This paper begins with an assumed nonlinear effect of climate on an economic outcome and then derives the resulting short run and long run behavior to changes in weather. The short run annual effects are a function of intertemporal weather deviations and the interaction between weather deviations and the long-run marginal effect of climate. One can identify the underlying nonlinear climate effects with these interaction terms. We illustrate these principles by showing how both weather and climate affect annual GDP per capita in a panel of countries. GDP is generally more sensitive to short run changes in temperature than long run changes. A long run 1 warming has a negligible effect on GDP per capita overall but it will cause losses in the hottest countries of -1% to -1.5% per C. Precipitation also has a very small effect on GDP per capita. These estimates just reflect the direct effect of temperature and precipitation on the economy and do not reflect nonmarket damages and damages caused by sea level rise and storms.
This paper provides fresh empirical evidence on households' climate change adaptation in Niger, a drought-prone country characterized by an adaptation deficit. It assessed the main drivers and welfare impacts of food storage, migration, and crop diversification, identified as part of the households' primary adaptation strategies. We composed a longitudinal dataset from the two cohorts of the harmonized survey on the households' living conditions (EHCVM) secondary data collected by the National Institute of Statistics of Niger, supported by the World Bank and the WAEMU commission. A Seemingly Unrelated Regression (SUR) model was used to assess the drivers of adaptation, while a quintile regression model was employed to evaluate the welfare effects of climate change adaptation.Our results show that adaptation is highly incentivized by a drier climate, where households rely on various techniques to smooth income and stabilize household calorie intake throughout the lean period. It is, however, weakly spurred by wetter climate and seasonal market fluctuations, although the welfare impact is still positive. These impacts are heterogeneous and pro-poor, although used by well-endowed households to improve their life opportunities. Adaptation remains largely autonomous and lacks policy support mechanisms from the government. We find that migration is an out-of-distress adaptation technique and has no welfare effects on the household's annual consumption and household calorie intake.Development efforts through the national adaptation programs should leverage and mainstream these adaptation options to alleviate rural vulnerability to multiple shocks. The government should institute interventions that build and sustain household assets to avoid distress migration. That consists of developing income-generating activities and safety nets in rural areas to create life opportunities and improve households' livelihood portfolios.
The relationship between water, energy, and food (WEF) has been recognized as an essential component that may affect the objectives of sustainable development. This research examines the price risk transfers within the energy, water, and agriculture sectors in an effort to further our economic comprehension of the World Economic Forum nexus. We also examine the important effects of transition and physical risks related to climate change on transmission patterns. We present an investigation using a quantile connectedness strategy in the timeframe-frequency domain, which takes into consideration both extreme and regular market situations, as well as long and short time periods. It is shown that under regular market conditions, the WEF security nexus is rather small, but under extreme market conditions, it increases significantly. When comparing the short-term and long-term horizons, the nexus is more obvious in the shorter term. The WEF nexus has usually been increased by the United States climate policy and uncertain economic policies, while it has been reduced by geopolitical risk and the United States term spread among the components examined. After conducting a thorough analysis of the effects of climate risks, we conclude that, under normal market conditions, investors are more concerned with transition climate risks than they are with physical climate risks in the short term. The time-frequency quantile connectedness of geopolitical, economic, and climate risks in the WEF nexus. Investors consider transitional and physical risks together over the long run, but only in very specific market conditions. Our investigation of the economic consequences adds validity to the concept that when building investment portfolios, consumers are more concerned with transition climate risks than physical risks.
Climate policy affects households not only through aggregate emissions reductions but also through its interaction with sector-specific policies that shape exposure and economic outcomes. In this study, we use causal forests to estimate heterogeneous effects of residential energy technologies on sick days and sickness-related work absences, out-of-pocket health expenditures, and lost earnings using nationally representative individual-level data from the India Human Development Survey. We combine these with projections of ambient PM2.5 from GAINS to simulate outcomes through 2030 under alternative climate and residential clean energy policies. We find that under current climate policies, reductions are modest, whereas more stringent, but feasible climate policies, combined with a full transition to clean fuels in the residential sector, produce considerably larger reductions, with routinely disadvantaged individuals benefiting the most. The results highlight the importance of complementing climate and residential clean energy policies to enhance both the magnitude and the distributional reach of economic benefits.
