[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120217-en":3,"doc-seo-120217-105":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},120217,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Energy poverty prediction and effective targeting for just transitions with machine learning","Energy poverty represents a major challenge as countries face escalating energy crises and accelerate net zero commitments under “just transitions.” Europe’s progress is constrained by limited recognition that energy vulnerable households are not always income poor. Machine learning can improve targeting by predicting energy vulnerability using objective, publicly available data, yet applications remain limited across many countries. This study develops a framework for fair prediction and targeting across EU members and the UK, primarily using Random Forest classifiers.","[www.ssoar. info](www.ssoar. info)  \nEnergy poverty prediction and effective targeting for just transitions with machine learning  \nSpandagos , Constantine; Tovar Reaños , Miguel Angel ; Lynch , Muireann Á .  \nVeröffentlichungsversion / Published Version Zeitschriftenartikel / journal article  \nEmpfohlene Zitierung / Suggested Citation:  \nSpandagos , C. , Tovar Reaños , M. A. , & Lynch , M. Á . (2023) . Energy poverty prediction and effective targeting for just transitions with machine learning. Energy Economics, 128, 1-19. [https://doi.org/10.1016/j.eneco.2023.107131](https://doi.org/10.1016/j.eneco.2023.107131)  \nNutzungsbedingungen:  \nDieser Text wird unter einer CC BY Lizenz (Namensnennung) zur Verfügung gestellt. Nähere Auskünfte zu den CC-Lizenzen finden Sie hier:  \n[https://creativecommons.org/licenses/by/4.0/deed.de](https://creativecommons.org/licenses/by/4.0/deed.de)  \nTerms of use:  \nThis document is made available under a CC BY Licence (Attribution). For more Information see:  \n[https://creativecommons.org/licenses/by/4.0](https://creativecommons.org/licenses/by/4.0)  \nDiese Version ist zitierbar unter / This version is citable under:  \n[https://nbn-resolving.org/urn:nbn:de:0168-ssoar-98025-2](https://nbn-resolving.org/urn:nbn:de:0168-ssoar-98025-2)  \nEnergy Economics 128 (2023) 107131  \nContents lists available at ScienceDirect  \nEnergy Economics  \njournal [homepage: www.elsevier.com/locate/eneeco](homepage: www.elsevier.com/locate/eneeco)  \n| Energy poverty prediction and effective targeting for just transitions with machine learning |  |  |  |\n| --- | --- | --- | --- |\n| Constantine Spandagosa, b, c, *, Miguel Angel Tovar Rea˜nosb, c, Muireann ´A. Lynch b, c\u003Cbr>a Department of Natural Resources and the Environment, University of New Hampshire, 56 College Road, Durham, NH 03824, United States b Economic and Social Research Institute, Sir John Rogerson’s Quay, Dublin, Ireland\u003Cbr>c Department of Economics, Trinity College Dublin, Dublin, Ireland |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| JEL codes:\u003Cbr>D10 I30 Q40\u003Cbr>Q41\u003Cbr>Q48\u003Cbr>Keywords:\u003Cbr>Energy poverty prediction\u003Cbr>Energy poverty targeting Machine learning\u003Cbr>Just energy transitions EU member states |  | The prevalence of energy poverty as a major challenge in numerous countries, the escalating energy crisis and the need to build just supporting mechanisms within the net zero energy transition add impetus to improving our ability to accurately predict energy vulnerable households. In Europe, this is hindered by limited recognition of the fact that energy vulnerable households are not necessarily income poor (and vice versa). Artificial Intelligence, and machine learning techniques in particular, may be applied to improve the targeting mechanism of energy poverty schemes, enabling accurate prediction of energy vulnerable households via objective, publicly available data. However, such applications are still limited, especially across a large number of countries. In response to the above, we develop an innovative machine learning framework for accurate prediction and fair targeting of energy poor households across all the current members of the European Union, and the United Kingdom. While we explore various machine learning algorithms, most of our analysis is performed using a Random Forest classifier. Our approach to explore energy poverty beyond income reveals household-level and country-level predictors of energy poverty, such as dwelling condition, energy efficiency, social protection payments and gas supplier switching rates. We also demonstrate how machine learning algorithms offer straightforward visualization of the mechanism that determines the energy poor classification, improving the transparency of alleviation schemes and assisting policy-makers in setting effective thresholds for assistance allocation. Finally, we evaluate the potential fairness of alleviation schemes and demonstrate that basing their targeting exclusively on i","cbCaiaO64ngNu5v8","https://ap.wps.com/l/cbCaiaO64ngNu5v8","pdf",3644468,1,20,"English","en",105,"# Abstract\n# Introduction\n# Energy poverty prediction and just transitions\n# Machine learning framework\n## Random Forest analysis\n## Predictors and policy implications\n# Fairness and effectiveness of targeting","[{\"question\":\"Why is predicting energy poverty important for just energy transitions?\",\"answer\":\"Energy poverty affects households’ ability to meet energy needs, and net zero transitions can create or worsen regressive outcomes without supportive mechanisms. Accurate prediction supports fair assistance allocation.\"},{\"question\":\"What data approach does the study emphasize for targeting energy vulnerable households?\",\"answer\":\"It uses objective, publicly available data to predict energy vulnerability and strengthen targeting mechanisms beyond income-only identification.\"},{\"question\":\"How does the study assess the fairness of energy alleviation targeting?\",\"answer\":\"It evaluates potential unfair exclusion by comparing outcomes when targeting relies exclusively on income-relevant or social welfare-relevant criteria, finding such approaches would be ineffective.\"}]","Energy poverty prediction and effective targeting for just transitions with machine learning | PDF",1785728775,50,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"energy-poverty-prediction-and-effective-targeting-for-just-transitions-with-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/energy-poverty-prediction-and-effective-targeting-for-just-transitions-with-machine-learning/120217/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is predicting energy poverty important for just energy transitions?","Question",{"text":75,"@type":76},"Energy poverty affects households’ ability to meet energy needs, and net zero transitions can create or worsen regressive outcomes without supportive mechanisms. Accurate prediction supports fair assistance allocation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data approach does the study emphasize for targeting energy vulnerable households?",{"text":80,"@type":76},"It uses objective, publicly available data to predict energy vulnerability and strengthen targeting mechanisms beyond income-only identification.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study assess the fairness of energy alleviation targeting?",{"text":84,"@type":76},"It evaluates potential unfair exclusion by comparing outcomes when targeting relies exclusively on income-relevant or social welfare-relevant criteria, finding such approaches would be ineffective.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":29,"slug":113},6,"Technology","technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":21,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":21,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]