Power Generation Sources and Carbon Dioxide Emissions in BRICS Countries: Static and Dynamic Panel Regression
Purpose: The threat of global warming has escalated as a result of industrialization, urbanization, population growth, and lifestyle changes in Brazil, Russia, India, China, and South Africa (BRICS). The amount of electricity generated by various sources is directly influenced by their respective carbon dioxide (CO2
) emissions. This study’s primary goal is to determine which sources are bad for the environment and which are not.
Methodology: Examining the impact of different energy generation sources on CO2
emissions using data from the BRICS. To analyze the data, pooled OLS and Generalized Method of Moments (GMM) are used, as well as Quantile Regression (QR).
Findings: We found that coal and gas power generation had a positive and large influence on CO2
emissions regardless of the method used. As compared to other emissions, coal-fired energy production has a more significant impact. In all regression models, hydroelectric and renewable energy generation can reduce CO2
Originality: Identifying an empirical link between CO2
emissions and energy production sources is the study’s most significant accomplishment. To obtain solid results, the paper used a combination of QR and GMM techniques. The conclusions presented in this article have important environmental policy consequences. CO2 emissions can be reduced by reducing the consumption of fossil fuels and promoting the development of alternative energy sources such as hydroelectric, wind, and solar power.
Abdallah, L., and El-Shennawy, T. (2013). Reducing carbon dioxide emissions from the electricity sector using smart electric grid applications. Journal of Engineering, 2013.
Al-Mulali, U. (2014). Investigating the impact of nuclear energy consumption on GDP growth and CO2
emission: A panel data analysis. Progress in Nuclear Energy, 73, 172–178.
Arellano, M., and Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The review of economic studies, 58(2), 277–297.
Arellano, M., and Bover, O. (1995). Another look at the instrumental variable estimation of error components models. Journal of Econometrics, 68(1), 29–51.
Awosusi, A. A., Adebayo, T. S., Altuntaş, M., Agyekum, E. B., Zawbaa, H. M., and Kamel, S. (2022). The dynamic impact of biomass and natural resources on the ecological footprint in BRICS economies: A quantile regression evidence. Energy Reports, 8, 1979–1994.
Aydin, M. (2019). The effect of biomass energy consumption on economic growth in BRICS countries: A country-specific panel data analysis. Renewable Energy, 138, 620–627.
Aytekin A. (2022). Energy, Environment, and Sustainability: A Multi-criteria Evaluation of Countries. Strategic planning for energy and the environment, 2022: Vol 41 Iss 3, 281–316.
Baloch, M. A., Mahmood, N., and Zhang, J. W. (2019). Effect of natural resources, renewable energy, and economic development on CO2
emissions in BRICS countries. Science of the Total Environment, 678, 632–638.
Baltagi B. H. (2008) Forecasting with panel data. J Forecast 27(2):153–173.
Bashir, M. F., Ma, B., Shahbaz, M., and Jiao, Z. (2020). The nexus between environmental tax and carbon emissions with the roles of environmental technology and financial development. Plos one, 15(11), e0242412.
Bayazıt, Y. (2021). The effect of hydroelectric power plants on carbon emission: An example of Gokcekaya dam, Turkey. Renewable Energy, 170, 181–187.
Bilgen, S., Kaygusuz, K., and Sari, A. (2004). Renewable energy for a clean and sustainable future. Energy sources, 26(12), 1119–1129.
Blundell, R., and Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143.
Buchinsky, M. (1994). Changes in the US wage structure 1963–1987: Application of quantile regression. Econometrica: Journal of the Econometric Society, 62(2), 405–458.
Cameron, A. C., and Trivedi, P. K. (2010). Microeconometrics using Stata (Vol. 2). College Station, TX: Stata Press.
Canay, I. A. (2011).A simple approach to quantile regression for panel data. The Econometrics Journal, 14(3), 368–386.
Chen, W., and Lei, Y. (2018). The impacts of renewable energy and technological innovation on environment-energy-growth nexus: New evidence from a panel quantile regression. Renewable energy, 123, 1–14.
Cho, Y., Lee, J., Kim, T.Y. (2007). The impact of ICT investment and energy price on industrial electricity demand: Dynamic growth model approach.Energy Policy, 35, 4730–4738.
Cowan, W. N., Chang, T., Inglesi-Lotz, R., and Gupta, R. (2014). The nexus of electricity consumption, economic growth, and CO2
emissions in the BRICS countries. Energy Policy, 66, 359–368. enpol.2020.111339
Dantama, Y. U., Abdullahi, Y. Z., and Inuwa, N. (2012). Energy consumption-economic growth nexus in Nigeria: An empirical assessment based on ARDL bound test approach. European Scientific Journal, 8(12).
