Share:


Investigating the nexus between fuel ethanol and CO2 emissions. A panel smooth transition regression approach

    Cosmin-Octavian Cepoi   Affiliation
    ; Mariana Bran Affiliation
    ; Mihai Dinu Affiliation

Abstract

In this paper, we fill the gap in the literature by identifying a negative relationship between fuel ethanol consumption and CO2 emissions, building on a sample of 17 European countries covering seven years, from 2010 to 2016. Based on a Panel Smooth Transition Regression approach we show that countries with high levels of income inequality have difficulties in avoiding environmental degradation by promoting policies and regulations for more intense use of biofuels. Furthermore, we bring strong empirical evidence suggesting that biofuels could be an alternative in the future to reducing CO2 emissions. In our opinion, this non-linear analysis could provide the scientific basis for authorities, especially the European Commission to promote environmental policies to a specific country with different levels of carbon emissions rather than to the entire group.

Keyword : CO2 emissions, biofuels, EKC, threshold effects, GINI Index, GDP growth

How to Cite
Cepoi, C.-O., Bran, M., & Dinu, M. (2020). Investigating the nexus between fuel ethanol and CO2 emissions. A panel smooth transition regression approach. Journal of Business Economics and Management, 21(6), 1774-1792. https://doi.org/10.3846/jbem.2020.13695
Published in Issue
Oct 22, 2020
Abstract Views
247
PDF Downloads
149
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Ahmad, M., Khan, Z., Rahman, Z. U., Khattak, S. I., & Khan, Z. U. (2019b). Can innovation shocks determine CO2 emissions (CO2 emissions) in the OECD economies? A new perspective. Economics of Innovation and New Technology. https://doi.org/10.1080/10438599.2019.1684643

Ahmad, M., Khan, Z., Ur Rahman, Z., & Khan, S. (2018). Does financial development asymmetrically affect CO2 emissions in China? An application of the nonlinear autoregressive distributed lag (NARDL) model. Carbon Management, 9(6), 631–644. https://doi.org/10.1080/17583004.2018.1529998

Ahmad, M., Ul Haq, Z., Khan, Z., Iqbal Khattak, S., Rahman Z., & Khan, S. (2019a). Does the inflow of remittances cause environmental degradation? Empirical evidence from China. Economic ResearchEkonomska Istraživanja, 32(1), 2099–2121. https://doi.org/10.1080/1331677X.2019.1642783

Al-Mulali, U., Solarin, S. A., Sheau-Ting, L., & Ozturk, I. (2016). Does moving towards renewable energy causes water and land inefficiency? An empirical investigation. Energy Policy, 93, 303–314. https://doi.org/10.1016/j.enpol.2016.03.023

Balat, M., & Balat, H. (2009). Recent trends in global production and utilization of bio-ethanol fuel. Applied Energy, 86(11), 2273–2282. https://doi.org/10.1016/j.apenergy.2009.03.015

Blomquist, J., & Westerlund, J. (2013). Testing slope homogeneity in large panels with serial correlation. Economics Letters, 121(3), 374–378. https://doi.org/10.1016/j.econlet.2013.09.012

Boyce, J. K. (1994). Inequality as a cause of environmental degradation. Ecological Economics, 11(3), 169–178. https://doi.org/10.1016/0921-8009(94)90198-8

Câmpeanu, E., Dumitrescu, D., Costică I., & Boitan, I. (2017). The impact of higher education funding on socio-economic variables: Evidence from EU countries. Journal of Economic Issues, 51(3), 748–781.

Cole, M. (2005). Re-examining the pollution-income relationship: A random coefficients approach. Economics Bulletin, 14(1), 1–7.

Crețan, G. C., & Iacob, M. (2009). Considerations regarding the efficiency of public expenditures for education. Annals of Faculty of Economics, 3(1), 142–146.

