Carbon Prices and Cooking Fires: How Decarbonization Pathway Design Shapes Household Energy Burdens

Carbon Prices and Cooking Fires: How Decarbonization Pathway Design Shapes Household Energy Burdens | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Carbon Prices and Cooking Fires: How Decarbonization Pathway Design Shapes Household Energy Burdens Mel George, Leon Clarke, Jihoon Min, Shonali Pachauri, Anand Patwardhan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9442656/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Climate mitigation pathways impose uneven burdens across income groups, yet analyses typically focus on revenue recycling rather than on how pathway architecture shapes distributional outcomes. We examine 17 pathways to 1.5°C and trace their impacts to household energy burdens across income deciles in India and the United States. Pathway architecture is a primary determinant of whether mitigation is progressive or regressive. Technology, pace, and cost-optimal pathways are regressive: the poorest households bear cost increases up to an order of magnitude larger than the richest. Demand-side pathways lower carbon prices by curbing demand among high-income consumers, creating development space that alleviates burdens in developing countries. Historical responsibility pathways with international transfers generate progressive outcomes for recipient countries at the expense of donor households. These patterns persist under consumption rebound, albeit attenuated. Distributional consequences are embedded in pathway design, not correctable through revenue policy alone. climate mitigation energy justice distributional impacts integrated assessment models demand-side mitigation effort-sharing Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Under cost-optimal climate mitigation, India’s poorest households face energy burden increases an order of magnitude larger than its richest. This disparity is not an unfortunate side effect to be corrected later through revenue recycling; it is a consequence of pathway architecture, the choices embedded in how we design routes to climate targets. Yet integrated assessment models (IAMs) typically report regional aggregates that render these consequences invisible 1 , 2 . Making low-carbon transitions just, equitable, and politically durable has become central to climate policy 3 – 5 , driven in part by evidence that unequal distribution of mitigation costs erodes social cohesion and undermines policy acceptability 6 , 7 . These concerns have spurred calls to integrate justice considerations, encompassing distributional, corrective, and recognitional dimensions, into IAMs 8 – 11 . Translating such calls into practice remains difficult, however, particularly when the goal is to trace how pathway designs propagate to vulnerable populations. Recent multi-model studies have sharpened the picture. Climate damages widen inequality; revenue recycling can narrow it 12 . Net-zero pathways tend to be regressive under uncompensated carbon pricing but become progressive with per-capita recycling 13 , 14 . Even so, stringent mitigation risks increasing poverty in developing regions absent careful pathway design 15 . A consistent theme in this literature is that revenue recycling determines distributional outcomes, a framing that treats pathways as distributionally neutral before compensation and locates all equity-relevant decisions at the revenue stage. But different pathway architectures, distinguished by technology choices, transition pace, effort allocation, or demand-side transformation, yield different distributional outcomes through their structure alone, before any revenue is collected or recycled. Effort-sharing pathways have been analyzed for country-level implications 16 – 18 but their within-country distributional effects remain unexplored. Demand-side mitigation has attracted attention for its well-being implications 19 – 22 , yet not all demand-side approaches are equivalent: electrification differs from behavioral demand reduction in ways that matter for who bears the cost. Here we examine 17 pathways to 1.5°C representing four architectural approaches (Table 1 ), tracing impacts to household energy burdens across income deciles in India and the United States using the Global Change Analysis Model (GCAM) linked with nationally representative household surveys 23 – 25 . This responds to calls for improved representation of heterogeneity in energy-climate models 10 , 26 , 27 . Standard IAM practice employs representative households, implicitly assuming that differences balance in aggregate 1 , 2 ; energy burden research reveals they do not 28 , 29 . Downscaling to income deciles exposes distributional variation that aggregate reporting obscures. We find that the architecture of mitigation pathways is a primary determinant of distributional outcomes. Most pathways are regressive, with the poorest deciles shouldering disproportionately large cost increases compared to the richest. A subset of pathways, those emphasizing demand reduction in wealthy populations or historical responsibility with transfers, instead yield progressive outcomes. These distributional consequences are embedded in pathway design; downstream revenue policies cannot fully undo them. Results Most pathways are regressive Technology, pace, and cost-optimal pathways concentrate burden on the poorest households in both countries (Figure 2). In India, the poorest decile (D1) faces energy burden increases roughly ten times larger than the richest (D10). This steep gradient reflects both higher price sensitivity among poor households and a counterintuitive fuel switching dynamic: under cost-optimal mitigation, the traditional biomass share of India’s residential consumption rises from roughly 54% in the reference scenario to 65%, as households retreat to self-collected fuels when modern alternatives become unaffordable. The United States shows the same direction but a compressed gradient. More uniform consumption patterns and universal modern energy access narrow the spread between deciles. Faster net-zero timelines (NZ2040 vs. NZ2060) amplify the regressive pattern without changing its character. Among effort-sharing pathways, grandfathering is particularly severe for India, locking in low per-capita carbon allocations irrespective of household income, while capability-based approaches (CAP-A, CAP-B) also skew costs toward the poorest. Demand-side measures and redistribution yield progressive outcomes A distinct subset of pathways reverses this pattern (Figure 3). Behavioral change (BEH) and comprehensive demand-side mitigation (COMPR) deliver progressive outcomes in India: the poorest decile sees burden reductions relative to reference, while richer deciles absorb modest increases. Reduced demand in high-consuming populations depresses global carbon prices, opening carbon space for developing regions (the mechanism is detailed in Figure 4). Non-CO2 reductions from dietary shifts and HFC phase-down extend this headroom further. Historical responsibility with international transfers (RESP) generates the most strongly progressive outcome for India. Financial transfers from wealthy countries more than offset domestic mitigation costs for the poorest households. The mirror image holds for the United States, where RESP imposes the steepest regressive burden as households bear transfer obligations on top of domestic mitigation costs. The sovereignty-based pathway (SOVER) shows near-neutral burden effects for India in 2030, reflecting lower near-term mitigation stringency rather than structural redistribution: weak ambition embedded in NDC trajectories permits continued access expansion without significant burden increases. Rebound attenuates but does not eliminate progressive transfers When Indian households receive transfer income under historical responsibility, they may channel part of it into additional energy consumption. We model this rebound in RESP-POST. Progressive outcomes persist: the poorest decile still experiences burden reductions relative to reference, though benefits shrink compared to the no-rebound case. This is, to our knowledge, the first household-level analysis of consumption responses to international climate transfers. It suggests that climate finance mechanisms can deliver within-country equity benefits under realistic behavioral assumptions. At longer time horizons (2050), however, cumulative rebound eventually overwhelms transfer benefits (Supplementary Information). Not all demand-side pathways are progressive The demand-side category masks critical heterogeneity. Electrification (ELE) is regressive in India: its benefits accrue to households that can afford to electrify, while costs are distributed through the carbon price. Traditional biomass phase-out (NTB) is regressive for the opposite reason, removing a fuel source that poor households depend on without providing affordable alternatives. Only pathways that reduce demand in wealthy populations, rather than mandating fuel transitions among the poor, yield progressive distributional patterns. Discussion Pathway architecture shapes distributional outcomes before revenue policy Decile-level burden analysis exposes distributional patterns that representative-household IAM reporting renders invisible. The gradient between poorest and richest households is not merely steep; it differs qualitatively across pathway categories, with technology, pace, and cost-optimal pathways systematically regressive while behavioral demand-side and historical responsibility pathways produce progressive outcomes for India's poorest without any revenue recycling. This pattern reframes the distributional question in the recent multi-model literature 12,13,14 , which has focused on revenue recycling as the equity lever and treated pathway architecture as distributionally neutral before compensation. Our results indicate that pathway structure shapes the distributional landscape on which revenue policy then operates. Some architectures are progressive before any revenue is collected; others embed regressivity that redistribution alone cannot fully counteract. The India-US pairing is analytically productive precisely because the two countries occupy different positions along several