metadata: authors: 0: author: Wollburg,Philip Randolph
1: author: Hallegatte,Stephane
2: author: Mahler,Daniel Gerszon
count: World
docna: 0: docna: The Climate Implications of Ending Global Poverty
docty: Policy Research Working Paper
abstracts: cdata!: Previous studies have explored potential
            conflicts between ending poverty and limiting global
            warming, by focusing on the carbon emissions of the
            world&#x2019;s poorest. This paper instead focuses on
            economic growth as the driver of poverty alleviation and
            estimates the emissions associated with the growth needed to
            eradicate poverty. With this framing, eradicating poverty
            requires not only increasing the consumption of poor people,
            but also the consumption of non-poor people in poor
            countries. Even in this more pessimistic framing, the global
            emissions increase associated with eradicating extreme
            poverty is small, at 2.37 gigatonnes of equivalent carbon
            dioxide in 2050, or 4.9 percent of 2019 global emissions.
            These additional emissions would not materially affect the
            global climate change challenge: global emissions would need
            to be reduced by 2.08 gigatonnes of equivalent carbon
            dioxide per year, instead of the 2.0 gigatonnes of
            equivalent carbon dioxide per year needed in the absence of
            any extreme poverty eradication. Lower inequality, higher
            energy efficiency, and decarbonization of energy can
            significantly ease this trade-off: assuming the best
            historical performance in all countries, the additional
            emissions for poverty eradication are reduced by 90 percent.
            Therefore, the need to eradicate extreme poverty cannot be
            used as a justification for reducing the world&#x2019;s
            climate ambitions. When trade-offs exist, the eradication of
            extreme poverty can be prioritized with negligible emissions
            implications. The estimated emissions of eradicating poverty
            are 15.3 percent of 2019 emissions with the
            lower-middle-income poverty line at $3.65 per day and or
            45.7 percent of 2019 emissions with the $6.85
            upper-middle-income poverty line. The challenge to align the
            world&#x2019;s development and climate objectives is not
            in reconciling extreme poverty alleviation with climate
            objectives but in providing middle-income standards of
            living in a sustainable manner.
display_title: The Climate Implications of Ending
            Global Poverty
listing_relative_url: /research/2023/02/34008886/climate-implications-ending-global-poverty
topicv3: Green Growth m1326430 476,Inclusive Growth m1327170 477,Poverty and Climate Change m1327423 587,Inequality and Shared Prosperity m1327655 700
publishtoextweb_dt: 2023-02-24T00:00:00Z
dois: 10.1596/1813-9450-10318
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 45
author_keys: 0: author_key: 1464872
1: author_key: 1103816
2: author_key: 1539953
model_extractions: Policy Research Working Paper 10318 The Climate Implications of Ending Global Poverty _Philip Wollburg Stephane Hallegatte Daniel Gerszon Mahler_ Development Data Group & Climate Change Group February 2023
Policy Research Working Paper 10318

# **Abstract**

Previous studies have explored potential conflicts between ending poverty and limiting global warming, by focusing on the carbon emissions of the world’s poorest. This paper instead focuses on economic growth as the driver of poverty alleviation and estimates the emissions associated with the growth needed to eradicate poverty. With this framing, eradicating poverty requires not only increasing the consumption of poor people, but also the consumption of non-poor people in poor countries. Even in this more pessimistic framing, the global emissions increase associated with eradicating extreme poverty is small, at 2.37 gigatonnes of equivalent carbon dioxide in 2050, or 4.9 percent of 2019 global emissions. These additional emissions would not materially affect the global climate change challenge: global emissions would need to be reduced by 2.08 gigatonnes of equivalent carbon dioxide per year, instead of the 2.0 gigatonnes of equivalent carbon dioxide per year needed in the absence of any extreme poverty eradication. Lower inequality, higher energy efficiency, and decarbonization of energy can significantly ease this trade-off: assuming the best historical performance in all countries, the additional emissions for poverty eradication are reduced by 90 percent. Therefore, the need to eradicate extreme poverty cannot be used as a justification for reducing the world’s climate ambitions. When trade-offs exist, the eradication of extreme poverty can be prioritized with negligible emissions implications. The estimated emissions of eradicating poverty are 15.3 percent of 2019 emissions with the lower-middle-income poverty line at $3.65 per day and or 45.7 percent of 2019 emissions with the $6.85 upper-middle-income poverty line. The challenge to align the world’s development and climate objectives is not in reconciling extreme poverty alleviation with climate objectives but in providing middle-income standards of living in a sustainable manner. .

