[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118604-en":3,"doc-seo-118604-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118604,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Who cuts emissions, who turns up the heat? causal machine learning estimates of energy efficiency interventions","Reducing domestic energy demand is central to climate mitigation and fuel poverty strategies, yet the impact of energy efficiency interventions is highly heterogeneous. A causal machine learning model trained on nationally representative English housing-stock data estimates average and subgroup-specific treatment effects of wall insulation on gas consumption. Results show average gas-demand reductions of about 19%, with substantial savings for low energy-burden groups and little to no reduction for high-burden households. The findings indicate a behaviorally driven mechanism in which households facing high costs-to-income ratios reallocate savings toward improved thermal comfort rather than lowering consumption, implying equity-relevant co-benefits for health and well-being.","Journal Pre-proof  \nWho cuts emissions, who turns up the heat? causal machine learning estimates of energy efficiency interventions  \nBernardino D ' Amico, Francesco Pomponi, Jay H. Arehart, Lina Khaddour  \nPII: S0378-7788(25)01343-X  \nDOI: [https://doi.org/10.1016/j.enbuild.2025.116613](https://doi.org/10.1016/j.enbuild.2025.116613)  \nReference: ENB 116613  \nTo appear in: Energy & Buildings  \nReceived date: 11 August 2025  \nRevised date: 1 October 2025  \nAccepted date: 18 October 2025  \nPlease cite this article as: Bernardino D ' Amico, Francesco Pomponi, Jay H. Arehart, Lina Khaddour, Who cuts emissions, who turns up the heat? causal machine learning estimates of energy efficiency interventions, Energy & Buildings (2025), doi: [https://doi.org/10.1016/j.enbuild.2025.116613](https://doi.org/10.1016/j.enbuild.2025.116613)  \nThis is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.  \n© 2025 Published by Elsevier B.V.  \nWho cuts emissions, who turns up the heat? causal machine learning estimates of energy efficiency  \ninterventions  \nBernardino D’Amico 1,2*, Francesco Pomponi3 , Jay H. Arehart4 ,  \nLina Khaddour 1,2  \n1* Digital Built Environment Group (DiBEG), Edinburgh Napier University,  \nEdinburgh, EH10 5DT, United Kingdom.  \n2 School of Computing, Engineering and the Built Environment (SCEBE), Edinburgh Napier University, Edinburgh, EH10 5DT, United Kingdom.  \n3York School of Architecture, University of York, York, YO10 5DD, United Kingdom.  \n4 Department of Civil, Environmental, and Architectural Engineering, University of Colorado Boulder, Boulder, 80309-0428, Colorado, United States.  \n*Corresponding author(s). E-mail(s): [b.damico@napier.ac.uk](b.damico@napier.ac.uk) ; Contributing authors: [francesco.pomponi@york.ac.uk](francesco.pomponi@york.ac.uk) ; [jay.arehart@colorado.edu](jay.arehart@colorado.edu) ;  \n[l.khaddour@napier.ac.uk](l.khaddour@napier.ac.uk) ;  \nAbstract  \nReducing domestic energy demand is central to climate mitigation and fuel poverty strategies, yet the impact of energy efficiency interventions is highly heterogeneous. Using a causal machine learning model trained on nationally representative data of the English housing stock, we estimate average and conditional treatment effects of wall insulation on gas consumption, focusing on distributional effects across energy burden subgroups. While interventions reduce gas demand on average (≈–19%), low energy burden groups achieve substantial savings, whereas those experiencing high energy burdens see little to no reduction. This pattern reflects a behaviourally-driven mechanism: households constrained by high costs-to-income ratios (>10%) reallocate savings toward improved thermal comfort rather than lowering consumption. Far from wasteful, such responses represent rational adjustmentsin contexts of prior deprivation, with potential co-benefits for health and well-being. These findings call for a broader evaluation framework that accounts for both climate impacts and the equity implications of domestic energy policy.  \nKeywords: Energy efficiency, Fuel poverty, Causal machine learning. Do-calculus  \n1 Introduction  \nDomestic energy use accounts for a substantial portion of global greenhouse gas (GHG) emissions, making its reduction a cornerstone of climate mitigation policies worldwide [1, 2] . In pursuit of decarbonisation, governments and international authorities have mandated laws, standards, subsidies and several other programmes and incentives to im","cbCaiuOSxlHXNw38","https://ap.wps.com/l/cbCaiuOSxlHXNw38","pdf",3499434,1,37,"English","en",105,"# Abstract\n# Introduction\n## Domestic energy use and climate mitigation\n## Energy efficiency, wellbeing, and fuel poverty\n## Heterogeneity, rebound, and behavioural mechanisms","[{\"question\":\"What is the document’s main question about energy efficiency interventions?\",\"answer\":\"It examines why energy efficiency interventions produce highly different outcomes across households—specifically, who benefits from reduced gas consumption and who does not.\"},{\"question\":\"How are the estimates produced in the study?\",\"answer\":\"The study uses a causal machine learning model trained on nationally representative data of the English housing stock to estimate average and conditional treatment effects of wall insulation.\"},{\"question\":\"What mechanism is proposed to explain the differing effects across energy-burden groups?\",\"answer\":\"Households with high costs-to-income ratios may use savings to improve thermal comfort instead of reducing consumption, leading to little or no gas reduction for high-burden groups.\"}]","Who cuts emissions, who turns up the heat? 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