04 0.009 0.002 0 0 0
Latvia 0.015 0.006 0.002 0 0 0 0 0 0
Lebanon 0.124 0.79 0.851 0.281 0.156 0.153 0.118 0.05 0.03
Lesotho 0.583 0.881 0.871 0.617 0.318 0.174 0.1 0.044 0.017
Liberia 2.095 3.005 2.873 2.001 0.643 0.136 0.035 0.007 0.001
Libya 0.034 0.05 0.007 0.006 0.003 0.002 0.001 0 0
Lithuania 0.029 0.009 0.003 0.001 0 0 0 0 0
Luxembourg 0.0016 0.0006 0.0004 0.0002 0.0003 0.0003 0.0006 0.0007 0.0007
Macedonia, North 0.095 0.109 0.069 0.031 0.01 0.003 0.001 0 0
Madagascar 19.55 22.45 22.89 20.52 12.92 7.411 4.718 2.671 1.681
Malawi 12.43 14.14 14.4 11.88 6.292 3.019 1.57 0.754 0.361
Malaysia 0.002 0.023 0.013 0.006 0.003 0.002 0.001 0 0
Maldives 0.022 0.039 0.039 0.025 0.02 0.014 0.008 0.004 0.001
Mali 8.276 8.624 9.213 8.078 3.348 1.044 0.328 0.075 0.018
Malta 0.0007 0.0001 0 0 0 0 0 0 0
Mauritania 0.279 0.294 0.25 0.216 0.093 0.029 0.009 0.002 0
Mauritius 0.003 0.008 0.01 0.003 0.001 0.001 0 0 0
Mexico 2.4 2.711 2.63 2.439 2.185 1.874 1.488 1.103 0.797
Micronesia 0.017 0.048 0.037 0.019 0.008 0.003 0.001 0 0
Moldova, Republic of 0.002 0.007 0.001 0 0 0 0 0 0
Mongolia 0.016 0.006 0.001 0 0 0 0 0 0
Montenegro 0.006 0.009 0.005 0.002 0.001 0 0 0 0
Morocco 0.25 0.166 0.11 0.064 0.029 0.012 0.005 0.002 0
Mozambique 17.82 22.28 22.48 21.01 11.62 5.77 4.311 2.647 1.547
Myanmar 0.728 0.247 0.075 0.069 0.069 0.024 0.004 0.001 0
Namibia 0.353 0.515 0.382 0.218 0.125 0.07 0.042 0.021 0.01
Working Paper | January 2023 76
Country 2017 2030 2040 2050 2060 2070 2080 2090 2100
Nepal 1.649 0.878 0.561 0.218 0.052 0.012 0.002 0 0
Netherlands 0.032 0.009 0.005 0.001 0.001 0 0 0 0
New Zealand 0 0.002 0.001 0.001 0 0 0 0 0
Nicaragua 0.137 0.108 0.088 0.098 0.071 0.037 0.023 0.012 0.006
Niger 9.375 6.236 3.936 1.874 0.398 0.056 0.01 0.002 0
Nigeria 78.95 93.33 93.41 83.45 55.15 26.33 11.13 4.643 1.945
Norway 0.013 0.005 0.003 0.002 0.001 0 0 0 0
Oman 0 0.006 0.002 0.001 0.001 0.001 0 0 0
Pakistan 4.859 2.82 2.126 1.086 0.288 0.073 0.029 0.011 0.003
Palestine 0.042 0.093 0.057 0.01 0.002 0.001 0 0 0
Panama 0.104 0.071 0.051 0.043 0.034 0.022 0.012 0.006 0.004
Papua New Guinea 2.22 1.196 0.842 0.897 0.416 0.214 0.123 0.056 0.025
Paraguay 0.07 0.047 0.031 0.025 0.019 0.012 0.007 0.003 0.002
Peru 1.076 0.706 0.445 0.372 0.294 0.196 0.119 0.059 0.024
Philippines 5.569 4.217 2.874 1.57 0.861 0.449 0.197 0.066 0.02
Poland 0.128 0.023 0.007 0.002 0.001 0 0 0 0
Portugal 0.042 0.022 0.014 0.006 0.003 0.001 0 0 0
Puerto Rico 0 0.0004 0.0001 0 0 0 0 0 0
Qatar 0 0.001 0 0 0 0 0 0 0
Romania 0.604 0.26 0.143 0.078 0.05 0.026 0.013 0.007 0.004
Russian Federation 0.063 0.081 0.029 0.026 0.025 0.014 0.005 0.002 0.001
Rwanda 6.729 5.971 4.45 2.304 0.937 0.414 0.163 0.045 0.014
Samoa 0.0011 0.0009 0.0005 0.0003 0.0001 0 0 0 0
