High wellbeing with lower energy consumption: scenarios for India’s residential sector

High wellbeing with lower energy consumption: scenarios for India’s residential sector | 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 Article High wellbeing with lower energy consumption: scenarios for India’s residential sector Alessio Mastrucci, Souran Chatterjee, Kaveri Ashok, Vassilis Daioglou, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8300022/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Ensuring equitable access to energy services to enhance human wellbeing is gaining prominence within climate negotiations. Despite rising energy demands in the Global South, disparities persist in access to decent living standards (DLS). Achieving DLS universally requires additional energy, yet the efficacy of demand-side strategies in meeting climate goals remains underexplored. Here, we assess energy and carbon dioxide emission implications of providing DLS to all within climate change mitigation efforts, focusing on India’s residential sector and using a multi-model analysis. Our findings show that providing DLS while leveraging demand-side measures, supported by enabling policies and widely available technologies, can substantially reduce residential energy demand by up to 27% by 2050 compared to a reference scenario. We find that enhanced access to DLS combined with demand-side measures not only improves wellbeing but also provides flexibility on the supply side while remaining compatible with ambitious climate targets. Earth and environmental sciences/Environmental social sciences/Climate-change mitigation Scientific community and society/Developing world Physical sciences/Energy science and technology/Energy modelling Scientific community and society/Energy and society/Energy access Climate change mitigation Sustainable development Energy poverty Global South Model intercomparison Decent Living Standards Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The Paris Agreement and the Sustainable Development Goals (SDG) have set an ambitious agenda requiring climate change mitigation action and broader sustainable development to go hand-in-hand 1 . The Global South is facing unique challenges in pursuing climate and development goals. Rapid population growth and development are driving demand for energy services in most emerging and developing countries 2 , while greenhouse gas (GHG) emissions continue to increase 3 , 4 . Southern Asia alone has seen a 3.6% annual rise in emissions since 2010 5 . Concurrently, large sections of the population still lack access to basic energy services essential for human wellbeing 6 , 7 . Approximately 3 billion people are estimated to live without access to decent living standards (DLS) that constitute the material prerequisites to support human wellbeing 8 , 9 . Around 1.2 billion people still live in inadequate housing infrastructure 10 , 675 million people lack access to electricity, and 2.3 billion to clean cooking 11 . Ensuring universal access to modern energy services is crucial for poverty alleviation and improvements of living conditions and wellbeing 12 . Within climate negotiations, considerations of equity and just transitions are increasingly emphasized 13 – 15 . With ambitious climate mitigation requiring significant emissions reductions and system transformations, the question of available development space to achieve DLS universally remains central 16 , 17 . Demand-side strategies, including energy efficiency improvements and behavioural shifts, offer large potential for reducing end-use sectors’ energy demands while being synergistic with raising wellbeing 18 – 20 . Yet, most existing mitigation scenarios focus on supply-side strategies and rely heavily on uncertain negative emission technologies, while overlooking demand-side strategies and considerations of equity and poverty eradication, especially for the Global South 21 – 23 . This gap is particularly evident in the buildings sector, which accounts 21% of global GHG emissions 24 and plays a central role in both decarbonization and support of human activities and wellbeing 25 , 26 . The residential sector is expanding rapidly in the Global South, driven by population growth and increasing demand for floorspace 27 . More than half of the buildings that will exist in 2050 are yet to be constructed in these regions 27 , presenting a unique opportunity to design buildings with already available low energy demand technologies that improve living conditions and comfort 28 . Mitigation scenarios for the building sector in the Global South show high emission reduction potentials 24 , but mostly rely on top-down and aggregate modelling, failing to account for DLS gaps and heterogeneous energy needs across populations 22 , 26 . The elimination of multiple deprivations has rarely been assessed in future scenario studies 17 , 29 – 31 . Existing literature mostly focuses on the minimum requirements to provide basic access to DLS without accounting for demand elasticities that are key in understanding the evolution of future energy demand 8 , 16 , 32 . These studies apply normative assumptions to calculate energy requirements and mostly overlook interactions between demand- and supply-side of energy systems and the synergies and trade-offs with climate mitigation policies. Thus, the effect of additional energy and emissions required to provide universal access to DLS on achieving climate mitigation targets remains underexplored. Ensuring higher levels of DLS while reducing energy demand and emissions is crucial to simultaneously achieve climate and sustainable development goals. Here, we assess a comprehensive set of scenarios to provide near-universal access to DLS by 2050 while achieving lower energy demand levels under different climate policies, focusing on India’s residential sector. India is a remarkable case study due large projected GHG emission levels 2 and, despite significant progress in recent years, large gaps in access to multiple dimensions of DLS 16 and social inequalities 33 . A key contribution of this study is the use of five detailed building sector models applied under a common scenario protocol with a focus on both climate mitigation and wellbeing outcomes. We include two national models – PIER 34 , 35 , and SAFARI 36 – and three global models – HEB 37 , IMAGE 38 , 39 , MESSAGEix-Buildings 40 – with the latter two being part of IAMs to account for energy supply transformations under different climate policy contexts. This approach enables robust assessment of findings across different modelling frameworks and exploration of uncertainty in future projections. These models explicitly account for access to key DLS services and amenities, making it possible to directly link service levels and related energy demands and carbon dioxide (CO 2 ) emissions. By systematically integrating DLS into energy modelling, this work contributes to a growing body of literature that seeks to make climate mitigation scenarios more inclusive and policy relevant. It also provides a foundation for future modelling efforts that aim to better represent the realities of the Global South, where the dual imperatives of development and decarbonization must be addressed together. Scenarios We explore three main scenarios investigating the effect of improved access to DLS and demand-side measures on energy demand and CO 2 emissions by the Indian residential sector between 2020 and 2050 (Table 1 ). The Reference (REF) scenario reflects a continuation of current trends and policies, aligned with Shared Socioeconomic Pathway SSP2 “Middle of the Road” 41 . The Decent living (DL) scenario assumes an improvement in access to basic services and amenities to provide near universal access to DLS by 2050. The Decent living – Demand measures (DL-DEM) scenario combines improved access to wellbeing in line with the DLS scenario and additional sector-wide energy efficiency improvements and behavioural changes achievable with currently available technologies. We combine the three main scenarios with two climate mitigation policy strategies, resulting in a total of six scenarios. The Current Policies strategy assumes continuation of national policies with no stringent climate policies. In the Mitigation Policies strategy, a set of stringent policies to achieve the 1.5°C climate targets are in place, including carbon pricing, leading to a decarbonization of the electricity supply system. Service levels, such as floorspace and access to appliances, remain unchanged across the Current Policies and Mitigation Policies strategies. We have established a modelling protocol to guide the implementation of these scenarios in the different models (see Supplementary Methods). Unlike similar multi-model analyses, we do not aim for full harmonization of scenario data and trends across the models, to better understand different model behaviours and complementary insights. We have, however, aligned the data for the year 2020 and the key underlying model drivers to the extent possible. Table 1 Overview of the three main scenarios. Scenario name Description Decent living standard (DLS) trends Energy demand trends Reference (REF) Continuation of current trends and policies. Likely DLS achievement under current trends and policies. Energy efficiency improvement in building shell, cooling systems, and appliances aligned with current trends and policies. Decent Living (DL) Improved access to DLS Achievement of near-universal DLS by 2050, including: - sufficient floorspace - durable homes - basic cooling thermal comfort - access to clean fuels for cooking - appliances (refrigerators, televisions, washing machine, lighting) Same as in the REF scenario, besides DLS interventions. Decent living – Demand measures (DL-DEM) Improved access to DLS and demand-side measures to achieve lower energy demand levels. Same as in the DL scenario. Sectoral interventions towards lower energy demand: improved building shell, energy-efficient heating and cooling systems, cooking stoves and appliances, electrification, behavioural change limiting energy demand. Access to decent living standards Levels of access to the services and amenities supporting DLS vary across different dimensions and over time (Fig. 1 , A-B). In 2020, access levels are particularly low for AC, refrigerators, and clean cooking. Average per-capita floorspace of buildings is at 13–15 m 2 /cap, close to the minimum threshold defined by DLS, namely 10 m 2 /cap with a minimum of 30m 2 per household 16 . In the REF scenario, national access to DLS improves gradually in most dimensions. Large gaps still exist in 2050, particularly for AC (48–82% access), and to a lesser extent for refrigerators (86–96% access) and for clean cooking (85–100%). Universal access to fans and televisions is almost achieved, and floorspace levels grow significantly to 22–29 m 2 /cap. Providing universal access to DLS requires additional efforts to close the existing gaps, as shown in the DL scenario results, especially for AC, clean cooking, and formal housing. Providing sufficient housing space requires increasing per-capita floorspace to an average of 22–33 m 2 /cap. These trends are robust across the different models, even though larger ranges of uncertainty exist on future access projections for specific dimensions, in particular AC, clean cooking, and per-capita floorspace. Despite additional provision of durable housing units under current trends and policies, the share of households with access to durable housing (Fig. 1 , C) does not significantly improve in the REF scenario due to growing population and urbanization levels. Additional efforts are required to fill this gap in the DL scenario, especially in the provision of affordable durable housing in urban areas. Energy demand projections Following rising service levels, the residential energy demand in India keeps growing in the REF scenario (Fig. 2 ). Cooling is the fastest-growing end-use with up to a ten-fold increase in projected energy demand by 2050, followed by appliances (Fig. 2 , A). Cooking energy demand is projected to decline already in the REF scenario, as the effects of switching to cleaner and more efficient energy carriers balances the increase in demand. While agreeing on the general trends, the models show large ranges of uncertainty, especially on cooling and cooking, stemming from different behavioural and conversion efficiency assumptions, and in the case of cooking, from different fuel shares. In the DL scenario, energy demand levels mostly increase compared to the REF scenario under higher service levels to provide DLS. The highest increases are projected for cooling (up to 75%) and for appliances (up to 29%) relative to the REF scenario in 2050 (Fig. 2 , B), due to broader access to AC, fans and other appliances (Fig. 1 ). Switches to cleaner and more energy efficient fuel systems further reduce cooking energy demand compared to the REF scenario. In the DL-DEM scenario, additional energy efficiency improvements results in comparable or lower energy demand levels relative to the DL scenario, while providing the same improved access to DLS. Energy demands for cooling and appliances decrease up