IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i16p10267-d891452.html
   My bibliography  Save this article

The Relationship between Transportation Industry Efficiency, Transportation Structure, and Regional Sustainability Development in China: Based on DEA and PVAR Models

Author

Listed:
  • Zhuyuan Li

    (College of Business Administration, Wonkwang University, No. 460, Iksandae-ro, Iksan 54538, Korea)

  • Tianxu Hao

    (College of Business Administration, Wonkwang University, No. 460, Iksandae-ro, Iksan 54538, Korea)

  • Run Zheng

    (Business School, Jiangsu Open University, Nanjing 210036, China)

Abstract

The sustainable development of the transportation industry has always been a major concern after China’s reform and opening up. Existing studies only examine the transportation efficiency of a single mode of transportation or a certain region without considering the overall efficiency of the national transportation industry. Furthermore, most studies do not consider the impact of transportation structure on transportation efficiency and economic development. Moreover, the correlations and interactions between transportation efficiency, transportation structure, and regional economic development have not been considered. Based on the research status, this study uses a panel vector autoregressive model to analyze the relationship between the three. The results show that the transportation efficiency value is the highest in the eastern region, followed by the central region, and it is the lowest in the western region. The equilibrium degree of transportation structure has a slight difference in the national transportation structure from 2011 to 2020, and the proportion of major transportation modes in each province is unchanged. The correlation of the three variables is as follows: (1) transportation efficiency and transportation structure have a mutually reinforcing effect in the short term; (2) regional economic development has a long-term contribution to transportation efficiency and structure improvement; and (3) the level of transportation efficiency plays a leading role in regional economic development. According to the empirical analysis results, this study puts forward relevant feasible suggestions for the decision makers who formulate the development policies of the transportation industry in order to optimize the structure, reduce resource waste, improve the service quality of various transportation modes, and promote the high-quality, sustainable development of the transportation industry and economy.

