Urban Green Innovation Efficiency in China: Spatiotemporal Evolution and Influencing Factors

Abstract

Investigating urban green innovation efficiency (UGIE) is imperative because it is correlated with the development of an ecological civilization and an innovative country. Spatiotemporal evolution and influencing factors of UGIE are two important scientific problems that are worth exploring. This study presents an indicator system for UGIE that includes input, expected output, and unexpected output, and employs a super-efficiency slacks-based measure (super-SBM) to calculate UGIE in 284 cities at or above the prefecture level in China from 2005 to 2020. Then, we adopted spatial auto-correlation to identify its spatial differences among these cities and Geodetector to evaluate its influencing factors. The results are as follows: (1) The overall UGIE tended to rise, except in northeastern China, megacities, and super large-sized cities. (2) The UGIE of Chinese cities exhibited remarkable spatial differences and auto-correlation, and the “low-low” type enjoyed the most local spatial auto-correlations. (3) Sociocultural factors represented by the number of collections in public libraries became the most important factors affecting the UGIE in China.

Township, County Town, Metropolitan Area, or Foreign Cities? Evidence from House Purchases by Rural Households in China

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“…The comprehensive framework constructed in this paper has more environmental elements, and traditional methods may have a co-integration that affects the conclusions. Therefore, this paper used GeoDetector, a method for detecting geospatial anisotropy without linear assumptions [63,64]. Basically, the study area is assumed to be divided into sub-regions, and spatial heterogeneity exists if the sum of the variances of the sub-regions is smaller than the total variance of the area.…”

Spatial Differences and Influencing Factors of Carbon Emission Intensity in China’s Urban Agglomerations toward the Carbon Neutrality Target

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Agglomeration Externalities vs. Network Externalities: Impact on Green Technology Innovation in 283 Chinese Cities

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