IDEAS home Printed from https://ideas.repec.org/a/eee/ecanpo/v65y2020icp11-20.html
   My bibliography  Save this article

Examining the determinants of CO2 emissions caused by the transport sector: Empirical evidence from 12 European countries

Author

Listed:
  • Georgatzi, Vasiliki V.
  • Stamboulis, Yeoryios
  • Vetsikas, Apostolos

Abstract

The transport sector is the second most important sector contributing to the production of CO2 emissions worldwide, and it is responsible for more than one third of the total energy consumption within the country-members of the European Environment Agency. With the overarching aim of mitigating climate change, policies, regulations, and new infrastructure investments are employed to facilitate the route to a low-carbon economy. In this paper, we investigate possible determinants of CO2 emissions caused by transport sector activity for 12 European countries from 1994 to 2014. We examine the effects of environmental policy stringency, climate change mitigation technologies related to transportation, and the share of value added by the transport sector and infrastructure investments (rail, inland waterways, and roads) on the amount of CO2 emissions caused by the transport sector. We employ panel data analysis – panel unit root tests, panel cointegration tests, the Fully-Modified OLS (FMOLS) approach, the Dynamic OLS (DOLS) approach, and the Granger causality test – in order to examine the relationship between CO2 emissions caused by the transport sector activity and their statistically significant determinants. Through our analysis, we highlight the importance of factors such as policies and technological innovation in the transition to a low carbon economy, something that is confirmed by our empirical findings. The empirical results of this study indicate that, for the examined countries: i) infrastructure investments do not seem to affect CO2 emissions caused by the transport sector; ii) CO2 emissions caused by the transport sector, environmental policy stringency, climate change mitigation technologies related to transportation, and the share of value added by the transport sector are cointegrated; and iii) both cointegrating regressions (FMOLS and DOLS) indicate that the environmental policy stringency and the CCMTs have positive policy outcomes (negative and statistically significant signs) on CO2 emissions, whereas the share of value added has a positive and statistically significant coefficient only in the case of the FMOLS method; iv) the panel Granger causality test presents a strong bidirectional causal relationship between environmental policy stringency index and CO2 emissions caused by the transport sector activity for one to three time lags. The findings of this study have important policy implications and warrant further research.

