Do Multi-Pilot Policies Accelerate Carbon Neutrality? A Reassessment of Low-Carbon City and Innovative City Policies Using a Double Machine Learning Model
マルチパイロット政策はカーボンニュートラルを加速するか?ダブルマシンラーニングモデルを用いた低炭素都市政策と革新的都市政策の再評価 (AI 翻訳)
(著者不明)
🤖 gxceed AI 要約
日本語
中国266都市のデータを用い、低炭素都市パイロット(LCCP)と革新都市パイロット(ICP)の効果をダブルマシンラーニングで推定。両政策は都市カーボンニュートラル性能(UCNP)を向上させ、相乗効果も確認。技術革新と産業高度化が経路。非資源型・沿岸都市で効果大。
English
Using double machine learning on 266 Chinese cities (2010-2020), this study finds that the Low-carbon City Pilot and Innovative City Pilot both significantly enhance urban carbon neutrality performance, with notable synergistic effects. Mechanisms include technological innovation and industrial upgrading, with stronger effects in non-resource-based and coastal cities.
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
日本でも複数の自治体主導の脱炭素政策が存在するが、本論文はパイロット政策間の連携強化の重要性を示唆。SSBJや地域脱炭素ロードマップの策定において、政策の相乗効果を考慮する必要性を提起。
In the global GX context
This paper demonstrates the value of machine learning for causal policy evaluation in carbon neutrality, offering global policymakers a framework to assess multi-policy synergy. It supports TCFD/ISSB-aligned evidence-based policymaking.
👥 読者別の含意
🔬研究者:Demonstrates application of double machine learning for evaluating multi-policy synergy in carbon neutrality, advancing causal inference in sustainability.
🏢実務担当者:City planners can learn from the finding that coordinating low-carbon and innovation policies amplifies effects.
🏛政策担当者:Indicates that aligning multiple pilot policies can accelerate carbon neutrality, urging policy coordination.
📄 Abstract(原文)
<p>Achieving carbon neutrality requires not only setting ambitious goals but also effectively integrating existing policy instruments. Although multiple pilot programs have been launched to promote green transformation, empirical evidence on whether these initiatives can synergistically advance regional low-carbon transition processes remains fragmented. This study reassesses the impacts of two landmark policies in China—the Low-carbon City Pilot (LCCP) and the Innovative City Pilot (ICP). We introduce an urban carbon neutrality performance (UCNP) index covering development foundation, low-carbon status, and emission reduction trends, thereby providing a comprehensive evaluation framework for the research. Based on data from 266 Chinese cities over the period 2010–2020, this paper employs a Double Machine Learning (DML) approach to examine the individual effects, synergistic effects, and underlying mechanisms of these two policies. Baseline regression results indicate that both the LCCP and ICP significantly enhance UCNP, and the two policies exhibit a notable synergistic effect. Moreover, compared with cities participating in only a single pilot, cities designated as dual pilot cities demonstrate stronger policy effects in improving UCNP. Heterogeneity analysis reveals that the impact of the dual pilot policy (DPP) on urban carbon neutrality performance is more pronounced in non-resource-based cities and coastal cities. This study also finds that policy effects vary across different pilot batches. Mechanism analysis shows that dual pilot cities influence the carbon neutrality process through two mediating channels: technological innovation and industrial upgrading. The findings indicate that the two pilot policies exert significant positive effects on advancing urban carbon neutrality processes. To further promote regional green and low-carbon development, policymakers and implementers urgently need to strengthen the coordination of policy objectives, policy contents, and policy instruments among different pilot policies. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;</p>
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.30955/gnj.08399first seen 2026-07-23 05:14:23
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