Applying the Super-EBM model and spatial Durbin model to examining total-factor ecological efficiency from a multi-dimensional perspective: evidence from China

Abstract

Ecological efficiency mainly emphasizes the importance of balancing the relationships between natural resources, energy, the ecological environment and economic growth, which has aroused widespread concern worldwide. China’s rapid economic development has inevitably been accompanied by serious resource exhaustion, environmental pollution and ecological deterioration in the past several decades, which has brought huge challenges to China’s sustainable development. Therefore, establishing the evaluation framework of total-factor ecological efficiency (TFEE) and identifying its driving force have a great significance for improving China’s sustainable development capabilities. First, an ecological efficiency evaluation framework is established based on the theory of total-factor analysis. Second, the super efficient hybrid distance model considers undesirable output and measures TFEE nationwide in 30 provinces and four regions during the period 2003–2017. Finally, the spatial effect of TFEE and its influencing factors are examined by using a spatial Durbin model. The empirical results show that (1) nationwide and regional TFEEs have different degrees of decline during the study period. There were significant differences among the 30 provinces and four regions. Beijing, Tianjin and Shanghai are efficient, while the other provinces have not been as effective. The TFEEs of the four regions are not effective with an ordering of eastern > northeast > central > western. (2) Moran’s I index shows that the TFEE nationwide has a positive spatial autocorrelation with strong spatial agglomeration. However, the spatial distribution pattern of TFEE in China was unstable and labile. The Moran scatter plot indicates that China’s provincial TFEE has not only spatial dependence characteristics but also differences in spatial correlation. (3) Most factors are bound up with TFEE to various degrees: technological progress (TP), industrial agglomeration (IG) and human capital (HC) play a positive role, while industrial structure (IS), the level of urbanization (CITY) and energy intensity (EI) play a negative role. Additionally, environmental regulation (GZ) shows a U-type relationship with TFEE. The level of economic development (GDP) and foreign direct investment (FDI) cannot have a significant impact on TFEE at this stage. (4) The spatial Durbin model results show that TFEE has a significant spatial spillover effect, and the improvement of the TFEE of a province will increase the TFEE of neighbouring provinces. The confirmed spatial spillover effects of technological progress (TP), industrial structure (IS), the level of urbanization (CITY), industrial agglomeration (IG) and human capital (HC) can significantly impact the TFEE of neighbouring provinces. Among them, technological progress (TP), the level of urbanization (CITY) and human capital (HC) can significantly improve the TFEE of neighbouring provinces, and the level of economic development (GDP) and foreign direct investment (FDI) can significantly inhibit the improvement of TFEE in neighbouring provinces.