资源科学 ›› 2021, Vol. 43 ›› Issue (10): 1933-1946.doi: 10.18402/resci.2021.10.01

• 资源经济 •    下一篇

中国绿色经济效率空间关联网络演变特征及影响因素

赵林1,2,3(), 曹乃刚1, 韩增林3(), 高晓彤1   

  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

摘要:

厘清绿色经济效率空间关联网络结构的演变特征及驱动因素,可为推进区域协同绿色发展提供参考依据。本文通过构建绿色经济效率评价体系,采用考虑非期望产出的Super-EBM模型对2000—2018年中国省域绿色经济效率进行了测度,运用修正的引力模型和社会网络分析方法分析了绿色经济效率的空间关联网络结构演变特征与关联网络效应,借助二次指派程序方法识别了其影响因素。研究表明:①2000—2018年中国绿色经济效率呈先下降后上升的“S”型变化趋势,空间上呈现东部>西部>中部的地区间不均衡特征。②中国绿色经济效率逐渐呈现出以京津、长三角为极核的复杂网络结构形态,省际间空间关联性日益提升,网络结构的稳定性逐渐增强,但整体网络仍存在较为森严的等级结构,网络结构尚待进一步优化。③京津、长三角和珠三角位于关联网络的中心位置,并兼具“中介”和“桥梁”功能;西北和东北地区处在关联网络的边缘位置;江西、重庆、甘肃和贵州在关联网络中具有“承东启西、贯南通北”的纽带功能。④绿色经济效率空间关联网络受到多种因素的综合作用,地理空间邻近和经济发展水平差异对空间关联强度具有正向效应,科技创新差异对空间关联网络具有抑制作用,资源禀赋和对外开放差异的正向效应趋于减弱。优化绿色经济效率空间关联网络,对于提高区域绿色经济效率和缩小绿色发展的区域差异具有重要意义。

关键词: 绿色经济效率, 空间关联网络, 影响因素, 效应, 社会网络分析, Super-EBM模型, 中国

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.

Key words: green economic efficiency, spatial correlation network, influencing factors, effects, social network analysis, Super-EBM model, China