This paper is a product of the Development Data Group, Development Economics and the Climate Change Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/ prwp. The authors may be contacted at shallegatte@worldbank.org, dmahler@worldbank.org, or pwollburg@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **The Climate Implications of Ending Global Poverty**

**Authors:** Philip Wollburg<sup>*</sup> r Stephane Hallegatte<sup>*</sup> r Daniel Gerszon Mahler<sup>*</sup>

Originally published in the Policy Research Working Paper Series on _February 2023._ This version is updated on _February 2024._ To obtain the originally published version, please email prwp@worldbank.org.

**Keywords:** Poverty, climate change, greenhouse gas emissions, inequality **JEL codes:** D63, I32, O44, Q54, Q56

*The author ordering was constructed through American Economic Association’s randomization tool (confirmation code: S0bg4jTASKt6). All authors are with the World Bank. Hallegatte may be contacted at <u>shallegatte@worldbank.org, Mahler may be contacted at dmahler@worldbank.org, Wollburg may be</u> contacted at <u>pwollburg@worldbank.org.</u> We are thankful for comments received from Aart Kray, Gero Carletto, Samuel Freije-Rodriguez, participants at a presentation to members of the Poverty and Equity Global Practice of the World Bank and to members of the Global Poverty and Inequality Data team of the World Bank. Mahler gratefully acknowledges financial support from the UK government through the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Programme and from the World Bank Group SDG fund. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Eradicating extreme poverty and stopping climate change are two urgent global challenges, and they can only be tackled together. Eradicating extreme poverty in a sustainable manner requires limiting the future socioeconomic and environmental impacts of climate change; and there is broad consensus that, to be feasible and sustained, the emissions reductions needed to stop climate change have to be achieved through a just transition, which in particular protects the poorest and most vulnerable.<sup>1,2</sup> The Sustainable Development Goals (SDGs), aspiring to a ‘better and more sustainable future for all’, attest to the combined importance of these issues. The first of the SDGs calls to ‘end poverty in all its forms everywhere’. Eradicating poverty is also atop the agenda of the World Bank, which aims to decrease the share of people living in extreme poverty – with less than $2.15 a day at 2017 PPP – to 3% or less by 2030.<sup>3</sup> SDG 13 focuses on ‘urgent action to combat climate change and its impacts’.<sup>4</sup> To this end, the Paris Agreement was adopted in 2015 by 196 countries with the goal to limit global warming to “ _well below 2°C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5°C above pre-industrial levels_ .” This would require reaching global net-zero emissions levels by or close to mid-century, with emission reductions going well beyond the reductions that have been achieved so far.<sup>5,6</sup> Eradicating extreme poverty requires raising the consumption levels of the global poor to at least $2.15 a day. However, rising income and consumption levels have historically been the main drivers of increasing CO2e emissions.<sup>7</sup> This raises the question whether, and under which conditions, containing climate change and eradicating poverty are compatible goals.

Existing research has approached this question by calculating the carbon footprint associated with the consumption of individuals at different income levels using consumption and expenditure surveys.<sup>8–10</sup> These studies simulate shifting up the consumption value of the world’s extreme poor to the poverty line and estimate the emissions associated with this hypothetical consumption shift. Studies using this approach have generally found that eradicating poverty leads to modest increases in global emissions, with estimates ranging from less than 1% to about 3%.

Here we approach this question with a different, more realistic, framing. Poverty reduction occurs by a combination of economic growth and distribution of this growth across households, with 90% of historical poverty alleviation driven by economic growth.<sup>11–14</sup> We estimate the carbon emissions implications of various growth scenarios (for aggregate growth and the distribution of this growth among the population) under which poverty would be eradicated. With this framing, eradicating poverty requires not only to increase the consumption of poor people but also, under realistic assumptions for the distribution of growth based on historical patterns, to increase the consumption of non-poor people. Therefore, eradicating poverty is expected to lead to higher emissions with this framing than in previous studies if the energy and carbon contents of economic growth follow historical patterns.

To achieve the world’s poverty and climate objectives, the future will need to differ markedly from the past. In particular, we analyze how changes in income inequality and in the energy and carbon intensity of economic growth can contribute to reconciling poverty and climate goals. Previous studies have documented stark inequalities in emissions between poorer and richer individuals and households, drawing attention to high emitters in all countries, which contrast with the minimal emissions contributions of the world’s poorest.<sup>15–17</sup> A separate body of research examines whether reducing income inequality increases the emissions intensity of GDP.<sup>10,18–23</sup> Here we show that lower inequality means that less growth is needed to eliminate extreme poverty, which in turn reduces the emissions of poverty alleviation significantly. On the flipside, rising inequality can escalate the carbon cost of poverty alleviation and exacerbate the trade-off between climate goals and eradicating even extreme poverty.

