On the Causal Nexus of Road Transport CO2 Emissions and Macroeconomic Variables in Tunisia: Evidence from Combined Cointegration Tests

Shahbaz, Muhammad and Khraief, Naceur and Dhaoui, Abderrazak (2015): On the Causal Nexus of Road Transport CO2 Emissions and Macroeconomic Variables in Tunisia: Evidence from Combined Cointegration Tests.

Abstract

This paper investigates the causal relationship between road transportation energy consumption, fuel prices, transport sector value added and CO2 emissions in Tunisia for the period 1980-2012. We apply the newly developed combined cointegration test proposed by Bayer and Hanck (2013) and the ARDL bounds testing approach to cointegration to establish the existence of long-run relationship in presence of structural breaks. The direction of causality between these variables is determined via vector error correction model (VECM).

Our empirical exercise reveals that the cointegration is present. Energy consumption adds in CO2 emissions. Fuel prices decline CO2 emissions. Road infrastructure boosts in CO2 emissions. Transport value-added also increases CO2 emissions. The causality analysis indicates the bidirectional casual relationship between energy consumption and CO2 emissions. Road infrastructure causes CO2 emissions and similar is true from opposite side in Granger sense. The bidirectional causality is also found between transport value-added and CO2 emissions. Fuel prices cause CO2 emissions, energy consumption, road infrastructure and transport value-added. This paper provides new insights to policy makers to design a comprehensive energy, transport and environment policies for sustainable economic growth in long run.

• Item Type: MPRA Paper
• Language: English
• Keywords: Road Transport, CO2 Emissions, Tunisia
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General

References

Abdallah B.K., Belloumi, M., and De Wolf D., (2013). Indicators for sustainable energy development: A multivariate Cointegration and causality analysis from Tunisian road transport sector. Renewable and Sustainable Energy Reviews 25: 34–43.

Akinboade. O., Ziramba, E. and Kumo, W. (2008). The demand for gasoline in South Africa: An empirical analysis using co-integration techniques. Energy Economics, 30(6), 3222-3229.

Banerjee, A., Dolado, J., and Mestre, R., (1998). Error‐Correction Mechanism Tests for Cointegration in A Single‐Equation Framework. Journal of Time Series Analysis, 19(3): 267-283.

Bayer, C., and Hanck, C., (2013). Combining Non‐Cointegration Tests. Journal of Time Series Analysis, 34(1): 83-95.

Boswijk, H.P., (1994) .Conditional and Structural Error Correction Models. Journal of Econometrics, 69(1): 159-171. Chemin, E.S., (2009). Panorama 2009: trends and challenges in passenger mobility.

Dargay, J., Gately, D. and Sommer, M., (2007). Vehicle ownership and income growth, worldwide: 1960–2030. Energy, 28(4): 143–170.

Dickey, D.A. and Fuller, W.A., (1981). Distribution of the estimators for autoregressive time series with a unit root. Econometrica, 49(4), 1057-72.

Engle, R.F., Granger, C.W.J., (1987) Cointegration and error correction representation: estimation and testing. Econometrica, 55(2), 251-276.

EuroMed (2010). Transport study road transport in Maghreb countries. http://euromedtransport.eu/En/image.php?id=1708

Fisher, R. A., (1932). Statistical methods for research workers. Edinburgh, 4th Edition.

Granger, C. W. J., (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3): 424-438.

He, K. Huo H., Zhang Q., He D., An, F. Wang M.and Walsh M.P (2005). Oil Consumption and CO2 Emissions in China’s Road Transport: Current Status, Future Trends, and Policy Implications. Energy Policy 33: 1499–1507.

International Energy Agency (IEA) (2006) World Energy Outlook 2006, IEA/OECD, Paris.

International Energy Agency (IEA), (2008). Energy Technology Perspectives 2008. IEA, Paris.

International Energy Agency (IEA), (2012a) .CO2 emissions from Fuel Combustion.

International Energy Agency, (2012b) .Key World Energy Statistics.

Johansen, S., (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2): 231-254.

Johansen, S., Juselius, K. Maximum likelihood estimation and inference on cointegration with applications to the demand for money. Oxford Bulletin of Economics and Statistics, 1990, 52(2): 169-210.

Kulshreshtha, M., Barnali, N. and Kulshreshtha, M., (2001). A multivariate cointegrating vector auto regressive model of freight transport demand: evidence from Indian railways, Transportation Research Part A, 35(1), 29-45.

Liddle, B., (2004). Demographic dynamics and per capita environmental impact: using panel regressions and household decompositions to examine population and transport. Population and Environment, 26(1), 23–39.

Liddle, B. and Lung S., (2013). The long-run causal relationship between transport energy consumption and GDP: Evidence from heterogeneous panel methods robust to cross-sectional dependence. Economics Letter, 121(3): 524-527.

Mraihi R., K. Ben Abdallah and Abid, M. (2013). Road transport‐related energy consumption: Analysis of driving factors in Tunisia, Energy Policy, 62 247–253 pp.

NAEC . National Agency for Energy Conservation; 1997.

Narayan, P.K., (2005). The saving and investment nexus for China: Evidence from co-integration tests. Applied Economics, 37(17): 1979-1990.

NIC. National Institute of Statistics; 2013.

Perron, P., (1989). The Great Crash, the Oil Price Shock and the Unit Root Hypothesis. Econometrica, 57(6): 1361- 1401.

Pesaran, M.H., Shin, Y., (1999). An autoregressive distributed lag modelling approach to cointegration analysis. Chapter 11 in Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium, Strom S. (ed.). Cambridge University Press: Cambridge.

Pesaran, M. H., Shin, Y., Smith, R. J., (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16, 289-326.

Phillips, P. C. B. and Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2): 335-346.

Phillips, P.C.B and Ouliaris, S., (1990). Asymptotic Properties of Residual Based Tests for Cointegration. Econometrica, 58(1): 165-193.

Pongthanaisawan J. and Sorapipatana C., (2010). Relationship between level of economic development and motorcycle and car ownerships and their impacts and greenhouse gas emission in Thailand. Renewable and Sustainable Energy Reviews, 14(9): 2966–2975.

Republic of Tunisia. Law n. 1999-71 dated on July 26. Official Journal of the Republic of Tunisia 1999.

Republic of Tunisia. Law n. 2000-147 dated on January 24. Official Journal of the Republic of Tunisia 2000.

Rodrigue, J.-P.,Comtois, C. and Slack, B., (2006). The Geography of Transport Systems. Routledge, NewYork.

Rudra PP., (2010). Transport infrastructure, energy consumption and economic growth triangle in India: cointegration and causality analysis. Journal of Sustainable Development, 3: 167–73.

Saboori, B., M. Sapri and bin Baba M., (2014). Economic growth, energy consumption and CO2 emissions in OECD (Organization for Economic Co-operation and Development)’s transport sector: A fully modified bi-directional relationship approach. Energy, 66(1): 150-161.

Samimi, R., (1995). Road Transport Energy Demand in Australia: A Cointegration Approach, Energy Economics, 17(4), 329-339.

Schafer, A., (2006). Long-term trends in global passenger mobility. The Bridge 36(4), 25–32.

Scholl, L., Schipper, L., Kiang, N., (1996). CO2 emissions from passenger transport: A comparison of international trends from 1973 to 1992. Energy Policy, 24, 17-30.

WDI. World development indicators. Washington, DC: IBRD, World Bank; 2013.

Zivot, E. and Andrews, D. (1992). Further evidence of the great crash, the oil price shock and the unit root hypothesis. Journal of Business and Economic Statistics, 10, 251-270.