We assess whether Article 6 carbon market transfers reduce between-region income inequality. Using an integrated assessment model (Global Change Analysis Model (GCAM)) with endogenous regional GDP, we model a maximalist form of Article 6 cooperation, with internationally transferred mitigation outcomes (ITMOs) traded across 32 regions under three socioeconomic baselines (SSP1, SSP2, SSP4) and a net-zero 2050 pathway. Our primary metric is the population-weighted global Gini coefficient, which we decompose into policy burden and transfer components and examine across the regional income distribution. Article 6 reduces inequality under most scenarios, with Gini reductions of 0.5-0.9 points by 2050 under SSP1, SSP2, and net-zero pathways. Under SSP4, where income divergence erodes lower-income regions' comparative advantage in low-cost mitigation, the progressive effect weakens to near-neutral (+0.03 Gini points). The financial transfer effect dominates the policy burden effect by a factor of three to six. Theil decomposition confirms this operates overwhelmingly through the between-region channel under convergent scenarios, weakening to 79% under SSP4. Africa's position as net seller or buyer serves as a diagnostic of this dependence. Article 6 can reduce global inequality, but the outcome depends on underlying development conditions rather than the market mechanism itself.
To evaluate the general equilibrium (GE) effects of partial equilibrium (PE) impacts on buildings, labor, roads, and transportation through 2100, we link sector-specific damages from the Framework for Evaluating Damages and Impacts (FrEDI) to the MIT's U.S. Regional Energy Policy (USREP) computable GE model to quantify economy-wide effects. We distinguish between market outcomes (e.g., GDP) and nonmarket valuation of mortality risk. We explain how PE effects propagate and amplify through GE channels. U.S. GDP falls by about 0.75% by 2100, with aggregate damage increasing by about 20% in 2050 and about 50% in 2100 from the PE result to the GE result. For road repairs in 2100, GE losses exceed PE damages by about 90%. Losses are often larger in the Southern Plains and Southeast. Lower-income households bear welfare losses many times the average. We also assess the valuation of climate-related mortality risk using the value of statistical life (VSL). We embed VSL directly in the utility function, allowing the valuation to evolve endogenously with income and demographic change. Nonmarket valuation of mortality-related losses, equivalent to about 6% of US GDP in 2100, is reported separately and should not be interpreted as part of a typical GDP accounting.
Climate has shaped pastoral production systems worldwide, influencing which livestock animals herders choose to raise. While most research has focused on genus choice - such as from cattle to goats to chickens - less attention has been paid to species choice within a single genus. This study uses a representative panel from Mongolia to examine climate-sensitive substitution between two closely related cattle species: cold-adapted yaks (Bos grunniens) and more heat-tolerant Mongolian cattle (Bos taurus). Both OLS and fractional logit models confirm that the yak share of all cattle is sensitive to seasonal temperature and precipitation. Fixed-effects panel regressions of yak and Mongolian cow populations reveal that warmer winter and spring temperatures reduce yak populations, but warmer winters increase Mongolian cattle populations. Summer warming is detrimental to Mongolian cattle but not yaks. Overall warming leads to losses for yaks (-439 head/degrees C) and Mongolian cattle (-256 head/degrees C), signaling that substitution may buffer but not fully offset warming. Increased precipitation decreases yak populations -1450 head/cm/mo, but increases Mongolian cattle 5364 head/cm/mo, largely because of the harmful winter effects of snow on yaks. These results highlight how subtle species changes within a genus can act as a climate adaptation even though they may not always prevent the harmful long-term impacts of warming.
More than 200 million people cook with charcoal daily. This has high climate costs: a Kenyan household using a charcoal stove emits as much CO2e per year as an average U.S. household using a gasoline vehicle. However, concerns around additionality (do subsidies increase sales?) and impact (do sales reduce emissions?) undermine the clean cooking transition. We conduct a randomized trial with 955 households in Nairobi to quantify the additionality and impact of subsidies for an improved cookstove in use by millions of households. Factoring in that 16% of total subsidy spending flows to nonadditional participants, incentivizing one additional stove sale requires US$32 in subsidy spending. Three-and-a-half years later, 83% of buyers own a working improved stove and 11% of nonbuyers do. Factoring in control group adoption and breakage rates, each additional stove generates at least 2.8 additional years of working improved stove ownership. Despite widespread stacking, stove ownership on average abates 1.7 tCO2e per year. Together, the subsidies abate CO2e at US$7 per ton (estimates range between US$3.5 and US$8.1). Each dollar of cookstove subsidy abates 168-383 times more CO2e than a dollar of electric vehicle subsidy.