dimensions that matter for distributional outcomes. India's residential energy system remains partially dependent on traditional biomass, its income distribution is steeper, and it stands on the receiving end of most effort-sharing frameworks. The United States has universal modern energy access, a compressed consumption gradient, and bears transfer obligations under historical responsibility. Neither country alone would reveal the mirror effect under RESP, nor would a single-country study expose how the same pathway architecture propagates through fundamentally different fuel mixes to produce opposite distributional outcomes. The causal chain runs through global commodity markets (Figure 4). Pathways that constrain carbon space elevate carbon prices that propagate to household energy bills, with the poorest households absorbing a disproportionate share due to higher price sensitivity and limited fuel-switching capacity. In India, this dynamic triggers a retreat to traditional biomass as modern fuels become unaffordable. The consequence is that climate policy partially reverses decades of progress on household energy transitions. Programs like India's Pradhan Mantri Ujjwala Yojana have expanded LPG access to hundreds of millions of households; carbon pricing under cost-optimal mitigation works against this trajectory by making the cleaner fuels these programs provide less affordable relative to self-collected biomass. The health implications are substantial: continued reliance on solid fuels for cooking is a leading cause of household air pollution and premature mortality, particularly among women and children. This tension between climate mitigation (SDG 13) and clean energy access (SDG 7) is often noted in abstract terms; our results quantify it at the household level. Pathways that expand carbon space, through demand contraction in affluent societies or explicit financial transfers, relieve this pressure and preserve room for development 32 . The double squeeze on the poorest households Our energy burden metric differs from standard gross-income approaches in a way that matters for the magnitude of estimated regressivity. By accounting for food price increases in the denominator alongside energy price increases in the numerator, the metric captures an amplification that gross-income calculations systematically understate. The effect is largest for households with high food budget shares. In India's poorest decile, where food expenditure can exceed half of household income, mitigation-induced agricultural price increases substantially shrink the income available for energy purchases, roughly doubling the apparent burden increase compared to what a gross-income denominator would show. This measurement distinction is particularly consequential in countries where food expenditure dominates household budgets at the bottom of the income distribution, and it suggests that existing analyses of carbon pricing regressivity in developing countries may systematically understate the burden on the poorest households. Demand-side heterogeneity and the cost-optimal default The demand-side findings give quantitative substance to the sufficiency literature 19,42,43 while complicating its narrative. Not all demand-side approaches produce equivalent distributional outcomes. The progressive character of behavioral demand reduction operates through a specific mechanism: lower aggregate demand depresses global carbon prices, creating space for developing regions. Electrification and traditional biomass phase-out lack this mechanism and instead impose costs on households least able to bear them. This distinction has practical implications for how "demand-side mitigation" is categorized in scenario assessments. Treating it as a single analytical category, as is common in IPCC scenario databases and model comparison exercises, obscures a consequential difference in who bears the cost. More broadly, cost-optimal pathways are not distributionally neutral defaults. They embed a particular allocation of burden that prioritizes aggregate economic efficiency, with disproportionate costs falling on the most vulnerable. The IPCC AR6 scenario database is heavily populated with cost-optimal and near-cost-optimal pathways; if each of these embeds regressive distributional consequences by construction, the evidence base informing climate policy carries an unexamined distributional assumption. Selecting a cost-optimal pathway is itself a distributive choice, even when framed as a technical benchmark. These results are consistent with arguments that reducing demand in affluent populations can create distributional space, though we do not assess whether resulting consumption levels meet sufficiency thresholds as defined in the decent living standards literature. Global versus domestic justice The effort-sharing results expose a tension that the existing literature on burden-sharing frameworks 16,17,18 has not traced to household level. Historical responsibility with transfers produces a mirror effect: the pathway that delivers the strongest progressive outcome for India simultaneously imposes the steepest regressive burden in the United States. This is not a modeling artifact; it reflects the arithmetic of transfers large enough to offset mitigation costs in recipient countries being financed by households in donor countries, where the burden falls disproportionately on those with the highest energy expenditure shares. This finding is directly relevant to ongoing negotiations over climate finance architecture. The Paris Agreement's Article 6 mechanisms and the loss and damage fund established at COP27 both involve financial flows from wealthy to developing countries, yet their design has proceeded largely without household-level distributional analysis in donor countries. Our results suggest that the political feasibility of such mechanisms depends not only on the aggregate fiscal burden but on how that burden distributes across income groups domestically. Transfer obligations that land disproportionately on a donor country's poorest households risk generating the same political backlash that has undermined domestic carbon pricing. International negotiations would benefit from anticipating these within-country consequences. Domestic policy must accompany international commitments so that transfers do not simply relocate burdens from Global South poor to Global North poor. The viability of transfer-based approaches is also time-dependent: at 2050, cumulative rebound overwhelms progressive benefits (S4), suggesting climate finance mechanisms require design features that account for consumption responses over longer horizons. This transfer-rebound dynamic extends the effort-sharing literature into household-level territory it has not previously reached, and it indicates that progressive outcomes at 2030 cannot be assumed to persist without active policy adjustment. Limitations Several limitations qualify these findings. Household survey data from 2011-2015 may not capture recent shifts in consumption patterns. We do not model the costs of implementing demand-side shifts, including infrastructure investment and behavior change programs; if financed regressively, some progressive benefits could be offset. Energy burden, while informative, does not capture the full welfare picture: health effects of continued traditional biomass use, time poverty from fuel collection, and non-monetary well-being dimensions all warrant separate study. We model representative households within deciles rather than full distributions, leaving intersecting vulnerabilities to future work. Our analysis relies on a single IAM; multi-model assessment would strengthen confidence in regional allocations. Results at 2050 amplify the 2030 patterns but carry correspondingly greater uncertainty. Sensitivity analyses using the MESSAGE-Access fuel choice model for India and heterogeneous demand elasticities for the US confirm the directional findings, though the latter shift peak burden increases from D1 to D2-D3 for some pathways (S2, S3). The food expenditure calculation applies GCAM agricultural price projections following the approach in Soergel et al. (2021), developed for REMIND; testing this linkage against model-specific agricultural price dynamics would strengthen confidence in the double-squeeze magnitudes. GCAM's biomass pricing may understate the true cost to households who collect rather than purchase fuel, since it does not capture the time poverty associated with fuel collection. Methods Modeling framework We use GCAM (Global Change Analysis Model), v6.0, an integrated model of energy, water, land, and climate systems across 32 regions 30,31 , solving for market-clearing prices and quantities at five-year intervals with detailed technology representation. All 17 pathways achieve approximately 1.5°C end-of-century warming, isolating the effects of pathway architecture from differences in climate ambition (Figure S8). Prior work analyzed how these pathways affect national energy goals 32 ; here we extend that work to household-level distributional impacts. Pathways are organized into four categories (Table 1). Technology pathways emphasize supply-side solutions: high renewables (RE), nuclear with carbon capture (CCS-NUC), and direct air capture (DAC). Pace pathways vary net-zero timelines (NZ2040, NZ2050, NZ2060). Effort-sharing pathways allocate responsibility through grandfathering (GR.FATH), historical responsibility with transfers (RESP), capability-based staging (CAP-A, CAP-B), and sovereignty-based NDC trajectories (SOVER). Demand-side pathways include electrification (ELE), traditional biomass phase-out (NTB), behavioral change (BEH), and comprehensive demand-side mitigation (COMPR). Reference (REF) and cost-optimal (C.OPT) scenarios serve as baselines. BEH operationalizes IPCC AR6 demand-side estimates through changes to income elasticities, floor space satiation levels, and vehicle load factors in GCAM (see S1.1 for all pathway specifications). RESP adjusts regional GDP values to reflect financial transfers computed from cumulative excess emissions, then re-runs cost-optimal mitigation (S1.1). Table 1. Pathways analyzed in this study. All pathways achieve approximately 1.5°C end-of-century warming. See