Energy efficiency measurement of Chinese Yangtze River Delta’s cities transportation: a DEA window analysis approach

In this paper, the transportation energy efficiency of Yangtze River Delta’s 15 cities is studied in the period from 2009 to 2013. To measure transportation’s dynamic performance, the window analysis of data envelopment analysis (DEA) is used to estimate the efficiency in cross-sectional and time-va...

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Bibliographic Details
Published in:Energy efficiency 2018-12, Vol.11 (8), p.1941-1953
Main Authors: Chen, Xiaohong, Gao, Yuyan, An, Qingxian, Wang, Zongrun, Neralić, Luka
Format: Article
Language:English
Subjects:
Carbon dioxide
Computer Science
Data envelopment analysis
Data processing
Economics and Management
Empirical analysis
Energy
Energy consumption
Energy Efficiency
Energy measurement
Energy Policy
Environment
Environmental Economics
Operations Research
Original Article
Passengers
Power efficiency
Public transportation
Regression analysis
Renewable and Green Energy
Rivers
Sustainable Development
Transportation energy
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Summary:In this paper, the transportation energy efficiency of Yangtze River Delta’s 15 cities is studied in the period from 2009 to 2013. To measure transportation’s dynamic performance, the window analysis of data envelopment analysis (DEA) is used to estimate the efficiency in cross-sectional and time-varying data. Capital inputs and labor inputs are selected as the two non-energy inputs, energy consumption as the energy input, passenger volume and freight volume are selected as the two desirable outputs, and carbon dioxide is chosen as the undesirable output. The empirical study shows that Shanghai had the highest transportation energy efficiency, followed by Zhejiang province, and the average efficiency of Jiangsu province was the worst. A regression analysis on the efficiency values and their three influencing factors was carried out. The results show that per capita gross domestic product and the per capita area of paved roads in a city negatively influenced the efficiency values, while the number of public transportation vehicles per 10,000 persons positively influenced the transportation efficiency values. Lastly, some policy recommendations, conclusions, and suggestions for further research are given.
ISSN:1570-646X
1570-6478
DOI:10.1007/s12053-018-9635-7