The Nexus between Renewable Energy, Economic Growth, and Carbon Dioxide Emissions: Evidence from MS-VAR and MS-Granger Causality Methods

Abstract

This study aims to examine the dynamic relationship between carbon dioxide (CO2) emissions, renewable energy consumption, and economic growth in Denmark, Sweden, and Chile. These countries were not randomly selected. They were chosen since they have the highest scores according to the Climate Change Performance Index (2023). In addition, Markov-switching vector autoregressive (MS-VAR) and Markov-switching Granger (MS-Granger) causality methods are applied to the annual data of the three countries over the period 1971–2021. Contrary to linear methods, MS-VAR and MS-Granger causality approaches allow us to estimate and interpret this relationship for different regimes, such as recession and expansion. These methods also provide insights into the likelihood and duration of the persistence of the current economic regime. The empirical results show that there is a two-way MS-Granger causality between renewable energy consumption and economic growth in all regimes for the three countries except for moderate and high expansion regimes for Chile. Moreover, in general, there is a two-way MS-Granger causality between economic growth and CO2 emissions in all regimes. Furthermore, the findings from the estimated models indicate that there is a two-way MS-Granger causality between renewable energy consumption and CO2 emissions in general, except for the second regime for Chile.

Keywords

• MS-VAR
• MS-Granger Causality
• Renewable Energy
• Carbon Dioxide Emissions
• Economic Growth
• Regime switching

Yenilenebilir Enerji, Ekonomik Büyüme ve Karbondioksit Emisyonları Arasındaki İlişki: MS-VAR ve MS-Granger Nedensellik Yöntemlerinden Kanıtlar

Abstract

Bu çalışma, Danimarka, İsveç ve Şili'deki CO2 emisyonları, yenilenebilir enerji tüketimi ve ekonomik büyüme arasındaki dinamik ilişkiyi incelemeyi amaçlamaktadır. Bu ülkeler rastgele belirlenmemiştir. İklim Değişikliği Performans Endeksi'ne (2023) göre en yüksek puanlara sahip ilk üç ülke oldukları için seçilmişlerdir. Ayrıca, üç ülkenin 1971-2021 yılları arası yıllık verilerine Markov rejim değişimli vektör otoregresif (MS-VAR) ve Markov rejim değişimli Granger (MS-Granger) nedensellik yöntemleri uygulanmıştır. Doğrusal yöntemlerin aksine, MS-VAR ve MS-Granger nedensellik yaklaşımları bu ilişkiyi durgunluk ve genişleme gibi farklı rejimler için tahmin etmemize ve yorumlamamıza olanak sağlamaktadır. Bu yöntemler aynı zamanda ülke ekonomisinin mevcut rejimde kalma olasılığı ve süresi hakkında da bilgiler sağlamaktadır. Ampirik sonuçlar, Şili için ılımlı ve hızlı büyüme rejimleri hariç, üç ülke için de tüm rejimlerde yenilenebilir enerji tüketimi ile ekonomik büyüme arasında iki yönlü MS-Granger nedenselliği olduğunu göstermektedir. Ayrıca, genel olarak, tüm rejimlerde ekonomik büyüme ve CO2 emisyonları arasında iki yönlü bir MS-Granger nedenselliği bulunmuştur. Son olarak, tahmin edilen modellerden elde edilen bulgular, Şili için ikinci rejim hariç, genel olarak yenilenebilir enerji tüketimi ile CO2 emisyonları arasında iki yönlü bir MS-Granger nedenselliği olduğunu göstermektedir.

Keywords

• MS-VAR
• MS-Granger Nedensellik
• Yenilenebilir Enerji
• Karbondioksit Emisyonları
• Ekonomik Büyüme
• Rejim Değişimi

