RUDN Journal of EconomicsRUDN Journal of EconomicsВестник Российского университета дружбы народов. Серия: Экономика2313-23292408-8986Peoples’ Friendship University of Russia named after Patrice Lumumba (RUDN University)3987710.22363/2313-2329-2024-32-2-287-302IXXILJINDUSTRIAL ORGANIZATION MARKETSЭКОНОМИКА ОТРАСЛЕВЫХ РЫНКОВResearch ArticleEvidence from the South African Energy Sector on the Impact of Gas Consumption and Technologies on the EnvironmentВлияние потребления газа и технологий на окружающую среду: опыт энергетического сектора Южной Африкиhttps://orcid.org/0000-0001-5270-2192YobeCollin L.ЙобеКоллин Л.

Postdoctoral Research Fellow

последипломный исследователь

collinyobe@gmail.comhttps://orcid.org/0000-0003-0578-576XMucharaBinganidzoМучараБинганидзо

Doctor, Senior Lecturer

доктор наук, доцент

Muchab@unisa.ac.zaUniversity of South Africa, Graduate School of Business LeadershipУниверситет Южной Африки, Высшая школа бизнес-лидерства30062024322INNOVATION AND INVESTMENT: OPPORTUNITIES AND PROSPECTSИННОВАЦИИ И ИНВЕСТИЦИИ: ВОЗМОЖНОСТИ И ПЕРСПЕКТИВЫ28730207072024Copyright ©; 2024, Yobe C.L., Muchara B.Copyright ©; 2024, Йобе К.Л., Мучара Б.2024Yobe C.L., Muchara B.Йобе К.Л., Мучара Б.https://creativecommons.org/licenses/by-nc/4.0https://journals.rudn.ru/economics/article/view/39877

Legacy emissions from fossil fuel consumption signify the lasting impact of past carbon dioxide (CO2) emissions on present-day emissions. Given that the current emission levels are also high; it has become urgent to deal with this crisis. This study aims to examine the effects of gas consumption, legacy CO2 emissions, energy decoupling, and population on carbon dioxide emissions in South Africa using the modified IPAT identity and the Markov Switching Dynamic Regression analysis. Integrating additional variables into the modified IPAT identity uncovered evidence from the South African energy sector on the impact of gas consumption on the environment. The Markov Switching Dynamic Regression Model (MSDRM) utilised annual data from the South African energy sector from 1966 to 2020, collected from diverse sources. Results indicate that the Gas model’s probability (i.e., 0.8475) would persist in high-emissions states over time. The MSDRM results showed that gas consumption suggests a statistically significant negative relationship between gas consumption (-0.0461) and CO2 emissions, meaning that despite the decrease in CO2 emissions from using gas, it does not imply instant reversals in the ambient CO2 as to reduce the overall CO2, likely contributed from other CO2-emitting fuels. The MSDRM results showed that legacy CO2 emissions positively impact (I) current CO2 emissions and that decoupling (T) leads to increased CO2 emissions-the latter relationship indicating likely energy rebounding. These findings highlight the need to prioritise interventions and strategies targeting the factors with higher probabilities of contributing to sustained high emissions, which may involve implementing policies to transition away from high-emission sources while exploring alternatives and adopting cleaner energy sources. The results emphasise the challenge of decoupling economic growth from high CO2 emissions and underscore the importance of sustained efforts to address and mitigate climate change.

Остаточные выбросы от потребления ископаемого топлива свидетельствуют о долгосрочном воздействии прошлых выбросов углекислого газа (CO2) на современные объемы. Учитывая, что текущие уровни выбросов остаются на высоком уровне, необходимо обращать внимание на накопительный характер данного процесса. Представленное исследование направлено на изучение влияния потребления газа и остаточных выбросов CO2 на выбросы углекислого газа в Южной африке с использованием модифицированной идентификации IPAT, а также скрытой модели маркова. внедрение дополнительных переменных в модифицированную идентификацию IPAT выявило доказательства влияния потребления газа на окружающую среду в энергетическом секторе Южной африки. в модели динамической регрессии маркова (MSDRM) использованы ежегодные данные из энергетического сектора Южной африки с 1966 по 2020 г., собранные из различных источников. результаты показывают, что вероятность модели (0,8475) будет сохраняться в состояниях с высокими выбросами с течением времени. результаты MSDRM показали, что потребление газа указывает на статистически значимую отрицательную связь между потреблением газа (-0,0461) и выбросами CO2, означающую, что, несмотря на снижение выбросов CO2 при использовании газа, это не влечет за собой мгновенных обратных изменений в атмосфере. вероятно, такие изменения вызваны другими источниками выбросов. результаты MSDRM показали, что остаточные выбросы CO2 положительно влияют (I) на нынешние выбросы CO2 и что декаплинг (T) приводит к увеличению выбросов CO2. Полученные результаты подчеркивают необходимость разработки приоритетных «зеленых» стратегий, направленных на борьбу с источниками с устойчивыми высокими выбросами.

