A novel stochastic semi-parametric frontier-based three-stage DEA window model to evaluate China's industrial green economic efficiency

Liu，Fangmei1; Li，Li2; Ye，Bin3; Qin，Quande2

2023-03-01

10.1016/j.eneco.2023.106566

ENERGY ECONOMICS   影响因子和分区

0140-9883

1873-6181

Traditional three-stage data envelopment analysis (DEA) models do not consider the problem of functional form and multicollinearity. This study develops a new stochastic semi-parametric frontier-based three-stage DEA model. The frontier incorporates the effects of both external environmental factors and statistical noise on efficiency. We adopt the StoNED (stochastic non-smooth envelopment of data) approach and use the quasi-likelihood estimation method to estimate the parameters of inefficiency term and stochastic noise. We conduct Monte Carlo experiments to examine the performance of the new frontier under different circumstances. Our results show that the new frontier provides a more realistic and accuracy estimator for efficiency measures. An empirical analysis is used to evaluate green economic efficiency (GEE) in China. We empirically compare different models and the results show that external environmental factors cause significant differences. We provide each provincial average GEE evaluated by the improved QLE-StoNED model, which are outperforms compared with other recently developed estimators. And a gradient difference emerges in the GEE among the eastern, central and western areas of China. The results also offer practical implications for the harmonious development of industrial production and a green economy in China.

Data envelopment analysis Industrial green economic efficiency Quasi-likelihood estimation Semi-parameter stochastic frontier

SSCI ; EI

英语

其他

National Natural Science Foundation of China["71871146","72174124"] ; Guangdong Special Support Program for Young Top-notch Talent in Science and Technology Innovation[2019TQ05L989] ; Natural Science Foundation of Guangdong Province[2021A1515011777] ; Research Platforms and Project in Ordinary Universities of Education Department of Guangdong Province[2020WTSCX079]

Business & Economics

Economics

WOS:000939859200001

ELSEVIER

20230913646500

Economic analysis ; Economic efficiency ; Industrial economics ; Parameter estimation ; Production efficiency ; Regression analysis ; Stochastic models ; Stochastic systems

Control Systems:731.1 ; Industrial Economics:911.2 ; Production Planning and Control; Manufacturing:913 ; Manufacturing:913.4 ; Statistical Methods:922 ; Probability Theory:922.1 ; Mathematical Statistics:922.2 ; Systems Science:961

ECONOMICS BUSINESS

2-s2.0-85148689995

Scopus

1.School of Management,Shenzhen Polytechnic,Shenzhen,518055,China
2.College of Management,Shenzhen University,Shenzhen,518060,China
3.School of Environmental Science & Engineering,Southern University of Science and Technology,Shenzhen,518055,China

Liu，Fangmei,Li，Li,Ye，Bin,et al. A novel stochastic semi-parametric frontier-based three-stage DEA window model to evaluate China's industrial green economic efficiency[J]. ENERGY ECONOMICS,2023,119.

Liu，Fangmei,Li，Li,Ye，Bin,&Qin，Quande.(2023).A novel stochastic semi-parametric frontier-based three-stage DEA window model to evaluate China's industrial green economic efficiency.ENERGY ECONOMICS,119.

Liu，Fangmei,et al."A novel stochastic semi-parametric frontier-based three-stage DEA window model to evaluate China's industrial green economic efficiency".ENERGY ECONOMICS 119(2023).