References

1. Adewuyi, A.O. and Awodumi, O.B. (2017). Biomass energy consumption, economic growth and carbon emissions: Fresh evidence from West Africa using a simultaneous equation model. Energy, 119, 453-471. https://doi.org/10.1016/j.energy.2016.12.059
2. Ang, A. and Bekaert, G. (1998). Regime switches in interest rates (NBER Working Paper No. 6508). Retrieved from http://www.nber.org/papers/w6508
3. Apergis, N. and Payne, J.E. (2010a). Energy consumption and growth in South America: Evidence from a panel error correction model. Energy Economics, 32(6), 1421-1426. https://doi.org/10.1016/j.eneco.2010.04.006
4. Apergis, N. and Payne, J.E. (2010b). Renewable energy consumption and economic growth: Evidence from a panel of OECD countries. Energy Policy, 38, 656–660. https://doi.org/10.1016/j.enpol.2009.09.002
5. Apergis, N., Payne, J.E., Menyah, K. and Wolde-Rufael, Y. (2010). On the causal dynamics between emissions, nuclear energy, renewable energy and economic growth. Ecological Economics, 69, 2255-2260. https://doi.org/10.1016/j.ecolecon.2010.06.014
6. Bhattacharya, M., Paramati, S.R., Ozturk, I. and Bhattacharya, S. (2016). The effect of renewable energy consumption on economic growth: Evidence from top 38 countries. Applied Energy, 162(C), 733-741. https://doi.org/10.1016/j.apenergy.2015.10.104
7. Can, M. and Gozgor, G. (2017). The impact of economic complexity on carbon emissions: Evidence from France. Environmental Science and Pollution Research, 24(19), 16364-16370. https://doi.org/10.1007/s11356-017-9219-7
8. Chen, C., Pinar, M. and Stengos, T. (2020). Renewable energy consumption and economic growth nexus: Evidence from a threshold model. Energy Policy, 139(C), 111295. https://doi.org/10.1016/j.enpol.2020.111295

