Study on the industrial Eco-Efficiency in East China based on the Super Efficiency DEA Model: an example of the 2003–2013 panel data

ABSTRACT

The article uses the panel data of six provinces and one chartered city in East China from 2003 to 2013 as an example, constructs the input–output indicators system of Eco-Efficiency, calculates each region’s industrial Eco-Efficiencies throughout the 11 years by the Super Efficiency DEA Model, carries out dynamic studies on the efficiencies by using the Malmquist Index and analyses the factors influencing the efficiencies by the Tobit model. The empirical studies suggest the following: the regions in East China have relatively high industrial Eco-Efficiencies, and the efficiencies turn out a downward then upward trend; there are disparities among the regions as Shanghai, Zhejiang and Jiangsu are regions of relatively high industrial Eco-Efficiencies; the region all have Tfpch (productivity changes) greater than 1, of which the average is 7.2%, and the industrial Eco-Efficiencies are mainly driven by technology advancement; research and development, foreign capital and industrial structure each has a positive relationship with the industrial Eco-Efficiencies, and research and development has a significant influence, while industrial structure has an insignificant influence, and environment governance has a negative and significant influence.

KEYWORDS:

• Industrial Eco-Efficiency
• Super Efficiency DEA Model
• Malmquist Index
• Tobit regression

JEL CLASSIFICATION:

• C33
• Q51
• Q57
• R15
• Q58

Acknowledgements

This work was supported by the National Natural Science Foundation of China [Grant Number 41361102], China Postdoctoral Science Foundation Funded Project [Grant Number 2015M580276] and China Postdoctoral Science Foundation Special Funded Project [Grant Number 2016T09087].

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [Grant Number 41361102] and China Postdoctoral Science Foundation Funded Project [Grant Number 2015M580276].