Digital Economy, Innovation Factor Mobility, and Urban Green Energy Efficiency: Evidence from Double Machine Learning
デジタル経済、イノベーション要素の流動性、都市のグリーンエネルギー効率：ダブル機械学習によるエビデンス (AI 翻訳)
J J Liu
🤖 gxceed AI 要約
日本語
本研究は2011年から2022年の中国281都市のパネルデータを用い、ダブル機械学習と空間ダービンモデルによりデジタル経済が都市のグリーンエネルギー効率に与える影響を分析した。結果、デジタル経済はグリーン技術革新、グリーンファイナンス、産業高度化を通じて効率を向上させ、イノベーション要素の流動性が重要な媒介効果を持つことを発見。また、近隣都市への正の空間波及効果も確認され、地域間協調の重要性を示した。
English
Using panel data of 281 Chinese cities (2011–2022), this study applies Double Machine Learning and Spatial Durbin Models to examine digital economy's impact on urban green energy efficiency. Findings show digital economy enhances local efficiency through green innovation, green finance, and industrial upgrading, with innovation factor mobility as a key mediator. Significant positive spatial spillovers to neighboring cities are also found.
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
日本のSSBJや有報におけるエネルギー開示強化の流れの中で、デジタル経済とグリーンエネルギー効率の連携は重要なテーマ。特に地域間波及効果のメカニズムは、日本の都道府県間連携やスマートシティ政策に示唆を与える。
In the global GX context
This paper provides empirical evidence on how digital economy drives green energy efficiency through factor mobility, relevant to global discussions on TCFD/ISSB energy transition metrics. The spatial spillover findings support cross-regional coordination policies, which can inform China's dual-carbon targets and similar strategies in other emerging economies.
👥 読者別の含意
🔬研究者:Introduces double machine learning to green energy efficiency analysis, overcoming endogeneity concerns; useful for methodology in energy-transition research.
🏢実務担当者:Shows that investing in digital infrastructure and green finance can improve energy efficiency; useful for corporate energy management and regional development planning.
🏛政策担当者:Provides evidence for promoting digital economy and factor mobility to enhance green energy efficiency, with spillover effects that justify cross-regional coordination.
📄 Abstract（原文）
Amidst booming digital economy and tightening climate governance, enhancing green total-factor energy efficiency has become pivotal for socioeconomic transformation. Whether digital economy drives urban green energy transition remains unresolved, particularly regarding factor mobility mechanisms and spatial spillovers. Using panel data of 281 Chinese cities from 2011 to 2022, this study applies Double Machine Learning and Spatial Durbin Models to examine digital economy’s impact on urban green energy efficiency. Findings indicate that (i) digital economy significantly enhances local green energy efficiency through green technological innovation, green finance development, and industrial upgrading; (ii) it facilitates talent and capital agglomeration toward digitally advanced regions, with innovation factor mobility serving as a crucial mediator; and (iii) significant spatial positive correlation exists, where digital economy generates pronounced spillovers to neighboring cities that exceed direct effects, fostering regional synergies. This research overcomes conventional methodological limitations and pioneers integrating innovation factor mobility into digital economy-green transition analysis, revealing factor reconfiguration as the core mechanism. Findings provide policy implications for cross-regional digital-energy coordination, factor marketization reforms, and differentiated green strategies.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18147057first seen 2026-07-13 06:22:59
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