to over 40% and 30% respectively relative to the REF scenario in 2050. The reduction of energy demand in the DL-DEM scenario compared to the REF scenario in 2050 is consistent across all but one model per end-use (IMAGE for cooling, MESSAGEix-Buildings for appliances), showing general agreement. In terms of total residential energy demand (Fig. 2 , C), the models consistently estimate an increase over time in the REF scenario. In the DL scenario, energy demand is 4–32% higher in 2050, and in the DL-DEM scenario comparable or up to 27% lower relative to the REF scenario in 2050. Large ranges of uncertainty exist across the estimates, especially in the DL and DL-DEM scenarios, stemming from different modelling dynamics and prevailing behaviour or technology drivers. Final energy mix and supply-side implications The investigated scenarios reveal important insights concerning the final energy mix and projected electricity demand (Fig. 3 , A). In the base year, the energy mix is dominated by solid biomass-primarily used for cooking and heating with low energy efficiency. In the REF scenario, the use of traditional biomass declines, replaced by oil, gas, and especially electricity, which becomes the dominant energy carrier by 2050 due to rising demand for cooling and appliances. In the DL scenario, most of the additional energy required to meet DLS is supplied by electricity, driven by increased access to cooling and appliances. The DL-DEM scenario shows overall lower energy use, with reductions in both electricity and fossil fuel demand due to efficiency improvements and behavioural changes. Given electricity’s central role, we provide more in-depth analysis to understand the broader implications of different scenarios. Electricity demand is set to increase steeply until 2050 in the REF scenario (Fig. 3 , B-C). Providing broad access to DLS (DL scenario) would require additional electricity in the range of 6–42% relative to the REF scenario in 2050. Implementing demand-side measures while providing access to DLS (DL-DEM scenario) substantially reduces electricity demand up to -27% relative to the REF scenario in 2050, with only one model (IMAGE) projecting an electricity increase. CO 2 emission pathways Total CO 2 emissions for the residential sector, including direct emissions from fossil fuel burning and indirect emissions from electricity supply, are projected to significantly increase until 2050 in the REF scenario under the Current Policies strategy (Fig. 4 , A). The additional emissions associated with DLS provision in the DL scenario can be mostly offset in the DL-DEM scenario with demand-side efforts and electrification, as consistently shown by all models. Despite similar trends, large uncertainties exist across the models, mostly reflecting the differences in projected energy demands. Under the Mitigation Policies strategy, the decarbonization of the electricity supply system drives major reductions in indirect emissions, with the three investigated scenarios REF, DL, and DL-DEM, reaching similar levels by 2050. In the base year, most emissions are indirect - from electricity supply (Fig. 4 , B). Under Current Policies, the further growth in CO 2 emissions is mostly due to indirect emissions, driven by electricity demand. Under Mitigation Policies, indirect emissions get close to zero as the electricity supply system decarbonizes while direct emissions become predominant by 2050. In the DL-DEM scenario, further electrification and energy efficiency improvements play a major role in reducing additional emissions for providing DLS. All models consistently show low direct emission levels in the DL-DEM scenario in 2050 both under Current Policies and Mitigation Policies. Larger uncertainties remain around indirect emissions under Current Policies, reflecting differences in electricity demand and model assumptions on future electricity system. In contrast, ranges of indirect emissions are minimal under the Mitigation Policies strategy due to electricity supply decarbonization, which is needed to achieve strict climate targets. Cumulative CO 2 emissions (2020–2050) are projected in the range of 12.3–22.9 Gt CO 2 in the REF scenario under Current Policies (Fig. 4 , C). They increase by 14–27% in the DL scenario and decrease up to 15% in the DL-DEM scenario, with only the IMAGE model projecting a 5% increase compared to the REF scenario. This demonstrates that combining improved access to DLS and demand-side measures can result in comparable or lower CO 2 emission levels compared to a REF scenario. Under Mitigation Policies, cumulative emissions amount to 7.6–10.5 Gt CO 2 in the REF scenario, with reductions of 38–54% compared to the Current Policies strategy. Cumulative emissions are slightly higher in the DL scenario (8.2–13.2 Gt CO 2 ) and comparable to REF in the DL-DEM scenario (7.6–11.7 Gt CO 2 ). While similar in emission terms, the REF and DL-DEM Mitigation Policies scenarios have very different implications on both DLS access levels and energy supply-side, due to different levels of electricity demand (see previous section). Discussion and conclusion Providing near-universal access to DLS in India by 2050 will require increasing energy and CO 2 emissions in the residential sector to bridge current and future gaps. We show that sector-level implementation of demand-side measures can largely offset and even reduce such additional energy requirements, resulting in comparable or lower total CO 2 emissions, even under ambitious climate policies. This shows the importance of combining improved access to DLS with both demand-side and supply-side decarbonization to pursue climate goals while providing higher levels of wellbeing and reducing inequalities. This conclusion is robust across the models. Thus, lowering electricity demand can provide more flexibility on the energy supply-side and avoid the construction of additional power plants and reliance on costly negative emission technologies. The results of our multi-model analysis, however, show relatively large ranges in future energy demand increase, especially for space cooling, and energy reduction potentials, highlighting critical uncertainties on the effects of different behavioural and technological drivers. This study advances current assessments of DLS in relation to energy and emissions in future scenarios in three important aspects. First, the use of detailed sectoral models that explicitly consider service levels and consistently model associated energy demands and emissions makes it possible to jointly assess DLS provision and demand-side measures to support both climate mitigation and SDGs in the Global South. This is a step change compared to existing climate mitigation scenarios where DLS provision and associated requirements are mostly overlooked and inequalities not adequately considered 26 . Second, we leverage the complementarity of different models to assess multiple dimensions of inequalities that are key to DLS achievement while considering the broader linkages between demand- and supply-side of energy systems. Most existing literature consists of single-model studies that focus either on access to DLS, sectoral mitigation scenarios, or supply-side scenarios employing IAMs, but not on the three combined together. Third, our multi-model analysis supports the exploration of uncertainty ranges across the models, improving the robustness of the insights derived. While multi-model comparisons exist for India and for other regions, they have mostly focused on IAM-based or sectoral climate change mitigation scenarios at an aggregate level 42 – 45 . Studies focusing on the provision of DLS have been mostly employed a single model with limited cross-model comparison of results 46 . Multi-model analyses accounting for DLS provision, as shown by this study, could open new avenues of research and application to other regions and sectors. The findings of this study are largely consistent with existing literature on scenarios for improved access to DLS in the Global South and associated energy and emissions. Similar to previous studies 46 , 47 , we show that ending energy and material poverty in countries of the Global South will require relatively low additional energy and emissions if highly efficient low-carbon technologies are implemented. The residential energy demands in this study are aligned with previous projections for India 45 , 48 – 50 . This study assumes future energy requirements for DLS based on projected energy demands whereas previous studies have mostly focused on estimating the minimum energy requirements 8 , 16 , 32 . Therefore, results cannot be directly compared to these due to inherently different assumptions and energy demand estimates. This study points to opportunities for further research on scenarios that can achieve higher levels of wellbeing with lower energy demand. Further analysis is warranted to deepen our understanding of the additional co-benefits associated with demand side measures. Relevant areas of research include evaluating the reduction in air pollution from increased adoption of clean residential fuels in lieu of traditional biomass and other non-clean fuels and associated health co-benefits. Reduced exposure to heat stress driven by improved building construction and access to affordable, low-energy and low-emission cooling and heating systems can contribute to improved health, wellbeing, and productivity. Construction of durable and affordable housing not only contributes to closing DLS gaps but can also results in job creation and economic benefits that could be quantified as additional co-benefits. Thus, further investigation of the heterogeneities in service levels and energy demand across housing types and household groups, e.g. by income levels and household types, is needed to better understand distributional aspects and inequalities to better target policy action. The scenarios presented in this work will require the mobilization of important resources, for which a cost-benefit analysis could provide a better understanding of the required investments vis-à-vis the benefits associated with higher wellbeing and low energy demand. The life-cycle implications of achieving universal access to DLS while pursuing energy efficiency strategies, including associated material demands and embodied impacts, could be evaluated for a broader assessment of the associated environmental consequences. Finally, additional analysis exploring the feedback effects of climate change on projected energy demands could be an additional domain of future research. The implementation of these proposed further analyses could enrich the findings of this study and provide enhanced support for informed policy decisions. Methods Models overview We use a set of five models to run scenarios for India’s residential sector (Table 2 ): two national models – PIER 34 , 35 , and SAFARI 36 – and three with global focus – HEB 37 , IMAGE 38 , 39 , MESSAGEix-Buildings 40 – with the latter two being part of IAMs, (see Supplementary Table 1 for detailed model documentation). Temporal resolution varies from annual to five-year. The models use either bottom-up engineering approaches or hybrid approaches combining bottom-up and top-down elements. The methods vary between models, including simulation, accounting, and system dynamics. All models have granular representations of the housing sector, accounting for key heterogeneities in geographical context, socio-economics, and housing characteristics (Supplementary Table 2). The model granularity and explicit representation of service levels are key for accounting access to DLS and energy requirements. All models cover all residential end-uses of energy except for HEB, which has a specific focus on services directly related to buildings and does not cover household appliances and cooking. For this reason, HEB was not used to calculate total residential energy demands and CO 2 emissions. Table 2 Models overview. Model Model full name Geographical coverage Temporal resolution (timestep) Overall approach Main methods HEB 37 High-efficiency Building model (HEB)-2.0 Global Annual Bottom-up (engineering approach) Accounting IMAGE 38 , 39 IMAGE 3.4 – Integrated Assessment Model Global Annual Hybrid Simulation MESSAGEix-Buildings 40 MESSAGEix-Buildings (CHILLED and STURM modules) Global Five-year Bottom-up (engineering approach) Simulation PIER 34 , 35 Perspectives on Indian Energy based on Rumi (PIER) 1.5 India Annual Bottom-up (engineering approach) Accounting SAFARI 36 Sustainable Alternative Futures for India India Annual Hybrid Simulation, System dynamics Decent living standards modelling The explicit inclusion of service levels in the models enables direct consideration of access to DLS and associated energy demands. We account here for key services and amenities related to housing and household appliances, including durable housing, sufficient floorspace, access to basic cooling thermal comfort, access to clean cooling, and access to essential appliances, such as refrigeration, washing machines, and televisions. Coverage of DLS aspects depends on the model scope and specifics. Three of the models (HEB, MESSAGEix-Buildings, and SAFARI) distinguish