IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i7p2399-d157119.html
   My bibliography  Save this article

Regional Differences and Spatial Aggregation of Sustainable Transport Efficiency: A Case Study of China

Author

Listed:
  • Fei Ma

    (School of Economics and Management, Chang’an University, Xi’an 710064, China)

  • Xiaodan Li

    (School of Economics and Management, Chang’an University, Xi’an 710064, China)

  • Qipeng Sun

    (School of Economics and Management, Chang’an University, Xi’an 710064, China)

  • Fei Liu

    (School of Economics and Management, Chang’an University, Xi’an 710064, China)

  • Wenlin Wang

    (School of Economics and Management, Chang’an University, Xi’an 710064, China)

  • Libiao Bai

    (School of Economics and Management, Chang’an University, Xi’an 710064, China)

Abstract

In the past few decades, traffic congestion, traffic accidents, and air pollution caused by transport become increasingly serious in China, so the issue of sustainable development of transport has attracted much attention. This study explores the development level of provincial sustainable transport in China, measures the level of sustainable development of China’s transport from the perspective of transport efficiency, and analyzes the differences and spatial effects of sustainable transport development. The undesired output of the super slacks-based-measurement data envelopment analysis (US-SBM-DEA) model is used to measure the sustainable transport efficiency ( STE ) in different provinces of China, and then the coefficient of variation ( CV ) and Gini coefficient ( GC ) are used to explore the regional differences of STE . Finally, we analyze the spatial aggregation of STE by the index of Moran’s I . The results show that the regional mean of STE presents a distribution in the order of eastern > western > central > northeastern regions. The CV reveals that there is a local σ-convergence in the STE differences among the four regions during the study period. The Moran scatter plot of STE shows that the provincial STE s in China are mainly the aggregations of high–low and low–low with the latter being more obvious. The GC basically remained at a relatively stable level during 2007–2015 while quickly decreased in 2016. i.e., the equity of sustainable transport increased dramatically in 2016. These results meet the actual development of the sustainable transport in China’s provinces and reflect the level of sustainable transport efficiency more objectively. The results of this study provides theoretical support for the provincial governments to formulate efficient transport policies.