Supplementary Information for detailed specifications. Category Pathway Description Baselines REF Reference scenario without climate policy C.OPT Cost-optimal pathway with uniform global carbon price minimizing aggregate mitigation cost Technology RE High renewable energy deployment with accelerated solar PV and wind cost reductions following SSP1 CCS-NUC Nuclear expansion with carbon capture and storage at reduced costs following SSP5 DAC Direct air capture deployment capped at 5 GtCO2, rising preference to 2050 Pace NZ2040 Global net-zero CO₂ emissions by 2040 NZ2050 Global net-zero CO₂ emissions by 2050 NZ2060 Global net-zero CO₂ emissions by 2060 Demand-side ELE Accelerated electrification of buildings and transport NTB Phase-out of traditional biomass in residential sector BEH Behavioral changes following IPCC AR6 demand-side estimates: diet shifts, reduced floor space, modal shifts, increased ridesharing COMPR Comprehensive demand-side: BEH measures plus Kigali HFC phase-down, bioenergy constraints, efficiency mandates Effort-sharing GR.FATH Grandfathering: emission rights and negative emission responsibilities allocated proportional to 2015 shares CAP-A Capability-based: regions advance net-zero timelines by 5-10 years based on GDP per capita relative to Brazil's 2050 level CAP-B Capability-based: regions exceeding Brazil's 2050 GDP per capita today move to net-zero in 2025 SOVER Sovereignty: NDC trajectories continued with gradual tapering RESP Historical responsibility: cumulative per-capita emissions since 1850 converted to financial transfers at prevailing carbon prices RESP-POST Historical responsibility with consumption rebound modeled Household data and downscaling For India, we use the India Human Development Survey (IHDS-II, 2011-12), covering approximately 42,000 households with detailed fuel expenditure data 33 . For the United States, we use the Residential Energy Consumption Survey (RECS 2015) covering approximately 5,700 households 34 . Income distributions are projected using log-normal parameterizations with regional GDP per capita and Gini coefficients 35-37 . We downscale GCAM regional outputs to income deciles by allocating residential energy consumption according to fuel-specific patterns observed in surveys (Figure 1). For each fuel, income-specific consumption shares preserve within-country heterogeneity while respecting regional energy balances. For India, we distinguish electricity, LPG, kerosene, firewood, dung and agricultural waste, and coal or charcoal. For the United States: electricity, natural gas, propane, and fuel oil. Household energy expenditure is the product of consumption and GCAM-projected prices, including carbon costs. Traditional biomass is valued at GCAM-projected prices, which reflect the model's representation of biomass supply costs in each region and period. Energy burden calculation: the double squeeze Our outcome variable is household energy burden: energy expenditure divided by income net of food costs. This formulation differs from standard approaches that use gross income, and the distinction matters. GCAM projects both energy prices and agricultural prices under mitigation scenarios. When mitigation raises energy prices, the numerator increases. When it raises food prices, net income in the denominator shrinks. Poor households, who allocate larger shares of income to food, are squeezed from both directions, an amplification that standard gross-income calculations miss. Food expenditure derives from GCAM agricultural projections and income-specific food budget shares, following the approach in Soergel et al. 38 where these shares are applied to GCAM agricultural price projections. We report changes relative to reference as percentage point differences: positive values indicate increased burden (regressive), negative values indicate reduced burden (progressive). The main text focuses on 2030 results; 2050 results, which amplify the same patterns with greater uncertainty, appear in Supplementary Information. Sensitivity analyses We test robustness through three approaches detailed in Supplementary Information. First, for India, we apply the MESSAGE-Access model to incorporate fuel choice dynamics across eight rural and urban household types 39,40 . Second, for the United States, we employ decile-specific income elasticities capturing heterogeneous price responses 41 . Third, we model consumption rebound under historical responsibility (RESP-POST), allowing households receiving transfer income to increase energy consumption in response. Conclusions The architecture of mitigation pathways shapes distributional outcomes independently of revenue recycling. Cost-optimal, technology, and pace pathways concentrate burden on the poorest households, and the regressivity is amplified when food price effects are accounted for in the burden metric. Behavioral and comprehensive demand-side pathways achieve equivalent climate outcomes with progressive profiles by depressing global carbon prices through demand contraction, while historical responsibility with transfers creates a mirror in which recipient and donor countries experience opposite distributional consequences from the same pathway. Several practical implications follow. Scenario design embeds distributive choices that representative-household aggregation obscures; making these visible requires the kind of decile-level analysis demonstrated here. Effort-sharing frameworks reshape within-country distributions in ways that require complementary domestic policy, particularly in donor countries where transfer obligations concentrate on the poorest households. Demand-side policy must distinguish between approaches that create space for the poor and those that eliminate options they depend on. And the tension between climate mitigation and energy access goals, visible in the biomass retreat under cost-optimal pathways, underscores that pathway design is not only a distributional choice but a development choice with consequences for health and welfare that extend beyond the energy burden metric. Declarations Data Availability GCAM is open source (https://github.com/JGCRI/gcam-core). The IHDS-II survey is available through ICPSR (ref 24). RECS 2015 is available from the U.S. Energy Information Administration (ref 25). GCAM scenario output databases are not publicly available due to their large size and complexity but are available from the corresponding author on reasonable request. Code Availability Scenario analysis was conducted using GCAM v6.0 (https://github.com/JGCRI/gcam-core). GCAM scenario outputs in IAMC format for all 17 pathways will be deposited in Zenodo before publication. Downscaling to income deciles and energy burden calculations were performed using structured spreadsheet tools implementing the procedures described in S1.2-S1.3; these are available from the corresponding author on request. Acknowledgments M.G. participated in the IIASA Young Scientists Summer Program (YSSP). Participation was supported by a fellowship from the National Academies of Sciences, Engineering, and Medicine, funded by the U.S. National Science Foundation. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Author Contributions M.G. conceived the study, developed methodology, conducted analysis, and wrote the manuscript. H.M. contributed to scenario design and GCAM modeling. S.P., J.M., and N.D.R. contributed to downscaling methodology, income projections, effort sharing pathway design, energy burden conceptualization and interpretation of results. S.P., N.D.R., H.M., L.C. and A.P. supervised the research. All authors reviewed the manuscript. Competing Interests The authors declare no competing interests. Declaration of generative AI and AI-assisted technologies in the manuscript preparation process During the preparation of this work, the author(s) used the GPT-5 artificial intelligence model for preparing the draft of the article to improve readability and language. After using this tool, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article. References Peng, W. et al. Climate policy models need to get real about people. Nature 594, 174-176 (2021). van Ruijven, B. J., O’Neill, B. C. & Chateau, J. Methods for including income distribution in global CGE models for long-term climate change research. Energy Econ. 51, 530-543 (2015). Carley, S. & Konisky, D. M. The justice and equity implications of the clean energy transition. Nat. Energy 5, 569-577 (2020). Sovacool, B. K. et al. Decarbonization and its discontents: a critical energy justice perspective on four low-carbon transitions. Clim. Change 155, 581-619 (2019). Stevis, D., Morena, E. & Krause, D. in Just Transitions (Pluto Press, 2020). Pahle, M. et al. Sequencing to ratchet up climate policy stringency. Nat. Clim. Change 8, 861-867 (2018). Gilmore, E. A. & Buhaug, H. Climate mitigation policies and the potential pathways to conflict. WIREs Clim. Change 12, e722 (2021). Zimm, C. et al. Justice considerations in climate research. Nat. Clim. Change 14, 22-30 (2024). Low, S. et al. Expert perspectives on incorporating justice considerations into integrated assessment modelling. npj Clim. Action 4, 10 (2025). Jafino, B. A., Kwakkel, J. H. & Taebi, B. Enabling assessment of distributive justice through models for climate change planning. WIREs Clim. Change 12, e721 (2021). Klinsky, S. & Winkler, H. Building equity in: strategies for integrating equity into modelling for a 1.5°C world. Phil. Trans. R. Soc. A 376, 20160461 (2018). Emmerling, J. et al. A multi-model assessment of inequality and climate change. Nat. Clim. Change 14, 1254-1260 (2024). Bistline, J. et al. Equity implications of net-zero emissions: a multi-model analysis of energy expenditures. Energy Clim. Change 5, 100118 (2024). Young-Brun, M. et al. Within-country inequality and the shaping of a just global climate policy. Proc. Natl Acad. Sci. USA 122, e2505239122 (2025). Zhao, S. et al. The multi-faceted global poverty and income inequality landscape in a decarbonizing world. Cell Rep. Sustain. 