Effects of new energy vehicle industry policies on manufacturing carbon emission efficiency: evidence from econometrics and doubly robust causal machine learning
新エネルギー自動車産業政策が製造業の炭素排出効率に与える影響：計量経済学と二重ロバスト因果機械学習からのエビデンス (AI 翻訳)
Wenxin Liu, Tao Xie
🤖 gxceed AI 要約
日本語
本研究は、中国30省のパネルデータ（2010-2023年）を用いて、新エネルギー自動車（NEV）産業政策が製造業の炭素排出効率に与える影響を分析。伝統的計量手法と機械学習を組み合わせた枠組みで、政策指数1%上昇が効率を0.0064向上させることを示した。地域的な不均一性も確認され、東部・西部や財政支援の強い省で効果が大きい。
English
This study examines the impact of new energy vehicle (NEV) industry policies on manufacturing carbon emission efficiency using panel data from 30 Chinese provinces (2010-2023). A hybrid framework combining traditional econometrics and machine learning reveals that a 1% increase in the policy index boosts efficiency by 0.0064. Regional heterogeneity shows stronger effects in eastern/western China and provinces with higher fiscal support.
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NEV政策が製造業の炭素効率に与える効果を実証した研究。日本のGX戦略においても、EV普及政策と産業構造転換の連動を検討する上で参考になる。特に、地域別の効果の違いや財政支援の重要性は、日本の地域特性を考慮した政策設計に示唆を与える。
In the global GX context
This paper provides robust empirical evidence on how industrial policy for new energy vehicles can drive manufacturing carbon efficiency. It adds to the global discourse on decarbonization policy design, particularly for countries (e.g., EU, Japan) promoting EV transitions and seeking to align manufacturing sector emissions with net-zero targets.
👥 読者別の含意
🔬研究者:Offers a rigorous causal inference framework combining econometrics and machine learning for policy evaluation in carbon efficiency.
🏢実務担当者:Provides evidence that NEV policies can improve manufacturing carbon efficiency, supporting corporate strategy alignment with policy trends.
🏛政策担当者:Highlights the importance of regional fiscal support and policy intensity in designing effective NEV and carbon reduction policies.
📄 Abstract（原文）
In light of global climate change and carbon-neutrality targets, carbon emissions from the manufacturing sector and the development of the new energy vehicle (NEV) industry have become central to policy agendas worldwide. NEV industry policies are a key instrument for enhancing manufacturing carbon-emission efficiency. Using provincial panel data from 30 Chinese provinces over 2010–2023, this study examines the effect of NEV industry policies on manufacturing carbon-emission efficiency. A hybrid analytical framework that combines traditional econometric methods with machine-learning techniques is employed for empirical analysis. The results indicate that a one–percentage-point increase in the policy index for the NEV industry is associated with a 0.0064 increase in manufacturing carbon-emission efficiency, significant at the 1% level. Regional heterogeneity is evident, with more pronounced policy effects in eastern and western China and in provinces with stronger fiscal support. Using a panel-consistent doubly robust causal machine-learning framework, we find that stronger NEV policy intensity improves manufacturing carbon emission efficiency. The analysis also highlights the importance of panel-aware estimation, overlap (common-support) diagnostics, and cluster-robust inference when evaluating policies using observational data. CATE-based heterogeneity analysis suggests that effect differences across covariate quantiles are generally modest, with the clearest separation observed for social consumption.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3389/fenvs.2026.1761204first seen 2026-05-17 07:23:38 · last seen 2026-05-20 05:16:27
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