Ecological Indicators (Jun 2024)

Analysis of the spatiotemporal evolution characteristics and policy factors of eco-innovation efficiency in Chinese urban agglomerations

  • Xinliang Wang,
  • Ting Nan,
  • Fei Liu,
  • Yuxin Xiao

Journal volume & issue
Vol. 163
p. 112106

Abstract

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These studies can’t explain the reality of China well, which researched on the spatiotemporal changes and influencing factors of ecological innovation efficiency in a single urban agglomeration. This study describes the spatiotemporal evolutionary characteristics of eco-innovation efficiency in eight major urban agglomerations in China based on the SE-U-SBM (Super-Efficiency Slacks-Based Measure) method of sequential DEA, Dagum Gini coefficient, kernel density estimation, and coefficient of variation method. The SDID and DID models were constructed to identify the policy causes of their spatiotemporal evolutionary characteristics. The results reveal that (1) The temporal and spatial evolution of eco-innovation efficiency in urban agglomerations is in “U” and inverted “U” shapes with the inflection point occurring in 2012. (2) Among the eight major urban agglomerations, the rest of them have a “U”-shaped trend except for the inverted “N”-shaped temporal trend of eco-innovation efficiency in the Harbin-Changchun (HC) and Guanzhong (GZ). The spatial convergence trend is the strongest in the Yangtze River’s midstream (UMYR) and Beijing-Tianjin-Hebei (BTH) and weakest in the Yangtze River Delta (YRD), Guangdong-Hong Kong-Macao (GHM), and Chengdu-Chongqing (CC). (3) Policies such as the Low-Carbon City Pilot (LCCP), National Demonstration City for the Circular Economy (CEDC), and Ecological Compensation Pilot (ECP) have caused the eco-innovation efficiency of urban agglomerations to increase after 2012. Additionally, the Ecological Civilization in the Pioneer Demonstration Zone (ECDAP) and LCCP can explain the inverted “N”-shaped characteristics of the urban agglomerations of the HC and GZ. Improvements in eco-innovation efficiency in the UMYR and Central Plains (CP) depend on ECP implementation. (4) ECDAP and CEDC can explain the inverted “U”-shaped spatial convergence of the eco-innovation efficiency in urban agglomerations. The spatial convergence of eco-innovation efficiency in the CP, GZ, and HC depends on policies other than the CEDC. The strong spatial convergence of BTH and UMYR was due to the ECDAP implementation.

Keywords