Forecasting 2030 CO₂ reduction targets for Russia as a major emitter using different estimation scenarios

References

• Abban, O. J., Wu, J., & Mensah, I. A. (2020). Analysis on the nexus amid CO2 emissions, energy intensity, economic growth, and foreign direct investment in belt and road economies: Does the level of income matter? Environmental Science and Pollution Research, 27(10), 11387–26.
   PubMed Web of Science ®Google Scholar
• Allen, M. R., Peters, G. P., Shine, K. P., Azar, C., Balcombe, P., Boucher, O., … Tanaka, K. (2022). Indicate separate contributions of long-lived and short-lived greenhouse gases in emission targets. Npj Clim Atmos Sci, 5(5). doi:10.1038/s41612-021-00226-2
   PubMedGoogle Scholar
• Al-Mulali, U., Ozturk, I., & Solarin, S. A. (2016). Investigating the environmental Kuznets curve hypothesis in seven regions: The role of renewable energy. Ecological Indicators, 67, 267–282.
   Web of Science ®Google Scholar
• Apergis, N., & Payne, J. E. (2010). The emissions, energy consumption, and growth nexus: Evidence from the commonwealth of independent states. Energy Policy, 38(1), 650–655.
   Web of Science ®Google Scholar
• Azevedo, V. G., Sartori, S., & Campos, L. M. S. (2018). CO2 emissions: A quantitative analysis among the BRICS nations. Renewable and Sustainable Energy Reviews, 81(1), 107–115.
   Google Scholar
• Banerjee, A., Dolado, J. J., & Mestre, R. (1998). Error-correction mechanism tests for cointegration in a single equation framework. Journal of Time Series Analysis, 19(3), 267–283.
   Google Scholar
• Berkeley Earth. (2021). Global temperature report for 2020. January, 14. http://berkeleyearth.org/global-temperature-report-for-2020/ (01.Feb.2022).
   Google Scholar
• Berkeley Earth. (2022). Global Temperature Report for 2021. http://berkeleyearth.org/global-temperature-report-for-2021/ (01.Feb.2022).
   Google Scholar
• Böttcher, K., Paunu, -V.-V., Kupiainen, K., Zhizhin, M., Matveev, A., Savolahti, M., … Karvosenoja, N. (2021). Black carbon emissions from flaring in Russia in the period 2012–2017. Atmospheric Environment, 254, 118390.
   Web of Science ®Google Scholar
• Brizga, J., Feng, K., & Hubacek, K. (2013). Drivers of CO2 emissions in the former Soviet Union: A country level IPAT analysis from 1990 to 2010. Energy, 59, 743–753.
   Web of Science ®Google Scholar
• Castle, J. L., Doornik, J. A., & Hendry, D. F. (2021). Robust discovery of regression models. Econometrics and Statistics, Econometrics and Statistics. doi:10.1016/j.ecosta.2021.05.004
   PubMed Web of Science ®Google Scholar
• Cheng, C., Ren, X., Wang, Z., & Yan, C. (2019). CO2 emissions, renewables, environmental patents, and economic growth - Evidence from BRIICS. Science of the Total Environment, 668, 1328–1338.
   PubMed Web of Science ®Google Scholar
• Chishti, M. Z., & Sinha, A. (2022). Do the shocks in technological and financial innovation influence the environmental quality? Evidence from BRICS Economies, Technology in Society, 68. doi:10.1016/j.techsoc.2021.101828
   Google Scholar
• Climate Action Tracker (CAT). 2022. Russian Federation. https://climateactiontracker.org/countries/russian-federation/ (11March 2022).
   Google Scholar
• Climate Watch. (2022a). Historical GHG emissions. https://www.climatewatchdata.org/ghg-emissions?breakBy=gas&chartType=percentage®ions=RUS&source=CAIT (03March 2022).
   Google Scholar
• Climate Watch. (2022b). Historical GHG emissions (World Resources Institute). 11March 2022 https://www.climatewatchdata.org/ghg-emissions?breakBy=countries&chartType=percentage&end_year=2018&gases=co2®ions=TOP&source=CAIT&start_year=1990
   Google Scholar
• Dickey, D., & Fuller, W. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(1981), 1057–1072.
