Existing demand-side climate change mitigation policies neglect avoid options

Existing demand-side climate change mitigation policies neglect avoid options | 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 Existing demand-side climate change mitigation policies neglect avoid options Alina Brad, Etienne Schneider, Christian Dorninger, Willi Haas, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5998199/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Demand-side options are increasingly recognized for their potential to mitigate climate change while reducing reliance on novel carbon dioxide removal. However, systematic analyses of implemented demand-side mitigation policy mixes remain scarce, compromising assessment and exploration of effective and feasible demand-side policies. Here, we provide a multilevel analysis of the evolution, composition, and foci of demand-side mitigation policy mixes in the transport and housing sector from 1995 to 2024, focusing on the EU, the federal Austrian and two provincial levels (Vienna, Lower Austria). Our high-resolution policy database features 351 demand-side measures, systematically classified according to policy target, instrument type, and the avoid-shift-improve framework. We find that existing policy mixes heavily rely on shift and improve measures, critically neglecting mitigation potentials of avoid options as well as certain policy areas. This suggests an urgent need to broaden demand-side policy mixes and explore strategies that increase the political feasibility of avoid options. Environmental Policy demand-side mitigation climate policy avoid-shift-improve Austria transport housing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction In climate change mitigation research, demand-side options are increasingly considered as a crucial supplement to technology-focused supply-side measures. They can serve to reduce the reliance on novel but potentially risky carbon dioxide removal methods in climate stabilization scenarios consistent with the Paris Agreement 1–4 as well as to mitigate the material impact of energy transitions 5 . However, despite surging research highlighting major mitigation potentials of demand-side options 6,7 , systematic analyses of actually realized or implemented demand-side mitigation policy are lacking. As a result, the current status of demand-side climate policies remains poorly understood, obstructing assessment and exploration of effective demand-side climate policies. Hence, there is a need to assess existing demand-side climate policy mixes, their evolution, composition and foci, in order to improve climate-policy making as well as scenario development 2,8 . Demand-side options refer to policies and measures designed to reduce material and energy consumption, along with associated greenhouse gas (GHG) emissions, by altering final demand for goods and services, without compromising or potentially improving well-being 9 . These options typically target adoption and choices of technologies in consumption, consumption and lifestyle patterns, as well as related systems of service provisioning and interconnected production-consumption infrastructures 2,9 . Demand-side mitigation options can be categorized into improve, shift and avoid measures 9 . Improve measures aim at enhancing the material and energy efficiency of existing technologies, modes of service provisioning, and end user adoption, such as driving less emission-intensive cars. Shift measures promote the switch to modes of service provisioning that are less emission-intensive per unit of service provided, e.g., using public transport. Avoid measures focus on avoiding demand for certain goods and services altogether, by redesigning service provisioning systems to reduce unnecessary resource consumption, e.g., by reducing distances travelled. While many consumption choices are ultimately made by individuals or households (e.g., which car to buy), these choices are strongly influenced by social norms and political decisions at various political levels that shape the available infrastructure (e.g., public transport availability, quality, and costs), economic incentives, and regulatory frameworks 10–12 . Approximately two-thirds of global GHG emissions are directly or indirectly linked to household consumption 13–16 with the remainder attributed to emissions from government infrastructures (e.g., hospitals) and capital investments. Therefore, literature on demand-side options highlights significant untapped mitigation potential, particularly if a comprehensive and targeted policy mix encompassing improve, shift, and avoid measures is implemented 10,17 . The most effective mix of improve, shift, and avoid measures is suggested to vary across sectors. High improve and shift potentials have been identified in the housing sector (e.g., refurbishment and renovation, shift to renewable energy), while high shift and avoid potentials are found in the transport sector (e.g., shift to public transport, avoid long-haul aviation, promote short-distance urban structures) 10,14 . However, while most improve measures are deemed more politically feasible, they are also susceptible to being undermined by rebound effects 18,19 . In contrast, many shift and, particularly avoid measures are considered unpopular and risky, as they often involve breaking entrenched routines or disrupting dominant modes of living 10,20,21 . Correspondingly, initial research on (implicit) demand-side policies in four OECD countries (France, Germany, Norway, Sweden) suggests that policy mixes have been skewed towards efficiency measures 10,13,19 . However, despite the prominence of demand-side options in the recent IPCC report 22 , the evolution and status of actual demand-side policy instruments and mixes have not yet been systematically analyzed using the avoid-shift-improve framework. Meanwhile, research on climate and energy transition policy has increasingly paid attention to policy mixes and their characteristics, such as coherence, comprehensiveness, and synergies, as well as temporal dynamics and patching 17,23–27 . Nonetheless, much of this work has primarily focused on supply-side measures, leaving demand-side mitigation policy mixes and methodologies for assessing them largely unexplored 28 . Advancing demand-side mitigation options, therefore, requires a deeper understanding of the policy mixes currently in place. Here, we provide a longitudinal, multilevel analysis of the evolution, composition, and foci of demand-side climate change mitigation policies for the case of Austria from 1995 to 2024 (excluding December) in the transport and housing sector. As in other OECD countries 13 , these two sectors are responsible for the majority of Austria’s household GHG footprint emissions (59.7% in 2022, Fig. 1 ), which in turn account for the majority of total footprint emissions and fall only slightly short of total production-based emissions ( Fig. 2 ). Our sample includes policies and major public investment decisions aimed at influencing investment or consumption choices of households, or households’ behaviour, in ways that reduce household GHG emissions (see Supplementary Methods for details on our classification and coding approach). We systematically coded, mapped and classified policies according to policy target, instrument type, and the demand-side mitigation option pursued, discerning improve, shift and avoid measures based on the established avoid-shift-improve framework ( Table 1 ). Focusing on a single country allowed us to zoom in on the multilevel nature of demand-side policy mixes over time, encompassing the EU, national, and provincial levels (selecting Austria’s two most populous provinces Vienna and Lower Austria). This approach enabled us to investigate how demand-side policy mixes vary across policy levels based on a high-resolution policy database. In contrast to large cross-national climate policy mix assessments that rely on established databases, such as the OECD Climate Policy Database e.g., 31 , which lists merely 12 policies for Austria between 1995 and 2024, our database includes a total of 185 federal-level policies for the same period. By additionally incorporating EU and provincial-level policies, we created a sample of 231 demand-side climate policies in the transport sector and 120 in the housing sector, respectively. Our study provides novel evidence on what demand-side policies are