Towards net-zero emissions in global residential heating and cooling: a global scenario analysis

Towards net-zero emissions in global residential heating and cooling: a global scenario analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Towards net-zero emissions in global residential heating and cooling: a global scenario analysis Alessio Mastrucci, Benigna Boza-Kiss, Bas van Ruijven This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4253226/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Apr, 2025 Read the published version in Climatic Change → Version 1 posted 4 You are reading this latest preprint version Abstract Accounting for 21% of global greenhouse gas (GHG) emissions, buildings play a crucial role in climate change mitigation. Demand-side interventions entail large energy and GHG emission reduction potentials. The effects of broader mitigation policies at the global level beyond energy efficiency improvements, including sufficiency and structural changes, and their interaction with cross-sectoral climate policies are, however, still unclear. Here, we assess a comprehensive set of scenarios to reduce residential space heating and cooling emissions towards net-zero targets. We find that activity reductions, fuel shifts, and technological improvements can reduce current global residential space heating and cooling CO 2 emissions by 61% until 2050. Combining these demand-side interventions and stringent climate policies entails up to 96% reduction of current CO 2 emissions by 2050. Neutralizing residual direct CO 2 emissions due to fossil fuels for space heating would require additional interventions. Buildings Climate change mitigation Energy demand Integrated assessment modelling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The building sector accounted for 21% of the global greenhouse gas (GHG) emissions in 2019 (Cabeza et al. 2022 ). Despite continuous improvements in energy efficiency (Saunders et al. 2021 ), GHG emissions from buildings have been increasing due to other factors, such as floor space and population growth (IEA 2019 ). Urgent action is required to reduce the total energy demand in buildings and to contribute to mid-century net-zero targets (Cabeza and Chàfer 2020 ; Ürge-Vorsatz et al. 2020 ; Mata et al. 2020 ; Creutzig et al. 2022 ). Energy demand reductions are central to carbon mitigation and can support avoiding the need for uncertain negative emission technologies (Grubler et al. 2018 ; Mundaca et al. 2019 ; Cabeza and Ürge-Vorsatz 2020 ). The building sector offers multifaceted mitigation options that are covered by the Avoid-Shift-Improve framework (Creutzig et al. 2021 , 2022 ), including: activity reduction through building design, size and use ( avoid ); electrification and fuel shifts ( shift ); and technological improvements of energy efficiency in building envelopes and technical systems ( improve ). The estimated global mitigation potential for buildings amounts to 8.2 GtCO 2 by 2050, equivalent to 61% of their baseline scenario (Cabeza et al. 2022 ). The assessment of buildings energy efficiency improvements and their mitigation potentials has a broad literature and detailed modelling (Mata et al. 2018 ; Cabeza and Chàfer 2020 ; Edelenbosch et al. 2021 ; Chatterjee et al. 2022 ). Conversely, activity level reductions – also known as sufficiency (Samadi et al. 2017 ; Lorek and Spangenberg 2019 ; Gaspard et al. 2023 ) – and structural changes (Francart et al. 2018 ; Kikstra et al. 2021 ) have been more scarcely investigated and mostly represented in a simplified way in existing models (Mastrucci et al. 2023 ). These interventions can deliver energy reductions additional to energy efficiency improvements and contribute to covering the full mitigation potential (Cabeza et al. 2022 ). Thus, the reduction potential of combined demand-side interventions and their interaction with cross-sectoral climate policies (Levesque et al. 2021 ) is also unclear. The building sector is characterized by a multitude of different building types and actors, high inertia and lock-in, and tight linkages to the context, making the modelling of GHG emissions and reduction potential challenging at large scales. At the global level, integrated assessment models (IAMs) are commonly used for exploration of mitigation scenarios, but they have traditionally focused on energy supply changes and decarbonization (Creutzig et al. 2018 ). The simplified accounting of the building sector in many IAMs has limitations in considering heterogeneities and key dynamics, including stock turnover, and energy efficiency investment decisions. In contrast, sectoral modelling has been used for investigating a range of mitigation interventions, including energy efficiency, structural changes, and sufficiency (Levesque et al. 2019 ; Camarasa et al. 2022 ), but is often limited in geographical coverage and in the lack of or inconsistent representation of the energy supply-side (Levesque et al. 2021 ). Recently, the modelling of end-use sectors in IAMs has been improved for stock turnover, buildings heterogeneity, and energy efficiency improvements (Knobloch et al. 2019 ; Edelenbosch et al. 2021 ; Mastrucci et al. 2021 ; Daioglou et al. 2022 ), though most of these studies only consider a narrow range of building sectoral interventions (energy efficiency improvements) and don’t investigate deep decarbonization scenarios. The trade-offs and synergies between demand-side and supply-side interventions, have been ignored or investigated with simplified approaches (Levesque et al. 2021 ), while more detailed approaches focus mostly on national or regional scales (Berrill et al. 2022 ). Here, we perform a comprehensive quantitative assessment of mitigation policies for the global residential sector towards mid-century net-zero targets, combining broad sectoral demand-side interventions and ambitious climate policies. We include building sector interventions targeting activity reduction, electrification and fuel shifts, and technological improvements and energy efficiency. We focus on two end-use services, space heating and cooling, which are respectively the largest and the fastest growing residential demand categories (IEA 2019 ), while they are both crucial for thermal comfort and well-being of occupants. We use the global building sector modelling framework MESSAGEix-Buildings (Mastrucci et al. 2021 ) soft-linked to the MESSAGEix-GLOBIOM IAM (Huppmann et al. 2019 ). This linkage further enables for accounting energy supply-side system transformation and interplay with the building demand-side for a more comprehensive assessment of broader decarbonization strategies. 2. Methods MESSAGEix-Buildings (Mastrucci et al. 2021 ) is a bottom-up framework to model energy demand and CO 2 emissions of the building sector in global change scenarios. In this study, we use two modules part of MESSAGEix-Buildings with a focus on the building stock: CHILLED (Cooling and Heating gLobaL Energy Demand model), a spatially-explicit energy demand model for space heating and cooling; and STURM (Stock TURnover Model of global buildings), a building stock turnover model including energy efficiency investment decisions assessment. MESSAGEix-Buildings is soft-linked to MESSAGEix-GLOBIOM (Huppmann et al. 2019 ), an IAM framework for the assessment of energy-environment-economy systems and development of global-change scenarios, enabling energy price feedback and explicit accounting of energy-supply side transformations in CO 2 emission calculations. MESSAGEix-Buildings has high level of detail in representing building dynamics, including stock turnover, energy-efficiency investment decisions, and energy demands, and has high granularity in geographical (regions, climates, urban and rural locations), socio-economic (income and tenure) and building characteristics (housing type, energy efficiency level, and energy carriers for heating), allowing the exploration of a broad set of policy and their effects on a global scale . Here, we run the model for 60 regions (Supplementary Information, section 1) with a 5-year timestep from 2015 (base year) to 2050. We perform model calibrations for the base year 2015 using statistical data on housing stock characteristics, floorspace, and final energy demand. Comprehensive descriptions of the methods are available in prior studies (Mastrucci et al. 2019 , 2021 ), to which we refer the reader for more details. 2.1. Energy demand modelling The CHILLED model calculates energy demand for space heating and cooling using the variable degree days (VDD) method (Al-Homoud 2001 ; Mastrucci et al. 2021 ). VDD are calculated as the annual sum of daily positive differences between outdoor temperature and a building-specific balance temperature, defined as the outdoor temperature at which neither heating nor cooling is required (Claridge et al. 1987 ; Al-Homoud 2001 ). Differently from traditional degree days, arbitrarily assuming fixed balance temperatures, the VDD method analytically calculates the balance temperature with a simplified thermal balance calculation, allowing for a more accurate calculation, accounting for building thermal characteristics and user behaviour-related parameters. The final energy demand for space heating and cooling is subsequently calculated based on the VDD results, by using building-specific heat transfer coefficients, user-related parameters and conversion efficiency of heating and cooling systems. The calculations are run over a spatial grid at 0.5° grid resolution (approximately 50 km at the equator) for the entire globe, using a set of building archetypes representative of different regions, housing types and energy efficiency levels. Results are aggregated by location (urban/rural), country and climatic zone for the different archetypes and energy intensities per unit of floorspace are passed over to the STURM model. 2.2. Energy efficiency decision modelling The STURM model can assess investment decisions on energy efficiency improvements in new constructions, renovations and heating fuel shifts via dedicated discrete choice models (Giraudet et al. 2012 ). A life-cycle cost approach is used to compare different options and to endogenously calculate market shares based on investment costs, operational costs, and intangible costs of alternative technologies, as well as renovation rates. Operational costs are calculated based on the energy demands from the STURM model and the energy price trajectories from the IAM MESSAGEix-GLOBIOM. The intangible costs represent non-monetizable technology-specific barriers towards investments. Discount rates differ across regions and household types, depending on the housing type and tenure, to represent different degree of predisposition to investment, e.g. lower for renting and multi-family homes to account for principal-agent problems (Ástmarsson et al. 2013 ). Constraints relative to the applicability of specific new construction and renovation options, and bounds to renovation rates, are set at the regional level. 2.3. Stock turnover modelling The STURM model accounts for the stock turnover of buildings using dynamic material flow analysis (MFA) (Sandberg et al. 2016 ). The model has population as key driver of housing demand, and consequent stock requirements, over time using “dwelling unit” as main unit of calculation. Outputs from the model include timeseries of housing stock, demolitions, and new constructions. The model runs calculations by region, location (urban and rural) and housing type, making use of exogenous population, urbanization, and housing types projections. At every timestep, the model calculates the number of housing units in the stock based on population, urbanization, household size, and housing type projections. Demolitions are estimated via a set of lifetime probability distributions by building type in different regions. New constructions are subsequently calculated by considering the number of housing units to replace due to demolitions and the new additions to the stock due to population increase. Renovation rates and market shares of different options for new construction, renovations, and fuel shifts (calculated using dedicated discrete choice models, see previous section) are applied to existing and new housing units to determine the updated configuration of the housing stock. Per-capita floorspace and energy intensity coefficients are used to calculate total floorspace and energy demands for space heating and cooling by region, location, housing type and heating energy carrier. Finally, CO 2 emissions are calculated by applying emission factors from consistent MESSAGEix-GLOBIOM scenario runs to final energy demands by energy carrier. We include both direct emission from fossil fuel combustion in buildings and indirect emissions from electricity and district heating. Results are aggregated and provided at the level of the 11 world regions in MESSAGEix-GLOBIOM and by Global North and Global South. 