Suggested Citation

  • Zhuyuan Li & Tianxu Hao & Run Zheng, 2022. "The Relationship between Transportation Industry Efficiency, Transportation Structure, and Regional Sustainability Development in China: Based on DEA and PVAR Models," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10267-:d:891452
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/16/10267/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/16/10267/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Ke & Lin, Boqiang, 2016. "Impact of energy conservation policies on the green productivity in China’s manufacturing sector: Evidence from a three-stage DEA model," Applied Energy, Elsevier, vol. 168(C), pages 351-363.
    2. Mingli Song & Guangshe Jia & Puwei Zhang, 2020. "An Evaluation of Air Transport Sector Operational Efficiency in China based on a Three-Stage DEA Analysis," Sustainability, MDPI, vol. 12(10), pages 1-16, May.
    3. Michael R.M. Abrigo & Inessa Love, 2016. "Estimation of Panel Vector Autoregression in Stata: a Package of Programs," Working Papers 201602, University of Hawaii at Manoa, Department of Economics.
    4. Wu, Haitao & Hao, Yu & Weng, Jia-Hsi, 2019. "How does energy consumption affect China's urbanization? New evidence from dynamic threshold panel models," Energy Policy, Elsevier, vol. 127(C), pages 24-38.
    5. Holtz-Eakin, Douglas & Newey, Whitney & Rosen, Harvey S, 1988. "Estimating Vector Autoregressions with Panel Data," Econometrica, Econometric Society, vol. 56(6), pages 1371-1395, November.
    6. Chang, Young-Tae & Zhang, Ning & Danao, Denise & Zhang, Nan, 2013. "Environmental efficiency analysis of transportation system in China: A non-radial DEA approach," Energy Policy, Elsevier, vol. 58(C), pages 277-283.
    7. Michael C. Carroll & James Ronald Stanfield, 2001. "Sustainable Regional Economic Development," Journal of Economic Issues, Taylor & Francis Journals, vol. 35(2), pages 469-476, June.
    8. John B. Parr, 2014. "The Regional Economy, Spatial Structure and Regional Urban Systems," Regional Studies, Taylor & Francis Journals, vol. 48(12), pages 1926-1938, December.
    9. Piotr Prus & Marek Sikora, 2021. "The Impact of Transport Infrastructure on the Sustainable Development of the Region—Case Study," Agriculture, MDPI, vol. 11(4), pages 1-15, March.
    10. H. Fried & C. Lovell & S. Schmidt & S. Yaisawarng, 2002. "Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis," Journal of Productivity Analysis, Springer, vol. 17(1), pages 157-174, January.
    11. Love, Inessa & Zicchino, Lea, 2006. "Financial development and dynamic investment behavior: Evidence from panel VAR," The Quarterly Review of Economics and Finance, Elsevier, vol. 46(2), pages 190-210, May.
    12. S. A. Tarkhov, 2021. "Geography of the Passenger Turnover Dynamics at Airports in Europe and Russia’s Regions in the First Year of the COVID-19 Pandemic," Regional Research of Russia, Springer, vol. 11(4), pages 435-453, October.
    13. Michael R. M. Abrigo & Inessa Love, 2016. "Estimation of panel vector autoregression in Stata," Stata Journal, StataCorp LP, vol. 16(3), pages 778-804, September.
    14. Jui-Kou Shang & Wei-Ting Hung & Chang-Fang Lo & Fei-Ching Wang, 2008. "Ecommerce and hotel performance: three-stage DEA analysis," The Service Industries Journal, Taylor & Francis Journals, vol. 28(4), pages 529-540, May.
    15. 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.
    16. Andrews, Donald W. K. & Lu, Biao, 2001. "Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models," Journal of Econometrics, Elsevier, vol. 101(1), pages 123-164, March.
    17. Qing Yin & Yadong Wang & Kaidi Wan & Delu Wang, 2020. "Evaluation of Green Transformation Efficiency in Chinese Mineral Resource-Based Cities Based on a Three-Stage DEA Method," Sustainability, MDPI, vol. 12(22), pages 1-29, November.
    18. 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.
    19. Levin, Andrew & Lin, Chien-Fu & James Chu, Chia-Shang, 2002. "Unit root tests in panel data: asymptotic and finite-sample properties," Journal of Econometrics, Elsevier, vol. 108(1), pages 1-24, May.
    20. Breitung, Jorg, 2002. "Nonparametric tests for unit roots and cointegration," Journal of Econometrics, Elsevier, vol. 108(2), pages 343-363, June.
    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. Weifan Zhong & Lijing Du, 2023. "Predicting Traffic Casualties Using Support Vector Machines with Heuristic Algorithms: A Study Based on Collision Data of Urban Roads," Sustainability, MDPI, vol. 15(4), pages 1-18, February.

