選好予測を通じた費用対効果の高い気候政策の設計：中国世帯の低炭素代替案に関するエビデンス
Designing cost-effective climate policies through preference prediction: Evidence from Chinese households’ low-carbon alternatives (原題)
Yuanyuan Tang, Bowei Guo
🤖 gxceed AI 要約
日本語
中国の世帯調査を用い、低炭素代替案への受容意思（WTA）の異質性を分析。人口統計や炭素中立政策の認知度がWTAと非線形に関連することを示す。計量経済学と機械学習による選好予測を比較し、予測モデルが受容率を高め補償額を削減することを確認。ただし公教育への投資が予測精度と同等以上の長期的効率性をもたらす可能性も示唆。
English
Using a Chinese household survey, this study examines heterogeneous willingness to accept (WTA) low-carbon alternatives. Demographic factors and carbon-neutrality policy awareness correlate non-linearly with WTA. Comparing econometric and machine-learning prediction models, it finds predictive targeting raises acceptance and lowers compensation, but public education may yield comparable long-term efficiency gains.
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
中国の炭素中立政策に対する世帯の受容性を分析した研究。日本でもカーボンプライシングや低炭素技術の導入において、市民の受容性や公平性の認識が政策設計の鍵となるため、選好予測手法や教育投資の効果は参考になる。
In the global GX context
This paper contributes to the global discourse on designing effective carbon pricing and low-carbon transitions by highlighting the role of heterogeneous household preferences and the potential of predictive analytics. It offers insights for policymakers worldwide on balancing targeted interventions with public education to enhance acceptance and cost-effectiveness.
👥 読者別の含意
🔬研究者:機械学習を用いた選好予測と政策設計の融合に関心のある研究者に、実証的手法と結果を提供。
🏢実務担当者:低炭素製品・サービスの市場導入戦略において、顧客セグメント別の受容性予測が参考になる。
🏛政策担当者:炭素税や補助金設計において、世帯選好の予測と公教育投資のバランスを検討する際のエビデンスを提供。
📄 Abstract（原文）
Designing cost-effective climate policies requires understanding not only who accepts low-carbon alternatives but also heterogeneous household preferences. Using a unique household survey from China, this study examines household willingness to accept (WTA) low-carbon alternatives and evaluates the potential efficiency gains from predicting these preferences. The results suggest that demographic characteristics such as location, gender, education, and income are associated with systematic differences in WTA. Knowledge of carbon neutrality and related policies is correlated with WTA in a non-linear manner. Households with basic awareness tend to report lower compensation requirements, while those with greater familiarity report higher stated WTA, possibly reflecting skepticism about the costs and perceived fairness of low-carbon transitions. To translate these insights into policy design, we compare econometric and machine-learning approaches to predicting household preferences. Compared with random assignments, predictive models substantially increase acceptance rates while reducing average compensation. However, the simulation results indicate that targeted investments in public education may generate long-term efficiency gains comparable to or even exceeding those from predictive precision alone
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.ijforecast.2026.08.003first seen 2026-09-14 04:42:11
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