Suggested Citation

  • Georgatzi, Vasiliki V. & Stamboulis, Yeoryios & Vetsikas, Apostolos, 2020. "Examining the determinants of CO2 emissions caused by the transport sector: Empirical evidence from 12 European countries," Economic Analysis and Policy, Elsevier, vol. 65(C), pages 11-20.
    • Handle: RePEc:eee:ecanpo:v:65:y:2020:i:c:p:11-20
    DOI: 10.1016/j.eap.2019.11.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0313592619300232
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eap.2019.11.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pesaran, M. Hashem & Shin, Yongcheol & Smith, Richard J., 2000. "Structural analysis of vector error correction models with exogenous I(1) variables," Journal of Econometrics, Elsevier, vol. 97(2), pages 293-343, August.
    2. Peter C. B. Phillips & Bruce E. Hansen, 1990. "Statistical Inference in Instrumental Variables Regression with I(1) Processes," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 57(1), pages 99-125.
    3. Alfredo Marvão Pereira & Rui Manuel Pereira, 2017. "On the Effects of Infrastructure Investments on Industrial CO2 Emissions in Portugal," GEE Papers 0081, Gabinete de Estratégia e Estudos, Ministério da Economia, revised Oct 2017.
    4. Pedroni, Peter, 2004. "Panel Cointegration: Asymptotic And Finite Sample Properties Of Pooled Time Series Tests With An Application To The Ppp Hypothesis," Econometric Theory, Cambridge University Press, vol. 20(3), pages 597-625, June.
    5. Sadorsky, Perry, 2009. "Renewable energy consumption and income in emerging economies," Energy Policy, Elsevier, vol. 37(10), pages 4021-4028, October.
    6. Bel, Germà & Joseph, Stephan, 2018. "Climate change mitigation and the role of technological change: Impact on selected headline targets of Europe's 2020 climate and energy package," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3798-3807.
    7. Wu, Libo & Kaneko, Shinji & Matsuoka, Shunji, 2005. "Driving forces behind the stagnancy of China's energy-related CO2 emissions from 1996 to 1999: the relative importance of structural change, intensity change and scale change," Energy Policy, Elsevier, vol. 33(3), pages 319-335, February.
    8. Xu, Bin & Lin, Boqiang, 2015. "Carbon dioxide emissions reduction in China's transport sector: A dynamic VAR (vector autoregression) approach," Energy, Elsevier, vol. 83(C), pages 486-495.
    9. Zhang, Runsen & Fujimori, Shinichiro & Dai, Hancheng & Hanaoka, Tatsuya, 2018. "Contribution of the transport sector to climate change mitigation: Insights from a global passenger transport model coupled with a computable general equilibrium model," Applied Energy, Elsevier, vol. 211(C), pages 76-88.
    10. Stock, James H & Watson, Mark W, 1993. "A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems," Econometrica, Econometric Society, vol. 61(4), pages 783-820, July.
    11. Kao, Chihwa, 1999. "Spurious regression and residual-based tests for cointegration in panel data," Journal of Econometrics, Elsevier, vol. 90(1), pages 1-44, May.
    12. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    13. Lee, Chien-Chiang, 2005. "Energy consumption and GDP in developing countries: A cointegrated panel analysis," Energy Economics, Elsevier, vol. 27(3), pages 415-427, May.
    14. Peter Pedroni, 1999. "Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(S1), pages 653-670, November.
    15. repec:bla:obuest:v:61:y:1999:i:0:p:631-52 is not listed on IDEAS
    16. Ronald Coase & Ning Wang, 2012. "China in Transition," Palgrave Macmillan Books, in: How China Became Capitalist, chapter 2, pages 22-40, Palgrave Macmillan.
    17. Scholl, Lynn & Schipper, Lee & Kiang, Nancy, 1996. "CO2 emissions from passenger transport : A comparison of international trends from 1973 to 1992," Energy Policy, Elsevier, vol. 24(1), pages 17-30, January.
    18. Su, Hsin-Ning & Moaniba, Igam M., 2017. "Does innovation respond to climate change? Empirical evidence from patents and greenhouse gas emissions," Technological Forecasting and Social Change, Elsevier, vol. 122(C), pages 49-62.
    19. Lu, I.J. & Lin, Sue J. & Lewis, Charles, 2007. "Decomposition and decoupling effects of carbon dioxide emission from highway transportation in Taiwan, Germany, Japan and South Korea," Energy Policy, Elsevier, vol. 35(6), pages 3226-3235, June.
    20. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    21. Neves, Sónia Almeida & Marques, António Cardoso & Fuinhas, José Alberto, 2017. "Is energy consumption in the transport sector hampering both economic growth and the reduction of CO2 emissions? A disaggregated energy consumption analysis," Transport Policy, Elsevier, vol. 59(C), pages 64-70.
    22. G. S. Maddala & Shaowen Wu, 1999. "A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(S1), pages 631-652, November.
    23. repec:bla:obuest:v:61:y:1999:i:0:p:653-70 is not listed on IDEAS
    24. 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.
    Full references (including those not matched with items on IDEAS)