    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. Joan Costa-Font & Cristina Vilaplana-Prieto, 2023. "‘Investing’ in care for old age? An examination of long-term care expenditure dynamics and its spillovers," Empirical Economics, Springer, vol. 64(1), pages 1-30, January.
    2. Marshall, Emily C. & Saunoris, James & Solis-Garcia, Mario & Do, Trang, 2023. "Measuring the size and dynamics of U.S. state-level shadow economies using a dynamic general equilibrium model with trends," Journal of Macroeconomics, Elsevier, vol. 75(C).
    3. Hertweck, Matthias & Brey, Bjoern, 2017. "The Persistent Effects of Monsoon Rainfall Shocks in India: A Nonlinear VAR Approach," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168256, Verein für Socialpolitik / German Economic Association.
    4. Kumeka, Terver Theophilus & Uzoma-Nwosu, Damian Chidozie & David-Wayas, Maria Onyinye, 2022. "The effects of COVID-19 on the interrelationship among oil prices, stock prices and exchange rates in selected oil exporting economies," Resources Policy, Elsevier, vol. 77(C).
    5. Kannyiri Banyen & Nicholas Biekpe, 2020. "Financial integration, competition and bank efficiency: evidence from Africa’s sub-regional markets," Economic Change and Restructuring, Springer, vol. 53(4), pages 495-518, November.
    6. Wang, Mengmeng & Zhou, Tao, 2022. "Understanding the dynamic relationship between smart city implementation and urban sustainability," Technology in Society, Elsevier, vol. 70(C).
    7. Abebe Hailemariam & Tutsirai Sakutukwa & Ratbek Dzhumashev, 2021. "Long-term determinants of income inequality: evidence from panel data over 1870–2016," Empirical Economics, Springer, vol. 61(4), pages 1935-1958, October.
    8. Ioannis Kostakis, 2020. "Is Tourism a Key Factor for Economic Growth? Fresh Evidence from South Europe Using Panel Cointegration and PVAR Analyses," World Journal of Applied Economics, WERI-World Economic Research Institute, vol. 6(2), pages 123-138, December.
    9. João Leitão & Joaquim Ferreira, 2021. "Dynamic Effects of Material Production and Environmental Sustainability on Economic Vitality Indicators: A Panel VAR Approach," JRFM, MDPI, vol. 14(2), pages 1-20, February.
    10. Cajas-Guijarro, John & Pérez-Almeida, Bryan, 2021. "Comercio, sobreexplotación laboral y ciclos en la periferia: una propuesta teórica y el caso ecuatoriano desde un modelo PVAR. || Trade, super-exploitation of labor power and cycles in the periphery: ," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 31(1), pages 161-197, June.
    11. Yen-Yao Wang & Chenhui Guo & Anjana Susarla & Vallabh Sambamurthy, 2021. "Online to Offline: The Impact of Social Media on Offline Sales in the Automobile Industry," Information Systems Research, INFORMS, vol. 32(2), pages 582-604, June.
    12. Ryan H. Murphy & Colin O’Reilly, 2019. "Applying panel vector autoregression to institutions, human capital, and output," Empirical Economics, Springer, vol. 57(5), pages 1633-1652, November.
    13. Caixia Wang & Huijie Li, 2023. "Variation in Global Policy Responses to COVID-19: A Bidirectional Analysis," IJERPH, MDPI, vol. 20(5), pages 1-13, February.
    14. Trofimov, Ivan D., 2022. "Determinants of the profit rates in the OECD economies: A panel data analysis of the Kalecki's profit equation," Structural Change and Economic Dynamics, Elsevier, vol. 61(C), pages 380-397.
    15. Sigmund, Michael & Ferstl, Robert, 2021. "Panel vector autoregression in R with the package panelvar," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 693-720.
    16. Oguzhan Cepni & Ibrahim Ethem Guney & Doruk Kucuksarac & M. Hasan Yilmaz, 2021. "Do local and global factors impact the emerging markets' sovereign yield curves? Evidence from a data‐rich environment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1214-1229, November.
    17. Kacou Yves Thierry Kacou & Yacouba Kassouri & Andrew Adewale Alola & Mehmet Altuntaş, 2022. "Examining the sustainable development approach of migrants' remittances and financial development in sub‐Saharan African countries," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(5), pages 804-816, October.
    18. Pazouki, Azadeh & Zhu, Xiaoxian, 2022. "The dynamic impact among oil dependence volatility, the quality of political institutions, and government spending," Energy Economics, Elsevier, vol. 115(C).
    19. Byomakesh Debata & Jitendra Mahakud, 2018. "Interdependence between Monetary Policy and Stock Liquidity: A Panel VAR Approach," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 12(4), pages 387-413, November.
    20. Rifai Afin, 2023. "Interlinka Terlinkage Of M Ge Of Macroeconomic Uncer Croeconomic Uncertainty And Macroeconomic Performance: Evidence From Asean-5 Countries Panel Var," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 26(1), pages 39-68, March.

    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:14:y:2022:i:16:p:10267-:d:891452. 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.