2, 100487 (2025). Raupach, M. R. et al. Sharing a quota on cumulative carbon emissions. Nat. Clim. Change 4, 873-879 (2014). Robiou du Pont, Y. et al. Equitable mitigation to achieve the Paris Agreement goals. Nat. Clim. Change 7, 38-43 (2017). van den Berg, N. J. et al. Implications of various effort-sharing approaches for national carbon budgets. Clim. Change 162, 1805-1822 (2020). Creutzig, F. et al. Demand-side solutions to climate change mitigation consistent with high levels of well-being. Nat. Clim. Change 12, 36-46 (2022). Wiese, F. et al. The key role of sufficiency for low demand-based carbon neutrality and energy security across Europe. Nat. Commun. 15, 9043 (2024). Niamir, L. & Creutzig, F. Closing the gap: integrating behavioral and social dynamics through a modular modelling framework for low-energy demand pathways. Energy Res. Soc. Sci. 122, 103988 (2025). Grubler, A. et al. A low energy demand scenario for meeting the 1.5°C target. Nat. Energy 3, 515-527 (2018). Calvin, K. et al. GCAM v5.1: representing the linkages between energy, water, land, climate, and economic systems. Geosci. Model Dev. 12, 677-698 (2019). Desai, S. & Vanneman, R. India Human Development Survey-II (ICPSR36151-v6). ICPSR (2018). U.S. Energy Information Administration. 2015 Residential Energy Consumption Survey (EIA, 2018). Rao, N. D. & Wilson, C. Advancing energy and well-being research. Nat. Sustain. 5, 98-103 (2022). Oswald, Y. et al. Large inequality in international and intranational energy footprints. Nat. Energy 5, 231-239 (2020). Drehobl, A., Ross, L. & Ayala, R. How High Are Household Energy Burdens? (ACEEE, 2020). Hernandez, D. & Bird, S. Energy burden and the need for integrated low-income housing and energy policy. Poverty Public Policy 2, 5-25 (2010). Bond-Lamberty, B. et al. gcamdata: An R package for GCAM input data. J. Open Res. Softw. 7, 6 (2019). Calvin, K. et al. GCAM documentation. PNNL (2023). George, M. (2022). Distributional Impacts of Different 1.5°C Pathways on Energy Poverty. IIASA YSSP Report. Laxenburg, Austria: IIASA; George, M. (2024). Equity and Distributional Impacts of Climate Mitigation Pathways. PhD Dissertation, University of Maryland. Desai, S. et al. Human Development in India (Oxford Univ. Press, 2010). U.S. EIA. RECS Technical Documentation (EIA, 2017). Rao, N. D., Sauer, P., Gidden, M. & Riahi, K. Income inequality projections for the shared socioeconomic pathways (SSPs). Futures 105, 27-39 (2019). Sahasranaman, A. & Kumar, N. Income distribution and inequality in India: 2014-19. SSRN 3707153 (2020). Darvas, Z. Global interpersonal income inequality decline. World Dev. 121, 16-32 (2019). Soergel, B. et al. Combining ambitious climate policies with efforts to eradicate poverty. Nat. Commun. 12, 2129 (2021). Cameron, C. et al. Policy trade-offs between climate mitigation and clean cook-stove access. Nat. Energy 1, 15010 (2016). Poblete-Cazenave, M. & Pachauri, S. A model of energy poverty and access. Energy Econ. 98, 105266 (2021). Sampedro, J. et al. Implications of different income distributions for future residential energy demand. Environ. Res. Lett. 17, 014031 (2022). Millward-Hopkins, J. Inequality can double the energy required to secure universal decent living. Nat. Commun. 13, 5028 (2022). Rao, N. D. & Min, J. Less global inequality can improve climate outcomes. WIREs Clim. Change 9, e513 (2018). Additional Declarations The authors declare no competing interests. Supplementary Files graphicalabstractv16.png Graphical Abstract Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9442656","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":624598954,"identity":"a399b2e5-df4a-41d5-ad24-9180278fe090","order_by":0,"name":"Mel George","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYDADfiCWYGBgJkGLZAPJWgwOEKtF3r352McvFdsSN9/ITrzBUGGd2EBIi+GZY8mzZc7cTtx2I3ezBcOZdCK0zMgxZpZsu21sdiN3mwRj22EitMx/A9Ty77ax8QyQln9EaJGX4DFm/NhwW85AAqSlgQgtBjxpycwMx27LSZx5u9ki4Vi6MWFb2g8fZvxRc5uHvz13440PNdayhG05AIwLHhgvgZBysC1AQxl/EKNyFIyCUTAKRi4AAJnNQJZ2fBFDAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-2688-3388","institution":"Center for Global Sustainability (CGS), University of Maryland - College Park","correspondingAuthor":true,"prefix":"","firstName":"Mel","middleName":"","lastName":"George","suffix":""},{"id":624598955,"identity":"d025a26b-f812-4ea8-b172-8422af642672","order_by":1,"name":"Leon Clarke","email":"","orcid":"","institution":"Center for Global Sustainability (CGS), University of Maryland - College Park","correspondingAuthor":false,"prefix":"","firstName":"Leon","middleName":"","lastName":"Clarke","suffix":""},{"id":624598956,"identity":"7c7313b0-6a21-4588-a763-682609089165","order_by":2,"name":"Jihoon Min","email":"","orcid":"","institution":"IIASA Laxenburg, Austria","correspondingAuthor":false,"prefix":"","firstName":"Jihoon","middleName":"","lastName":"Min","suffix":""},{"id":624599129,"identity":"b2fa383a-056d-499a-8150-f9016e57d7d0","order_by":3,"name":"Shonali Pachauri","email":"","orcid":"","institution":"IIASA Laxenburg, Austria","correspondingAuthor":false,"prefix":"","firstName":"Shonali","middleName":"","lastName":"Pachauri","suffix":""},{"id":624599130,"identity":"0a1e2a8b-d33e-43ac-8289-b53bf14d0066","order_by":4,"name":"Anand Patwardhan","email":"","orcid":"","institution":"School of Public Policy, Univ of Maryland - College Park","correspondingAuthor":false,"prefix":"","firstName":"Anand","middleName":"","lastName":"Patwardhan","suffix":""},{"id":624599131,"identity":"30dbdec4-d302-4d8f-8478-56d57e38636a","order_by":5,"name":"Narasimha D Rao","email":"","orcid":"","institution":"Yale School of the Environment, Yale University","correspondingAuthor":false,"prefix":"","firstName":"Narasimha","middleName":"D","lastName":"Rao","suffix":""},{"id":624599132,"identity":"c926f7f2-5de9-452b-863f-d3a936d557cf","order_by":6,"name":"Haewon McJeon","email":"","orcid":"","institution":"KAIST School of Green Growth \u0026 Sustainability, Daejeon Korea","correspondingAuthor":false,"prefix":"","firstName":"Haewon","middleName":"","lastName":"McJeon","suffix":""}],"badges":[],"createdAt":"2026-04-17 00:58:34","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9442656/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9442656/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107433407,"identity":"35fd9c66-0118-47a8-8568-72796496496a","added_by":"auto","created_at":"2026-04-21 12:52:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99525,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological framework. Panel A: Standard IAM approaches use representative households that obscure distributional variation; our approach incorporates income heterogeneity across deciles. Panel B: The 'double squeeze' on energy burden. Under mitigation, GCAM projects both higher energy prices (increasing the numerator) and higher food prices (decreasing net income in the denominator). Poor households, with higher food budget shares, experience amplified burden increases that standard gross-income calculations miss.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9442656/v1/3b1b7614ef1825fe69a40ca1.png"},{"id":107488616,"identity":"c335d36e-0d84-4249-95a6-5bf8d0560b0b","added_by":"auto","created_at":"2026-04-22 02:45:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":76976,"visible":true,"origin":"","legend":"\u003cp\u003eEnergy burden changes across all pathways and income deciles, 2030. Heatmaps show burden changes (percentage points relative to reference) for India (left) and USA (right). Pathways grouped by category. Red indicates regressive impacts (burden increase); blue indicates progressive impacts (burden decrease). Most pathways show regressive gradients with poorest deciles (D1) experiencing larger increases than richest (D10).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9442656/v1/06ec48c816d5307371d127f6.png"},{"id":107433410,"identity":"7ca00a85-45f6-4a50-bb3d-42955564eeb2","added_by":"auto","created_at":"2026-04-21 12:52:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":150692,"visible":true,"origin":"","legend":"\u003cp\u003eDistributional character of pathways: D1 versus D10 burden changes. Scatter plots for India (left) and USA (right). Points above the diagonal (shaded red) indicate regressive outcomes where D1 impact exceeds D10; points below (shaded blue) indicate progressive outcomes. Markers distinguish pathway categories. Note the contrast between RESP outcomes: progressive for India (below diagonal), highly regressive for USA (far above diagonal).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9442656/v1/453ec9f2aabce25abd35e01c.png"},{"id":107488587,"identity":"a03d8221-336f-4243-9b87-de8827b435bb","added_by":"auto","created_at":"2026-04-22 02:45:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":79688,"visible":true,"origin":"","legend":"\u003cp\u003eMechanism linking pathway architecture to distributional outcomes. Space-constraining pathways (most technology, pace, and cost-optimal approaches) produce high carbon prices that burden poorest households most. Space-creating pathways (demand reduction, historical responsibility) reduce demand in wealthy populations, lower global prices, and create development space for the Global South.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9442656/v1/14f9982c8e41da52d0ad635b.png"},{"id":107868310,"identity":"d5a3152a-0cda-4806-9999-cb00006a7c94","added_by":"auto","created_at":"2026-04-27 07:10:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":570738,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9442656/v1/526ac45c-52d4-48b5-b486-062fc22795db.pdf"},{"id":107488601,"identity":"d6899d57-6a91-4aaf-aefd-bdecc3df6593","added_by":"auto","created_at":"2026-04-22 02:45:18","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":74344,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical Abstract\u003c/p\u003e","description":"","filename":"graphicalabstractv16.png","url":"https://assets-eu.researchsquare.com/files/rs-9442656/v1/0cb0055cc2f335d5d9c55e76.png"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eCarbon Prices and Cooking Fires: How Decarbonization Pathway Design Shapes Household Energy Burdens\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUnder cost-optimal climate mitigation, India\u0026rsquo;s poorest households face energy burden increases an order of magnitude larger than its richest. This disparity is not an unfortunate side effect to be corrected later through revenue recycling; it is a consequence of pathway architecture, the choices embedded in how we design routes to climate targets. Yet integrated assessment models (IAMs) typically report regional aggregates that render these consequences invisible\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMaking low-carbon transitions just, equitable, and politically durable has become central to climate policy\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, driven in part by evidence that unequal distribution of mitigation costs erodes social cohesion and undermines policy acceptability\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. These concerns have spurred calls to integrate justice considerations, encompassing distributional, corrective, and recognitional dimensions, into IAMs\u003csup\u003e\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Translating such calls into practice remains difficult, however, particularly when the goal is to trace how pathway designs propagate to vulnerable populations.