Spatiotemporal pattern evolution and influencing factors of green innovation efficiency: A China’s city level analysis

Abstract

Based on employing the global super efficiency epsilon-based measure (GSE-EBM) model to evaluation the green innovation efficiency (GIE) of 285 prefecture-level or above cities in China during the period 2004–2018, this paper combines the approaches of kernel density estimation, cold hot spot analysis and standard deviation ellipse to intuitively describe GIE’s spatiotemporal pattern evolution features, and then utilizes the geographical weighted regression (GWR) model to explore the spatial heterogeneity of GIE’s affecting factors. The results show that: (1) China’s urban GIE displayed a fluctuating increasing trend, revealing clearly regional disparities, and gradually decreased from the Eastern coastal region to the Central, the Western and the Northeast region. (2) The spatial difference of China’s urban GIE exhibited the characteristics of expansion, polarization, and spatial agglomeration with the center of gravity gradually shifting to the Southeast region. (3) In the analysis of socio-economic factors of China’s urban GIE, the GWR model effectively identified the spatial heterogeneity, and improved the explanatory ability compared to ordinary least squares (OLS) model. (4) The GWR model analysis indicate that population density, economic development, transportation infrastructure, openness and industrial structure played significant impacts on China’s urban GIE, and there exists significant spatial heterogeneity in the impact of each influencing factor. The findings of this study can provide valuable references for urban green transformation and high-quality development in China.

Keywords

• GIE
• Spatiotemporal pattern evolution
• Influencing factors
• GSE-EBM model