9. Cho, S., Heo, E. and Kim, J. (2015). Causal relationship between renewable energy consumption and economic growth: Comparison between developed and less-developed countries. Geosystem Engineering, 18, 284–291. https://doi.org/10.1080/12269328.2015.1053540
10. CCPI. (2018). Climate Change Performance Index - Results 2018. Retrieved from https://ccpi.org/wp-content/uploads/climate_change_performance_index_2018_20503.pdf
11. CCPI. (2023). Climate Change Performance Index - Results 2023. Retrieved from https://ccpi.org/wp-content/uploads/CCPI-2023-Results-3.pdf
12. Dempster, A.P., Laird, N.M. and Rubin, D.B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39, 1-38. http://dx.doi.org/10.2307/2984875
13. Dickey, D.A. and Fuller, W.A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49, 1057-1072. https://doi.org/10.2307/1912517
14. Dinda, S. (2004). Environmental Kuznets curve hypothesis: A survey. Ecological Economics, 49, 431-455. https://doi.org/10.1016/j.ecolecon.2004.02.011
15. Dong, K., Hochman, G., Zhang, Y., Sun, R., Li, H. and Liao, H. (2018). CO2 emissions, economic and population growth, and renewable energy: Empirical evidence across regions. Energy Economics, 75(C), 180-192. https://doi.org/10.1016/j.eneco.2018.08.017
16. Fallahi, F. (2011). Causal relationship between energy consumption (EC) and GDP: A Markov-switching (MS) causality. Energy, 36(7), 4165-4170. https://doi.org/10.1016/j.energy.2011.04.027
17. Fang, Y. (2011). Economic welfare impacts from renewable energy consumption: The China experience. Renewable and Sustainable Energy Reviews, 15, 5120–5128. https://doi.org/10.1016/j.rser.2011.07.044
18. Goldfeld, S.M. and Quandt, R.E. (1973). A Markov model for switching regressions. Journal of Econometrics, 1(1), 3–15. https://doi.org/10.1016/0304-4076(73)90002-X
19. Grossman, G. and Krueger, A. (1991). Environmental impacts of a North American free trade agreement (NBER Working Paper No. 3914). https://doi.org/10.3386/w3914
20. Hamilton, J.D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57, 357-384. https://doi.org/10.2307/1912559
21. Hamilton, J.D. (1994). Time series analysis. New Jersey: Princeton University Press.
22. Inglesi-Lotz, R. (2016). The impact of renewable energy consumption to economic growth: A panel data application. Energy Economics, 53, 58–63. https://doi.org/10.1016/j.eneco.2015.01.003
23. IEA. (2015). World energy outlook 2015 (International Energy Agency, OECD). https://doi.org/10.1787/weo-2015-en
24. IEA. (2016). World energy outlook 2016 (International Energy Agency, OECD). https://doi.org/10.1787/weo-2016-en
25. IEA. (2018). Energy policies beyond IEA countries - Chile 2018 (International Energy Agency, OECD). https://doi.org/10.1787/9789264290242-en
26. IEA. (2019). Energy policies of IEA countries: Sweden 2019 (International Energy Agency, OECD). https://doi.org/10.1787/d4ff3340-en
27. IEA. (2022). World energy outlook 2022 (International Energy Agency, OECD). https://doi.org/10.1787/3a469970-en
28. Irandoust, M. (2016). The renewable energy-growth nexus with carbon emissions and technological innovation: Evidence from the Nordic countries. Ecological Indicators, 69, 118–125. https://doi.org/10.1016/j.ecolind.2016.03.051
29. Jebli, M.B., Farhani, S. and Guesmi, K. (2020). Renewable energy, CO2 emissions and value added: Empirical evidence from countries with different income levels. Structural Change and Economic Dynamics, 53, 402-410. https://doi.org/10.1016/j.strueco.2019.12.009
30. Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12, 231-254. https://doi.org/10.1016/0165-1889(88)90041-3
31. Johansen, S. (1995). Likelihood based inference in cointegrated vector autoregressive models. Oxford: Oxford University Press. https://doi.org/10.1093/0198774508.001.0001
32. Joo, Y.J., Kim, C.S. and Yoo, S.H. (2015). Energy consumption, CO2 emission, and economic growth: Evidence from Chile. International Journal of Green Energy, 12(5), 543-550. https://doi.org/10.1080/15435075.2013.834822
33. Kocak, E. and Sarkgunesi, A. (2018). The impact of foreign direct investment on CO2 emissions in Turkey: New evidence from cointegration and bootstrap causality analysis. Environmental Science and Pollution Research, 25(1), 790-804. https://doi.org/10.1007/s11356-017-0468-2
34. Krolzig, H.M. (1997). Markov-switching vector autoregressions modelling, statistical inference, and application to business cycle analysis. Berlin: Springer. https://doi.org/10.1007/978-3-642- 51684-9
35. Krolzig, H.M. (1998). Econometric modelling of Markov-switching vector autoregressions using MSVAR for Ox (Unpublished doctoral dissertation). Nuffield College, Oxford.
36. Krolzig, H.M. (2003). Constructing turning point chronologies with Markov-switching vector autoregressive models: The Euro-zone business cycle. Paper presented at the Colloquium on Modern Tools for Business Cycle Analysis. Eurostat, Luxembourg. Retrieved from https://www.diw.de/documents/dokumentenarchiv/17/41185/abstract_krolzig240204.pdf
37. Kula, F. (2014). The long-run relationship between renewable electricity consumption and GDP: Evidence from panel data. Energy Sources, Part B: Economics, Planning, and Policy, 9(2), 156-160. https://doi.org/10.1080/15567249.2010.481655
38. Kuznets, S. (1955). Economic growth and income inequality. American Economic Review, 45(1), 1-28. Retrieved from https://assets.aeaweb.org/