formal and informal housing explicitly, while all models but one (HEB, IMAGE, MESSAGEix-Buildings, and SAFARI) use floorspace as indicator of housing size. Access to cooling, appliances, and clean cooking is considered in all models, except for HEB, due to his focus on building-related services. The representations of DLS aspects differ across the five models in terms of model dynamics and parameters (Supplementary Table S3). The parameters defining service levels for DLS differ based on their endogenous or exogenous nature. For instance, the floorspace projections in MESSAGEix-Buildings, while being scenario-dependent, are external static inputs to the model. In contrast, endogenous parameters can change based on model dynamics and interaction with other parameters. For example, access to clean cooking in IMAGE is calculated based on a logit model, driven by parameters such as household income and energy prices. In some cases, model parameters can have both endogenous and exogenous nature. Most dimensions of DLS in this study can be represented in access terms, e.g. share of households with access to specific services or amenities. Sufficient floorspace is commonly represented by the per-capita floorspace. Energy demand modelling The models in this study use detailed approaches for calculating residential energy demands for different end-uses. Detailed bottom-up approaches are critical to account for service levels, particularly services supporting DLS, and related energy demands. The selected models, even with some significant methodological differences, share similar accounting of service levels as key drivers of energy demand. In general, service levels are quantified at the level of different population groups (i) and technologies (t), and multiplied by related energy intensities to calculate total energy demand by end-use (E e ), using a set of equations based on the Kaya identity (Eq. 1 ): $$\:{E}_{e}=\sum\:{E}_{t,i}=\sum\:{P}_{i}\bullet\:({S}_{t,i}/{P}_{i})\bullet\:({E}_{t,i}/{S}_{t,i})/{\eta\:}_{t}$$ 1 Where: E e is the total energy demand for the end-use e P i is the population belonging to the group i, (number of people or households), based on different geographical context, socioeconomic, or housing characteristics. S t,i /P i is the service level (e.g. floorspace, or appliances ownership) per population unit (per-capita or per-household) in time t E t,i /S t,i is the energy demand intensity per unit of service level (e.g. energy for cooling per floorspace unit or electricity per appliance unit) η t is the technology-specific energy efficiency coefficient Energy demands for space cooling are calculated by using floorspace as the basic service level unit (HEB, IMAGE, MESSAGEix-Buildings, SAFARI) or appliances unit (PIER), and energy intensities relative to different housing types, technologies, or household groups. Energy intensities are either exogenously assumed or calculated via dedicated energy demand models and depending on standard or variable Degree Days or Degree Hours to account for different climate conditions. Energy demands for cooking and appliances (IMAGE, MESSAGEix-Buildings, PIER, and SAFARI) are calculated based on the penetration of different types of appliances and fuels and related energy intensities. Energy efficiency improvements are modelled by introducing technology-specific energy intensities and energy efficiency coefficients, and by considering either the uptake of technologies with different efficiency levels over time (using exogenous assumptions or dedicated decision models), or assuming continuous improvement in the average efficiency level at the scale of the entire stock. In the case of models that are part of IAMs (IMAGE and MESSAGEix-Buildings), linkages with the energy supply-side systems are accounted for via energy price signals influencing household decisions. The building stock turnover is explicitly considered by some of the models (IMAGE, MESSAGEix-Buildings, and SAFARI) for improved accounting of building cycles and new building construction. CO 2 emission modelling We use data from the models IMAGE, MESSAGEix-Buildings, PIER, and SAFARI to project residential CO 2 emissions, including direct and indirect emissions. Direct CO 2 emissions from fuel combustion in buildings are calculated natively in all four models. For indirect CO 2 emissions from electricity supply, we use emission factors from the IMAGE and MESSAGEix-GLOBIOM IAMs and apply them to electricity projections from the different models. Emission factors for electricity are consistent with the different climate policy scenarios, accounting for different supply-system transformation and electricity mix. In the case of IMAGE, emissions are fully endogenously calculated and account for changes in demand driven by different energy prices. Emission factors from MESSAGEix-GLOBIOM are based on existing scenarios 51 . Scenarios implementation The scenario implementation follows a modelling protocol, detailing the scenario narratives and basic assumptions and drivers (see Supplementary Methods). The scenario implementation varies across the five models due to different modelling approaches, underlying dynamics, and specific model assumptions. Scenarios can be run as normative (target-seeking, e.g. requiring full access to DLS aspects by a target year) or explorative (establishing the underlying dynamics rather than the targets, e.g. the dynamics bringing to improved access to DLS aspects), depending on the nature of the model. The DL and DL-DEM scenarios were run as explorative in the PIER model and as normative in the other models. Due to the variety of approaches and differences across the models, complete harmonization of the scenarios was not possible and beyond the scope of this study. Moreover, changing or forcing model dynamics can hide specific insights stemming from the comparison of different approaches. We have, however, ensured basic alignment between the models via the modelling protocol, specifically regarding the basic drivers in the models, such as population, and main narrative storylines and assumptions (see Supplementary Methods and Supplementary Note). Prior to running the scenarios, we have compared the levels of access to DLS and energy demands in the year 2020 across the models. This process highlighted some key differences in terms of input data and basic assumptions. We therefore agreed on a set of common input data and assumptions and revised the 2020 implementation as far as possible. In the case of the PIER model, scenario runs are only until 2040, and results were extrapolated to 2050 by linear extrapolation. Declarations Acknowledgements This project has received funding from the Energy Demand changes Induced by Technological and Social innovations (EDITS) project, which is an initiative coordinated by the Research Institute of Innovative Technology for the Earth (RITE) and International Institute for Applied Systems Analysis (IIASA), and funded by Ministry of Economy, Trade, and Industry (METI), Japan. Authors contributions. AM: Conceptualization, Methodology, Formal analysis, Investigation, Writing – Original draft, Visualization; SC: Conceptualization, Methodology, Investigation, Writing – Original draft; KA, VD, SD, and AS: Conceptualization, Methodology, Investigation, Writing - Review & Editing; OE: Conceptualization, Methodology, Writing - Review & Editing; PK: Conceptualization, Methodology; DÜV: Conceptualization, Supervision; SK, RN, and VZ: Methodology; SP: Conceptualization, Writing - Review & Editing, Supervision. References Soergel B et al (2021) Combining ambitious climate policies with efforts to eradicate poverty. Nat Commun 12:2342 IEA. World Energy Outlook 2023 . https://www.iea.org/reports/world-energy-outlook-2023 (2023) Dhakal S et al (2022). Chapter 2 : Emissions trends and drivers. IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK and New York, NY, USA Fuhr H (2021) The rise of the Global South and the rise in carbon emissions. Third World Q 42:2724–2746 Lamb WF et al (2021) A review of trends and drivers of greenhouse gas emissions by sector from 1990 to 2018. Environ Res Lett 16:073005 Broto VC et al (2017) A research agenda for a people-centred approach to energy access in the urbanizing global south. Nat Energy 2:776–779 Samarakoon S (2019) A justice and wellbeing centered framework for analysing energy poverty in the Global South. Ecol Econ 165:106385 Kikstra JS, Mastrucci A, Min J, Riahi K (2021) Rao, N. D. Decent living gaps and energy needs around the world. Environ Res Lett 16:095006 Rao ND, Min J (2018) Decent Living Standards: Material Prerequisites for Human Wellbeing. Soc Indic Res 138:225–244 Behr DM et al (2021) Introducing the Adequate Housing Index: A New Approach to Estimate the Adequate Housing Deficit within and across Emerging Economies. Policy Research Working Paper No. 9830 IEA IRENA, World UNSD, WHO (2023) Bank &. Tracking SDG 7: The Energy Progress Report Rao ND, Pachauri S (2017) Energy access and living standards: some observations on recent trends. Environ Res Lett 12:025011 Hickel J, Slamersak A (2022) Existing climate mitigation scenarios perpetuate colonial inequalities. Lancet Planet Health 6:e628–e631 Méjean A, Lecocq F, Mulugetta Y (2015) Equity, burden sharing and development pathways: reframing international climate negotiations. Int Environ Agreements 15:387–402 Markkanen S, Anger-Kraavi A (2019) Social impacts of climate change mitigation policies and their implications for inequality. Clim Policy 19:827–844 Rao ND, Min J, Mastrucci A (2019) Energy requirements for decent living in India, Brazil and South Africa. Nat Energy 4:1025–1032 Huo J, Meng J, Zheng H, Parikh P, Guan D (2023) Achieving decent living standards in emerging economies challenges national mitigation goals for CO2 emissions. Nat Commun 14:6342 Creutzig F et al (2022) Demand-side solutions to climate change mitigation consistent with high levels of well-being. Nat Clim Chang 12:36–46 Grubler A et al (2018) A low energy demand scenario for meeting the 1.5°C target and sustainable development goals without negative emission technologies. Nat Energy 3:515–527 Pauliuk S et al (2021) Global scenarios of resource and emission savings from material efficiency in residential buildings and cars. Nat Commun 12:5097 Creutzig F et al (2018) Towards demand-side solutions for mitigating climate change. Nat Clim Change 8:260–263 Chatterjee S et al (2024) Balancing energy transition: Assessing decent living standards and future energy demand in the Global South. Energy Research Social Science 118:103757 Kanitkar T, Mythri A, Jayaraman T (2024) Equity assessment of global mitigation pathways in the IPCC Sixth Assessment Report. Clim Policy 24:1129–1148 Cabeza LF et al (2022) Chapter 9 : Buildings. Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC Fell MJ (2017) Energy services: A conceptual review. Energy Research Social Science 27:129–140 Mastrucci A et al (2023) Modeling Low Energy Demand Futures for Buildings: Current State and Research Needs. Annu Rev Environ Resour 48:761–792 IEA. 2019 Global Status Report for Buildings and Construction: Towards a Zero-Emission, Efficient and Resilient Buildings and Construction Sector. 