2
With increasingly stringent climate policies and green technologies – like renewable energy and electric transportation – becoming cheaper than fossil fuel equivalent, future economic growth is expected to be more energy efficient and less carbon intensive. We further show the extent to which these changes can reduce the emissions of poverty eradication<sup>24</sup> and estimate the rates of decarbonization that would be required in non-poor countries to offset these emissions.

# **Results**

**How much economic growth is needed to end extreme poverty?** We estimate the historical relationship between growth in GDP per capita and growth in consumption per capita in a random slope regression model, taking into account trends across and within countries. We use data for 168 countries from the World Bank’s 2022 Poverty and Shared Prosperity Report<sup>25</sup> , converting income distributions to consumption distributions where needed. We also winsorize daily per capita consumption at a value of $0.50 as lower values likely reflect measurement error. We find that, on average, when GDP per capita grows by 1%, consumption per capita grows by 0.7%, with variation between countries (the remainder of growth in GDP per capita is likely being allocated to savings which would imply that the savings rate converges to around 30% in the long term). Using the international extreme poverty line at $2.15 in 2017 USD PPP, we focus, in our baseline model, on the poverty target of reducing the share of people living in extreme poverty to 3% or less, the World Bank’s global poverty target and the UN’s interpretation of ending extreme poverty in the SDGs.<sup>3</sup> We estimate the growth necessary to reach this target in each country, assuming to begin with an unchanged distribution of consumption within countries and modeling population growth until 2050 according to UN forecasts.<sup>26</sup>

The per capita GDP growth needed to reduce extreme poverty to 3% ranges from zero to nearly 600% -- a sixfold expansion of the economy (Figure 1, panel A). Non-poor countries require zero growth as the poverty target is already reached. North America is the only region in which all countries have achieved this poverty target, while countries in Sub-Saharan Africa need to grow on average by 215% to reach it (Figure 1, Panel B). Targeting higher poverty lines ($3.65, $6.85 – poverty lines typically of lower and upper-middle income countries respectively<sup>27</sup> ) requires more growth in more countries than targeting extreme poverty, as more countries are considered poor against higher poverty lines (zero to 1117% at $3.65; zero to 2251% at $6.85; Extended Figure 5). A minimum consumption level of $15 per day in all countries would need per capita GDP growth ranging between zero (in 28 countries) and 5140% (Extended Figure 5). We show the implications of targeting poverty rates other than 3% (5%, 4%, 2%, 1%, 0%; Extended Figure 13).

**With historical energy-intensity patterns, how much energy is required for economic growth?** To link GDP growth with greenhouse gas (GHG) emissions, we first relate GDP per capita to energy consumption per capita and then relate energy consumption per capita to greenhouse gas emissions. For this, we combine GDP data from the World Development Indicators (WDI) with data from the Energy Information Administration (EIA) on primary energy consumption and with data on GHG emissions from Climatewatch/CAIT, all for 2010 to 2019 (the latest year the data are available).

We again use a random slope regression to model the relationship between GDP and energy consumption, which also allows for efficiency gains over time. The random slope model exploits both variation between countries and variation within countries, and in this case allows for countries to convert GDP to energy needs at different rates and improve (or deteriorate) in energy efficiency at different rates. We cap the distribution of country-level coefficients at the 10<sup>th</sup> and 90<sup>th</sup> percentiles as the extreme values likely reflect measurement error or historical patterns that are unlikely to continue in the future.

3
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grows according to UN projections and the estimated historical patterns and rates of improvement in energy consumption and carbon intensity hold going forward until 2050.

- For the reference _poverty-eradication_ scenario, all parameters remain the same as in the _no-poverty reduction_ scenario, except for per capita GDP growth which is calibrated to achieve full poverty eradication by 2050. In practice, we extrapolate current economic growth forecasts into the future in each country until the poverty reduction target is reached. For instance, in India, the 3% target would be met in 2027, based on current growth trends. Once the target is met in a country, we only count the GHG emissions associated with countries maintaining GDP per capita levels to keep people out of poverty. For countries that are not expected to grow enough to reach the poverty target by 2050, such as Nigeria, we instead model an annualized per capita economic growth rate that meets the poverty target in 2050. We show the implications of using other target years than 2050 in Extended Figure 13.

