LAPSE:2023.8912v1
Published Article

LAPSE:2023.8912v1
Evaluation of the Operational Efficiency and Energy Efficiency of Rail Transit in China’s Megacities Using a DEA Model
February 24, 2023
Abstract
To date, along with the rapid development of urban rail transit (URT) in China, the evaluation of operational efficiency and energy efficiency has become one of the most important topics. However, the extant literature regarding the efficiency of URT at the line level and considering carbon emissions is limited. To fill the gap, an evaluation model based on slacks-based measure (SBM) data envelopment analysis (DEA) is proposed to measure the efficiencies, which is applied to 61 URT lines in China’s four megacities. The findings are summarized as follows: (1) The average operational efficiency and energy efficiency of URT lines are low, and both have great room for improvement. (2) There are significant disparities in the efficiency of URT lines in the case cities. For instance, the average operational efficiency of URT lines in Guangzhou is higher than that of other cities, while the average energy efficiency of URT lines in Shanghai is higher than that of other cities. (3) The URT lines operated by state-owned enterprises have higher average operational efficiency, while the lines operated by joint ventures have higher average energy efficiency. Finally, some suggestions are provided to improve the efficiencies.
To date, along with the rapid development of urban rail transit (URT) in China, the evaluation of operational efficiency and energy efficiency has become one of the most important topics. However, the extant literature regarding the efficiency of URT at the line level and considering carbon emissions is limited. To fill the gap, an evaluation model based on slacks-based measure (SBM) data envelopment analysis (DEA) is proposed to measure the efficiencies, which is applied to 61 URT lines in China’s four megacities. The findings are summarized as follows: (1) The average operational efficiency and energy efficiency of URT lines are low, and both have great room for improvement. (2) There are significant disparities in the efficiency of URT lines in the case cities. For instance, the average operational efficiency of URT lines in Guangzhou is higher than that of other cities, while the average energy efficiency of URT lines in Shanghai is higher than that of other cities. (3) The URT lines operated by state-owned enterprises have higher average operational efficiency, while the lines operated by joint ventures have higher average energy efficiency. Finally, some suggestions are provided to improve the efficiencies.
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Keywords
carbon emission, data envelopment analysis, Energy Efficiency, operational efficiency, urban rail transit
Subject
Suggested Citation
Zhang H, Wang X, Chen L, Luo Y, Peng S. Evaluation of the Operational Efficiency and Energy Efficiency of Rail Transit in China’s Megacities Using a DEA Model. (2023). LAPSE:2023.8912v1
Author Affiliations
Zhang H: Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
Wang X: Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
Chen L: Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
Luo Y: School for Business and Society, University of York, York YO10 5GD, UK
Peng S: Faculty of Business and Management, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai 519087, China [ORCID]
Wang X: Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
Chen L: Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
Luo Y: School for Business and Society, University of York, York YO10 5GD, UK
Peng S: Faculty of Business and Management, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai 519087, China [ORCID]
Journal Name
Energies
Volume
15
Issue
20
First Page
7758
Year
2022
Publication Date
2022-10-20
ISSN
1996-1073
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PII: en15207758, Publication Type: Journal Article
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LAPSE:2023.8912v1
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https://doi.org/10.3390/en15207758
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Feb 24, 2023
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