41 (2019) Ürge-Vorsatz D et al (2020) Advances Toward a Net-Zero Global Building Sector. Annu Rev Environ Resour 45:227–269 Islam S, Rana MJ, Shupler M (2023) Deepened socioeconomic inequality in clean cooking fuel use in India from 2005–2006 to 2015–2016. Heliyon 9:e17041 Perez J, Fusco G (2020) Exploring inequalities in India through housing overcrowding. J Hous Built Environ 35:593–616 Poblete-Cazenave M, Pachauri S, Byers E, Mastrucci A, van Ruijven B (2021) Global scenarios of household access to modern energy services under climate mitigation policy. Nat Energy 6:824–833 Millward-Hopkins J, Steinberger JK, Rao ND, Oswald Y (2020) Providing decent living with minimum energy: A global scenario. Glob Environ Change 65:102168 Yawale SK, Hanaoka T, Kapshe M (2021) Development of energy balance table for rural and urban households and evaluation of energy consumption in Indian states. Renew Sustain Energy Rev 136:110392 Prayas (Energy Group). PIER: Modelling the Indian energy system through the 2020s (2021) Prayas (Energy Group) (2023) More with Less: Insights from Residential Energy Demand Assessment Using PIER . https://energy.prayaspune.org/our-work/research-report/more-with-less Kumar P, Natarajan R, Ashok K (2021) Sustainable alternative futures for urban India: the resource, energy, and emissions implications of urban form scenarios. Environ Res : Infrastruct Sustain 1:011004 Chatterjee S, Kiss B, Ürge-Vorsatz D, Teske S (2022) Decarbonisation Pathways for Buildings. In: Teske S (ed) Achieving the Paris Climate Agreement Goals. Springer International Publishing, Cham, pp 161–185. doi: 10.1007/978-3-030-99177-7_7 . Daioglou V, Van Ruijven BJ, Van Vuuren (2012) D. P. Model projections for household energy use in developing countries. Energy 37:601–615 Daioglou V, Mikropoulos E, Gernaat D (2022) Vuuren, D. P. Efficiency improvement and technology choice for energy and emission reductions of the residential sector. Energy 243:122994van Mastrucci A, van Ruijven B, Byers E, Poblete-Cazenave M, Pachauri S (2021) Global scenarios of residential heating and cooling energy demand and CO2 emissions. Clim Change 168:14 O’Neill BC et al (2017) The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob Environ Change 42:169–180 Camarasa C et al (2022) A global comparison of building decarbonization scenarios by 2050 towards 1.5–2°C targets. Nat Commun 13:3077 Krey V et al (2019) Looking under the hood: A comparison of techno-economic assumptions across national and global integrated assessment models. Energy 172:1254–1267 Luderer G et al (2012) The economics of decarbonizing the energy system—results and insights from the RECIPE model intercomparison. Clim Change 114:9–37 Spencer T et al (2017) The 1.5°C target and coal sector transition: at the limits of societal feasibility. Clim Policy 18:335–351 Pachauri S et al (2013) Pathways to achieve universal household access to modern energy by 2030. Environ Res Lett 8:024015 Wollburg P, Hallegatte S, Mahler DG (2023) Ending extreme poverty has a negligible impact on global greenhouse gas emissions. Nature 623:982–986 Akpinar-Ferrand E, Singh A (2010) Modeling increased demand of energy for air conditioners and consequent CO2 emissions to minimize health risks due to climate change in India. Environmental Science Policy 13:702–712 Bhattacharyya SC (2015) Influence of India’s transformation on residential energy demand. Appl Energy 143:228–237 Chaturvedi V, Eom J, Clarke LE, Shukla PR (2014) Long term building energy demand for India: Disaggregating end use energy services in an integrated assessment modeling framework. Energy Policy 64:226–242 Riahi K et al (2021) Cost and attainability of meeting stringent climate targets without overshoot. Nat Clim Chang 11:1063–1069 Additional Declarations There is NO Competing Interest. Supplementary Files SIdatav1.0.xlsx Dataset 1 SIv1.0.docx Supplementary Information Cite Share Download PDF Status: Under Review 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. 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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-8300022","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":560336774,"identity":"7439e2c4-37b4-4d74-a1db-39c373b3a3d9","order_by":0,"name":"Alessio Mastrucci","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYDADfmbGBqIVQ5RKNiO0ENQMUWBwAF0EF9BtP/78cWWbnbzxcebGjz9qGOT5G5ifP8CnxexMjmHj2bZkw22HGZuleY4xGM44wGaI1xazAzmMjY1tBxLMDjM2SDOwMTBuYGAgoOX884dgLcbNjM0/f/xjsN/AwP4Rv5YbCYZgLQbMjG0SvG0MiRsYeAjYcuON4cyGc8mGMw4ztlnz9kkkzzjMUzgDv8PSH3xsKLOT5+8//vjmj282tv3t7Rs+4NOCDiQYGJhJUT8KRsEoGAWjACsAAHD/SxJpCbK1AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-5611-7780","institution":"International Institute for Applied Systems Analysis (IIASA)","correspondingAuthor":true,"prefix":"","firstName":"Alessio","middleName":"","lastName":"Mastrucci","suffix":""},{"id":560336775,"identity":"bdba04a6-0755-4d25-8d29-accaf585f54d","order_by":1,"name":"Souran Chatterjee","email":"","orcid":"https://orcid.org/0000-0002-3500-6061","institution":"University of Plymouth","correspondingAuthor":false,"prefix":"","firstName":"Souran","middleName":"","lastName":"Chatterjee","suffix":""},{"id":560336776,"identity":"9135f931-46ea-45a4-9da0-3362500a0230","order_by":2,"name":"Kaveri Ashok","email":"","orcid":"","institution":"Center for Study of Science, Technology and Policy (CSTEP)","correspondingAuthor":false,"prefix":"","firstName":"Kaveri","middleName":"","lastName":"Ashok","suffix":""},{"id":560336777,"identity":"8eff8216-b091-4983-9532-d2af3bfa2856","order_by":3,"name":"Vassilis Daioglou","email":"","orcid":"https://orcid.org/0000-0002-6028-352X","institution":"PBL Netherlands Environmental Assessment Agency","correspondingAuthor":false,"prefix":"","firstName":"Vassilis","middleName":"","lastName":"Daioglou","suffix":""},{"id":560336778,"identity":"b0833227-a71f-4276-b021-dec66ba20b4f","order_by":4,"name":"Srihari Dukkipati","email":"","orcid":"","institution":"Prayas (Energy Group)","correspondingAuthor":false,"prefix":"","firstName":"Srihari","middleName":"","lastName":"Dukkipati","suffix":""},{"id":560336779,"identity":"3515c9b7-9781-49d8-8a21-7884e3dd7429","order_by":5,"name":"Ashok Sreenivas","email":"","orcid":"","institution":"Prayas (Energy Group)","correspondingAuthor":false,"prefix":"","firstName":"Ashok","middleName":"","lastName":"Sreenivas","suffix":""},{"id":560336780,"identity":"9a026650-e2e2-4c73-91de-208cd2343d6b","order_by":6,"name":"Oreane Edelenbosch","email":"","orcid":"https://orcid.org/0000-0002-6588-5255","institution":"PBL Netherlands Environmental Assessment Agency","correspondingAuthor":false,"prefix":"","firstName":"Oreane","middleName":"","lastName":"Edelenbosch","suffix":""},{"id":560336781,"identity":"fa1642be-3418-4d06-8565-2258abaeefd9","order_by":7,"name":"Poornima Kumar","email":"","orcid":"","institution":"Environmental Change Institute (ECI), Oxford University","correspondingAuthor":false,"prefix":"","firstName":"Poornima","middleName":"","lastName":"Kumar","suffix":""},{"id":560336782,"identity":"5a62ac3c-adbe-44e0-bed1-b59b0d830b95","order_by":8,"name":"Diana Ürge-Vorsatz","email":"","orcid":"https://orcid.org/0000-0003-2570-5341","institution":"Central European University","correspondingAuthor":false,"prefix":"","firstName":"Diana","middleName":"","lastName":"Ürge-Vorsatz","suffix":""},{"id":560336783,"identity":"e96b6860-d6c9-43da-95d7-9aba6056738f","order_by":9,"name":"Sarah Khan","email":"","orcid":"","institution":"Center for Study of Science, Technology and Policy (CSTEP)","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Khan","suffix":""},{"id":560336784,"identity":"6d3f7ab6-6cdd-4c27-aedb-67d5db3277a8","order_by":10,"name":"Ramya Natarajan","email":"","orcid":"","institution":"Center for Study of Science, Technology and Policy (CSTEP)","correspondingAuthor":false,"prefix":"","firstName":"Ramya","middleName":"","lastName":"Natarajan","suffix":""},{"id":560336785,"identity":"db26f88f-7ddb-4ecf-a998-04aacbf24b20","order_by":11,"name":"Victhalia Zapata","email":"","orcid":"https://orcid.org/0000-0001-8274-0173","institution":"Utrecht University","correspondingAuthor":false,"prefix":"","firstName":"Victhalia","middleName":"","lastName":"Zapata","suffix":""},{"id":560336786,"identity":"8de25460-2487-46d1-9893-49ae66641ca2","order_by":12,"name":"Shonali Pachauri","email":"","orcid":"https://orcid.org/0000-0001-8138-3178","institution":"International Institute for Applied Systems Analysis","correspondingAuthor":false,"prefix":"","firstName":"Shonali","middleName":"","lastName":"Pachauri","suffix":""}],"badges":[],"createdAt":"2025-12-07 13:25:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8300022/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8300022/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100206180,"identity":"a00e3d15-77af-442e-be19-79a2487d9798","added_by":"auto","created_at":"2026-01-14 06:27:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":275367,"visible":true,"origin":"","legend":"\u003cp\u003eProjections of access to residential services and amenities to provide Decent Living Standards (DLS) in India. A) National access in 2020 and in 2050 in the Reference (REF) and Decent Living (DL) scenarios. Bars indicate ranges across models, points indicate individual model results. Data sources: all models in this study. B) Average per-capita floorspace in 2020 and in 2050 in the REF and DL scenarios. Bars indicate ranges across models, points indicate individual model results. Data sources: all models in this study. C) Share of non-durable and durable housing types in the REF and DL scenarios. Data source: SAFARI model.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8300022/v1/f5863411af18d77db79beb2d.png"},{"id":100206184,"identity":"77986564-d466-46d0-9114-291e53936a2a","added_by":"auto","created_at":"2026-01-14 06:27:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":249540,"visible":true,"origin":"","legend":"\u003cp\u003eProjections of residential final energy demand in India in the Reference (REF), Decent Living (DL), and Decent living – Demand measures (DL-DEM) scenarios. A) National projections by end-use. Bars represent model ranges, points individual model results. B) Percentage change compared to Reference in 2050. Bars represent model ranges, points individual model results. C) Total final energy projections for residential. Data source: all models in this study.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8300022/v1/1330a0bbfa63bf3a8c0f69ca.png"},{"id":100206183,"identity":"37ba83d1-25e0-4bc8-b31c-8eaa608903d1","added_by":"auto","created_at":"2026-01-14 06:27:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":237689,"visible":true,"origin":"","legend":"\u003cp\u003eProjections of residential final energy mix and electricity in India in the Reference (REF), Decent Living (DL), and Decent living – Demand measures (DL-DEM) scenarios. A) Final energy by energy carrier. B) Electricity projection. C) Percentage change in electricity compared to Reference in 2050. Bars represent model ranges, points individual model results. Data source: IMAGE, MESSAGEix-Buildings, PIER, and SAFARI models.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8300022/v1/47051ea1fbec6fa7bdadc853.png"},{"id":100206185,"identity":"049309b9-2ca3-47fb-ba22-f873aff3f2b4","added_by":"auto","created_at":"2026-01-14 06:27:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":399991,"visible":true,"origin":"","legend":"\u003cp\u003eProjections of CO\u003csub\u003e2\u003c/sub\u003e emissions for the residential sector in India in the Reference (REF), Decent Living (DL), and Decent living – Demand measures (DL-DEM) scenarios under different climate policy strategies (Current Policies and Mitigation Policies). \u0026nbsp;Results are from four building sector models, IMAGE, MESSAGEix-Buildings, PIER, and SAFARI, with indirect emission calculated based on electricity emission factors from the IMAGE model.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8300022/v1/dab4b45ab1e2a0654c2e5bb5.png"},{"id":100369848,"identity":"e1596b04-8cf1-4d5b-a9c1-ecd8abf6ebe6","added_by":"auto","created_at":"2026-01-16 07:59:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1759822,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8300022/v1/9b2e7a0f-b7c5-4bd3-886f-51f66bd4e6e5.pdf"},{"id":100206181,"identity":"3d7536ba-a8d3-46f1-85bd-25ebd81151a2","added_by":"auto","created_at":"2026-01-14 06:27:21","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":66713,"visible":true,"origin":"","legend":"Dataset 1","description":"","filename":"SIdatav1.0.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8300022/v1/b809cde0decd172b78d845a6.xlsx"},{"id":100206182,"identity":"925f07f0-a11c-4fe0-bdcc-00fbcf721f59","added_by":"auto","created_at":"2026-01-14 06:27:21","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":484508,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"SIv1.0.docx","url":"https://assets-eu.researchsquare.com/files/rs-8300022/v1/f59d0b3c410dae9f9149a242.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"High wellbeing with lower energy consumption: scenarios for India’s residential sector","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Paris Agreement and the Sustainable Development Goals (SDG) have set an ambitious agenda requiring climate change mitigation action and broader sustainable development to go hand-in-hand\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The Global South is facing unique challenges in pursuing climate and development goals. Rapid population growth and development are driving demand for energy services in most emerging and developing countries\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, while greenhouse gas (GHG) emissions continue to increase\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Southern Asia alone has seen a 3.6% annual rise in emissions since 2010 \u003csup\u003e5\u003c/sup\u003e. Concurrently, large sections of the population still lack access to basic energy services essential for human wellbeing\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Approximately 3\u0026nbsp;billion people are estimated to live without access to decent living standards (DLS) that constitute the material prerequisites to support human wellbeing\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Around 1.2\u0026nbsp;billion people still live in inadequate housing infrastructure\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, 675\u0026nbsp;million people lack access to electricity, and 2.3\u0026nbsp;billion to clean cooking\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Ensuring universal access to modern energy services is crucial for poverty alleviation and improvements of living conditions and wellbeing\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWithin climate negotiations, considerations of equity and just transitions are increasingly emphasized\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. With ambitious climate mitigation requiring significant emissions reductions and system transformations, the question of available development space to achieve DLS universally remains central\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Demand-side