摘要
随着绿色发展理念得到贯彻落实,科学测算与解析绿色全要素生产率增长对于经济高质量发展意义重大。为直接完整测度区域层面的绿色全要素生产率增长及其构成,本文构造聚合环境广义Luenberger-Hicks-Moorsteen生产率指标及分解子项,利用蒙特卡洛模拟检验该指标测算结果的有效性,发现其相对于聚合L指标更具优势。本文根据2003—2019年地级以上城市的投入产出指标数据,测算八大综合经济区的绿色全要素生产率增长及动力来源。结果表明,八大经济区绿色全要素生产率均实现增长,但增速呈现分化特征。八大综合经济区绿色全要素生产率增长在技术层面的动力主要源于技术进步与规模效率增长,在要素层面主要源于期望产出效率增长与非期望产出治理效率增长。技术进步和期望产出效率增长是八大经济区绿色全要素生产率增长差异的主要成因。本文结论对于有针对性地提升地区绿色全要素生产率具有启示意义。
With the implementation of the green development philosophy in China, scientific measurement and analysis of the growth of green total factor productivity(GTFP) are of great significance for high-quality development.In order to measure the growth of GTFP and its compositions at the regional level, this paper innovatively proposes the aggregate environmental generalized Luenberger-Hicks-Moorsteen(AEGLHM) productivity indicator and its decomposition subitems.The results of Monte Carlo simulation show that the AEGLHM indicator is more effective and accurate than the aggregate Luenberger indicator.Then, this paper applies this new indicator to measure the growth of GTFP and its components in eight economic zones with data of prefecture-level cities from 2003 to 2019.Results show that the GTFP of the eight economic zones has increased, but the growth rate highlights the characteristics of polarization.Moreover, the main driving forces of GTFP growth in the eight economic zones are technological progress and scale efficiency growth from the technical perspective, as well as desirable output efficiency growth and undesirable output governance efficiency growth from the factorial perspective.Finally, this paper finds that the technological progress and the desirable output efficiency growth are the dominant causes that drive the difference of GTFP growth in the eight economic zones.The conclusions have some enlightenment for improving regional GTFP with targeted strategies.
作者
程开明
李泗娥
CHENG Kaiming;LI Sie(School of Statistics and Mathematics,Zhejiang Gongshang University)
出处
《经济理论与经济管理》
CSSCI
北大核心
2022年第7期49-65,共17页
Economic Theory and Business Management
基金
国家社科基金重点项目(19ATJ003)
浙江省重点建设高校优势特色学科(浙江工商大学统计学)项目(1020JYN4120004G-081)的资助。