気候変動に対する態度と需要主導のエネルギー削減：機械学習アプローチ
Attitudes toward climate change to demand-driven energy mitigation: A machine-learning approach (原題)
Giacomo A. Campagnola, Bruno S. Sergi, Emiliano Sironi
🤖 gxceed AI 要約
日本語
本研究は、気候変動に対する個人の態度と省エネ行動の関係を、欧州23カ国の調査データに機械学習を適用して分析した。その結果、気候変動に対する個人的責任の認識が、エネルギー意識の高い行動の最も強力な予測因子であることを明らかにした。需要側のエネルギー削減を促進するための政策的示唆を提供する。
English
This study applies machine learning to survey data from 23 European countries to examine the relationship between personal attitudes toward climate change and energy-saving behaviors. It finds that perceived personal responsibility for climate change is the strongest predictor of energy-conscious behavior, offering policy implications for promoting demand-side energy mitigation.
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
日本では、家庭部門の省エネ行動や需要側対策がカーボンニュートラル実現に重要であり、本研究成果は日本の行動変容を促す政策設計や企業の省エネサービス開発に示唆を与える。
In the global GX context
This paper contributes to global understanding of demand-side mitigation by identifying key attitudinal drivers of energy-saving behavior across Europe, which can inform international climate policies and behavioral interventions.
👥 読者別の含意
🔬研究者:Provides empirical evidence on the relative importance of attitudes in predicting energy behavior, useful for designing behavioral interventions.
🏢実務担当者:Highlights the role of personal responsibility in energy-saving, which can inform customer engagement strategies for energy efficiency programs.
🏛政策担当者:Suggests that fostering a sense of personal responsibility may be more effective than other attitudinal factors in promoting energy conservation.
📄 Abstract（原文）
Promoting mitigation behaviors among the public is a fundamental step in addressing climate change, yet such efforts have achieved uneven success across demographic groups. This paper surveys the literature on the relationship between personal attitudes toward climate change and mitigation behaviors, with a particular focus on demand-side energy consumption reductions. An interpretative framework for understanding the key drivers of and barriers to common mitigation strategies is provided. Applying a machine-learning approach to survey data from 23 European countries to disentangle the relative importance of attitudinal predictors, the analysis finds that perceived personal responsibility for climate change is the single most powerful predictor of energy-conscious behavior. This paper thus offers a useful guide to understanding successful approaches to incentivizing climate-friendly action within this regional context.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.energy.2026.142189first seen 2026-09-04 04:58:54
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