TY | POVERTY | POVERTY POVERTY | POVERTY | POVERTY | POVERTY | POVERTY | POVERTY | POVERTY POVERTY | POVERTY | POVERTY | POVERTY | POVERTY | POVERTY TY | POVERTY | POVERTY POVERTY | POVERTY | POVERTY | POVERTY | POVERTY | POVERTY POVERTY | POVERTY POVERTY | POVERTY | POVERTY | POVERTY | POVERTY | POVERTY Prosperity Insight Series Ruth Hill, Trang Nguyen, Miki Khanh Doan Impacts and Guide Policy Action A Framework to Understand Welfare CLIMATE AND EQUITY: VERTY | POVERTY | POVERTY | POVERTY | POVERTY | POVERTY | POVERTY | POVERTY CLIMATE AND EQUITY: A Framework to Understand Welfare Impacts and Guide Policy Action Ruth Hill, Trang Nguyen, Miki Khanh Doan1 1. The American Economic Association author randomization tool was used to randomize author names. This paper builds on an earlier policy brief to which Ben Brunckhorst and Ghazala Mansuri also contributed. It has benefited from comments by Richard Damania, Alan Fuchs, and Maria Ana Lugo, as well as suggestions by Haroon Bhorat, Francois Bourguignon, Shanta Devarajan, Francisco Ferreira, Susanna Gable, Stephane Hallegatte, Reema Hanna, Ravi Kanbur, Nora Lustig, Danielle Resnick, and Florencia Torche. This paper has also benefited from many staff in the World Bank’s Poverty and Equity Global Practice who provided inputs and comments through a series of presentations (in particular, Javier Baez, Ana Maria Munoz Boudet, Sandra Baquie, Carlos Rodriguez Castelan, Lourdes Rodriguez Chamussy, Alejandro de la Fuente, Daniel Valderama Gonzalez, Johannes Hoogeveen, Gabriela Inchauste, Rinku Murgai, Pierella Paci, Lokendra Phadera, Ambar Narayan, Reena Badiani, Monica Robayo, Jorge Luis Castaneda Nunez, Samuel Rodriguez, Sailesh Tiwari, Ailin Tomio, Matthew Wai-Poi, and Salman Zaidi). It was prepared under the guidance of Luis Felipe Lopez-Calva and Benu Bidani. © 2024 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW Washington DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy, completeness, or currency of the data included in this work and does not assume responsibility for any errors, omissions, or discrepancies in the information, or liability with respect to the use of or failure to use the information, methods, processes, or conclusions set forth. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. Nothing herein shall constitute or be construed or considered to be a limitation upon or waiver of the privileges and immunities of The World Bank, all of which are specifically reserved. Rights and Permissions The material in this work is subject to copyright. Because The World Bank encourages dissemination of its knowledge, this work may be reproduced, in whole or in part, for noncommercial purposes as long as full attribution to this work is given. Any queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; e-mail: pubrights@worldbank.org. Cover photo: © brazzo / iStock. Further permission required for reuse. Document photos: © Ivan Bruno / iStock. Further permission required for reuse, © Lingbeek / iStock. Further permission required for reuse, © georgeclerk / iStock. Further permission required for reuse Cover design: Anatol Ursu, https://www.behance.net/olywebart CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION iv Prosperity Note Abstract Reducing the impact of climate change on poor and vulnerable households is essential for hastening poverty reduction. In thinking about policies that do this, it is useful to apply the same hazard, exposure, and vulnerability framework that is often used to understand the physical impacts of climate change and add the non-climate benefits and costs to households that these policies can also bring. Policies that reduce hazards and/or vulnerability while bringing non-climate benefits should be prioritized where possible. However, some development policies that bring non-climate benefits, particularly in higher-income and higher- growth countries, may increase emissions by enough to worsen future hazards, so their emissions impact needs to be managed with compensating actions. Policies that reduce the hazards faced by poor households are needed, and the non-climate cost of these policies on poor people should be minimized or compensated where it cannot be avoided. TABLE OF CONTENTS Executive Summary 1 1. Background 3 2. The Welfare Impacts of Climate 5 2.1. The Asset-Based Framework 5 2.2. The Role of Climate in Determining Household Welfare 7 2.3. The Impact of Climate Change on Welfare 12 2.4. Measuring the Welfare Impacts of Climate Change 13 3. The Welfare Impacts of Climate Policies 23 3.1. The Impact of Climate Policies on Welfare 23 3.2. Policies with Minimal Trade-off 28 3.3. Addressing Trade-offs: Reducing Vulnerability at a Cost to Income Growth 32 3.4. Addressing Trade-offs: Reducing Hazards While Minimizing Welfare Costs 34 3.5. Cross-Border Issues and Climate Justice 43 References 45 CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION vi Prosperity Note CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 1 Prosperity Note ES. EXECUTIVE SUMMARY Reducing the impact of climate change on poor and vulnerable households is essential for hastening poverty reduction. Climate change disproportionately affects poor and vulnerable people whose livelihoods often depend on natural resources and whose lack of access to savings, credit, and insurance makes hazards more costly. To guide policy action, this document sets out a framework for thinking about the relationship between climate and poverty, and how climate change and climate policies affect household welfare. It also provides a review of the analytical methods and tools that can be used to quantify the risks climate and climate change pose to welfare, and the impacts of climate action on welfare. The framework uses an asset-based approach to examine how climate outcomes affect household income and consumption, and how households use assets and markets to cope with climate shocks. Poor households often rely on natural resources for their livelihoods, which makes their incomes more dependent on weather and thus highly susceptible to changes in the climate. Additionally, the lack of capital that accompanies a life in poverty makes hazards more costly. They are less likely to be able to rely on savings, access to credit, or insurance to manage their losses of income or assets. This paper also adapts the hazard, exposure, and vulnerability framework to explore how climate change and policies impact welfare through three channels: the hazard distribution, changes in exposure and vulnerability, and other benefits or costs to households that do not rely on climate outcomes. The net welfare impact of policy will vary across people. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 2 Prosperity Note Policies that address climate hazards or reduce exposure and vulnerability and bring non-climate benefits for poor and vulnerable people--so-called double or triple wins--should be prioritized. Some examples of triple wins include certain climate- smart agricultural practices, sustainable forest management, investments in clean energy access, and reducing inefficiencies in trade. These actions have minimal trade-offs in achieving progress on both climate goals and development outcomes. In many cases, trade-offs are present in the short run. For example, rising energy prices and job losses in carbon-intensive sectors can significantly affect poorer and more vulnerable communities. Transitioning to renewable energy sources, for instance, can impose costs on communities that rely on fossil fuel industries. Addressing the trade- offs inherent in climate policies requires a nuanced understanding of the diverse impacts that these policies can have across different populations and time horizons. While the long-run benefits are clear, it is important to measure and manage trade-offs that exist in the short to medium term and design compensatory actions to ensure an equitable transition. Policy packages will often be required to ensure that both climate and poverty targets are simultaneously met. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 3 Prosperity Note 1. BACKGROUND The World Bank has adopted a new vision on poverty reduction that saw 1 billion people statement: to end poverty on a livable planet. The move out of poverty came to a halt in 2020. Climate mission underscores the relationship between change poses a significant threat to the lives and poverty and climate objectives. The livelihoods of livelihoods of poor and vulnerable people. Urgent poor households are often particularly reliant on action is necessary to end extreme poverty and their environment and the natural capital to which prevent catastrophic climate-related hazards and they have access. Both aspects of the mission are biodiversity loss. currently under threat. Three decades of progress CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 4 Prosperity Note However, actions that are particularly good for one The focus of the framework is on the economic objective may not be as effective, and could even impacts of climate change and climate action. be detrimental, for the other. Understanding and However, it is important to note that much of the managing these trade-offs is essential for success. welfare impact of climate change and related This paper presents a framework for thinking about extreme events comes through loss of life and the relationship between climate and poverty, and adverse effects on physical and mental health, the channels through which climate action impacts particularly in poor places with fewer means to cope. household welfare. This framework is then used to Natural disasters intensified by climate change can highlight the synergies and trade-offs in meeting cause significant loss of life. The Intergovernmental climate and poverty and equity objectives, along Panel on Climate Change (IPCC) Sixth Assessment with analytical methods and tools that can be used Report projects 250,000 deaths in excess per year to quantify the trade-offs where possible. The goal by 2050 attributable to climate change due to heat, is to provide a primer to microeconomists working undernutrition, malaria, and diarrheal disease on welfare analysis of climate change and climate (IPCC, 2023a). Africa accounts for more than half action that explains key concepts, provides a rubric of this projected excess mortality. for thinking through the issues, and describes available tools to use. At various points, a review of the literature is also provided (or review pieces referenced), but the goal of the paper is not to provide a comprehensive review of the literature. Although written primarily for an internal World Bank audience, the paper may have interest beyond that. An earlier policy brief discusses the same framework and policy guiding principles (Brunckhorst et al., 2023). CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 5 Prosperity Note 2. THE WELFARE IMPACTS OF CLIMATE 2.1. The Asset-Based Framework We start the framework by focusing on a household assets are used to generate income (López-Calva and its ability to earn income and increase its & Rodríguez-Castelán, 2016). Household income welfare. The ability of a household to earn income is determined by these factors, as well as by any depends on its ability to accumulate and use assets, taxes paid and transfers and subsidies received. and the return the household earns when these CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 6 Prosperity Note A household can accumulate and use many kinds in hot days, extreme rainfall, and storms, imposes of assets. They can be grouped into five main significant costs on the income-earning capacity of categories: financial (cash in hand, bank accounts, poor people (Birkmann et al., 2022). net loans outstanding), physical (agricultural tools, Second, the impacts of climate change on welfare— livestock), human (knowledge, skills, health), social whether that is increased risk of extreme events or (networks, norms, social trust), and natural (land, gradual increase in temperatures, sea level rise biodiversity). Income growth comes from investing or changes in wetness—depend on a household’s in and protecting these assets, and from increasing assets and the context in which the household the returns to these assets. The returns to assets finds itself. For example, households residing near can be increased through technological change healthy mangrove ecosystems (that is, households and through investments and policies that impact with more natural capital) have lower coastal flood the context—the market structure and macro risks (Gijsman et al., 2021). Households with conditions—in which a household lives and works. higher levels of education (that is, households with Figure 1 illustrates the core concept of this asset- more human capital) are better equipped to adapt based framework. their livelihood activities, households with savings There are two important points to note from (that is, more financial capital) are more capable of the asset-based framework for a discussion on restarting businesses, and households with access the relationship between climate and poverty. to remittances (that is, more social capital) can First, poor households often engage in livelihood finance immediate consumption after a weather activities that heavily rely on the use of natural shock (Mohapatra et al., 2012; Jack & Suri, 2014; capital, such as farming, pastoralism, and fishing. Rahman & Akter, 2014; Anik et al., 2018). Consequently, climate change, such as an increase Figure 1: Asset-based Framework Context: FINANCIAL Market structure & CAPITAL macro conditions ASSET ACCUMULATION HUMAN X SOCIAL CAPITAL INTENSITY OF USE CAPITAL X RETURNS = INCOME GENERATION CAPACITY + taxes, transfers and subsidies PHYSICAL NATURAL CAPITAL CAPITAL Source: López-Calva and Rodríguez-Castelán, 2016. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 7 Prosperity Note Given the focus of this framework on the impact of and use of assets, X. For then, which is our climate on welfare, we focus on the welfare that is interest, we can write: determined by climate outcomes. The welfare of (Eq. 1) household i living in location k at time t, can be decomposed into two components: the part where f(.) captures how exposure to a given weather determined by climate outcomes, and the part outcome, translates into household’s welfare independent of climate, As the discussion in given and The general functional form this subsection has underscored, both components allows for flexibility in capturing interactions across depend on the environmental and socioeconomic and within each component. context, ESEC, and the household’s accumulation 2.2. The Role of Climate in Determining Household Welfare Hazard, Exposure, and Vulnerability Framework outlined by the IPCC. Hazard refers to a potential adverse weather event that may affect people’s In this section, we focus on further unpacking welfare. Exposure captures the number of people To do this, we use the hazard, exposure, and or assets exposed to a given hazard. Vulnerability vulnerability framework that is commonly used is the extent to which a hazard adversely impacts a in modeling the impact of climate events. Figure household’s welfare. 2 provides an overview of this framework, incorporating the definitions of these components Figure 2: Hazard, Exposure, and Vulnerability Framework The propensity or predisposition to be adversely affected. Vulnerability encompasses a variety of concepts and elements, including sensitivity or susceptibility to harm and lack of capacity to cope and adapt. The presence of people; livelihoods; species or ecosystems; environmental functions, services, and resources; infrastructure; or economic, social, or cultural assets in places and settings that could be adversely affected. The potential occurence of a natural or human-induced physical event or trend that may cause loss of life, injury or other health impacts. as well as damage and loss to property, infrastructure, livelihoods, service provision, ecosystems and environmenal resources. Source: IPCC, 2023b. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 8 Prosperity Note In the notation of equation (1), reflects the loss in a flood. If agricultural income is lost, the size weather outcome experienced by the household of the total income loss will depend on whether at time t and thus reflects exposure to a given the household is able to compensate for losses in hazard. Vulnerability is the marginal change in agricultural income by earning income from other welfare experienced by a household in response to activities. If total income is lost, the degree to which a change in the magnitude of the weather outcome: this impacts consumption will depend on its ability to smooth consumption through borrowing and (Eq. 2) saving or receiving remittances from friends and family. If welfare is being defined by consumption, all these aspects of a household’s characteristics Two aspects of the definition are worth noting. First, will determine its vulnerability to weather shocks. there is a difference between how vulnerability is In terms of the notation of equation (2) the cross- defined in the IPCC definition and how it has been derivative identifies how much used in the poverty measurement literature (see vulnerability depends on household circumstances. Hoddinott and Quisumbing [2010] for a review). In the poverty measurement literature, vulnerability is The setup discussed so far has good grounding usually defined as the likelihood that a household’s in the applied microeconomic literature and is welfare, falls below a minimum level of the same setup used by Hsiang et al. (2019) as consumption given by the poverty line. It is related they consider the distribution of environmental to the measure given in equation (2) in that if the damages. However, to properly align equation (1) vulnerability of_ikt^ to is high, then it is more with the framework set out in figure 2, we need likely that W_ikt will fall below a poverty line in to switch from looking at the welfare impact of a expectation, but it is a different concept. Given particular weather event that a household has there is an increasing consensus on the use of been exposed to, c_kt to looking at “the potential vulnerability as part of the hazard, exposure, and occurrence” of a weather event, h(c_kt ), as vulnerability framework, we use vulnerability as indicated by the definition of hazard in figure 2. defined in equation (2) in the rest of this note. In other words, instead of examining the ex-post welfare effects of a weather event, we need to Second, the vulnerability of a household to a climate think about the expected impact of weather events, event does not refer only to the initial impact of and how this expected impact changes as the the event on assets or income; it also reflects the probability distribution of weather events changes ability of a household to protect its welfare from (climate change). For a risk neutral person, this can that impact. This is reflected in the IPCC definition, be written as where vulnerability includes both “the sensitivity (Eq. 3) or susceptibility to harm” and “the lack of capacity to cope and adapt.” For example, a household may where E[W_ikt^ is the expectation of W_ikt and experience a loss in agricultural income from a h(c_kt) represents the probability distribution of weather event. The size of that agricultural income possible realized weather outcomes in location loss will depend on the ability of the household to k (that is, the climate). In reality, people are risk protect yields from a drought by using irrigation, averse and derive lower utility from extreme say, or its ability to move cattle to protect them from outcomes and expected welfare is given as: CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 9 Prosperity Note will be a large part of the welfare impact of climate change, Artuc et al. (2023), which specifically where u(.) is the utility derived from the welfare model trade between locations, suggest that this is outcome achieved when the weather event is c_k. unlikely to be a large impact. However, for a given An example of h(c_kt) would be the distribution climate shock it is quite possible that interruptions of potential number of days with average in nearby markets and price impacts occur. temperatures above 30 degrees Celsius in a year This can be represented in equation (3) in two ways. in a given location, with the hazard referring to the First, h(.) can be a matrix of weather outcomes across right tail of the distribution where the number of places that may impact a household in location k. days in this category significantly affect human’s Alternately, the climate shock in location k can refer living and working conditions. to weather-induced changes in prices, even if the Hazard, exposure, and vulnerability differ across weather conditions have occurred elsewhere. households. The distribution of hazards is partially determined by ESEC_k, which, in turn, is shaped by households’ accumulation and use of assets. Costly Income Smoothing Exposure and vulnerability to a specific hazard The subtler welfare impact of extreme weather also vary across households because these two events occurs not when disasters strike but in components are determined by the context and the costly behavior driven by the anticipation of households’ accumulation and use of assets. When shocks that households are ill placed to cope with climate shocks occur, they tend to have a larger (uninsured risk). The direct impact of a calamity impact on households that have less access to on well-being is the visible, headline-grabbing markets, capital, and basic services (Dercon, 2004; way that conflict or poorly managed disasters set Lybbert et al., 2004). back progress. However, the persistent impact of With this basic setup in place, the following uninsured risk on household behavior every year— subsections indicate how the framework can be regardless of whether the feared event occurs— extended to reflect other aspects of the relationship is arguably the larger constraint to accelerating between welfare and climate. poverty reduction. In Zimbabwe, this was found to be twice as large an impact on income growth (Elbers et al., 2007). Indirect Effects of Weather Conditions When households face risk, their rational response Equation (3) indicates that the welfare of a is to act to avoid it. When households cannot household in location k is dependent on the weather manage the risk in their environment, they may realized in location k. However, it is quite possible eschew investments and livelihood strategies that the weather conditions in another location (such as cultivation of high-risk, high-return impact W_ikt through their impacts on markets. crops) that offer great reward but leave them too Weather conditions can disrupt markets or reduce exposed to the elements of nature, the economic supply of food or other commodities and impact ups and downs of a weak economy, or the uncertain prices. While Hallegatte et al. (2016) suggest this behavior of others. