“Emergence” and “Dissolution” of Green Innovation Bubbles in Power Industry Chain Enterprises
電力産業チェーン企業におけるグリーンイノベーションバブルの「発生」と「解消」 (AI 翻訳)
Yanbing Zhang, Changzheng Zhang, Chengyu Li
🤖 gxceed AI 要約
日本語
本研究は、2016年から2023年までの中国の電力産業チェーン企業を対象に、グリーンイノベーションバブルの発生要因と抑制策を分析している。能力・動機・機会（AMO）フレームワークに基づき、ダブル機械学習（DDML）を用いて因果関係を特定し、勾配ブースティングツリー（GBT）とSHAP解釈可能性分析により非線形関係を明らかにした。省エネ政策とグリーン金融政策がバブル形成を抑制すること、またそのメカニズムとして技術スピルオーバーや同業模倣などが確認された。上流企業と中流企業で政策への感応度が異なることも示された。
English
This paper investigates the emergence and dissolution of green innovation bubbles among Chinese power industry chain enterprises from 2016 to 2023. Using the AMO framework and double machine learning (DDML), it identifies causal drivers, and employs gradient boosting trees with SHAP for interpretability. Results show that energy-saving and green finance policies significantly inhibit bubble formation through innovation incentive, peer imitation, and technology spillover channels. Upstream and midstream enterprises respond differently to policies.
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やグリーン金融政策が進む中、グリーンイノベーションの質的評価が重要になっている。本論文は中国の事例だが、イノベーションバブルの抑制策としての政策効果の分析は、日本の電力業界における過剰投資の防止にも示唆を与える。また、機械学習を用いた因果推論手法は、日本のGX政策評価に応用可能である。
In the global GX context
This Chinese case study on green innovation bubbles in the power sector offers methodological and policy insights for global GX practitioners. The use of double machine learning and SHAP for causal inference and interpretability is relevant to ISSB/TCFD-aligned impact assessment. The finding that energy-saving and green finance policies curb bubbles via peer imitation and technology spillovers highlights the importance of policy design in preventing greenwashing and ensuring efficient capital allocation.
👥 読者別の含意
🔬研究者:Researchers in green innovation and energy economics can adopt the DDML+SHAP approach for causal analysis of policy impacts on innovation quality.
🏢実務担当者:Corporate sustainability teams can use these findings to assess overinvestment risks in green innovation and refine R&D strategies.
🏛政策担当者:Policymakers can learn how energy and green finance policies deflate innovation bubbles via spillover and imitation mechanisms, aiding in efficient policy design.
📄 Abstract（原文）
The clean and low-carbon transition of new-type power systems imposes increasingly stringent demands on green technology innovation among enterprises along the power industry chain. Identifying the drivers and potential remedies for green innovation bubble can offer China-originated solutions to the sustainable development of the global power sector. This paper focuses on Chinese power industry chain enterprises over the period 2016–2023. Drawing on the AMO framework, a three-dimensional analytical framework encompassing ability, motivation, and opportunity is developed. Double machine learning (DDML) is employed to perform benchmark regression and causal identification. Subsequently, gradient boosting trees (GBT) combined with SHAP interpretability analysis are applied to uncover nonlinear relationships and heterogeneous transmission pathways among key variables. The results indicate that energy-saving policies and green financial policies significantly inhibit the formation of the green innovation bubble in power industry chain enterprises. Specifically, these policies curb the green innovation bubble via three channels: an innovation incentive management mechanism, a peer imitation and convergence mechanism, and an industrial chain technology spillover mechanism. Upstream enterprises exhibit greater sensitivity to direct regulatory measures and backward technology spillovers from energy-saving and green finance policies, whereas midstream enterprises are more reliant on peer carbon emission pressure. The findings are validated through cross-verification among DDML, mechanism analysis, and interpretable analysis. The results provide empirical evidence and policy implications for optimizing energy-saving and green finance policies and for precisely deflating the green innovation bubble.
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.3390/admsci16060251first seen 2026-07-09 04:38:29
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