Spatial Correlation Network of Eco-Efficiency of Service Industry in China and Its Driving Mechanism
Clarifying the spatial correlation network and driving mechanism of eco-efficiency of service industry in China is conducive to providing a basis for promoting green transformation of service industry in a coordinated manner.The Super-EBM model based on undesirable output,the modified gravity model and the social network analysis were combined to explore the evolution characteristics and the driving mechanisms of the spatial correlation network of eco-efficiency of service industry in China.The main results are as follows.① During the study period,the eco-efficiency of provincial service industry in China showed an evolutionary trend of fluctuation and decline.The spatial correlation strength of service industry eco-efficiency among provinces was increasing,showing a distribution pattern of"dense in the east and sparse in the west,strong in the east and weak in the west".② The eco-efficiency of service industry in China exhibited the characteristics of multi-thread spatial spillover,but the overall network density was still relatively low and the cohesion has declined.Provinces such as Beijing,Shanghai and Jiangsu were always dominant in the eco-efficiency spatial correlation network of service industry in China,which were the important bridge for other provinces to link the spatial correlation of eco-efficiency of service industry.③ The spatial spillover effect between net spillover,net benefit,broker and two-way spillover sectors became stronger,but the spatial connection within the sector tended to be weaken.At the end of the study period,there was no spatial connection among the members within the net spillover sector or the broker sector.④ The combination of factor input potential difference,market resource allocation and government macro-regulation drove the formation of spatial correlation network of eco-efficiency in service industry,and accelerated the evolution and reorganization of spatial correlation network through the changes in the direction and degree of each driving factor.
green and low carbon transitionservice industryeco-efficiency of service industryspatial correlation networksocial network analysis
万方数据
维普
