• 资源经济 •

中国绿色经济效率空间关联网络演变特征及影响因素

1. 1.曲阜师范大学地理与旅游学院,日照 276826
2.日照市国土空间规划与生态建设重点实验室,日照 276826
3.辽宁师范大学海洋可持续发展研究院,大连 116029
• 收稿日期:2021-01-06 修回日期:2021-04-14 出版日期:2021-10-25 发布日期:2021-12-25
• 通讯作者: 韩增林,男,山东商河人,教授,研究方向为经济地理。E-mail: hzl@lnnu.edu.cn
• 作者简介:赵林,男,山东东平人,副教授,研究方向为经济地理与区域可持续发展。E-mail: zhaolin19880112@126.com
• 基金资助:
国家自然科学基金项目(41701117);国家自然科学基金项目(42071150);山东省高等学校青创科技支持计划项目(2020RWG010)

Spatial correlation network and influencing factors of green economic efficiency in China

ZHAO Lin1,2,3(), CAO Naigang1, HAN Zenglin3(), GAO Xiaotong1

1. 1. School of Geography and Tourism, Qufu Normal University, Rizhao 276826, China
2. Rizhao Key Laboratory of Territory Spatial Planning and Ecological Construction, Rizhao 276826, China
3. Institute of Marine Sustainable Development, Liaoning Normal University, Dalian 116029, China
• Received:2021-01-06 Revised:2021-04-14 Online:2021-10-25 Published:2021-12-25

Abstract:

Clarifying the characteristics of change and driving factors of the spatial correlation network structure of green economic efficiency can provide some reference for promoting regional coordination of green development. By constructing a green economic efficiency evaluation system, this study used the Super-Epsilon-Based Measure (Super-EBM) model that considers undesired output to measure the green economic efficiency in China’s provinces from 2000 to 2018. The structural change characteristics of green economic efficiency and its effect were analyzed by using a modified gravity model and the social network analysis method, and the influencing factors were identified by the quadratic assignment procedure method. The results show that: (1) From 2000 to 2018, China’s green economic efficiency showed a S-shaped trend that declined first and then increased, showing spatial imbalance between the regions with east > west > central region. (2) China’s green economic efficiency has gradually developed a complex network structure with the Beijing-Tianjin area and the Yangtze River Delta as the polar core. The spatial correlation between provinces has been increasing, and the stability of the network structure has gradually enhanced. However, there is still a relatively strict hierarchical structure of the overall network, and the network structure needs to be further optimized. (3) The Beijing-Tianjin area, the Yangtze River Delta, and the Pearl River Delta are located in the core areas of the network and have both “intermediary” and “bridge” functions. Northwest and northeast regions are located in the periphery areas of the network. Jiangxi, Chongqing, Gansu, and Guizhou have played the role of “connecting the east and the west, and connecting the south and the north” in the spatial correlation network. (4) The spatial correlation network of green economic efficiency is affected by the joint action of multiple factors. Geographical proximity and the difference in economic development levels have significant positive impact on the improvement of the spatial correlation strength of green economic efficiency. The difference in scientific and technological innovation levels has an inhibitory effect on the spatial correlation network. The positive effects of the differences in resource endowment and opening up tend to weaken. Optimizing the spatial correlation network of green economic efficiency is of great significance for improving the regional green economic efficiency and reducing the regional differences of green development.