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 10 Prosperity Note A number of well-identified studies across contexts predicted by a profit maximation strategy every show that when households have higher access to period (Sandmo, 1971). climate risk management instruments, there is a 15–30 percent increase in investment regardless of whether shocks occur (Mobarak & Rosenzweig Long-Run Impacts and the Concept of Resilience [2013] for rainfall index insurance in India; Elabed & Carter [2014] for area yield insurance in Mali; Karlan Much evidence points to the fact that shocks et al. [2014] for rainfall index insurance in Ghana; cast a long shadow on welfare. Income shocks Cai et al. [2015] for swine insurance in China; Cai increase the probability of being infected by the [2016] for area-yield insurance in China; Fuchs & human immunodeficiency virus (Burke et al., Wolff [2016] for rainfall index insurance in Mexico; 2015). When a household experiences a shock, Jensen et al. [2017] for livestock insurance in Kenya; investments in education and nutrition are reduced, Hill et al. [2019] for rainfall and area yield insurance and this increases a child’s likelihood of being in in Bangladesh; Bulte et al. [2020] for multiperil crop poverty as an adult. The impact of nutritional and insurance in Kenya; Stoeffler et al. [2022] for area educational shocks on incomes earned as an adult yield in Burkina Faso). When farm households have is substantial, including a 3 percent reduction in an opportunity to insure their crops, investment annual earnings in Ethiopia, 20 percent lower wages in agricultural inputs goes up: in Ghana, spending in Burundi, and 14 percent lower lifetime earnings on inputs rose by 88 percent, from US$375 to in Zimbabwe (Alderman et al., 2006; Bundervoet US$705 (Karlan et al., 2014); in Mali, spending on et al., 2009). Shocks can matter even before one is inputs increased by 14 percent (Elabed & Carter, born: in Bangladesh, in utero exposure to a tornado 2014). The returns to these inputs vary in any increased the probability of low birth weight and given year based on weather conditions and prices decreased birth length (Gunnsteinsson et al., 2022); (Rosenzweig & Udry, 2016), but even assuming a in Mozambique, those exposed to drought in utero relatively low average return to input use, these had fewer years of schooling (Baez et al., 2017). increases amount to an average increase in income Not only do the initial impacts of climate events growth of 1–9 percent a year—enough to move many vary by household characteristics, but so does the of these farmers out of poverty and to offset the ability to recover from shocks. This is depicted in losses associated with one-in-five-year events. figure 3, which indicates how poorer households Equation (3) considers the welfare of household may experience both larger losses in a crisis and i at one point in time, but in reality, welfare is slower recovery from a crisis. maximized across multiple periods (Deaton, 1997): where w_ikt is welfare in one period. When households are unable to insure welfare from shocks to income, the amount that is invested in productions decisions can be less than that CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 11 Prosperity Note Figure 3: Resilience is Often Lower for Lower-Income Households SHORT-RUN IMPACT IN CRISIS LONG-RUN RECOVERY NEXT CRISIS Crises have larger welfare effects Income growth is often slower for Without intervention, this cycle on poorer households. This poor households in recovery as repeats increasing inequality means initial welfare differences they lost human capital and assets as it goes. between rich and poor get larger. during the crisis. This also makes the welfare difference larger. A richer than average household A poorer than average household Source: Hill and Narayan, 2020. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 12 Prosperity Note Large and sustained income losses require losses were passed on to reduced consumption households to use coping mechanisms with long- (Fafchamps et al., 1998; Kazianga & Udry, 2006). In run impacts: inadequate nutrition in first 500 days southern Ethiopia, drought did not trigger the sale of life, high rates of indebtedness, and, at times, the of livestock, and in northern Kenya, households loss of productive assets and permanent withdrawal chose to protect the assets they had by reducing of children from schools. Past evidence suggests food intake and energy levels (Lybbert et al., 2004; that reducing food consumption is most frequently McPeak & Barrett, 2001). used as a coping strategy after a fast onset shock, A household that is less vulnerable or more whereas sale of productive assets seems to be resilient will recover more quickly; a household mainly used by households as a coping strategy that is more vulnerable (or less resilient) will of last resort. Dercon (2004) finds that 8 percent recover more slowly. In equation (3), this can be of households affected by the famine in Ethiopia represented by considering welfare at time t to be in the mid-1980s reduced food consumption, 39 dependent also on weather conditions in a prior percent sold valuables (on average 29 percent period t-n. The size of n to consider will depend of livestock holdings were liquidated), 7 percent on the context. The concept of resilience refers migrated in distress, and 11 percent had at least to this ability of households to recover in a timely one member go to a feeding camp. This ordering manner. Although resilience used to be used to of the prevalence of coping strategies was constant capture the notion of “bouncing back,” it has over in every village, even though the severity of time become broader, taking into account the harvest failure varied across villages. In Burkina process by which households recover, the degree Faso in 1984, combined livestock sales offset to which shocks can be anticipated and managed between 15 percent and 30 percent of the income (Lavell et al., 2012). losses resulting from drought during this period, while more than half of the rainfall-induced crop 2.3. The Impact of Climate Change on Welfare Climate change is altering the distribution of All of these changes can be reflected in a change hazards faced by individuals in all locations in the in the distribution h(.). The change in welfare that a world. The probability of extreme events—“tail” change in the climate brings is thus given by events that reflect events in the tails of the hazard distribution—is expected to increase. This includes an increasing likelihood of catastrophic floods, (Eq. 4) drought, heat waves, and cyclones. Additionally, the average weather is expected to change with assuming everything else stays equal. Where h’(.) temperatures increasing everywhere and wetness is the probability distribution of weather outcomes increasing in some places. under climate change. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 13 Prosperity Note There are six aspects to the changing climate that iv. Higher temperatures will reduce labor will impact welfare: productivity and in the extreme increase i. The frequency and intensity of extreme weather mortality (as the risk of heat-associated deaths events that cause asset and income losses will increases) and morbidity, as a changing climate increase (see, for example, Hallegatte et al., will increase the prevalence of some pathogens 2016; Birkmann et al., 2022). (see, for example, Hsiang et al., 2019; Carleton ii. Agricultural incomes will, in most cases, fall et al., 2022). (see, for example, Ortiz-Bobea et al., 2021). v. Sea level rise will make some places In most countries close to the equator, climate uninhabitable (see, for example, Oppenheimer change will bring yield reductions with income et al., 2019). Land currently occupied by losses for agricultural households (Bezner Kerr approximately a third of Bangladesh’s et al., 2022) and ambiguous impacts on food and Vietnam’s populations is projected to prices (see, for example, Costinot et al., 2016). permanently fall below the high tide lines by In some countries (for example, in Eastern 2100 (Kulp & Strauss, 2019). Africa and Mongolia), this will bring yield vi. Higher temperatures and rainfall shocks have increases and income gains. been linked to more civil conflicts (see Burke, iii. Although less well understood, droughts will Hsiang, and Miguel [2015] and Koubi [2019] for also have impacts in urban areas through their a comprehensive review), crimes (Blakeslee & impacts on health and productivity. In Latin Fishman, 2018), and gender-based violence America, droughts are estimated to have four (Miguel, 2005; Sekhri & Storeygard, 2014). times the impact on income losses than floods in urban areas (Damania et al., 2017). 2.4. Measuring the Welfare Impacts of Climate Change Damage or Vulnerability Functions relies on estimates derived from observed data and identifies the causal impact of weather changes on A key component of any measure of the welfare outcomes of interest by examining the effects of impacts of climate change is the vulnerability specific weather events in a particular location at or damage function (these terms can be used a specific point in time. The underlying assumption interchangeably) that relates losses in welfare is that while the probability distribution of an event to climate conditions. This has been estimated may not be considered exogenous, its timing— several ways in the climate economics literature conditional on the distribution—is assumed to be (see Auffhammer [2018] for a good review). exogenous to the households. Auffhammer (2018) and Dell et al. (2014) provide an overview of the Analyses focusing on weather shocks can be method. In summary, data on hazards are matched used to examine the impact of extreme weather to household observations in survey data using events on welfare, corresponding to estimate the the best available data on the geographic location vulnerability function described in equation (2). The of a household (either admin codes or GPS) and method most commonly used in welfare analysis a fixed effects regression is run to identify the CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 14 Prosperity Note impact of weather in determining welfare. Baquie being estimated reflects the causal impact of the & Foucault (2023), Gascoigne et al. (2024), and changing climate on the outcome of interest. Table Strobl & Spencer (2024) provide a recent overview 1 summarizes and highlights the advantages and of methodological considerations. caveats of each approach. While these analyses are well identified and provide The first approach, estimating the impact of hazards information on the impacts of extreme weather on welfare outcomes (income, consumption, events that are becoming more frequent with poverty, or nutrition outcomes) for exposed people, climate change, the response to random short- has been a core aspect of applied microeconomic term fluctuations in weather might be very different analysis, including much work by the World Bank. from adaptation responses to permanent shifts Dell et al. (2014) provide a review of earlier work and changes in average conditions. For instance, on this and Kala et al. (2023) review some of the groundwater irrigation may provide a short-term recent work. Recent examples of World Bank work adaptation strategy, but that may not be available in in this area include Hill & Porter (2017), Pape & the long term if the resource is depleted. In addition, Wollburg (2019), Baez et al. (2019), Baquie & Fuje given few extreme events are observed, the ability (2020), and Kochhar & Knippenberg (2023). Other to estimate the impact of truly extreme events is work looks at how extreme weather events have also limited. It is not necessarily the case that the impacted local economic activity. For example, impact of these events can be extrapolated using Zaveri et al. (2023) use subnational GDP data and coefficients estimated on more moderate events. find that weather shocks reduce GDP growth by 0.4–0.8 percentage points. An alternative approach is using observed trend changes to examine the impact of climate Although damage/vulnerability functions have change. In these analyses, the c_t in equation (2) been estimated in many contexts for many years, does not reflect a specific event but rather the they have not typically been used to model those at change in the climate over time. The coefficient risk to climate shocks or the likely poverty impacts being estimated is much closer to the coefficient of future shocks or climate trends. The next section required to estimate the impact of climate change summarizes work on welfare risk from extreme on welfare, but the challenge of these analyses is weather events that are likely to become more that it is much less plausible that the coefficient frequent with climate change. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 15 Prosperity Note Table 1: Estimating Damage Functions Summary Data Approach of method requirements Advantages Caveats Examples 1. Regress welfare on 1. Gridded historical 1. Impact is causally 1. The impact estimated Baez & measures of the climate data identified (for is: Santos weather (linearly 2. Repeated survivors, see • The direct loss and (2007); or nonlinearly), measurements caveats) not impacts beyond Deschênes & controlling for of welfare 2. Can be used to the local area, such as Greenstone unit-specific and with location understand changes those that might arise (2007, time-period fixed coordinates (can in welfare over time, from wider disruption of 2011); Pape effects be on different predict impacts markets, macro effects & Wollburg Panel or 2. To estimate individuals (cross- of climate shocks, • Often a moderate (2019); repeated differential sectional) or the generate probability shock: extreme Baquie & cross- damage impacts same individual distributions of events are unlikely Fuje (2020); sections for different types (panel data) welfare, and simulate to be observed in the Kochhar & of welfare of individuals, how welfare changes household data, limiting Knippenberg data and interact the shock with a changing ability to estimate (2023); Hill weather with household climate losses for extreme & Porter shocks to characteristics events (2017); estimate • The impact after Gascoigne et the currently available al. (2024) welfare ex post and ex ante impact of measures are used, extreme masking some of the events true cost of shocks and uninsured risk 2. The estimates are also subject to survivor bias (consumption only measured for those still in place after the shock) Regress welfare on 1. Gridded historical 1. Allow for private 1. Omitted variable bias Mendelsohn long-term weather weather data adaptation to climate since average climate et al. (1994); variables (allowing over a substantial conditions, based could be correlated with Seo & for nonlinear effects), period of time on the intuition other unobserved time- Mendelsohn along with control 2. Cross-sectional that in a stationary invariant factors (2008); Dell variables measure climate, individuals 2. Assuming costless et al. (2009); of welfare optimize according adaptation Severen et Ricardian with location to the environment 3. Traditional Ricardian al. (2018) cross- coordinates they face and then approach only considers sectional compare outcomes in historical climate approach hot versus cold areas matters, assuming to 2. Rely on data readily individuals base their estimate available actions on historical the impact climate. Forward- of a looking Ricardian changing approach (Severen et climate al., 2018) corrects the direction and magnitude of the bias caused by failure to incorporate climate expectations into current behaviors CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 16 Prosperity Note Construct long- 1. Gridded historical 1. Accounting for 1. Omitted variable bias Dell et al. term welfare and weather data time-invariant may remain—e.g., (2012); weather variables 2. Panel measure and time-trending trends in local Burke & at two different of welfare unobservables, thus emissions could affect Emerick points in time for a with location provide plausibly both climate and (2016) given location, then coordinates causal estimates welfare outcomes Long calculate changes of damages 2. Differential trends could difference in outcome as a that account be driven by short-run estimation function of long-term for observable variation in weather to changes in average adaptation by using around the chosen estimate climate conditions differential climate endpoint periods the impact trends (e.g., decadal 3. Baseline used of a changes) for comparison changing 2. Can be used to test incorporates some climate whether the shorter degree of adaptation run damages of 4. Significant data climatic variation requirements (broad on outcomes are in spatial coverage of data fact mitigated in the over long periods of longer run time) Source: This table is drawn from three review articles: Dell et al. (2014), Auffhammer (2018), and Baquie & Foucault (2024). Quantifying the Welfare Risk of Extreme Weather In looking at Malawi, Gascoigne et al. (2024) use Events historical data on a soil moisture index (figure 4) and simulated weather data (based on historical Once a vulnerability function is estimated, it can be weather patterns). The graph shows the increase in combined with hazard and exposure data to estimate the poverty rate that results from different weather the expected welfare loss of different climate events events. Figure 5 comes from Bodewig et al. (2021), (equation [3]). These can be aggregated up to who use the Stress Testing Social Protection tool provide maps or national estimates of climate risk to to determine the number of households that social welfare outcomes. Examples of this work are Porter protection support needs to reach in the event of a & White (2016), Hill & Porter (2017), and Kochhar & drought shock in Ethiopia. Knippenberg (2023). CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 17 Prosperity Note Figure 4: Impact of Drought on Poverty Rate in Malawi Figure 5: Change in Number of Rural Pool as a Result of Rainfall in Ethiopia Source: Gascoigne et al., 2024. Source: Bodewig et al., 2021. Both examples use the historical distribution Another concern with this approach, as discussed of shocks rather than the future distribution of briefly above, is that it may fail to capture the shocks. Baquie & Foucault (2023) describe how impacts of future extreme events, particularly if the this approach can be used to provide estimates predicted intensity of these events falls outside the of risk that take into account projected weather historical distribution. This limitation is significant changes. The analysis involves two steps: (1) in the context of climate change, where extreme estimating the historical impact of weather on events are likely to exceed historical norms in both welfare using methods already highlighted, and