already represented in the transport and housing sector across different policy levels over time. This allows us to identify relevant gaps in sectoral demand-side policy mixes and to discuss our findings against the background of newly available data on carbon inequality trends in Austria from 2000 to 2020 32 , along with broader political implications. Table 1 : Demand-side mitigation policy classification based on Creutzig et al. 2022 9 Avoid Shift Improve Transport Policies aimed at avoiding or reducing mobility and related emissions, e.g., teleworking or compact city structures Policies promoting switches from the private car to other modes of transport (public transport, cycling, walking) or car-sharing Policies aimed at encouraging choices for smaller or more efficient combustion engines or using sustainable fuels, or encouraging end-user adoption of electric vehicles Housing Policies aimed at avoiding demand for space conditioning services and carbon-intensive building materials through building design (e.g., passive houses) or adapting dwelling size to household size; or aimed at avoiding household energy demand through behavioural energy saving Policies enhancing the access to or support the switch to renewable energy, including the electrification of cooking and water heating with green power Policies aimed at improving energy efficiency of new buildings and existing building stock or of household appliances Demand-side policy mixes exist but remain partial Among the 351 demand-side mitigation policies identified in the transport and housing sectors, 215 are shift measures and 121 are improve measures. By contrast, avoid options are largely neglected in both sectors. In the transport sector, shift measures constitute 64.5%, improve measures 32.5%, and avoid measure only 3%. Similarly, in the housing sector, shift measures account for 55%, improve measures 38.3%, and avoid measures 6.7% ( Fig. 3 ). Transport sector: Targeting cars and modal shifts, neglecting avoid options and air travel In the transport sector, private cars (including battery electric vehicles) were the most frequently targeted focus of policies, accounting for 47.6% of all measures. Most improve policies aimed to reduce the attractiveness of driving or buying cars with large or inefficient internal combustion engines. These policies, particularly prevalent in the policy mix in the period from 1995 to 2010, increased fuel taxes, introduced tax benefits for low-CO 2 -emission cars (as part of an engine capacity-related insurance tax), and integrated a fuel combustion and CO 2 -emission-based component into the motor vehicle registration tax ( Fig. 4 ). Measures that increased the cost of driving internal combustion engine cars may have indirectly encouraged households to adopt alternative transport modes. However, these were categorized as improve measures, as their primary objective was to incentivize the purchase of more fuel-efficient cars – at least for households that could afford them. Especially since the mid-2010s, additional improve measures directly targeted the adoption of electric vehicles. These measures have predominantly relied on price premiums, tax exemptions, investments in the deployment and accessibility of charging infrastructure, and other forms of regulatory privileging, such as designated parking spaces. Shift measures targeting private cars primarily aimed at disincentivizing their use, rather than promote the adoption of more efficient vehicles, thus encouraging a transition to alternative transport modes. A key example of these measures is the introduction of short-term parking zones and parking fees in Vienna in 1995, followed by their gradual expansion, which increased the attractiveness of other transport options for inner-city mobility. Beyond discouraging private car use, policies aimed at promoting shifts toward other transport modes – primarily public transport and cycling, but also walking and car-sharing – constitute a significant proportion of demand-side measures in the transport sector ( Fig. 3 ). In Vienna, a distinct policy pattern emerged combining push measures such as parking zones and fees, with pull measures encouraging shifts to alternative transport modes 33 . These pull measures include investments in public transport infrastructure, cycling and park-and-ride facilities, subsidies for cargo e-bikes, and the introduction of the highly affordable 365-Euro annual public transport ticket. We identified relatively few policies aimed at promoting train travel beyond urban public transport ( Fig. 3 ). These include significant investment plans to expand and enhance rail infrastructure and the introduction of the KlimaTicket in 2021, a yearly flat-rate ticket covering all rail and public transport services. However, many policies explicitly disincentivizing private car use may also implicitly encourage increased train travel. Stunningly, we only found four demand-side climate policies addressing air travel. Overall, the transport sector exhibits a relatively extensive policy mix emphasizing shift measures, aimed at encouraging individuals to transition away from private car use toward alternative modes of transport. Beyond the growing focus within improve measures on promoting the adoption of electric vehicles, we could not identify substantial changes in the focus of the policy mix over time. Simultaneously, certain areas (notably air travel) and policy options (particularly avoid measures) have remained largely neglected. The avoid measures identified were predominantly included in strategy or planning policy documents, i.e., declarations of intent to explore options for reducing mobility needs through spatial planning and promoting teleworking. Housing sector: Improving efficiency, shifting to renewables, omitting avoid potentials Improve and particularly shift measures also dominate the policy mix in the housing sector ( Fig. 3 ). The strong presence of shift measures is largely attributable to federal and provincial subsidies for photovoltaic systems, solar thermal energy, heat pumps, and biomass-based heating systems, introduced in the mid- to late 2000s ( Fig. 5 ). These subsidies were typically renewed annually over extended periods and were complemented by bans on fossil fuel-based heating systems at both federal and provincial levels, initially targeting coal and oil, and most recently, natural gas. Additional shift measures include information campaigns and regulatory instruments, such as building standards and the suspension of permit requirements, to facilitate the transition to renewable energy and heating options. Improve policies primarily targeted on enhancing the energy efficiency of buildings. Central to these efforts were federal and EU building and construction regulations, alongside the mandatory provision of energy performance certificates for houses and apartments. A smaller subset of improve measures aimed at directing household demand towards more efficient household appliances, mostly through information campaigns. The few avoid measures identified, slightly more prevalent in the housing sector’s policy mix than in transport, primarily aimed to raise public awareness of energy-saving practices or to promote passive houses through regulatory standards (e.g., introducing quality standard labels) and subsidies for buildings with very low energy consumption indicators. Notably, these measures aimed at avoiding energy consumption rather than GHG emissions associated with carbon-intensive construction materials. Only within the past two years were additional avoid measures introduced at the EU and federal levels to encourage the repair of goods using regulatory and economic instruments. Remarkably, no avoid measures were found that directly addressed the avoidance of new construction. Discussion Our country-focused, high-resolution analysis of demand-side policy mixes indicate that actually implemented demand-side measures have remained partial, with a notable neglect of avoid options. Although the Green Party's first participation in the national government led to a surge in demand-side policymaking, accompanied by significant reductions in production-based GHG emissions (-5.8% in 2022, -6.4% projected for 2023), the dominant policy focus remained on shift and improve measures persisted, alongside continuously high household GHG footprint emissions (Fig. 2 ). Sectoral trends in household GHG