The emissions needed for poverty eradication are defined as the difference in emissions between the _poverty-eradication_ scenario and the counterfactual _no-poverty-reduction scenario,_ that is, the emissions from all per capita GDP growth occurring in poor countries until the poverty target is reached. For countries that are expected to reach the extreme poverty target by 2050, based on current growth trends, like India, the most likely future scenario will see more economic growth (and consequently emissions) than both the counterfactual no-poverty-eradication scenario and the poverty eradication scenario. For countries that are not expected to reach the target by 2050, like Nigeria, the most likely future scenario will see less growth (and emissions) than we model in the poverty-eradication scenario but more than in the no-poverty-reduction scenario (Extended Figure 12).

The important difference of this approach relative to previous studies is that we count the additional emissions from higher consumption of all people in each country (including non-poor people), not only the additional emissions from the people moving out of poverty. In India, for instance, around 6% of the population would need to exit extreme poverty for the target to be reached, but we count the additional emissions _from the entire population_ caused by the economic growth needed for eradicating poverty.

Figure 2, panel A shows the number of people lifted out of extreme poverty between 2023 and 2050 relative to the _no-poverty-reduction_ scenario, amounting to just over 1 billion in 2050. Of the 1 billion, 69% are in Sub-Saharan Africa, 19% in South Asia, and 5% in the Middle East and North Africa. Panel B shows the emissions associated with poverty eradication at different poverty lines.

We find the emissions associated with extreme poverty alleviation to be modest. Annual emissions are estimated to be 2.37 gigatons of CO2e (or 4.9% of 2019 global emissions) higher in 2050 when the extreme poverty target is reached, relative to the no poverty reduction scenario. Figure 3 shows that emissions increases are small in the initial years (0.3% in 2023) and increase through time as more and more people are lifted and kept out of poverty (1.7% in 2030, 2.9% in 2040). Of the additional emissions in 2050, 60% accrues in Sub-Saharan Africa, followed by 21% in South Asia, and 12% in East Asia & Pacific. At the country-level, 20% of additional emissions in 2050 accrues in India, followed by 6.3% in the Philippines, and 6% in Angola.

Achieving more ambitious poverty targets has more significant emissions consequences. Using the lower-middle-income poverty line of $3.65 per day, rather than the extreme poverty line of 5
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The results are relatively modest at the $2.15 line because the emissions of poor countries are small relative to wealthier countries – even if they reach the income level necessary to eliminate poverty (Figure 2, Panel C). In contrast, at the $6.85 line, and even more so with a minimum consumption level of $15 per day, the added GHG for poor countries to reach the poverty target starts to have a notable impact on the global emissions. At the same time more countries, such as China, Brazil, Indonesia, and the Russian Federation, require growth to meet the target at that line and the regional distribution of the additional emissions changes (Extended Figure 5). In the case of $6.85 per day, 29% accrue in East Asia & Pacific, 28% in Sub-Saharan Africa, and 24% in South Asia (Figure 2, panel B), while the countries with the largest shares of added emissions in 2050 are India (18%), China (10%), and the Philippines (6%).

**What is the trade-off between ending poverty and limiting global warming?** Regardless of future poverty alleviation, containing global warming requires aggressive reductions in global GHG emissions to reach zero net emissions: to reach the most ambitious temperature target of the Paris Agreement, net emissions should reach zero by mid-century. With a 2-degree target, net zero emissions need to be achieved soon thereafter. Even if all new growth in poor countries would (unrealistically) follow historical energy- and carbon-intensity patterns and rates of improvement, eradicating extreme poverty does not affect materially the climate change challenge: instead of reducing emissions by 100%, the world needs to reduce emissions by 104.9%. (As net zero emissions is expected to be achieved with a combination of emission reductions and carbon removals from the atmosphere, this would require more carbon removals or more emission reductions.)

In the _no-poverty-reduction_ scenario, reaching net zero emissions in 2050 requires reducing annual global emissions by 2.0 GtCO2e per year, factoring in energy and non-energy emissions as well as population growth, but no growth in GDP per capita in poor countries. How much harder is it to achieve net-zero while also eradicating extreme poverty by 2050? In our reference _povertyeradication_ scenario, annual reduction requirements rise modestly by 4% to 2.08 gigatons.

Another way of measuring the trade-off is to look at the change in energy carbon intensity needed to compensate for the additional emissions of ending extreme poverty. To end extreme poverty without increasing emissions compared to the baseline without poverty reduction at all, non-poor countries would need to decarbonize energy consumption at an average rate of 0.28% per year above and beyond current trends. This represents a small increase in ambition relative to what would be required to reach the 1.5C or 2C warming goals, at 5.9% and 2.4% per year, respectively.