frequency and severity. Baquie & Foucault (2023) (2) predicting the impact of future weather by recommend that researchers carefully review the simulating consumption distribution resulting from variables and model specifications to effectively the forecasted distribution of weather. account for non-linearities in climate phenomena. It is also worth noting that these models capture It is important to note that this approach (and the vulnerability of households with the characteristics mapping and proxy approach described below) and behaviors they have today. Future adaptation focuses on the direct impacts of climate shocks, measures may alter how households respond to in that those exposed are those living in locations shocks of similar magnitude to those historically where the shock occurred. Having a clearer observed. For example, Barreca et al. (2016) find understanding of the degree to which indirect that the adoption of residential air-conditioning in effects (such as impacts on food prices) are an the United States led to the decline in the mortality important part of the welfare impact of these impact of hot days by 75 percent. shocks is important for understanding the degree to which this partial equilibrium analysis captures This approach is data intensive but provides the overall welfare cost of extreme events. The information on the size of need resulting from answer to this question may well differ by context, climate risk. However, this is not needed for many depending on the level of market integration. policy questions, such as which parts of the country CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 18 Prosperity Note are more at risk from extreme weather events. (for risk modeling, this is usually expressed in Additionally, in the absence of data to estimate return period, the expected number of years vulnerability, reasonable proxies have been used in which the event will occur once). Much can to estimate who is likely to be vulnerable, and be learned from existing work on mapping those approaches can also be used to provide an hazards (see, for example, the Global Facility indication of the number of people at risk even for Disaster Reduction and Recovery’s overview when intensive modeling work is not possible. We of commonly used hazard data sets). There are turn to these approaches now. some hazards for which choosing the data is more challenging, such as drought, but recent work suggests that the Normalized Difference Counting and Mapping Those at Risk Vegetation Index (NDVI) or a soil moisture index works well across settings. Much work has been undertaken by the World Bank, particularly in Poverty and Equity Assessments • Exposure: Gridded population data are used and Country Climate and Development Reports for exposure when it comes to outcomes, (CCDRs), to identify which places have more people such as welfare, which affect people. If there at risk of being negatively impacted by an extreme are shocks that some people are not exposed weather event. This work uses variables that are to—for example, drought conditions for those expected to be correlated with the vulnerability living in a city—then the population that is function (for example, current welfare level or exposed (in this example, the rural population) asset holdings of a household) and overlays these can be used for the exposure layer. Most measures with measures of hazard and exposure to mapping work in the practice uses the Global provide an indication of which parts of a country are Human Settlement Layer from the European more or less at risk. Commission – Joint Research Centre). One size does not fit all, and the indicators that • Vulnerability: The Geospatial Poverty Portal are selected will likely need to be tailored to the provides poverty data at the admin 1 or 2 country level to reflect the nature of the climate level for most countries in the world and is a risks that the country is most exposed to. However, useful starting point for looking at an income some useful points can be noted from the work dimension of vulnerability (box 1). In countries done to date: where poverty maps are available, poverty at a finer level of disaggregation can be used. • Hazard: Choosing the right hazard data In the absence of poverty map data, some requires choosing the nature of the risk (for teams have used the relative wealth index example, flood, drought), the severity of event (Chi et al., 2022). Data on additional aspects that would be considered (for example, water of vulnerability, such as access to markets, depth in the case of flood, or water deficit in finance, and social protection, which are the case of drought), and the probability that captured in household surveys have also been a given location will experience such an event used (Doan et al., 2023). CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 19 Prosperity Note BOX 1: The Difference Between Measuring Vulnerability and Vulnerability to Poverty In the hazard, exposure, and vulnerability framework, vulnerability is a characteristic of a person or household. In the welfare literature, vulnerability refers to the likelihood of being poor—a function of both the characteristics of the household and the uncertainty the households face as a result of climate and other shocks. While the “vulnerability to poverty” concept is not being used in this paper, we review in this box how the literature has measured the concept, as it is still used in welfare analysis. Many of the early estimates on measuring vulnerability to poverty used panel data that had repeated observations of welfare outcomes on the same individual. Given this type of data is often not available, there is a long tradition in the World Bank of providing an estimate of the vulnerability to poverty by using insights from available panel data work. This requires using panel data to estimate the level of consumption or income that an individual would need in order to have a low probability of falling into poverty. López-Calva & Ortiz-Juarez (2014) proposed using a 10 percent probability of becoming poor to identify those vulnerable to poverty. Using this approach, they calculated that the vulnerability threshold for Chile, Mexico, and Peru is about 2.5 times the poverty line. Similar analysis in Indonesia found that the vulnerability threshold is 1.5 times the poverty line (Jellema et al., 2017; World Bank, 2019b). This approach—of setting a vulnerability to poverty line at some multiple of the poverty line—has been used in World Bank regional reports and poverty assessments for Brazil, Cambodia, Mongolia, Tanzania, Türkiye, Uganda, Vietnam, and others. The multiples used range from 1.25 to 2.5 times the poverty line depending on the context.a In the context of the United States, several studies have considered the threshold between 1.25 and 2 times the Federal Poverty Guidelines to identify those who are poor or near poor, including Montgomery et al. (1996), Heggeness & Hokayem (2013), Hair et al. (2015), Saczewska-Piotrowska (2016), and Dube (2019). a. Recent poverty assessment reports use a range of multiples of the poverty line to define vulnerability to poverty: 1.25 for Cambodia (Karamba et al., 2022), 1.5 for Indonesia and Myanmar (Pape & Ali, 2023; World Bank, 2022h), 1.7 for Vietnam (World Bank, 2022c), 1.75 for EAP (Ruggeri Laderchi et al., 2017), 2 for Brazil, Europe and Central Asia, Honduras, Peru, and Uganda (World Bank, 2022a; Bussolo et al., 2018; Robayo-Abril et al., 2023; Word Bank, 2023d; World Bank, 2016), and almost 2.5 for Latin American and the Caribbean (World Bank, 2021). A number of reports undertaken by the World Bank Pakistan (World Bank, 2023a; World Bank, 2022f). have overlaid exposure and poverty. For instance, Other work explores overlaying other dimensions of figure 6, taken from the 2022 Vietnam Poverty and vulnerability (see, for example, the Project Targeting Equity Assessment, illustrates the overlap between Index work). Doan et al. (2023) provide a global poverty and environmental risk maps in Vietnam estimate of the number of people at risk of extreme (World Bank, 2022c). Additional examples can be weather events (flood, drought, cyclone, or heat found in the Poverty and Equity Assessments of wave); these are the people who are both exposed Brazil and Honduras (World Bank, 2022a; Robayo- and highly vulnerable. Vulnerability is assessed by Abril et al., 2023) and the CCDRs for Cambodia and a set of indicators capturing the physical propensity CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 20 Prosperity Note to experience severe losses (proxied by the lack (proxied by low income, not having education, not of access to basic infrastructure services) and having access to financial services, and not having the inability to cope with and recover from losses access to social protection). Figure 6: Overlapping Maps of Vulnerability (Measured by Poverty) and Exposure to Hazards in Vietnam Source: World Bank, 2022c. Modeling the Welfare Impacts of Climate Change impact of climate change: (a) top-down approaches, consisting of the reweighting and behavioral A body of literature on assessing the welfare approaches, and (b) the shock waves approach. impacts of climate change uses damage functions Both approaches consist of two main steps: (i) using to estimate welfare losses resulting from the the latest household survey data, demographic changing trend in climate conditions. Much of projections, and the outcomes of macroeconomic the published literature uses impacts on GDP models2 to project the distribution of households per capita as a proxy for welfare impacts rather and household incomes into the future, and (ii) than modeling the distributional implications using predictions of the impact of climate changes of climate change (Costinot et al., 2016; Cruz & on incomes and prices to estimate the impact of Rossi-Hansberg, 2021). Prior to the establishment climate change on this future income distribution. of CCDRs, few World Bank studies examined the Hernani-Limarino et al. (forthcoming) review the impacts of climate change on poverty, with the strengths and challenges of each method and offer notable exception of Hallegatte et al. (2016), which guidance for applications. Table 2 is taken from this introduced the shock waves approach. review and goes into more detail on how each step Two main ex ante microsimulation approaches are is done, and the main strengths and limitations of used in CCDRs to assess the future distributional each approach. 2. The two most commonly used macroeconomic models are a macro-fiscal model (MFMod, Burns et al., 2019) and full general equilibrium models Environmental Impact and Sustainability Applied General Equilibrium (ENVISAGE) model (Van der Mensbrugghe, 2010) and Mitigation, Adaptation, and New technologies Applied General Equilibrium (MANAGE) model (Van der Mensbrugghe, 2020). CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 21 Prosperity Note Table 2: A Summary of Microsimulation Approaches Top-down reweighting Top-down behavioral Shock waves • Demographic changes (age, • Demographic changes • Demographic changes (age, schooling, location) and (age, schooling, location) schooling, location) and employment status changes incorporated through changes employment status changes incorporated through changes in the population weights in the incorporated through changes in the population weights in the survey. Changes in employment in the population weights in the survey (and corresponding wage survey. • Wages adjusted through changes) simulated from an • Wages adjusted through Method used residual growth to match labor econometric labor supply residual growth to match labor to establish productivity growth assumption model productivity growth assumption baseline • Nonlabor incomes adjusted • Additional wage adjustment • Nonlabor incomes adjusted to match nonlabor incomes through residual growth to to match nonlabor incomes and tax/transfers changes match labor productivity growth and tax/transfers changes assumptions assumption assumptions • Nonlabor incomes adjusted to match nonlabor incomes and tax/transfers changes assumptions • Use demographic forecasts • Use demographic forecasts • Use demographic forecasts from global institutions for from global institutions for from global institutions for demographic changes demographic changes demographic changes • Use macro model simulation • Use macro model without • Use a sample of a combinatorial without climate change for climate change for assumptions of historical trends for assumptions on changes in on residual labor productivity changes in distribution of distribution of employment growth, nonlabor income employment status (e.g., status (e.g., activity, changes and tax/transfer activity, unemployment, unemployment, and sector of changes and sector of employment), Baseline model employment), residual labor labor productivity growth, inputs productivity growth, nonlabor nonlabor income changes income changes and tax/ and tax/transfer changes. The transfer changes sample can be created with or without correlation between assumptions. If some of these variables are available from a macro model, it is possible to use a macro model simulation without climate change • New set of changes in • New set of changes for labor • Household specific climate distribution of employment productivity growth, nonlabor shocks that reduce labor and status (e.g., activity, income changes and tax/ nonlabor income depending on unemployment, and sector of transfer changes provided by a the household characteristics Climate change employment), labor productivity macro model simulation with impacts growth, nonlabor income climate change impacts changes and tax/transfer changes provided by a macro model simulation with climate change impacts CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 22 Prosperity Note • Typically, one or two baseline • Typically, one or two baseline • Hundreds of baseline scenarios scenarios versus two or more scenarios versus two or more combined one by one with two Treatment of climate change scenarios climate change scenarios climate impacts scenarios, or uncertainty with random draws of climate scenarios taken within the two extremes • Macroeconomic consistency • Macroeconomic consistency • Macroeconomic consistency not ensured but requires a macro ensured but requires a macro required but can be run in the model model absence of a macro model • Limits the set of households to • Labor market dynamics • Limits the set of households to the ones initially present in the modeled, but calibrations of the ones initially present in the survey behavioral equations may not survey • General equilibrium effects of hold in the long run • Some—not all—general Advantages and climate impacts are captured • General equilibrium effects of equilibrium effects of climate disadvantages • Climate impacts are only climate impacts are captured impacts are modeled captured if they significantly • Climate impacts are only • Captures the heterogeneity impact employment and wages captured if they significantly of impacts as well as targeted at the macro level, some of the impact employment and wages impacts on small segments of heterogeneity of impacts is lost at the macro level, some of the the population heterogeneity of impacts is lost Source: Hernani-Limarino et al. (forthcoming). Although there are some differences in projecting waves approach uses damage functions to directly the distribution of incomes into the future, the main estimate impacts of climate change on households. difference comes in how the impacts of climate This allows for much more heterogeneity in impacts change are dealt with. The top-down approaches to be retained. However, the predictions are not take the outputs of a macro-model that uses necessarily consistent with a macro-model and not damage functions to estimate the impacts of climate all general equilibrium impacts of climate change change on the economy. The predicted impacts on impacts will necessarily be taken into account. income and prices from these models are then used Some of the impacts will be missed as a result. The to predict distributional impacts. The advantage of shockwaves approach also has a much stronger this approach is that the impacts on households are treatment of uncertainty, running hundreds of consistent with a macro model, but the disadvantage baseline scenarios and multiple draws of climate is that much of the heterogeneity of impacts on scenarios within two extremes. households is lost in this approach. The shock CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 23 Prosperity Note 3. THE WELFARE IMPACTS OF CLIMATE POLICIES 3.1. The Impact of Climate Policies on Welfare Addressing the environmental impacts of human costs on others that the actors do not consider. activities requires a comprehensive understanding These externalities call for policy interventions to of their effects and the implementation of targeted target the source of environmental harm and set policy interventions. Fossil fuel use and degradation the prices accordingly to reflect environmental of natural resources come with significant damages while protecting the affected, vulnerable environmental side effects. Negative externalities population from both climate change and the occur when the actions of individuals or firms impose negative impacts of climate policies. Although CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 24 Prosperity Note climate policies are often categorized based on responses to climate change can impact welfare whether they primarily contribute to mitigation or outcomes and generate distributional effects. adaptation efforts, these objectives do not have Climate actions bring welfare benefits by improving to be mutually exclusive. Mitigation actions aim to the probability distribution of hazards and/or reduce and/or stabilize greenhouse gas emissions, reducing exposure and vulnerability. For the such as transitioning from fossil fuels to renewable purpose of the following discussion, we combine energy or implementing carbon capture and storage actions to reduce exposure and vulnerability into or enhance natural carbon sinks through forest and one category. Additionally, climate actions can also land management. Adaptation or resilience building have welfare implications separate from the effect actions aim to moderate harm or seize beneficial they have on hazards, exposure, and vulnerability. opportunities, including preventive measures like They can carry a cost (either an actual cost or a cost urban greening, early warning systems, and resilient in terms of opportunity when money is not used for infrastructure, as well as coping mechanisms such other purposes) or bring an additional benefit that as shock-responsive social protection or disaster directly impacts welfare. We thus add an additional management systems. channel of impact to the IPCC framework. Figure 7 summarizes this adapted framework that we use to further discuss the welfare impacts of a Adapting the Hazard, Exposure, Vulnerability climate policy on an individual through three main Framework for Welfare Analysis of Climate Action channels: (i) hazard, (ii) exposure and vulnerability, and (iii) other benefits or costs to households. The hazard, exposure, and vulnerability conceptual framework can be adapted to explore how policy Figure 7: Welfare Impacts of Climate Policy CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 25 Prosperity Note The first channel—hazard—involves the impact of associated with weather shocks (Burgess & climate policies on the alteration of the probability Donaldson, 2010; Baffes et al., 2015). Additionally, distribution of climate, leading to a reduction or policies can support households in pursuing dampening of the intensity and occurrence of future economic diversification and resilient livelihoods, climate hazards. The timing of this channel may reducing their reliance on income activities that vary depending on the policy. Mitigation measures, are more susceptible to climate-related hazards. such as transitioning to low-carbon industrial Higher level of education may allow households to processes and materials or implementing carbon diversify income or switch sectors when a shock capture and storage technologies, can stabilize occurs (Reardon, 1997; Tesfaye & Tirivayi, 2020). the climate, but their effects are realized over an Public or private insurance instruments that pay extended period (Martin et al. [2014] in the United out or provide transfers to households in bad Kingdom, Andersson [2019] in Sweden, Metcalf weather outcomes (for example, shock-response [2019] in British Colombia; Rafaty et al. [2021] in social protection policies) also reduce vulnerability Europe). The IPCC Climate Change 2023: Synthesis by increasing the ability of households to cope Report suggests that significant and sustained with the impacts. The strategic choice between reductions in emissions can result in noticeable focusing on reducing exposure