footprint emissions (Fig. 4 , Fig. 5 ) suggest, however, that the lack of avoid measures has been particularly impactful in the transport sector. Despite notable reductions in the mid-2000s and 2010s, mobility-related household emissions in 2022 remain significantly higher (17.6%) than in 1995. By contrast, housing-related household footprint emissions have experienced a considerable decline, falling well below the 1995 level (-33.4%). While direct attribution of emission reductions to specific policies is beyond the scope of this paper, these trends indicate that shift measures, which incentivize and enable households to transition to renewable energy, along with improve measures aimed at increasing buildings energy efficiency, have been relatively effective, despite counterproductive subsidies (specifically an industry-funded subsidy for oil-fired heating systems). Direct housing-related emissions (i.e., fuel use) correspondingly saw the most significant reduction, both in absolute terms and in their relative contribution to overall household footprint emissions 32 (from 12.1% in 1995 to 8.2% in 2022) (Fig. 1 ). Meanwhile, mobility-related emissions have remained the largest contributor to household GHG emissions, with their share increasing from 25.6% in 1995 to 33% in 2022 (Fig. 1 ). This contrasts with the significantly higher density of demand-side policies in the transport sector compared to the housing sector (231 as opposed to 120 policies in housing). A variety of policies has been implemented that make driving fuel-inefficient cars more expensive, incentivize the adoption of electric vehicles, and encourage shifts to alternative transport modes. However, these measures appear to have been partially undermined by insufficient policy integration, including counterproductive subsidies such as the design of the commuter allowances, the diesel tax privilege, and tax benefits for company cars 34 , 35 . Moreover, our findings suggest that household demand for air travel has remained hardly addressed by existing demand-side policies, echoing results from other country-focused studies 36 . Overall, stubbornly high mobility-related household emissions highlight the urgent need to exploit the mitigation potentials of avoid measures identified for this sector 6 , 14 . The significantly above-average income elasticity of mobility demand highlighted in research on carbon inequality in high-income countries, including Austria 32 , 37 , 38 , and the lack of demand-side policies addressing air travel, suggests that avoid measures could unleash special potential when targeting mobility patterns of high-income groups, e.g., through levies on frequent flying, or a ban of short-haul flights or private jet flights. Such a focus could increase the political feasibility of avoid measures, often deemed unpopular or risky 10 , 39 , as their costs remain concentrated on a relatively small subset of high-emitting households, and particularly a ban of private jet flights enjoys broad support across party affiliations in several EU countries 40 . Concomitantly, other avoid options increasing well-being or providing co-benefits by lowering mobility needs through urban design or teleworking, could target a wider spectrum of households across various income levels 9 , 41 . While continuously high mobility-related emissions underscore the urgent need for avoid measures in the transport sector, our findings also suggest that the potential for avoid options in the housing sector have not yet been fully leveraged 42 , 43 . For example, policies could incentivize and support the adjustment of household to dwelling size through relocation grants, for instance after children have moved out cf. 44 . Additionally, strategies that increase political feasibility of avoid options by selectively targeting high-income lifestyles are conceivable, e.g., limiting excessive floor space per capita as a major contributor to household GHG emissions 32 , or avoiding new construction and the associated use of carbon intensive-building materials by mobilizing existing housing stock through effective vacancy fees. While infrastructure investment constitutes a significant portion of shift measures, particularly in the transport sector, our findings also show that both improve and shift measures heavily rely on economic instruments (Fig. 3 ). Worryingly in light of growing concerns over popular backlash to climate policy 45 – 47 , however, the composition of these measures suggests that they tend to make emission intensive practices (e.g., driving fuel-inefficient cars) more expensive, but access to subsidy schemes promoting the adoption of low-carbon alternatives (e.g., electric vehicles, renewable energy heating systems in privately owned homes) has tended to be biased towards high-income households that can afford such investments 48 . While this may be less problematic in urban areas with existing infrastructure that facilitate modal shifts, as evidenced here by Vienna, it is specifically concerning with respect to low-income households in rural areas, who face rising costs but lack enabling conditions for modal shifts. Our findings therefore also indicate an urgent need to make demand-side subsidy schemes more inclusive, ensuring broader access for low- and medium income households. Conclusion While a surging body of literature points to the potentials of demand-side option for climate change mitigation 6 , 10 , our study shows that current multi-level demand-side policy mixes remain partial, particularly lacking avoid options. Further research is needed to examine whether similar biases exist in other sectors beyond transport and housing, and beyond our case study focus on Austria. Additional studies are also required to identify what factors explain the lack of avoid options in demand-side policy mixes. Literature on demand-side solutions highlights concerns over controversiality and popularity of such measures, especially given political sensitivities surrounding governmental initiatives to alter individual behaviour 10 , 19 . However, political economy factors, such as vested interests, may also be at play 6 , e.g., when measures to avoid new building construction evidently collide with the priorities of the construction industry. In any case, our findings point to the need to better understand what factors determine the popularity and political feasibility of avoid measure, especially given imminent or actual climate policy backlash 45 , 46 . This is particularly crucial, as emerging contributions on political strategies for climate policy 39 highlight broad support for supply-side measures like green industrial policy subsidies, but have largely overlooked demand-side options cf. 40 . To design feasible avoid measures as part of comprehensive demand-side policy mixes, stressing positive effects on human well-being 9 and participation 10 is essential. Equally important are novel findings on public support for sufficiency or degrowth measures 49 , 50 and how such support can be actively shaped 51 , as well as dissociating avoid measures from generalizing notions of renunciation, except when setting upper limits specifically targeting high-income, emission-intensive lifestyles 52 – 54 . 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Supplementary Files SupplementaryMethods.docx SourceData1Demandsidepolicydata.xlsx SourceData2calculationsofhouseholdfootprintGHGemissions.xlsx SourceData3exiobaseaggregates.