Aiming for more ambitious poverty targets creates a more acute trade-off. At the lower-middleincome poverty line of $3.65 per day, the emissions reductions required to achieve net-zero by 2050 are 2.14 GtCO2e per year. Decarbonization at a rate of 1% in non-poor countries and 2.15% in poor countries above the current trends would offset the additional emissions from poverty alleviation.

With the upper-middle-income poverty line of $6.85 per day, the annual global emissions reductions required to achieve net-zero rise to 2.42 gigatons per year each year between 2023 and 2050. To offset the emissions of poverty eradication, non-poor countries would need to decarbonize at a rate of 9.3% per year. This rate substantially exceeds decarbonization rates consistent with 1.5C or 2C warming trajectories. In comparison, poor countries (relative to the $6.85 per day poverty line) would need to decarbonize at a rate of 2.6% above current trends.

7
So far, we have explored scenarios based on historical patterns and relationships between growth, poverty, energy consumption and GHG emissions. However, historical patterns will need to change for the world to achieve its climate and poverty goals. How does this affect our results? Here we explore changes in inequality (affecting the link between growth and poverty), energy efficiency (affecting the link between growth and energy consumption) and carbon intensity (affecting the link between energy consumption and GHG emissions).

**How does inequality impact the emissions needed for poverty eradication?** Changes in inequality matter for the emissions of poverty alleviation since they affect the economic growth needed to eradicate poverty. As countries become more equal, the poorest will move closer to the poverty line, and less growth is needed to alleviate poverty. We explore how reducing inequality affects the emissions associated with achieving the poverty target. The Gini coefficient is the most common measure for inequality, ranging from 0 (equal) to 1 (unequal). A 1% decline in the Gini coefficient, in our model, can be interpreted as the reduction in inequality that would come about if a flat 1% consumption tax was levied, and its revenues evenly redistributed among all individuals (ignoring behavioral effects). We model a scenario in which poor countries experience a decline in the Gini coefficient at the rate of the top 10% historical Gini declines over a 28-year period – a reduction of around 17% until 2050.

There is debate over whether lowering inequality may also directly affect emissions by changing consumption patterns along the income distribution, or by enabling climate policies that reduce emissions, with mixed empirical evidence.<sup>18,19,22,28</sup> We assess the relationship of inequality and energy consumption using the random slope model and find no significant effect of inequality on energy consumption conditional on GDP, and we consequently leave it out of our model.

We find that lower inequality significantly reduces the carbon emissions needed for poverty alleviation with historical energy and carbon patterns. In the _reduced-inequality_ scenario, the carbon emissions increase associated with eradicating extreme poverty in 2050 is 876 million tons (or 1.8% of 2019 emissions levels) – just over a third of the 4.9% in the baseline scenario with no inequality change (Figure 3).

**How do changes in energy- and carbon-intensity impact the emissions needed for poverty eradication?** Achieving the world’s climate goals will require unprecedented changes in energyand carbon-intensity and future economic growth cannot be expected to have the same intensities as historical patterns. Even without climate policies, renewable energies have now become cheaper than fossil fuels in most countries, and further progress in energy efficiency (including through electrification of heat and transportation) will make future growth less intensive in energy and carbon.<sup>29</sup>

To explore these effects, we consider first cases in which all poor countries increase energy efficiency and decarbonize energy consumption related to _new_ production at the rate of the top 10% historical performers as estimated in the random slope regression model. (For now, the existing production level is assumed to maintain the same energy efficiency and carbon intensity.) This represents the best historical performance and translates to improvements in energy efficiency of new GDP of 2.2% per year and reductions in carbon intensity of energy of 3.2% per year. For reference, reducing energy and carbon intensity to levels consistent with a 2C warming trajectory would require average annual improvement rates of 3.5% and 2.4%, respectively. For levels consistent with a 1.5C warming trajectory, these reductions need to reach 4% and 5.9%, respectively.<sup>30</sup> 8
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We finally consider cases in which all poor countries follow the path not of historical best but historical worst performers. In these scenarios, poor countries experience rising inequality (13% increase in the Gini by 2050), increasing energy intensity of GDP (+2.1% per year), and increasing carbon intensity of energy (+2.5% per year; Extended Table 4). In these cases, emissions even for extreme poverty eradication increase substantially, to 11.3% with rising inequality and to 8.8% and 10.3% with increasing energy and carbon intensity, respectively, compared to 4.9% in the baseline scenario. We explore a wider range of scenarios and their associated emissions in the appendix, based on the different combinations of inequality, energy and carbon intensity, and other key modeling parameters.