Suggested Citation

  • Fei Ma & Xiaodan Li & Qipeng Sun & Fei Liu & Wenlin Wang & Libiao Bai, 2018. "Regional Differences and Spatial Aggregation of Sustainable Transport Efficiency: A Case Study of China," Sustainability, MDPI, vol. 10(7), pages 1-23, July.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:7:p:2399-:d:157119
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/7/2399/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/10/7/2399/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Holden, Erling & Linnerud, Kristin & Banister, David, 2013. "Sustainable passenger transport: Back to Brundtland," Transportation Research Part A: Policy and Practice, Elsevier, vol. 54(C), pages 67-77.
    2. Woo, Chungwon & Chung, Yanghon & Chun, Dongphil & Seo, Hangyeol & Hong, Sungjun, 2015. "The static and dynamic environmental efficiency of renewable energy: A Malmquist index analysis of OECD countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 367-376.
    3. Weibin Lin & Bin Chen & Lina Xie & Haoran Pan, 2015. "Estimating Energy Consumption of Transport Modes in China Using DEA," Sustainability, MDPI, vol. 7(4), pages 1-15, April.
    4. Abbas Rajabifard & Russell G. Thompson & Yiqun Chen, 2015. "An intelligent disaster decision support system for increasing the sustainability of transport networks," Natural Resources Forum, Blackwell Publishing, vol. 0(2), pages 83-96, May.
    5. Dorina Pojani & Dominic Stead, 2015. "Sustainable Urban Transport in the Developing World: Beyond Megacities," Sustainability, MDPI, vol. 7(6), pages 1-22, June.
    6. Nazmus Sakib & Federica Appiotti & Filippo Magni & Denis Maragno & Alberto Innocenti & Elena Gissi & Francesco Musco, 2018. "Addressing the Passenger Transport and Accessibility Enablers for Sustainable Development," Sustainability, MDPI, vol. 10(4), pages 1-21, March.
    7. Haibo Zhou & Hanhui Hu, 2017. "Sustainability Evaluation of Railways in China Using a Two-Stage Network DEA Model with Undesirable Outputs and Shared Resources," Sustainability, MDPI, vol. 9(1), pages 1-23, January.
    8. Maria Giovanna MONTI, 2007. "A note on the Dagum decomposition of the Gini inequality index," Departmental Working Papers 2007-16, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    9. Lindholm, Maria Eleonor & Blinge, Magnus, 2014. "Assessing knowledge and awareness of the sustainable urban freight transport among Swedish local authority policy planners," Transport Policy, Elsevier, vol. 32(C), pages 124-131.
    10. United Nations UN, 2015. "Transforming our World: the 2030 Agenda for Sustainable Development," Working Papers id:7559, eSocialSciences.
    11. Fei Ma & Wenlin Wang & Qipeng Sun & Fei Liu & Xiaodan Li, 2018. "Ecological Pressure of Carbon Footprint in Passenger Transport: Spatio-Temporal Changes and Regional Disparities," Sustainability, MDPI, vol. 10(2), pages 1-17, January.
    12. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    13. Shivi Agarwal, 2016. "DEA-neural networks approach to assess the performance of public transport sector of India," OPSEARCH, Springer;Operational Research Society of India, vol. 53(2), pages 248-258, June.
    14. Cui, Qiang & Kuang, Hai-bo & Wu, Chun-you & Li, Ye, 2014. "The changing trend and influencing factors of energy efficiency: The case of nine countries," Energy, Elsevier, vol. 64(C), pages 1026-1034.
    15. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    16. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    17. Duygun, Meryem & Prior, Diego & Shaban, Mohamed & Tortosa-Ausina, Emili, 2016. "Disentangling the European airlines efficiency puzzle: A network data envelopment analysis approach," Omega, Elsevier, vol. 60(C), pages 2-14.
    18. Xu, Xin & Cui, Qiang, 2017. "Evaluating airline energy efficiency: An integrated approach with Network Epsilon-based Measure and Network Slacks-based Measure," Energy, Elsevier, vol. 122(C), pages 274-286.
    19. Gernot Pauli, 2010. "Sustainable transport: A case study of Rhine navigation," Natural Resources Forum, Blackwell Publishing, vol. 34(4), pages 236-254, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Qipeng Sun & Xiu Wang & Fei Ma & Yanhu Han & Qianqian Cheng, 2019. "Synergetic Effect and Spatial-Temporal Evolution of Railway Transportation in Sustainable Development of Trade: An Empirical Study Based on the Belt and Road," Sustainability, MDPI, vol. 11(6), pages 1-22, March.
    2. Hao Xu & Yeqing Wang & Hongwei Liu & Ronglu Yang, 2020. "Environmental Efficiency Measurement and Convergence Analysis of Interprovincial Road Transport in China," Sustainability, MDPI, vol. 12(11), pages 1-16, June.
    3. Fei Ma & Fei Liu & Qipeng Sun & Wenlin Wang & Xiaodan Li, 2018. "Measuring and Spatio-Temporal Evolution for the Late-Development Advantage in China’s Provinces," Sustainability, MDPI, vol. 10(8), pages 1-27, August.
    4. Liangen Zeng, 2021. "China’s Eco-Efficiency: Regional Differences and Influencing Factors Based on a Spatial Panel Data Approach," Sustainability, MDPI, vol. 13(6), pages 1-19, March.