Using data from 144 countries from 2002 to 2019, we employ a bilateral trade matrix to capturethe link between trade, economic development, and carbon emissions. We find evidence thatcountries engaged in trade are associated with greater emissions when their trading partnersexperience an increase in economic development. Specifically, we find that emissions decreasedwithin a trading network of developed countries and increased within a trading network ofdeveloping countries. Our findings suggest the prospect of forming multi-tier Climate Clubs-designed along Nordhaus [(2015). Climate clubs: Overcoming free-riding in international cli-mate policy.American Economic Review, 105(4), 1339-1370, https://doi.org/10.1257/aer.15000001; (2021). Dynamic climate clubs: On the effectiveness of incentives in globalclimate agreements.Proceedings of the National Academy of Sciences, 118(45), 1-6, https://doi.org/10.1073/pnas.2109988118]-where trade agreements are tied to members'commit-ment to reduce emissions based on their development status and technological transfersbetween coalitions.
This paper examines how policies intended to reduce carbon dioxide (CO2) emissions affect air pollution exposure, mortality risk, and monetary benefits across the income distribution in the United States (U.S.). We use an energy system optimization model (ESOM) to translate several climate change mitigation policies into CO2-equivalent emission reductions. The ESOM also tracks emissions of three air pollutants: fine particulate matter, sulfur dioxide, and nitrogen oxides. The AP3 model links changes in emissions of local air pollutants to county-level ambient concentrations, exposure, mortality risk, and monetary damages. We present three central results. First, the monetary benefits from reduced air pollution exposure of the climate policies amount to less than 1% of real per capita income. Second, the monetary benefits are progressively distributed. Specifically, counties with a 10% higher real median income level tend to incur between 5% and 6% lower benefits from the carbon tax, the net zero scenario, and the clean electricity standard in 2030. These estimated elasticities are closer to zero in 2040 and 2050. Third, benefits are distributed progressively in the northeast and the western census regions, and regressively in the Midwest. In the southeast, benefits and income are uncorrelated.
In this paper, we estimate the domestic social cost of carbon using a recent meta-analysis of the total impact of climate change and a standard integrated assessment model. The average national social cost of carbon closely follows per capita income, the total domestic social cost of carbon the size of the population. The domestic social cost of carbon measures self-harm. Net liability is defined as the harm done by a country's emissions on other countries minus the harm done to a country by other countries' emissions. Net liability is positive for middle-income, carbon-intensive countries; it is particularly large for China. Poor and rich countries would be compensated because their current emissions are relatively low, poor countries additionally because they are vulnerable.
This study examines how climate variability affects child labor in the Occupied Palestinian Territories, where ongoing conflict and scarce resources amplify household vulnerabilities. Drawing on Palestinian Labor Force Survey data from 2000 to 2018 linked with district-level data on climate indicators, we employ a linear probability model to measure the effects of climate shocks on child labor. Our analysis reveals that rainfall increases child labor, particularly among boys in agricultural roles, while extreme heat discourages it. We also find that girls often respond to rainfall shocks through early marriage. These patterns vary across northern, central, and southern districts, highlighting the importance of place-based adaptation. Our findings show that structural constraints mediate how households cope with environmental stress. The results suggest that effective policy must integrate climate-resilient agricultural investments with targeted social-protection measures, educational support, and gender-responsive programming to mitigate the risks of child labor and early marriage under increasing climate pressure.
Climate change is expected to impose heterogeneous damages throughout the world due to varying levels of hazards, exposure, and vulnerability. The social cost of carbon (SCCO2), the net present value of welfare losses from an additional ton of CO2, provides a useful synthesis of these damages globally, and marginal damages at the national level highlight the spatial pattern of these damages. This paper presents the PAGE-2025 integrated assessment model with 183 individual countries and five small country groups. We incorporate several critical improvements: a novel mapping of climate uncertainty to country-level warming patterns, an empirical approach to dynamic vulnerability using risk indices, updated abatement cost estimates using NGFS energy model scenarios, and an approximation of the effects of subnational heterogeneity in impacts. These changes result in a global SCCO2 distribution with a median of $359 per ton of CO2. Damages are dominated by market damages, which are high in tropical regions and negative at high latitudes, and nonmarket damages, which are high in temperate regions that show the greatest rates of warming. We then decompose the spatial pattern of country-level SCCO2 into the role of hazard, exposure, and vulnerability, by calibrating a simple emulator with temperature, population, and income, which captures the country-level SCCO2 pattern with high precision. The spatial pattern roughly follows GDP, but with tropical countries at twice their GDP-only SCCO2 level and cooler countries below this level. We use this decomposition to evaluate the role of different modeling decisions, including different scenarios, downscaling, damage assumptions, abatement costs, and assumptions about sub-national vulnerability. Across modeling decisions, the greatest changes in spatial pattern are driven by the use of SSP scenarios (rather than the default RFFSPs) and the updating of nonmarket damages damages to reflect Howard and Sterner (2017).