# **Discussion**

We study the potential trade-offs between poverty eradication and limiting global warming. Our approach to this issue is to estimate the GHG emissions from the economic growth needed to eradicate extreme poverty under different inequality, energy-intensity, and carbon-intensity assumptions.

The main finding of this study is that, at the global level, the trade-off between eradicating extreme poverty and containing global warming is small, and the need to eradicate extreme poverty cannot be used as a justification for reducing the world’s climate ambitions. This result of course does not mean that there are no trade-offs between poverty alleviation and GHG emissions _for specific policies or investments or related to some budgetary constraints_ , even though recent work points toward more synergies than trade-offs _._<sup>31</sup> When poverty reduction requires increases in GHG emissions, our second conclusion is that the eradication of extreme poverty can be prioritized, since the emissions implications will always remain very limited (at least if the energy- and carbonintensities are as good as historical standards). Therefore, our results show that the challenge to align the world’s development and climate objectives is not in reconciling extreme poverty alleviation with climate objectives _but in providing middle-income standards of living while decarbonizing the world economy_ . Our analysis faces important limitations. The modeling framework is deliberately simple to allow transparently comparing the emissions associated with different growth and income distribution scenarios consistent with poverty eradication. It is designed to explore scenarios, not to provide forecasts or predictions of future economic growth or carbon emissions.

Moreover, the model draws on historical data, which by construction constrains the range of modeled pathways to what has been observed in the past and makes the results overly pessimistic by ignoring the recent progress in green technologies and solutions. Indeed, new technologies and circumstances create new possibilities for future pathways that did not exist in the past.<sup>32</sup> For instance, new evidence shows that renewable energy sources, rather than fossil fuels, present the most cost-effective way to meet growing electricity demand in many low- and middle-income countries, suggesting that the carbon content of economic growth will be much lower in the future than historically.<sup>31</sup> Particularly relevant for extreme poverty alleviation is the potential from small-scale solar mini-grid in rural areas.<sup>33</sup> Also, there is growing evidence of the potential from energy efficiency measures to generate energy savings and economic benefits, especially linked to electrification of heat (e.g., with heat pumps) and transportation (from electric bikes to electric buses).<sup>34</sup> When low-energy and low-carbon options become more competitive than alternatives, trade-offs between climate and development objectives disappear, even though higher upfront costs and investment needs can represent a major financial challenge.

10
Our estimates rely on monetary poverty measures, based in large part on the consumption value of goods and services. Monetary welfare measures fail to capture all dimensions of well-being or deprivation.<sup>25,35</sup> Importantly, previous research suggested that pathways to ending deprivation and satisfying basic human needs can differ from pathways to eradicating monetary poverty and may be achieved at lower emissions intensity.<sup>36</sup> There are further some methodological challenges concerning the poverty estimates we rely on. Methods and survey designs vary across countries and, since poverty estimates are susceptible to such methodological choices, these differences may affect their comparability<sup>37–39</sup> , despite efforts at harmonizing the data across countries.

Our model does not consider certain plausible indirect impacts of the modeling parameters affecting total GHG emissions. For instance, policies to reduce the carbon intensity of growth, such as carbon taxes with a share of revenues used to scale up poverty-reduction programs, can also contribute to reduced inequalities and poverty. And changes in inequality may also lead to additional changes in total emissions which our model fails to capture. Global warming itself is expected to impact poverty levels and may also impact income distributions,<sup>40</sup> while economic growth and reduced poverty may affect population growth.

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rather save some for investing and savings. There will likely always be some individuals with very low or negative income each year due to a negative capital shock or for other reasons. Yet this may not imply that their consumption falls below any problematic threshold. This poses a challenge for our study because it is not obvious that income poverty can be ended. For that reason, we convert income distributions to consumption distributions by applying the following formula: ln(c m m m )) cc) = llcc(iic<sup>0.93</sup> + 0.68 + 0.26 ∗ln(iic (1) This is derived by exploiting 150 surveys spanning 16 countries where for a given year, there exists both an income aggregate and consumption aggregate in the World Bank’s Poverty and Inequality Platform. For each of these surveys, the distributions are converted to 100 quantiles of matching income-consumption pairs (i.e. the income and consumption level at a particular quantile for a particular country-year). The functional form in equation (1) is chosen as it reflects a scenario where both the consumption and income distributions follow a 3-parameter log normal distribution while allowing for these distributions to differ based on a country’s median income. The fit gives an R<sup>2</sup> of 0.965 and hence highly accurately converts income to consumption distributions. Note that the conversion is not done at the household level, meaning that we do not convert a particular household’s income level to a consumption level. Rather, we convert the 2022 distributions to 1,000 quantiles and convert each of the income quantiles to consumption based on the equation above. More details about the income-consumption conversion are available in the appendix.

