Combining ambitious climate policies with efforts to eradicate poverty

The paper investigates how ambitious climate mitigation policies, particularly carbon pricing aligned with the Paris Agreement’s 1.5 °C target, interact with global poverty eradication goals (SDG 1.1). While unabated climate change is expected to exacerbate poverty—especially in the Global South—mitigation measures can also raise energy and food prices, potentially burdening low-income households. The research question is whether and how the adverse distributional side effects of ambitious climate policies on poverty can be mitigated or turned into co-benefits through revenue recycling and international finance, across different socioeconomic futures (SSPs). The study’s purpose is to quantify poverty trajectories to mid-century, assess policy-induced changes, and evaluate national redistribution and international transfers as strategies to reconcile SDG 13 with SDG 1.1.

Prior multi-country studies often examined moderate carbon prices, had static setups or narrower country coverage, and frequently omitted land-use mitigation effects on food prices. Analyses of NDC-level ambition suggested modest global poverty impacts by 2030, but evidence under Paris-compatible ambition remained limited. Research has highlighted the vulnerability of poorer countries and households to climate impacts and the potential regressivity of uniform carbon pricing without compensating measures. Some sectoral and CGE-based studies indicated increases in poverty under ambitious mitigation absent redistribution, while integrated assessments rarely captured within-country distributional impacts. This work addresses these gaps by combining a state-of-the-art IAM with a new distributional and poverty projection framework, explicitly including land-use measures and revenue recycling schemes.

- Modeling framework: The study uses the REMIND-MAgPIE integrated assessment framework, coupling the REMIND energy-economy-climate model with the spatially explicit MAgPIE land-use model. Scenarios cover SSP1, SSP2, and SSP5 socioeconomic pathways. Baselines exclude climate impacts to serve as counterfactuals. - Climate policy design: Ambitious mitigation scenarios meet a 1.5 °C-consistent CO2 budget via carbon pricing with regionally differentiated initial prices (staged accession). Developing regions start with lower carbon prices that converge toward a global uniform price by 2050. Land-use related emissions are priced in the modeling; revenue use for redistribution varies by scenario. - Distributional framework: Policy cost channels include average income (GDP per capita) losses and increased expenditures for energy and food. Starting from country-level baseline income distributions (lognormal, calibrated to GDP per capita and projected Gini coefficients from SSPs), the study allocates policy costs across incomes using empirically informed expenditure shares and elasticities (energy and food shares from World Bank consumption data). Carbon pricing revenues are redistributed under three schemes: (i) distributionally neutral (proportional to income), (ii) progressive equal-per-capita dividends, and (iii) strongly progressive transfers (greater transfers to lower incomes). A Monte Carlo simulation (1 million representative individuals per country-year) computes post-policy income distributions and implied Gini changes. - Poverty projection model: A regression links country-level poverty incidence (below international lines) to average income and Gini coefficient, with country fixed effects. Main results use the $1.90/day (2011 PPP) line; extensions assess $5.50/day. National poverty headcounts are aggregated to regional and global levels. Uncertainties are quantified via 68% prediction intervals derived from the regression model and propagated to headcounts and differences between policy and baseline. - Additional mechanisms: The study explores (a) adding land-use emission pricing revenues to the redistribution base and (b) an international climate finance mechanism transferring 5% of industrialized countries’ energy-sector carbon pricing revenues to Sub-Saharan Africa for domestic redistribution. Sensitivity analyses vary mitigation stringency (well-below 2 °C, 2 °C).