Destek, M. A., & Sinha, A. (2020). Renewable, non-renewable energy consumption, economic growth, trade openness and ecological footprint: Evidence from organisation for economic Co-operation and development countries. Journal of Cleaner Production, 242, 118537. https://doi.org/10.1016/j.jclepro.2019.118537

Dias de Oliveira, M. E., Vaughan, B. E., & Rykiel, Jr. E. J. (2005). Ethanol as fuel: energy, carbon dioxide balances, and ecological footprint. Bioscience, 55(7), 593–602. https://doi.org/10.1641/0006-3568(2005)055[0593:EAFECD]2.0.CO;2

Dietz, T., & Rosa, E. A. 1997. Effects of population and affluence on CO2 emissions. Proceedings of the National Academy of Sciences of the United States of America, 94(1), 175–179. https://doi.org/10.1073/pnas.94.1.175

Fan, Y., Liu, L. C., Wu, G., & Wei, Y. M. (2006). Analysis impact factors of CO2 emissions using the STIRPAT model. Environmental Impact Assessment Review, 26(4), 377–395. https://doi.org/10.1016/j.eiar.2005.11.007

Galeotti, M., Lanza, A., & Pauli, F. (2006). Reassessing the environmental Kuznets curve for CO2 emissions: A robustness exercise. Ecological Economics, 57(1), 152–163. https://doi.org/10.1016/j.ecolecon.2005.03.031

Ghayur, M. A., Huang, Y., McIlveen-Wright, D., & Hewitt, N. (2011). Process simulations for wheat and wheat straw based ethanol biorefinery concepts. In A. V. Bridgwater (Ed.), Proceedings of the bioten conference on biomass, bioenergy and biofuels 2010 (pp. 146–152). CPL Press.

Gherghina, R., & Duca, I. (2013). The contribution of education to the economic development process of the states. Journal of Knowledge Management, Economics and Information Technology, 3(1), 1–11.

Glithero, N. J., Ramsden, S. J., & Wilson, P. (2013). Barriers and incentives to the production of bioethanol from cereal straw: A farm business perspective. Energy Policy, 59, 161–171. https://doi.org/10.1016/j.enpol.2013.03.003

González, A., Teräsvirta, T., Dijk, van D., & Yang, Y. (2005). Panel smooth transition regression models (SSE/EFI Working Paper Series in Economics and Finance No 604). http://swopec.hhs.se/hastef/papers/hastef0604.pdf

Griffith, R., Redding, S., & van Reenen, J. (2004). Mapping the two faces of R&D: productivity growth in a panel of OECD industries. The Review of Economics and Statistics, 86(4), 883–895. https://doi.org/10.1162/0034653043125194

Grossman, G. M., & Krueger, A. B. (1991). Environmental impacts of a North American free trade agreement (NBER Working Paper 3914). https://doi.org/10.3386/w3914

Guo, W., Sun, T., & Dai, H. J. (2015). Effect of population structure change on carbon emission in China. Sustainability, 8(3). https://doi.org/10.3390/su8030225

Hansen, B. (1999). Threshold effects in non-dynamic panels: Estimation, testing, and inference. Journal of Econometrics, 93(2), 345–368. https://doi.org/10.1016/S0304-4076(99)00025-1

Hill, J., Nelson, E., Tilman, D., Polasky, S., & Douglas, T. (2006). Environmental,economic, and energetic costs and benefits of biodiesel and ethanol biofuels. Proceedings of the National Academy of Sciences, 103(30), 11206–11210. https://doi.org/10.1073/pnas.0604600103

Hübler, M. (2017). The inequality-emissions nexus in the context of trade and development: A quantile regression approach. Ecological Economics, 134, 174–185. https://doi.org/10.1016/j.ecolecon.2016.12.015

Kang, Y. Q., Zhao, T., & Yang, Y. Y. (2016). Environmental Kuznets curve for CO2 emissions in China: A spatial panel data approach. Ecological Indicators, 63, 231–239. https://doi.org/10.1016/j.ecolind.2015.12.011

Kasman, A., & Duman, Y. S. (2015). CO2 emissions, economic growth, energy consumption, trade and urbanization in new EU member and candidate countries: A panel data analysis. Economic Modelling, 44, 97–103. https://doi.org/10.1016/j.econmod.2014.10.022

Khan, Z., Shahbaz, M., Ahmad, M., Rabbi, F., & Siqun, Y. (2019). Total retail goods consumption, industry structure, urban population growth and pollution intensity: An application of panel data analysis for China. Environmental Science and Pollution Research, 26, 32224–32242. https://doi.org/10.1007/s11356-019-06326-0

Kline, R. B. (1998). Principles and practice of structural equation modeling. The Guilford Press.