For about 50 economies home to less than 2% of the world’s population, we have no prior income or consumption data at all. These tend to be small high-income countries that do not share microdata as well as very closed countries, such as Cuba and the Democratic People’s Republic of Korea. To make the exercise truly global, we impute consumption distributions for these countries by taking the median value of the 1,000 consumption quantiles of their respective World Bank income group and region. For region-income group pairs with fewer than five countries with data, we take the median from the income group for the country with missing data.

Finally, we winsorize the consumption distributions at 50 cents per person per day. Consumption levels below that would reflect a daily caloric intake that likely is impossible to sustain over periods of time, and hence likely reflect measurement error. This winsorization affects 0.4% of the observations. If we do not winsorize consumption, our baseline additional emissions in 2050 relative to 2019 levels for the three poverty lines go from 4.9% to 5.6% ($2.15), from 15.3% to 16.3% ($3.65), and from 45.7% to 47.5% ($6.85). If we winsorize consumption at $1, our results decrease to 3.7%, 13.4%, and 42.5% for the three lines, respectively.

# **1.2 Poverty lines**

We are primarily interested in the greenhouse gases necessary to end extreme poverty, currently measured as falling short of a daily consumption of $2.15 in 2017 purchasing power parity adjusted dollars.<sup>27</sup> This is the international poverty line used for the first target of the Sustainable Development Goals and the poverty line used for the World Bank’s mission goal. It reflects the typical national poverty line of low-income countries. These low-income countries tend to define their national poverty lines as the expenditure necessary to consume about 2,200 calories per day and a small non-food allotment.

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# **1.3 Consumption growth necessary to end poverty**

**With constant distribution** . Calculating the consumption growth necessary to end poverty in each country is straightforward in the case where growth accrues to all equally, i.e. is distributionneutral. First, we identify the consumption level of the third percentile. Take the case of Benin where the third percentile reflects a consumption per day of $1.33. For the country to reach the poverty target for the international poverty line in a manner where the consumption of all individuals grows at an equal rate, the third percentile needs to just pass the poverty threshold. This means that the consumption value of individuals at the third percentile needs to grow by $2.15−$1.33 = 62% . Since we assume growth is distribution-neutral, the entire consumption $1.33 distribution of Benin would need to grow by 62% to reach the poverty target. By the same logic, everyone’s consumption needs to grow by 174% for the country to reach the target at the $3.65 poverty line. More generally, to reach the poverty rate target of (which unless otherwise specified is 3% in our analysis) at the poverty line , then consumption per capita ( g hc c PP<sup>∗</sup> in country needs to grow by zz ccg cc) cc g hc c = −1 , (2) ) zz where ) is the consumption level of percentile at ccg cccc<sup>∗</sup> FFcc<sup>−1</sup> (PP<sup>∗</sup> . cc<sup>−1</sup> FF (PP<sup>∗</sup> PP<sup>∗</sup>

**With changing distribution.** The calculation is more complicated in the inequality-sensitive scenarios. The main reason for this is that there are many different inequality metrics and infinitely many ways consumption growth can be allocated for a given change in inequality. We use the Gini coefficient as the inequality metric due to its popularity. We implement changes in inequality that correspond to taxing consumption by x% and distributing the proceedings equally to everyone. It turns out that such a tax and transfer scheme precisely reduces the Gini coefficient by x%.<sup>48,49</sup> This particular change in inequality has been shown to occur frequently on historical data.<sup>50</sup> Concretely it means that if the Gini reduces by x%, then each individual’s consumption will be given by: c m = c o (3) where c o is the consumption before the inequality change, nn mm ccoommc m is the consumption after cc cc (1 −xx) + xx∗μμ the inequality change, and is mean consumption per capita. From this equation it is also clear mm nn that the Gini reduction at most lowers consumption by x%, so if average growth is larger than x%, cc cc ccoomm then no one will lose consumption as a result of this inequality reduction. μμ Given that inequality reductions will increase the consumption of the bottom more than average, the 3<sup>rd</sup> percentile will now move closer (or above) the poverty line, so ) will increase, and the growth needed for the 3<sup>rd</sup> percentile to reach the poverty line will be lower. In Benin, the mean cc<sup>−1</sup> consumption is $5.04, so if the Gini is reduced by 10%, the 3<sup>rd</sup> percentile obtains a consumption FF (PP<sup>∗</sup> level of $1.70, and the growth needed to reach the target drops from 62% to 26%. All of this is illustrated in Extended Figure 3 below.