   Google Scholar
• Dietz, T., & Rosa, E. A. (1994). Rethinking the environmental impacts of population, affluence and technology. Human Ecology Review, 1, 277–300.
   Google Scholar
• Dietz, T., & Rosa, E. (1997). Effects of population and affluence on CO 2  emissions. Proceedings of the National Academy of Sciences, 94(1), 175–179.
   PubMed Web of Science ®Google Scholar
• Dong, K., Dong, X., & Dong, C. (2019a). Determinants of the global and regional CO 2 emissions: What causes what and where? Applied Economics, 51(46), 5031–5044.
   Web of Science ®Google Scholar
• Dong, K., Dong, X., & Jiang, Q. (2019b). How renewable energy consumption lower global CO2 emissions? Evidence from countries with different income levels. The World Economy, 43(6), 1665–1698.
   Web of Science ®Google Scholar
• Doornik, J. A., & Hansen, H. (1994). A practical test for univariate and multivariate normality. Discussion paper, Nuffield College.
   Google Scholar
• Doornik, J. A., & Hendry, D. F. (2018). Empirical econometric modelling using PcGive (8th, Vol. I). London: Timberlake Consultants Press.
   Google Scholar
• EC (European Commission). (2022). Launch by United States, the European Union, and partners of the global methane pledge to keep 1.5C within reach. https://ec.europa.eu/commission/presscorner/detail/en/statement_21_5766 (03.March.2022).
   Google Scholar
• Enerdata. (2022). https://www.enerdata.net/ (03.March.2022).
   Google Scholar
• Engle, R. F. (1982). Autoregressive conditional heteroscedasticity, with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.
   Web of Science ®Google Scholar
• Enrlich, P., & Holdren, J. (1971). Impact of population growth. Science, 171(3977), 1212–1217.
   PubMed Web of Science ®Google Scholar
• Europe, & Lancet Regional Health – Europe, T.; The Lancet Regional Health. (2022). The regional and global impact of the Russian invasion of Ukraine. The Lancet Regional Health - Europe, 15(2022), 100379.
   PubMedGoogle Scholar
• Global Methane Pledge. (2022). Fast action on methane to keep a 1.5°C future within reach. https://www.globalmethanepledge.org/ (03.March.2022).
   Google Scholar
• Godfrey, L. G. (1978). Testing for higher order serial correlation in regression equations when the regressors include lagged dependent variables. Econometrica, 46(6), 1303–1313.
   Web of Science ®Google Scholar
• Grossman, G. M., & Krueger, A. B. (1991). Environmental impacts of a north American free trade agreement. In Working paper (pp. 3914). Cambridge: National Bureau of Economic Research.
   Google Scholar
• Harvey, A. C. (1989). Forecasting, structural time series and the kalman filter. Cambridge, UK: Cambridge University Press.
   Google Scholar
• Hasanov, F. J., Khan, Z., Hussain, M., & Tufail, M. (2021). Theoretical framework for the carbon emissions effects of technological progress and renewable energy consumption. Sustainable Development, 1–13. doi:10.1002/sd.2175
   Web of Science ®Google Scholar
• Hendry, D. F. (2020). First in, first out: econometric modelling of UK annual CO2 emissions, 1860–2017. Accessed on October 9, 2022. https://www.nuffield.ox.ac.uk/economics/papers/2020/2020W02_CO2UKEmissionsModel20.pdf
   Google Scholar
• Hendry, D. F., & Doornik, J. A. (2014). In Empirical model discovery and theory evaluation. Cambridge, MA: MIT Press.
   Google Scholar
• Hobbs, P. V., & Radke Lawrence, F. (1992). Airborne studies of the smoke from the Kuwait oil fires. Science, 256(5059), 987–991.
   PubMed Web of Science ®Google Scholar
• Inglesi-Lotz, R. (2017). Decomposing the south African CO2 emissions within a BRICS Countries context: Signalling potential energy rebound effects. Energy. doi:10.1016/j.energy.2017.12.150
   Google Scholar
• Intergovernmental Panel on Climate Change (IPCC). (2021). AR6 climate change 2021: The physical science basis. https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_SPM_final.pdf (01.Feb.2022).