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5998199","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":413564349,"identity":"1042624b-d6a8-494a-9596-c0ef852bb030","order_by":0,"name":"Alina Brad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYDACdsYGIHkAiJOBhAEDD2EtzHAtaQnIWgzwaAGTIC05KKpwa+FvZm578IPhTj5/e8436YKCwzL8DMwPPzDU/MGpReIwY7thD8Mzyxln3m6TnmFwmEeygc1YguEYHocdZmyT4GE4bMBwI3ebNI9BGo/BAQYzBgY23FrkgVok/wC1yN/IeQbWYn+A/RsDwz/cWgyAWqRBthjcyGEDarHhAQaaGQNjG24thiAtMgbPDAzPPDO2BmmROMxTLJHYZ4xTi9zx9meSbyruGMgdT354m+ePhD1/e/vGDx++yeH2PsR5yBxQTCUQ0DAKRsEoGAWjAD8AAACwSOipyCwsAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-5342-1632","institution":"University of Vienna","correspondingAuthor":true,"prefix":"","firstName":"Alina","middleName":"","lastName":"Brad","suffix":""},{"id":413565351,"identity":"b97eb3b0-a4d8-4958-8056-aec63d505b51","order_by":1,"name":"Etienne Schneider","email":"","orcid":"https://orcid.org/0000-0002-4243-7337","institution":"University of Vienna","correspondingAuthor":false,"prefix":"","firstName":"Etienne","middleName":"","lastName":"Schneider","suffix":""},{"id":413565352,"identity":"e73ba72e-e83e-48b3-8474-2186d90c3b35","order_by":2,"name":"Christian Dorninger","email":"","orcid":"https://orcid.org/0000-0001-5203-8471","institution":"University of Natural Resource and Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Dorninger","suffix":""},{"id":413565353,"identity":"95b27347-b435-41a4-a0d2-ddb798fb0dca","order_by":3,"name":"Willi Haas","email":"","orcid":"https://orcid.org/0000-0001-5599-9227","institution":"University of Natural Resource and Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Willi","middleName":"","lastName":"Haas","suffix":""},{"id":413565354,"identity":"ae1cc5dd-aec8-4bb4-9cd7-d6eb833c0e2a","order_by":4,"name":"Carolin Hirt","email":"","orcid":"","institution":"University of Vienna","correspondingAuthor":false,"prefix":"","firstName":"Carolin","middleName":"","lastName":"Hirt","suffix":""},{"id":413565355,"identity":"90670345-9c03-4726-a786-b66e159ec280","order_by":5,"name":"Dominik Wiedenhofer","email":"","orcid":"https://orcid.org/0000-0001-7418-3477","institution":"University of Natural Resource and Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Dominik","middleName":"","lastName":"Wiedenhofer","suffix":""},{"id":413565356,"identity":"38d4765e-eb7f-4117-adb9-73ac5e4e767e","order_by":6,"name":"Simone Gingrich","email":"","orcid":"https://orcid.org/0000-0001-7891-8688","institution":"University of Natural Resource and Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Simone","middleName":"","lastName":"Gingrich","suffix":""}],"badges":[],"createdAt":"2025-02-10 10:25:23","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5998199/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5998199/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76778166,"identity":"32c24f49-1866-439c-8fb1-92778cccecd9","added_by":"auto","created_at":"2025-02-20 15:45:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":140305,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProduct group composition of the GHG footprint of Austrian households for 1995, 2000, 2005, 2010, 2015, 2020, and 2022.\u003c/strong\u003e Calculations are based on input-output tables of EXIOBASEv.3.8.2\u003csup\u003e29\u003c/sup\u003e, enhanced with Austrian production-based GHG emissions data from the Environment Agency Austria\u003csup\u003e30\u003c/sup\u003e (see Supplementary Methods). Indirect housing GHG emissions comprise electricity, residential buildings operating services, district heating, production of heating fuels, materials for housing maintenance etc. Direct housing GHG emissions result from burning of heating oil, gas and coal in private households. Indirect mobility GHG emissions include production of fuels, travels (flights, other transport modes like bus or train), production of cars, vehicle spare parts, maintenance etc. Direct mobility emissions result from burning of diesel and gasoline in private cars.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5998199/v1/df27652057afd96d404ecfe3.png"},{"id":76778169,"identity":"dcf0147d-2201-427d-848b-bfd7cdbe7b89","added_by":"auto","created_at":"2025-02-20 15:45:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":147109,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAustrian production and differentiated consumption-based GHG emissions, 1995-2022.\u003c/strong\u003e Consumption-based GHG emissions are based on own calculations using input-output tables of EXIOBASEv.3.8.2\u003csup\u003e29\u003c/sup\u003e; production-based emissions are from the Environment Agency Austria\u003csup\u003e30\u003c/sup\u003e (see Supplementary Methods).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5998199/v1/4475660f39a450449a6b44cc.png"},{"id":76778957,"identity":"e2e7b88e-7272-48ee-b879-0b9be8b244b7","added_by":"auto","created_at":"2025-02-20 15:53:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":339053,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentified demand-side policies in the transport and housing sector\u003c/strong\u003e. Policies are categorized based on the avoid-shift-improve framework and policy target addressed (upper part), and policy instrument type for different demand-side options and the total policy mix (lower part). The underlying policy dataset is provided in the Supplementary information.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5998199/v1/2ade67410144337604ad7bca.png"},{"id":76778181,"identity":"b11c642a-46f6-4c0e-ae87-842ba5ee2b00","added_by":"auto","created_at":"2025-02-20 15:45:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":497999,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMulti-level demand-side policy mix and development of household GHG footprint emissions in the transport sector.\u003c/strong\u003e The upper part presents selected demand-side policies. Note that many policies included in the policy dataset (Supplementary information) are contained in strategy documents and are not displayed individually here. Strategies containing multiple demand-side measures were not assigned to the avoid, shift, or improve colour code unless they targeted only one option. The lower part shows the development of the household GHG footprint emission in the transport sector, including direct and indirect GHG emissions. Data are based on own calculations using EXIOBASEv.3.8.2 and data from the Environment Agency Austria (see Supplementary information).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5998199/v1/86cb3a380eb3e29e5bec6774.png"},{"id":76778175,"identity":"07767961-d74c-4b2a-b2a4-b8e4a159a429","added_by":"auto","created_at":"2025-02-20 15:45:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":476260,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMulti-level demand-side policy mix and development of household GHG footprint emissions in the housing sector.\u003c/strong\u003eThe upper part presents selected demand-side policies. Note that many policies included in the policy dataset (Supplementary information) are contained in strategy documents and are not displayed individually here. Strategies containing multiple demand-side measures were not assigned to the avoid, shift, or improve colour code unless they targeted only one option. The lower part shows the development of the household GHG footprint emission in the housing sector, including direct and indirect GHG emissions. Data are based on own calculations using EXIOBASEv.3.8.2 and data from the Environment Agency Austria (see Supplementary information).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5998199/v1/01c46a0a6bdddf208e62fcd9.png"},{"id":76780302,"identity":"cb4b74c3-d4be-4a72-913f-03fe18d7f81e","added_by":"auto","created_at":"2025-02-20 16:17:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2170762,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5998199/v1/d8d4c155-f06f-4751-9f2a-6a86541fb8f1.pdf"},{"id":76778167,"identity":"ec2bd169-7d6e-40b3-910d-a6f9f7606245","added_by":"auto","created_at":"2025-02-20 15:45:28","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":51481,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMethods.docx","url":"https://assets-eu.researchsquare.com/files/rs-5998199/v1/c3c19185d3fb30be2a2c9fa8.docx"},{"id":76778938,"identity":"db9b25bf-2de1-499d-8918-6aaa47876311","added_by":"auto","created_at":"2025-02-20 15:53:28","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":49726,"visible":true,"origin":"","legend":"","description":"","filename":"SourceData1Demandsidepolicydata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5998199/v1/046c5f8c8a60dd8a061cd1da.xlsx"},{"id":76778180,"identity":"209b726a-eab0-4fa7-a5e0-fc5576847fcf","added_by":"auto","created_at":"2025-02-20 15:45:28","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":27695,"visible":true,"origin":"","legend":"","description":"","filename":"SourceData2calculationsofhouseholdfootprintGHGemissions.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5998199/v1/8fa313c25c85de97232653dd.xlsx"},{"id":76778173,"identity":"e9d43954-e028-4095-a963-79979355d43e","added_by":"auto","created_at":"2025-02-20 15:45:28","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":26551,"visible":true,"origin":"","legend":"","description":"","filename":"SourceData3exiobaseaggregates.