Estimation of eco-efficiency and its influencing factors in Guangdong province based on Super-SBM and panel regression models

• Published in: Ecological indicators 2018-03, Vol.86, p.67-80
• Main Authors: Zhou, Chunshan, Shi, Chenyi, Wang, Shaojian, Zhang, Guojun
• Format: Article
• Language: English
• Subjects: Eco-efficiency; Guangdong; Influencing factors; Panel regression model; Super-SBM model; Topsis model
• ISSN: 1470-160X; 1872-7034
• DOI: 10.1016/j.ecolind.2017.12.011

[Display omitted] •A Super-SBM model with undesirable outputs was used to evaluate eco-efficiency in Guangdong.•A panel data regression technique for influential factors was employed during 2005–2014.•Eco-efficiency was characterized by increasing regional disparity.•Technical innovation, government regulation and openness were the main facilitators.•Land-use intensity, industrial structure and economic growth decreased eco-efficiency. Eco-efficiency is an indicator that is tied to economic activities and ecology; it serves as a useful instrument for sustainability analysis. Based on a panel data set for the period 2005–2014, this paper estimated the eco-efficiency of 21 cities in Guangdong province, China by applying a super-slack-based measure (Super-SBM) model that considers undesirable output indicators and a Topsis model. Using a panel data model with fixed effects, the influencing factors on eco-efficiency were also explored. The results indicate that during the study period, the three indexes—resource inputs (RI), economic benefits (EB) and environmental impacts (EI)—showed obvious spatiotemporal differentiation with high values mainly being located in the core region of Guangdong (the Pearl River Delta, PRD) and low values being primarily distributed in the northern area of the province. Influenced by inputs and outputs, the eco-efficiency presented significant disparities among four areas and 21 cities. The highest eco-efficiency was in the eastern area and then in the PRD, areas whose eco-efficiency averaged around 1.16 and 1.10, respectively, while the north had the lowest value, averaging below 1.00. Regional disparity was found to increase and spatial autocorrelation to decrease gradually between 2005 and 2014. The results of the panel data analysis indicate that technical innovation had the greatest positive influence on eco-efficiency, followed by government regulation, openness and population density. Conversely, land-use intensity was identified as the main inhibiting factor among the negative influencing factors, which also included industrial structure and per capita GDP. Interestingly, the study found that a high level of per capita GDP would not necessarily lead to high eco-efficiency. The findings of this study hold important implications for both policy makers and urban planners.