Institutional Investors and Green Innovation Under Double Externalities: A Machine Learning Optimization Perspective
二重の外部性下における機関投資家とグリーンイノベーション：機械学習最適化の視点から (AI 翻訳)
Siqi Luo, Chengkun Liu
🤖 gxceed AI 要約
日本語
本研究は、中国の移行経済において機関投資家がグリーンイノベーションの二重の外部性（知識スピルオーバーと環境便益）にどう対処するかを分析。固定効果モデルとダブル機械学習（ランダムフォレスト、勾配ブースティング、Lasso、Ridge）を統合し、機関所有がグリーンイノベーションの規模と質を高めることを示す。企業の透明性が補完的ガバナンス手段として機能し、非公式制度（儒教文化の弱さ、市場センチメント）が影響を増幅する。
English
This study examines how institutional investors address the double externalities of green innovation in China's transition economy. Integrating fixed-effects models with double machine learning (random forest, gradient boosting, Lasso, Ridge), it finds that institutional ownership enhances both the scale and quality of green innovation. Corporate transparency acts as a compensatory governance tool, and effects are stronger in weaker Confucian cultures and higher market sentiment.
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
中国の事例だが、日本でも機関投資家によるグリーンイノベーション促進の議論は重要。SSBJや有報での非財務情報開示が進む中、企業透明性の役割や機械学習を用いた因果推論手法は、日本の投資家・企業にも示唆を与える。
In the global GX context
While focused on China, this paper offers methodological innovation by combining econometrics with machine learning for causal inference in sustainable finance. The findings on institutional investors' role in green innovation and the moderating effect of transparency are relevant for global discussions on ESG integration and climate finance, though context-specific.
👥 読者別の含意
🔬研究者:Methodological contribution: double machine learning for causal inference in green innovation studies.
🏢実務担当者:Insights on how institutional ownership and transparency can drive green innovation, applicable to corporate sustainability strategies.
🏛政策担当者:Evidence that institutional investors can compensate for weak formal institutions, suggesting policy support for investor engagement in green innovation.
📄 Abstract（原文）
This paper investigates how institutional investors address the double externalities of green innovation (knowledge spillovers and environmental benefits) in China’s transition economy. Methodologically, we integrate fixed-effects econometric models with a double machine learning framework, employing random forest, gradient boosting, Lasso, and Ridge to optimize causal inference under high-dimensional controls. The results consistently show that institutional ownership significantly enhances both the scale and quality of green innovation, particularly when formal institutions inadequately internalize externalities. Mechanism analysis further reveals that corporate transparency acts as a compensatory governance tool, strengthening the role of institutional investors in mitigating market failures. We also document heterogeneous effects across informal institutional environments, where weaker Confucian culture and stronger market sentiment amplify investor influence. By combining econometric identification with machine learning optimization, this study advances methodological approaches to sustainable finance and offers policy insights into leveraging institutional investors as catalysts for environmental governance.
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.3390/math13223718first seen 2026-05-05 19:07:31
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