A Study on the Statistical Measurement and Determinants of China’s Energy Transition from a Justice Perspective
正義の視点から見た中国のエネルギー移行の統計的測定と決定要因に関する研究 (AI 翻訳)
Shuyu Xue
🤖 gxceed AI 要約
日本語
本論文は、中国31省の2010〜2023年のパネルデータを用いて、エネルギー移行の公平性を評価する総合指標を構築。エントロピー法、DEA、TOPSISで各省の公平性スコアを算出し、二方向固定効果モデルで要因を分析。結果、全国の公平性は改善しているが地域差が大きいこと、経済発展が公平性に段階的に負の影響を与えることなどを発見。
English
This paper constructs a comprehensive evaluation system for energy transition fairness using panel data from 31 Chinese provinces (2010-2023). It applies entropy method, DEA, and TOPSIS to measure provincial fairness, and a two-way fixed effects panel model to identify driving mechanisms. Findings show national fairness improving with significant regional disparities, and economic development having a stage-specific negative impact.
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
中国のエネルギー移行における公平性指標の開発は、日本のGX政策（地域間格差・公正な移行）の評価手法として参考になる。ただしデータ・制度は中国固有。
In the global GX context
This paper provides a methodological framework for measuring fairness in energy transitions that could inform global just transition debates, though its provincial-level Chinese data limits direct transferability. It highlights the tension between economic growth and equitable transition.
👥 読者別の含意
🔬研究者:Offers a replicable methodology for measuring energy transition fairness at subnational level.
🏛政策担当者:Provides evidence for region-specific policies to balance efficiency and equity in energy transition.
📄 Abstract（原文）
Currently, research on measuring energy transition fairness at the provincial level is still insufficient. Therefore, in this paper, based on the dual carbon goals, we used panel data from 31 Chinese provinces from 2010 to 2023 to build a comprehensive evaluation system that combines transition outcomes with energy fairness. We applied the entropy method, DEA model, and TOPSIS model to calculate the energy transition fairness level of each province, and then used a two-way fixed effects panel model to identify the driving mechanisms. The results show that the national level of energy transition fairness has steadily improved, but there are significant regional differences: first, the western region has the best overall transition foundation, while the eastern region leads in transition efficiency; second, economic development has a stage-specific negative impact on transition fairness, while per capita fiscal capacity is a crucial positive driving factor; third, the impact of various factors differs across regions. Based on the findings of this study, it’s recommended to adopt targeted policies, such as region-specific support, optimizing green financial investments, and setting up a dynamic provincial monitoring system, so that energy use can be both efficient and fair during the transition.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.53941/eem.2026.100009first seen 2026-07-17 04:53:49
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