国际经济与贸易学院知识库

在经历了四十余年的高速增长后，中国正面临资源消耗严重、环境污染加剧
的严峻社会问题。党的二十大报告中，习近平总书记强调要推动绿色发展，促进
人与自然的和谐共生。但从人均收入水平状况看，我国将长期处于发展阶段。因
此环境资源破坏和经济发展需求的矛盾是亟需解决的。自十九大报告首次提出应
提高全要素生产率后，二十大报告又再次强调着力提高全要素生产率，推动经济
的高质量发展。考虑非期望产出的绿色全要素生产率是评价绿色经济发展的重要
指标，能同时度量经济增长状况和环境资源节约情况。绿色全要素生产率水平与
我国的产业结构和产业发展有着必然联系。高技术产业具有低能耗、高知识性、
高生产率等特点，当形成产业集聚时发挥其外部性通过知识溢出等效应提升地区
的绿色全要素生产率水平。
本文以 2011-2021 年中国 30 个省份的高技术产业集聚情况和绿色全要素生
产率水平为研究对象，采用区位熵和赫芬达尔--赫希曼指数测算了高技术产业的
MAR 外部性和 Jacobs 外部性水平，采用 Malmquist-Luenberger 指数法测算了地
区的绿色全要素生产率及其分解项技术效率和技术进步水平。建立基准回归模型，
从实证角度检验了集聚外部性对绿色全要素生产率的影响；建立影响机制模型，
检验了集聚外部性对技术效率以及技术进步的影响。
通过对各指标的现状分析以及计量结果分析得出如下结论：第一，各省的
MAR 外部性水平和 Jacobs 外部性水平都能显著提升地区绿色全要生产率水平；
第二，MAR 外部性水平和 Jacobs 外部性水平能显著提升技术效率和技术进步水
平；第三，技术进步在集聚外部性的绿色全要素生产率提升效应中扮演主要角色。
因此本文提出如下建议：第一，全面优化政策，提高绿色全要素生产率；第二，
建设高质量高新技术开发区，发挥集聚池作用；第三，引导企业进行合理集聚，
充分发挥集聚外部性效应。

After more than 40 years of rapid growth, China is facing severe social problems such as severe resource consumption and intensified environmental pollution. However, from the perspective of per capita income level, China will be in a long-term stage of development. Therefore, the contradiction between environmental resource destruction and economic development needs urgently needs to be resolved. Since the 19th National Congress report first proposed the need to increase total factor productivity, the 20th National Congress report once again emphasizes the focus on improving total factor productivity and promoting high-quality economic development. The green total factor productivity considering unexpected output is an important indicator for evaluating the development of green economy, which can simultaneously measure economic growth and environmental resource conservation. The level of green total factor productivity is inevitably related to China's industrial structure and development. High tech industries have the characteristics of low energy consumption, high knowledge, and high productivity. When forming industrial clusters, their externalities are utilized to enhance the green total factor productivity level of the region through knowledge spillover effects.

This paper takes the high-tech industry agglomeration and green total factor productivity level of 30 provinces in China in 2011-2021 as the research object（Xizang, Hong Kong, Macao and Taiwan are temporarily excluded from the study due to lack of data）, uses the location entropy and Herfindahl Herchmann index to measure the MAR externality and Jacobs externality level of high-tech industries, and uses the Malmquist Luenberger index method to measure the regional green total factor productivity and its decomposition technical efficiency and technical progress level. Establishing a benchmark regression model to empirically examine the impact of agglomeration externalities on green total factor productivity; We established an impact mechanism model to examine the impact of agglomeration externalities on technological efficiency and progress.

Based on the analysis of the current situation and measurement results of various indicators, the following conclusions can be drawn:: firstly, the levels of MAR and Jacobs externalities in each province can significantly improve the regional green total productivity level; Secondly, the level of MAR and Jacobs externalities can significantly improve technological efficiency and progress; Thirdly, technological progress plays a major role in the green total factor productivity enhancement effect of agglomeration externalities. Therefore, this article proposes the following suggestions: firstly, comprehensively optimize policies and improve green total factor productivity; Secondly, build high-quality high-tech development zones and leverage the role of agglomeration pools; Thirdly, guide enterprises to conduct reasonable agglomeration and fully leverage the externalities of agglomeration