Rethinking Regional Energy Poverty:An Intersectional Modelling Approach
地域エネルギー貧困の再考：交差性モデリングアプローチ (AI 翻訳)
Sara; id_orcid 0000-0003-1053-1641 Tavakoli, Pedro Sampaio, Ali Hassanzadeh
🤖 gxceed AI 要約
日本語
本研究は、エネルギー貧困の要因を独立的に扱う従来の手法では脆弱性の形成を捉えきれないと指摘し、交差性を媒介構造とする3段階モデリングフレームワークを開発。イングランドの地域データに統計・機械学習を適用し、同程度のエネルギー貧困でも異なる条件の組み合わせから生じることを実証。place-based政策の基盤として、介入が異なる集団に与える影響を評価する政策ラボツールへの拡張を目指す。
English
This study develops a three-level modeling framework operationalizing intersectionality to capture how combinations of socio-economic characteristics interact to produce regional energy poverty. Using statistical and machine learning methods on England data, it shows that similar poverty levels arise from different condition combinations, with socio-economic gradient dominating but intersectional patterns revealing life-stage and heating-type effects. The work aims to support place-based policy and create a policy lab tool for assessing intervention impacts across groups.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本でもエネルギー価格高騰や脱炭素移行に伴うエネルギー貧困の顕在化が懸念されており、本手法は地域特性を考慮した支援策の設計に示唆を与える。特に、SSBJやESG情報開示における社会的側面（S）の評価において、脆弱性の複合的把握手法として応用可能性がある。
In the global GX context
This paper addresses energy poverty within Europe's energy transition, using an innovative intersectional ML approach. It contributes to the global just transition discourse by showing that policy must consider interacting drivers rather than single factors. The policy lab concept aligns with fairness and social sustainability goals increasingly emphasized in frameworks like the EU's Social Climate Fund and corporate disclosure standards.
👥 読者別の含意
🔬研究者:Demonstrates how ML and intersectionality can model complex socio-economic drivers of energy poverty, offering a methodological advance for vulnerability research.
🏢実務担当者:Provides a framework for local authorities and energy companies to identify vulnerable household groups beyond simple income thresholds, enabling targeted support programs.
🏛政策担当者:Highlights the need for place-based, multi-dimensional energy poverty policies and presents a policy lab tool to simulate intervention effects on different groups.
📄 Abstract（原文）
Energy poverty remains a persistent policy challenge across Europe, particularly in the context of the ongoing energy transition and rising pressure on household energy affordability (Washington2016). Despite extensive research, its underlying drivers are often treated as separate and additive. This study examines whether such approaches are sufficient to capture how vulnerability actually forms at the regional level. This assumption of separable effects can lead to misclassification of vulnerable groups and limit the effectiveness of policy targeting.<br/>To address this, a three-level modelling framework is developed in which intersectionality is operationalised as a mediating structure linking socio-economic characteristics to energy poverty (Crenshaw2013). Rather than treating drivers as independent, the framework captures how combinations of characteristics interact to produce vulnerability.<br/>The analysis uses regional data for England and combines statistical and machine learning methods to identify underlying patterns in the data. The results show that similar levels of energy poverty can emerge from different combinations of conditions. While a socio-economic gradient, related to labour market position, education, and health, plays a dominant role, additional intersectional patterns capture life-stage differences and energy system characteristics, particularly heating types.<br/>This suggests that identifying where energy poverty occurs is not enough on its own. What matters is identifying the root causes of energy poverty and how different drivers intersect to produce it. Modelling these relationships provides a more useful basis for place-based policy, especially in the context of Europe’s energy transition.<br/>Ongoing work extends this approach to finer spatial scales and develops a policy-oriented modelling framework. This is intended to function as a policy lab tool to assess how different interventions may affect different population groups, with particular attention to fairness across regions.<br/>
🔗 Provenance — このレコードを発見したソース
- openalex https://research.manchester.ac.uk/en/publications/b68ef231-570c-4727-a712-c7b8e6202aa1first seen 2026-07-04 04:38:20
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