Research on the impact of green finance on regional carbon emission reduction and its role mechanisms
グリーンファイナンスが地域の炭素排出削減に与える影響とそのメカニズムに関する研究 (AI 翻訳)
Huiyun Li, Zongbao Yu, Gang Chen, Yingjun Nie
🤖 gxceed AI 要約
日本語
本論文は、中国のグリーン金融改革・革新パイロットゾーンを自然実験として、2010~2021年の270都市のデータを用い、ダブル脱バイアス機械学習モデルでグリーンファイナンス政策の炭素排出削減効果を実証した。結果、政策は炭素削減に有意に寄与し、そのメカニズムはグリーン技術革新と産業構造最適化を通じたものである。また、東部地域や非資源依存都市で効果が高く、他のデジタル政策との相乗効果も確認された。
English
Using China's green finance reform and innovation pilot zones as a quasi-natural experiment, this study employs a double debiased machine learning model on 270 cities from 2010-2021 to assess the impact of green finance policy on regional carbon emission reduction. Results show that the policy significantly reduces emissions through green technology innovation and industrial structure optimization, with stronger effects in eastern regions and non-resource-based cities, and synergistic effects with digital policies like 'Broadband China'.
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
中国のグリーンファイナンス政策の効果を実証した研究だが、日本でもグリーンファイナンスの促進や地域別の政策効果の分析手法として参考になる。特に、政策の異質性や他のデジタル政策との相乗効果の分析は、日本のGX政策設計に示唆を与える。
In the global GX context
This paper provides empirical evidence on the effectiveness of green finance pilot policies in reducing carbon emissions, using a rigorous causal inference method. It offers insights for global policymakers on how green finance can be leveraged alongside digital policies for synergistic effects, and highlights regional heterogeneity that is relevant for designing targeted climate policies.
👥 読者別の含意
🔬研究者:Provides a robust empirical framework (DDML) for evaluating green finance policy impacts, with insights on mechanisms and heterogeneity.
🏢実務担当者:Demonstrates how green finance can support corporate green innovation and industrial upgrading, offering evidence for sustainability strategy.
🏛政策担当者:Shows that green finance pilot policies effectively reduce emissions, especially when combined with digital infrastructure policies, informing policy design.
📄 Abstract(原文)
As a crucial instrument, green finance policy facilitates the transition toward a green and low-carbon regional economic structure, which is essential for realizing the target of "double carbon" and the harmonious coexistence of nature and humans. Therefore, taking the green financial reform and innovation pilot zone as a quasi-natural experiment, we select 270 cities from 2010 to 2021 as research samples and empirically assess the effects of the green finance policy on reducing regional carbon emissions through the double debiased machine learning (DDML) model. This study demonstrates that (1) green finance policy plays a significant role in promoting regional carbon emission reduction, and this conclusion remains valid after a variety of robustness tests; (2) the mechanism of action indicates that green finance policy contributes to regional carbon emission reduction by supporting green technological innovation and promoting the optimization of the industrial structure; (3) the analysis of heterogeneity reveals that green finance policy has a more pronounced effect on carbon emission reduction in the eastern region and in non-resource-based cities than in the central and western regions and in resource-dependent cities; and (4) the pilot policy of "Broadband China", the pilot policy of information consumption, and the comprehensive experimental zone of big data has a synergistic effect on carbon reduction and emission reduction with green finance policy. The findings of this study not only contribute to deepening the understanding of the effects of the green finance pilot policy on regional carbon emission reduction but also provide policy support for local governments to explore green technology comprehensively, grasp new opportunities for green development, and expand the space for sustainable economic development with the assistance of the green finance pilot policy.
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.1038/s41598-025-02481-2first seen 2026-05-05 19:07:05
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。