or vulnerability changes in atmospheric composition within a few must be informed by an assessment of whether it years and a slowdown in global warming within is exposure or vulnerability that drives the greater approximately two decades. However, policies impact of climate on poor households’ welfare. can also bring about immediate changes in local While vulnerabilities are nearly always higher for weather conditions by modifying the natural and poorer households, exposure may or may not be built environment. For example, urban trees can higher for poorer households and may or may not reduce the urban heat island effect and the risk of be driving differential effects (see, for example, heat waves (Harlan et al., 2006; Ziter et al., 2019; Banzhaf et al., 2019). Schwaab et al., 2021). The third channel involves the costs and benefits In the second channel—exposure and of climate policies in relation to households’ vulnerability—policies can reduce household’s accumulation, use of, and return to assets that exposure and vulnerability to climate. Policies are independent of climate impacts. This policy- such as timely evacuation or planned relocations induced change in welfare can be positive; for for communities in uninhabitable areas enable example, investments in education or health households to move themselves or their assets can increase the returns to household’s human to less hazardous locations, thereby reducing capital by increasing their productive capacity their exposure (Cools et al., 2016; Miller & Dun, and, subsequently, their income. It can also be 2019). Policies can also reduce vulnerability by negative; for example, removal of energy subsidies increasing the various forms of capital available or the introduction of carbon pricing may reduce to poor households, enabling them to better the net returns earned from livelihoods that use cope with climate shocks. For example, resilient carbon (Dorband et al., 2019). In addition, given infrastructure can reduce vulnerability to floods constrained public resources, financing climate (Pant et al., 2018), while well-integrated markets action could come at the cost of reducing other and value chains can reduce price impacts welfare-enhancing investments. It is important CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 26 Prosperity Note to consider the direct and opportunity cost of and upskill coal miners, can improve their financing climate action in assessing the net welfare. Such programs not only provide an welfare impact, though accurate costing of climate alternative to an industry that is becoming financing is challenging because of a lack of obsolete but also have the potential to uniform methodologies, especially for adaptation, increase their income (Pao-Yu et al., 2020). and standardized reporting on climate finance.3 3. The full beneficial impacts of climate actions There are three important points to note for on hazards are realized over the long run welfare analysis: (Tomorrow in figure 8), whereas the costs of climate actions are more likely to be felt in the 1. Environmental externalities mean that the near term (Today in figure 8). However, future benefits from climate action that improves benefits are discounted when they are valued the distribution of hazards are likely to be today and discount rates can vary across felt by many more people than are impacted people—they are often higher for poorer by policy-induced changes in welfare. This people (Tanaka et al., 2010; Haushofer & means that while the costs of climate action Fehr, 2014)—and between citizens and policy may be felt by few, the benefits will be enjoyed makers (policy makers often discount the by many. Importantly, in the case of climate future at high rates). While a discount rate has change, the externality extends across to be chosen for policy analysis, it is important countries, and also across generations (see to do robustness checks for different discount point 3). The fact that the externality is across rates, to see how that changes the valuation of countries requires a separate consideration to different policies (for an example, see Clarke cross-country issues for policy making. & Hill [2013]). 2. There can be considerable heterogeneity The total social welfare impact of a policy on a across households in the magnitude and society will be the sum of the welfare impacts on direction of the second (exposure and each person across the welfare distribution, taking vulnerability) and third (policy-induced change into account discount rates and social welfare in welfare) channel in figure 7. This means that weights. There can be trade-offs across different the overall welfare impact of a climate action generations and groups of people. can be negative for some households while positive for others. For example, the cost of switching from coal to renewable energy is higher for coal miners who lack human capital related to green skills (Hanson, 2023). Nevertheless, a comprehensive package of policies, including training programs to reskill 3. For some global estimates, see World Bank and IMF (2022). CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 27 Prosperity Note Figure 8: The Welfare Impacts of Climate Policy: Taking Timing Into Account Note: Changes in a household’s exposure and vulnerability are labeled simply as vulnerability for ease of exposition We can reflect this using the equations set out in While the benefits of mitigation actions are often section 2. The welfare impact of climate action realized in the future through the first channel, implemented at time t (Policy_t) is the sum of the there can be both short- and medium-term policy’s impact on W_ik and on the aspect of welfare costs and benefits through the second and third that is not dependent on weather outcomes, channels. These policies have a cost now and there is thus a trade-off between paying now (whether in monetary terms or in growth forgone) (Eq. 5) and benefiting in the future. These trade-offs can take various forms, including potential increases This can be written as the impact of the policy in energy prices and the prices of other inelastic on the hazard distribution, the vulnerability of a goods (Boyce, 2018). They can also involve shifts household with a given exposure, and the welfare in sectoral labor demand, requiring adjustments in impacts independent of climate, given by g(.) the workforces and skill sets (Borissov et al., 2019; Carbone et al., 2020). Furthermore, mitigation actions may require investments in new production processes, green technologies, and infrastructure (Eq. 6) upgrades (Blanco et al., 2022). They may bring As noted in point 3 above, many policies impact about changes in ways of living, consuming, and h_kt only in the future. Consider a policy that only thinking (Adger et al., 2013). Moreover, mitigation impacts the h_kt, at time t+n, but affects household actions can lead to changes in land-use patterns, assets in the present. The discounted expected shifts in agricultural practices, and the restoration welfare impacts (V_ik) of Policyt can be written as of natural systems (Hurlbert et al., 2019). Although trade-offs are often most evident in mitigation actions, they can also arise in adaptation actions that redirect resources toward coping with disasters, potentially affecting productive uses of (Eq. 7) those resources (Antwi-Agyei et al., 2018). where δ_i reflects the rate at which individual i discounts the future. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 28 Prosperity Note 3.2. Policies with Minimal Trade-off Policies that bring the largest welfare gains across [2022]). We highlight a few areas where policy time should be prioritized. These may be policies support is likely to bring triple wins: with positive welfare impacts across all three • Climate-smart agricultural practices increase channels—mitigating future hazards, reducing agricultural productivity while sequestering exposure and vulnerability, and generating income carbon and reducing the vulnerability of for low-income households—or policies that have agricultural income to extreme weather large benefits through one or two of the channels events. For example, Sahel farmers use low- with little or low costs from the other channels. cost, efficient traditional practices, such These policy choices are being made under as agroforestry and rainwater harvesting considerable uncertainty about future states of the techniques, to accumulate rainfall, reduce world. In reality, hk,t+n. is not known; instead, runoff, and restore soils. Rainwater harvesting we have expectations over this with uncertainty and agroforestry have been shown to increase around these expectations. The risk of investing in soil carbon sequestration at the estimated rates the wrong thing is very real, and policy making is of 839 kilograms and 1,359 kilograms of carbon being undertaken in this environment. This makes per hectare per year, respectively, in Africa the idea of no-regrets policies particularly important. (World Bank, 2012). In Niger, these practices No-regrets policies bring benefits today because were found to increase yields (Aker & Jack, of contemporaneous (or near-contemporaneous) 2021), which is consistent with the findings of impacts on the hazard distribution, or in reducing older studies that showed that yields were 16– exposure of vulnerability, or policies that bring 30 percent higher among farmers implementing benefits of income growth regardless of the weather. these techniques in Niger and Burkina Faso This is another reason why prioritizing triple- and (Matlon, 1985). These practices also reduce double-win policies is particularly important. vulnerability to low rainfall, allowing yield increases in low rainfall years.4 • Policies that protect forests and mangroves Triple Wins for Poor People can generate incomes for local communities Policies that have positive welfare impacts across while improving the climate and reducing all three channels for people at the bottom of the vulnerability to weather extremes (Zaveri income distribution can be thought of as triple- et al., 2023). For example, in the United win policies for poor people. Although few policies States, a large-scale reforestation program may be triple wins, clear sets of policies fall into in the 1930s across six Midwestern states this category. The IPCC Sixth Assessment Report led to enduring impacts over several decades focuses on adaptation and contains a useful of increased precipitation and reduced summary of policies that reduce vulnerability while temperatures as well as increased crop yields bringing improvements in the hazard distribution by 11–22 percent (Grosset et al., 2023). and income benefits (see figure SPM.4(b) in IPCC Halting annual mangrove deforestation in Indonesia could reduce emissions by 10–31 4. For more information on the Sahel example, see Baquie & Hill (2023). CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 29 Prosperity Note percent of estimated annual emissions from inefficient fleet management or the prevalence land-use sectors at present (Murdiyarso et of empty cargos. In Central America, imperfect al., 2015). Additionally, mangroves serve as competition accounts for at least 35 percent of barriers against cyclones, storms, and tidal mean prices on national routes and improving surges and as a main source of income for cost efficiencies and increasing competition nearby households (Das et al., 2022). could significantly reduce freight transport prices (Osborne et al., 2014). Addressing • Improving access to clean and improved these inefficiencies can simultaneously impact energy sources can also bring triple wins. emissions positively, reduce vulnerability Over half of all wood harvested worldwide is by better integrating markets, and promote used as fuel which contributes to black carbon economic growth. and other greenhouse gas emissions but also accelerate forest degradation and deforestation This initial list of actions that offer triple wins (Hutton et al., 2006; Bond et al., 2013; FAO, for households at the bottom of the income 2023). The depletion of wood resources distribution across various contexts needs further leads to increased time and labor burden to work to expand it. While the potential for triple collect firewood for households, particularly wins exists across different geographical contexts for women. Additionally, traditional biomass and sectors, the key policy question becomes how stoves increase health risks associated with to identify the constraints that prevent progress in poor air quality, even resulting in premature these areas, and to design policies to overcome deaths (Person et al., 2012). Transitioning to these challenges. For instance, climate-smart improved and cleaner cooking stoves thus agricultural practices or agroforestry may require significantly contributes to mitigating climate a fundamental shift in traditional techniques, change, reducing exposure and vulnerability necessitating new skills or knowledge that farmers through decreased deforestation and habitat may not initially possess. Training increased the destruction, and improving household welfare adoption of soil conservation practices in the Sahel. by reducing health risks and pressures on Farmers trained on traditional soil conservation women’s time use. practices in the Sahel were 90 percent more likely to adopt these practices, and 50 percent more • Implementing energy-saving measures in likely to have neighbors adopting the technique buildings not only decrease energy consumption than untrained farmers (Aker & Jack, 2023). and energy costs, thereby increasing disposable However, providing this type of knowledge at scale income for households, they also reduce can be challenging. exposure to indoor air quality pollutants as well as extreme climate events such as heat waves Financial constraints are another common barrier, or cold spells (World Bank, 2019a). as up-front costs might be prohibitive for low- income households, and credit products are often • Reducing inefficiencies in trade, particularly in not available, particularly for investments with road freight transport, presents an opportunity long-run benefits. Addressing these constraints can for a triple-win policy. Trucking inefficiencies significantly increase take-up. In Poland, energy- are a significant challenge, often exacerbated saving measures in buildings were supported by regulations that limit competition and lead to through the Clean Air Priority Program, which CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 30 Prosperity Note provided financial and operational schemes to Cash transfers that support climate-smart support low-income single-family building owners agricultural practices and natural resource in upgrading their heat sources and investing in management can simultaneously address thermo-modernization. climate change, enhance resilience, and improve livelihoods. Ethiopia’s flagship public works Even when people have enough information, program alleviated poverty and food insecurity, training, and financial resources to access these bolstered community resilience against shocks technologies, behavioral barriers can hinder their (Wiseman et al., 2010), and increased tree possibility to access them—for example, the factor cover through soil and water conservation and of risk aversion; farmers might be hesitant to reforestation initiatives (Hirvonen et al., 2022).6 adopt new practices due to uncertainty about the In Burkina Faso, conditional environmental cash outcomes. Moreover, cultural and social norms can transfers raised household consumption by 12 influence the willingness to adopt new methods, as percent and reduced severe food insecurity by 60 practices deeply ingrained in community identity percent while aiming to increase the survival rate of may not be easily altered. trees planted on degraded forest lands (Adjognon Importantly, the existing policy environment and & Guthoff, 2021). the incentives it provides can impede widespread There is thus an important role in identifying take-up in these areas. Traditional agricultural both the high-value triple-win opportunities and support through input subsidies, output payments, the constraints that impede progress in a given or market price interventions often results in context. The type of work undertaken in the Rural extensive environmental damage (Damania et Income Diagnostic tool can help in this regard. al., 2023). These subsidies can distort farmers’ For example, in Burkina Faso the tool highlighted agricultural production decisions, discouraging the opportunity of soil conservation practices for climate-smart agricultural practices and natural agricultural income growth for poor households resource management.5 Refocusing on triple-win and the knowledge, credit, and policy incentives strategies may require a strategic shift in policy, that constrained the realization of this opportunity repurposing agricultural subsidies toward climate- (World Bank, 2019c). smart agriculture and resilient natural resource management. Existing incentives also shape the design and implementation of climate actions. For Double Wins for Poor People example, research by Healy & Malhotra (2009) demonstrates that voters tend to reward incumbent While it is useful to identify the triple wins, many politicians for allocating funds to disaster relief policies that offer significant benefits in reducing efforts but not for investments in disaster vulnerability and improving income growth, even preparedness. Recent work by Hallegatte et al. after accounting for their costs, have minimal (2023) explores the political economy dimensions impacts on the future hazard distribution. These in more detail. policies are just as important to prioritize. Indeed, it is not the number of areas of positive impact 5. It is important to caution that the process of removing input subsidies may have significant impacts on households. Ghose et al. (2023) estimate that a fertilizer import ban in Sri Lanka leads to declines in crop yields by 1–14 percent across crops and welfare for both landowners and workers. While workers suffered less than landowners everywhere, workers living in heavily agricultural regions still face as much as a 3 percent income reduction. 6. Analyzing a similar public works program in Malawi, Beegle et al. (2017) find no evidence that the program improves food security and suggestive evidence of negative spillovers to untreated households. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 31 Prosperity Note that matter but the overall welfare benefit from policies, can increase emissions. At low levels each policy. In this section, we highlight some of of growth or for very poor countries, growth in the evidence on what these policies are. Additional emissions may have a negligible impact on the review work will add to this set of examples. hazard distribution in the future (Wollburg et al., 2023). The anticipated welfare impact of these For example, mobile money spurs development, policies is thus expected to be very high. thereby increasing welfare (Batista & Vicente, 2023). When a weather crisis strikes, it also In middle-income countries, or in scenarios where allows households to quickly receive transfers or growth rates are high, the increase in emissions remittances quickly from relatives or migrant family is substantial, potentially accelerating the members who live elsewhere (Jack & Suri, 2014). worsening of future hazard distributions (Wollburg Similarly, better access to roads in remote areas et al., 2023). For example, Gertler et al. (2016) increases access to markets, goods, and services, demonstrate the nonlinear relationship between thereby bringing development. When drought income and both asset ownership and energy reduces local food availability, improved access to consumption in Mexico. Policies that promote markets reduces the impact of this weather shock renewable energy and increase the cost of carbon on local food prices (Burgess & Donaldson, 2010). can sustain growth and alleviate poverty while Education increases an individual’s ability to earn reducing emissions. This approach aligns with key income—and the gain in lifetime earnings usually findings from evaluating policy packages in the more than offsets the cost of public spending Pakistan CCDR (World Bank, 2022f). (Hendren & Sprung Keyser, 2020)—but it also allows households to switch sectors when climate shocks reduce returns in the sector in which they are engaged. Furthermore, a recent study shows each additional year of education leads to increases in pro-climate beliefs, behaviors, most policy preferences, and even green voting across 16 European countries (Angrist et al., 2023). Moreover, the impact of education on voting is significant and equates to a substantial 35 percent increase. None of these policies and similar policies would be considered adaptation investments designed to reduce the vulnerability of households to climate events, but they can be highly effective in reducing vulnerability. Without actions to reduce the carbon footprint of goods and energy consumed in a country, development policies, like many growth-enhancing CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 32 Prosperity Note 3.3. Addressing Trade-offs: Reducing Vulnerability at a Cost to Income Growth In many cases, however, the cost of reducing levels given the cost of reducing the variance of hazard, exposure, or vulnerability is not offset by consumption and risk preferences. Policies that resulting income growth. There is a net cost for increase the availability of low-cost risk reduction these actions. In this section, we consider the costs and management strategies need to be prioritized. arising from reducing exposure/vulnerability (with The framework set out by Ehrlich & Becker (1972) a focus on vulnerability), and in the next section highlights the need to think about optimizing we consider the costs arising from improving the investments in risk reduction and risk management hazard distribution. concurrently. The framework examines the In the case of vulnerability, many of the available interaction between market-provided insurance, risk reduction and risk management strategies self-insurance, and self-protection, defining market come with a cost for average incomes.7 For insurance as a contract in which an individual pays example, in the case of risk reduction, agricultural in advance to reduce losses when they occur, self- practices that reduce vulnerability by reducing protection as actions taken to reduce the probability yield losses under weather extremes often also of loss, and self-insurance as actions taken to result in lower agricultural income in normal reduce the size of loss when it occurs. The authors weather years. Kala et al. (2023) provide a review note that actions that reduce the probability of loss of this evidence. One notable paper is by Hultgren often also reduce the size of the loss when this et al. (2022), who show that agricultural practices occurs, and this has implications for investments in that adapt to rising temperatures depress yields both self-insurance and in the demand for market during periods of moderate temperatures. In insurance. The key insight is that the availability of the case of risk management, the cost of private market insurance, if appropriately priced, need not insurance is always higher than the actuarial fair reduce the demand for self-protection, avoiding price of insurance. The cost of publicly financing the usual concerns with moral hazard. shock-responsive safety nets may or may not be While the model self-protection refers to actions recovered through the income growth enabled; undertaken by individuals, it could also be public there is currently not enough evidence in this area actions that need to be taken to protect the well- to say. being of those most at risk. Such actions include The welfare benefit to spending on reducing a range of risk reduction investments, which vulnerability comes from the fact that individuals simultaneously change both the probability and the are risk averse and thus prefer to reduce size of losses. These include efforts to green cities, their consumption to minimize its variance, in climate-proof key infrastructure, increase the particular the downside losses. Given there is energy efficiency of buildings, and improve water a cost to reducing the variance in consumption, management, as well as investments in climate- no variance in consumption is not always the resilient agricultural practices and landscape optimal solution. Welfare is maximized when the restoration and reforestation, among others. variance in consumption is reduced to acceptable 7. Risk reduction strategies are often called ex ante strategies, in that action is taken that reduces the impact of the shock prior to the shock occurring. They are also referred to as income smoothing. Risk management strategies are often called ex post strategies, in that they reduce the impact of the shock once it has occurred. They can also be referred to as consumption smoothing. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 33 Prosperity Note Self-insurance refers to a range of actions that are in place, there is evidence that they play a individuals take to manage shocks to income without protective role (Gehrke, 2019; Gelb et al., 2022; relying on formal risk transfer arrangements. This Afridi et al., 2022). could be holding savings to draw on when income There is also considerable evidence that cash falls, relying on informal networks for a temporary transfers help households manage shocks. consumption loan, or increasing income from other However, the evidence is predominantly for the sources. Hill et al. (2021) and Kala et al. (2023) impact of regular cash transfers (see Hill et al. look at the degree to which these strategies are [2021] for a summary). Rigorous evidence on the used by poor households and their impact. impact of cash transfers provided in response to Market insurance can be reframed as formal a disaster is much more limited, but it also shows insurance. Market insurance refers to contracts in significant long-run welfare benefits. In Fiji, an which there is an up-front payment to a second impact assessment conducted three months after party that undertakes to provide income in the a tropical cyclone found that households that event of a state-contingent realization. In practice, received cash transfers recovered more quickly. although impactful when provided (as discussed For example, they were 8–10 percent more likely in section 2), cost-effective insurance products to have recovered from housing damage than non- are not widely present for the types of climate beneficiaries (Mansur et al., 2018). Assessing risks faced by poor and near-poor households the same cash transfer program, Ivaschenko et in low- and middle-income countries and take- al. (2020) use a sharp regression discontinuity up tends to be low (see Hill et al. [2021] for a design to show that beneficiaries who received review). Some types of product do show promise cash transfers recovered much more quickly than (such as livestock insurance) and supporting those who did not. Del Carpio & Macours (2009) the development of these markets may become and Macours et al. (2013) both use a cluster RCT increasingly important. However, in principle, such to evaluate a conditional cash transfer (consisting state-contingent payments could be financed and of a transfer paid every two months) implemented possibly also managed by the state—say, through by the Nicaraguan government in the aftermath adaptative social protection schemes that provide of a drought. The evaluations document positive support to farm households faced with crop failure persistent impacts on height-for-age scores, caused by drought, flood, or other extreme weather cognitive and psycho-social development, and event, or support poor households if food prices child labor (particularly for boys). rise above a threshold value because of a climate- One paper rigorously examines the impact of related event, and so on. anticipatory cash transfers—cash transfers provided Programs that have built-in automatic stabilizers, before the full impacts of a disaster materialize— such as unemployment insurance or self-tar¬geted and similarly finds strongly positive impacts. Pople employment guarantee schemes, automatically et al. (2021) exploit administrative constraints target those in need as a result of a climate disaster experienced during the rollout of anticipatory through self-selection. However, unemployment cash transfers in Bangladesh to compare treated insurance covers few people in contexts where households with otherwise comparable households rates of self-employment or informal employment that did not receive the cash transfer. Households are high. When employment guarantee schemes receiving the transfer were less likely to go without CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 34 Prosperity Note eating during the flood and reported higher child in unintended and negative consequences (New and adult food consumption (4 percent and 7 et al., 2022). Such findings underscore the need percent, respectively) after the flood. Asset losses for careful planning and implementation, as these and costly borrowing were lower and earning maladaptive actions can worsen inequalities and activity was higher for those receiving the transfers. increase vulnerabilities in affected communities. This issue is particularly critical in relation to Lastly, a significant concern when policies overlook irrigation infrastructure and commonly implemented incentives is the risk of maladaptation. Responses insurance schemes, which are prone to well- to climate change can inadvertently create or documented moral hazard problems. aggravate risks through maladaptation actions. For instance, well-intentioned investments aimed at providing short-term relief may lead to costly lock-in effects. Over time, path dependence can set in and make corrective measures more difficult and costly to implement. An example of this is the construction of sea walls, which can cause coastal erosion farther down the coastline, or irrigation practices upstream that exacerbate water scarcity downstream. A comprehensive study referenced in the The IPCC Sixth Assessment Report, which analyzed over 300 climate change adaptation initiatives, discovers that approximately a third of these efforts resulted 3.4. Addressing Trade-offs: Reducing Hazards While Minimizing Welfare Costs Transitioning toward a low-carbon, climate- To manage the trade-off, the goal should be resilient economy, often referred to as the green to pursue a “just transition” (see box 2 for a transition, usually involves a trade-off between discussion of how this term has been used in the paying now and future benefits. These policies bring World Bank and other multilateral development future climate benefits by altering the probability banks) with a policy mix of climate mitigation distribution of hazards (the first channel in figure policies accompanied by compensatory actions 8’s Tomorrow panel), but they have a cost now that would increase the benefits for poor people (the second and third channels in figure 8’s Today while minimizing the costs. For example, revenue panel). In addition, at a given point in time, the cost from carbon taxes could be recycled not only of the green transition is higher for specific people to targeted transfers but also to productive even though the future benefits are shared by all. investments in reskilling and upskilling, facilitating Short-term costs, such as higher energy prices mobility, and reducing market frictions and credit or job losses in carbon-intensive sectors, can be market failures, among others. Such investments particularly hard for poorer people to manage. actively support poor households in transitioning Therefore, assessing how the green transition rather than just compensate them for losses. impacts poor and vulnerable people and designing policies to reduce negative impacts are essential. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 35 Prosperity Note BOX 2: What Is Meant by a “Just Transition”? At the 2019 UN Secretary General’s Climate Action Summit, the multilateral development banks (MDBs) collectively pledged to “support a just transition that promotes diversification and inclusion” and developed a set of high-level principles to guide MDB support for a just transition.a Among these, principle 4 emphasizes that “MDB support for a just transition seeks to mitigate negative socio-economic impacts and increase opportunities associated with the transition to a net zero economy, supporting affected workers and communities, and enhancing access to sustainable, inclusive and resilient livelihoods for all.”b At the World Bank, the Just Transition for All Initiative primarily focuses on the shift away from coal. This initiative aims to “put people and communities at the center of the transition” and “work with stakeholders to create the plans, policies, and reforms needed to mitigate environmental impacts, support impacted people, and build a new clean energy future.”c The main emphasis is on facilitating the transition for workers and communities affected by the closure of coal mines and power plants (Calice & Demekas, 2023). However, the World Bank undertakes a wide range of work that supports countries to transition to a lower-carbon growth path, reflecting the people-centered approach outlined in the World Bank Group Climate Change Action Plan 2021-2025. a. High level MDB statement: For publication at the UNSG Climate Action Sumit, 22 September 2019. https://www. adb.org/sites/default/files/page/41117/climate-change-finance-joint-mdb-statement-2019-09-23.pdf. b. While the World Bank Group was not part of the MDB group that prepared the principles, inputs from the World Bank Group were included. See MDB Just Transition High-Level Principles (https://www.eib.org/ attachments/documents/mdb-just-transition-high-level-principles-en.pdf). c. Just Transition for All: The World Bank Group’s Support to Countries Transitioning Away from Coal, Understanding Poverty, Extractive Industries, World Bank, https://www.worldbank.org/en/topic/ extractiveindustries/justtransition. There are two key aspects of a just transition:8 go up and deepen, as well as support to transition to more efficient appliances, better-quality housing, 1. Minimizing the cost to real household and cleaner transport use. consumption and supporting the transition to clean energy consumption for the bottom quintiles Indirect subsidies, like those for energy, are often and vulnerable groups. Poor and vulnerable equivalent to a higher share of market income for households often face higher energy prices caused poorer households (World Bank, 2022h). Removing by reforms aimed at reducing fossil fuel subsidies these subsidies without offering compensatory or implementing carbon taxes. Energy costs can be measures would disproportionately affect poor a large share of the budget of poor and vulnerable households. Figure 9 presents an incidence analysis households, as they tend to be inefficient users of from 94 economies, illustrating the amount that energy because of outdated appliances and poorly would be lost in household consumable income insulated housing. When policy reform increases (shown as a share of market income) if energy energy prices, poor and vulnerable households subsidies were removed. need compensation to ensure that poverty does not 8. We focus here on welfare impacts rather than aspects related to the process of a just transition. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 36 Prosperity Note Figure 9: Subsidy Incidence Curve (Benefits as a Percentage of Market Income, by Decile) Source: World Bank, 2022h. Compensatory measures, such as social for enabling substitution toward cleaner and more assistance, can help reduce the impact on real efficient technologies. household consumption for poor people when energy prices rise, as seen with carbon taxes or the elimination of energy subsidies. However, 2. Supporting the transition to low-carbon/low- the current design of social protection structures methane employment and livelihoods for bottom is not entirely effective in protecting the poor quintiles and vulnerable groups: This requires and vulnerable from rising energy prices. The support for workers, communities, and regions World Bank (2022h) highlights that spending on affected by the transition away from carbon- subsidies dwarfs social protection spending in intensive economic activity. The improved hazard lower-middle-income countries (LMICs) and low- distribution is often shared across people in a given income countries (LICs). This finding is confirmed location, but the cost can be different for different in Mukherjee et al. (2023). A recent study by Hoy people in the same location. For example, the cost et al. (2023) shows the importance of considering of switching from coal to solar energy is larger for other types of compensatory measures, such as a person who works in a coal mine. According to investments in roads and education. They find the World Bank’s 2022 China CCDR, the profile of that public support for reducing energy subsidies people in sectors that contract with climate action is low in the absence of compensatory measures will be very different from the profile of those in but can double or even triple when such reforms sectors that expand with climate action (figure are paired with compensatory policies such as 10). This illustrates the need for policy packages investments in roads and education financed by the to support individuals at risk of job losses and job savings from subsidy cuts. Furthermore, in addition displacement, including compensatory transfers, to compensatory policies to compensate poor insurance programs, and active labor market households for the income effect of raising prices, labor policies such as reskilling, upskilling, and job complementary policies like investments in public searching and matching programs. transit or clean cooking programs will be critical CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 37 Prosperity Note Figure 10: Workers in Contracting Sectors Have a Different Profile (Low-Skilled Males Working Inland) Than Workers in Expanding Sectors (High-Skilled Males Working in Coastal Urban Areas) a. Workers’ Characteristics in Contracting Sectors. b. Workers’ Characteristics in Expanding Sectors. (% Relative to 2018) (% Relative to 2018) Source: World Bank, 2022b. Note: Graphs show the distribution of jobs lost and gained, holding job characteristics fixed. Assessing how the green transition impacts poor 1. The impact on real household consumption and vulnerable people is important for designing 2. The adoption of clean energy consumption just transition policies. The following subsections 3. The transition to low-carbon/low-methane highlight examples of recent World Bank work employment and livelihoods undertaken in this area (summarized in figure 11): Figure 11: Approaches to Measure the Welfare Impact of Green Transition Policies Change in real household consumption, estimated Clean energy adoption, estimated using using Partial equilibrium General equilibrium Household surveys, accompanied by behavioral analysis (CEQ, FiscalSim, analysis (macro-to-micro analysis of the barriers to adopt SUBSIM, WELCOM) modeling) + Increased energy price Green transition Compensatory policies Job and livelihood displacement Change in employment and livelihood, estimated using Bottom-up approaches (household Partial equilibrium analysis (SIMLAB, General equilibrium analysis and worker surveys, task-based Integrated Assessment of Agricultural (macro-to-micro modeling) analysis using occupational and System) employment data) CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 38 Prosperity Note Assessing the Cost of Increases in Energy Prices tools is available in Lustig (2023). Based on the on Real Household Consumption CEQ foundation, the World Bank has developed a customized fiscal-policy microsimulation There has been substantial research assessing the (FiscalSim) tool to assess the distributional effects distributional impacts of carbon tax and subsidy of fiscal and social spending reforms (Gao & policies. Changes in households’ real incomes Inchauste, 2020). FiscalSim can be used to estimate and expenditures can be estimated using partial how policy reforms may affect income distribution, equilibrium analysis (including fiscal incidence work incentives, and government budgets as well as and fiscal microsimulation analysis) or general the effects of changes in policies over time. The tool equilibrium analysis (macro-micro modeling). can also be used to compare proposed, alternative, Additionally, the Climate Policy Assessment Tool or hypothetical policy changes, and the implications (CPAT), an Excel-based tool developed by the World of alternative economic or demographic scenarios. Bank and the International Monetary Fund (IMF), Two other prepackaged simulation models are provides a quick overview of the economic effects, SUBsidy SIMulation (SUBSIM), which facilitates including distributional effects, of carbon price rapid distributional analyses of energy subsidies intervention and subsidies.9 and simulations of subsidy and price reforms (Araar & Verme, 2012), and Welfare and Competition (WELCOM), which measures the distributional Partial Equilibrium Analysis effects of changes in market structure induced Fiscal microsimulation analyses can help analyze through either regulatory reform or easing of trade existing policies as well as simulate alternative barriers (Rodriguez-Castelan et al., 2019). policy scenarios, including alternative compensation schemes and mitigation policies. The Commitment The above methods can be used to evaluate the size to Equity (CEQ) methodology is a diagnostic and mechanisms needed for compensatory transfers tool used to determine the distributional impact to minimize the cost faced by poor households. For of fiscal policy by assessing the incidence and example, simulations for Tunisia show that poverty impact on poverty and inequality of each tax and increases can be compensated through a combination social spending intervention and their combined of additional safety net and pension transfers (Molini impact. An overview of the CEQ methods and & Pavelesku, 2018) (figure 12). Figure 12: Change in Poverty After Subsidy Removal and Compensatory Mechanism Source: Molini & Pavelesku, 2018. 