   Google Scholar
• Intergovernmental Panel on Climate Change (IPCC). (2022). Climate change 2022: Mitigation of climate change. Working group III contribution to the IPCC sixth assessment report (AR 6). https://www.ipcc.ch/report/ar6/wg3/ (28.April.2022).
   Google Scholar
• International Energy Agency (IEA). (2022a). Net Zero by 2050: A roadmap for global energy sector 2021 (Paris). https://www.iea.org/reports/net-zero-by-2050 (22.May.2022).
   Google Scholar
• International Energy Agency (IEA). (2022b). Global CO2 emissions rebounded to their highest level in history in 2021 (Paris). https://www.iea.org/news/global-co2-emissions-rebounded-to-their-highest-level-in-history-in-2021 (11.March.2022).
   Google Scholar
• International Monetary Fund (IMF). (2022). World economic outlook report, chapter 3: Near-term macroeconomic impact of decarbonization policies (Washington, DC). https://www.imf.org/en/Publications/WEO/Issues/2022/10/11/world-economic-outlook-october-2022 (10.Oct.2022).
   Google Scholar
• International Renewable Energy Agency (IRENA). (2022). World energy transitions outlook 2022: 1.5°C pathway (Abu Dhabi). https://www.irena.org/publications/2022/Mar/World-Energy-Transitions-Outlook-2022 (22.May.2022).
   Google Scholar
• Ito, K. (2017). CO2 emissions, renewable and non-renewable energy consumption, and economic growth: Evidence from panel data for developing countries. International Economics, 151, 1–6.
   Google Scholar
• Jacobson, M. Z., von Krauland, A.-K., Coughlin, S. J., Dukas, E., Nelson, A. J. H., Palmer, F. C., & Rasmussen, K. R. (2022). Low-cost solutions to global warming, air pollution, and energy insecurity for 145 countries. Energy & Environmental Science, 15(8), 3343–3359.
   Web of Science ®Google Scholar
• Jaforullah, M., & King, A. (2017). The econometric consequences of an energy consumption variable in a model of CO 2 emissions. Energy Economics, 63, 84–91.
   Web of Science ®Google Scholar
• Kaya, Y. (1990). Impacts of carbon dioxide emission control on GNP growth: Interpretation of proposed scenarios. In Proceedings of the IPCC Energy and Industry Subgroup, Response Strategies Working Group, Paris, France.
   Google Scholar
• Ketenci, N. (2018). The environmental Kuznets curve in the case of Russia. Russian Journal of Economics, 4(2018), 249–265.
   Google Scholar
• Kijima, M., Nishide, K., & Ohyama, A. (2010). Economic models for the environmental Kuznets curve: A survey. J. Econ Dynam Contr, 34(7), 1187–1201.
   Web of Science ®Google Scholar
• Kilinc-Ata, N., & Dolmatov, I. A. (2022a). The Russian federation’s renewable energy development determinants: Evidence from empirical research. International Journal of Energy Sector Management, ahead-of-print No. ahead-of-print. doi:10.1108/IJESM-04-2022-0018.
   Web of Science ®Google Scholar
• Kilinc-Ata, N., & Dolmatov, I. A. (2022b). Which factors influence the decisions of renewable energy investors? Empirical evidence from OECD and BRICS countries. Environmental Science and Pollution Research. doi:10.1007/s11356-022-22274-8
   Web of Science ®Google Scholar
• Kilinc-Ata, N., & Likhachev, V. L. (2022). Validation of the environmental kuznets curve hypothesis and role of carbon emission policies in the case of Russian Federation. Research Square, Preprint. doi:10.21203/rs.3.rs-1279598/v1.
   Google Scholar
• Kim, T.-Y. (2022). Critical minerals threaten a decades-long trend of cost declines for clean energy technologies. https://www.iea.org/commentaries/critical-minerals-threaten-a-decades-long-trend-of-cost-declines-for-clean-energy-technologies?utm_content=buffer46523&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer (20.May.2022).
   Google Scholar
• Lieb, C. M. (2003). The environmental Kuznets curve—a survey of the empirical evidence and of possible causes. University of Heidelberg discussion paper, No. 391.
   Google Scholar
• Meinshausen, M., Lewis, J., McGlade, C., Gütschow, J., Nicholls, Z., Burdon, R., & Hackmann, B. (2022). Realization of Paris agreement pledges may limit warming just below 2 °C. Nature, 604(7905), 304–309.