3. Scenario setup 3.1. Scenarios overview Our set of scenarios combines demand-side interventions for buildings, including relevant policy instruments for their implementation, and climate policies (Table 1 ). The starting point is the shared socio-economic pathway SSP2 “middle of the road” (O’Neill et al. 2017 ), assumed as baseline for demographics and socio-economics. The Avoid-Shift-Improve framework (Creutzig et al. 2021 , 2022 ) provides comprehensive assessment of sectoral demand-side interventions , reflecting opportunities for socio-cultural, infrastructural, and technological change. In a similar fashion, we frame interventions and their strategical combinations into activity reductions and activity shifts ( Activity ), electrification and fuel shifts ( Fuel shifts ), technology and energy efficiency improvements ( Technology ) (Kriegler et al. 2023 ). We combine all investigated demand-side measures under the ALL scenario and contrast them against a reference scenario (REF) assuming continuation of current policies and regulation. We assess the effect of stringent climate policies in line with the 1.5C target (1.5C) contrasted with a baseline scenario with continuation of national policies until 2030 and no stringent climate policies (NPi), based on existing scenarios from the IAM MESSAGEix-GLOBIOM (Riahi et al. 2021 ). Table 1 Overview of interventions and policy instruments in the modelled scenarios. Policy focus Intervention type Description Policy instruments Demand-side Reference (REF) Continuation of current demand-side interventions Current policy instruments with current stringencies. Activity (ACT) - Reduce per-cap floorspace by 5% by 2050. - Shift to multi-family housing - Switch to more conservative temperature set-points, reaching − 1°C for heating and + 1°C for cooling by 2030 compared to 2015. Policies limiting floorspace in new construction, policies limiting new construction of single-family housing, information campaigns. Electrification and fuel shifts (ELE) - Increase electrification rate in existing buildings. - Ban fossil fuels in new construction and renovations. - Phase-down coal and traditional biomass in individual heating systems. Subsidies, fuel mandates. Fuel mandates, subsidies, and incentives, building codes, neighbourhood-based approaches. Technology (TEC) - Mandatory advanced renovation (Global North only) and advanced new construction (all regions) as from 2030, including building shells and technical systems. Advanced corresponds to passive building standard for new construction in the Global North and energy savings for renovation of at least 40%. - Increase in yearly renovation rates up to 3% in the Global North and 1.5% in the Global South. Building codes and regulations, subsidies and incentives, energy performance certification. Combined (ALL) Combination of all demand-side interventions above Combination of the policy instruments above. Climate policies No stringent climate policy (NPi) Continuation of current national policies until 2030; no additional stringent climate policies Current policy instruments. Climate policy (1.5C) National policies until 2030; climate policies in line with the 1.5°C targets; cumulative CO 2 emissions (2020–2100) 600 GtCO2 Carbon taxes, supply-side oriented policies. 3.2. Model implementation of scenarios Policy interventions are represented with specific model implementations and dynamics (see the Supplementary Information, section 2, for complete information). Activity reductions mostly concern exogenous model parameters. Scenarios are modelled by adjusting the relevant model parameters and exogenous projections, i.e. per-capita floorspace, share of multi-family housing, and set-point temperature. Electrification and fuel shifts are represented by introducing constraints in the relevant discrete choice models, i.e. on minimum electrification rates and uptake of fossil fuels-based heating systems in new constructions and renovations. Model constraints are also used to model technology and energy efficiency improvements, i.e. on minimum energy efficiency standards for new constructions and renovations, and minimum renovation rates. Climate policies and energy supply-side interventions are modelled with the help of the soft-linkage with the MESSAGEix-GLOBIOM IAM. Energy price signals and CO 2 emission factors for electricity and district heating are determined based on a consistent set of scenario runs (Riahi et al. 2021 ) in MESSAGEix-GLOBIOM and fed into the STURM model (Supplementary Information, section 3). The effects on the model results are twofold. First, energy prices influence household investment decisions on energy efficiency, e.g. under higher prices, advanced energy efficiency options are favoured in the LCC calculations, and therefore energy demands for space heating and cooling. Second, scenario-consistent emission factors affect the resulting CO 2 emission projections. 4. Results 4.1. Effects of demand-side interventions The sectoral demand-side interventions drive major changes both in the future configuration of the global housing stock and the mix of energy carriers for space heating (Fig. 1 ). In the reference scenario (NPi-REF), a stark increase in total floor space in the Global South is driven by population growth and increase in average affluence. As a result, the housing stock in 2050 is mostly composed of new buildings (70% of total floorspace). Conversely, in the Global North, most of the existing housing stock of 2015 is still standing in 2050 (64% of total floorspace), though a part of it is renovated under current policies. The NPi-ACT scenario results in a 14% reduction of total global floorspace compared to the NPi-REF, but no substantial changes in the mix of energy carriers. The NPi-ELE scenario entails a major shift in energy carriers for space heating, including significant increase in electrification and phase-out of fossil fuels, especially in the Global South. The NPi-TEC scenario is characterized by advanced technology solutions leading to higher shares of advanced new construction and renovations by 2050. In the Global North, acceleration of the deep renovation rates results in higher share of renovated buildings, and larger electrification due to the uptake of heat-pumps combined with higher insulation standards. The NPi-ALL scenario combines all considered demand-side interventions, resulting in both lower floorspace levels, and higher penetration of advanced new construction and renovation. Natural gas and other fossil fuels are reduced in the Global South and almost completely phased-out in the Global North, as a result of high electrification levels. The final energy demand for space heating and cooling differs across the investigated scenarios in the Global South and Global North regions (Fig. 2 ), resulting from the interplay of activity drivers, buildings stock dynamics, and energy efficiency improvements. In the reference scenario (NPi-REF), the energy demand for space heating in the Global South peaks around 2035 and then starts declining under energy efficiency improvements from continuation of current policies, offsetting the floor space growth. In the Global North, the total final energy demand for space heating is 53% lower in 2050 than in 2015 driven by energy efficiency improvements under current policies. The scenarios based on individual demand-side interventions reflect different potentials to reduce energy demand for space heating, the most effective being NPi-TEC (-30%), followed by NPi-ACT (-17%) and then NPi-ELE (-6%) compared to NPi-REF in 2050. The combination of all demand-side interventions (NPi-ALL) has the largest energy demand reduction potential for space heating (-44%), due to the high complementarity of the individual measures. The energy demand for space cooling is projected to double globally between 2015 and 2050 in the NPi-REF scenario, driven by growing access to air-conditioning and larger floor space in the Global South. In the Global North, the energy demand for cooling is lower due to different climatic conditions. Similar to space heating, the combination of demand-side interventions (NPi-ALL) can significantly reduce the global energy demand for space cooling by 38% in 2050 compared to the NPi-REF scenario, with different reduction potential in the Global South (-40%) and in the Global North (-30%). For space cooling, the global energy reduction provided by Activity interventions (-22%) and Technology interventions (-25%) are comparable, highlighting the weightier role of behavioural aspects for this end-use. 4.2. Decarbonization pathways We explore here the combined effect of demand-side interventions and climate policies consistent with the 1.5°C target towards residential space heating and cooling decarbonization. In the reference scenario (NPi-REF), global final energy demands for space heating and cooling reach 22.3 EJ/yr by 2050 (-31% compared to 2015) and global CO 2 emissions reach 1.44 GtCO 2 /yr (-37% compared to 2015). Demand-side interventions drive major energy demand reduction for heating and cooling, up to 61% in NPi-ALL relative to 2015, corresponding to 43% reduction compared to NPi-REF in 2050 (Fig. 3 , left panel). Climate policies, through higher energy prices, only drive down energy demand by 37% in the 1.5C-REF scenario relative to 2015, and, combined with demand-side interventions (1.5C-ALL), only add marginal energy demand reductions to NPi-ALL, reaching 62% reductions. The major effect of the stringent climate policies is on CO 2 emission reductions (Fig. 3 , right panel), through the decarbonization of district heating and the electricity supply. CO 2 emission reductions amount to 84% between 2015 and 2050 in the 1.5C-REF scenario, while the combination of all demand-side interventions alone cause only 61% CO 2 mitigation (NPi-ALL). The CO 2 emissions reduction associated with the NPi-ALL and 1.5C-REF scenarios are respectively 38% and 75% compared to the reference scenario NPi-REF in 2050 (Table 2 ). Only the combination of all demand-side interventions and climate policies (1.5C-ALL) leads to emission levels closer to zero in 2050 (0.083 GtCO 2 /yr), with 96% reductions compared to 2015 and 94% cut compared to the NPI-REF scenario (Table 2 ). Table 2 Final energy and total (direct and indirect) CO 2 emission reduction potential in 2050 compared to the NPi-REF reference scenario. Scenario Final energy reduction potential in 2050 (%) CO 2 emission reduction potential in 2050 (%) Global Global North Global South Global Global North Global South NPi-ACT 18 18 18 18 18 19 NPi-ELE 4 3 6 0 2 0 NPi-TEC 29 30 28 27 29 26 NPi-ALL 43 43 42 38 41 36 1.5C-REF 9 6 13 75 66 83 1.5C-ALL 44 44 45 94 94 95 The analysis of CO 2 emissions by region, end-use and emission type – direct from fossil fuel burning in buildings and indirect from district heating and electricity supply – provides further important insights on the mitigation pathways (Fig. 4 ). In the Global South, CO 2 emissions from space heating and cooling increase by 13% by 2050 in the NPi-REF scenario and constitute more than half of global emissions, mostly due to tripling indirect emissions for space cooling under stark demand increase. CO 2 emissions for space heating decrease by 29%, mostly driven by the decarbonization of the electricity system and district heating. The combination of sectoral interventions (NPi-ALL) leads to 28% reduction in 2050 compared to 2015 in CO 2 emissions for space heating and cooling in the Global South. Reductions are driven by lower energy demand levels, and fossil fuel switches, resulting in lower emissions from cooling and direct emissions for heating. The decarbonization of the supply system under 1.5°C-consistent climate policies (15C-REF), contributes to neutralizing indirect CO 2 emissions, while the direct emissions for heating are significantly reduced only under the combined demand-side interventions scenario (15C-ALL), bringing down total emissions by 94% compared to 2015. In the Global North (Fig. 4 , right panel), significant reductions in CO 2 emissions are expected until 2050 already in the NPi-REF scenario (-57% compared to 2015), mostly due to reduced direct emissions for space heating driven by increase in energy efficiency under current policies. While major reductions in indirect CO 2 emissions are achievable under more stringent climate scenarios (1.5C-REF), only the sectoral interventions drive the abatement of direct emissions for space heating (15C-ALL), up to a total 97% reduction of direct and indirect emissions. Compared to CO 2 emission levels in 2015, in the NPi-REF scenario (Fig. 4 , top panel) most countries in the Global South increase CO 2 emissions by 2050, largely driven by space cooling demand increase, with the notable exception of China. India is contributing the largest increase in CO 2 emissions (+ 79 MtCO 2 ), quintuplicating the emissions between 2015 and 2050. In contrast, most the Global North and China, experience significant CO 2 emission reductions. The largest absolute reduction potential by 2050 include China (-224 MtCO 2 , corresponding to -42%) and the USA (-294 MtCO 2 , corresponding to -61%). The picture changes in the most ambitious scenario 1.5C-ALL (Fig. 4 , bottom panel), where all world regions reduce CO 2 emissions for space heating and cooling by 2050. The countries with the largest reduction potential between 2015 and 2050include China (-500MtCO 2 , corresponding to -95%), USA (-475MtCO 2 , corresponding to -98%) and Russia (-161MtCO 2 , corresponding to -91%). 