中国全要素能源效率测算及其驱动因素

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摘要为敦促中国全要素能源效率提升,遵循中国全要素能源效率测算、驱动因素分析的研究路径,首先以中国30个省份、直辖市、自治区(不包括西藏、香港、澳门和台湾)的面板数据为研究对象,将能源足迹纳入非期望产出指标,旨在使全要素能源效率测度指标更加科学;其次采用更适宜于面板数据的动态随机非参数数据包络分析法(StoNED)测度全要素能源效率,并按全国、八大经济区、分省3个层面分析测算结果;再次结合因变量为截断数据且可能存在空间效应的特性,构建包含环境规制、对外贸易、科研经费、能源消费结构、人口规模、产业结构6个驱动因素的空间误差面板Tobit回归模型(SEM-Tobit),研究中国全要素能源效率驱动因素效应;最后探求提升全要素能源效率的政策建议.研究发现:就全要素能源效率测度结果而言,从全国来看,中国全要素能源效率大致呈先下降后上升的分布特征;从区域来看,八大综合经济区全要素能源效率呈现由沿海向内陆逐渐收敛的发展态势;从具体省域来看,沿海省份全要素能源效率相对较高,但同一地区不同省份间全要素能源效率水平存在一定差异;从驱动因素分析结果来看,环境规制、能源消费结构对全要素能源效率呈现显著的负向作用,而产业结构、人口规模、对外贸易、科研经费对全要素能源效率呈现积极推动作用.

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关键词 ：全要素能源效率, 动态StoNED模型, 空间误差面板Tobit回归, 驱动因素分析

Abstract：In order to urge the improvement of total factor energy efficiency in China, Firstly, the panel data of 30 provinces, municipalities directly under the central government and autonomous regions of China (excluding Tibet, Hong Kong, Macao and Taiwan) were used to incorporate the energy footprint into the non-expected output indicators, aiming to make the total factor energy efficiency measures more scientific. Secondly, a dynamic stochastic non-parametric data envelopment analysis (StoNED), which is more suitable for panel data, was used to measure total factor energy efficiency, and the results are analysed at three levels: national, eight economic regions and sub-provinces. A spatial error panel Tobit regression model (SEM-Tobit) was constructed to investigate the effects of the drivers of total factor energy efficiency in China, and finally, policy recommendations are explored to improve total factor energy efficiency. The study finds that: in terms of total factor energy efficiency measurement results, from a national perspective, China's total factor energy efficiency generally shows a decreasing and then increasing distribution, from a regional perspective, the total factor energy efficiency of the eight comprehensive economic zones shows a gradual convergence from coastal to inland, from a provincial perspective, the total factor energy efficiency of coastal provinces is relatively high, but there are some differences in the level of total factor energy efficiency between different provinces in the same region. From the analysis of the drivers, environmental regulation and energy consumption structure have a significant negative effect on total factor energy efficiency, while industrial structure, population size, foreign trade and research funding have a positive effect on total factor energy efficiency.

Key words： total factor energy efficiency dynamic StoNED model spatial error panel-Tobit regression driver analysis

收稿日期: 2021-10-16

PACS:

X24

基金资助:国家社会科学基金资助项目(19VDL002,21&ZD148)

通讯作者: 陈菁泉,研究员,9488297@qq.com E-mail: 9488297@qq.com

作者简介: 陈菁泉(1979-),男,辽宁辽阳人,研究员,博士,研究方向为世界经济、中国城市热点问题等.发表论文20余篇.

引用本文:

陈菁泉, 连欣燕, 马晓君, 米军. 中国全要素能源效率测算及其驱动因素[J]. 中国环境科学, 2022, 42(5): 2453-2463. CHEN Jing-quan, LIAN Xin-yan, MA Xiao-jun, MI Jun. Total factor energy efficiency measurement and drivers in China. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(5): 2453-2463.

链接本文:

http://www.zghjkx.com.cn/CN/ 或 http://www.zghjkx.com.cn/CN/Y2022/V42/I5/2453

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