\u003c/p\u003e \u003cp\u003eRecent multi-model studies have sharpened the picture. Climate damages widen inequality; revenue recycling can narrow it\u003csup\u003e12\u003c/sup\u003e. Net-zero pathways tend to be regressive under uncompensated carbon pricing but become progressive with per-capita recycling\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Even so, stringent mitigation risks increasing poverty in developing regions absent careful pathway design\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. A consistent theme in this literature is that revenue recycling determines distributional outcomes, a framing that treats pathways as distributionally neutral before compensation and locates all equity-relevant decisions at the revenue stage. But different pathway architectures, distinguished by technology choices, transition pace, effort allocation, or demand-side transformation, yield different distributional outcomes through their structure alone, before any revenue is collected or recycled. Effort-sharing pathways have been analyzed for country-level implications\u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e but their within-country distributional effects remain unexplored. Demand-side mitigation has attracted attention for its well-being implications\u003csup\u003e\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, yet not all demand-side approaches are equivalent: electrification differs from behavioral demand reduction in ways that matter for who bears the cost.\u003c/p\u003e \u003cp\u003eHere we examine 17 pathways to 1.5\u0026deg;C representing four architectural approaches (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), tracing impacts to household energy burdens across income deciles in India and the United States using the Global Change Analysis Model (GCAM) linked with nationally representative household surveys\u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. This responds to calls for improved representation of heterogeneity in energy-climate models\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Standard IAM practice employs representative households, implicitly assuming that differences balance in aggregate\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e; energy burden research reveals they do not\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Downscaling to income deciles exposes distributional variation that aggregate reporting obscures.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe find that the architecture of mitigation pathways is a primary determinant of distributional outcomes. Most pathways are regressive, with the poorest deciles shouldering disproportionately large cost increases compared to the richest. A subset of pathways, those emphasizing demand reduction in wealthy populations or historical responsibility with transfers, instead yield progressive outcomes. These distributional consequences are embedded in pathway design; downstream revenue policies cannot fully undo them.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eMost pathways are regressive\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTechnology, pace, and cost-optimal pathways concentrate burden on the poorest households in both countries (Figure 2). In India, the poorest decile (D1) faces energy burden increases roughly ten times larger than the richest (D10). This steep gradient reflects both higher price sensitivity among poor households and a counterintuitive fuel switching dynamic: under cost-optimal mitigation, the traditional biomass share of India\u0026rsquo;s residential consumption rises from roughly 54% in the reference scenario to 65%, as households retreat to self-collected fuels when modern alternatives become unaffordable.\u003c/p\u003e\n\u003cp\u003eThe United States shows the same direction but a compressed gradient. More uniform consumption patterns and universal modern energy access narrow the spread between deciles. Faster net-zero timelines (NZ2040 vs. NZ2060) amplify the regressive pattern without changing its character. Among effort-sharing pathways, grandfathering is particularly severe for India, locking in low per-capita carbon allocations irrespective of household income, while capability-based approaches (CAP-A, CAP-B) also skew costs toward the poorest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemand-side measures and redistribution yield progressive outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA distinct subset of pathways reverses this pattern (Figure 3). Behavioral change (BEH) and comprehensive demand-side mitigation (COMPR) deliver progressive outcomes in India: the poorest decile sees burden reductions relative to reference, while richer deciles absorb modest increases. Reduced demand in high-consuming populations depresses global carbon prices, opening carbon space for developing regions (the mechanism is detailed in Figure 4). Non-CO2 reductions from dietary shifts and HFC phase-down extend this headroom further.\u003c/p\u003e\n\u003cp\u003eHistorical responsibility with international transfers (RESP) generates the most strongly progressive outcome for India. Financial transfers from wealthy countries more than offset domestic mitigation costs for the poorest households. The mirror image holds for the United States, where RESP imposes the steepest regressive burden as households bear transfer obligations on top of domestic mitigation costs.\u003c/p\u003e\n\u003cp\u003eThe sovereignty-based pathway (SOVER) shows near-neutral burden effects for India in 2030, reflecting lower near-term mitigation stringency rather than structural redistribution: weak ambition embedded in NDC trajectories permits continued access expansion without significant burden increases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRebound attenuates but does not eliminate progressive transfers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen Indian households receive transfer income under historical responsibility, they may channel part of it into additional energy consumption. We model this rebound in RESP-POST. Progressive outcomes persist: the poorest decile still experiences burden reductions relative to reference, though benefits shrink compared to the no-rebound case. This is, to our knowledge, the first household-level analysis of consumption responses to international climate transfers. It suggests that climate finance mechanisms can deliver within-country equity benefits under realistic behavioral assumptions. At longer time horizons (2050), however, cumulative rebound eventually overwhelms transfer benefits (Supplementary Information).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNot all demand-side pathways are progressive\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe demand-side category masks critical heterogeneity. Electrification (ELE) is regressive in India: its benefits accrue to households that can afford to electrify, while costs are distributed through the carbon price. Traditional biomass phase-out (NTB) is regressive for the opposite reason, removing a fuel source that poor households depend on without providing affordable alternatives. Only pathways that reduce demand in wealthy populations, rather than mandating fuel transitions among the poor, yield progressive distributional patterns.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003ePathway architecture shapes distributional outcomes before revenue policy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDecile-level burden analysis exposes distributional patterns that representative-household IAM reporting renders invisible. The gradient between poorest and richest households is not merely steep; it differs qualitatively across pathway categories, with technology, pace, and cost-optimal pathways systematically regressive while behavioral demand-side and historical responsibility pathways produce progressive outcomes for India\u0026apos;s poorest without any revenue recycling. This pattern reframes the distributional question in the recent multi-model literature\u003csup\u003e12,13,14\u003c/sup\u003e, which has focused on revenue recycling as the equity lever and treated pathway architecture as distributionally neutral before compensation. Our results indicate that pathway structure shapes the distributional landscape on which revenue policy then operates. Some architectures are progressive before any revenue is collected; others embed regressivity that redistribution alone cannot fully counteract.\u003c/p\u003e\n\u003cp\u003eThe India-US pairing is analytically productive precisely because the two countries occupy different positions along several dimensions that matter for distributional outcomes. India\u0026apos;s residential energy system remains partially dependent on traditional biomass, its income distribution is steeper, and it stands on the receiving end of most effort-sharing frameworks. The United States has universal modern energy access, a compressed consumption gradient, and bears transfer obligations under historical responsibility. Neither country alone would reveal the mirror effect under RESP, nor would a single-country study expose how the same pathway architecture propagates through fundamentally different fuel mixes to produce opposite distributional outcomes.