9. CPAT documentation: https://cpmodel.github.io/cpat_public/. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 39 Prosperity Note A study in Ukraine shows that the poorest quintile Housing and Utility Subsidy (HUS) program was was getting the lowest energy and housing subsidies extended to most eligible households (figure 14). and privileges (Olivier et al., 2023). Strikingly, only The actual data from the Ukraine Household Living 16 percent of the energy subsidies were going to Condition Survey 2016 show that 55 percent of the the poorest quintile (see figure 13). Simulations poorest quintile benefited from the HUS program with alternative thresholds and norms show that in 2016, after coverage of the program grew from 8 this share would increase to 30 percent when the percent in 2013 to 42 percent in 2016. Figure 13: Distribution of Total Energy Subsidies (HUS) and Figure 14: Distribution of Simulated Benefits When Housing and Utility Privileges Before the Reform (2013) in Extending HUS Coverage in Ukraine Ukraine Source: Olivier et al., 2023. Note: Data from Ukraine Household Source: Olivier et al., 2023. Note: Data from Ukraine Household Living Conditions Survey 2013. HUS = Housing and Utility Living Conditions Survey 2013. Norms are social norms for Subsidy program, PRIV = Housing and Utility Privileges program housing and utility consumption. Thresholds are the thresholds for the benefit formula. General Equilibrium Analysis CCDR also shows that tax reform to increase the The top-down microsimulation approaches progressivity of the tax system can create space to described in the section on “Modeling the Welfare compensate the most vulnerable households for Impacts of Climate Change” can estimate both losses from higher carbon prices (figure 16) (World the distribution of climate change and climate Bank, 2023b). action on real household consumption. The example illustrated in figure 15 comes from the Kazakhstan CCDR, showing that phasing out fossil fuel subsidies can generate savings to the budget to offset the adverse impacts of price changes on the poor (World Bank, 2022d). The Colombia CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 40 Prosperity Note Figure 15: Potential impact of US$20 Carbon Price with Figure 16: Percentage Change in Household Consumption, Full Redistribution to the Bottom 40 Percent: Relative Mean by Quintile, Relative to a Scenario Without Comprehensive Consumption Effect Tax Reforms Source: World Bank, 2022d. Source: World Bank, 2023b. Note: Analysis is based on a US$20 carbon price by 2030, 10 percent auctioning by 2030, an equivalent carbon tax on transport, 100 percent recycling of revenue, and 20 percent leakage in transfers. Addressing Financial and Behavioral Constraints households in need of stove upgrades or retrofitting, to Adoption which informed the program to allocate costs to households for upgrades and assess affordability Addressing the barriers faced by households, after receiving support from CAPP and tax relief particularly low-income households, in transitioning (World Bank, 2020). to clean energy consumption, is also an important part of ensuring a just transition. Identifying these World Bank analysis in Europe, Central Asia, and barriers is a first step to overcoming them, and there Africa increasingly shows the nonfinancial and is an increasing literature that explores how to do behavioral barriers faced by households and how this (e.g., Berkouwer & Dean 2022). Increasingly, to support behavioral change in transitioning to World Bank analytical work has also focused on this. clean energy consumption. A behavioral diagnostic For example, distributional analysis undertaken to of sustainable heating transitions in the Western inform the World Bank’s operational support for Balkans emphasizes the need to design and the Clean Air Priority Program (CAPP) in Poland implement programs that consider the attitudes, shows that lower-income households are less beliefs, and preferences of the target population able to afford the cost of heat source upgrades. On toward investments in cleaner, more efficient the basis of this analysis, the program formulated technologies. The study finds that awareness financial and operational schemes to support and knowledge about available programs and single-family building owners upgrading their heat initiatives significantly increase the willingness sources and investing in thermo-modernization. to upgrade heating systems (Karver et al., 2023). The project uses a modeling approach to identify Relative to respondents with no awareness about CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 41 Prosperity Note the existence of the programs, those aware with directly, indirectly, or through induced effects. In full knowledge and those who know someone who the context where rich data are available, such as benefited from subsidies are 18 and 17 percentage the United States, researchers can possibly explore points (respectively) more likely to want to upgrade these questions in great details. For example, their heating systems. Furthermore, there is Hanson (2023) shows the spatial distribution heterogeneity in the respondent characteristics in of employment in fossil-fuel-intensive activities the willingness to upgrade, with older respondents, across the United States from 2000 forward and single-person households, and couples around then examines how policy options address the 12 percentage points less likely to be willing to consequences of job loss caused by the decline in upgrade heating. coal mining since 1980. However, more often than not, data availability is more constrained in the LIC Underlying biases, norms, and mental models that and LMIC context, and even with a narrow focus, impede the adoption of solar home systems and tracking workers, households, or firms directly clean cooking by households in Sub-Saharan Africa impacted by the green transition requires panel was similarly explored by Coony et al. (2021). surveys. For example, understanding the impacts Interviews with households reveal that many on employment requires detailed surveys with barriers center around perceptions of the utility of information on whether displaced workers remain solar home systems, including their financial values, unemployed—with or without compensation—or thus requiring behavior change interventions to whether they manage to secure greener jobs with increase the uptake. comparable wages. Furthermore, the impacts of some green policies, such as a carbon border- adjustment tax or levies on non-green technologies, Assessing the Implications of a Green Transition are mostly indirect. Despite these challenges, it is on Employment and Livelihoods important to proactively address the essential data and methodologies required for monitoring the As discussed, the economic risks and benefits progress of a just transition, with a specific focus arising from the green transition may not be on the employment and livelihood dimension.10 uniformly distributed across worker profiles, time, and geographical locations. Consequently, there is This section reviews recent efforts undertaken an urgent need to assess distributional impacts on by the World Bank and others to understand jobs and address two key questions: the employment and livelihood impacts of the green transition in developing country contexts. 1. How can vulnerable workers be supported in Examples of emerging approaches include: (i) transitioning to a greener sector or occupation? measuring current green versus brown jobs and 2. How can agricultural households and small- who hold these jobs, (ii) modeling possible shifts in scale, informal enterprises be supported in labor demand, who may be affected, and impacts transitioning to greener production? of complementary policies (iii) assessing the possibility to move jobs given shifting demands, Attributing employment and livelihood impact and (iv) assessing the impacts of coal region to the green transition is challenging because it transition specifically. is difficult to identify who is impacted, whether 10. For an overview of studies that analyze the impacts of green policies on jobs, see Bowen & Kuralbayeva (2015) and Van der Ree (2019). CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 42 Prosperity Note First, measuring green versus brown jobs and Green Jobs Assessment Model (GJAM), which the profile of workers in those jobs can yield uses a dynamic-recursive model combining insights into who may be affected by the green macroeconomic equations with a supply-and-use transition. A new strand of work leverages detailed table (SUT) system. Several published reports occupation information to determine the extent using GJAM included low- and middle-income of an occupation’s “greenness.” Doan & Nguyen countries. The ILO’s training guidebook reviews (forthcoming) briefly review measuring green skills. methods of documenting green jobs and provides This approach considers a spectrum of greenness, assessment tools, including recommended surveys thus allowing for jobs to be identified as directly on employment in the environmental sector and or indirectly green. The most dominant bottom-up green jobs (ILO, 2017). approach is task-based analysis, which categorizes Regarding labor market policies, a tool like occupations based on the overall greenness of their Simulation of Labor Economics (SIMLAB), while task content. Vona et al. (2018) pioneered this originally not intended to look at climate, shows approach by leveraging comprehensive data sets the potential to explore the trade-offs associated from the US Occupational Information Network with specific climate policies and the impacts of (O*NET) taxonomy and granular employment data complementary actions. SIMLAB is a structural from the Bureau of Labor Statistics to identify jobs macro-micro simulation tool designed to analyze more susceptible to green transitions and explore the distributional impacts of human capital associated skills requirements in the United States. policies on the labor market and poverty. For Researchers have extended the O*NET taxonomy a comprehensive review, refer to Robayo-Abril to the occupational classification systems of other (forthcoming). This tool facilitates ex ante analysis economies based on the assumption that these of selected labor policy reforms, including human countries share the same set of green tasks (Valero capital policies, public sector employment policies, et al., 2021; IMF, 2022; OECD, 2023; Di Maro et formalization, and labor market policies. al., forthcoming). The World Bank has recently supported three initiatives in improving country- In the agrifood sector, Nico & Christiaensen (2023) specific occupational classification in Indonesia, analyze the implications of the green transition on Uruguay, and Vietnam. jobs by merging the results from existing, integrated agricultural assessment models with insights from Second, to assess possible labor market impacts, structural transformation and leveraging econometric a general equilibrium approach combined with estimates to provide a comprehensive view. macro-to-micro modeling can be used to study the impacts on employment, similar to the example of Third, to assess the possibility to move jobs given the China CCDR (figure 13). The Model of Innovation shifting demands, a complementary method is in Dynamic Low-Carbon Structural Economic the occupational mobility network analysis, which and Employment Transformations (MINDSET) visualizes the clustering and segregation patterns macro-to-micro model can also assess possible in occupational mobility and task similarity as shifts in labor demand, who may be affected, and economies transition to greener sectors and the impacts of select complementary policies.11 industries (Mealy, 2022). Since the economic Alternatively, the International Labour Organization benefits and risks of the green transition may not (ILO) has established the Green Jobs Assessment be uniformly distributed across worker profiles, Institution Network (GAIN) and developed the time, and geographical locations, this method helps 11. MINDSET is a post-Keynesian single-year model developed by the World Bank that focuses on the demand side and assumes that the economy is always operating below full capacity, meaning that more resources (such as labor) may be brought into production when needed. It is previously named Multiregional input-output (MRIO) model. Additional details on the modeling approach are in World Bank (2023a). CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 43 Prosperity Note inform the required skills, job quality, and locations regarding job attributes, and implementing a of jobs that might be affected by green policies and new artificial intelligence–powered job matching the green transition. tool designed to identify optimal alternative employment opportunities for affected individuals Fourth, assessing employment impacts is critical (similar idea to Mealy [2022]). Closely related to supporting a just coal region transition. to the last method, online job postings have Recent studies from the Lower Silesia, Silesia, increasingly become a powerful source of data for and Wielkopolska regions in Poland have researchers. Lassébie et al. (2021) apply machine introduced several innovative methods to study learning to classify skill requirements in online job the employment impacts of the coal transition postings in several Organisation for Economic Co- (Christiaensen, Ferré, Gajderowicz, & Wrona, 2022; operation and Development (OECD) countries into Christiaensen, Ferré, Gajderowicz, Ruppert Bulmer, broader skill categories and validate against the et al., 2022; Christiaensen, Ferré, Honorati, et al., O*NET information on skills by occupations. A few 2022). These methods include employing a novel other papers apply natural language processing bottom-up approach to calculate the number of and expert survey to online job vacancy data to workers indirectly affected by coal mine closures gain job-level perspectives in the United States (defined as positions in associated subsidiaries or (Saussay et al., 2022; Curtis et al., 2023). subcontractors with the mining conglomerates), utilizing discrete choice experiments to gauge the preferences of workers impacted by these closures 3.5. Cross-Border Issues and Climate Justice This framework has focused on country-level Report 2023 shows that about 75 percent of total welfare analysis. However, given that climatic historical emissions since 1850 can be attributed shocks can transcend national boundaries, to North America, Europe, and China alone, with and that climate change is a global externality, North America and Europe together accounting for policies in one economy can impact the probability nearly half of this total (Chancel et al., 2023). This, distribution of hazards in another. Thus, cross- combined with the fact that the largest losses are border issues are important to consider, even in in the world’s poorest countries, means that the country welfare analysis. In this section, we set poorest 50 percent of the world’s population faces out some of the recent work in this area to provide 75 percent of the relative losses caused by climate- some context and then discuss what this means for related events despite contributing only 12 percent country-level analysis. of emissions. In contrast, the world’s richest 10 percent are responsible for a massive 48 percent Overconsumption of carbon in high-income of emissions. countries has adversely altered the probability distribution of hazards in LICs and middle-income Two types of country-level analysis can be useful countries (MICs), causing the problem the world in providing more nuance to this story. First, collectively faces today. The Climate Inequality estimating energy growth that comes from income CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 44 Prosperity Note growth (see, for example, Gertler et al. [2016]), and second, showing that energy consumption within countries is also unequal and as a result the richest share of the population in some MICs consume more energy per capita than households in high- income countries. Even noting this nuance, policies in high- and upper- middle-income countries, paid for by the people in those countries, have the potential to bring benefits for poor people living in LICs and LMICs. If these wealthier nations were to implement policies to reduce current emissions, there would be a significant impact on global emissions with benefits for the future global hazard distribution. In contrast, policies that reduce emissions in LICs and LMICs will have less of an impact on the hazard distribution as a result of changing the trajectory of climate change, even though they may bring local environmental benefits such as improved air and water quality and lower temperatures. Kanbur (2023) makes the point that documenting the global benefit of mitigation policies undertaken by individual countries can help underscore the contribution they are making to a global good. He argues that given the historically low contribution of LICs and LMICs to the global climate crisis, this contribution to the global benefit merits financial compensation. The first step in this discussion is documenting the global benefit from domestic policy choices. Climate change is a global challenge that requires urgent, equitable global actions. While this entails a complex interplay of trade-offs and policy packages, thoughtful design and implementation of global climate interventions could help rectify the disproportionate burden shouldered by LMICs, contributing to a more equitable and effective global response to climate change. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 45 Prosperity Note References Adger, W. N., Barnett, J., Brown, K., & Marshall, N. (2013). Cultural dimensions of climate change impacts and adaptation. Nature Climate Change, 3(2), 112–117. Adjognon, G. S., & Guthoff, J. (2021). Reducing hunger with payments for environmental services (PES): Experimental evidence from Burkina Faso. American Journal of Agricultural Economics, 103(3), 831–857. Afridi, F., Mahajan, K., & Sangwan, N. (2022). Employment guaranteed? Social protection during a pandemic. Oxford Open Economics, 1. Aker, J. C., & Jack, B. K. (2023). Harvesting the rain: The adoption of environmental technologies in the Sahel. Review of Economics and Statistics, 1–52. Alderman, H., Hoddinott, J., & Kinsey, B. (2006). Long term consequences of early childhood malnutrition. Oxford Economic Papers, 58(3), 450–474. Andersson, J. J. (2019). Carbon taxes and CO2 emissions: Sweden as a case study. American Economic Journal: Economic Policy, 11(4), 1–30. Angrist, N., Winseck, K., Patrinos, H. A., & Zivin, J. S. G. (2023). Human capital and climate change (Working Paper No. 31000). National Bureau of Economic Research. Anik, A. R., Ranjan, R., & Ranganathan, T. (2018). Estimating the impact of salinity stress on livelihood choices and incomes in rural Bangladesh. Journal of International Development, 30(8), 1414–1438. Antwi-Agyei, P., Dougill, A. J., Stringer, L. C., & Codjoe, S. N. A. (2018). Adaptation opportunities and maladaptive outcomes in climate vulnerability hotspots of northern Ghana. Climate Risk Management, 19, 83–93. Araar, A., & Verme, P. (2012). Reforming subsidies: A toolkit for policy simulations (Policy Research Working Paper No. 6148). World Bank. Artuc, E., Porto, G., & Rijkers, B. (2023). Crops, conflict, and climate change [Working paper]. Auffhammer, M. (2018). Quantifying economic damages from climate change. Journal of Economic Perspectives, 32(4), 33–52. Baez, J. E., Caruso, G., & Niu, C. (2017). Tracing back the weather origins of human welfare: Evidence from Mozambique (Policy Research Working Paper No. 8167). World Bank Group. Baez, J. E., Kshirsagar, V., & Skoufias, E. (2019). Adaptive safety nets for rural Africa: Drought-sensitive targeting with sparse data (Policy Research Working Paper No. 9071). World Bank Group. Baez, J. E., & Santos, I. V. (April, 2007). Children’s vulnerability to weather shocks: A natural disaster as a natural experiment. Social science research network, New York, 1-28. Baffes, J., Kshirsagar, V., & Mitchell, D. (2015). What drives local food prices? Evidence from the Tanzanian maize market (Policy Research Working Paper No. 7338). World Bank. Baquie, S., & Fuje, H. (2020). Vulnerability to poverty following extreme weather events in Malawi (Policy Research Working Paper No. 9435). World Bank Group. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 46 Prosperity Note Baquie, S., & Hill, R. (2023). Real-world policies for a low-carbon future (improving water availability and restoring soil fertility in the Sahel). World Bank. Baquie, S., & Foucault, G. (2023). Background note on bringing climate change into vulnerability analysis. Equitable Growth, Finance, & Institutions Insight. World Bank. Barreca, A., Clay, K., Deschenes, O., Greenstone, M. & Shapiro, J.S. (2016). Adapting to climate change: The remarkable decline in the US temperature-mortality relationship over the twentieth century. Journal of Political Economy, 124(1), 105-159. Barrett, C. B. (2021). Overcoming global food security challenges through science and solidarity. American Journal of Agricultural Economics, 103(2), 422–447. Barrett, C. B., Ortiz-Bobea, A., & Pham, T. (2023). Structural transformation, agriculture, climate, and the environment. Review of Environmental Economics and Policy, 17(2). Batista, C., & Vicente, P. C. (2023). Is mobile money changing rural Africa? Evidence from a field experiment. Review of Economics and Statistics, 1–29. Beegle, K., Galasso, E., & Goldberg, J. (2017, September). Direct and indirect effects of Malawi’s public works program on food security. Journal of Development Economics, 128, 1–23. Banzhaf, S., Ma, L., & Timmins, T. (2019). Environmental justice: The economics of race, place, and pollution. Journal of Economic Perspectives, 33(1), 185–208. Berkouwer, S. B., & Dean, J. T. (2022). Credit, attention, and externalities in the adoption of energy efficient technologies by low-income households. American Economic Review, 112(10), 3291-3330. Bezner Kerr, R., Hasegawa, T., Lasco, R., Bhatt, I., Deryng, D., Farrell, A., Gurney-Smith, H., Ju, H., Lluch-Cota, S., Meza, F., Nelson, G., Neufeldt, H., & Thornton, P. (2022). Food, fibre, and other ecosystem products. In H.-O. Pörtner, D. C. Roberts, M. Tignor, E. S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, & B. Rama (Eds.), Climate change 2022: Impacts, adaptation, and vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, pp. 713–906. Cambridge University Press. Birkmann, J., Liwenga, E., Pandey R., Boyd, E., Djalante, R., Gemenne, F., Leal Filho, W., Pinho, P. F., Stringer, L., & Wrathall, D. (2022). Poverty, livelihoods, and sustainable development. In H.-O. Pörtner, D. C. Roberts, M. Tignor, E. S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, & B. Rama (Eds.), Climate change 2022: Impacts, adaptation, and vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, pp. 1171–1274. Cambridge University Press. Blakeslee, D. S., & Fishman, R. (2018). Weather shocks, agriculture, and crime: Evidence from India. Journal of Human Resources, 53(3), 750–782. Blanco, G., de Coninck, H., Agbemabiese, L., Diagne, E. H. M., Diaz Anadon, L., Lim, Y. S., Pengue, W. A., Sagar, A. D., Sugiyama, T., Tanaka, K., Verdolini, E., & Witajewski-Baltvilks, J. (2022). Innovation, technology development and transfer. In P. R. Shukla, J. Skea, R. Slade, A. Al Khourdajie, R. van Diemen, D. McCollum, M. Pathak, S. Some, P. Vyas, R. Fradera, M. Belkacemi, A. Hasija, G. Lisboa, S. Luz, & J. Malley (Eds.), Climate change 2022: Mitigation of climate change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, pp. 1641–1726. Cambridge University Press. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 47 Prosperity Note Bodewig, C., Calcutt, E., Conner, G., Cook, S., Gentilini, U., Hill, R., Majoka, Z., Narayan, A., Saidi. M., & Tassot, C. (2021). Stress testing social protection: A rapid appraisal of the adaptability of social protection systems and their readiness to scale-up. A guide for practitioners. World Bank Group. Bond, T. C., Doherty, S. J., Fahey, D. W., Forster, P. M., Berntsen, T., DeAngelo, B. J., Flanner, M. G., Ghan, S., Kärcher, B., Koch, D., Kinne, S., Kondo, Y., Quinn, P. K., Sarofim, M. C., Schultz, M. G., Schulz, M., Venkataraman, C., Zhang, H., Zhang, S., … Zender, C. S. (2013). Bounding the role of black carbon in the climate system: A scientific assessment. Journal of Geophysical Researcher: Atmospheres, 118(11), 5380–5552. Borissov, K., Brausmann, A., & Bretschger, L. (2019, September). Carbon pricing, technology transition, and skill- based development. European Economic Review, 118, 252–269. Boyce, J. K. (2018, August). Carbon pricing: Effectiveness and equity. Ecological Economics, 150, 52–61. Bowen, A., & Kuralbayeva, K. (2015). Looking for green jobs: the impact of green growth on employment. Grantham Research Institute Working Policy Report. London: London School of Economics and Political Science, 1-28. Brunckhorst, B., Hill, R., Mansuri, G., Nguyen, T., & Doan, M. 2023. Climate and Equity: A Framework to Guide Policy Action. Poverty and Equity Global Practice. World Bank. Bulte, E., Cecchi, F., Lensink, R., Marr, A., & Van Asseldonk, M. (2020, December). Does bundling crop insurance with certified seeds crowd-in investments? Experimental evidence from Kenya. Journal of Economic Behavior & Organization, 180, 744–757. Bundervoet, T., Verwimp, P., & Akresh, R. (2009). Health and civil war in rural Burundi. The Journal of Human Resources, 44(2), 536–563. Burgess, R., & Donaldson, D. (2010). Can openness mitigate the effects of weather shocks? Evidence from India’s famine era. The American Economic Review, 100(2), 449–453. Burke, M., & Emerick, K. (2016). Adaptation to climate change: Evidence from US agriculture. American Economic Journal: Economic Policy, 8(3), 106–140. Burke, M., Gong, E., & Jones, K. (2015). Income shocks and HIV in Africa. The Economic Journal, 125(585), 1157– 1189. Burke, M., Hsiang, S. M., & Miguel, E. (2015). Climate and conflict. Annual Review of Economics, 7(1), 577–617. Burns, A., Campagne, B. P. M., Jooste, C., Stephan, D. A., & Bui, T. T. (2019). The World Bank macro-fiscal model technical description (Policy Research Working Paper No. 8965). World Bank. Bussolo, M., Davalos, M. E., Peragine, V., & Sundaram, R. (2018). Toward a new social contract: Taking on distributional tensions in Europe and Central Asia. Europe and Central Asia Studies. World Bank. Cai, H., Chen, Y., Fang, H., & Zhou, L. A. (2015). The effect of microinsurance on economic activities: Evidence from a randomized field experiment. Review of Economics and Statistics, 97(2), 287–300. Cai, J. (2016). The impact of insurance provision on household production and financial decisions. American Economic Journal: Economic Policy, 8(2), 44–88. Calice, P., & Demekas, D. G. (2023). Mobilizing finance for the just energy transition in the European Union. Equitable Growth, Finance, & Institutions Insight. World Bank. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 48 Prosperity Note Carbone, J. C., Rivers, N., Yamazaki, A., & Yonezawa, H. (2020). Comparing applied general equilibrium and econometric estimates of the effect of an environmental policy shock. Journal of the Association of Environmental and Resource Economists, 7(4). Carleton, T., Jina, A., Delgado, M., Greenstone, M., Houser, T., Hsiang, S., Hultgren, A., Kopp, R. E., McCusker, K. E., Nath, I., Rising, J., Rode, A., Seo, H. K., Viaene, A., Yuan, J., & Zhang, A. T. (2022). Valuing the global mortality consequences of climate change accounting for adaptation costs and benefits. The Quarterly Journal of Economics, 137(4), 2037–2105. Carter, M. R., Little, P. D., Mogues, T., & Negatu, W. (2007). Poverty traps and natural disasters in Ethiopia and Honduras. World Development, 35(5), 835–856. Chancel, L., Bothe, P., & Voituriez, T. (2023). Climate inequality report 2023: Fair taxes for a sustainable future in the Global South. World Inequality Lab. Chi, G., Fang, H., Chatterjee, S., & Blumenstock, J. E. (2022). Microestimates of wealth for all low- and middle- income countries. Proceedings of the National Academy of Sciences, 119(3), Article e2113658119. Christiaensen, L., Ferré, C., Gajderowicz, T., & Wrona, S. (2022). Towards a just coal transition: Labor market challenges and people’s perspectives from Lower Silesia (Jobs Working Paper No. 69). World Bank. Christiaensen, L., Ferré, C., Gajderowicz, T., Ruppert Bulmer, E., & Wrona, S. (2022). Towards a just coal transition: Labor market challenges and people’s perspectives from Silesia (Jobs Working Paper No. 70). World Bank. Christiaensen, L., Ferré, C., Honorati, M., Gajderowicz, T., & Wrona, S. (2022). Towards a just coal transition: Labor market challenges and people’s perspectives from Wielkopolska (Jobs Working Paper). World Bank. Clarke, D., & Hill, R. V. (2013). Cost-benefit analysis of the African Risk Capacity facility (IFPRI Discussion Paper 1292). International Food Policy Research Institute. Coony, J., Lourenco, J. S., & de Martino, S. (2021, June 10). Shining a light on misconceptions about solar home systems. Development and a Changing Climate. Cools, J., Innocenti, D., & O’Brien, S. (2016, April). Lessons from flood early warning systems. Environmental Science & Policy, 58, 117–122. Costinot, A., Donaldson, D., & Smith, C. (2016). Evolving comparative advantage and the impact of climate change in agricultural markets: Evidence from 1.7 million fields around the world. Journal of Political Economy, 124(1), 205–248. Cruz, J. L., & Rossi-Hansberg, E. (2021). The economic geography of global warming (Working Paper No. 28466). National Bureau of Economic Research. Curtis, E., O’Kane, L., & Park, R. J. (2023). Workers and the green-energy transition: Evidence from 300 million job transitions (Working Paper No. 31539). National Bureau of Economic Research. Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A., & Hansen, M. C. (2018). Classifying drivers of global forest loss. Science, 361(6407), 1108–1111. Damania, R., Desbureaux, S., Hyland, M., Islam, A., Moore, S., Rodella, A., Russ, J., & Zaveri, E. (2017). Uncharted waters: The new economics of water scarcity and variability. World Bank CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 49 Prosperity Note Damania, R., Balseca, E., De Fontaubert, C., Gill, J., Rentschler, J., Russ, J., & Zaveri, E. (2023). Detox development: Repurposing environmentally harmful subsidies. World Bank. Das, S. C., Das, S., & Tah, J. (2022). Mangrove forests and people’s livelihoods. In S. C. Das Pullaiah, & E. C. Ashton (Eds.), Mangroves: Biodiversity, livelihoods and conservation, pp. 153–173. Springer. Deaton, A. S. (1997). The analysis of household surveys: A microeconometric approach to development policy. World Bank Group. Del Carpio, X. V., & Macours, K. (2009). Leveling the intra-household playing field: compensation and specialization in child labor allocation. World Bank Policy Research Working Paper, (4822). Dell, M., Jones, B. F., & Olken, B. A. (2009). Temperature and income: Reconciling new cross-sectional and panel estimates. The American Economic Review, 99(2), 198–204. Dell, M., Jones, B. F., & Olken, B. A. (2012). Temperature shocks and economic growth: Evidence from the last half century. American Economic Journal: Macroeconomics, 4(3), 66–95. Dell, M., Jones, B. F., & Olken, B. A. (2014). What do we learn from the weather? The new climate-economy literature. Journal of Economic Literature, 52(3), 740–798. Dercon, S. (2004). Growth and shocks: Evidence from rural Ethiopia. Journal of Development Economics, 74(2), 309–329. Deschênes, O., & Greenstone, M. (2007). The economic impacts of climate change: Evidence from agricultural output and random fluctuations in weather. The American Economic Review, 97(1), 354–385. Deschênes, O., & Greenstone, M. (2011). Climate change, mortality, and adaptation: Evidence from annual fluctuations in weather in the US. American Economic Journal: Applied Economics, 3(4), 152–185. Di Maro, V., Montoya, K., Olivieri, S., Vazquez, E., Winkler, H. (Forthcoming). Green Jobs, Dirty Sectors, and the Implications for a Just Transition: Evidence from Cross-Country Comparisons with a Particular Focus on Latin America and Caribbean Countries. World Bank. Doan, M. K., Hill, R., Hallegatte, S., Corral Rodas, P. A., Brunckhorst, B. J., Nguyen, M., Freije-Rodriguez, S., & Naikal, E. G. (2023). Counting people exposed to, vulnerable to, or at high risk from climate shocks—A methodology (Policy Research Working Paper No. 10619). World Bank Group. Doan, M. K., & Nguyen, T. (Forthcoming). Literature review on quantifying green jobs. World Bank Group. Donato, D. C., Kauffman, J. B., Murdiyarso, D., Kurnianto, S., Stidham, M., & Kanninen, M. (2011). Mangroves among the most carbon-rich forests in the tropics. Nature Geoscience, 4(5), 293–297. Dorband, I. I., Jakob, M., Kalkuhl, M., & Steckel, J. C. (2019, March). Poverty and distributional effects of carbon pricing in low- and middle-income countries—A global comparative analysis. World Development, 115, 246–257. Dube, A. (2019). Minimum wages and the distribution of family incomes. American Economic Journal: Applied Economics, 11(4), 268–304. Eberenz, S., Lüthi, S., & Bresch, D. N. (2021). Regional tropical cyclone impact functions for globally consistent risk assessments. Natural Hazards and Earth System Sciences, 21(1), 393–415. Ehrlich, I., & Becker, G. S. (1972). Market insurance, self-insurance, and self-protection. Journal of political Economy, 80(4), 623-648. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 50 Prosperity Note Elabed, G., & Carter, M. (2014). Ex-ante impacts of agricultural insurance: Evidence from a field experiment in Mali [Unpublished manuscript]. Elbers, C., Gunning, J. W., & Kinsey, B. (2007). Growth and risk: Methodology and micro evidence. The World Bank Economic Review, 1. eMBeD. (Forthcoming). Behavioral approaches to address climate change [eMBeD brief]. World Bank. Fafchamps, M., Udry, C., & Czukas, K. (1998). Drought and saving in West Africa: Are livestock a buffer stock? Journal of Development Economics, 55(2), 273–305. FAO. (2023). Sustainability by numbers—Forest products at FAO. Food and Agriculture Organization. Fuchs, A., & Wolff, H. (2016). Drought and retribution: Evidence from a large-scale rainfall-indexed insurance program in Mexico (Policy Research Working Paper No. 7565). World Bank. Gao, J., & Inchauste, G. (2020). A customizable microsimulation tool to analyze distributional effects of country fiscal policies (Poverty and Equity Note No. 37). World Bank. Gascoigne, J., Baquie, S., Vinha, K., Skoufias, E., Calcutt, E., Kshirsagar, V., Meenan, C., & Hill, R. (2024). The welfare cost of drought in Sub-Saharan Africa. World Bank. Gehrke, E. (2019). An employment guarantee as risk insurance? Assessing the effects of the NREGS on agricultural production decisions. The World Bank Economic Review, 33(2), 413-435. Gelb, A., Giri, A., Mukherjee, A., Rautela, R., Thapliyal, M., & Webster, B. (2022). Beyond India’s Lockdown: PMGKY Benefits During the COVID-19 Crisis and the State of Digital Payments. Policy Paper, 257. Gertler, P. J., Shelef, O., Wolfram, C. D., & Fuchs, A. (2016). The demand for energy-using assets among the world’s rising middle classes. The American Economic Review, 106(6), 1366–1401. Gijsman, R., Horstman, E. M., Friess, D. A., Swales, A., & Wijnberg, K. M. (2021, July 8). Nature-based engineering: A review on reducing coastal flood risk with mangroves. Frontiers in Marine Science, 8. Ghose, D., Fraga, E., & Fernandes, A. (2023). Fertilizer import bans, agricultural exports, and welfare: Evidence from Sri Lanka (Policy Research Working Paper No. 10642). World Bank Group. Grosset, F., Papp, A., & Taylor, C. (2023, January 26). Rain follows the forest: Land use policy, climate change, and adaptation. Climate Change, and Adaptation. Gunnsteinsson, S., Molina, T., Adhvaryu, A., Christian, P., Labrique, A., Sugimoto, J., Shamim, A. A., & West, K. P. (2022, September). Protecting infants from natural disasters: The case of vitamin A supplementation and a tornado in Bangladesh. Journal of Development Economics, 158, 102914. Hair, N. L., Hanson, J. L., Wolfe, B. L., & Pollak, S. D. (2015). Association of child poverty, brain development, and academic achievement. JAMA Pediatrics, 169(9), 822–829. Hallegatte, S., Godinho, C., Rentschler, J., Avner, P., Knudsen, C., Lemke, J., & Mealy, P. (2023). Within reach: Navigating the political economy of decarbonization. World Bank. Hallegatte, S., Bangalore, M., Bonzanigo, L., Fay, M., Narloch, U., Rozenberg, J., & Vogt-Schilb, A. (2014). Climate change and poverty—An analytical framework (Policy Research Working Paper No. 7126). World Bank. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 51 Prosperity Note Hallegatte, S., Bangalore, M., Bonzanigo, L., Fay, M., Kane, T., Narloch, U., Rozenberg, J., Treguer, D., & Vogt-Schilb, A. (2016). Shock waves: Managing the impacts of climate change on poverty. World Bank. Hallegatte, S., & Rozenberg, J. (2017, April 5). Climate change through a poverty lens. Nature Climate Change, 7, 250–256. Hallegatte, S., Vogt-Schilb, A., Bangalore, M., & Rozenberg, J. (2017). Unbreakable: Building the resilience of the poor in the face of natural disasters. Climate Change and Development. World Bank. Hanson, G. (2023). Local labor market impacts of the energy transition: Prospects and policies (Working Paper No. 30871). National Bureau of Economic Research. Harlan, S. H., Brazel, A. J., Prashad, L., Stefanov, W. L., & Larsen, L. (2006). Neighborhood microclimates and vulnerability to heat stress. Social Science & Medicine, 63(11), 2847–2863. Haushofer, J., & Fehr, E. (2014). On the psychology of poverty. Science, 344(6186), 862–867. Healy, A., & Malhotra, N. (2009). Myopic voters and natural disaster policy. American Political Science Review, 103(3), 387–406. Heggeness, M. L., & Hokayem, C. (2013). Life on the edge: Living near poverty in the United States, 1966–2011 (SEHSD Working Paper). U.S. Census Bureau. Hendren, N., & Sprung-Keyser, B. (2020). A unified welfare analysis of government policies. The Quarterly Journal of Economics, 135(3), 1209–1318. Hernani-Limarino, W., Freije-Rodriguez, S., & Rozenberg, J., (Forthcoming). Microsimulation approaches for the assessment of household welfare impacts of climate change and climate action (Guidance note). World Bank. Hill, R. V., Kumar, N., Magnan, N., Makhija, S., De Nicola, F., Spielman, D. J., & Ward, P. S. (2019, January). Ex ante and ex post effects of hybrid index insurance in Bangladesh. Journal of Development Economics, 136, 1–17. Hill, R. V., & Narayan, A. (2020). Covid-19 and inequality: A review of the evidence on likely impact and policy options (Working Paper No. 3). Centre for Disaster Protection, London. Hill, R. V., Campero Peredo, A., & Tarazona, M. (2021). The impact of pre-arranged disaster finance: Evidence gap assessment (Working Paper No. 7). Centre for Disaster Protection, London. Hill, R. V., & Porter, C. (2017). Vulnerability to drought and food price shocks: Evidence from Ethiopia. World Development, 96, 65–77. Hirvonen, K., Machado, E. A., Simons, A. M., & Taraz, V. (2022, July). More than a safety net: Ethiopia’s flagship public works program increases tree cover. Global Environmental Change, 75, 102549. Hoddinott, J., & Quisumbing, A. (2010). Methods for microeconometric risk and vulnerability assessment. In R. Fuentes-Nieva, & P. A. Seck (Eds.), Risk, shocks, and human development: On the brink, pp. 62–100. Palgrave Macmillan. Hoy, C., Kim, Y. S., Nguyen, M., Sosa, M., & Tiwari, S. (2023). Building public support for reducing fossil fuel subsidies: Evidence across 12 middle-income countries (Policy Research Working Paper No. 10615). World Bank. Hsiang, S. (2016). Climate econometrics. Annual Review of Resource Economics, 8(1), 43–75. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 52 Prosperity Note Hsiang, S., Oliva, P., & Walker, R. (2019). The distribution of environmental damages. Review of Environmental Economics and Policy, 13(1), 83–103. Hultgren, A., Carleton, T., Delgado, M., Gergel, D. R., Greenstone, M., Houser, T., Hsiang, S., Jina, A., Kopp, R. E., Malevich, S. B., McCusker, K. E., Mayer, T., Nath, I., Rising, J., Rode, A., & Yuan, J. (2022). Estimating global impacts to agriculture from climate change accounting for adaptation. SSRN Electronic Journal. Huizinga, J., de Moel, H., & Szewczyk, W. (2017, April 12). Global flood depth-damage functions: Methodology and the database with guidelines. JRC Publications Repository. Hutton, G., Rehfuess, E., Tediosi, F., & Weiss, S. (2006). Evaluation of the costs and benefits of household energy and health interventions at global and regional levels. World Health Organization. Hurlbert, M., Krishnaswamy, J., Davin, E., Johnson, F. X., Mena, C. F., Morton, J., Myeong, S., Viner, D., Warner, K., Wreford, A., Zakieldeen, S., & Zommers, Z. (2019). Risk management and decision-making in relation to sustainable development. In P. R. Shukla, J. Skea, E., Calvo Buendia, V. Masson-Delmotte, H.