strategies, including energy efficiency improvements and behavioural shifts, offer large potential for reducing end-use sectors\u0026rsquo; energy demands while being synergistic with raising wellbeing\u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Yet, most existing mitigation scenarios focus on supply-side strategies and rely heavily on uncertain negative emission technologies, while overlooking demand-side strategies and considerations of equity and poverty eradication, especially for the Global South\u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis gap is particularly evident in the buildings sector, which accounts 21% of global GHG emissions\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e and plays a central role in both decarbonization and support of human activities and wellbeing\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The residential sector is expanding rapidly in the Global South, driven by population growth and increasing demand for floorspace\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. More than half of the buildings that will exist in 2050 are yet to be constructed in these regions\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, presenting a unique opportunity to design buildings with already available low energy demand technologies that improve living conditions and comfort\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Mitigation scenarios for the building sector in the Global South show high emission reduction potentials\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, but mostly rely on top-down and aggregate modelling, failing to account for DLS gaps and heterogeneous energy needs across populations\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe elimination of multiple deprivations has rarely been assessed in future scenario studies\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Existing literature mostly focuses on the minimum requirements to provide basic access to DLS without accounting for demand elasticities that are key in understanding the evolution of future energy demand\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. These studies apply normative assumptions to calculate energy requirements and mostly overlook interactions between demand- and supply-side of energy systems and the synergies and trade-offs with climate mitigation policies. Thus, the effect of additional energy and emissions required to provide universal access to DLS on achieving climate mitigation targets remains underexplored. Ensuring higher levels of DLS while reducing energy demand and emissions is crucial to simultaneously achieve climate and sustainable development goals.\u003c/p\u003e \u003cp\u003eHere, we assess a comprehensive set of scenarios to provide near-universal access to DLS by 2050 while achieving lower energy demand levels under different climate policies, focusing on India\u0026rsquo;s residential sector. India is a remarkable case study due large projected GHG emission levels\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and, despite significant progress in recent years, large gaps in access to multiple dimensions of DLS\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e and social inequalities\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. A key contribution of this study is the use of five detailed building sector models applied under a common scenario protocol with a focus on both climate mitigation and wellbeing outcomes. We include two national models \u0026ndash; PIER\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, and SAFARI\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e \u0026ndash; and three global models \u0026ndash; HEB\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, IMAGE\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, MESSAGEix-Buildings\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e \u0026ndash; with the latter two being part of IAMs to account for energy supply transformations under different climate policy contexts. This approach enables robust assessment of findings across different modelling frameworks and exploration of uncertainty in future projections. These models explicitly account for access to key DLS services and amenities, making it possible to directly link service levels and related energy demands and carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) emissions. By systematically integrating DLS into energy modelling, this work contributes to a growing body of literature that seeks to make climate mitigation scenarios more inclusive and policy relevant. It also provides a foundation for future modelling efforts that aim to better represent the realities of the Global South, where the dual imperatives of development and decarbonization must be addressed together.\u003c/p\u003e\n\u003ch3\u003eScenarios\u003c/h3\u003e\n\u003cp\u003eWe explore three main scenarios investigating the effect of improved access to DLS and demand-side measures on energy demand and CO\u003csub\u003e2\u003c/sub\u003e emissions by the Indian residential sector between 2020 and 2050 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The \u003cem\u003eReference\u003c/em\u003e (REF) scenario reflects a continuation of current trends and policies, aligned with Shared Socioeconomic Pathway SSP2 \u0026ldquo;Middle of the Road\u0026rdquo; \u003csup\u003e41\u003c/sup\u003e. The \u003cem\u003eDecent living (DL)\u003c/em\u003e scenario assumes an improvement in access to basic services and amenities to provide near universal access to DLS by 2050. The \u003cem\u003eDecent living \u0026ndash; Demand measures (DL-DEM)\u003c/em\u003e scenario combines improved access to wellbeing in line with the DLS scenario and additional sector-wide energy efficiency improvements and behavioural changes achievable with currently available technologies. We combine the three main scenarios with two climate mitigation policy strategies, resulting in a total of six scenarios. The \u003cem\u003eCurrent Policies\u003c/em\u003e strategy assumes continuation of national policies with no stringent climate policies. In the \u003cem\u003eMitigation Policies\u003c/em\u003e strategy, a set of stringent policies to achieve the 1.5\u0026deg;C climate targets are in place, including carbon pricing, leading to a decarbonization of the electricity supply system. Service levels, such as floorspace and access to appliances, remain unchanged across the \u003cem\u003eCurrent Policies\u003c/em\u003e and \u003cem\u003eMitigation Policies\u003c/em\u003e strategies. We have established a modelling protocol to guide the implementation of these scenarios in the different models (see Supplementary Methods). Unlike similar multi-model analyses, we do not aim for full harmonization of scenario data and trends across the models, to better understand different model behaviours and complementary insights. We have, however, aligned the data for the year 2020 and the key underlying model drivers to the extent possible.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of the three main scenarios.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScenario name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDecent living standard (DLS) trends\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnergy demand trends\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReference (REF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContinuation of current trends and policies.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLikely DLS achievement under current trends and policies.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnergy efficiency improvement in building shell, cooling systems, and appliances aligned with current trends and policies.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecent Living (DL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImproved access to DLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAchievement of near-universal DLS by 2050, including:\u003c/p\u003e \u003cp\u003e- sufficient floorspace\u003c/p\u003e \u003cp\u003e- durable homes\u003c/p\u003e \u003cp\u003e- basic cooling thermal comfort\u003c/p\u003e\u003cp\u003e- access to clean fuels for cooking\u003c/p\u003e\u003cp\u003e- appliances (refrigerators, televisions, washing machine, lighting)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSame as in the REF scenario, besides DLS interventions.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecent living \u0026ndash; Demand measures (DL-DEM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImproved access to DLS and demand-side measures to achieve lower energy demand levels.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSame as in the DL scenario.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSectoral interventions towards lower energy demand: improved building shell, energy-efficient heating and cooling systems, cooking stoves and appliances, electrification, behavioural change limiting energy demand.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAccess to decent living standards\u003c/h2\u003e \u003cp\u003eLevels of access to the services and amenities supporting DLS vary across different dimensions and over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, A-B). In 2020, access levels are particularly low for AC, refrigerators, and clean cooking. Average per-capita floorspace of buildings is at 13\u0026ndash;15 m\u003csup\u003e2\u003c/sup\u003e/cap, close to the minimum threshold defined by DLS, namely 10 m\u003csup\u003e2\u003c/sup\u003e/cap with a minimum of 30m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e per household \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In the REF scenario, national access to DLS improves gradually in most dimensions. Large gaps still exist in 2050, particularly for AC (48\u0026ndash;82% access), and to a lesser extent for refrigerators (86\u0026ndash;96% access) and for clean cooking (85\u0026ndash;100%). Universal access to fans and televisions is almost achieved, and floorspace levels grow significantly to 22\u0026ndash;29 m\u003csup\u003e2\u003c/sup\u003e/cap. Providing universal access to DLS requires additional efforts to close the existing gaps, as shown in the DL scenario results, especially for AC, clean cooking, and formal housing. Providing sufficient housing space requires increasing per-capita floorspace to an average of 22\u0026ndash;33 m\u003csup\u003e2\u003c/sup\u003e/cap. These trends are robust across the different models, even though larger ranges of uncertainty exist on future access projections for specific dimensions, in particular AC, clean cooking, and per-capita floorspace. Despite additional provision of durable housing units under current trends and policies, the share of households with access to durable housing (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, C) does not significantly improve in the REF scenario due to growing population and urbanization levels. Additional efforts are required to fill this gap in the DL scenario, especially in the provision of affordable durable housing in urban areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEnergy demand projections\u003c/h3\u003e\n\u003cp\u003eFollowing rising service levels, the residential energy demand in India keeps growing in the REF scenario (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Cooling is the fastest-growing end-use with up to a ten-fold increase in projected energy demand by 2050, followed by appliances (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, A). Cooking energy demand is projected to decline already in the REF scenario, as the effects of switching to cleaner and more efficient energy carriers balances the increase in demand. While agreeing on the general trends, the models show large ranges of uncertainty, especially on cooling and cooking, stemming from different behavioural and conversion efficiency assumptions, and in the case of cooking, from different fuel shares. In the DL scenario, energy demand levels mostly increase compared to the REF scenario under higher service levels to provide DLS. The highest increases are projected for cooling (up to 75%) and for appliances (up to 29%) relative to the REF scenario in 2050 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, B), due to broader access to AC, fans and other appliances (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Switches to cleaner and more energy efficient fuel systems further reduce cooking energy demand compared to the REF scenario. In the DL-DEM scenario, additional energy efficiency improvements results in comparable or lower energy demand levels relative to the DL scenario, while providing the same improved access to DLS. Energy demands for cooling and appliances decrease up to over 40% and 30% respectively relative to the REF scenario in 2050. The reduction of energy demand in the DL-DEM scenario compared to the REF scenario in 2050 is consistent across all but one model per end-use (IMAGE for cooling, MESSAGEix-Buildings for appliances), showing general agreement. In terms of total residential energy demand (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, C), the models consistently estimate an increase over time in the REF scenario. In the DL scenario, energy demand is 4\u0026ndash;32% higher in 2050, and in the DL-DEM scenario comparable or up to 27% lower relative to the REF scenario in 2050. Large ranges of uncertainty exist across the estimates, especially in the DL and DL-DEM scenarios, stemming from different modelling dynamics and prevailing behaviour or technology drivers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eFinal energy mix and supply-side implications\u003c/h3\u003e\n\u003cp\u003eThe investigated scenarios reveal important insights concerning the final energy mix and projected electricity demand (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, A). In the base year, the energy mix is dominated by solid biomass-primarily used for cooking and heating with low energy efficiency. In the REF scenario, the use of traditional biomass declines, replaced by oil, gas, and especially electricity, which becomes the dominant energy carrier by 2050 due to rising demand for cooling and appliances. In the DL scenario, most of the additional energy required to meet DLS is supplied by electricity, driven by increased access to cooling and appliances. The DL-DEM scenario shows overall lower energy use, with reductions in both electricity and fossil fuel demand due to efficiency improvements and behavioural changes.