Our baseline scenario uses the distribution-neutral case. Though consumption inequality surely will change in the coming decades, historical evidence shows that around 90% of changes in poverty are driven by shifts in mean consumption rather than changes in the distribution of consumption.<sup>13</sup> In addition, a recent exercise attempting to predict changes in the Gini using more 15
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income countries and mostly uses information on inequality backed out from distributional national accounts rather than from observed consumption vectors, which is less relevant for our purposes.

In the appendix, we expand the scenario analysis by looking at different combinations of inequality changes and energy and carbon intensity, and we further vary other key modelling parameters: the population growth forecasts until 2050 and passthrough rates from GDP growth to consumption growth. Population scenarios are based on the low, medium, and high variant of the UN’s Population Prospects (United Nation 2022). For the passthrough rates from GDP growth to consumption growth, we use the 10<sup>th</sup> and 90<sup>th</sup> percentile of the distribution from Extended Figure 4, Panel A for all countries. In total, we analyze 243 scenarios, which are outlined in Extended Table 4.

# **Extended Table 4. Parameters in different model scenarios**

|**Parameter**||**Interpretation**|**Value**|
|---|---|---|---|
||High|90<sup>th</sup>percentile of changes in<br>inequality (increase)|+13% in Gini in 2050|
|Inequality|Baseline|No change<br>|0%|
||Low|10<sup>th</sup>percentile of changes in<br>inequality (decline)|-17% in Gini in 2050|
||High|Less growth passes through to<br>consumption|0.44%<br>increase<br>in<br>consumption<br>for<br>1%<br>growth in GDP|
|Passthrough rate of GDP<br>growth to consumption|Baseline<br>Low|Country-specific<br>estimate<br>of<br>passthrough rate<br>More growth passes through to<br>consumption|On<br>average,<br>0.7%<br>increase in consumption<br>for 1% growth in GDP<br>0.98%<br>increase<br>in<br>consumption<br>for<br>1%<br>growth in GDP|
||High|90<sup>th</sup><br>percentile<br>of<br>annual<br>improvements<br>in<br>energy<br>efficiency<br> <br> <br>|Deteriorations in energy<br>efficiency of 2.1% per<br>year<br>|
|Energy consumption per<br>GDP|Baseline<br>Low|Country-specific<br>estimate<br>of<br>annual improvements in energy<br>efficiency<br>10<sup>th</sup><br>percentile<br>of<br>annual<br>improvements<br>in<br>energy<br>efficiency|On average, improvement<br>in energy efficiency of<br>0.9% per year<br>Improvement in energy<br>efficiency of 3.2% per<br>year|
||High|90<sup>th</sup><br>percentile<br>of<br>annual<br>improvement in GHG emissions<br>of<br>energy<br>consumption<br>(increase)|Increase<br>in<br>carbon<br>intensity of energy of<br>2.5% per year|
|GHG emissions of energy<br>consumption|Baseline<br>Low|<br>Country-specific<br>estimate<br>of<br>annual improvement in energy<br>intensity of GDP<br>10<sup>th</sup><br>percentile<br>of<br>annual<br>improvement in energy intensity<br>of GDP (decarbonization)|On average, no change in<br>carbon intensity of energy<br>of 0.1% per year<br>Reduction<br>in<br>carbon<br>intensity of energy of<br>2.2% per year|
||High|High variant of UN’s population<br>projections|Country-specific<br>growth<br>rate|
|Population growth|Baseline<br>Low|Medium<br>variant<br>of<br>UN’s<br>population projections<br>Low variant of UN’s population<br>projections|Country-specific<br>growth<br>rate<br>Country-specific<br>growth<br>rate|

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Note that for each of these scenarios, we do not only change the poverty eradication projection but also the counterfactual projection. This means that when we, for example, switch from the medium variant of population projections to a low variant of population projections, we are looking at the greenhouse gas implications of ending poverty in a world where populations grow slower. We are _not_ looking at the impact of ending poverty in a world with low population growth vis-à-vis a world with no growth and medium population growth. Likewise, in the scenario with strong reductions in energy intensity, we are looking at the greenhouse gas implications of ending poverty in a world where countries reduce their energy intensity at the rate of historical best performers.

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