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5998199/v1/edc7fcece26036d3451dd06f.xlsx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eExisting demand-side climate change mitigation policies neglect avoid options\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn climate change mitigation research, demand-side options are increasingly considered as a crucial supplement to technology-focused supply-side measures. They can serve to reduce the reliance on novel but potentially risky carbon dioxide removal methods in climate stabilization scenarios consistent with the Paris Agreement\u003csup\u003e1\u0026ndash;4\u003c/sup\u003e as well as \u0026nbsp; to mitigate the material impact of energy transitions\u003csup\u003e5\u003c/sup\u003e. However, despite surging research highlighting major mitigation potentials of demand-side options\u003csup\u003e6,7\u003c/sup\u003e, systematic analyses of actually realized or implemented demand-side mitigation policy are lacking. As a result, the current status of demand-side climate policies remains poorly understood, obstructing assessment and exploration of effective demand-side climate policies. Hence, there is a need to assess existing demand-side climate policy mixes, their evolution, composition and foci, in order to improve climate-policy making as well as scenario development\u003csup\u003e2,8\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDemand-side options refer to policies and measures designed to reduce material and energy consumption, along with associated greenhouse gas (GHG) emissions, by altering final demand for goods and services, without compromising or potentially improving well-being\u003csup\u003e9\u003c/sup\u003e. These options typically target adoption and choices of technologies in consumption, consumption and lifestyle patterns, as well as related systems of service provisioning and interconnected production-consumption infrastructures\u003csup\u003e2,9\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eDemand-side mitigation options can be categorized into improve, shift and avoid measures\u003csup\u003e9\u003c/sup\u003e. Improve measures aim at enhancing the material and energy efficiency of existing technologies, modes of service provisioning, and end user adoption, such as driving less emission-intensive cars. Shift measures promote the switch to modes of service provisioning that are less emission-intensive per unit of service provided, e.g., using public transport. Avoid measures focus on avoiding demand for certain goods and services altogether, by redesigning service provisioning systems to reduce unnecessary resource consumption, e.g., by reducing distances travelled. While many consumption choices are ultimately made by individuals or households (e.g., which car to buy), these choices are strongly influenced by social norms and political decisions at various political levels that shape the available infrastructure (e.g., public transport availability, quality, and costs), economic incentives, and regulatory frameworks\u003csup\u003e10\u0026ndash;12\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eApproximately two-thirds of global GHG emissions are directly or indirectly linked to household consumption\u003csup\u003e13\u0026ndash;16\u003c/sup\u003e with the remainder attributed to emissions from government infrastructures (e.g., hospitals) and capital investments. Therefore, literature on demand-side options highlights significant untapped mitigation potential, particularly if a comprehensive and targeted policy mix encompassing improve, shift, and avoid measures is implemented\u003csup\u003e10,17\u003c/sup\u003e. The most effective mix of improve, shift, and avoid measures is suggested to vary across sectors. High improve and shift potentials have been identified in the housing sector (e.g., refurbishment and renovation, shift to renewable energy), while high shift and avoid potentials are found in the transport sector (e.g., shift to public transport, avoid long-haul aviation, promote short-distance urban structures)\u003csup\u003e10,14\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, while most improve measures are deemed more politically feasible, they are also susceptible to being undermined by rebound effects\u003csup\u003e18,19\u003c/sup\u003e. In contrast, many shift and, particularly avoid measures are considered unpopular and risky, as they often involve breaking entrenched routines or disrupting dominant modes of living\u003csup\u003e10,20,21\u003c/sup\u003e. Correspondingly, initial research on (implicit) demand-side policies in four OECD countries (France, Germany, Norway, Sweden) suggests that policy mixes have been skewed towards efficiency measures\u003csup\u003e10,13,19\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHowever, despite the prominence of demand-side options in the recent IPCC report\u003csup\u003e22\u003c/sup\u003e, the evolution and status of actual demand-side policy instruments and mixes have not yet been systematically analyzed using the avoid-shift-improve framework. Meanwhile, research on climate and energy transition policy has increasingly paid attention to policy mixes and their characteristics, such as coherence, comprehensiveness, and synergies, as well as temporal dynamics and patching\u003csup\u003e17,23\u0026ndash;27\u003c/sup\u003e. Nonetheless, much of this work has primarily focused on supply-side measures, leaving demand-side mitigation policy mixes and methodologies for assessing them largely unexplored\u003csup\u003e28\u003c/sup\u003e. Advancing demand-side mitigation options, therefore, requires a deeper understanding of the policy mixes currently in place.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHere, we provide a longitudinal, multilevel analysis of the evolution, composition, and foci of demand-side climate change mitigation policies for the case of Austria from 1995 to 2024 (excluding December) in the transport and housing sector. As in other OECD countries\u003csup\u003e13\u003c/sup\u003e, these two sectors are responsible for the majority of Austria\u0026rsquo;s household GHG footprint emissions (59.7% in 2022, \u003cstrong\u003eFig. 1\u003c/strong\u003e), which in turn account for the majority of total footprint emissions and fall only slightly short of total production-based emissions (\u003cstrong\u003eFig. 2\u003c/strong\u003e). Our sample includes policies and major public investment decisions aimed at influencing investment or consumption choices of households, or households\u0026rsquo; behaviour, in ways that reduce household GHG emissions (see Supplementary Methods for details on our classification and coding approach). We systematically coded, mapped and classified policies according to policy target, instrument type, and the demand-side mitigation option pursued, discerning improve, shift and avoid measures based on the established avoid-shift-improve framework (\u003cstrong\u003eTable 1\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFocusing on a single country allowed us to zoom in on the multilevel nature of demand-side policy mixes over time, encompassing the EU, national, and provincial levels (selecting Austria\u0026rsquo;s two most populous provinces Vienna and Lower Austria). This approach enabled us to investigate how demand-side policy mixes vary across policy levels based on a high-resolution policy database. In contrast to large cross-national climate policy mix assessments that rely on established databases, such as the OECD Climate Policy Database\u003csup\u003ee.g., 31\u003c/sup\u003e, which lists merely 12 policies for Austria between 1995 and 2024, our database includes a total of 185 federal-level policies for the same period. By additionally incorporating EU and provincial-level policies, we created a sample of 231 demand-side climate policies in the transport sector and 120 in the housing sector, respectively.