Gas consumptionenergy decouplingSouth African energy sectormodified IPAT identityIPATпотребление газаэнергетический сектор Южной африкиAbas, N., Kalair, A., & Khan, N. (2015). Review of fossil fuels and future energy technologies. Futures, 69, 31-49. https://doi.org/10.1016/j.futures.2015.03.003Andrew, R., & Peters, G. (2021). The Global Carbon Project’s Fossil CO2 Emissions Dataset. Zenodo: Geneva, Switzerland.Benli, B., Gezer, M., & Karakas, E. (2020). Smart City transformation for mid-sized cities: Case of Canakkale, Turkey. Handbook of smart cities, 1-20.Berry, E.X. (2019). Human CO2 emissions have little effect on atmospheric CO2. International Journal of Atmospheric and Oceanic Sciences, 3(1), 13-26. https://doi.org/10.1007/978-3030-15145-4_23-1. https:10.11648/j.ijaos.20190301.13Caineng, Z.O. U., Xiong, B., Huaqing, X.U. E., Zheng, D., Zhixin, G.E., Ying, W.A. N.G., & Songtao, W.U. (2021). The role of new energy in carbon neutral. Petroleum exploration and development, 48(2), 480-491. https://doi.org/10.1016/S1876-3804 (21)60039-3Chertow, M.R. (2000). The IPAT equation and its variants. Journal of industrial ecology, 4(4), 13-29. https://doi.org/10.1162/10881980052541927Chovancová, J., & Tej, J. (2020). Decoupling economic growth from greenhouse gas emissions: The case of the energy sector in V4 countries. Equilibrium. Quarterly Journal of Economics and Economic Policy, 15(2), 235-251Covert, T., Greenstone, M., & Knittel, C.R. (2016). Will we ever stop using fossil fuels? Journal of Economic Perspectives, 30(1), 117-138Du, K., Li, P., & Yan, Z. (2019). Do green technology innovations contribute to carbon dioxide emission reduction? Empirical evidence from patent data. Technological Forecasting and Social Change, 146, 297-303. https://doi.org/10.1016/j.techfore.2019.06.010Gielen, D., Boshell, F., Saygin, D., Bazilian, M.D., Wagner, N., & Gorini, R. (2019). The role of renewable energy in the global energy transformation. Energy strategy reviews, 24, 38-50. https://doi.org/10.1016/j.esr.2019.01.006Granshaw, F.D. (2020). 5: The Carbon Cycle. Climate Toolkit 2.0.Gujarati, D.N. (2004). Basic Econometrics, The McGraw. Hill Companies.Hamilton, J.D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica: Journal of the econometric society, 357-384. https://doi.org/10.2307/1912559Hubacek, K., Chen, X., Feng, K., Wiedmann, T., & Shan, Y. (2021). Evidence of decoupling consumption-based CO2 emissions from economic growth. Advances in Applied Energy, 4, 100074. https://doi.org/10.1016/j.adapen.2021.100074Islam, M.M., Hasanuzzaman, M., Pandey, A.K., & Rahim, N.A. (2020). Modern energy conversion technologies. In Energy for sustainable development (pp. 19-39). Academic Press. https://doi.org/10.1016/B978-0-12-814645-3.00002-XJacal, S., Straubinger, F.B., Benjamin, E.O., & Buchenrieder, G. (2022). Economic costs and environmental impacts of fossil fuel dependency in sub-Saharan Africa: A Nigerian dilemma. Energy for Sustainable Development, 70, 45-53. https://doi.org/10.1016/j. esd.2022.07.007Li, R., & Li, S. (2021). Carbon emission post-coronavirus: continual decline or rebound? Structural Change and Economic Dynamics, 57, 57-67. https://doi.org/10.1016/j. strueco.2021.01.008Lin, B., & Zhu, J. (2019). The role of renewable energy technological innovation on climate change: Empirical evidence from China. Science of the Total Environment, 659, 1505-1512. https://doi.org/10.1016/j.scitotenv.2018.12.449Martins, T., Barreto, A.C., Souza, F.M., & Souza, A.M. (2021). Fossil fuels consumption and carbon dioxide emissions in G7 