5. Policies to deliver demand-side driven climate mitigation Closing the gap between the model-estimated theoretical potential for climate change mitigation and the realized emission reductions has been under scrutiny and shown to be challenging for multiple reasons. This study estimates an energy demand reduction potential of 43% and a CO 2 emission mitigation potential of 38% resulting from the combination of all building sector interventions (NPi-ALL scenario) relative to the reference (NPi-REF) scenario in 2050. Market forces alone fail to deliver deep transformations in residential space heating and cooling demands because of diverse market barriers, market inertia, the fragmentation of the user side and the market of supply, the lock-ins of infrastructure, social norm impacts, and split incentives (Golove and Eto 1996 ; Wilson et al. 2015 ; Cristino et al. 2021 ). A wide range of public policies in the building sector are already well-known and implemented to address these barriers in countries all around the world (Nascimento et al. 2022 ; IEA 2023a ). However, the focus of sectoral policies has been traditionally on supply and end-use technology solutions, and the potential of socio-behavioural and infrastructural interventions is gaining attention only recently (Rosenow et al. 2016 ; Maor and Howlett 2021 ). The broader set of interventions and their combinations assessed in this study can deliver impacts towards reaching net-zero targets via a number of policy instruments ranging from already implemented measures to opportunities for scaling-up. Policies related to activity level reductions or sufficiency are underrepresented in decision making as much as in modelling studies (Samadi et al. 2017 ), even though we show here that such measures have a potential of 18% CO 2 emission reductions, and would also address the issue of fair consumption of space and resources (Cabeza et al. 2022 ). (Best et al. 2022 ) identified over 45 sufficiency measures aimed at avoiding consumption in the building sector in Europe, including regulatory and financial measures, such as bonuses for switching to smaller apartments, limitations to secondary and holiday homes to avoid sprawl of vacant living spaces. Policies targeting voluntary change of behaviour through awareness raising and information programs were found to have moderate effectiveness and lower costs compared to standards or financial instruments (Boza-Kiss et al. 2013 ). The limitations of sufficiency policies lie in the low level of acceptance by the target groups (Akenji, Lewis, et al. 2021) and the potential for rebound effects (Sorrell et al. 2020 ). While activity level reductions directly help reduce emissions, related policies are appropriate for overconsumption and to reduce inequality. On the other hand, it is important to stress that providing adequate services, such as access to minimum housing as defined e.g. through decent living standards (Rao and Min 2018 ), can become part of successful policies, exemplified by national cooling action plans, which prioritizes passive and low-cost measures to achieve the reduction of space cooling to ensure the material preconditions for human wellbeing (Hu et al. 2023 ). Electrification and fuel shifts have become a critical component of emission reduction, sometimes even contrasted to energy efficiency solutions. Policies related to electrification, fuel shifts, and rolling-out renewable technologies were introduced progressively since the 1950s, increasing in the 1970s, and peaking in the early 2000s, with another boost in the 2020s (IEA 2023b ). Phasing out fossil fuels for heating can be achieved in different ways, but must combine incentivizing electrification with restraining fossil fuel solutions (Braungardt et al. 2023 ). Electrification of the buildings at the household or district level distils largely to heat pumps as the main technology pathway. Many European countries opt for mandatory regulations, such as banning of new gas, oil, and coal heating systems boilers, swaps of existing heaters, and combined with financial incentives (Braungardt et al. 2023 ; Torné and Trutnevyte 2024 ). In the Global South, e.g. Brazil and India measures to connect urban space heating and cooling with industrial waste heat or co-generation at a district level have been on the rise (IRENA, IEA and REN21 2018). Though policies are varied for demand electrification, our study has shown a limited impact or only − 6% and − 3% energy demand reduction compared to NPi-REF in 2050 for Global South and Global North, respectively. Further to fuel shifts, the recent report of the Intergovernmental Panel on Climate Change (IPCC) estimates that renewable energy policies contribute 9% of the total potential of emission reduction (IPCC 2023). Policies to improve technologies and their adoption have the longest history and can achieve CO 2 emission reduction potentials of 27% in 2050, as shown by our results. Building codes and standards are among the most widely adopted policy instruments, reported in 79 countries in 2022 (40% of all nations) (IEA 2019 ; UNEP 2022 ). However, only 26% of all countries use mandatory codes for both residential and non-residential buildings (UNEP 2022 ). Building codes and product standards deliver the highest net savings at the societal level, however their emission saving potential strongly depends on the effectiveness of enforcement (Boza-Kiss et al. 2013 ). In response to the Paris Agreement, there has been a growing interest to develop building codes that deliver towards the net zero emission commitments (UNEP 2022 ), including enforcement of passive level standards, public building demonstration sites, and programs for exemplary buildings (Brussels Environment 2016 ). Renovation rates, however, are still too low due to technical, financial and social barriers, such as principal agent issues (Ástmarsson et al. 2013 ; D’Oca et al. 2018 ). Targeted policies are needed to overcome these barriers and accelerate and increase the depth of interventions to deliver the necessary energy savings (Baek and Park 2012 ). In developing countries, upscaling the uptake of affordable low-energy buildings through targeted policies can contribute not only to reduce energy and GHG emissions, but also to bridge the current housing gaps and promote the sustainable development agenda (Bulkeley et al. 2014 ; Mastrucci and Rao 2019 ). 6. Discussion and conclusions This study explored the effect of a broad set of interventions for decarbonization of the global residential sector, focusing on space heating and cooling and accounting for both demand-side interventions and ambitious climate policies. The results showed that demand-side interventions centred on activity reduction, electrification and fuel shifts, and technology improvements are highly complementary and, when combined, entail the highest energy demand reduction potential, up to 61% in 2050 compared to the base year 2015, even in the absence of supply-side interventions and carbon taxes. Stringent climate policies, enabling the decarbonization of the electricity supply system, are critical for CO 2 emission abatement. However, this study showed that only the combination of demand-side interventions and stringent climate policies enables achieving future CO 2 emission levels close to net-zero, with reductions up to 96% by 2050 compared to 2015. Reaching full carbon neutrality would require additional efforts to abate the residual direct CO 2 emissions, e.g. due to remaining use of gas and other fossil fuels for space heating. Our results are well aligned with those of available residential global space heating and cooling projections (Chatterjee et al. 2022 ; Daioglou et al. 2022 ; Camarasa et al. 2022 ). The estimated mitigation potentials in this study are more conservative compared to the findings of the recent report of the IPCC on climate change mitigation (Cabeza et al. 2022 ). We found a global CO 2 emission reduction potential of 38% from demand-side measures alone (NPi-ALL scenario) compared to our reference (NPi-REF) scenario in 2050, whereas a global mitigation potential of 61% was estimated in the IPCC report compared to baseline scenarios, by aggregating the results of available bottom-up studies. However, the IPCC findings relate to the entire building sector while this study focuses on residential space heating and cooling only. Overall, these findings are consistent in highlighting that a broader range of policies beyond technology and energy efficiency improvements, including activity reductions and fuel shifts, would be needed achieve the required transformations and achieve ambitious climate goals. While there are many barriers and uncovered policy areas, we also found many successful examples of policies pursuing the directions indicated in this paper. In general, policy instruments rarely work well when isolated while policy mixes deliver larger change and can address complex policy goals (Irrek and Jarcynski 2007 ; Givoni et al. 2010 ; Boza-Kiss et al. 2013 ). This study contributes to advancing climate change mitigation assessment for the building sector in three ways: 1) by improving the representation of the global building sector in the context of IAM, via enhanced model granularity and dynamics, including building stock turnover and energy efficiency investments; 2) by providing evidence on the effect of a broad set of demand-side mitigation policies encompassing activity reduction, fuel shifts and technology improvements; 3) by showing the interaction of these demand-side policies with energy supply-side policies and combined effects on energy demands and CO 2 emissions. Future developments will focus on expanding the model to cover other energy services, e.g. cooking and appliances, and the commercial sector. Accounting for the material dimension of buildings is critical for assessing the broader effect of mitigation policies while considering all stages of the building life cycle beyond operational energy, e.g. including the impacts of material production as effect of building activities. With improved data availability and empirical evidence on the effect of different interventions in different contexts, the representation of heterogeneities and building dynamics can be improved to better reflect local contexts. Coupling with models focusing on specific aspects, e.g. social and behavioural aspects, can contribute to overcoming current limitations in the exogenous representation of some key dimensions, e.g. behaviour and lifestyle changes. Declarations Competing interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding: This study has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements no. 821124 (NAVIGATE) and 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. Author contributions: Alessio Mastrucci: Conceptualization, Methodology, Formal analysis, Investigation, Writing – Original draft, Visualization Data Availability: The datasets generated during the current study are available in the Supplementary Information. References Akenji L, Bengtsson, Magnus, Toivio V et al (2021) 1.5-Degree Lifestyles: Towards A Fair Consumption Space for All. Hot or Cool Institute, Berlin Al-Homoud MS (2001) Computer-aided building energy analysis techniques. 