\u003c/p\u003e \u003cp\u003eGiven electricity\u0026rsquo;s central role, we provide more in-depth analysis to understand the broader implications of different scenarios. Electricity demand is set to increase steeply until 2050 in the REF scenario (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, B-C). Providing broad access to DLS (DL scenario) would require additional electricity in the range of 6\u0026ndash;42% relative to the REF scenario in 2050. Implementing demand-side measures while providing access to DLS (DL-DEM scenario) substantially reduces electricity demand up to -27% relative to the REF scenario in 2050, with only one model (IMAGE) projecting an electricity increase.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eCO\u003csub\u003e2\u003c/sub\u003e emission pathways\u003c/h3\u003e\n\u003cp\u003eTotal CO\u003csub\u003e2\u003c/sub\u003e emissions for the residential sector, including direct emissions from fossil fuel burning and indirect emissions from electricity supply, are projected to significantly increase until 2050 in the REF scenario under the Current Policies strategy (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, A). The additional emissions associated with DLS provision in the DL scenario can be mostly offset in the DL-DEM scenario with demand-side efforts and electrification, as consistently shown by all models. Despite similar trends, large uncertainties exist across the models, mostly reflecting the differences in projected energy demands. Under the Mitigation Policies strategy, the decarbonization of the electricity supply system drives major reductions in indirect emissions, with the three investigated scenarios REF, DL, and DL-DEM, reaching similar levels by 2050.\u003c/p\u003e \u003cp\u003eIn the base year, most emissions are indirect - from electricity supply (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, B). Under Current Policies, the further growth in CO\u003csub\u003e2\u003c/sub\u003e emissions is mostly due to indirect emissions, driven by electricity demand. Under Mitigation Policies, indirect emissions get close to zero as the electricity supply system decarbonizes while direct emissions become predominant by 2050. In the DL-DEM scenario, further electrification and energy efficiency improvements play a major role in reducing additional emissions for providing DLS. All models consistently show low direct emission levels in the DL-DEM scenario in 2050 both under Current Policies and Mitigation Policies. Larger uncertainties remain around indirect emissions under Current Policies, reflecting differences in electricity demand and model assumptions on future electricity system. In contrast, ranges of indirect emissions are minimal under the Mitigation Policies strategy due to electricity supply decarbonization, which is needed to achieve strict climate targets.\u003c/p\u003e \u003cp\u003eCumulative CO\u003csub\u003e2\u003c/sub\u003e emissions (2020\u0026ndash;2050) are projected in the range of 12.3\u0026ndash;22.9 Gt CO\u003csub\u003e2\u003c/sub\u003e in the REF scenario under Current Policies (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, C). They increase by 14\u0026ndash;27% in the DL scenario and decrease up to 15% in the DL-DEM scenario, with only the IMAGE model projecting a 5% increase compared to the REF scenario. This demonstrates that combining improved access to DLS and demand-side measures can result in comparable or lower CO\u003csub\u003e2\u003c/sub\u003e emission levels compared to a REF scenario. Under Mitigation Policies, cumulative emissions amount to 7.6\u0026ndash;10.5 Gt CO\u003csub\u003e2\u003c/sub\u003e in the REF scenario, with reductions of 38\u0026ndash;54% compared to the Current Policies strategy. Cumulative emissions are slightly higher in the DL scenario (8.2\u0026ndash;13.2 Gt CO\u003csub\u003e2\u003c/sub\u003e) and comparable to REF in the DL-DEM scenario (7.6\u0026ndash;11.7 Gt CO\u003csub\u003e2\u003c/sub\u003e). While similar in emission terms, the REF and DL-DEM Mitigation Policies scenarios have very different implications on both DLS access levels and energy supply-side, due to different levels of electricity demand (see previous section).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion and conclusion","content":"\u003cp\u003eProviding near-universal access to DLS in India by 2050 will require increasing energy and CO\u003csub\u003e2\u003c/sub\u003e emissions in the residential sector to bridge current and future gaps. We show that sector-level implementation of demand-side measures can largely offset and even reduce such additional energy requirements, resulting in comparable or lower total CO\u003csub\u003e2\u003c/sub\u003e emissions, even under ambitious climate policies. This shows the importance of combining improved access to DLS with both demand-side and supply-side decarbonization to pursue climate goals while providing higher levels of wellbeing and reducing inequalities. This conclusion is robust across the models. Thus, lowering electricity demand can provide more flexibility on the energy supply-side and avoid the construction of additional power plants and reliance on costly negative emission technologies. The results of our multi-model analysis, however, show relatively large ranges in future energy demand increase, especially for space cooling, and energy reduction potentials, highlighting critical uncertainties on the effects of different behavioural and technological drivers.\u003c/p\u003e \u003cp\u003eThis study advances current assessments of DLS in relation to energy and emissions in future scenarios in three important aspects. First, the use of detailed sectoral models that explicitly consider service levels and consistently model associated energy demands and emissions makes it possible to jointly assess DLS provision and demand-side measures to support both climate mitigation and SDGs in the Global South. This is a step change compared to existing climate mitigation scenarios where DLS provision and associated requirements are mostly overlooked and inequalities not adequately considered\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Second, we leverage the complementarity of different models to assess multiple dimensions of inequalities that are key to DLS achievement while considering the broader linkages between demand- and supply-side of energy systems. Most existing literature consists of single-model studies that focus either on access to DLS, sectoral mitigation scenarios, or supply-side scenarios employing IAMs, but not on the three combined together. Third, our multi-model analysis supports the exploration of uncertainty ranges across the models, improving the robustness of the insights derived. While multi-model comparisons exist for India and for other regions, they have mostly focused on IAM-based or sectoral climate change mitigation scenarios at an aggregate level \u003csup\u003e\u003cspan additionalcitationids=\"CR43 CR44\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e–\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Studies focusing on the provision of DLS have been mostly employed a single model with limited cross-model comparison of results \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Multi-model analyses accounting for DLS provision, as shown by this study, could open new avenues of research and application to other regions and sectors.\u003c/p\u003e \u003cp\u003eThe findings of this study are largely consistent with existing literature on scenarios for improved access to DLS in the Global South and associated energy and emissions. Similar to previous studies\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, we show that ending energy and material poverty in countries of the Global South will require relatively low additional energy and emissions if highly efficient low-carbon technologies are implemented. The residential energy demands in this study are aligned with previous projections for India \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e–\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. This study assumes future energy requirements for DLS based on projected energy demands whereas previous studies have mostly focused on estimating the \u003cem\u003eminimum\u003c/em\u003e energy requirements \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Therefore, results cannot be directly compared to these due to inherently different assumptions and energy demand estimates.\u003c/p\u003e \u003cp\u003eThis study points to opportunities for further research on scenarios that can achieve higher levels of wellbeing with lower energy demand. Further analysis is warranted to deepen our understanding of the additional co-benefits associated with demand side measures. Relevant areas of research include evaluating the reduction in air pollution from increased adoption of clean residential fuels in lieu of traditional biomass and other non-clean fuels and associated health co-benefits. Reduced exposure to heat stress driven by improved building construction and access to affordable, low-energy and low-emission cooling and heating systems can contribute to improved health, wellbeing, and productivity. Construction of durable and affordable housing not only contributes to closing DLS gaps but can also results in job creation and economic benefits that could be quantified as additional co-benefits. Thus, further investigation of the heterogeneities in service levels and energy demand across housing types and household groups, e.g. by income levels and household types, is needed to better understand distributional aspects and inequalities to better target policy action.\u003c/p\u003e \u003cp\u003eThe scenarios presented in this work will require the mobilization of important resources, for which a cost-benefit analysis could provide a better understanding of the required investments vis-à-vis the benefits associated with higher wellbeing and low energy demand. The life-cycle implications of achieving universal access to DLS while pursuing energy efficiency strategies, including associated material demands and embodied impacts, could be evaluated for a broader assessment of the associated environmental consequences. Finally, additional analysis exploring the feedback effects of climate change on projected energy demands could be an additional domain of future research. The implementation of these proposed further analyses could enrich the findings of this study and provide enhanced support for informed policy decisions.\u003c/p\u003e\u003c/div\u003e"},{"header":"Methods","content":"\u003cp\u003eModels overview\u003c/p\u003e\n\u003cp\u003eWe use a set of five models to run scenarios for India\u0026rsquo;s residential sector (Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e): two national models \u0026ndash; PIER\u003csup\u003e\u003cspan\u003e34\u003c/span\u003e,\u003cspan\u003e35\u003c/span\u003e\u003c/sup\u003e, and SAFARI\u003csup\u003e\u003cspan\u003e36\u003c/span\u003e\u003c/sup\u003e \u0026ndash; and three with global focus \u0026ndash; HEB\u003csup\u003e\u003cspan\u003e37\u003c/span\u003e\u003c/sup\u003e, IMAGE\u003csup\u003e\u003cspan\u003e38\u003c/span\u003e,\u003cspan\u003e39\u003c/span\u003e\u003c/sup\u003e, MESSAGEix-Buildings\u003csup\u003e\u003cspan\u003e40\u003c/span\u003e\u003c/sup\u003e \u0026ndash; with the latter two being part of IAMs, (see Supplementary Table\u0026nbsp;1 for detailed model documentation). Temporal resolution varies from annual to five-year. The models use either bottom-up engineering approaches or hybrid approaches combining bottom-up and top-down elements. The methods vary between models, including simulation, accounting, and system dynamics. All models have granular representations of the housing sector, accounting for key heterogeneities in geographical context, socio-economics, and housing characteristics (Supplementary Table\u0026nbsp;2). The model granularity and explicit representation of service levels are key for accounting access to DLS and energy requirements. All models cover all residential end-uses of energy except for HEB, which has a specific focus on services directly related to buildings and does not cover household appliances and cooking. For this reason, HEB was not used to calculate total residential energy demands and CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eModels overview.