\u003c/p\u003e\n\u003cp\u003eOur study provides novel evidence on what demand-side policies are already represented in the transport and housing sector across different policy levels over time. This allows us to identify relevant gaps in sectoral demand-side policy mixes and to discuss our findings against the background of newly available data on carbon inequality trends in Austria from 2000 to 2020\u003csup\u003e32\u003c/sup\u003e, along with broader political implications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e: Demand-side mitigation policy classification based on Creutzig et al. 2022\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003e9\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"background: rgb(231, 230, 230); border-collapse: collapse; border: none; width: 100%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 70.9pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid white;background: rgb(242, 242, 242);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:16px;font-family:\"Calibri\",sans-serif;margin-top:12.0pt;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid white;background: rgb(209, 167, 201);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:16px;font-family:\"Calibri\",sans-serif;margin-top:6.0pt;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;text-align:center;'\u003e\u003cspan style=\"color:black;\"\u003eAvoid\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid white;background: rgb(168, 210, 184);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:16px;font-family:\"Calibri\",sans-serif;margin-top:6.0pt;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;text-align:center;'\u003e\u003cspan style=\"color:black;\"\u003eShift\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid white;background: rgb(255, 234, 205);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:16px;font-family:\"Calibri\",sans-serif;margin-top:6.0pt;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;text-align:center;'\u003e\u003cspan style=\"color:black;\"\u003eImprove\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 70.9pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid white;background: rgb(242, 242, 242);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:16px;font-family:\"Calibri\",sans-serif;margin-top:3.0pt;margin-right:0in;margin-bottom: 3.0pt;margin-left:0in;'\u003e\u003cspan style=\"color:black;\"\u003eTransport\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid white;background: rgb(209, 167, 201);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:16px;font-family:\"Calibri\",sans-serif;margin-top:3.0pt;margin-right:0in;margin-bottom: 3.0pt;margin-left:0in;'\u003e\u003cspan style=\"font-size:14px;color:black;\"\u003ePolicies aimed at avoiding or reducing mobility and related emissions, e.g., teleworking or compact city structures\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid white;background: rgb(168, 210, 184);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:16px;font-family:\"Calibri\",sans-serif;margin-top:3.0pt;margin-right:0in;margin-bottom: 3.0pt;margin-left:0in;'\u003e\u003cspan style=\"font-size:14px;color:black;\"\u003ePolicies promoting switches from the private car to other modes of transport (public transport, cycling, walking) or car-sharing\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid white;background: rgb(255, 234, 205);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:16px;font-family:\"Calibri\",sans-serif;margin-top:3.0pt;margin-right:0in;margin-bottom: 3.0pt;margin-left:0in;'\u003e\u003cspan style=\"font-size:14px;color:black;\"\u003ePolicies aimed at encouraging choices for smaller or more efficient combustion engines or using sustainable fuels, or encouraging end-user adoption of electric vehicles\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 70.9pt;border: none;background: rgb(242, 242, 242);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:16px;font-family:\"Calibri\",sans-serif;margin-top:3.0pt;margin-right:0in;margin-bottom: 3.0pt;margin-left:0in;'\u003e\u003cspan style=\"color:black;\"\u003eHousing\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border: none;background: rgb(209, 167, 201);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:16px;font-family:\"Calibri\",sans-serif;margin-top:3.0pt;margin-right:0in;margin-bottom: 3.0pt;margin-left:0in;'\u003e\u003cspan style=\"font-size:14px;color:black;\"\u003ePolicies aimed at avoiding demand for space conditioning services and carbon-intensive building materials through building design (e.g., passive houses) or adapting dwelling size to household size; or aimed at avoiding household energy demand through behavioural energy saving\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border: none;background: rgb(168, 210, 184);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:16px;font-family:\"Calibri\",sans-serif;margin-top:3.0pt;margin-right:0in;margin-bottom: 3.0pt;margin-left:0in;'\u003e\u003cspan style=\"font-size:14px;color:black;\"\u003ePolicies enhancing the access to or support the switch to renewable energy, including the electrification of cooking and water heating with green power\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"border: none;background: rgb(255, 234, 205);padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;font-size:16px;font-family:\"Calibri\",sans-serif;margin-top:3.0pt;margin-right:0in;margin-bottom: 3.0pt;margin-left:0in;'\u003e\u003cspan style=\"font-size:14px;color:black;\"\u003ePolicies aimed at improving energy efficiency of new buildings and existing building stock or of household appliances\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eDemand-side policy mixes exist but remain partial\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 351 demand-side mitigation policies identified in the transport and housing sectors, 215 are shift measures and 121 are improve measures. By contrast, avoid options are largely neglected in both sectors. In the transport sector, shift measures constitute 64.5%, improve measures 32.5%, and avoid measure only 3%. Similarly, in the housing sector, shift measures account for 55%, improve measures 38.3%, and avoid measures 6.7% (\u003cstrong\u003eFig. 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTransport sector: Targeting cars and modal shifts, neglecting avoid options and air travel\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the transport sector, private cars (including battery electric vehicles) were the most frequently targeted focus of policies, accounting for 47.6% of all measures. Most improve policies aimed to reduce the attractiveness of driving or buying cars with large or inefficient internal combustion engines. These policies, particularly prevalent in the policy mix in the period from 1995 to 2010, increased fuel taxes, introduced tax benefits for low-CO\u003csub\u003e2\u003c/sub\u003e-emission cars (as part of an engine capacity-related insurance tax), and integrated a fuel combustion and CO\u003csub\u003e2\u003c/sub\u003e-emission-based component into the motor vehicle registration tax (\u003cstrong\u003eFig. 4\u003c/strong\u003e). Measures that increased the cost of driving internal combustion engine cars may have indirectly encouraged households to adopt alternative transport modes. However, these were categorized as improve measures, as their primary objective was to incentivize the purchase of more fuel-efficient cars \u0026ndash; at least for households that could afford them. Especially since the mid-2010s, additional improve measures directly targeted the adoption of electric vehicles. These measures have predominantly relied on price premiums, tax exemptions, investments in the deployment and accessibility of charging infrastructure, and other forms of regulatory privileging, such as designated parking spaces.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eShift measures targeting private cars primarily aimed at disincentivizing their use, rather than promote the adoption of more efficient vehicles, thus encouraging a transition to alternative transport modes. A key example of these measures is the introduction of short-term parking zones and parking fees in Vienna in 1995, followed by their gradual expansion, which increased the attractiveness of other transport options for inner-city mobility. Beyond discouraging private car use, policies aimed at promoting shifts toward other transport modes \u0026ndash; primarily public transport and cycling, but also walking and car-sharing \u0026ndash; constitute a significant proportion of demand-side measures in the transport sector (\u003cstrong\u003eFig. 3\u003c/strong\u003e). In Vienna, a distinct policy pattern emerged combining push measures such as parking zones and fees, with pull measures encouraging shifts to alternative transport modes\u003csup\u003e33\u003c/sup\u003e. These pull measures include investments in public transport infrastructure, cycling and park-and-ride facilities, subsidies for cargo e-bikes, and the introduction of the highly affordable 365-Euro annual public transport ticket.