-O. Pörtner, D. C. Roberts, P. Zhai, R. Slade, S. Connors, R. van Diemen, M. Ferrat, E. Haughey, S. Luz, S. Neogi, M. Pathak, J. Petzold, J. Portugal Pereira, P. Vyas, E. Huntley, … J. Malley (Eds.), Climate change and land: An IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems, pp. 673–800. Cambridge University Press. ILO. (2017). How to measure and model social and employment outcomes of climate and sustainable development policies. International Labour Organization. IMF. (2022). World economic outlook: War sets back the global recovery. International Monetary Fund. Inchauste, G., & Victor, D. (2017). The political economy of energy subsidy reform. Directions in Development. World Bank. IPBES. (2019). Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science- Policy Platform on Biodiversity and Ecosystem Services. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. IPCC. (2023a). Health [Factsheet derived from the IPCC Sixth Assessment Report by Working Group II – Impacts, Adaptation and Vulnerability]. Intergovernmental Panel on Climate Change. IPCC. (2023b). Summary for policymakers. In Core Writing Team, H. Lee, & J. Romero (Eds.), Climate change 2023: Synthesis report. A report of the Intergovernmental Panel on Climate Change. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, pp. 1–34. Intergovernmental Panel on Climate Change. IPCC. (2022). Summary for policymakers. In H.-O. Pörtner, D. C. Roberts, M. Tignor, E. S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, & B. Rama (Eds.), Climate change 2022: Impacts, adaptation, and vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, pp. 3–36. Cambridge University Press. Jack, W., & Suri, T. (2014). Risk sharing and transactions costs: Evidence from Kenya’s mobile money revolution. The American Economic Review, 104(1), 183–223. Jellema, J., Wai-Poi, M., & Afka, R. (2017). The distributional impact of fiscal policy in Indonesia. World Bank. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 53 Prosperity Note Jensen, N. D., Barrett, C. B., & Mude, A. G. (2017, November). Cash transfers and index insurance: A comparative impact analysis from northern Kenya. Journal of Development Economics, 129, 14–28. Ivaschenko, O., Doyle, J., Kim, J., Sibley, J., & Majoka, Z. (2020). Does ‘Manna from Heaven’ help? The role of cash transfers in disaster recovery—lessons from Fiji after Tropical Cyclone Winston. Disasters, 44(3), 455-476. Kala, N., Balboni, C., & Bhogale, S. (2023, June). Climate Adaptation. VoxDevLit, 7.1. Kanbur, R. (2023). What is the World Bank good for? [Presentation]. EFI Africa Directors Inspirational Breakfast Series, Washington, DC. Karamba, W., Tong, K., & Salcher, I. (2022). Cambodia poverty assessment: Toward a more inclusive and resilient Cambodia. World Bank. Karlan, D., Osei, R., Osei-Akoto, I., & Udry, C. (2014). Agricultural decisions after relaxing credit and risk constraints. The Quarterly Journal of Economics, 129(2), 597–652. Karver, J., Lucchetti, L., Fruttero, A., Sajaia, Z., Nguyen, T., Oviedo, A. M., & Silvestri, A. (2023). Behavioral diagnostic of sustainable heating transitions in the Western Balkans: Evidence from Bosnia & Herzegovina, Kosovo, North Macedonia, and Serbia. World Bank. Kazianga, H., & Udry, C. (2006). Consumption smoothing? Livestock, insurance, and drought in rural Burkina Faso. Journal of Development Economics, 79(2), 413–446. Kochhar, N., & Knippenberg, E. (2023). Droughts and welfare in Afghanistan (Policy Research Working Paper No. 10272). World Bank. Kosmowski, F. (2018, May). Soil water management practices (terraces) helped to mitigate the 2015 drought in Ethiopia. Agricultural Water Management, 204, 11–16. Kulp, S. A., & Strauss, B. H. (2019). New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nature Communications, 10(1), 1–12. Koubi, V. (2019, May). Climate change and conflict. Annual Review of Political Science, 22, 343–360. Lassébie, J., Marcolin, L., Vandeweyer, M., & Vignal, B. (2021). Speaking the same language: A machine learning approach to classify skills in Burning Glass Technologies data (OECD Social, Employment and Migration Working Paper No. 263). OECD Publishing. Lavell, A., Oppenheimer, M., Diop, C., Hess, J., Lempert, R., Li, J., Muir-Wood, R., & Myeong, S. (2012). Climate change: New dimensions in disaster risk, exposure, vulnerability, and resilience. In C. B. Field, V. Barros, T. F. Stocker, D. Qin, D. J. Dokken, K. L. Ebi, M. D. Mastrandrea, K. J. Mach, G.-K. Plattner, S. K. Allen, M. Tignor, & P. M. Midgley (Eds.), Managing the risks of extreme events and disasters to advance climate change adaptation: Special report of Working Groups I and II of the Intergovernmental Panel on Climate Change, pp. 25–64. Cambridge University Press. López-Calva, L. F., & Ortiz-Juarez, E. (2014, January). A vulnerability approach to the definition of the middle class. Journal of Economic Inequality, 12, 23–47. López-Calva, L. F., & Rodríguez-Castelán, C. (2016). Pro-growth equity: A policy framework for the twin goals (Policy Research Working Paper No. 7897). World Bank. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 54 Prosperity Note Lustig, N. (Ed.). (2023). Commitment to equity handbook: Estimating the impact of fiscal policy on inequality and poverty. Brookings Institution Press. Lybbert, T. J., Barrett, C. B., Desta, S., & Layne Coppock, D. (2004). Stochastic wealth dynamics and risk management among a poor population. The Economic Journal, 114(498), 750–777. Macours, K. (2013). Volatility, agricultural risk, and household poverty: micro-evidence from randomized control trials. Agricultural Economics, 44(s1), 79-84. Mansur, A., Doyle, J., & Ivaschenko, O. (2018). Cash transfers for disaster response: lessons from Tropical Cyclone Winston. Development Policy Centre Discussion Paper, (67). Martin, R., de Preux, L. B., Wagner, U. J. (2014). The impact of a carbon tax on manufacturing: Evidence from microdata. Journal of Public Economics, 117, 1–14. Matlon, P. J. (1985). Annual report of ICRISAT/Burkina economics program. International Crops Research Institute for the Semi-arid Tropics (ICRISAT). Mauerman, M., Ross, C., Ilboudo Nébié, E., Anderson, W., Jensen, N., & Chelanga, P. (2023, April). The long-term impact of multi-season droughts on livestock holdings and pastoralist decision-making in Marsabit, Kenya. Journal of Arid Environments, 211, 104928. McPeak, J. G., & Barrett, C. B. (2001). Differential risk exposure and stochastic poverty traps among East African pastoralists. American Journal of Agricultural Economics, 83(3), 674–679. Mealy, P. (2022). Capturing benefits of the green transition: Green competitiveness and jobs in Brazil [Background paper for the Brazil CCDR]. World Bank. Mendelsohn, R., Nordhaus, W. D., & Shaw, D. (1994). The impact of global warming on agriculture: A Ricardian analysis. The American Economic Review, 84(4), 753–771. Metcalf, G. (2019). On the economics of a carbon tax for the United States. Brookings Papers on Economic Activity. Miguel, E. (2005). Poverty and witch killing. The Review of Economic Studies, 72(4), 1153–1172. Miller, F., & Dun, O. (2019). Resettlement and the environment in Vietnam: Implications for climate change adaptation planning. Asia Pacific Viewpoint, 60(2), 132–147. Mobarak, A. M., & Rosenzweig, M. R. (2013). Informal risk sharing, index insurance, and risk taking in developing countries. The American Economic Review, 103(3), 375–380. Mohapatra, S., Joseph, G., & Ratha, D. (2012, April). Remittances and natural disasters: Ex-post response and contribution to ex-ante preparedness. Environment, Development, and Sustainability, 14, 365–387. Molini, V., & Pavelesku, D. (2018). Poverty and social impact analysis Tunisia: The impact of reductions in electricity and gas subsidies. World Bank Group. Montgomery, L. E., Kiely, J. L., & Pappas, G. (1996). The effects of poverty, race, and family structure on US children’s health: Data from the NHIS, 1978 through 1980 and 1989 through 1991. American Journal of Public Health, 86(10), 1401–1405. Mukherjee, A., Okamura, Y, Gentilini, U., Gencer, D., Almenfi, M., Kryeziu, A., Montenegro, M., & Umapathi, N. (2023). Cash transfers in the context of energy subsidy reform: Insights from recent experience. Energy Subsidy Reform in Action Series. World Bank. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 55 Prosperity Note Murdiyarso, D., Purbopuspito, J., Kauffman, J. B., Warren, M. W., Sasmito, S. D., Donato, D. C., Manuri, S., Krisnawati, H., Taberima, S., & Kurnianto, S. (2015). The potential of Indonesian mangrove forests for global climate change mitigation. Nature Climate Change, 5(12), 1089–1092. New, M., Reckien, D., Viner, D., Adler, C., Cheong, S.-M., Conde, C., Constable, A., Coughlan de Perez, E., Lammel, A., Mechler, R., Orlove, B., & Solecki, W. (2022). Decision-making options for managing risk. In H.-O. Pörtner, D. C. Roberts, M. Tignor, E. S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, & B. Rama (Eds.), Climate change 2022: Impacts, adaptation, and vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, pp. 2539–2654. Cambridge University Press. Nguyen, T., & Torrachi, F. (2017). Impacts of electricity tariff reforms on energy affordability in the Western Balkans. World Bank Group. Nico, G., & Christiaensen, L. (2023). Jobs, food, and greening: Exploring implications of the green transition for jobs in the agri-food system (Jobs Working Paper No. 75). World Bank Group. OECD. (2023). Job creation and local economic development 2023: Bridging the great green divide. OECD Publishing. Olivier, A., Matytsin, M., & Gencer, D. (2023). Distributional analysis for informing energy subsidy reforms: Review of recent approaches. Energy Subsidy Reform in Action Series. World Bank. Ortiz-Bobea, A., Ault, T. R., Carrillo, C. M., Chambers, R. G., & Lobell, D. B. (2021). Anthropogenic climate change has slowed global agricultural productivity growth. Nature Climate Change, 11(4), 306–312. Oppenheimer, M., Glavovic, B. C., Hinkel, J., van de Wal, R., Magnan, A. K., Abd-Elgawad, A., Cai, R., Cifuentes-Jara, M., DeConto, R. M., Ghosh, T., Hay, J., Isla, F., Marzeion, B., Meyssignac, B., & Sebesvari, Z. (2019). Sea level rise and implications for low-lying islands, coasts, and communities. In H.-O. Pörtner, D. C. Roberts, V. Masson- Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. M. Weyer (Eds.), IPCC special report on the ocean and cryosphere in a changing climate, pp. 321–445. Cambridge University Press. Osborne, T., Pachón, M. C., & Araya, G. E. (2014). What drives the high price of road freight transport in Central America? (Policy Research Working Paper No. 6844). World Bank. Pape, U. J., & Ali, R. (2023). Indonesia poverty assessment—Pathways towards economic security. World Bank. Pape, U. J., & Wollburg, P. R. (2019). Impact of drought on poverty in Somalia (Policy Research Working Paper No. 8698). World Bank. Pant, R., Thacker, S., Hall, J. W., Alderson, D., & Barr, S. (2018). Critical infrastructure impact assessment due to flood exposure. Journal of Flood Risk Management, 11(1), 22–33. Pao-Yu, O., Brauers, H., & Herpich, P. (2020). Lessons from Germany’s hard coal mining phase-out: Policies and transition from 1950 to 2018. Climate Policy, 20(8), 963–979. Person, B., Loo, J. D., Owuor, M., Ogange, L., Jefferds, M. E. D., & Cohen, A. L. (2012). “It is good for my family’s health and cooks food in a way that my heart loves”: Qualitative findings and implications for scaling up an improved cookstove project in rural Kenya. International Journal of Environmental Research and Public Health, 9(5), 1566–1580. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 56 Prosperity Note Pople, A., Hill, R., Dercon, S., & Brunckhorst, B. (2021). Anticipatory cash transfers in climate disaster response. Centre for the Study of African Economies. Porter, C., & White, E. (2016). Potential for application of a probabilistic catastrophe risk modelling framework to poverty outcomes: General form vulnerability functions relating household poverty outcomes to hazard intensity in Ethiopia (Policy Research Working Paper No. 7717). World Bank Posadas, J., Alatas, H., & Granata, M. J. (2020). Indonesia’s occupational tasks and skills from occupational employment demand to tasks and skills requirements 2020. World Bank Group. Rafaty R., Dolphin, G., & Pretis, F. (2021). Carbon pricing and the elasticity of CO2 emissions (Working Paper 21–33). Resources for the Future. Rahman, S., & Akter, S. (2014). Determinants of livelihood choices. Journal of South Asian Development, 9(3). Reardon, T. (1997). Using evidence of household income diversification to inform study of the rural nonfarm labor market in Africa. World Development, 25(5), 735–747. Robayo-Abril, M. (Forthcoming). Labor market search, informality, and human capital shocks. Robayo-Abril, M., & Barroso, R. (2022). El Salvador Systematic Country Diagnostic: Addressing vulnerabilities to sustain poverty reduction and inclusive growth. World Bank. Robayo-Abril, M., Rude, B., Cadena, K., & Espino, I. (2023). Honduras poverty assessment: Toward a path of poverty reduction and inclusive growth. World Bank. Rodriguez-Castelan, C., Araar, A., Malasquez, E. A., Olivieri, S., & Vishwanath, T. (2019). Distributional effects of competition: A simulation approach (Policy Research Working Paper No. 8838). World Bank. Rosenzweig, M. R., & Udry, C. (2016). External validity in a stochastic world (Yale University Economic Growth Center Discussion Paper No. 1054). Ruggeri Laderchi, C., Spatafora, N. L., Sudhir, S., & Zaidi, S. (2017). Riding the wave: An East Asian miracle for the 21st century. World Bank East Asia and Pacific Regional Report. World Bank Group. Saczewska-Piotrowska, A. (2016). Near poverty definition, factors, prediction. Econometrics: Advances in Applied Data Analytic, 4(54), 82–94. Sandmo, A. (1971). On the theory of the competitive firm under price uncertainty. The American Economic Review, 61(1), 65–73. Saussay, A., Sato, M., Vona, F., & O’Kane, L. (2022). Who’s fit for the low-carbon transition? Emerging skills and wage gaps in job and data (Granthan Research Institute on Climate Change and the Environment Working Paper No. 381). Schwaab, J., Meier, R., Mussetti, G., Seneviratne, S., Bürgi, C., & Davin, E. L. (2021). The role of urban trees in reducing land surface temperatures in European cities. Nature Communications, 12(1), 1–11. Sekhri, S., & Storeygard, A. (2014, November). Dowry deaths: Response to weather variability in India. Journal of Development Economics, 111, 212–223. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 57 Prosperity Note Seneviratne, S. I., Nicholls, N., Easterling, D., Goodess, C. M., Kanae, S., Kossin, J., Luo, Y., Marengo, J., McInnes, K., Rahimi, M., Reichstein, M., Sorteberg, A., Vera, C., & Zhang, X. (2012). Changes in climate extremes and their impacts on the natural physical environment. In C. B. Field, V. Barros, T. F. Stocker, D. Qin, D. J. Dokken, K. L. Ebi, M. D. Mastrandrea, K. J. Mach, G.-K. Plattner, S. K. Allen, M. Tignor, & P. M. Midgley (Eds.), Managing the risks of extreme events and disasters to advance climate change adaptation: Special report of Working Groups I and II of the Intergovernmental Panel on Climate Change, pp. 109–230. Cambridge University Press. Seo, S. N., & Mendelsohn, R. (2008). Measuring impacts and adaptations to climate change: A structural Ricardian model of African livestock management. Agricultural Economics, 38(2), 151–165. Severen, C., Costello, C., & Deschenes, O. (2018, May). A forward-looking Ricardian approach: Do land markets capitalize climate change forecasts? Journal of Environmental Economics and Management, 89, 235–254. Strobl, E., & Spencer, N. (2024). Modeling the impact of extreme climate events on household welfare: A review of the empirical challenges. Stoeffler, Q., Carter, M., Guirkinger, C., & Gelade, W. (2022). The spillover impact of index insurance on agricultural investment by cotton farmers in Burkina Faso. World Bank Economic Review, 36(1), 114–140. Tanaka, T., Camerer C., & Nguyen, Q. (2010). Risk and time preferences: Linking experimental and household survey data from Vietnam. The American Economic Review, 100(1), 557–571. Tesfaye, W., & Tirivayi, N. (2020, January). Crop diversity, household welfare and consumption smoothing under risk: Evidence from rural Uganda. World Development, 125, 104686. Triyana, M., Jiang, A. W., Hu, Y., & Naoaj, M. S. (2024). Climate shocks and the poor: A review of the literature (Policy Research Working Paper 10742). World Bank. Valero, A., Li, J., Muller, S., Riom, C., Nguyen-Tien, V., & Draca, M. (2021). Are ‘green’ jobs good jobs? How lessons from the experience to-date can inform labor market transitions of the future. Grantham Research Institute on Climate Change and the Environment and Centre for Economic Performance, London School of Economics and Political Science. Van der Mensbrugghe, D. (2010). The ENVironmental Impact and Sustainability Applied General Equilibrium (ENVISAGE) model, version 7.1. World Bank. Van der Mensbrugghe, D. (2020). The Mitigation, Adaptation, and New technologies Applied General Equilibrium (MANAGE) model, version 2.0g. GTAP Technical Paper. Center for Global Trade Analysis, Purdue University. Van der Ree, K. (2019). Promoting green jobs: Decent work in the transition to low-carbon, green economies. In C. Gironde, & G. Carbonnier (Eds.), The ILO @ 100: Addressing the past and future of work and social protection, pp. 248–272. Brill. Vona, F., Marin, G., Consoli, D., & Popp, D. (2018). Environmental regulation and green skills: An empirical exploration. Journal of the Association of Environmental and Resource Economists, 5(4). Wiseman, W., Van Domelen, J., & Coll-Black, S. (2010). Designing and implementing a rural safety net in a low- income setting: Lessons learned from Ethiopia’s Productive Safety Net Program 2005–2009. World Bank. Wollburg, P., Hallegatte, S., & Mahler, D. G. (2023). Ending extreme poverty has a negligible impact on global greenhouse gas emissions. Nature, 623(7989), 982–986. World Bank. (2012). Carbon sequestration in agricultural soils. World Bank. CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION 58 Prosperity Note World Bank. (2016). The Uganda poverty assessment report 2016: Farms, cities, and good fortune—Assessing poverty reduction in Uganda from 2006 to 2014. World Bank. World Bank. (2018). Poverty and shared prosperity 2018: Piecing together the poverty puzzle. World Bank. World Bank. (2019a). Air quality management in Poland [Final report]. World Bank. World Bank. (2019b). Aspiring Indonesia: Expanding the middle class. World Bank. World Bank. (2019c). Burkina Faso rural income diagnostic. World Bank. World Bank. (2020). Poland catching-up regions: Towards robust, scalable, and inclusive clear air program for all. World Bank. World Bank. (2021). The gradual rise and rapid decline of the middle class in Latin America and the Caribbean. World Bank. World Bank. (2022a). Brazil poverty and equity assessment: Looking ahead of two crises. World Bank. World Bank. (2022b). China Country Climate and Development Report. CCDR Series. World Bank. World Bank. (2022c). 2022 Vietnam poverty & equity assessment: From the last mile to the next mile. World Bank. World Bank. (2022d). Kazakhstan Country Climate and Development Report. CCDR Series. World Bank. World Bank. (2022e). G5 Sahel Region Country Climate and Development Report. CCDR Series. World Bank. World Bank. (2022f). Pakistan Country Climate and Development Report. CCDR Series. World Bank. World Bank. (2022g). Peru Country Climate and Development Report. CCDR Series. World Bank. World Bank. (2022h). Poverty and shared prosperity 2022: Correcting course. World Bank. World Bank. (2022i). Progress, setbacks, and uncertainty: Effects of Covid-19 and coup on poverty in Myanmar [Myanmar Poverty Synthesis Note]. World Bank. World Bank. (2023a). Cambodia Country Climate and Development Report. CCDR Series. World Bank. World Bank. (2023b). Colombia Country Climate and Development Report. CCDR Series. World Bank. World Bank. (2023c). Green jobs: Upskilling and reskilling Vietnam’s workforce for a greener economy. World Bank Group. World Bank. (2023d). Rising strong: Peru poverty and equity assessment. World Bank. World Bank. (2023e). Tunisia Country Climate and Development Report. CCDR Series. World Bank. World Bank, & IMF. (2022). Achieving climate and development goals: The financing question [Document for the October 14, 2022, Development Committee Meeting]. Development Committee. Zaveri, E., Damania, R., & Engle, N. (2023). Droughts and deficits: The global impact of droughts on economic growth (Policy Research Working Paper No. 10453). World Bank. Ziter, C. D., Pedersen, E. J., Kucharik, C. J., & Turner, M. G. (2019). Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer. Proceedings of the National Academy of Sciences, 116(15), 7575–7580. Prosperity Note CLIMATE AND EQUITY: A FRAMEWORK TO UNDERSTAND WELFARE IMPACTS AND GUIDE POLICY ACTION TY | POVERTY | POVERTY POVERTY | POVERTY | POVERTY | POVERTY | POVERTY | 59 POVERTY | POVERTY POVERTY | POVERTY | POVERTY | POVERTY | POVERTY | POVERTY TY | POVERTY | POVERTY POVERTY | POVERTY | POVERTY | POVERTY | POVERTY | POVERTY | POVERTY POVERTY | POVERTY | POVERTY | POVERTY | POVERTY | POVERTY