Do Low-Carbon and New Energy Demonstration City Pilots Generate Synergy? Evaluating the Dual-Pilot Policy on Carbon Emission Performance with Double Machine Learning
低炭素・新能源実証都市パイロットは相乗効果を生むか？二重機械学習による炭素排出パフォーマンスに関するデュアルパイロット政策の評価 (AI 翻訳)
Mingyang Li, Qiancheng Jiang
🤖 gxceed AI 要約
日本語
中国の低炭素都市パイロット（LCCP）と新能源実証都市パイロット（NEDC）のデュアル政策が、炭素排出パフォーマンス（CEP）に与える影響を、2006～2023年の274都市パネルデータと二重機械学習を用いて評価。デュアル政策は単独政策より高いCEP改善効果を示し、グリーン技術革新、産業構造高度化、エネルギー効率向上が経路として特定された。また、経済発展度や環境規制の厳しさによる異質性や、負の空間的波及効果も確認。
English
This study evaluates the synergistic effect of China's Low-Carbon City Pilot (LCCP) and New Energy Demonstration City Pilot (NEDC) on carbon emission performance (CEP) using panel data from 274 Chinese cities (2006-2023) and double machine learning. The dual-pilot policy significantly improves CEP more than either single policy alone, with mechanisms including green technology innovation, industrial upgrading, and energy efficiency. Heterogeneous effects are found based on economic development, path dependence, and environmental governance, along with negative spatial spillovers.
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
本論文は中国の都市レベルでの低炭素・新能源政策の組み合わせ効果を検証しており、日本における自治体の脱炭素政策（例：ゼロカーボンシティ）の設計や、SSBJの地域別情報開示枠組みへの示唆を与える可能性がある。ただし、中国特有の制度やデータに基づくため、日本への直接応用には慎重な解釈が必要。
In the global GX context
This paper provides rigorous causal evidence on the synergy of combining low-carbon and new energy policies at the city level, a relevant topic for global climate policy design. The double machine learning methodology offers a robust approach for evaluating policy mixes, which can inform international frameworks like the UNFCCC or ISSB's policy engagement. The findings on spatial spillovers also highlight the need for coordinated regional policies.
👥 読者別の含意
🔬研究者:Researchers in environmental economics and policy evaluation can learn from the application of double machine learning and the integration of social welfare into carbon performance measurement.
🏛政策担当者:Policymakers can gain insights into the design of combined low-carbon and renewable energy policies to achieve synergistic emission reductions, though contextual differences with Japan require careful adaptation.
📄 Abstract（原文）
To advance sustainable development, China has introduced low-carbon city pilots (LCCP) and new energy demonstration city pilots (NEDC) as important institutional innovations. Using 2006–2023 panel data for 274 Chinese cities, we treat the dual-pilot policy of LCCP and NEDC as a quasi-natural experiment. We measure carbon emission performance (CEP) via a super-efficiency SBM-GML index incorporating social welfare and undesirable outputs, and use double machine learning (DML) to estimate the policy’s impact on CEP. We find the dual-pilot policy is associated with significantly improved urban CEP, with a stronger effect than either single pilot alone. Mechanism tests suggest the policy may contribute to improved CEP by promoting green technology innovation, industrial structure upgrading, and energy efficiency. Heterogeneity test results demonstrate that the dual-pilot policy yields more pronounced impacts in cities characterized by higher economic development, weaker path dependence, and more stringent environmental governance. Additionally, negative cross-regional spatial spillovers are identified. Different from the existing literature, this study integrates social welfare dimensions into CEP’s measurement framework and further validates that the dual-pilot policy generates more outstanding efficiency benefits compared with separate LCCP and NEDC pilots
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18115734first seen 2026-06-06 04:56:34
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