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003eModel\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eModel full name\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eGeographical coverage\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eTemporal resolution (timestep)\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eOverall approach\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\"\u003eMain methods\u003cbr\u003e\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eHEB \u003csup\u003e\u003cspan\u003e37\u003c/span\u003e\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eHigh-efficiency Building model (HEB)-2.0\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eGlobal\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eAnnual\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eBottom-up (engineering approach)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eAccounting\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eIMAGE \u003csup\u003e\u003cspan\u003e38\u003c/span\u003e,\u003cspan\u003e39\u003c/span\u003e\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eIMAGE 3.4 \u0026ndash; Integrated Assessment Model\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eGlobal\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eAnnual\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eHybrid\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eSimulation\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eMESSAGEix-Buildings \u003csup\u003e\u003cspan\u003e40\u003c/span\u003e\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eMESSAGEix-Buildings\u003cbr\u003e(CHILLED and STURM modules)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eGlobal\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eFive-year\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eBottom-up (engineering approach)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eSimulation\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003ePIER \u003csup\u003e\u003cspan\u003e34\u003c/span\u003e,\u003cspan\u003e35\u003c/span\u003e\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003ePerspectives on Indian Energy based on Rumi (PIER) 1.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eIndia\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eAnnual\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eBottom-up (engineering approach)\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eAccounting\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003eSAFARI \u003csup\u003e\u003cspan\u003e36\u003c/span\u003e\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eSustainable Alternative Futures for India\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eIndia\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eAnnual\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eHybrid\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003eSimulation, System dynamics\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eDecent living standards modelling\u003c/p\u003e\n\u003cp\u003eThe explicit inclusion of service levels in the models enables direct consideration of access to DLS and associated energy demands. We account here for key services and amenities related to housing and household appliances, including durable housing, sufficient floorspace, access to basic cooling thermal comfort, access to clean cooling, and access to essential appliances, such as refrigeration, washing machines, and televisions. Coverage of DLS aspects depends on the model scope and specifics. Three of the models (HEB, MESSAGEix-Buildings, and SAFARI) distinguish formal and informal housing explicitly, while all models but one (HEB, IMAGE, MESSAGEix-Buildings, and SAFARI) use floorspace as indicator of housing size. Access to cooling, appliances, and clean cooking is considered in all models, except for HEB, due to his focus on building-related services. The representations of DLS aspects differ across the five models in terms of model dynamics and parameters (Supplementary Table S3). The parameters defining service levels for DLS differ based on their endogenous or exogenous nature. For instance, the floorspace projections in MESSAGEix-Buildings, while being scenario-dependent, are external static inputs to the model. In contrast, endogenous parameters can change based on model dynamics and interaction with other parameters. For example, access to clean cooking in IMAGE is calculated based on a logit model, driven by parameters such as household income and energy prices. In some cases, model parameters can have both endogenous and exogenous nature. Most dimensions of DLS in this study can be represented in access terms, e.g. share of households with access to specific services or amenities. Sufficient floorspace is commonly represented by the per-capita floorspace.\u003c/p\u003e\n\u003cp\u003eEnergy demand modelling\u003c/p\u003e\n\u003cp\u003eThe models in this study use detailed approaches for calculating residential energy demands for different end-uses. Detailed bottom-up approaches are critical to account for service levels, particularly services supporting DLS, and related energy demands. The selected models, even with some significant methodological differences, share similar accounting of service levels as key drivers of energy demand. In general, service levels are quantified at the level of different population groups (i) and technologies (t), and multiplied by related energy intensities to calculate total energy demand by end-use (E\u003csub\u003ee\u003c/sub\u003e), using a set of equations based on the Kaya identity (Eq.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e):\u003c/p\u003e\n\u003cdiv id=\"Equ1\"\u003e\n \u003cdiv id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:{E}_{e}=\\sum\\:{E}_{t,i}=\\sum\\:{P}_{i}\\bullet\\:({S}_{t,i}/{P}_{i})\\bullet\\:({E}_{t,i}/{S}_{t,i})/{\\eta\\:}_{t}$$\u003c/div\u003e\n \u003cdiv\u003e1\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cp\u003eE\u003csub\u003ee\u003c/sub\u003e is the total energy demand for the end-use e\u003c/p\u003e\n\u003cp\u003eP\u003csub\u003ei\u003c/sub\u003e is the population belonging to the group i, (number of people or households), based on different geographical context, socioeconomic, or housing characteristics.\u003c/p\u003e\n\u003cp\u003eS\u003csub\u003et,i\u003c/sub\u003e/P\u003csub\u003ei\u003c/sub\u003e is the service level (e.g. floorspace, or appliances ownership) per population unit (per-capita or per-household) in time t\u003c/p\u003e\n\u003cp\u003eE\u003csub\u003et,i\u003c/sub\u003e/S\u003csub\u003et,i\u003c/sub\u003e is the energy demand intensity per unit of service level (e.g. energy for cooling per floorspace unit or electricity per appliance unit)\u003c/p\u003e\n\u003cp\u003e\u0026eta;\u003csub\u003et\u003c/sub\u003e is the technology-specific energy efficiency coefficient\u003c/p\u003e\n\u003cp\u003eEnergy demands for space cooling are calculated by using floorspace as the basic service level unit (HEB, IMAGE, MESSAGEix-Buildings, SAFARI) or appliances unit (PIER), and energy intensities relative to different housing types, technologies, or household groups. Energy intensities are either exogenously assumed or calculated via dedicated energy demand models and depending on standard or variable Degree Days or Degree Hours to account for different climate conditions. Energy demands for cooking and appliances (IMAGE, MESSAGEix-Buildings, PIER, and SAFARI) are calculated based on the penetration of different types of appliances and fuels and related energy intensities. Energy efficiency improvements are modelled by introducing technology-specific energy intensities and energy efficiency coefficients, and by considering either the uptake of technologies with different efficiency levels over time (using exogenous assumptions or dedicated decision models), or assuming continuous improvement in the average efficiency level at the scale of the entire stock. In the case of models that are part of IAMs (IMAGE and MESSAGEix-Buildings), linkages with the energy supply-side systems are accounted for via energy price signals influencing household decisions. The building stock turnover is explicitly considered by some of the models (IMAGE, MESSAGEix-Buildings, and SAFARI) for improved accounting of building cycles and new building construction.\u003c/p\u003e\n\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e emission modelling\u003c/p\u003e\n\u003cp\u003eWe use data from the models IMAGE, MESSAGEix-Buildings, PIER, and SAFARI to project residential CO\u003csub\u003e2\u003c/sub\u003e emissions, including direct and indirect emissions. Direct CO\u003csub\u003e2\u003c/sub\u003e emissions from fuel combustion in buildings are calculated natively in all four models. For indirect CO\u003csub\u003e2\u003c/sub\u003e emissions from electricity supply, we use emission factors from the IMAGE and MESSAGEix-GLOBIOM IAMs and apply them to electricity projections from the different models. Emission factors for electricity are consistent with the different climate policy scenarios, accounting for different supply-system transformation and electricity mix. In the case of IMAGE, emissions are fully endogenously calculated and account for changes in demand driven by different energy prices. Emission factors from MESSAGEix-GLOBIOM are based on existing scenarios\u003csup\u003e\u003cspan\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eScenarios implementation\u003c/p\u003e\n\u003cp\u003eThe scenario implementation follows a modelling protocol, detailing the scenario narratives and basic assumptions and drivers (see Supplementary Methods). The scenario implementation varies across the five models due to different modelling approaches, underlying dynamics, and specific model assumptions. Scenarios can be run as normative (target-seeking, e.g. requiring full access to DLS aspects by a target year) or explorative (establishing the underlying dynamics rather than the targets, e.g. the dynamics bringing to improved access to DLS aspects), depending on the nature of the model. The DL and DL-DEM scenarios were run as explorative in the PIER model and as normative in the other models. Due to the variety of approaches and differences across the models, complete harmonization of the scenarios was not possible and beyond the scope of this study. Moreover, changing or forcing model dynamics can hide specific insights stemming from the comparison of different approaches. We have, however, ensured basic alignment between the models via the modelling protocol, specifically regarding the basic drivers in the models, such as population, and main narrative storylines and assumptions (see Supplementary Methods and Supplementary Note). Prior to running the scenarios, we have compared the levels of access to DLS and energy demands in the year 2020 across the models. This process highlighted some key differences in terms of input data and basic assumptions. We therefore agreed on a set of common input data and assumptions and revised the 2020 implementation as far as possible. In the case of the PIER model, scenario runs are only until 2040, and results were extrapolated to 2050 by linear extrapolation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThis project has received funding from the Energy Demand changes Induced by Technological and Social innovations (EDITS) project, which is an initiative coordinated by the Research Institute of Innovative Technology for the Earth (RITE) and International Institute for Applied Systems Analysis (IIASA), and funded by Ministry of Economy, Trade, and Industry (METI), Japan.\u003c/p\u003e\n\u003cp\u003eAuthors contributions. AM: Conceptualization, Methodology, Formal analysis, Investigation, Writing \u0026ndash; Original draft, Visualization;\u003csup\u003e\u0026nbsp;\u003c/sup\u003eSC: Conceptualization, Methodology, Investigation, Writing \u0026ndash; Original draft;\u003csup\u003e\u0026nbsp;\u003c/sup\u003eKA, VD, SD, and AS: Conceptualization, Methodology, Investigation, Writing - Review \u0026amp; Editing; OE: Conceptualization, Methodology, Writing - Review \u0026amp; Editing;\u003csup\u003e\u0026nbsp;\u003c/sup\u003ePK: Conceptualization, Methodology;\u003csup\u003e\u0026nbsp;\u003c/sup\u003eD\u0026Uuml;V: Conceptualization, Supervision;\u003csup\u003e\u0026nbsp;\u003c/sup\u003eSK, RN, and VZ: Methodology;\u003csup\u003e\u0026nbsp;\u003c/sup\u003eSP: Conceptualization, Writing - Review \u0026amp; Editing, Supervision.