エネルギー正義と住宅：地区レベルにおけるエネルギー関連格差の探索的データ分析
Energy justice and housing: exploratory data analysis of energy-related disparities at the neighborhood level (原題)
Martha M. Hoffmann, Sophie Wanner Fandrych
🤖 gxceed AI 要約
日本語
ドイツ・ベルリンの542計画地区について、所得分布・住宅エネルギー消費・エネルギー支出・エネルギー負担率・エネルギー貧困人口比率を含む新たな空間データセットを構築した。中央値のエネルギー負担率は5.5%、二重中央値基準の貧困閾値は11.1%で、約19.5%の世帯がエネルギー貧困にあると推計。建物効率化は賃貸比率の高い地区で遅れており、エネルギー貧困は移民歴・失業・福祉受給者の比率が高い地区と関連する。脱炭素政策の社会的影響評価の基盤を提供する。
English
Builds a new spatial dataset for Berlin's 542 planning areas covering income, residential energy consumption, expenditures, energy burden, and energy-poor population share. Median energy burden is 5.5%, setting a double-median poverty threshold of 11.1%, with ~19.5% of households estimated energy-poor. Building efficiency lags in high-tenant areas, and energy poverty correlates with immigration history, unemployment, and welfare receipt. Provides a baseline for assessing social impacts of decarbonization measures.
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
日本でも脱炭素政策と住宅・エネルギー貧困の社会的公平性は、GX推進における「公正な移行」論点として重要性が増している。地域別のエネルギー負担データ整備は、自治体の気候政策と社会政策の統合に示唆を与える。
In the global GX context
Adds empirical granularity to the 'just transition' dimension of global decarbonization debates, showing how building-efficiency policy and energy poverty intersect spatially. Relevant to CSRD/ISSB social-disclosure discussions and to regulators designing distributional safeguards in climate policy.
👥 読者別の含意
🔬研究者:Provides a replicable spatial methodology linking energy burden, building stock, and socio-demographics at neighborhood scale.
🏢実務担当者:Highlights that building-efficiency retrofits and tenant-heavy portfolios carry social-equity exposure that disclosure teams may need to address.
🏛政策担当者:Offers a distributional baseline for designing decarbonization measures that avoid worsening energy poverty in specific districts.
📄 Abstract（原文）
Abstract Greenhouse gas emissions from Germany’s residential sector must be reduced significantly to achieve climate targets, yet the necessary policy measures can interact with and intensify existing tensions between social and environmental objectives. This study aims to support an empirically grounded discourse on questions of justice in the context of decarbonization, energy, and housing. To this end, it links energy justice to the housing question through the issue of energy poverty, and investigates energy-related disparities in Berlin. A new spatial dataset is created for Berlin’s 542 planning areas (PLRs), containing income distribution, residential energy consumption, energy expenditures, energy burden, and the share of energy-poor population. The dataset is subjected to exploratory data analysis, including correlational analysis, with parameters reflecting living space, urban typology, construction period, ownership, and socio-demographic aspects. The analysis of the modelled dataset reveals a median energy burden of households in Berlin (M) of 5.5 %, which defines the double median (2 M) energy poverty threshold of Berlin at 11.1 % energy burden. We estimate that 19.5 % of households are experiencing energy poverty. The share of energy-burdened population is distributed unevenly across PLRs. Spearman correlation factors illustrate the interconnectedness of energy-related indicators, built environment, and socio-demographic aspects at PLR level. While this method cannot determine causality, we do find that energy building efficiency, and thus likely modernization, appears to lag in PLRs with higher share of tenants. Energy poverty relates to PLRs with higher shares of population with immigration history, unemployment, and welfare recipients. This study provides future research with a baseline to assess social impacts of decarbonization measures in Berlin.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1088/2753-3751/aeb306first seen 2026-10-31 04:46:36 · last seen 2026-11-06 04:35:37
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。