Lan, J., Kakinaka, M., & Huang, X. (2012). Foreign direct investment, human capital and environmental pollution in China. Environmental and Resource Economics, 51, 255–275. https://doi.org/10.1007/s10640-011-9498-2

Liddle, B., & Lung, S. (2010). Age-structure, urbanization, and climate change in developing countries: Revisiting STIRPAT for disaggregated population and consumption-related environmental impacts. Population and Environment, 31, 317–343. https://doi.org/10.1007/s11111-010-0101-5

Liu, Y., Hao, Y., & Gao, Y. (2017). The environmental consequences of domestic and foreign investment: Evidence from China. Energy Policy, 108, 271–280. https://doi.org/10.1016/j.enpol.2017.05.055

Mader, S. (2018). The nexus between social inequality and CO2 emissions revisited: Challenging its empirical validity. Environmental Science & Policy, 89, 322–329. https://doi.org/10.1016/j.envsci.2018.08.009

Nasir, M. A., Huynh, T., & Tram, H. (2019). Role of financial development, economic growth & foreign direct investment in driving climate change: A case of emerging ASEAN. Journal of Environmental Management, 242, 131–141. https://doi.org/10.1016/j.jenvman.2019.03.112

Nasreen, S., Anwar, S., & Ozturk, I. (2017). Financial stability, energy consumption and environmental quality: Evidence from South Asian economies. Renewable and Sustainable Energy Reviews, 67, 1105–1122. https://doi.org/10.1016/j.rser.2016.09.021

Ohia, G. N., Ohia, N. P., Ekwueme, S. T., & Nwankwo, I. V. (2020). Hydrolysis of cellulose wastes: Feasibility of fuel ethanol as alternative to gasoline from petroleum as a usable energy source in Nigeria. Petroleum Science and Engineering, 4(1), 16–22. https://doi.org/10.11648/j.pse.20200401.12

Omay, T., & Öznur Kan, E. (2010). Re-examining the threshold effects in the inflation growth-nexus with cross-sectionally dependent non-linear panel: Evidence from six industrialized economies. Economic Modelling, 27(5), 996–1005. https://doi.org/10.1016/j.econmod.2010.04.011

Patni, N., Pillai, S. G., & Dwivedi, A. H. (2013). Wheat as a promising substitute of corn for bioethanol production. Procedia Engineering, 51, 355–362. https://doi.org/10.1016/j.proeng.2013.01.049

Ponce de, L. B. D., & Marshall, J. (2014). Relationship between urbanization and CO2 emissions depends on income level and policy. Environmental Science & Technology, 48(7), 3632–3639. https://doi.org/10.1021/es405117n

Rahman, Z., Cai, H., & Ahmad, M. (2019a). A new look at the Remittances-FDI-Energy-Environment nexus in the case of selected Asian Nations. The Singapore Economic Review, 1–19. https://doi.org/10.1142/S0217590819500176

Rahman, Z., Chongbo, W., & Ahmad, M. (2019b). An (a)symmetric analysis of the pollution haven hypothesis in the context of Pakistan: A non-linear approach. Carbon Management, 10(3), 227–239. https://doi.org/10.1080/17583004.2019.1577179

Sarkodie, S. A., & Strezov, V. (2019). Effect of foreign direct investments, economic development and energy consumption on greenhouse gas emissions in developing countries. Science of the Total Environment, 646, 862–871. https://doi.org/10.1016/j.scitotenv.2018.07.365

Shahbaz, M., Solarin, S. A., Sbia, R., & Bibi, S. (2015). Does energy intensity contribute to CO2 emissions? A trivariate analysis in selected African countries. Ecological Indicators, 50, 215–224. https://doi.org/10.1016/j.ecolind.2014.11.007

Stern, D. I., & Common, M. S. (2001). Is there an environmental Kuznets curve for sulfur? Journal of Environmental Economics and Management, 41(2), 162–178. https://doi.org/10.1006/jeem.2000.1132

Străchinaru, A. I., & Dumitrescu, B. A. (2019). Assessing the sustainability of inflation targeting: Evidence from EU countries with Non-EURO currencies. Sustainability, 11(20), 5654. https://doi.org/10.3390/su11205654

Teräsvirta, T. (1994). Specification, estimation, and evaluation of smooth transition autoregressive models. Journal of the American Statistical Association, 89(425), 208–218. https://doi.org/10.1080/01621459.1994.10476462

Xie, Q., Wang, X., & Cong, X. (2020). How does foreign direct investment affect CO2 emissions in emerging countries? New findings from a nonlinear panel analysis. Journal of Cleaner Production, 249, 119422. https://doi.org/10.1016/j.jclepro.2019.119422