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Cambridge University Press IRENA IEA (2018) and REN21 Renewable Energy Policies in a Time of Transition. IRENA, OECD/ IEA and REN21 Irrek W, Jarcynski L (2007) Overall impact assessment of current energy efficiency policies and potential ‘good practice’ policies, Report within the framework of the project AID-EE within the framework of the Intelligent Energy Europe Programme by the European Commission. Wuppertal Institute, Wuppertal Kikstra JS, Vinca A, Lovat F et al (2021) Climate mitigation scenarios with persistent COVID-19-related energy demand changes. Nat Energy. https://doi.org/10.1038/s41560-021-00904-8 Knobloch F, Pollitt H, Chewpreecha U et al (2019) Simulating the deep decarbonisation of residential heating for limiting global warming to 1.5°C. Energ Effi 12:521–550. https://doi.org/10.1007/s12053-018-9710-0 Kriegler E, Strefler J, Gulde R et al (2023) How to achieve a rapid, fair, and efficient transformation to net zero emissions: Policy findings from the NAVIGATE project. Potsdam Institute for Climate Impact Research Levesque A, Pietzcker RC, Baumstark L, Luderer G (2021) Deep decarbonisation of buildings energy services through demand and supply transformations in a 1.5°C scenario. Environ Res Lett 16:054071. https://doi.org/10.1088/1748-9326/abdf07 Levesque A, Pietzcker RC, Luderer G (2019) Halving energy demand from buildings: The impact of low consumption practices. Technol Forecast Soc Chang 146:253–266. https://doi.org/10.1016/j.techfore.2019.04.025 Lorek S, Spangenberg JH (2019) Energy sufficiency through social innovation in housing. Energy Policy 126:287–294. https://doi.org/10.1016/j.enpol.2018.11.026 Maor M, Howlett M (2021) Policy Instrument Interactions in Policy Mixes: Surveying the Conceptual and Methodological Landscape. SSRN J. https://doi.org/10.2139/ssrn.3790007 Mastrucci A, Byers E, Pachauri S, Rao ND (2019) Improving the SDG energy poverty targets: Residential cooling needs in the Global South. Energy Build 186:405–415. https://doi.org/10.1016/j.enbuild.2019.01.015 Mastrucci A, Niamir L, Boza-Kiss B et al (2023) Modelling low energy demand futures for buildings – Current state and research needs. Annu Rev Environ Resour Mastrucci A, Rao ND (2019) Bridging India’s housing gap: lowering costs and CO2 emissions. Building Res Inform 47:8–23. https://doi.org/10.1080/09613218.2018.1483634 Mastrucci A, van Ruijven B, Byers E et al (2021) Global scenarios of residential heating and cooling energy demand and CO2 emissions. Clim Change 168:14. https://doi.org/10.1007/s10584-021-03229-3 Mata É, Kalagasidis AS, Johnsson F (2018) Contributions of building retrofitting in five member states to EU targets for energy savings. Renew Sustain Energy Rev 93:759–774. https://doi.org/10.1016/j.rser.2018.05.014 Mata É, Korpal AK, Cheng SH et al (2020) A map of roadmaps for zero and low energy and carbon buildings worldwide. Environ Res Lett 15:113003. https://doi.org/10.1088/1748-9326/abb69f Mundaca L, Ürge-Vorsatz D, Wilson C (2019) Demand-side approaches for limiting global warming to 1.5°C. Energ Effi 12:343–362. https://doi.org/10.1007/s12053-018-9722-9 Nascimento L, Kuramochi T, Iacobuta G et al (2022) Twenty years of climate policy: G20 coverage and gaps. Clim Policy 22:158–174. https://doi.org/10.1080/14693062.2021.1993776 O’Neill BC, Kriegler E, Ebi KL et al (2017) The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob Environ Change 42:169–180. https://doi.org/10.1016/j.gloenvcha.2015.01.004 Rao ND, Min J (2018) Decent Living Standards: Material Prerequisites for Human Wellbeing. Soc Indic Res 138:225–244. https://doi.org/10.1007/s11205-017-1650-0 Riahi K, Bertram C, Huppmann D et al (2021) Cost and attainability of meeting stringent climate targets without overshoot. Nat Clim Chang 11:1063–1069. https://doi.org/10.1038/s41558-021-01215-2 Rosenow J, Fawcett T, Eyre N, Oikonomou V (2016) Energy efficiency and the policy mix. Building Res Inform 44:562–574. https://doi.org/10.1080/09613218.2016.1138803 Samadi S, Gröne M-C, Schneidewind U et al (2017) Sufficiency in energy scenario studies: Taking the potential benefits of lifestyle changes into account. Technol Forecast Soc Chang 124:126–134. https://doi.org/10.1016/j.techfore.2016.09.013 Sandberg NH, Sartori I, Heidrich O et al (2016) Dynamic building stock modelling: Application to 11 European countries to support the energy efficiency and retrofit ambitions of the EU. Energy Build 132:26–38. https://doi.org/10.1016/j.enbuild.2016.05.100 Saunders HD, Roy J, Azevedo IML et al (2021) Energy Efficiency: What Has Research Delivered in the Last 40 Years? Annu Rev Environ Resour 46:135–165. https://doi.org/10.1146/annurev-environ-012320-084937 Sorrell S, Gatersleben B, Druckman A (2020) The limits of energy sufficiency: A review of the evidence for rebound effects and negative spillovers from behavioural change. Energy Res Social Sci 64:101439. https://doi.org/10.1016/j.erss.2020.101439 Torné A, Trutnevyte E (2024) Banning fossil fuel cars and boilers in Switzerland: Mitigation potential, justice, and the social structure of the vulnerable. Energy Res Social Sci 108:103377. https://doi.org/10.1016/j.erss.2023.103377 UNEP (2022) 2022 Global Status Report for Buildings and Construction: Towards a Zero–emission, Efficient and Resilient Buildings and Construction Sector. United Nations Environment Programme (UNEP) Ürge-Vorsatz D, Khosla R, Bernhardt R et al (2020) Advances Toward a Net-Zero Global Building Sector. Annu Rev Environ Resour 45:227–269. https://doi.org/10.1146/annurev-environ-012420-045843 Wilson C, Crane L, Chryssochoidis G (2015) Why do homeowners renovate energy efficiently? Contrasting perspectives and implications for policy. Energy Res Social Sci 7:12–22. https://doi.org/10.1016/j.erss.2015.03.002 Supplementary Files SI01v1.0.docx SI02v1.0.xlsx Cite Share Download PDF Status: Published Journal Publication published 15 Apr, 2025 Read the published version in Climatic Change → Version 1 posted Reviewers agreed at journal 07 May, 2024 Reviewers invited by journal 07 May, 2024 Editor assigned by journal 16 Apr, 2024 First submitted to journal 11 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4253226","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":299705440,"identity":"7f142633-426c-4b5e-b254-1117b53b8f7d","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":"IIASA: International Institute for Applied Systems Analysis","correspondingAuthor":true,"prefix":"","firstName":"Alessio","middleName":"","lastName":"Mastrucci","suffix":""},{"id":299705441,"identity":"e4775b3e-5e98-4c13-9634-3079bf933e91","order_by":1,"name":"Benigna Boza-Kiss","email":"","orcid":"","institution":"IIASA: International Institute for Applied Systems Analysis","correspondingAuthor":false,"prefix":"","firstName":"Benigna","middleName":"","lastName":"Boza-Kiss","suffix":""},{"id":299705442,"identity":"01cce16b-d7e9-4bc6-8d3e-7ade28c28eaf","order_by":2,"name":"Bas van Ruijven","email":"","orcid":"","institution":"IIASA: International Institute for Applied Systems Analysis","correspondingAuthor":false,"prefix":"","firstName":"Bas","middleName":"van","lastName":"Ruijven","suffix":""}],"badges":[],"createdAt":"2024-04-11 15:05:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4253226/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4253226/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10584-025-03923-6","type":"published","date":"2025-04-15T15:57:42+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":56486605,"identity":"abe08c1c-4128-491f-94de-c16fa2a7039d","added_by":"auto","created_at":"2024-05-14 20:48:00","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":528333,"visible":true,"origin":"","legend":"\u003cp\u003eTop panel: Housing stock floorspace breakdown by building energy efficiency cohort in 2015 (base year) and in 2050 for different demand-side interventions (see Table 1) without stringent climate policies (NPi). Standard (std) indicates current new construction and renovation practices. Advanced (adv.) indicates passive building standard for new construction in the Global North and energy savings of at least 40% for renovation of existing buildings. Bottom panel: Housing stock breakdown by energy carriers for space heating in 2015 (base year) and in 2050 for different demand-side interventions (see Table 1) without stringent climate policies (NPi).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4253226/v1/3ecfd56ef44150d5b5949608.jpeg"},{"id":56486607,"identity":"13ddc45e-b2bd-41de-97d7-186498a07e21","added_by":"auto","created_at":"2024-05-14 20:48:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28677,"visible":true,"origin":"","legend":"\u003cp\u003eProjections of final energy demand for space heating and cooling in the Global South and Global North until 2050 for different demand-side interventions (see Table 1 for full definition of scenarios) without stringent climate policies (NPi).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4253226/v1/78d17ce45da189846c8b75fc.png"},{"id":56486606,"identity":"7c398427-1f0d-4eb6-a1d0-bede1378f0c8","added_by":"auto","created_at":"2024-05-14 20:48:00","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":192399,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal final energy demand (left) and CO\u003csub\u003e2 \u003c/sub\u003eemissions for space heating and cooling (right) in 2050 for combinations of all demand-side interventions (REF and ALL) and climate policies (NPi and 1.5C) scenarios. See Table 1 for scenarios definition.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4253226/v1/d40a7ac29ae5c39822f92ccb.jpeg"},{"id":56486608,"identity":"5d226be0-7e1b-4a34-8ba0-958b65f91d52","added_by":"auto","created_at":"2024-05-14 20:48:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":26633,"visible":true,"origin":"","legend":"\u003cp\u003e\u0026nbsp;CO\u003csub\u003e2\u003c/sub\u003e emissions for space heating and cooling in the Global South and Global North in 2015 (base year) and in 2050 for different combinations of demand-side interventions (REF and ALL) and climate policy (NPi and 1.5C) scenarios. See Table 1 for scenarios definition.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4253226/v1/9cf852c5581e04bd9e2e17b6.png"},{"id":56486610,"identity":"011332dc-71d8-4238-a5cc-9c64faff71c5","added_by":"auto","created_at":"2024-05-14 20:48:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":58938,"visible":true,"origin":"","legend":"\u003cp\u003eMap of CO\u003csub\u003e2\u003c/sub\u003e emission reduction potential for space heating and cooling by 2050 compared to the base year 2015 for different combinations of demand-side interventions (REF and ALL) and climate policy (NPi and 1.5C) scenarios. See Table 1 for scenarios definition.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4253226/v1/566842a4d720cb407c698624.png"},{"id":81051140,"identity":"d0dc1a4b-ab9c-4d69-a940-104b3c68c778","added_by":"auto","created_at":"2025-04-21 16:10:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1626687,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4253226/v1/1039a9c9-b074-4aac-abc8-896b6825fc08.pdf"},{"id":56487026,"identity":"98ce3583-1b21-40ef-890d-6dddb04f454a","added_by":"auto","created_at":"2024-05-14 20:56:01","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":92281,"visible":true,"origin":"","legend":"","description":"","filename":"SI01v1.0.docx","url":"https://assets-eu.researchsquare.com/files/rs-4253226/v1/57acc85d9abad814cab1b810.docx"},{"id":56487025,"identity":"42e05ad0-e1b3-4c8d-af2c-eaabc1d1908c","added_by":"auto","created_at":"2024-05-14 20:56:00","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":820475,"visible":true,"origin":"","legend":"","description":"","filename":"SI02v1.0.