Sao Tome and Principe 0.074 0.1 0.114 0.087 0.052 0.036 0.03 0.019 0.011
Saudi Arabia 0 0.008 0.004 0.001 0.001 0.001 0.001 0.001 0.001
Senegal 4.738 3.725 2.462 1.26 0.443 0.139 0.048 0.012 0.002
Serbia 0.477 0.183 0.081 0.034 0.015 0.005 0.001 0 0
Seychelles 0.0009 0.0012 0.0012 0.0007 0.0001 0 0 0 0
Sierra Leone 3.294 3.832 4.357 3.985 1.873 0.537 0.169 0.046 0.011
Singapore 0 0.001 0 0 0 0 0 0 0
Slovakia 0.072 0.031 0.014 0.006 0.004 0.002 0.001 0 0
Slovenia 0 0.0002 0 0 0 0 0 0 0
Solomon Islands 0.157 0.146 0.109 0.096 0.037 0.009 0.003 0.001 0
Somalia 6.041 9.568 9.725 2.975 0.68 0.121 0.02 0.003 0
South Africa 10.86 12.52 12.38 11.98 11.09 9.525 8.033 6.39 4.652
Spain 0.321 0.209 0.123 0.08 0.057 0.035 0.017 0.007 0.003
Sri Lanka 0.18 0.145 0.087 0.047 0.021 0.008 0.003 0.001 0
St. Lucia 0.008 0.008 0.007 0.006 0.004 0.003 0.002 0.001 0.001
St. Vincent and the 
Grenadines 0.009 0.007 0.005 0.004 0.002 0.001 0 0 0
Climate Change, Economic Production, Inequality, and Poverty 77
Country 2017 2030 2040 2050 2060 2070 2080 2090 2100
Sudan 4.171 5.45 3.864 2.216 1.12 0.513 0.26 0.105 0.038
Sudan South 9.241 11.14 10.2 7.093 5.72 3.689 1.994 0.912 0.297
Suriname 0.11 0.087 0.056 0.046 0.036 0.026 0.019 0.011 0.005
Sweden 0.025 0.011 0.005 0.002 0.001 0 0 0 0
Switzerland 0 0.005 0.004 0.002 0.001 0.001 0 0 0
Syrian Arab Republic 5.915 9.604 6.874 3.905 2.197 1.649 1.511 1.219 0.632
Taiwan 0 0.001 0 0 0 0 0 0 0
Tajikistan 0.248 0.079 0.018 0.005 0.001 0 0 0 0
Tanzania 26.85 21.87 15.71 9.826 4.092 1.136 0.325 0.093 0.021
Thailand 0.021 0.022 0.01 0.007 0.005 0.002 0.001 0 0
Timor-Leste 0.262 0.245 0.103 0.025 0.003 0 0 0 0
Togo 3.688 3.478 2.855 1.66 0.607 0.182 0.061 0.018 0.004
Tonga 0.0007 0.0023 0.0025 0.0017 0.0006 0.0004 0.0002 0 0
Trinidad and Tobago 0.004 0.004 0.002 0.001 0 0 0 0 0
Tunisia 0.028 0.058 0.042 0.012 0.005 0.002 0.001 0 0
Turkey 0.112 0.035 0.015 0.006 0.003 0.001 0.001 0 0
Turkmenistan 0.078 0.003 0 0 0 0 0 0 0
Uganda 17.08 14.73 10.73 6.527 1.855 0.478 0.181 0.064 0.018
Ukraine 0.01 0.306 0.039 0.017 0.011 0.008 0.004 0.002 0.001
United Arab Emirates 0 0.006 0.002 0.001 0.001 0.001 0 0 0
United Kingdom 0.137 0.189 0.177 0.097 0.066 0.049 0.03 0.014 0.007
United States of America 3.249 3.023 2.895 1.92 1.005 0.788 0.752 0.543 0.335
Uruguay 0.002 0.002 0.001 0 0 0 0 0 0
Uzbekistan 4.058 1.947 1.11 0.516 0.162 0.049 0.01 0.001 0
Vanuatu 0.042 0.049 0.026 0.015 0.005 0.001 0 0 0
Venezuela, Bolivarian 
Republic 4.199 12.54 10.96 10.25 8.495 5.732 3.957 2.433 2.233
Viet Nam 1.774 0.31 0.062 0.02 0.006 0.001 0 0 0
Yemen 13.14 17.35 16.58 13.31 9.308 5.204 2.282 0.76 0.25
Zambia 9.851 10.87 10.14 11.35 10.62 7.097 5.718 4.465 2.843
Zimbabwe 4.821 4.739 3.499 2.344 1.149 0.454 0.201 0.091 0.03