39. Lee, J.W. (2019). Long-run dynamics of renewable energy consumption on carbon emissions and economic growth in the European Union. International Journal of Sustainable Development & World Ecology, 6(1), 1-10. https://doi.org/10.1080/13504509.2018.1492998
40. Lindmark, M. (2002). An EKC-pattern in historical perspective: Carbon dioxide emissions, technology, fuel prices and growth in Sweden 1870-1997. Ecological Economics, 42(1-2), 333-347. https://doi.org/10.1016/S0921-8009(02)00108-8
41. Mazur, A., Phutkaradze, Z. and Phutkaradze, J. (2015). Economic growth and environmental quality in the European Union countries - Is there evidence for the environmental Kuznets curve? International Journal of Management and Economics, 45, 108–126. https://doi.org/10.1515/ijme-2015-0018
42. Menegaki, A.N. (2011). Growth and renewable energy in Europe: A random effect model with evidence for neutrality hypothesis. Energy Economics, 33(2), 257-263. https://doi.org/10.1016/j.eneco.2010.10.004
43. Musah, M., Kong, Y., Mensah, I.A., Antwi, S.K. and Donkor, M. (2020). The link between carbon emissions, renewable energy consumption, and economic growth: A heterogeneous panel evidence from West Africa. Environmental Science and Pollution Research, 27(23), 28867-28889. https://doi.org/10.1007/s11356-020-08488-8
44. Nguyen, K.H. and Kakinaka, M. (2019). Renewable energy consumption, carbon emissions, and development stages: Some evidence from panel cointegration analysis. Renewable Energy, 132(C), 1049-1057. https://doi.org/10.1016/j.renene.2018.08.069
45. Ocal, O. and Aslan, A. (2013). Renewable energy consumption–economic growth nexus in Turkey. Renewable and Sustainable Energy Reviews, 28(C), 494-499. https://doi.org/10.1016/j.rser.2013.08.036
46. Olivier, J., Janssens-Maenhout, G. and Peters, J. (2012). Trends in global CO2 emissions: 2012 report. Retrieved from https://data.europa.eu/doi/10.2788/33777
47. Ozturk, I. and Acaravci, A. (2010). CO2 emissions, energy consumption and economic growth in Turkey. Renewable and Sustainable Energy Reviews, 14(9), 3220-3225. https://doi.org/10.1016/j.rser.2010.07.005
48. Payne, J.E. (2009). On the dynamics of energy consumption and output in the US. Applied Energy, 86(4), 575-577. https://doi.org/10.1016/j.apenergy.2008.07.003
49. Phillips, P.C. and Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346. https://doi.org/10.2307/2336182
50. Psaradakis, Z., Ravn, M.O. and Sola, M. (2005). Markov switching causality and the money–output relationship. Journal of Applied Econometrics, 20, 665–683. https://doi.org/10.1002/jae.819
51. Radmehr, R., Henneberry, S.R. and Shayanmehr, S. (2021). Renewable energy consumption, CO2 emissions, and economic growth nexus: A simultaneity spatial modeling analysis of EU countries. Structural Change and Economic Dynamics, 57(C), 13-27. https://doi.org/10.1016/j.strueco.2021.01.006
52. REN21. (2022). Renewables 2022 global status report (REN21). Retrieved from https://www.ren21.net/wp-content/uploads/2019/05/AnnualReport_2022.pdf
53. Sadorsky, P. (2009). Renewable energy consumption and income in emerging economies. Energy Policy, 37(10), 4021–4028. https://doi.org/10.1016/j.enpol.2009.05.003
54. Saidi, K. and Mbarek, M.B. (2016). Nuclear energy, renewable energy, CO2 emissions, and economic growth for nine developed countries: Evidence from panel Granger causality tests. Progress in Nuclear Energy, 88, 364-374. https://doi.org/10.1016/j.pnucene.2016.01.018
55. Saidi, K. and Omri, A. (2020). The impact of renewable energy on carbon emissions and economic growth in 15 major renewable energy-consuming countries. Environmental Research, 186, 109567. https://doi.org/10.1016/j.envres.2020.109567
56. Salim, R.A. and Rafiq, S. (2012). Why do some emerging economies proactively accelerate the adoption of renewable energy? Energy Economics, 34(4), 1051–1057. https://doi.org/10.1016/j.eneco.2011.08.015
57. Shahbaz, M. and Sinha, A. (2019). Environmental Kuznets curve for CO2 emissions: A literature survey. Journal of Economic Studies, 46(1), 106-168. https://doi.org/10.1108/JES-09-2017-0249
58. Shahbaz, M., Loganathan, N., Zeshan, M. and Zaman, K. (2015). Does renewable energy consumption add in economic growth? An application of auto-regressive distributed lag model in Pakistan. Renewable and Sustainable Energy Reviews, 44(C), 576-585. https://doi.org/10.1016/j.rser.2015.01.017
59. Tiwari, A.K., Shahbaz, M. and Hye, Q.M.A. (2013). The environmental Kuznets curve and the role of coal consumption in India: Cointegration and causality analysis in an open economy. Renewable and Sustainable Energy Reviews, 18(C), 519-527. https://doi.org/10.1016/j.rser.2012.10.031
60. Warne, A. (2000). Causality and regime inference in a Markov switching VAR (Sveriges Riksbank Working Paper Series No. 118). Retrieved from https://www.econstor.eu/bitstream/10419/82444/1/wp_118.pdf
61. WDI. (2023). World development indicators [Dataset]. Retrieved from https://databank.worldbank.org/source/world-development-indicators
62. Yao, S., Zhang, S. and Zhang, X. (2019). Renewable energy, carbon emission and economic growth: A revised environmental Kuznets curve perspective. Journal of Cleaner Production, 235, 1338-1352. https://doi.org/10.1016/j.jclepro.2019.07.069