Can green credit promote the synergy of carbon reduction and efficiency improvement in enterprises?—The transmission mechanism and contextual effect of green technology innovation
グリーンクレジットは企業の炭素削減と効率向上の相乗効果を促進できるか？—グリーン技術イノベーションの伝達メカニズムとコンテクスト効果 (AI 翻訳)
Zizhen Cai, Xinli Li
🤖 gxceed AI 要約
日本語
本論文は、中国の電力業界の上場企業データ（2010-2021年）を用い、グリーンクレジットが企業の炭素削減と効率向上の相乗効果を促進するメカニズムを実証分析。グリーン技術イノベーションが重要な媒介役割を果たすことを確認し、内部ガバナンスなどのコンテクスト効果も検討。グリーンファイナンスと技術革新に関する理論を拡張し、政策設計に示唆を与える。
English
Using data from Chinese listed power companies (2010-2021), this paper empirically analyzes how green credit promotes synergy between carbon reduction and efficiency improvement. It finds green technology innovation as a key mediator and explores contextual effects like internal governance. Provides theoretical expansion and practical insights for green finance policy and low-carbon strategies.
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「ダブルカーボン」目標下でのグリーンクレジット政策の効果を実証した本論文は、日本においてもグリーンファイナンス政策の設計や企業の脱炭素戦略の策定に示唆を与える。特に、グリーン技術イノベーションを媒介とするメカニズムは、日本企業の環境投資行動の理解に有用である。
In the global GX context
This paper provides empirical evidence on the effectiveness of green credit as a transition finance tool, demonstrating its role in driving corporate decarbonization and efficiency gains through green innovation. The findings on contextual factors (e.g., governance) are relevant for designing green finance policies globally, particularly for power sectors undergoing transition.
👥 読者別の含意
🔬研究者:Provides empirical evidence on the mediating role of green technology innovation between green credit and decarbonization synergy, expanding theoretical frameworks in green finance and innovation economics.
🏢実務担当者:Offers evidence that green credit can simultaneously enhance environmental performance and economic efficiency via innovation, useful for corporate sustainability strategy and green investment decisions.
🏛政策担当者:Demonstrates the effectiveness of green credit in promoting carbon reduction and efficiency synergy, with implications for optimizing policy design and considering firm-level contextual factors.
📄 Abstract（原文）
Against the background of the "Dual Carbon" goal, enterprises, as the key subjects of economic growth and carbon emission reduction, face the challenge of realizing the coordinated development of environmental performance and economic benefits. Green technology innovation is the core path to resolve the contradiction between "growth and emission reduction", but its features of high input and high risk leave enterprises insufficient motivation. As an important policy tool, green credit provides impetus for enterprises' green transformation through financial support and incentive-restraint mechanisms. Based on the data of listed companies in China's power industry from 2010 to 2021, this paper constructs a theoretical framework of "green credit—green technology innovation—synergy of carbon reduction and efficiency improvement", and verifies the positive effect of green credit on enterprises' carbon reduction and efficiency synergy as well as its functioning mechanism through empirical analysis. The study finds that green credit can significantly improve enterprises' level of green technology innovation, and further promote the synergy of carbon reduction and efficiency improvement; green technology innovation plays a vital intermediary role between green credit and enterprises' carbon reduction-efficiency synergy; the positive effect of green credit varies notably under different internal and external contexts, such as internal governance level. The conclusions of this paper not only expand the theoretical boundary in the fields of green finance and technological innovation, but also provide practical reference for the government to optimize green credit policies and for enterprises to formulate low-carbon strategies.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.54254/2977-5701/2026.35407first seen 2026-07-16 05:25:27
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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。