The impact of digitalization and energy transition policies on urban energy rebound effects in China: A double machine learning-based causal identification.
中国におけるデジタル化とエネルギー移行政策が都市のエネルギーリバウンド効果に与える影響：ダブル機械学習に基づく因果識別 (AI 翻訳)
Peng Gao, Kunpeng Zhang, Zongchuan Liu
🤖 gxceed AI 要約
日本語
本論文は、ダブル機械学習を用いて中国都市のエネルギーリバウンド効果（ERE）を測定し、国家ビッグデータ総合実験区と新エネルギー模範都市の二重政策の影響を評価。結果は、二重政策が特に資源都市や旧工業都市でEREを抑制し、産業構造最適化、グリーン技術革新、エネルギー消費転換を通じて効果を発揮することを示す。
English
This paper uses double machine learning to measure urban energy rebound effects (ERE) in China and evaluates the impact of dual-pilot policies (National Big Data Comprehensive Experimental Zones and New Energy Demonstration Cities). Results show that the dual policies significantly mitigate ERE, especially in resource-based and old industrial cities, through industrial structure optimization, green technological innovation, and energy consumption transition.
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SSBJ対応やエネルギー政策評価が進む中、本論文の機械学習を用いた政策効果の因果識別手法は、日本のGX政策評価にも応用可能な示唆を与える。特に、複数政策の相乗効果を検証する枠組みは参考になる。
In the global GX context
This paper contributes to global GX scholarship by applying causal machine learning to evaluate energy transition policies, addressing the Jevons Paradox. The findings on policy synergy and urban heterogeneity offer insights for designing effective decarbonization strategies worldwide.
👥 読者別の含意
🔬研究者:Offers a rigorous causal framework (double ML) for policy evaluation in energy rebound studies.
🏢実務担当者:Demonstrates how combining digitalization and energy policies can mitigate rebound effects, relevant for corporate energy strategy.
🏛政策担当者:Provides evidence that dual-pilot policies outperform single policies, informing integrated policy design for energy transition.
📄 Abstract（原文）
The digital economy has injected fresh momentum into China's growth, yet the accompanying energy rebound effect (ERE) deserves attention. This study measures the urban-level ERE in China from the technological progress perspective and treats the dual-pilot policies of China's National Big Data Comprehensive Experimental Zones (NBDCEZs) and New Energy Demonstration Cities (NEDCs) as a quasi-natural experiment. Using a double machine learning approach, we evaluate the impact of dual-pilot policies on urban ERE. The results show that dual-pilot policies significantly mitigate urban ERE, especially in resource-based cities, old industrial cities, and cities with advanced green finance. Mechanism analysis indicates that dual-pilot energy policies can mitigate the ERE through industrial structure optimization, green technological innovation, and energy consumption transition. Moreover, compared with a single policy, the combined implementation of the dual-pilot policies has a stronger mitigating effect on the ERE. These findings offer empirical evidence and policy insights for addressing the Jevons Paradox and advancing sustainable development.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1016/j.jenvman.2026.129446first seen 2026-06-10 05:43:20
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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。