Dynamic links between ICT, transport energy, environmental degradation and growth: empirical evidence from Tunisia

  • Received August 17, 2017;
    Accepted September 18, 2017;
    Published September 26, 2017
  • Author(s)
    Mounir Ben Mbarek , Nahed Zghidi
  • DOI
    http://dx.doi.org/10.21511/ee.08(3).2017.08
  • Article Info
    Volume 8 2017, Issue #3, pp. 76-83
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    6 articles
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The transport sector, particularly road transport, is a major factor in the overall emissions balance of the substances involved in air pollution for the majority of developing countries. This paper investigates the dynamic links between information and communication technology (ICT), transport energy, environmental degradation and growth for Tunisia. The authors used a Johansen co-integration analysis to determine this econometric relationship using data during 1990–2015. In order to test the Granger causality links in the short and long run, a panel Vector Error Correction Model is used. The variance decomposition is used to confirm the existing links between the different variables. Different results are found. These findings show the existence of bidirectional in short- and long-run causality between transport energy and CO2 emissions. By cons, ICT does not minimize significantly pollution in Tunisia. These findings are very important for the transport sector and in terms of the choice of government policy decisions in order to minimize the pollution.

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    • Table 1. Data and descriptive statistic of variables
    • Table 2. Unit root test (Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP))
    • Table 3. Results of Johansen cointegration Test
    • Table 4. The VECM Granger causality analysis
    • Table 5. Variance decomposition: Cholesky ordering: LNGDP FFEC LNICT CO2
    • Ajanovic, A., Dahl, C., & Schipper, L. (2012). Modelling transport (energy) demand and policies  An introduction. Energy Policy, 41, 3-16.
    • Alshehry, A. S., & Belloumi, M. (2017). Study of the environmental Kuznets curve for transport carbon dioxide emissions in Saudi Arabia. Renewable and Sustainable Energy Reviews, 75, 1339-1347.
    • Boyle, S. (1990). Transport and energy policies  only connect. Energy Policy, 18(1), 34-41.
    • Bruglieri, M., Bruschi, F., Colorni, A., Luè, A., Nocerino, R., & Rana, V. (2015). A real-time information system for public transport in case of delays and service disruptions. Transportation Research Procedia, 10, 493-502.
    • Fedderke, J. W., Perkins, P., & Luiz, J. M. (2006). Infrastructural investment in long-run economic growth: South Africa 1875–2001. World development, 34(6), 1037-1059.
    • Gao, Z., Wu, Y., & Huang, J. (2009). Analysis of weld pool dynamic during stationary laser–MIG hybrid welding. The International Journal of Advanced Manufacturing Technology, 44(9-10), 870.
    • Ghali, K. H., & El-Sakka, M. I. (2004). Energy use and output growth in Canada: a multivariate cointegration analysis. Energy economics, 26(2), 225-238.
    • Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration  with applications to the demand for money. Oxford Bulletin of Economics and statistics, 52(2), 169-210.
    • Le Breton, É. (2001). Les compromis fragiles du traitement des conflits publics dans les transports urbains. Sociologie du travail, 43(4), 515-531.
    • Mbarek, M. B., Nasreen, S., & Feki, R. (2017). The contribution of nuclear energy to economic growth in France: short and long run. Quality & Quantity, 51(1), 219-238.
    • Pradhan, R. P., & Bagchi, T. P. (2013). Effect of transportation infrastructure on economic growth in India: the VECM approach. Research in Transportation Economics, 38(1), 139-148.
    • Rojas-Rueda, D., De Nazelle, A., Teixidó, O., & Nieuwenhuijsen, M. J. (2013). Health impact assessment of increasing public transport and cycling use in Barcelona: a morbidity and burden of disease approach. Preventive medicine, 57(5), 573-579.
    • Saidi, S., & Hammami, S. (2017). Modeling the causal linkages between transport, economic growth and environmental degradation for 75 countries. Transportation Research Part D: Transport and Environment, 53, 415-427.
    • Sims, C. A. (1980). Macroeconomics and reality. Econometrica: Journal of the Econometric Society, 1-48.
    • Stelzer, A., Englert, F., Hörold, S., & Mayas, C. (2016). Improving service quality in public transportation systems using automated customer feedback. Transportation Research Part E: Logistics and Transportation Review, 89, 259-271.
    • Wang, S., Fan, J., Zhao, D., Yang, S., & Fu, Y. (2016). Predicting consumers’ intention to adopt hybrid electric vehicles: using an extended version of the theory of planned behavior model. Transportation, 43(1), 123-143.
    • Xylia, M., & Silveira, S. (2017). On the road to fossil-free public transport: The case of Swedish bus fleets. Energy Policy, 100, 397-412.
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