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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.
Liu et al. (Wed,) studied this question.