\u003c/p\u003e\n\u003cp\u003eThe causal chain runs through global commodity markets (Figure 4). Pathways that constrain carbon space elevate carbon prices that propagate to household energy bills, with the poorest households absorbing a disproportionate share due to higher price sensitivity and limited fuel-switching capacity. In India, this dynamic triggers a retreat to traditional biomass as modern fuels become unaffordable. The consequence is that climate policy partially reverses decades of progress on household energy transitions. Programs like India\u0026apos;s Pradhan Mantri Ujjwala Yojana have expanded LPG access to hundreds of millions of households; carbon pricing under cost-optimal mitigation works against this trajectory by making the cleaner fuels these programs provide less affordable relative to self-collected biomass. The health implications are substantial: continued reliance on solid fuels for cooking is a leading cause of household air pollution and premature mortality, particularly among women and children. This tension between climate mitigation (SDG 13) and clean energy access (SDG 7) is often noted in abstract terms; our results quantify it at the household level. Pathways that expand carbon space, through demand contraction in affluent societies or explicit financial transfers, relieve this pressure and preserve room for development\u003csup\u003e32\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe double squeeze on the poorest households\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur energy burden metric differs from standard gross-income approaches in a way that matters for the magnitude of estimated regressivity. By accounting for food price increases in the denominator alongside energy price increases in the numerator, the metric captures an amplification that gross-income calculations systematically understate. The effect is largest for households with high food budget shares. In India\u0026apos;s poorest decile, where food expenditure can exceed half of household income, mitigation-induced agricultural price increases substantially shrink the income available for energy purchases, roughly doubling the apparent burden increase compared to what a gross-income denominator would show. This measurement distinction is particularly consequential in countries where food expenditure dominates household budgets at the bottom of the income distribution, and it suggests that existing analyses of carbon pricing regressivity in developing countries may systematically understate the burden on the poorest households.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemand-side heterogeneity and the cost-optimal default\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe demand-side findings give quantitative substance to the sufficiency literature\u003csup\u003e19,42,43\u0026nbsp;\u003c/sup\u003ewhile complicating its narrative. Not all demand-side approaches produce equivalent distributional outcomes. The progressive character of behavioral demand reduction operates through a specific mechanism: lower aggregate demand depresses global carbon prices, creating space for developing regions. Electrification and traditional biomass phase-out lack this mechanism and instead impose costs on households least able to bear them. This distinction has practical implications for how \u0026quot;demand-side mitigation\u0026quot; is categorized in scenario assessments. Treating it as a single analytical category, as is common in IPCC scenario databases and model comparison exercises, obscures a consequential difference in who bears the cost.\u003c/p\u003e\n\u003cp\u003eMore broadly, cost-optimal pathways are not distributionally neutral defaults. They embed a particular allocation of burden that prioritizes aggregate economic efficiency, with disproportionate costs falling on the most vulnerable. The IPCC AR6 scenario database is heavily populated with cost-optimal and near-cost-optimal pathways; if each of these embeds regressive distributional consequences by construction, the evidence base informing climate policy carries an unexamined distributional assumption. Selecting a cost-optimal pathway is itself a distributive choice, even when framed as a technical benchmark. These results are consistent with arguments that reducing demand in affluent populations can create distributional space, though we do not assess whether resulting consumption levels meet sufficiency thresholds as defined in the decent living standards literature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGlobal versus domestic justice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe effort-sharing results expose a tension that the existing literature on burden-sharing frameworks\u003csup\u003e16,17,18\u003c/sup\u003e has not traced to household level. Historical responsibility with transfers produces a mirror effect: the pathway that delivers the strongest progressive outcome for India simultaneously imposes the steepest regressive burden in the United States. This is not a modeling artifact; it reflects the arithmetic of transfers large enough to offset mitigation costs in recipient countries being financed by households in donor countries, where the burden falls disproportionately on those with the highest energy expenditure shares.\u003c/p\u003e\n\u003cp\u003eThis finding is directly relevant to ongoing negotiations over climate finance architecture. The Paris Agreement\u0026apos;s Article 6 mechanisms and the loss and damage fund established at COP27 both involve financial flows from wealthy to developing countries, yet their design has proceeded largely without household-level distributional analysis in donor countries. Our results suggest that the political feasibility of such mechanisms depends not only on the aggregate fiscal burden but on how that burden distributes across income groups domestically. Transfer obligations that land disproportionately on a donor country\u0026apos;s poorest households risk generating the same political backlash that has undermined domestic carbon pricing.\u003c/p\u003e\n\u003cp\u003eInternational negotiations would benefit from anticipating these within-country consequences. Domestic policy must accompany international commitments so that transfers do not simply relocate burdens from Global South poor to Global North poor. The viability of transfer-based approaches is also time-dependent: at 2050, cumulative rebound overwhelms progressive benefits (S4), suggesting climate finance mechanisms require design features that account for consumption responses over longer horizons. This transfer-rebound dynamic extends the effort-sharing literature into household-level territory it has not previously reached, and it indicates that progressive outcomes at 2030 cannot be assumed to persist without active policy adjustment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral limitations qualify these findings. Household survey data from 2011-2015 may not capture recent shifts in consumption patterns. We do not model the costs of implementing demand-side shifts, including infrastructure investment and behavior change programs; if financed regressively, some progressive benefits could be offset. Energy burden, while informative, does not capture the full welfare picture: health effects of continued traditional biomass use, time poverty from fuel collection, and non-monetary well-being dimensions all warrant separate study. We model representative households within deciles rather than full distributions, leaving intersecting vulnerabilities to future work. Our analysis relies on a single IAM; multi-model assessment would strengthen confidence in regional allocations. Results at 2050 amplify the 2030 patterns but carry correspondingly greater uncertainty. Sensitivity analyses using the MESSAGE-Access fuel choice model for India and heterogeneous demand elasticities for the US confirm the directional findings, though the latter shift peak burden increases from D1 to D2-D3 for some pathways (S2, S3). The food expenditure calculation applies GCAM agricultural price projections following the approach in Soergel et al. (2021), developed for REMIND; testing this linkage against model-specific agricultural price dynamics would strengthen confidence in the double-squeeze magnitudes. GCAM\u0026apos;s biomass pricing may understate the true cost to households who collect rather than purchase fuel, since it does not capture the time poverty associated with fuel collection.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eModeling framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe use GCAM (Global Change Analysis Model), v6.0, an integrated model of energy, water, land, and climate systems across 32 regions\u003csup\u003e30,31\u003c/sup\u003e, solving for market-clearing prices and quantities at five-year intervals with detailed technology representation. All 17 pathways achieve approximately 1.5\u0026deg;C end-of-century warming, isolating the effects of pathway architecture from differences in climate ambition (Figure S8). Prior work analyzed how these pathways affect national energy goals\u003csup\u003e32\u003c/sup\u003e; here we extend that work to household-level distributional impacts.\u003c/p\u003e\n\u003cp\u003ePathways are organized into four categories (Table 1). Technology pathways emphasize supply-side solutions: high renewables (RE), nuclear with carbon capture (CCS-NUC), and direct air capture (DAC). Pace pathways vary net-zero timelines (NZ2040, NZ2050, NZ2060). Effort-sharing pathways allocate responsibility through grandfathering (GR.FATH), historical responsibility with transfers (RESP), capability-based staging (CAP-A, CAP-B), and sovereignty-based NDC trajectories (SOVER). Demand-side pathways include electrification (ELE), traditional biomass phase-out (NTB), behavioral change (BEH), and comprehensive demand-side mitigation (COMPR). Reference (REF) and cost-optimal (C.OPT) scenarios serve as baselines. BEH operationalizes IPCC AR6 demand-side estimates through changes to income elasticities, floor space satiation levels, and vehicle load factors in GCAM (see S1.1 for all pathway specifications). RESP adjusts regional GDP values to reflect financial transfers computed from cumulative excess emissions, then re-runs cost-optimal mitigation (S1.1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003ePathways analyzed in this study. All pathways achieve approximately 1.5\u0026deg;C end-of-century warming. See Supplementary Information for detailed specifications.