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSoergel B et al (2021) Combining ambitious climate policies with efforts to eradicate poverty. Nat Commun 12:2342\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIEA. \u003cem\u003eWorld Energy Outlook 2023\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iea.org/reports/world-energy-outlook-2023\u003c/span\u003e\u003cspan address=\"https://www.iea.org/reports/world-energy-outlook-2023\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDhakal S et al (2022). \u003cem\u003eChapter 2\u003c/em\u003e: Emissions trends and drivers. IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK and New York, NY, USA\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFuhr H (2021) The rise of the Global South and the rise in carbon emissions. Third World Q 42:2724\u0026ndash;2746\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLamb WF et al (2021) A review of trends and drivers of greenhouse gas emissions by sector from 1990 to 2018. Environ Res Lett 16:073005\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBroto VC et al (2017) A research agenda for a people-centred approach to energy access in the urbanizing global south. Nat Energy 2:776\u0026ndash;779\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamarakoon S (2019) A justice and wellbeing centered framework for analysing energy poverty in the Global South. Ecol Econ 165:106385\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKikstra JS, Mastrucci A, Min J, Riahi K (2021) Rao, N. D. Decent living gaps and energy needs around the world. Environ Res Lett 16:095006\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRao ND, Min J (2018) Decent Living Standards: Material Prerequisites for Human Wellbeing. Soc Indic Res 138:225\u0026ndash;244\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBehr DM et al (2021) Introducing the Adequate Housing Index: A New Approach to Estimate the Adequate Housing Deficit within and across Emerging Economies. Policy Research Working Paper No. 9830\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIEA IRENA, World UNSD, WHO (2023) Bank \u0026amp;. \u003cem\u003eTracking SDG 7: The Energy Progress Report\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRao ND, Pachauri S (2017) Energy access and living standards: some observations on recent trends. Environ Res Lett 12:025011\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHickel J, Slamersak A (2022) Existing climate mitigation scenarios perpetuate colonial inequalities. Lancet Planet Health 6:e628\u0026ndash;e631\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM\u0026eacute;jean A, Lecocq F, Mulugetta Y (2015) Equity, burden sharing and development pathways: reframing international climate negotiations. Int Environ Agreements 15:387\u0026ndash;402\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarkkanen S, Anger-Kraavi A (2019) Social impacts of climate change mitigation policies and their implications for inequality. Clim Policy 19:827\u0026ndash;844\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRao ND, Min J, Mastrucci A (2019) Energy requirements for decent living in India, Brazil and South Africa. Nat Energy 4:1025\u0026ndash;1032\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuo J, Meng J, Zheng H, Parikh P, Guan D (2023) Achieving decent living standards in emerging economies challenges national mitigation goals for CO2 emissions. Nat Commun 14:6342\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCreutzig F et al (2022) Demand-side solutions to climate change mitigation consistent with high levels of well-being. Nat Clim Chang 12:36\u0026ndash;46\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrubler A et al (2018) A low energy demand scenario for meeting the 1.5\u0026deg;C target and sustainable development goals without negative emission technologies. Nat Energy 3:515\u0026ndash;527\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePauliuk S et al (2021) Global scenarios of resource and emission savings from material efficiency in residential buildings and cars. Nat Commun 12:5097\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCreutzig F et al (2018) Towards demand-side solutions for mitigating climate change. Nat Clim Change 8:260\u0026ndash;263\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChatterjee S et al (2024) Balancing energy transition: Assessing decent living standards and future energy demand in the Global South. Energy Research Social Science 118:103757\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanitkar T, Mythri A, Jayaraman T (2024) Equity assessment of global mitigation pathways in the IPCC Sixth Assessment Report. Clim Policy 24:1129\u0026ndash;1148\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCabeza LF et al (2022) \u003cem\u003eChapter 9\u003c/em\u003e: Buildings. Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFell MJ (2017) Energy services: A conceptual review. Energy Research Social Science 27:129\u0026ndash;140\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMastrucci A et al (2023) Modeling Low Energy Demand Futures for Buildings: Current State and Research Needs. Annu Rev Environ Resour 48:761\u0026ndash;792\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIEA. 2019 Global Status Report for Buildings and Construction: Towards a Zero-Emission, Efficient and Resilient Buildings and Construction Sector. 41 (2019)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Uuml;rge-Vorsatz D et al (2020) Advances Toward a Net-Zero Global Building Sector. Annu Rev Environ Resour 45:227\u0026ndash;269\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIslam S, Rana MJ, Shupler M (2023) Deepened socioeconomic inequality in clean cooking fuel use in India from 2005\u0026ndash;2006 to 2015\u0026ndash;2016. Heliyon 9:e17041\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerez J, Fusco G (2020) Exploring inequalities in India through housing overcrowding. J Hous Built Environ 35:593\u0026ndash;616\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoblete-Cazenave M, Pachauri S, Byers E, Mastrucci A, van Ruijven B (2021) Global scenarios of household access to modern energy services under climate mitigation policy. Nat Energy 6:824\u0026ndash;833\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMillward-Hopkins J, Steinberger JK, Rao ND, Oswald Y (2020) Providing decent living with minimum energy: A global scenario. Glob Environ Change 65:102168\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYawale SK, Hanaoka T, Kapshe M (2021) Development of energy balance table for rural and urban households and evaluation of energy consumption in Indian states. Renew Sustain Energy Rev 136:110392\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrayas (Energy Group). PIER: Modelling the Indian energy system through the 2020s (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrayas (Energy Group) (2023) \u003cem\u003eMore with Less: Insights from Residential Energy Demand Assessment Using PIER\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://energy.prayaspune.org/our-work/research-report/more-with-less\u003c/span\u003e\u003cspan address=\"https://energy.prayaspune.org/our-work/research-report/more-with-less\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar P, Natarajan R, Ashok K (2021) Sustainable alternative futures for urban India: the resource, energy, and emissions implications of urban form scenarios. Environ Res : Infrastruct Sustain 1:011004\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChatterjee S, Kiss B, \u0026Uuml;rge-Vorsatz D, Teske S (2022) Decarbonisation Pathways for Buildings. In: Teske S (ed) Achieving the Paris Climate Agreement Goals. Springer International Publishing, Cham, pp 161\u0026ndash;185. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-3-030-99177-7_7\u003c/span\u003e\u003cspan address=\"10.1007/978-3-030-99177-7_7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaioglou V, Van Ruijven BJ, Van Vuuren (2012) D. P. Model projections for household energy use in developing countries. Energy 37:601\u0026ndash;615\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaioglou V, Mikropoulos E, Gernaat D (2022) Vuuren, D. P. Efficiency improvement and technology choice for energy and emission reductions of the residential sector. Energy 243:122994van\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMastrucci A, van Ruijven B, Byers E, Poblete-Cazenave M, Pachauri S (2021) Global scenarios of residential heating and cooling energy demand and CO2 emissions. Clim Change 168:14\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Neill BC et al (2017) The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob Environ Change 42:169\u0026ndash;180\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCamarasa C et al (2022) A global comparison of building decarbonization scenarios by 2050 towards 1.5\u0026ndash;2\u0026deg;C targets. Nat Commun 13:3077\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrey V et al (2019) Looking under the hood: A comparison of techno-economic assumptions across national and global integrated assessment models. Energy 172:1254\u0026ndash;1267\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuderer G et al (2012) The economics of decarbonizing the energy system\u0026mdash;results and insights from the RECIPE model intercomparison. Clim Change 114:9\u0026ndash;37\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpencer T et al (2017) The 1.5\u0026deg;C target and coal sector transition: at the limits of societal feasibility. Clim Policy 18:335\u0026ndash;351\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePachauri S et al (2013) Pathways to achieve universal household access to modern energy by 2030. Environ Res Lett 8:024015\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWollburg P, Hallegatte S, Mahler DG (2023) Ending extreme poverty has a negligible impact on global greenhouse gas emissions. Nature 623:982\u0026ndash;986\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkpinar-Ferrand E, Singh A (2010) Modeling increased demand of energy for air conditioners and consequent CO2 emissions to minimize health risks due to climate change in India. Environmental Science Policy 13:702\u0026ndash;712\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhattacharyya SC (2015) Influence of India\u0026rsquo;s transformation on residential energy demand. Appl Energy 143:228\u0026ndash;237\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChaturvedi V, Eom J, Clarke LE, Shukla PR (2014) Long term building energy demand for India: Disaggregating end use energy services in an integrated assessment modeling framework. Energy Policy 64:226\u0026ndash;242\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiahi K et al (2021) Cost and attainability of meeting stringent climate targets without overshoot. Nat Clim Chang 11:1063\u0026ndash;1069\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Climate change mitigation, Sustainable development, Energy poverty, Global South, Model intercomparison, Decent Living Standards","lastPublishedDoi":"10.21203/rs.3.rs-8300022/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8300022/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEnsuring equitable access to energy services to enhance human wellbeing is gaining prominence within climate negotiations. Despite rising energy demands in the Global South, disparities persist in access to decent living standards (DLS). Achieving DLS universally requires additional energy, yet the efficacy of demand-side strategies in meeting climate goals remains underexplored. Here, we assess energy and carbon dioxide emission implications of providing DLS to all within climate change mitigation efforts, focusing on India\u0026rsquo;s residential sector and using a multi-model analysis. Our findings show that providing DLS while leveraging demand-side measures, supported by enabling policies and widely available technologies, can substantially reduce residential energy demand by up to 27% by 2050 compared to a reference scenario. We find that enhanced access to DLS combined with demand-side measures not only improves wellbeing but also provides flexibility on the supply side while remaining compatible with ambitious climate targets.\u003c/p\u003e","manuscriptTitle":"High wellbeing with lower energy consumption: scenarios for India’s residential sector","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-14 06:27:17","doi":"10.21203/rs.3.rs-8300022/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c8422104-c5e3-490c-ac24-8c286da99aff","owner":[],"postedDate":"January 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59644389,"name":"Earth and environmental sciences/Environmental social sciences/Climate-change mitigation"},{"id":59644390,"name":"Scientific community and society/Developing world"},{"id":59644391,"name":"Physical sciences/Energy science and technology/Energy modelling"},{"id":59644392,"name":"Scientific community and society/Energy and society/Energy access"}],"tags":[],"updatedAt":"2026-01-14T06:27:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-14 06:27:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8300022","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8300022","identity":"rs-8300022","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}