Heterogeneous Effects of Green Finance on Urban Decarbonization: Evidence from 285 Cities in China
グリーンファイナンスの都市脱炭素化に対する不均一効果：中国285都市の証拠 (AI 翻訳)
Xueyang Li, Jin Ma
🤖 gxceed AI 要約
日本語
本研究は、中国285都市のデータを用いて、グリーンファイナンスが炭素排出強度に与える影響を、計量経済モデルと機械学習（SHAP分析）により検証した。グリーンボンドとグリーン投資が特に効果的であり、その効果は都市の開発段階に応じて異なり、第4・5線都市で最大となる。また、エネルギー構造の最適化が主要な伝達経路であることが示された。これらの結果は、地域差に配慮したグリーンファイナンスシステム構築への政策示唆を提供する。
English
This study examines the impact of green finance on city-level carbon intensity using econometric models and machine learning (SHAP) on data from 285 Chinese cities. Green bonds and green investment show the strongest decarbonization effects, with greater impacts in lower-tier cities. Energy structure optimization is the primary transmission channel. The findings provide guidance for building a regionally differentiated green finance system.
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や移行金融の枠組みが進む中、地域ごとの脱炭素効果の差異を定量的に示す本知見は、日本の自治体向けグリーンファイナンス施策の設計に示唆を与える。
In the global GX context
As global disclosure frameworks (ISSB, TCFD) and transition finance evolve, this paper provides empirical evidence from China on how green finance instruments vary in effectiveness across city tiers, offering insights for tailoring financial policies to local contexts.
👥 読者別の含意
🔬研究者:Provides a methodological template combining econometrics and ML (SHAP) for causal analysis of green finance impacts on decarbonization.
🏢実務担当者:Highlights which green finance instruments (bonds, investment) are most effective and how to target cities based on development level.
🏛政策担当者:Evidence for designing tier-specific green finance policies to achieve inclusive decarbonization, applicable beyond China.
📄 Abstract（原文）
While green finance has become a key instrument for low-carbon city transitions, its actual decarbonization effects and transmission mechanisms remain unclear. This study employs econometric models and machine learning-based analysis to examine whether and how green finance reduces city-level carbon intensity. Results show that green finance significantly lowers carbon intensity, with green bonds and green investment having the strongest impacts and evident spatial spillovers. The effects vary by development level, being most pronounced in Fourth- and Fifth-tier cities. Mediation analysis reveals that green finance operates mainly through energy structure optimization, followed by industrial upgrading, foreign direct investment, and technological innovation. SHAP analysis confirms substantial differences across financial instruments, with green bonds, funds, and credit contributing most to decarbonization. Moreover, the marginal impact is stronger in cities with low technological capacity, high industrial dependency, and coal-based energy mixes. These findings provide theoretical support and policy guidance for building a multi-level, regionally differentiated green finance system to promote inclusive low-carbon transitions. Keywords: Green Finance; Carbon Intensity; Decarbonization Effect; Machine Learning; City
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.63386/628630first seen 2026-06-09 04:52:43
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