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathway\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 451px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eBaselines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eREF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 451px;\"\u003e\n \u003cp\u003eReference scenario without climate policy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eC.OPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 451px;\"\u003e\n \u003cp\u003eCost-optimal pathway with uniform global carbon price minimizing aggregate mitigation cost\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eTechnology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 451px;\"\u003e\n \u003cp\u003eHigh renewable energy deployment with accelerated solar PV and wind cost reductions following SSP1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eCCS-NUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 451px;\"\u003e\n \u003cp\u003eNuclear expansion with carbon capture and storage at reduced costs following SSP5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eDAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 451px;\"\u003e\n \u003cp\u003eDirect air capture deployment capped at 5 GtCO2, rising preference to 2050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003ePace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eNZ2040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 451px;\"\u003e\n \u003cp\u003eGlobal net-zero CO₂ emissions by 2040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eNZ2050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 451px;\"\u003e\n \u003cp\u003eGlobal net-zero CO₂ emissions by 2050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eNZ2060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 451px;\"\u003e\n \u003cp\u003eGlobal net-zero CO₂ emissions by 2060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eDemand-side\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eELE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 451px;\"\u003e\n \u003cp\u003eAccelerated electrification of buildings and transport\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eNTB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 451px;\"\u003e\n \u003cp\u003ePhase-out of traditional biomass in residential sector\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eBEH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 451px;\"\u003e\n \u003cp\u003eBehavioral changes following IPCC AR6 demand-side estimates: diet shifts, reduced floor space, modal shifts, increased ridesharing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eCOMPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 451px;\"\u003e\n \u003cp\u003eComprehensive demand-side: BEH measures plus Kigali HFC phase-down, bioenergy constraints, efficiency mandates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eEffort-sharing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eGR.FATH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 451px;\"\u003e\n \u003cp\u003eGrandfathering: emission rights and negative emission responsibilities allocated proportional to 2015 shares\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eCAP-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 451px;\"\u003e\n \u003cp\u003eCapability-based: regions advance net-zero timelines by 5-10 years based on GDP per capita relative to Brazil\u0026apos;s 2050 level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eCAP-B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 451px;\"\u003e\n \u003cp\u003eCapability-based: regions exceeding Brazil\u0026apos;s 2050 GDP per capita today move to net-zero in 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eSOVER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 451px;\"\u003e\n \u003cp\u003eSovereignty: NDC trajectories continued with gradual tapering\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eRESP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 451px;\"\u003e\n \u003cp\u003eHistorical responsibility: cumulative per-capita emissions since 1850 converted to financial transfers at prevailing carbon prices\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eRESP-POST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 451px;\"\u003e\n \u003cp\u003eHistorical responsibility with consumption rebound modeled\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eHousehold data and downscaling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor India, we use the India Human Development Survey (IHDS-II, 2011-12), covering approximately 42,000 households with detailed fuel expenditure data\u003csup\u003e33\u003c/sup\u003e. For the United States, we use the Residential Energy Consumption Survey (RECS 2015) covering approximately 5,700 households\u003csup\u003e34\u003c/sup\u003e. Income distributions are projected using log-normal parameterizations with regional GDP per capita and Gini coefficients\u003csup\u003e35-37\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWe downscale GCAM regional outputs to income deciles by allocating residential energy consumption according to fuel-specific patterns observed in surveys (Figure 1). For each fuel, income-specific consumption shares preserve within-country heterogeneity while respecting regional energy balances. For India, we distinguish electricity, LPG, kerosene, firewood, dung and agricultural waste, and coal or charcoal. For the United States: electricity, natural gas, propane, and fuel oil. Household energy expenditure is the product of consumption and GCAM-projected prices, including carbon costs. Traditional biomass is valued at GCAM-projected prices, which reflect the model\u0026apos;s representation of biomass supply costs in each region and period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnergy burden calculation: the double squeeze\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur outcome variable is household energy burden: energy expenditure divided by income net of food costs. This formulation differs from standard approaches that use gross income, and the distinction matters. GCAM projects both energy prices and agricultural prices under mitigation scenarios. When mitigation raises energy prices, the numerator increases. When it raises food prices, net income in the denominator shrinks. Poor households, who allocate larger shares of income to food, are squeezed from both directions, an amplification that standard gross-income calculations miss.\u003c/p\u003e\n\u003cp\u003eFood expenditure derives from GCAM agricultural projections and income-specific food budget shares, following the approach in Soergel et al.\u003csup\u003e38\u003c/sup\u003e where these shares are applied to GCAM agricultural price projections. We report changes relative to reference as percentage point differences: positive values indicate increased burden (regressive), negative values indicate reduced burden (progressive). The main text focuses on 2030 results; 2050 results, which amplify the same patterns with greater uncertainty, appear in Supplementary Information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe test robustness through three approaches detailed in Supplementary Information. First, for India, we apply the MESSAGE-Access model to incorporate fuel choice dynamics across eight rural and urban household types\u003csup\u003e39,40\u003c/sup\u003e. Second, for the United States, we employ decile-specific income elasticities capturing heterogeneous price responses\u003csup\u003e41\u003c/sup\u003e. Third, we model consumption rebound under historical responsibility (RESP-POST), allowing households receiving transfer income to increase energy consumption in response.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe architecture of mitigation pathways shapes distributional outcomes independently of revenue recycling. Cost-optimal, technology, and pace pathways concentrate burden on the poorest households, and the regressivity is amplified when food price effects are accounted for in the burden metric. Behavioral and comprehensive demand-side pathways achieve equivalent climate outcomes with progressive profiles by depressing global carbon prices through demand contraction, while historical responsibility with transfers creates a mirror in which recipient and donor countries experience opposite distributional consequences from the same pathway.\u003c/p\u003e\n\u003cp\u003eSeveral practical implications follow. Scenario design embeds distributive choices that representative-household aggregation obscures; making these visible requires the kind of decile-level analysis demonstrated here. Effort-sharing frameworks reshape within-country distributions in ways that require complementary domestic policy, particularly in donor countries where transfer obligations concentrate on the poorest households. Demand-side policy must distinguish between approaches that create space for the poor and those that eliminate options they depend on. And the tension between climate mitigation and energy access goals, visible in the biomass retreat under cost-optimal pathways, underscores that pathway design is not only a distributional choice but a development choice with consequences for health and welfare that extend beyond the energy burden metric.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGCAM is open source (https://github.com/JGCRI/gcam-core). The IHDS-II survey is available through ICPSR (ref 24). RECS 2015 is available from the U.S. Energy Information Administration (ref 25). GCAM scenario output databases are not publicly available due to their large size and complexity but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eScenario analysis was conducted using GCAM v6.0 (https://github.com/JGCRI/gcam-core). GCAM scenario outputs in IAMC format for all 17 pathways will be deposited in Zenodo before publication. Downscaling to income deciles and energy burden calculations were performed using structured spreadsheet tools implementing the procedures described in S1.2-S1.3; these are available from the corresponding author on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.G. participated in the IIASA Young Scientists Summer Program (YSSP). Participation was supported by a fellowship from the National Academies of Sciences, Engineering, and Medicine, funded by the U.S. National Science Foundation. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.G. conceived the study, developed methodology, conducted analysis, and wrote the manuscript. H.M. contributed to scenario design and GCAM modeling. S.P., J.M., and N.D.R. contributed to downscaling methodology, income projections, effort sharing pathway design, energy burden conceptualization and interpretation of results. S.P., N.D.R., H.M., L.C. and A.P. supervised the research. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the manuscript preparation process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work, the author(s) used the GPT-5 artificial intelligence model for preparing the draft of the article to improve readability and language. After using this tool, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePeng, W. et al. Climate policy models need to get real about people. Nature 594, 174-176 (2021).\u003c/li\u003e\n\u003cli\u003evan Ruijven, B. J., O\u0026rsquo;Neill, B. C. \u0026amp; Chateau, J. Methods for including income distribution in global CGE models for long-term climate change research. Energy Econ. 51, 530-543 (2015).\u003c/li\u003e\n\u003cli\u003eCarley, S. \u0026amp; Konisky, D. M. The justice and equity implications of the clean energy transition. Nat. Energy 5, 569-577 (2020).\u003c/li\u003e\n\u003cli\u003eSovacool, B. K. et al. Decarbonization and its discontents: a critical energy justice perspective on four low-carbon transitions. Clim. Change 155, 581-619 (2019).\u003c/li\u003e\n\u003cli\u003eStevis, D., Morena, E. \u0026amp; Krause, D. in Just Transitions (Pluto Press, 2020).\u003c/li\u003e\n\u003cli\u003ePahle, M. et al. Sequencing to ratchet up climate policy stringency. Nat. Clim. Change 8, 861-867 (2018).\u003c/li\u003e\n\u003cli\u003eGilmore, E. A. \u0026amp; Buhaug, H. Climate mitigation policies and the potential pathways to conflict. WIREs Clim. Change 12, e722 (2021).\u003c/li\u003e\n\u003cli\u003eZimm, C. et al. Justice considerations in climate research. Nat. Clim. Change 14, 22-30 (2024).\u003c/li\u003e\n\u003cli\u003eLow, S. et al. Expert perspectives on incorporating justice considerations into integrated assessment modelling. npj Clim. Action 4, 10 (2025).\u003c/li\u003e\n\u003cli\u003eJafino, B. A., Kwakkel, J. H. \u0026amp; Taebi, B. Enabling assessment of distributive justice through models for climate change planning. WIREs Clim. Change 12, e721 (2021).\u003c/li\u003e\n\u003cli\u003eKlinsky, S. \u0026amp; Winkler, H. Building equity in: strategies for integrating equity into modelling for a 1.5\u0026deg;C world. Phil. Trans. R. Soc. A 376, 20160461 (2018).\u003c/li\u003e\n\u003cli\u003eEmmerling, J. et al. A multi-model assessment of inequality and climate change. Nat. Clim. Change 14, 1254-1260 (2024).\u003c/li\u003e\n\u003cli\u003eBistline, J. et al. Equity implications of net-zero emissions: a multi-model analysis of energy expenditures. Energy Clim. Change 5, 100118 (2024).\u003c/li\u003e\n\u003cli\u003eYoung-Brun, M. et al. Within-country inequality and the shaping of a just global climate policy. Proc. Natl Acad. Sci. USA 122, e2505239122 (2025).\u003c/li\u003e\n\u003cli\u003eZhao, S. et al. The multi-faceted global poverty and income inequality landscape in a decarbonizing world. Cell Rep. Sustain. 2, 100487 (2025).\u003c/li\u003e\n\u003cli\u003eRaupach, M. R. et al. Sharing a quota on cumulative carbon emissions. Nat. Clim. Change 4, 873-879 (2014).\u003c/li\u003e\n\u003cli\u003eRobiou du Pont, Y. et al. Equitable mitigation to achieve the Paris Agreement goals. Nat. Clim. Change 7, 38-43 (2017).\u003c/li\u003e\n\u003cli\u003evan den Berg, N. J. et al. Implications of various effort-sharing approaches for national carbon budgets. Clim. Change 162, 1805-1822 (2020).\u003c/li\u003e\n\u003cli\u003eCreutzig, F. et al. Demand-side solutions to climate change mitigation consistent with high levels of well-being. Nat. Clim. Change 12, 36-46 (2022).\u003c/li\u003e\n\u003cli\u003eWiese, F. et al. The key role of sufficiency for low demand-based carbon neutrality and energy security across Europe. Nat. Commun. 15, 9043 (2024).\u003c/li\u003e\n\u003cli\u003eNiamir, L. \u0026amp; Creutzig, F. Closing the gap: integrating behavioral and social dynamics through a modular modelling framework for low-energy demand pathways. Energy Res. Soc. Sci. 122, 103988 (2025).\u003c/li\u003e\n\u003cli\u003eGrubler, A. et al. A low energy demand scenario for meeting the 1.5\u0026deg;C target. Nat. Energy 3, 515-527 (2018).\u003c/li\u003e\n\u003cli\u003eCalvin, K. et al. GCAM v5.1: representing the linkages between energy, water, land, climate, and economic systems. Geosci. Model Dev. 12, 677-698 (2019).\u003c/li\u003e\n\u003cli\u003eDesai, S. \u0026amp; Vanneman, R. India Human Development Survey-II (ICPSR36151-v6). ICPSR (2018).\u003c/li\u003e\n\u003cli\u003eU.S. Energy Information Administration. 2015 Residential Energy Consumption Survey (EIA, 2018).\u003c/li\u003e\n\u003cli\u003eRao, N. D. \u0026amp; Wilson, C. Advancing energy and well-being research. Nat. Sustain. 5, 98-103 (2022).\u003c/li\u003e\n\u003cli\u003eOswald, Y. et al. Large inequality in international and intranational energy footprints. Nat. Energy 5, 231-239 (2020).\u003c/li\u003e\n\u003cli\u003eDrehobl, A., Ross, L. \u0026amp; Ayala, R. How High Are Household Energy Burdens? (ACEEE, 2020).\u003c/li\u003e\n\u003cli\u003eHernandez, D. \u0026amp; Bird, S. Energy burden and the need for integrated low-income housing and energy policy. Poverty Public Policy 2, 5-25 (2010).\u003c/li\u003e\n\u003cli\u003eBond-Lamberty, B. et al. gcamdata: An R package for GCAM input data. J. Open Res. Softw. 7, 6 (2019).\u003c/li\u003e\n\u003cli\u003eCalvin, K. et al. GCAM documentation. PNNL (2023).\u003c/li\u003e\n\u003cli\u003eGeorge, M. (2022). Distributional Impacts of Different 1.5\u0026deg;C Pathways on Energy Poverty. IIASA YSSP Report. Laxenburg, Austria: IIASA; George, M. (2024). Equity and Distributional Impacts of Climate Mitigation Pathways. PhD Dissertation, University of Maryland.\u003c/li\u003e\n\u003cli\u003eDesai, S. et al. Human Development in India (Oxford Univ. Press, 2010).\u003c/li\u003e\n\u003cli\u003eU.S. EIA. RECS Technical Documentation (EIA, 2017).\u003c/li\u003e\n\u003cli\u003eRao, N. D., Sauer, P., Gidden, M. \u0026amp; Riahi, K. Income inequality projections for the shared socioeconomic pathways (SSPs). Futures 105, 27-39 (2019).\u003c/li\u003e\n\u003cli\u003eSahasranaman, A. \u0026amp; Kumar, N. Income distribution and inequality in India: 2014-19. SSRN 3707153 (2020).\u003c/li\u003e\n\u003cli\u003eDarvas, Z. Global interpersonal income inequality decline. World Dev. 121, 16-32 (2019).\u003c/li\u003e\n\u003cli\u003eSoergel, B. et al. Combining ambitious climate policies with efforts to eradicate poverty. Nat. Commun. 12, 2129 (2021).\u003c/li\u003e\n\u003cli\u003eCameron, C. et al. Policy trade-offs between climate mitigation and clean cook-stove access. Nat. Energy 1, 15010 (2016).\u003c/li\u003e\n\u003cli\u003ePoblete-Cazenave, M. \u0026amp; Pachauri, S. A model of energy poverty and access. Energy Econ. 98, 105266 (2021).\u003c/li\u003e\n\u003cli\u003eSampedro, J. et al. Implications of different income distributions for future residential energy demand. Environ. Res. Lett. 17, 014031 (2022).\u003c/li\u003e\n\u003cli\u003eMillward-Hopkins, J. Inequality can double the energy required to secure universal decent living. Nat. Commun. 13, 5028 (2022).\u003c/li\u003e\n\u003cli\u003eRao, N. D. \u0026amp; Min, J. Less global inequality can improve climate outcomes. WIREs Clim. Change 9, e513 (2018).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"66c5e166-34e5-406b-a2d5-225c696efcd6","identifier":"10.13039/100000209","name":"National Academy of Sciences","awardNumber":"YSSP","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"International Institute for Applied Systems Analysis","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"climate mitigation, energy justice, distributional impacts, integrated assessment models, demand-side mitigation, effort-sharing","lastPublishedDoi":"10.21203/rs.3.rs-9442656/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9442656/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate mitigation pathways impose uneven burdens across income groups, yet analyses typically focus on revenue recycling rather than on how pathway architecture shapes distributional outcomes. We examine 17 pathways to 1.5\u0026deg;C and trace their impacts to household energy burdens across income deciles in India and the United States. Pathway architecture is a primary determinant of whether mitigation is progressive or regressive. Technology, pace, and cost-optimal pathways are regressive: the poorest households bear cost increases up to an order of magnitude larger than the richest. Demand-side pathways lower carbon prices by curbing demand among high-income consumers, creating development space that alleviates burdens in developing countries. Historical responsibility pathways with international transfers generate progressive outcomes for recipient countries at the expense of donor households. These patterns persist under consumption rebound, albeit attenuated. Distributional consequences are embedded in pathway design, not correctable through revenue policy alone.\u003c/p\u003e","manuscriptTitle":"Carbon Prices and Cooking Fires: How Decarbonization Pathway Design Shapes Household Energy Burdens","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 12:52:18","doi":"10.21203/rs.3.rs-9442656/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"025700d0-5648-459d-b019-e2f3429ee049","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T12:52:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 12:52:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9442656","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9442656","identity":"rs-9442656","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}