    5. Liu, Hongwei & Shao, Liangyu & Min, Jie & Ji, Xiang, 2024. "Regional differences and determinants of environmental efficiency in China's road transportation industry," Economic Analysis and Policy, Elsevier, vol. 81(C), pages 931-946.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Nengcheng & Xu, Lei & Chen, Zeqiang, 2017. "Environmental efficiency analysis of the Yangtze River Economic Zone using super efficiency data envelopment analysis (SEDEA) and tobit models," Energy, Elsevier, vol. 134(C), pages 659-671.
    2. António Afonso & Ana Patricia Montes & José M. Domínguez, 2024. "Measuring Tax Burden Efficiency in OECD Countries: An International Comparison," CESifo Working Paper Series 11333, CESifo.
    3. Khanal, Aditya & Koirala, Krishna & Regmi, Madhav, 2016. "Do Financial Constraints Affect Production Efficiency in Drought Prone Areas? A Case from Indonesian Rice Growers," 2016 Annual Meeting, February 6-9, 2016, San Antonio, Texas 230087, Southern Agricultural Economics Association.
    4. Noel Uri, 2003. "The Effect of Incentive Regulation in Telecommunications in the United States," Quality & Quantity: International Journal of Methodology, Springer, vol. 37(2), pages 169-191, May.
    5. Badunenko, Oleg & Galeotti, Marzio & Hunt, Lester C., 2021. "Better to grow or better to improve? Measuring environmental efficiency in OECD countries with a Stochastic Environmental Kuznets Frontier," FEEM Working Papers 316226, Fondazione Eni Enrico Mattei (FEEM).
    6. Michaelides, Panayotis G. & Vouldis, Angelos T. & Tsionas, Efthymios G., 2010. "Globally flexible functional forms: The neural distance function," European Journal of Operational Research, Elsevier, vol. 206(2), pages 456-469, October.
    7. Kuosmanen, Timo & Johnson, Andrew, 2017. "Modeling joint production of multiple outputs in StoNED: Directional distance function approach," European Journal of Operational Research, Elsevier, vol. 262(2), pages 792-801.
    8. Tovar, Beatriz & Wall, Alan, 2015. "Can ports increase traffic while reducing inputs? Technical efficiency of Spanish Port Authorities using a directional distance function approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 71(C), pages 128-140.
    9. Vaneet Bhatia & Sankarshan Basu & Subrata Kumar Mitra & Pradyumna Dash, 2018. "A review of bank efficiency and productivity," OPSEARCH, Springer;Operational Research Society of India, vol. 55(3), pages 557-600, November.
    10. Fabio Pammolli & Francesco Porcelli & Francesco Vidoli & Guido Borà, 2014. "La spesa sanitaria delle Regioni in Italia - Saniregio 3," Working Papers CERM 02-2014, Competitività, Regole, Mercati (CERM).
    11. repec:cuf:journl:y:2017:v:18:i:1:valles-gimenez is not listed on IDEAS
    12. Fabio Pammolli & Francesco Porcelli & Francesco Vidoli & Monica Auteri & Guido Borà, 2017. "La spesa sanitaria delle Regioni in Italia - Saniregio2017," Working Papers CERM 01-2017, Competitività, Regole, Mercati (CERM).
    13. Ahn, Heinz & Clermont, Marcel & Langner, Julia, 2023. "Comparative performance analysis of frontier-based efficiency measurement methods – A Monte Carlo simulation," European Journal of Operational Research, Elsevier, vol. 307(1), pages 294-312.
    14. Subal Kumbhakar & Efthymios Tsionas, 2008. "Scale and efficiency measurement using a semiparametric stochastic frontier model: evidence from the U.S. commercial banks," Empirical Economics, Springer, vol. 34(3), pages 585-602, June.
    15. Musshoff, Oliver & Hirschauer, Norbert & Herink, Michael, 2009. "Bei welchen Problemstrukturen sind Data-Envelopment-Analysen sinnvoll? Eine kritische Würdigung," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 58(02), pages 1-11, February.
    16. Caitlin O’Loughlin & Léopold Simar & Paul W. Wilson, 2023. "Methodologies for assessing government efficiency," Chapters, in: António Afonso & João Tovar Jalles & Ana Venâncio (ed.), Handbook on Public Sector Efficiency, chapter 4, pages 72-101, Edward Elgar Publishing.
    17. Otsuka, Akihiro, 2023. "Industrial electricity consumption efficiency and energy policy in Japan," Utilities Policy, Elsevier, vol. 81(C).
    18. Moritz Flubacher & George Sheldon & Adrian Müller, 2015. "Comparison of the Economic Performance between Organic and Conventional Dairy Farms in the Swiss Mountain Region Using Matching and Stochastic Frontier Analysis," Journal of Socio-Economics in Agriculture (Until 2015: Yearbook of Socioeconomics in Agriculture), Swiss Society for Agricultural Economics and Rural Sociology, vol. 7(1), pages 76-84.
    19. Gil, Guilherme Dôco Roberti & Costa, Marcelo Azevedo & Lopes, Ana Lúcia Miranda & Mayrink, Vinícius Diniz, 2017. "Spatial statistical methods applied to the 2015 Brazilian energy distribution benchmarking model: Accounting for unobserved determinants of inefficiencies," Energy Economics, Elsevier, vol. 64(C), pages 373-383.
    20. Maruyama, Eduardo & Schollard, Phoebe, 2021. "Geographic prioritization of agricultural investments: Prioritization of agricultural and nutrition investments," 2021 Conference, August 17-31, 2021, Virtual 315292, International Association of Agricultural Economists.
    21. Herings, P.J.J. & Kubler, F., 2000. "Computing equilibria in finance economies," Research Memorandum 022, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:10:y:2018:i:7:p:2399-:d:157119. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.