Data-Driven Robust Data Envelopment Analysis for Evaluating the Carbon Emissions Efficiency of Provinces in China

Shaojian Qu, Yuting Xu, Ying Ji (), Can Feng, Jinpeng Wei and Shan Jiang
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Shaojian Qu: School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Yuting Xu: School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Ying Ji: School of Management, Shanghai University, Shanghai 200444, China
Can Feng: School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Jinpeng Wei: School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Shan Jiang: School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China

Sustainability, 2022, vol. 14, issue 20, 1-26

Abstract: To combat global warming, China proposed the “dual carbon” policy in 2020. In this context, it becomes crucial to improve carbon emissions efficiency. Currently, some scholars have utilized data envelopment analysis (DEA) to study carbon emissions efficiency. However, uncertainty about climate and government economic policy is ignored. This paper establishes a robust DEA model to reduce uncertainty and improve robustness. First, robust optimization theory is combined with DEA to establish the robust DEA model. Second, considering three uncertainty sets (box set, ellipsoid set, and polyhedron set), a robust DEA model for different situations is considered. Finally, to address the problem of over-conservatism in robust optimization, this paper applies the data-driven robust DEA model to further analyze the carbon emissions efficiency of China. The results of the data-driven robust DEA model suggest that the government should focus on coordinated regional development, promote the transformation and upgrading of the energy structure, innovate in green technology, and advocate for people to live a green and low-carbon lifestyle.

Keywords: carbon emissions efficiency; robust optimization; data envelopment analysis (DEA); data-driven; uncertainty set (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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