The results of five Integrated Assessment Models are discussed in this concluding paper. All the scenarios measure the National Social Cost of Carbon (NSCC) across approximately 200 countries in a scenario with no mitigation. Despite assuming similar population and economic growth rates (RFF-SPs), the five models imply a wide range of forecasted climate impacts. Summing the mean NSCC estimates of all countries leads to a mean global Social Cost of Carbon (SCC) that varies from $33/tCO2 to $1373/tCO2 across the models.Most of the literature on climate IAMs has focused on these SCC values. This special issue, in contrast, is focused on the NSCC. However, the assumptions in each IAM model that led to different SCC values also determine the cumulative magnitude of the NSCCs from each model. The discount rate, the climate damage function, and the modeling of uncertainty strongly influence the magnitude of the NSCCs across models. What is unique to the analysis of the NSCCs is how they are distributed across countries.The NSCC values explain how climate damages and, therefore, the benefits of mitigation are distributed across nations. The IAMs predict that China and especially the United States are particularly exposed to climate change due to their large asset bases. About half of the global damage is assumed to happen in the 10 largest countries in the world, measured using GDP PPP. Most of the models predict every country has some damage, but one model predicts 29 countries - mostly in high latitudes - will benefit from near-term climate change. Regression analysis confirms that the distribution of climate damages across countries is primarily determined by the total size of the national economy. Because IAMs scale physical risks by economic exposure, they predict that two-thirds of the global climate burden is concentrated in the mid-to-high latitudes, where the majority of global economic activity is located. Finally, the IAMs predict a very wide range of NSCC values in 2100, ranging from a 40% increase to an eight-fold increase over current NSCC values, depending on structural assumptions regarding adaptation and catastrophic risk.
This paper characterizes the distribution of marginal climate damages across countries by combining global social cost of carbon (GSCC) and national social cost of carbon (NSCC) estimates within a unified probabilistic integrated assessment framework. Using the RICE50+ model, I estimate the GSCC and NSCC for 200 countries, incorporating uncertainty in socio-economic trajectories, climate sensitivity, damage functions, and discounting. The 2025 GSCC is $194/tCO2 (5-95%: $135-$275), consistent with recent literature. NSCC is evaluated under a no-mitigation baseline and is highly concentrated: the United States and China together bear approximately 40% of global marginal damages in 2025. This pattern persists through 2100, even as fast-growing economies like Nigeria rise in the rankings. The sum of NSCC under this no-mitigation baseline exceeds the cooperative GSCC, highlighting the gap between the globally efficient carbon price and countries' unilateral incentives. Variance decompositions show that discounting, damages, and climate sensitivity dominate uncertainty in NSCC, while socio-economic pathways are quantitatively secondary.
Calculating the proper social cost of carbon (SCC) is essential for effective climate policy. This paper argues for differentiated regional SCCs and a global SCC indicator based on the Lindahl equilibrium by treating climate change as an externality. The regional Lindahl SCC represents the regional "personalized prices" or "willing-to-pay" cost-sharing in global GHG mitigation efforts. It also acts as a consensus tool for international cooperation on carbon emissions. To demonstrate the feasibility and advantages of the Lindahl SCC, it is calculated using the RICE2020 model and compared with the regional SCC based on a utilitarian (equal-weight) social optimum. Numerical simulations support the findings. Finally, the policy implications of the Lindahl SCC are discussed.
We provide a probabilistic multi-model assessment of the Social Cost of Greenhouse Gases (SCGHG) by implementing a bottom-up damage function in two Integrated Assessment Models (IAMs): a detailed process-based model (WITCH) and a cost-benefit model with country-level resolution (RICE50+). We generate over 18,000 estimates of the social costs of CO2, CH4 and N2O through extensive uncertainty analysis of economic development, population dynamics, climate response, and damage functions. Our global Social Cost of Carbon (SCC) estimates range from 65$/tCO2 (RICE50+) to 99$/tCO2 (WITCH) using a 3% discount rate, rising to 130-171$/tCO2 with 2% discounting. We find comparable importance of model choice, and key parameters such population dynamics, and economic growth projections. We demonstrate the methodology by computing the global and national SCC and their distribution. We find that countries with high exposure and large economies at risks (e.g., India, Nigeria and the US) have the largest national SCC. Our results emphasize the importance of model uncertainty alongside parametric uncertainty in computing the global and national values of the SCC.
