主管：中国科学院
主办：中国优选法统筹法与经济数学研究会
　　　中国科学院科技战略咨询研究院
中国管理科学

• •    

我国制造业数字化绿色化协同发展时空分异及影响因素研究

李根, 刁天乐, 刘家国, 任宇萌   

  1. 江苏科技大学, 212004
  • 收稿日期:2025-01-13 修回日期:2026-01-08 接受日期:2026-01-23
  • 通讯作者: 刘家国
  • 基金资助:
    国家自科基金项目(71503106); 江苏省软科学资助项目(BR2024029); 江苏高校哲学社会科学研究重大项目(2024SJZD126)

The Spatiotemporal Differentiation and Influencing Factors of Collaborative Development of Manufacturing Digitalization and Greening in China

  1. , 212004,
  • Received:2025-01-13 Revised:2026-01-08 Accepted:2026-01-23

PDF (PC)

0

摘要： 实现数字化绿色化“双化协同”已成为我国制造业高质量发展的必然选择。本文基于“经济—社会—环境”复合系统理论分别构建制造业数字化绿色化综合评价指标，运用CRITIC-TOPSIS综合评价法与耦合协调度模型测算2016-2023年我国30个省市制造业数字化绿色化协同发展水平，采用Dagum基尼系数分解、空间自相关分析及地理探测器模型揭示其时空分异特征及影响因素。研究发现：制造业数字化与绿色化发展水平均呈上升趋势，在发展前期增长快，近两年则下降；二者协同发展水平逐年上升，但整体水平不高且局部不均衡；协同发展水平基尼系数呈现“上升—下降—上升”波浪式浮动趋势，变化幅度较小；协同发展水平空间上存在较为显著的相关性，位于高高集聚区的省份较为稳定，而位于低低集聚区和低高集聚区的省份则逐渐减少；创新能力、政府行为和产业结构是协同发展水平空间分异的主要影响因素，地区教育水平则是重要的间接因素。本文从打造数字经济新引擎、促进数字化绿色化有机融合、制定差异发展战略等方面促进制造业数字化绿色化协同发展。

关键词: 数字化, 绿色化, 协同发展, 时空分异, 地理探测器

Abstract: Achieving "dual coordination" of digitalization and greening has become an inevitable choice for the high-quality development of China's manufacturing industry. This study constructs comprehensive evaluation indicators for the digitalization and greening of China's manufacturing sector based on the theory of "economic-social-environment" composite system and applies methods such as the CRITIC-TOPSIS comprehensive evaluation method, the coupling coordination degree model, Dagum Gini coefficient and its decomposition, spatial autocorrelation analysis, and the geographic detector model. These methods are used to measure the coordinated development level of manufacturing digitalization and greening and to analyze their spatiotemporal variation characteristics and influencing factors. The findings reveal that the development levels of manufacturing digitalization and greening exhibit an overall upward trend, with rapid growth in the early stages and a decline in recent years. The coupling coordination degree has increased yearly, but remains at a low level overall, with regional imbalances. The Gini coefficient of collaborative development levels follows a “rise-fall-rise” wave-like fluctuation pattern with minor variations. Spatially, there is significant correlation in collaborative development levels, with provinces in high-high agglomeration zones remaining stable, while those in low-low and low-high zones gradually decrease. Innovation capability, government actions, and industrial structure are the main drivers of spatial differentiation in collaborative development, while regional education levels play an important indirect role. The study suggests enhancing the collaborative development of manufacturing digitalization and greening by fostering a digital economy engine, promoting organic integration of digitalization and greening, and formulating differentiated development strategies.

Key words: digitalization, greening, collaborative development, spatiotemporal differentiation, geographical detector