Spatiotemporal characteristics and influential factors of eco-efficiency in Chinese prefecture-level cities: A spatial panel econometric analysis. (1st July 2020)

• Record Type: Journal Article
• Authors: Ren, Yufei; Fang, Chuanglin; Li, Guangdong
• Is Part Of: Journal of cleaner production. Volume 260(2020)
• Publication Date: 2020-07-01
• Subjects: Eco-efficiency -- Spillover effect -- Super epsilon-based measure model (S-EBM) -- Influential factors -- Spatial Durbin model -- China; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2020.120787
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Eco-efficiency, which emphasizes the significance of balancing the relationship between resource input, environmental pollution, and economic growth, has aroused extensive attention worldwide. China has seen profound economic growth in the past several decades, however, this has led to challenges for energy consumption growth and environmental issues (e.g., PM2.5 concentration). Improving eco-efficiency plays an important role in achieving sustainable development in China. Here, a novel evaluation framework of eco-efficiency is established by collecting satellite-derived data. Then, a hybrid distance model, named S-EBM with undesirable outputs, is applied to measure the eco-efficiency of Chinese 283 prefecture-level cities during the period 2003–2013. Finally, the spatial externality of eco-efficiency and its driving factors are examined by using a spatial Durbin model. Given that our results show the robustness in different spatial matrix settings, the research framework is applicable to the similar areas out of China (e.g., India). The empirical results show that: (1) eco-efficiency exhibits, by and large, a geographical gradual decrease trend from relatively developed areas to developing areas, however, a few exceptions indicate that a higher economic development level does not necessarily lead to a greater eco-efficiency, and vice versa; (2) estimation results of Moran's I index and hotspot analysis suggest that positive spatial autocorrelation of ecoefficiency is gradually increasing and the spatial distribution of statistically significant high and low agglomerations is changing over time; (3) The estimation results of the spatial Durbin model show that an increase in eco-efficiency in surrounding cities will improve the value of eco-efficiency in local cities; (4) The spatial externality of explanatory variables is also found, in which financial development, foreign direct investment, environmental regulation, and spatial urbanization level in neighboring cities impact the eco-efficiency in local cities. Highlights: The nighttime light data was collected to extend evaluation index of eco-efficiency. Hybrid distance model was used to measure eco-efficiency of China's prefecture cities. The economic development level does not necessarily match with eco-efficiency. Increase in eco-efficiency in adjacent cities will lift eco-efficiency in local city. Environmental regulation and economic urbanization can greatly improve eco-efficiency.