    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. El-Shazly, Alaa, 2013. "Electricity demand analysis and forecasting: A panel cointegration approach," Energy Economics, Elsevier, vol. 40(C), pages 251-258.
    2. Skare, Marinko & Ozturk, Ilhan & Porada-Rochoń, Małgorzata & Stjepanovic, Sasa, 2024. "Energy as the new frontier: Dynamic panel data analysis revealing energy's transformative role in economic growth and technological progress," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    3. Nguyen, Kim Hanh & Kakinaka, Makoto, 2019. "Renewable energy consumption, carbon emissions, and development stages: Some evidence from panel cointegration analysis," Renewable Energy, Elsevier, vol. 132(C), pages 1049-1057.
    4. Kahia, Montassar & Ben Aïssa, Mohamed Safouane & Charfeddine, Lanouar, 2016. "Impact of renewable and non-renewable energy consumption on economic growth: New evidence from the MENA Net Oil Exporting Countries (NOECs)," Energy, Elsevier, vol. 116(P1), pages 102-115.
    5. Alessandro Bellocchi & Edgar J. Sanchez Carrera & Giuseppe Travaglini, 2021. "What drives TFP long-run dynamics in five large European economies?," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 38(2), pages 569-595, July.
    6. Edmore Mahembe & Nicholas Mbaya Odhiambo, 2019. "Foreign aid, poverty and economic growth in developing countries: A dynamic panel data causality analysis," Cogent Economics & Finance, Taylor & Francis Journals, vol. 7(1), pages 1626321-162, January.
    7. Dobnik, Frauke, 2011. "Energy Consumption and Economic Growth Revisited: Structural Breaks and Cross-section Dependence," Ruhr Economic Papers 303, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    8. Juan Carlos Aquino & N. R. Ramírez-Rondán, 2020. "Estimating factor shares from nonstationary panel data," Empirical Economics, Springer, vol. 58(5), pages 2353-2380, May.
    9. Sadorsky, Perry, 2012. "Energy consumption, output and trade in South America," Energy Economics, Elsevier, vol. 34(2), pages 476-488.
    10. Mohammad Mafizur Rahman & Xuan-Binh (Benjamin) Vu & Son Nghiem, 2022. "Economic Growth in Six ASEAN Countries: Are Energy, Human Capital and Financial Development Playing Major Roles?," Sustainability, MDPI, vol. 14(8), pages 1-17, April.
    11. Tarik Dogru & Umit Bulut & Ercan Sirakaya-Turk, 2021. "Modeling tourism demand: Theoretical and empirical considerations for future research," Tourism Economics, , vol. 27(4), pages 874-889, June.
    12. Farhani, Sahbi & Shahbaz, Muhammad & Sbia, Rashid & Chaibi, Anissa, 2014. "What does MENA region initially need: Grow output or mitigate CO2 emissions?," Economic Modelling, Elsevier, vol. 38(C), pages 270-281.
    13. Frauke Dobnik, 2011. "Energy Consumption and Economic Growth Revisited: Structural Breaks and Cross-section Dependence," Ruhr Economic Papers 0303, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    14. Helmi Hamdi & Ali Said & Rashid Sbia, 2015. "Empirical Evidence on the Long-Run Money Demand Function in the Gulf Cooperation Council Countries," International Journal of Economics and Financial Issues, Econjournals, vol. 5(2), pages 603-612.
    15. Xuejiao Ma & Qichuan Jiang, 2019. "How to Balance the Trade-off between Economic Development and Climate Change?," Sustainability, MDPI, vol. 11(6), pages 1-30, March.
    16. Kahia, Montassar & Aïssa, Mohamed Safouane Ben & Lanouar, Charfeddine, 2017. "Renewable and non-renewable energy use - economic growth nexus: The case of MENA Net Oil Importing Countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 127-140.
    17. Pedro M. G. Martins, 2010. "Aid Absorption and Spending in Africa: A Panel Cointegration Approach," Working Paper Series 1010, Department of Economics, University of Sussex Business School.
    18. Igor Petruška & Eva Litavcová & Jana Chovancová, 2022. "Impact of Renewable Energy Sources and Nuclear Energy on CO 2 Emissions Reductions—The Case of the EU Countries," Energies, MDPI, vol. 15(24), pages 1-23, December.
    19. Ouedraogo, Nadia S., 2013. "Energy consumption and human development: Evidence from a panel cointegration and error correction model," Energy, Elsevier, vol. 63(C), pages 28-41.
    20. Sofien Tiba & Musavir Ul Habib, 2024. "Examining the Causal Linkages Between Nuclear Energy, Environment, and Economic Growth: An Application from the SAARC Economies," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(2), pages 9699-9722, June.

    More about this item

    Keywords

    Transport sector; Climate change mitigation technology; CO2 emissions; European countries; Policy stringency;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy

    Statistics

    Access and download statistics

    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:eee:ecanpo:v:65:y:2020:i:c:p:11-20. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/economic-analysis-and-policy .

    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.