countries: Empirical evidence from ARDL bounds testing approach. Environmental Pollution, 291, 118093. https://doi.org/10.1016/j. envpol.2021.118093Neves, S.A., & Marques, A.C. (2021). The substitution of fossil fuels in the US transportation energy mix: Are emissions decoupling from economic growth?. Research in Transportation Economics, 90, 101036. https://doi.org/10.1016/j.retrec.2021.101036Pham, N.M., Huynh, T.L. D., & Nasir, M.A. (2020). Environmental consequences of population, affluence and technological progress for European countries: A Malthusian view. Journal of environmental management, 260, 110143. https://doi.org/10.1016/j.jenvman.2020.110143Project, G.C. 2021. Supplemental data of global carbon budget 2021 (version 1.0). Global Carbon ProjectRaihan, A., Muhtasim, D.A., Farhana, S., Pavel, M.I., Faruk, O., Rahman, M., & Mahmood, A. (2022). Nexus between carbon emissions, economic growth, renewable energy use, urbanization, industrialization, technological innovation, and forest area towards achieving environmental sustainability in Bangladesh. Energy and Climate Change, 3, 100080. https://doi.org/10.1016/j.egycc.2022.100080Rehman, A., Rauf, A., Ahmad, M., Chandio, A.A., & Deyuan, Z. (2019). The effect of carbon dioxide emission and the consumption of electrical energy, fossil fuel energy, and renewable energy, on economic performance: evidence from Pakistan. Environmental Science and Pollution Research, 26, 21760-21773. https://doi.org/10.1007/s11356-01905550-yRipple, W.J., Wolf, C., Lenton, T.M., Gregg, J.W., Natali, S.M., Duffy, P.B., & Schellnhuber, H.J. (2023). Many risky feedback loops amplify the need for climate action. One Earth, 6(2), 86-91Skånberg, K., & Svenfelt, Å. (2022). Expanding the IPAT identity to quantify backcasting sustainability scenarios. Futures & Foresight Science, 4(2), e116. https://doi.org/10.1002/ ffo2.116Sorrell, S., Gatersleben, B., & Druckman, A. (2020). The limits of energy sufficiency: A review of the evidence for rebound effects and negative spillovers from behavioural change. Energy Research & Social Science, 64, 101439. https://doi.org/10.1016/j.erss.2020.101439Stančin, H., Mikulčić, H., Wang, X., & Duić, N. (2020). A review on alternative fuels in future energy system. Renewable and sustainable energy reviews, 128, 109927. https://doi.org/10.1016/j. rser.2020.109927Wang, Q., & Su, M. (2020). Drivers of decoupling economic growth from carbon emission- an empirical analysis of 192 countries using decoupling model and decomposition method. Environmental Impact Assessment Review, 81, 106356. https://doi.org/10.1016/j. eiar.2019.106356Wang, Q., & Zhang, F. (2020). Does increasing investment in research and development promote economic growth decoupling from carbon emission growth? An empirical analysis of BRICS countries. Journal of Cleaner Production, 252, 119853. https://doi.org/10.1016/j. jclepro.2019.119853Wang, Q., & Zhang, F. (2021). The effects of trade openness on decoupling carbon emissions from economic growth-evidence from 182 countries. Journal of cleaner production, 279, 123838. https://doi.org/10.1016/j.jclepro.2020.123838Wooldridge, J.M. (2010). Econometric analysis of cross section and panel data. MIT press.Wooldridge, J.M. (2015). Control function methods in applied econometrics. Journal of Human Resources, 50(2), 420-445. https://doi.org/10.3368/jhr.50.2.420Zou, C., Zhao, Q., Zhang, G., & Xiong, B. (2016). Energy revolution: From a fossil energy era to a new energy era. Natural Gas Industry B, 3(1), 1-11. https://doi.org/10.1016/j. ngib.2016.02.001