Impact of Public Transportation on European Countries’ Development: a Spatial Perspective

• Authors: Andreea Matyas

Abstracts

EN

Sustainability is a key topic nowadays, mostly because in the last decade the pollution levels have reached an all-time high. National governments are searching for sustainable and environmentally friendly solutions to decrease the amount of pollution. This study is a cross-sectional study on 27 European countries, using data gathered in 2020. This study’s main goal is to show the environmental sustainability of public transportation and its impact on country development in Europe. The methodology used in this study will consist of spatial econometrics methods with visual maps and graphs to help with a better visual representation of the phenomena presented. The empirical evidence will be confirmed by the spatial regression’s results. Because the spatial diagnostic tests revealed that the spatial processes are present in terms of both spatial lag and spatial errors, the model that was used was a Spatial Autoregressive Moving Average Model (SARMA). Moreover, the environmental sustainability of public transport is also a significant factor. The expected results from which this study began – specifically, that the spatiality has a significant impact in modelling the relationship between public transportation and economic development – were confirmed.

Keywords

EN

Spatial Econometrics; Econometric Methods; Spatial Autoregressive Moving Average Model; Spatial Autocorrelation; Transportation Economics

• Publisher: Uniwersytet Warszawski. Wydawnictwo Naukowe Wydziału Zarządzania
• Journal: Central European Economic Journal
• Year: 2023
• Volume: 10
• Issue: 57
• Pages: 403-413

Dates

published

2023

Contributors

author

Andreea Matyas

• Babes-Bolyiai University

References

• Anselin, L. (1988). Spatial Econometrics: Methods and Models. Dordrecht: Kluwer Academic Publishers. https://doi.org/10.1007/978-94-015-7799-1
• Anselin, L., & Bera, A.K. (1998). Spatial Dependence in Linear Regression Models with an Introduction to Spatial Econometrics. Statistics: Textbooks and Monographs, 155, 237–289. http://www.econ.uiuc.edu/~hrtdmrt2/Teaching/SE_2016_19/References/Spatial_Dependence_in_Linear_Regression_Models_With_an_Introduction_to_Spatial_Econometrics_281_29.pdf
• Beirão, G., & Cabral, J.A.S. (2007). Understanding attitudes towards public transport and private car: A qualitative study. Transport Policy, 14(6), 478–489. https://doi.org/10.1016/j.tranpol.2007.04.009
• Cambridge Systematics, Inc. and Economic Development Research Group (1999). Public Transportation and the Nation's Economy. A Quantitative Analysis of Public Transportation's Economic Impact. Washington: American Public Transit Association. https://www.kcata.org/documents/uploads/T_PublicTransportationNationsEconomy.pdf
• Cervero, R., & Glub, A. (2007). Informal Transport: A global perspective. Transport Policy, 14(6), 445–457. https://doi.org/10.1016/j.tranpol.2007.04.011
• Elburz, Z., & Cubukcu, K.M. (2018). Public Infrastructure and Regional Growth Relationship from a Spatial Perspective with a Time-Varying Spatial Weight Matrix. Proceedings 12th World Congress of the RSAI, 58–64. https://www.regionalscience.org/images/PDF/Proceedings_RSAICongress2018.pdf
• Eurostat. Eurostat Data [Data Set.] https://ec.europa.eu/eurostat/web/main/data/database.
• Gärling, T., & Schuitema, G. (2007). Travel demand management targeting reduced private car use: effectiveness, public acceptability and political feasibility. Journal of Social Issues, 63 (1), 139–153. https://doi.org/10.1111/j.1540-4560.2007.00500.x
• Gujarati, D.N. (2004). Basic Econometrics Fourth Edition. New York: United States Military Academy. https://zalamsyah.staff.unja.ac.id/wp-content/uploads/sites/286/2019/11/7-Basic-Econometrics-4th-Ed.-Gujarati.pdf.
• Horcher, D., & Tirachini, A. (2021). A review of public transport economics. Economics of Transportation, 25(1). https://doi.org/10.1016/j.ecotra.2021.100196
• Huang, J.S. (1984). THE AUTOREGRESSIVE MOVING AVERAGE MODEL FOR SPATIAL ANALYSIS. Australian Journal of Statistics, 26(2), 169–178. https://doi.org/10.1111/j.1467-842X.1984.tb01231.x
• Jeon, C.M. (2007). Incorporating sustainability into transportation planning and decision making: Definitions, performance measures, and evaluation. Atlanta: Georgia Institute of Technology. https://doi.org/10.3141/2174-09
• LeSage, J. P. (1997). Bayesian Estimation of Spatial Autoregressive Models. International Regional Science Review, 20(1–2), 113–129. https://doi.org/10.1177/016001769702000107
• LeSage, J.P. (1999). Spatial Econometrics. The Web Book of Regional Science. Morgantown: Regional Research Institute, West Virginia University. https://rri.wvu.edu/web-book
• Moran, P.A.P. (1950). Notes on Continuous Stochastic Phenomena. Biometrika, 37(1),17–23. https://doi.org/10.1093/biomet/37.1-2.17
• Newbold, P., Carlson, W.L., & Thorne, B.M. (2013). Statistics for Business and Economics, London: Pearson Education Limited. https://handoutset.com/wp-content/uploads/2022/07/Statistics-for-business-and-economics-Paul-Newbold-William-Lee-Carlson-etc..pdf
• Vauchic, V.R. (1999). Transportation for livable cities, New Brunswick: Centre for Urban Policy Research. https://doi.org/10.1023/A:1017551925033
• Weisbrod, G., & Reno, A. (2009). Economic Impact of Public Transport Development, Washington: American Public Transportation Association. https://onlinepubs.trb.org/onlinepubs/tcrp/docs/TCRPJ-11Task7-FR.pdf

Identifiers

DOI

10.2478/ceej-2023-0023

Biblioteka Nauki

20433505