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Symmetry
Volume 16
Issue 8
10.3390/sym16080960
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

A Hybrid Data Envelopment Analysis–Random Forest Methodology for Evaluating Green Innovation Efficiency in an Asymmetric Environment

by
Limei Chen
1,2,*,
Xiaohan Xie
1,
Yao Yao
1,
Weidong Huang
1,2 and
Gongzhi Luo
1
1
School of Management, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2
Key Research Base of Philosophy and Social Sciences in Jiangsu, Information Industry Integration Innovation and Emergency Management Research Center, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Symmetry 2024, 16(8), 960; https://doi.org/10.3390/sym16080960 (registering DOI)
Submission received: 26 June 2024 / Revised: 15 July 2024 / Accepted: 16 July 2024 / Published: 28 July 2024
(This article belongs to the Special Issue Symmetric or Asymmetric Distributions and Its Applications)
Versions Notes

Abstract

The accurate evaluation of green innovation efficiency is a critical prerequisite for enterprises to achieve sustainable development goals and improve environmental performance and economic efficiency. This paper evaluates the green innovation efficiency of 72 new-energy enterprises by using a hybrid method of Data Envelopment Analysis (DEA) and a random forest model. The non-parametric DEA model is combined with the parametric SFA model to analyze the real green innovation efficiency on the basis of removing environmental factors and random factors. Then, the random forest model based on a nonlinear relationship is used to evaluate factors impacting green innovation efficiency. This paper proposes a comprehensive evaluation method designed to assess the green innovation efficiency of new-energy enterprises. By applying this method, companies can gain a comprehensive understanding of the current performance in green innovation, facilitating informed decision-making and accelerating sustainable development.
Keywords: three-stage DEA model; random forest method; green innovation efficiency; new-energy companies three-stage DEA model; random forest method; green innovation efficiency; new-energy companies

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MDPI and ACS Style

Chen, L.; Xie, X.; Yao, Y.; Huang, W.; Luo, G. A Hybrid Data Envelopment Analysis–Random Forest Methodology for Evaluating Green Innovation Efficiency in an Asymmetric Environment. Symmetry 2024, 16, 960. https://doi.org/10.3390/sym16080960

AMA Style

Chen L, Xie X, Yao Y, Huang W, Luo G. A Hybrid Data Envelopment Analysis–Random Forest Methodology for Evaluating Green Innovation Efficiency in an Asymmetric Environment. Symmetry. 2024; 16(8):960. https://doi.org/10.3390/sym16080960

Chicago/Turabian Style

Chen, Limei, Xiaohan Xie, Yao Yao, Weidong Huang, and Gongzhi Luo. 2024. "A Hybrid Data Envelopment Analysis–Random Forest Methodology for Evaluating Green Innovation Efficiency in an Asymmetric Environment" Symmetry 16, no. 8: 960. https://doi.org/10.3390/sym16080960

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