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绿色技术创新、环境规制与能源强度——基于中国制造业的实证分析 被引量:78

Green technology innovation,environmental regulation and energy intensity——An empirical study based on Chinese manufacturing industry
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摘要 基于核密度函数模型描绘我国制造业能源强度的动态演变规律和行业异质性,从行业环境规制视角构建绿色技术创新对能源强度的非线性门槛模型,探讨行业环境规制的门槛效应和时序变化,进而利用各因素多个分位数全面分析对能源强度的影响效应。研究结果表明:样本期间我国制造业能源强度整体发展较为收敛,行业差距有扩大趋势;能源相对价格、技术改造和人力资本水平与能源强度显著负相关;企业规模和技术引进在一定程度上制约能源强度降低;环境规制强度的增加促进绿色技术创新降低能源强度,但受限于行业低环境规制(ER≤0.101)和中环境规制(0. This paper describes the dynamic evolution of China's manufacturing energy intensity and industry heterogeneity based on the Kernel density function model. In view of environmental regulation and constructs a nonlinear threshold model for green technology innovation to energy intensity. Moreover,it discusses the threshold effect and the change of time and employs multiple quantile to comprehensively analyze the effect on energy intensity. The results show that the overall development of China's manufacturing energy intensity is more convergent and the gap of the industry is expanding during the sample period. The relative price of energy,technological transformation and human capital are significantly negatively correlated with energy intensity. The increase of environmental regulation intensity promotes green technology innovation to reduce energy intensity in section ER≤0. 101 and 0. 101 ER≤2. 160. Industry heterogeneity exists in environmental regulation and there is a big difference in the same industry in different years. Quantile regression results show that threshold regression exaggerates the positive impact of firm size on energy intensity and only high human capital could significantly reduce energy intensity.
作者 徐建中 王曼曼 XU Jian - zhong;WANG Man - man(School of Economics and Management, Harbin Engineering University, Harbin 150001, China)
出处 《科学学研究》 CSSCI CSCD 北大核心 2018年第4期744-753,共10页 Studies in Science of Science
基金 国家自然科学基金资助项目(71273072) 黑龙江省青年科学项目(JJ2016QN0645)
关键词 绿色技术创新 环境规制 能源强度 制造业 门槛效应 分位数回归 green technology innovation environmental regulation energy intensity manufacturing industry threshold effect quan-tile regression
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