- Baseline poverty (no climate impacts, no policy): Under SSP2, ~350 million (308–411 million) people remain in extreme poverty (≤$1.90/day) in 2030; ~90 million by 2050. Faster reduction under SSP1/SSP5 (~230 million and ~190 million in 2030, respectively); slower under SSP3/SSP4 (~500 million in 2030). - Ambitious mitigation without progressive redistribution: In SSP2, a neutral (distributionally proportional) revenue use increases global extreme poverty by ~50 million people in 2030 relative to baseline, with notable increases in Sub-Saharan Africa and smaller increases in India, Latin America, and Southeast Asia. - Equal-per-capita climate dividend (progressive national redistribution): Recycling all national carbon pricing revenues equally per capita offsets the global poverty increase, yielding a small net reduction of ~6 million globally by 2030 (SSP2). Effects are heterogeneous: Sub-Saharan Africa still faces a residual increase (~10 million) by 2030, while countries like India can see net reductions (about −10 million by 2030). - Regional heterogeneity: Sub-Saharan Africa has high baseline poverty and relatively low per-capita emissions (and initially low carbon prices), limiting domestic revenue bases; hence, even progressive national recycling may not fully offset poverty increases there. Most other regions can avoid increases with domestic progressive recycling. - Options to achieve poverty co-benefits (SSP2): • Strongly progressive national redistribution (more targeted to low incomes) can more than compensate policy side effects, reducing global poverty by about 50 million in 2030 and fully offsetting SSA increases in 2030. • Including land-use emissions revenues for redistribution increases the domestic revenue base—particularly important in SSA—and is sufficient to offset increases in all SSA countries in 2030 and 2050; globally, ~30 million fewer poor than baseline in 2030 and near-baseline levels by 2050. • International climate finance: Transferring 5% of energy-sector carbon pricing revenues from industrialized countries to SSA (initially ~US$100 billion/yr, ~0.2% of donor GDP, declining toward mid-century) combined with equal-per-capita domestic recycling leads to lower poverty than baseline in 2030 (about −30 million in SSA, −45 million globally). As transfers cease by 2050 with carbon neutrality, residual increases reappear (+20 million SSA, +30 million global), indicating a need for sustained funding beyond carbon revenue. - Sensitivity to mitigation target: After progressive national recycling, more stringent temperature targets slightly worsen net poverty outcomes. For a 2 °C target, a small net global increase remains (+5 million in 2030). - Higher poverty line ($5.50/day): Baseline headcounts remain very large to mid-century, especially in SSA. Policy side effects are more persistent at this higher line; among explored measures, adding land-use revenues is most effective at compensating long-term side effects.

Ambitious climate policy can create short-term poverty risks through higher energy and food prices, especially where domestic revenue bases are small and vulnerability is high. The study shows that progressive recycling of national carbon pricing revenues can largely reconcile mitigation with poverty eradication, yielding net global benefits by 2030 and avoiding trade-offs in many regions. However, regional disparities—most notably in Sub-Saharan Africa—necessitate complementary measures: stronger progressivity, inclusion of land-use revenues, and modest international climate finance. These instruments jointly transform potential trade-offs between SDG 13 and SDG 1.1 into co-benefits and align with SDG 17 on partnerships. Sensitivity analyses indicate that stricter temperature targets require even more attention to redistribution due to higher near-term prices and diminishing revenues as carbon neutrality is reached. The results underscore the importance of governance capacity for progressive redistribution and of sustained international support to ensure that mitigation pathways do not disproportionately burden the poor.

The paper demonstrates that, under Paris-compatible mitigation, national equal-per-capita recycling of carbon pricing revenues can offset global poverty increases and even slightly reduce headcounts by 2030. To secure benefits in regions with limited revenue bases, especially Sub-Saharan Africa, enhancing progressivity, expanding the revenue base to land-use emissions, and deploying modest international climate finance are effective and complementary strategies. Over longer horizons, as carbon revenues decline with deep decarbonization, sustained resources and broader pro-poor investments will be needed to maintain gains, particularly under higher poverty lines (e.g., $5.50/day). Future research should integrate climate impact damages, COVID-19 effects, sectoral heterogeneity, and multi-dimensional SDG outcomes to refine policy design and ensure equitable burden sharing.

- Climate impacts of warming (e.g., on productivity, agriculture, disasters) are not included; baselines serve as counterfactuals without climate damages. - National-level, aggregate distributional approach: does not resolve subnational/sectoral heterogeneity, detailed energy carriers or specific food commodities, or input–output linkages; potential heterogeneous income effects (e.g., farm income from higher food prices) are not fully captured. - Assumes effective institutions and low transaction costs for redistribution; feasibility, targeting accuracy, and administrative costs, especially for strongly progressive schemes, are uncertain. - International transfer design is stylized (fixed 5% share of donor revenues to SSA) and does not assess potential macroeconomic side effects (e.g., ‘climate finance curse’) in depth. - Uncertainties remain in projections of income, inequality, and poverty dynamics; results rely on regression-based poverty estimates and scenario assumptions (SSPs, technology, policy paths).