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The coordinated development of industrialization and its ecological environment are vital antecedents to sustainable development in China. However, along with the accelerating development of industrialization in China, the contradiction between industrial development and environment preservation has turned out to be increasingly evident and inevitable. Eco-efficiency can be seen either as an indicator of environmental performance, or as a business strategy for sustainable development. Hence, industrial eco-efficiency promotion is the key factor for green industrial development. This study selects indicators relevant to resources, economy, and the environment of industrial development, and the indicators can well reflect the characteristics of industrial eco-efficiency. The SBM (Slacks-Based Measure) model overcomes the limitations of a radial model and directly accounts for input and output slacks in the efficiency measurements, with the advantage of capturing the entire aspect of inefficiency. This study evaluates the industrial eco-efficiency of nine cities in Fujian province during the period of 2006–2016, based on undesired output SBM (Slacks-Based Measure) model and also uses a Tobit regression model to analyze the influencing factors. The results show that there is a positive correlation among the economic development level, opening level, research and development (R&amp;D) innovation, and industrial eco-efficiency in Fujian Province. However, a negative correlation was found between the industrial structure and industrial eco-efficiency in Fujian Province. Moreover, environmental regulation in Fujian Province was not found to significantly influence the industrial eco-efficiency. Hence, through the systematic analysis of industrial eco-efficiency and its influencing factors in Fujian, the study gives further insight on how policy-making can help achieve sustainable development, balancing between economic benefits and ecological improvements.

Fujian’s Industrial Eco-Efficiency: Evaluation Based on SBM and the Empirical Analysis of lnfluencing Factors