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4253226/v1/c947d058fa69911ab3209e13.xlsx"}],"financialInterests":"","formattedTitle":"Towards net-zero emissions in global residential heating and cooling: a global scenario analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe building sector accounted for 21% of the global greenhouse gas (GHG) emissions in 2019 (Cabeza et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Despite continuous improvements in energy efficiency (Saunders et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), GHG emissions from buildings have been increasing due to other factors, such as floor space and population growth (IEA \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Urgent action is required to reduce the total energy demand in buildings and to contribute to mid-century net-zero targets (Cabeza and Ch\u0026agrave;fer \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; \u0026Uuml;rge-Vorsatz et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mata et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Creutzig et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Energy demand reductions are central to carbon mitigation and can support avoiding the need for uncertain negative emission technologies (Grubler et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mundaca et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Cabeza and \u0026Uuml;rge-Vorsatz \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe building sector offers multifaceted mitigation options that are covered by the Avoid-Shift-Improve framework (Creutzig et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), including: activity reduction through building design, size and use (\u003cem\u003eavoid\u003c/em\u003e); electrification and fuel shifts (\u003cem\u003eshift\u003c/em\u003e); and technological improvements of energy efficiency in building envelopes and technical systems (\u003cem\u003eimprove\u003c/em\u003e). The estimated global mitigation potential for buildings amounts to 8.2 GtCO\u003csub\u003e2\u003c/sub\u003e by 2050, equivalent to 61% of their baseline scenario (Cabeza et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The assessment of buildings energy efficiency improvements and their mitigation potentials has a broad literature and detailed modelling (Mata et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cabeza and Ch\u0026agrave;fer \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Edelenbosch et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chatterjee et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Conversely, activity level reductions \u0026ndash; also known as sufficiency (Samadi et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lorek and Spangenberg \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gaspard et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) \u0026ndash; and structural changes (Francart et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kikstra et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have been more scarcely investigated and mostly represented in a simplified way in existing models (Mastrucci et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These interventions can deliver energy reductions additional to energy efficiency improvements and contribute to covering the full mitigation potential (Cabeza et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, the reduction potential of combined demand-side interventions and their interaction with cross-sectoral climate policies (Levesque et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) is also unclear.\u003c/p\u003e \u003cp\u003eThe building sector is characterized by a multitude of different building types and actors, high inertia and lock-in, and tight linkages to the context, making the modelling of GHG emissions and reduction potential challenging at large scales. At the global level, integrated assessment models (IAMs) are commonly used for exploration of mitigation scenarios, but they have traditionally focused on energy supply changes and decarbonization (Creutzig et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The simplified accounting of the building sector in many IAMs has limitations in considering heterogeneities and key dynamics, including stock turnover, and energy efficiency investment decisions. In contrast, sectoral modelling has been used for investigating a range of mitigation interventions, including energy efficiency, structural changes, and sufficiency (Levesque et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Camarasa et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), but is often limited in geographical coverage and in the lack of or inconsistent representation of the energy supply-side (Levesque et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recently, the modelling of end-use sectors in IAMs has been improved for stock turnover, buildings heterogeneity, and energy efficiency improvements (Knobloch et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Edelenbosch et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mastrucci et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Daioglou et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), though most of these studies only consider a narrow range of building sectoral interventions (energy efficiency improvements) and don\u0026rsquo;t investigate deep decarbonization scenarios. The trade-offs and synergies between demand-side and supply-side interventions, have been ignored or investigated with simplified approaches (Levesque et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), while more detailed approaches focus mostly on national or regional scales (Berrill et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHere, we perform a comprehensive quantitative assessment of mitigation policies for the global residential sector towards mid-century net-zero targets, combining broad sectoral demand-side interventions and ambitious climate policies. We include building sector interventions targeting activity reduction, electrification and fuel shifts, and technological improvements and energy efficiency. We focus on two end-use services, space heating and cooling, which are respectively the largest and the fastest growing residential demand categories (IEA \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), while they are both crucial for thermal comfort and well-being of occupants. We use the global building sector modelling framework MESSAGEix-Buildings (Mastrucci et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) soft-linked to the MESSAGEix-GLOBIOM IAM (Huppmann et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This linkage further enables for accounting energy supply-side system transformation and interplay with the building demand-side for a more comprehensive assessment of broader decarbonization strategies.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eMESSAGEix-Buildings (Mastrucci et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) is a bottom-up framework to model energy demand and CO\u003csub\u003e2\u003c/sub\u003e emissions of the building sector in global change scenarios. In this study, we use two modules part of MESSAGEix-Buildings with a focus on the building stock: CHILLED (Cooling and Heating gLobaL Energy Demand model), a spatially-explicit energy demand model for space heating and cooling; and STURM (Stock TURnover Model of global buildings), a building stock turnover model including energy efficiency investment decisions assessment. MESSAGEix-Buildings is soft-linked to MESSAGEix-GLOBIOM (Huppmann et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), an IAM framework for the assessment of energy-environment-economy systems and development of global-change scenarios, enabling energy price feedback and explicit accounting of energy-supply side transformations in CO\u003csub\u003e2\u003c/sub\u003e emission calculations.\u003c/p\u003e \u003cp\u003eMESSAGEix-Buildings has high level of detail in representing building dynamics, including stock turnover, energy-efficiency investment decisions, and energy demands, and has high granularity in geographical (regions, climates, urban and rural locations), socio-economic (income and tenure) and building characteristics (housing type, energy efficiency level, and energy carriers for heating), allowing the exploration of a broad set of policy and their effects on a global scale .\u003c/p\u003e \u003cp\u003eHere, we run the model for 60 regions (Supplementary Information, section 1) with a 5-year timestep from 2015 (base year) to 2050. We perform model calibrations for the base year 2015 using statistical data on housing stock characteristics, floorspace, and final energy demand. Comprehensive descriptions of the methods are available in prior studies (Mastrucci et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), to which we refer the reader for more details.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Energy demand modelling\u003c/h2\u003e \u003cp\u003eThe CHILLED model calculates energy demand for space heating and cooling using the variable degree days (VDD) method (Al-Homoud \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Mastrucci et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). VDD are calculated as the annual sum of daily positive differences between outdoor temperature and a building-specific balance temperature, defined as the outdoor temperature at which neither heating nor cooling is required (Claridge et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Al-Homoud \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Differently from traditional degree days, arbitrarily assuming fixed balance temperatures, the VDD method analytically calculates the balance temperature with a simplified thermal balance calculation, allowing for a more accurate calculation, accounting for building thermal characteristics and user behaviour-related parameters. The final energy demand for space heating and cooling is subsequently calculated based on the VDD results, by using building-specific heat transfer coefficients, user-related parameters and conversion efficiency of heating and cooling systems. The calculations are run over a spatial grid at 0.5\u0026deg; grid resolution (approximately 50 km at the equator) for the entire globe, using a set of building archetypes representative of different regions, housing types and energy efficiency levels. Results are aggregated by location (urban/rural), country and climatic zone for the different archetypes and energy intensities per unit of floorspace are passed over to the STURM model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Energy efficiency decision modelling\u003c/h2\u003e \u003cp\u003eThe STURM model can assess investment decisions on energy efficiency improvements in new constructions, renovations and heating fuel shifts via dedicated discrete choice models (Giraudet et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). A life-cycle cost approach is used to compare different options and to endogenously calculate market shares based on investment costs, operational costs, and intangible costs of alternative technologies, as well as renovation rates. Operational costs are calculated based on the energy demands from the STURM model and the energy price trajectories from the IAM MESSAGEix-GLOBIOM. The intangible costs represent non-monetizable technology-specific barriers towards investments. Discount rates differ across regions and household types, depending on the housing type and tenure, to represent different degree of predisposition to investment, e.g. lower for renting and multi-family homes to account for principal-agent problems (\u0026Aacute;stmarsson et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Constraints relative to the applicability of specific new construction and renovation options, and bounds to renovation rates, are set at the regional level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Stock turnover modelling\u003c/h2\u003e \u003cp\u003eThe STURM model accounts for the stock turnover of buildings using dynamic material flow analysis (MFA) (Sandberg et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The model has population as key driver of housing demand, and consequent stock requirements, over time using \u0026ldquo;dwelling unit\u0026rdquo; as main unit of calculation. Outputs from the model include timeseries of housing stock, demolitions, and new constructions. The model runs calculations by region, location (urban and rural) and housing type, making use of exogenous population, urbanization, and housing types projections.