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe identified relatively few policies aimed at promoting train travel beyond urban public transport (\u003cstrong\u003eFig. 3\u003c/strong\u003e). These include significant investment plans to expand and enhance rail infrastructure and the introduction of the KlimaTicket in 2021, a yearly flat-rate ticket covering all rail and public transport services. However, many policies explicitly disincentivizing private car use may also implicitly encourage increased train travel. Stunningly, we only found four demand-side climate policies addressing air travel.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, the transport sector exhibits a relatively extensive policy mix emphasizing shift measures, aimed at encouraging individuals to transition away from private car use toward alternative modes of transport. Beyond the growing focus within improve measures on promoting the adoption of electric vehicles, we could not identify substantial changes in the focus of the policy mix over time. Simultaneously, certain areas (notably air travel) and policy options (particularly avoid measures) have remained largely neglected. The avoid measures identified were predominantly included in strategy or planning policy documents, i.e., declarations of intent to explore options for reducing mobility needs through spatial planning and promoting teleworking.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHousing sector: Improving efficiency, shifting to renewables, omitting avoid potentials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImprove and particularly shift measures also dominate the policy mix in the housing sector (\u003cstrong\u003eFig. 3\u003c/strong\u003e). The strong presence of shift measures is largely attributable to federal and provincial subsidies for photovoltaic systems, solar thermal energy, heat pumps, and biomass-based heating systems, introduced in the mid- to late 2000s (\u003cstrong\u003eFig. 5\u003c/strong\u003e). These subsidies were typically renewed annually over extended periods and were complemented by bans on fossil fuel-based heating systems at both federal and provincial levels, initially targeting coal and oil, and most recently, natural gas. Additional shift measures include information campaigns and regulatory instruments, such as building standards and the suspension of permit requirements, to facilitate the transition to renewable energy and heating options.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eImprove policies primarily targeted on enhancing the energy efficiency of buildings. Central to these efforts were federal and EU building and construction regulations, alongside the mandatory provision of energy performance certificates for houses and apartments. A smaller subset of improve measures aimed at directing household demand towards more efficient household appliances, mostly through information campaigns.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe few avoid measures identified, slightly more prevalent in the housing sector\u0026rsquo;s policy mix than in transport, primarily aimed to raise public awareness of energy-saving practices or to promote passive houses through regulatory standards (e.g., introducing quality standard labels) and subsidies for buildings with very low energy consumption indicators. Notably, these measures aimed at avoiding energy consumption rather than GHG emissions associated with carbon-intensive construction materials. Only within the past two years were additional avoid measures introduced at the EU and federal levels to encourage the repair of goods using regulatory and economic instruments. Remarkably, no avoid measures were found that directly addressed the avoidance of new construction.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur country-focused, high-resolution analysis of demand-side policy mixes indicate that actually implemented demand-side measures have remained partial, with a notable neglect of avoid options. Although the Green Party's first participation in the national government led to a surge in demand-side policymaking, accompanied by significant reductions in production-based GHG emissions (-5.8% in 2022, -6.4% projected for 2023), the dominant policy focus remained on shift and improve measures persisted, alongside continuously high household GHG footprint emissions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSectoral trends in household GHG footprint emissions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) suggest, however, that the lack of avoid measures has been particularly impactful in the transport sector. Despite notable reductions in the mid-2000s and 2010s, mobility-related household emissions in 2022 remain significantly higher (17.6%) than in 1995. By contrast, housing-related household footprint emissions have experienced a considerable decline, falling well below the 1995 level (-33.4%). While direct attribution of emission reductions to specific policies is beyond the scope of this paper, these trends indicate that shift measures, which incentivize and enable households to transition to renewable energy, along with improve measures aimed at increasing buildings energy efficiency, have been relatively effective, despite counterproductive subsidies (specifically an industry-funded subsidy for oil-fired heating systems). Direct housing-related emissions (i.e., fuel use) correspondingly saw the most significant reduction, both in absolute terms and in their relative contribution to overall household footprint emissions\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e (from 12.1% in 1995 to 8.2% in 2022) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMeanwhile, mobility-related emissions have remained the largest contributor to household GHG emissions, with their share increasing from 25.6% in 1995 to 33% in 2022 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This contrasts with the significantly higher density of demand-side policies in the transport sector compared to the housing sector (231 as opposed to 120 policies in housing). A variety of policies has been implemented that make driving fuel-inefficient cars more expensive, incentivize the adoption of electric vehicles, and encourage shifts to alternative transport modes. However, these measures appear to have been partially undermined by insufficient policy integration, including counterproductive subsidies such as the design of the commuter allowances, the diesel tax privilege, and tax benefits for company cars\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Moreover, our findings suggest that household demand for air travel has remained hardly addressed by existing demand-side policies, echoing results from other country-focused studies\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Overall, stubbornly high mobility-related household emissions highlight the urgent need to exploit the mitigation potentials of avoid measures identified for this sector\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe significantly above-average income elasticity of mobility demand highlighted in research on carbon inequality in high-income countries, including Austria\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, and the lack of demand-side policies addressing air travel, suggests that avoid measures could unleash special potential when targeting mobility patterns of high-income groups, e.g., through levies on frequent flying, or a ban of short-haul flights or private jet flights. Such a focus could increase the political feasibility of avoid measures, often deemed unpopular or risky\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, as their costs remain concentrated on a relatively small subset of high-emitting households, and particularly a ban of private jet flights enjoys broad support across party affiliations in several EU countries\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Concomitantly, other avoid options increasing well-being or providing co-benefits by lowering mobility needs through urban design or teleworking, could target a wider spectrum of households across various income levels\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile continuously high mobility-related emissions underscore the urgent need for avoid measures in the transport sector, our findings also suggest that the potential for avoid options in the housing sector have not yet been fully leveraged\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. For example, policies could incentivize and support the adjustment of household to dwelling size through relocation grants, for instance after children have moved out\u003csup\u003ecf. