Temporal-Spatial Analysis of Chinese Railway Efficiency Under CO 2 Emissions: A Malmquist–Network Data Envelopment Analysis Model

Wenxin Ji and Feifei Qin ()
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Wenxin Ji: School of Rail Transportation, Soochow University, No. 1 Shizi Street, Suzhou 215006, China
Feifei Qin: School of Rail Transportation, Soochow University, No. 1 Shizi Street, Suzhou 215006, China

Sustainability, 2024, vol. 16, issue 20, 1-16

Abstract: With the rapid development of China’s economy, railway transport has increasingly become the main mode of medium and long-distance transport in China. At the same time, we find that in the process of technical improvement, the greenhouse gases emitted from railway locomotives not only affect the environment but also have a big influence on operational effectiveness. In order to clearly understand whether the total undesired output—CO 2 emissions—will have an impact on railway efficiency and the environment, we proposed a Malmquist–Network DEA model. Based on the data of 18 railway bureaus in China during the period of 2006–2020, we adopted the Malmquist–NDEA model to analyze the different efficiencies of each stage of the railway operation in China and analyze the environmental efficiency of China’s railway using temporal and spatial dimensions. We found that (1) including the CO 2 emissions as an undesirable output in the model has an inverse effect on both the overall efficiency and the production consumption and profit stage efficiencies; (2) the average overall efficiency of these 18 rail bureaus has shown relative stability, and the negative effects of CO 2 on the construction development and production stages are much lower than on the consumption and profit stages; and (3) the rail systems in the eastern areas have higher efficiencies in their construction development stage compared to the other two areas.

Keywords: railway transport efficiency; Malmquist–NDEA model; CO 2 emissions (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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