\u003c/p\u003e \u003cp\u003eAt every timestep, the model calculates the number of housing units in the stock based on population, urbanization, household size, and housing type projections. Demolitions are estimated via a set of lifetime probability distributions by building type in different regions. New constructions are subsequently calculated by considering the number of housing units to replace due to demolitions and the new additions to the stock due to population increase. Renovation rates and market shares of different options for new construction, renovations, and fuel shifts (calculated using dedicated discrete choice models, see previous section) are applied to existing and new housing units to determine the updated configuration of the housing stock. Per-capita floorspace and energy intensity coefficients are used to calculate total floorspace and energy demands for space heating and cooling by region, location, housing type and heating energy carrier. Finally, CO\u003csub\u003e2\u003c/sub\u003e emissions are calculated by applying emission factors from consistent MESSAGEix-GLOBIOM scenario runs to final energy demands by energy carrier. We include both direct emission from fossil fuel combustion in buildings and indirect emissions from electricity and district heating. Results are aggregated and provided at the level of the 11 world regions in MESSAGEix-GLOBIOM and by Global North and Global South.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Scenario setup","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Scenarios overview\u003c/h2\u003e \u003cp\u003eOur set of scenarios combines demand-side interventions for buildings, including relevant policy instruments for their implementation, and climate policies (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The starting point is the shared socio-economic pathway SSP2 \u0026ldquo;middle of the road\u0026rdquo; (O\u0026rsquo;Neill et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), assumed as baseline for demographics and socio-economics. The \u003cem\u003eAvoid-Shift-Improve\u003c/em\u003e framework (Creutzig et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) provides comprehensive assessment of sectoral \u003cem\u003edemand-side interventions\u003c/em\u003e, reflecting opportunities for socio-cultural, infrastructural, and technological change. In a similar fashion, we frame interventions and their strategical combinations into activity reductions and activity shifts (\u003cem\u003eActivity\u003c/em\u003e), electrification and fuel shifts (\u003cem\u003eFuel shifts\u003c/em\u003e), technology and energy efficiency improvements (\u003cem\u003eTechnology\u003c/em\u003e) (Kriegler et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We combine all investigated demand-side measures under the ALL scenario and contrast them against a reference scenario (REF) assuming continuation of current policies and regulation.\u003c/p\u003e \u003cp\u003eWe assess the effect of stringent climate policies in line with the 1.5C target (1.5C) contrasted with a baseline scenario with continuation of national policies until 2030 and no stringent climate policies (NPi), based on existing scenarios from the IAM MESSAGEix-GLOBIOM (Riahi et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\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 interventions and policy instruments in the modelled 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\u003ePolicy focus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntervention type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolicy instruments\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemand-side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference (REF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuation of current demand-side interventions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCurrent policy instruments with current stringencies.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActivity (ACT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Reduce per-cap floorspace by 5% by 2050.\u003c/p\u003e \u003cp\u003e- Shift to multi-family housing\u003c/p\u003e \u003cp\u003e- Switch to more conservative temperature set-points, reaching \u0026minus;\u0026thinsp;1\u0026deg;C for heating and +\u0026thinsp;1\u0026deg;C for cooling by 2030 compared to 2015.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolicies limiting floorspace in new construction, policies limiting new construction of single-family housing, information campaigns.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElectrification and fuel shifts (ELE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Increase electrification rate in existing buildings.\u003c/p\u003e \u003cp\u003e- Ban fossil fuels in new construction and renovations.\u003c/p\u003e \u003cp\u003e- Phase-down coal and traditional biomass in individual heating systems.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSubsidies, fuel mandates.\u003c/p\u003e \u003cp\u003eFuel mandates, subsidies, and incentives, building codes, neighbourhood-based approaches.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnology (TEC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Mandatory \u003cem\u003eadvanced\u003c/em\u003e renovation (Global North only) and \u003cem\u003eadvanced\u003c/em\u003e new construction (all regions) as from 2030, including building shells and technical systems. \u003cem\u003eAdvanced\u003c/em\u003e corresponds to passive building standard for new construction in the Global North and energy savings for renovation of at least 40%.\u003c/p\u003e \u003cp\u003e- Increase in yearly renovation rates up to 3% in the Global North and 1.5% in the Global South.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBuilding codes and regulations, subsidies and incentives, energy performance certification.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCombined (ALL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCombination of all demand-side interventions above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCombination of the policy instruments above.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClimate policies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo stringent climate policy (NPi)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuation of current national policies until 2030; no additional stringent climate policies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCurrent policy instruments.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClimate policy (1.5C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNational policies until 2030; climate policies in line with the 1.5\u0026deg;C targets; cumulative CO\u003csub\u003e2\u003c/sub\u003e emissions (2020\u0026ndash;2100) 600 GtCO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCarbon taxes, supply-side oriented policies.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Model implementation of scenarios\u003c/h2\u003e \u003cp\u003ePolicy interventions are represented with specific model implementations and dynamics (see the Supplementary Information, section 2, for complete information). Activity reductions mostly concern exogenous model parameters. Scenarios are modelled by adjusting the relevant model parameters and exogenous projections, i.e. per-capita floorspace, share of multi-family housing, and set-point temperature. Electrification and fuel shifts are represented by introducing constraints in the relevant discrete choice models, i.e. on minimum electrification rates and uptake of fossil fuels-based heating systems in new constructions and renovations. Model constraints are also used to model technology and energy efficiency improvements, i.e. on minimum energy efficiency standards for new constructions and renovations, and minimum renovation rates.\u003c/p\u003e \u003cp\u003eClimate policies and energy supply-side interventions are modelled with the help of the soft-linkage with the MESSAGEix-GLOBIOM IAM. Energy price signals and CO\u003csub\u003e2\u003c/sub\u003e emission factors for electricity and district heating are determined based on a consistent set of scenario runs (Riahi et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) in MESSAGEix-GLOBIOM and fed into the STURM model (Supplementary Information, section 3). The effects on the model results are twofold. First, energy prices influence household investment decisions on energy efficiency, e.g. under higher prices, advanced energy efficiency options are favoured in the LCC calculations, and therefore energy demands for space heating and cooling. Second, scenario-consistent emission factors affect the resulting CO\u003csub\u003e2\u003c/sub\u003e emission projections.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Effects of demand-side interventions\u003c/h2\u003e \u003cp\u003eThe sectoral demand-side interventions drive major changes both in the future configuration of the global housing stock and the mix of energy carriers for space heating (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the reference scenario (NPi-REF), a stark increase in total floor space in the Global South is driven by population growth and increase in average affluence. As a result, the housing stock in 2050 is mostly composed of new buildings (70% of total floorspace). Conversely, in the Global North, most of the existing housing stock of 2015 is still standing in 2050 (64% of total floorspace), though a part of it is renovated under current policies. The NPi-ACT scenario results in a 14% reduction of total global floorspace compared to the NPi-REF, but no substantial changes in the mix of energy carriers. The NPi-ELE scenario entails a major shift in energy carriers for space heating, including significant increase in electrification and phase-out of fossil fuels, especially in the Global South. The NPi-TEC scenario is characterized by advanced technology solutions leading to higher shares of \u003cem\u003eadvanced\u003c/em\u003e new construction and renovations by 2050. In the Global North, acceleration of the deep renovation rates results in higher share of renovated buildings, and larger electrification due to the uptake of heat-pumps combined with higher insulation standards. The NPi-ALL scenario combines all considered demand-side interventions, resulting in both lower floorspace levels, and higher penetration of advanced new construction and renovation. Natural gas and other fossil fuels are reduced in the Global South and almost completely phased-out in the Global North, as a result of high electrification levels.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe final energy demand for space heating and cooling differs across the investigated scenarios in the Global South and Global North regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), resulting from the interplay of activity drivers, buildings stock dynamics, and energy efficiency improvements. In the reference scenario (NPi-REF), the energy demand for space heating in the Global South peaks around 2035 and then starts declining under energy efficiency improvements from continuation of current policies, offsetting the floor space growth. In the Global North, the total final energy demand for space heating is 53% lower in 2050 than in 2015 driven by energy efficiency improvements under current policies.\u003c/p\u003e \u003cp\u003eThe scenarios based on individual demand-side interventions reflect different potentials to reduce energy demand for space heating, the most effective being NPi-TEC (-30%), followed by NPi-ACT (-17%) and then NPi-ELE (-6%) compared to NPi-REF in 2050. The combination of all demand-side interventions (NPi-ALL) has the largest energy demand reduction potential for space heating (-44%), due to the high complementarity of the individual measures.