Evaluation and determinants of total unified efficiency of China's manufacturing sector under the carbon neutrality target

Ctu da has one of the st rgest m'iota ukcmring Mclusnies in he mald. At he same dme, the carbon emissions induced oy China's manufactming production pose a severe threat to the environment. With China recently setting the carbon neutrality target, the manufacturing indusny faces more urgent messtue to reduce emissions By adopting the data enveloprnent analysis, this paper first evaluates the total factor ttnified efficiency of China's manufacnuing industry at regional and sectoral levels. Then, the panel robit model is ttsed to examine the deteiminants of the efficiency level of China's manufacttuing industry. ktrthel, this paper utilizes the economic importance indicator to rank the importance of each factor. The results show regional and sectoral aficiency heterogeneity in China's manufactufing indusny. The raid and western China had a slower efficiency growth than the coastal cohort. in addition, the light manufacttuing industry showed lower efficiency than the heavy manufacturing industly. Labor piality, industrial agglomeration, and environmental regttlation can positively impact efficiency. Stuprisingly, degrees tu- results show that R&D investment and openness hann the manufacturing industry's efficiency. he funne policy design should foctts on cultivating specialized talent, encouraging point to-point manufacttuing agglomention, and unifying the emission mandate across the nation. Meanwhile, China should be more cautious of the emissions fiorn foreign funded rnanufacturing enterprises and set emission standards on exports of manttfactured goods.

Automated summary

China has one of the strongest manufacturing industries in the world, but the carbon emissions from its production pose a serious threat to the environment. This study evaluates the efficiency of China's manufacturing industry using data envelopment analysis and examines the factors that affect its efficiency using a panel regression model. The results show regional and sectoral heterogeneity in efficiency, with slower growth in efficiency in the western and inland regions compared to the coastal areas, and lower efficiency in the light manufacturing industry compared to heavy manufacturing. Factors such as labor quality, industrial agglomeration, and environmental regulation positively impact efficiency, while R&D investment and openness have a negative impact. The study suggests policy measures to focus on nurturing specialized talent, promoting point-to-point manufacturing agglomeration, and unifying emission standards nationwide, while also being cautious of emissions from foreign-funded manufacturing enterprises and setting emission standards for manufactured goods exports.