Gasser, P. (2020). A review of energy security indices to compare country performances. Energy Policy, 139, 111339.
Jin, T., and Kim, J. (2018). What is better for mitigating carbon emissions–Renewable energy or nuclear energy? A panel data analysis. Renewable and Sustainable Energy Reviews, 91, 464–471.
Menyah, K., and Wolde-Rufael, Y. (2010). CO2
emissions, nuclear energy, renewable energy, and economic growth in the US. Energy Policy, 38(6), 2911–2915.
Nickell, S. (1981). “Biases in dynamic models with fixed effects.” Econometrica: J Econo Soc: 1417–1426.
Ozturk, I. (2017). Measuring the impact of alternative and nuclear energy consumption, carbon dioxide emissions, and oil rents on specific growth factors in the panel of Latin American countries. Progress in Nuclear Energy, 100, 71–81.
Pao, H. T., and Tsai, C. M. (2010). CO2
emissions, energy consumption, and economic growth in BRIC countries. Energy Policy, 38(12), 7850–7860.
Pao, H. T., and Tsai, C. M. (2011). Multivariate Granger causality between CO2
emissions, energy consumption, FDI (foreign direct investment), and GDP (gross domestic product): evidence from a panel of BRIC (Brazil, Russian Federation, India, and China) countries. Energy, 36(1), 685–693.
Paul, S., and Bhattacharya, R. N. (2004). CO2
emission from energy use in India: a decomposition analysis. Energy Policy, 32(5), 585–593. Performances. Energy Policy 139:111339. https://doi.org/10.1016/j.
Rahman, M. H., Majumder, S. C., and Debbarman, S. (2020). Examine the Role of Agriculture to Mitigate the CO2
Emission in Bangladesh. Asian Journal of Agriculture and Rural Development, 10(1), 392–405.
Rahman, M. M. (2017). Do population density, economic growth, energy use and exports adversely affect environmental quality in Asian populous countries?. Renewable and Sustainable Energy Reviews, 77, 506–514.
Roodman, D. (2009). How to do xtabond2: An introduction to difference and system GMM in Stata. The Stata Journal, 9(1), 86–136.
Sebos, I., Progiou, A.G., and Kallinikos, L.E. (2021). Methodological Framework for the Quantification of GHG Emission Reductions from Climate Change Mitigation Actions. Strategic planning for energy and the environment, 219–242.
Statista. Total population of the BRICS countries from 2000 to 2026 (in million inhabitants). https://www.statista.com/statistics/254205/total-population-of-the-bric-countries/Summit, F. B. (2013). Thekweni declaration and action plan.
The Guardian (2012). World Carbon Emissions: the League Table of Every Country. 21 June 2012.
Urry, J. (2015).Climate change and society.In Why the Social Sciences Matter. Palgrave Macmillan: London, UK, pp. 45–59.
Wang, W., Mu, H., Kang, X., Song, R., and Ning, Y. (2010).Changes in industrial electricity consumption in China from 1998 to 2007. Energy Policy, 38, 3684–3690.
World Bank(2019).World development indicators. Accessed at: http://www.worldbank.org/data/onlinedatabases.
Yang, L., and Lin, B. (2016). Carbon dioxide-emission in China’s power industry: Evidence and policy implications.Renewable and Sustainable Energy Reviews, 60, 258–267.
Yoo, S.H. (2005). Electricity consumption and economic growth: Evidence from Korea.Energy Policy, 33, 1627–1632.
Yu, Z., Liu, W., Chen, L., Eti, S., Dinçer, H., and Yüksel, S. (2019). The effects of electricity production on industrial development and sustainable economic growth: A VAR analysis for BRICS countries. Sustainability, 11(21), 5895.
Zhang, M., Mu, H., and Ning, Y. (2009). Accounting for energy-related CO2
emission in China, 1991–2006. Energy Policy, 37(3), 767–773.
Zhao, W., Cao, Y., Miao, B., Wang, K., and Wei, Y. M. (2018). Impacts of shifting China’s final energy consumption to electricity on CO2
emission reduction. Energy Economics, 71, 359–369.
Zhu, L., He, L., Shang, P., Zhang, Y., and Ma, X. (2018). Influencing factors and scenario forecasts of carbon emissions of the Chinese power industry: Based on a Generalized Divisia Index Model and Monte Carlo Simulation. Energies, 11(9), 2398.