\u003c/p\u003e \u003cp\u003eThe energy demand for space cooling is projected to double globally between 2015 and 2050 in the NPi-REF scenario, driven by growing access to air-conditioning and larger floor space in the Global South. In the Global North, the energy demand for cooling is lower due to different climatic conditions. Similar to space heating, the combination of demand-side interventions (NPi-ALL) can significantly reduce the global energy demand for space cooling by 38% in 2050 compared to the NPi-REF scenario, with different reduction potential in the Global South (-40%) and in the Global North (-30%). For space cooling, the global energy reduction provided by \u003cem\u003eActivity\u003c/em\u003e interventions (-22%) and \u003cem\u003eTechnology\u003c/em\u003e interventions (-25%) are comparable, highlighting the weightier role of behavioural aspects for this end-use.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Decarbonization pathways\u003c/h2\u003e \u003cp\u003eWe explore here the combined effect of demand-side interventions and climate policies consistent with the 1.5\u0026deg;C target towards residential space heating and cooling decarbonization. In the reference scenario (NPi-REF), global final energy demands for space heating and cooling reach 22.3 EJ/yr by 2050 (-31% compared to 2015) and global CO\u003csub\u003e2\u003c/sub\u003e emissions reach 1.44 GtCO\u003csub\u003e2\u003c/sub\u003e/yr (-37% compared to 2015). Demand-side interventions drive major energy demand reduction for heating and cooling, up to 61% in NPi-ALL relative to 2015, corresponding to 43% reduction compared to NPi-REF in 2050 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, left panel). Climate policies, through higher energy prices, only drive down energy demand by 37% in the 1.5C-REF scenario relative to 2015, and, combined with demand-side interventions (1.5C-ALL), only add marginal energy demand reductions to NPi-ALL, reaching 62% reductions.\u003c/p\u003e \u003cp\u003eThe major effect of the stringent climate policies is on CO\u003csub\u003e2\u003c/sub\u003e emission reductions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, right panel), through the decarbonization of district heating and the electricity supply. CO\u003csub\u003e2\u003c/sub\u003e emission reductions amount to 84% between 2015 and 2050 in the 1.5C-REF scenario, while the combination of all demand-side interventions alone cause only 61% CO\u003csub\u003e2\u003c/sub\u003e mitigation (NPi-ALL). The CO\u003csub\u003e2\u003c/sub\u003e emissions reduction associated with the NPi-ALL and 1.5C-REF scenarios are respectively 38% and 75% compared to the reference scenario NPi-REF in 2050 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Only the combination of all demand-side interventions and climate policies (1.5C-ALL) leads to emission levels closer to zero in 2050 (0.083 GtCO\u003csub\u003e2\u003c/sub\u003e/yr), with 96% reductions compared to 2015 and 94% cut compared to the NPI-REF scenario (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFinal energy and total (direct and indirect) CO\u003csub\u003e2\u003c/sub\u003e emission reduction potential in 2050 compared to the NPi-REF reference scenario.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScenario\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eFinal energy reduction potential in 2050 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e emission reduction potential in 2050 (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlobal North\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlobal South\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGlobal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGlobal North\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGlobal South\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPi-ACT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPi-ELE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPi-TEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNPi-ALL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e42\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1.5C-REF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e66\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1.5C-ALL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e94\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e94\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe analysis of CO\u003csub\u003e2\u003c/sub\u003e emissions by region, end-use and emission type \u0026ndash; direct from fossil fuel burning in buildings and indirect from district heating and electricity supply \u0026ndash; provides further important insights on the mitigation pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the Global South, CO\u003csub\u003e2\u003c/sub\u003e emissions from space heating and cooling increase by 13% by 2050 in the NPi-REF scenario and constitute more than half of global emissions, mostly due to tripling indirect emissions for space cooling under stark demand increase. CO\u003csub\u003e2\u003c/sub\u003e emissions for space heating decrease by 29%, mostly driven by the decarbonization of the electricity system and district heating. The combination of sectoral interventions (NPi-ALL) leads to 28% reduction in 2050 compared to 2015 in CO\u003csub\u003e2\u003c/sub\u003e emissions for space heating and cooling in the Global South. Reductions are driven by lower energy demand levels, and fossil fuel switches, resulting in lower emissions from cooling and direct emissions for heating. The decarbonization of the supply system under 1.5\u0026deg;C-consistent climate policies (15C-REF), contributes to neutralizing indirect CO\u003csub\u003e2\u003c/sub\u003e emissions, while the direct emissions for heating are significantly reduced only under the combined demand-side interventions scenario (15C-ALL), bringing down total emissions by 94% compared to 2015.\u003c/p\u003e \u003cp\u003eIn the Global North (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, right panel), significant reductions in CO\u003csub\u003e2\u003c/sub\u003e emissions are expected until 2050 already in the NPi-REF scenario (-57% compared to 2015), mostly due to reduced direct emissions for space heating driven by increase in energy efficiency under current policies. While major reductions in indirect CO\u003csub\u003e2\u003c/sub\u003e emissions are achievable under more stringent climate scenarios (1.5C-REF), only the sectoral interventions drive the abatement of direct emissions for space heating (15C-ALL), up to a total 97% reduction of direct and indirect emissions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCompared to CO\u003csub\u003e2\u003c/sub\u003e emission levels in 2015, in the NPi-REF scenario (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, top panel) most countries in the Global South increase CO\u003csub\u003e2\u003c/sub\u003e emissions by 2050, largely driven by space cooling demand increase, with the notable exception of China. India is contributing the largest increase in CO\u003csub\u003e2\u003c/sub\u003e emissions (+\u0026thinsp;79 MtCO\u003csub\u003e2\u003c/sub\u003e), quintuplicating the emissions between 2015 and 2050. In contrast, most the Global North and China, experience significant CO\u003csub\u003e2\u003c/sub\u003e emission reductions. The largest absolute reduction potential by 2050 include China (-224 MtCO\u003csub\u003e2\u003c/sub\u003e, corresponding to -42%) and the USA (-294 MtCO\u003csub\u003e2\u003c/sub\u003e, corresponding to -61%). The picture changes in the most ambitious scenario 1.5C-ALL (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, bottom panel), where all world regions reduce CO\u003csub\u003e2\u003c/sub\u003e emissions for space heating and cooling by 2050. The countries with the largest reduction potential between 2015 and 2050include China (-500MtCO\u003csub\u003e2\u003c/sub\u003e, corresponding to -95%), USA (-475MtCO\u003csub\u003e2\u003c/sub\u003e, corresponding to -98%) and Russia (-161MtCO\u003csub\u003e2\u003c/sub\u003e, corresponding to -91%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Policies to deliver demand-side driven climate mitigation","content":"\u003cp\u003eClosing the gap between the model-estimated theoretical potential for climate change mitigation and the realized emission reductions has been under scrutiny and shown to be challenging for multiple reasons. This study estimates an energy demand reduction potential of 43% and a CO\u003csub\u003e2\u003c/sub\u003e emission mitigation potential of 38% resulting from the combination of all building sector interventions (NPi-ALL scenario) relative to the reference (NPi-REF) scenario in 2050. Market forces alone fail to deliver deep transformations in residential space heating and cooling demands because of diverse market barriers, market inertia, the fragmentation of the user side and the market of supply, the lock-ins of infrastructure, social norm impacts, and split incentives (Golove and Eto \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Wilson et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Cristino et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A wide range of public policies in the building sector are already well-known and implemented to address these barriers in countries all around the world (Nascimento et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; IEA \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). However, the focus of sectoral policies has been traditionally on supply and end-use technology solutions, and the potential of socio-behavioural and infrastructural interventions is gaining attention only recently (Rosenow et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Maor and Howlett \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The broader set of interventions and their combinations assessed in this study can deliver impacts towards reaching net-zero targets via a number of policy instruments ranging from already implemented measures to opportunities for scaling-up.\u003c/p\u003e \u003cp\u003ePolicies related to \u003cem\u003eactivity level reductions or sufficiency\u003c/em\u003e are underrepresented in decision making as much as in modelling studies (Samadi et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), even though we show here that such measures have a potential of 18% CO\u003csub\u003e2\u003c/sub\u003e emission reductions, and would also address the issue of fair consumption of space and resources (Cabeza et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). (Best et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) identified over 45 sufficiency measures aimed at avoiding consumption in the building sector in Europe, including regulatory and financial measures, such as bonuses for switching to smaller apartments, limitations to secondary and holiday homes to avoid sprawl of vacant living spaces. Policies targeting voluntary change of behaviour through awareness raising and information programs were found to have moderate effectiveness and lower costs compared to standards or financial instruments (Boza-Kiss et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The limitations of sufficiency policies lie in the low level of acceptance by the target groups (Akenji, Lewis, et al. 2021) and the potential for rebound effects (Sorrell et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While activity level reductions directly help reduce emissions, related policies are appropriate for overconsumption and to reduce inequality. On the other hand, it is important to stress that providing adequate services, such as access to minimum housing as defined e.g. through decent living standards (Rao and Min \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), can become part of successful policies, exemplified by national cooling action plans, which prioritizes passive and low-cost measures to achieve the reduction of space cooling to ensure the material preconditions for human wellbeing (Hu et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eElectrification and fuel shifts\u003c/em\u003e have become a critical component of emission reduction, sometimes even contrasted to energy efficiency solutions. Policies related to electrification, fuel shifts, and rolling-out renewable technologies were introduced progressively since the 1950s, increasing in the 1970s, and peaking in the early 2000s, with another boost in the 2020s (IEA \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). Phasing out fossil fuels for heating can be achieved in different ways, but must combine incentivizing electrification with restraining fossil fuel solutions (Braungardt et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Electrification of the buildings at the household or district level distils largely to heat pumps as the main technology pathway. Many European countries opt for mandatory regulations, such as banning of new gas, oil, and coal heating systems boilers, swaps of existing heaters, and combined with financial incentives (Braungardt et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Torn\u0026eacute; and Trutnevyte \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the Global South, e.g. Brazil and India measures to connect urban space heating and cooling with industrial waste heat or co-generation at a district level have been on the rise (IRENA, IEA and REN21 2018). Though policies are varied for demand electrification, our study has shown a limited impact or only \u0026minus;\u0026thinsp;6% and \u0026minus;\u0026thinsp;3% energy demand reduction compared to NPi-REF in 2050 for Global South and Global North, respectively. Further to fuel shifts, the recent report of the Intergovernmental Panel on Climate Change (IPCC) estimates that renewable energy policies contribute 9% of the total potential of emission reduction (IPCC 2023).