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Additionally, strategies that increase political feasibility of avoid options by selectively targeting high-income lifestyles are conceivable, e.g., limiting excessive floor space per capita as a major contributor to household GHG emissions\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, or avoiding new construction and the associated use of carbon intensive-building materials by mobilizing existing housing stock through effective vacancy fees.\u003c/p\u003e \u003cp\u003eWhile infrastructure investment constitutes a significant portion of shift measures, particularly in the transport sector, our findings also show that both improve and shift measures heavily rely on economic instruments (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Worryingly in light of growing concerns over popular backlash to climate policy\u003csup\u003e\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, however, the composition of these measures suggests that they tend to make emission intensive practices (e.g., driving fuel-inefficient cars) more expensive, but access to subsidy schemes promoting the adoption of low-carbon alternatives (e.g., electric vehicles, renewable energy heating systems in privately owned homes) has tended to be biased towards high-income households that can afford such investments\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. While this may be less problematic in urban areas with existing infrastructure that facilitate modal shifts, as evidenced here by Vienna, it is specifically concerning with respect to low-income households in rural areas, who face rising costs but lack enabling conditions for modal shifts. Our findings therefore also indicate an urgent need to make demand-side subsidy schemes more inclusive, ensuring broader access for low- and medium income households.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWhile a surging body of literature points to the potentials of demand-side option for climate change mitigation\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, our study shows that current multi-level demand-side policy mixes remain partial, particularly lacking avoid options. Further research is needed to examine whether similar biases exist in other sectors beyond transport and housing, and beyond our case study focus on Austria. Additional studies are also required to identify what factors explain the lack of avoid options in demand-side policy mixes. Literature on demand-side solutions highlights concerns over controversiality and popularity of such measures, especially given political sensitivities surrounding governmental initiatives to alter individual behaviour\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. However, political economy factors, such as vested interests, may also be at play\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, e.g., when measures to avoid new building construction evidently collide with the priorities of the construction industry. In any case, our findings point to the need to better understand what factors determine the popularity and political feasibility of avoid measure, especially given imminent or actual climate policy backlash\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. This is particularly crucial, as emerging contributions on political strategies for climate policy\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e highlight broad support for supply-side measures like green industrial policy subsidies, but have largely overlooked demand-side options\u003csup\u003ecf. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo design feasible avoid measures as part of comprehensive demand-side policy mixes, stressing positive effects on human well-being\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e and participation\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e is essential. Equally important are novel findings on public support for sufficiency or degrowth measures\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e and how such support can be actively shaped\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, as well as dissociating avoid measures from generalizing notions of renunciation, except when setting upper limits specifically targeting high-income, emission-intensive lifestyles\u003csup\u003e\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Ultimately, however, advancing urgently needed avoid options\u0026mdash;despite potential political opposition\u0026mdash;will require political strategies, such as recruiting allies, expanding the set of policy winners through issue linkage, or limiting the access and influence of vested interests that act as policy opponents\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHickel, J. \u003cem\u003eet al.\u003c/em\u003e Urgent need for post-growth climate mitigation scenarios. \u003cem\u003eNat Energy\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 766\u0026ndash;768 (2021).\u003c/li\u003e\n\u003cli\u003eSugiyama, M. \u003cem\u003eet al.\u003c/em\u003e High with low: Harnessing the power of demand-side solutions for high wellbeing with low energy and material demand. \u003cem\u003eJoule\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 1\u0026ndash;6 (2024).\u003c/li\u003e\n\u003cli\u003eGrubler, A. \u003cem\u003eet al.\u003c/em\u003e A low energy demand scenario for meeting the 1.5 \u0026deg;C target and sustainable development goals without negative emission technologies. \u003cem\u003eNat Energy\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 515\u0026ndash;527 (2018).\u003c/li\u003e\n\u003cli\u003eCap, S., De Koning, A., Tukker, A. \u0026amp; Scherer, L. 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Strategic State Capacity: How States Counter Opposition to Climate Policy. \u003cem\u003eComparative Political Studies\u003c/em\u003e \u003cstrong\u003e55\u003c/strong\u003e, 493\u0026ndash;523 (2022).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"demand-side mitigation, climate policy, avoid-shift-improve, Austria, transport, housing","lastPublishedDoi":"10.21203/rs.3.rs-5998199/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5998199/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDemand-side options are increasingly recognized for their potential to mitigate climate change while reducing reliance on novel carbon dioxide removal. However, systematic analyses of implemented demand-side mitigation policy mixes remain scarce, compromising assessment and exploration of effective and feasible demand-side policies. Here, we provide a multilevel analysis of the evolution, composition, and foci of demand-side mitigation policy mixes in the transport and housing sector from 1995 to 2024, focusing on the EU, the federal Austrian and two provincial levels (Vienna, Lower Austria). Our high-resolution policy database features 351 demand-side measures, systematically classified according to policy target, instrument type, and the avoid-shift-improve framework. We find that existing policy mixes heavily rely on shift and improve measures, critically neglecting mitigation potentials of avoid options as well as certain policy areas. This suggests an urgent need to broaden demand-side policy mixes and explore strategies that increase the political feasibility of avoid options.\u003c/p\u003e","manuscriptTitle":"Existing demand-side climate change mitigation policies neglect avoid options","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-20 15:45:24","doi":"10.21203/rs.3.rs-5998199/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b55d3a85-675c-4412-ae0d-1ae85d4bca9a","owner":[],"postedDate":"February 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44097370,"name":"Environmental Policy"}],"tags":[],"updatedAt":"2025-02-20T15:45:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-20 15:45:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5998199","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5998199","identity":"rs-5998199","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}