\u003c/p\u003e \u003cp\u003ePolicies to \u003cem\u003eimprove technologies and their adoption\u003c/em\u003e have the longest history and can achieve CO\u003csub\u003e2\u003c/sub\u003e emission reduction potentials of 27% in 2050, as shown by our results. Building codes and standards are among the most widely adopted policy instruments, reported in 79 countries in 2022 (40% of all nations) (IEA \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; UNEP \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, only 26% of all countries use mandatory codes for both residential and non-residential buildings (UNEP \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Building codes and product standards deliver the highest net savings at the societal level, however their emission saving potential strongly depends on the effectiveness of enforcement (Boza-Kiss et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In response to the Paris Agreement, there has been a growing interest to develop building codes that deliver towards the net zero emission commitments (UNEP \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), including enforcement of passive level standards, public building demonstration sites, and programs for exemplary buildings (Brussels Environment \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Renovation rates, however, are still too low due to technical, financial and social barriers, such as principal agent issues (\u0026Aacute;stmarsson et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; D\u0026rsquo;Oca et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Targeted policies are needed to overcome these barriers and accelerate and increase the depth of interventions to deliver the necessary energy savings (Baek and Park \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In developing countries, upscaling the uptake of affordable low-energy buildings through targeted policies can contribute not only to reduce energy and GHG emissions, but also to bridge the current housing gaps and promote the sustainable development agenda (Bulkeley et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mastrucci and Rao \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e"},{"header":"6. Discussion and conclusions","content":"\u003cp\u003eThis study explored the effect of a broad set of interventions for decarbonization of the global residential sector, focusing on space heating and cooling and accounting for both demand-side interventions and ambitious climate policies. The results showed that demand-side interventions centred on activity reduction, electrification and fuel shifts, and technology improvements are highly complementary and, when combined, entail the highest energy demand reduction potential, up to 61% in 2050 compared to the base year 2015, even in the absence of supply-side interventions and carbon taxes. Stringent climate policies, enabling the decarbonization of the electricity supply system, are critical for CO\u003csub\u003e2\u003c/sub\u003e emission abatement. However, this study showed that only the combination of demand-side interventions and stringent climate policies enables achieving future CO\u003csub\u003e2\u003c/sub\u003e emission levels close to net-zero, with reductions up to 96% by 2050 compared to 2015. Reaching full carbon neutrality would require additional efforts to abate the residual direct CO\u003csub\u003e2\u003c/sub\u003e emissions, e.g. due to remaining use of gas and other fossil fuels for space heating.\u003c/p\u003e \u003cp\u003eOur results are well aligned with those of available residential global space heating and cooling projections (Chatterjee et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Daioglou et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Camarasa et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The estimated mitigation potentials in this study are more conservative compared to the findings of the recent report of the IPCC on climate change mitigation (Cabeza et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). We found a global CO\u003csub\u003e2\u003c/sub\u003e emission reduction potential of 38% from demand-side measures alone (NPi-ALL scenario) compared to our reference (NPi-REF) scenario in 2050, whereas a global mitigation potential of 61% was estimated in the IPCC report compared to baseline scenarios, by aggregating the results of available bottom-up studies. However, the IPCC findings relate to the entire building sector while this study focuses on residential space heating and cooling only. Overall, these findings are consistent in highlighting that a broader range of policies beyond technology and energy efficiency improvements, including activity reductions and fuel shifts, would be needed achieve the required transformations and achieve ambitious climate goals. While there are many barriers and uncovered policy areas, we also found many successful examples of policies pursuing the directions indicated in this paper. In general, policy instruments rarely work well when isolated while policy mixes deliver larger change and can address complex policy goals (Irrek and Jarcynski \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Givoni et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Boza-Kiss et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study contributes to advancing climate change mitigation assessment for the building sector in three ways: 1) by improving the representation of the global building sector in the context of IAM, via enhanced model granularity and dynamics, including building stock turnover and energy efficiency investments; 2) by providing evidence on the effect of a broad set of demand-side mitigation policies encompassing activity reduction, fuel shifts and technology improvements; 3) by showing the interaction of these demand-side policies with energy supply-side policies and combined effects on energy demands and CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e \u003cp\u003eFuture developments will focus on expanding the model to cover other energy services, e.g. cooking and appliances, and the commercial sector. Accounting for the material dimension of buildings is critical for assessing the broader effect of mitigation policies while considering all stages of the building life cycle beyond operational energy, e.g. including the impacts of material production as effect of building activities. With improved data availability and empirical evidence on the effect of different interventions in different contexts, the representation of heterogeneities and building dynamics can be improved to better reflect local contexts. Coupling with models focusing on specific aspects, e.g. social and behavioural aspects, can contribute to overcoming current limitations in the exogenous representation of some key dimensions, e.g. behaviour and lifestyle changes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis study has received funding from the European Union\u0026rsquo;s Horizon 2020 research and innovation programme under grant agreements no. 821124 (NAVIGATE) and 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\u003ch2\u003eAuthor contributions:\u003c/h2\u003e \u003cp\u003eAlessio Mastrucci: Conceptualization, Methodology, Formal analysis, Investigation, Writing \u0026ndash; Original draft, Visualization\u003c/p\u003e\u003ch2\u003eData Availability:\u003c/h2\u003e \u003cp\u003eThe datasets generated during the current study are available in the Supplementary Information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkenji L, Bengtsson, Magnus, Toivio V et al (2021) 1.5-Degree Lifestyles: Towards A Fair Consumption Space for All. 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Energy Res Social Sci 7:12\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.erss.2015.03.002\u003c/span\u003e\u003cspan address=\"10.1016/j.erss.2015.03.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"climatic-change","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clim","sideBox":"Learn more about [Climatic Change](https://www.springer.com/journal/10584)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/clim/default.aspx","title":"Climatic Change","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Buildings, Climate change mitigation, Energy demand, Integrated assessment modelling","lastPublishedDoi":"10.21203/rs.3.rs-4253226/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4253226/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccounting for 21% of global greenhouse gas (GHG) emissions, buildings play a crucial role in climate change mitigation. Demand-side interventions entail large energy and GHG emission reduction potentials. The effects of broader mitigation policies at the global level beyond energy efficiency improvements, including sufficiency and structural changes, and their interaction with cross-sectoral climate policies are, however, still unclear. Here, we assess a comprehensive set of scenarios to reduce residential space heating and cooling emissions towards net-zero targets. We find that activity reductions, fuel shifts, and technological improvements can reduce current global residential space heating and cooling CO\u003csub\u003e2\u003c/sub\u003e emissions by 61% until 2050. Combining these demand-side interventions and stringent climate policies entails up to 96% reduction of current CO\u003csub\u003e2\u003c/sub\u003e emissions by 2050. Neutralizing residual direct CO\u003csub\u003e2\u003c/sub\u003e emissions due to fossil fuels for space heating would require additional interventions.\u003c/p\u003e","manuscriptTitle":"Towards net-zero emissions in global residential heating and cooling: a global scenario analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-14 20:47:56","doi":"10.21203/rs.3.rs-4253226/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-05-07T15:47:09+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-07T12:11:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-16T13:03:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Climatic Change","date":"2024-04-11T11:05:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"climatic-change","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clim","sideBox":"Learn more about [Climatic Change](https://www.springer.com/journal/10584)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/clim/default.aspx","title":"Climatic Change","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c8422104-c5e3-490c-ac24-8c286da99aff","owner":[],"postedDate":"May 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-21T16:06:04+00:00","versionOfRecord":{"articleIdentity":"rs-4253226","link":"https://doi.org/10.1007/s10584-025-03923-6","journal":{"identity":"climatic-change","isVorOnly":false,"title":"Climatic Change"},"publishedOn":"2025-04-15 15:57:42","publishedOnDateReadable":"April 15th, 2025"},"versionCreatedAt":"2024-05-14 20:47:56","video":"","vorDoi":"10.1007/s10584-025-03923-6","vorDoiUrl":"https://doi.org/10.1007/s10584-025-03923-6","workflowStages":[]},"version":"v1","identity":"rs-4253226","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4253226","identity":"rs-4253226","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}