   PubMed Web of Science ®Google Scholar
• Mikayilov, J. I., Marzio, G., & Hasanov, F. J. (2018). The Impact of Economic Growth on CO2 Emissions in Azerbaijan. Journal of Cleaner Production, 197, 1558–1572.
   Web of Science ®Google Scholar
• Mitic, P., Ivanovic, O. M., & Zdravkovic, A. (2017). A cointegration analysis of real GDP and CO2 emissions in transitional countries. Sustainability, 9(4), 1–18.
   Web of Science ®Google Scholar
• Narayan, P. K., Saboori, B., & Soleymani, A. (2016). Economic growth and carbon emissions. Economic Modelling, 53, 388–397.
   Web of Science ®Google Scholar
• National Academy of Sciences, Engineering and Medicine (NASEM). (2022). Greenhouse gas emissions information for decision making: A framework going forward. Washington, DC: The National Academies Press. doi:10.17226/26641
   Google Scholar
• Osobajo, O. A., Otitoju, A., Otitoju, M. A., & Oke, A. (2020). The Impact of Energy Consumption and Economic Growth on Carbon Dioxide Emissions. Sustainability, 12(19), 7965.
   Web of Science ®Google Scholar
• Oxford Economics. (2022). Oxford economics global economic model database. March release. https://www.oxfordeconomics.com/service/subscription-services/macro/global-economic-model/ (01.Feb.2022).
   Google Scholar
• Pao, H., Yu, H., & Yang, Y. (2011). Modelling the CO2 emissions, energy use, and economic growth in Russia. Energy, 36(8), 5094–5100.
   Web of Science ®Google Scholar
• Parry, I. W. H., Black, S., & Vernon, N. (2021). Still not getting energy prices right: A global and country update of fossil fuel subsidies, IMF Working Paper, No. 2021/236 (10.11.)
   Google Scholar
• Perez Suarez, R., & Lopez-Menendez, A. J. (2015). Growing green? Forecasting CO2 emissions with environmental kuznets curves and logistic growth models. Environmental Science & Policy, 54, 428–437.
   Web of Science ®Google Scholar
• Pesaran, M. H., & Shin, Y. (1999). An autoregressive distributed lag modeling approach to cointegration analysis. In S. Strom (Ed.), Econometrics and economic theory in the 20th century: The Ragnar Frisch Centennial Symposium. Cambridge, UK: Cambridge University Press, 371–413.
   Google Scholar
• Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326.
   Web of Science ®Google Scholar
• Phillips, P. C. B., & Hansen, B. E. (1990). Statistical inference in instrumental variables regression with I(1) processes. The Review of Economic Studies, 57(1), 99–125.
   Web of Science ®Google Scholar
• Ramsey, J. B. (1969). Tests for specification errors in classical linear least squares regression analysis. Journal of the Royal Statistical Society, Series B, 31, 350–371.
   Google Scholar
• Shafik, N., & Bandyopadhyay, S. (1992). Economic growth and environmental quality: Time series and cross-country evidence. Background paper for the world development report 1992. The World Bank, Washington, D.C.
   Google Scholar
• Shapovalova, D. (2020). Oil spill in Siberia: Are we prepared for permafrost thaw? https://www.thearcticinstitute.org/oil-spill-siberia-prepared-permafrost-thaw/ (01.Feb.2022).
   Google Scholar
• Shuai, C., Chen, X., Shen, L., Tan, Y., Tan, Y., & Tan, Y. (2017). The turning points of carbon Kuznets curve: Evidences from panel and time-series data of 164 countries. Journal of Cleaner Production, 162, 1031–1047.
   Web of Science ®Google Scholar
• STATISTA, (2022). https://www.statista.com/statistics/1048675/greenhouse-gas-emissions-by-sector-russia/. (15.May.2022).
   Google Scholar
• Steel, D., DesRoches, C. T., & Mintz-Woo, K. (2022). Climate change and the threat to civilization. Proceedings of the National Academy of Sciences, 119(42). doi:10.1073/pnas.2210525119
   Web of Science ®Google Scholar
• Stepanov, I. A., & Makarov, I. A. (2021). Greenhouse gas emissions regulation in fossil fuels exporting countries: Opportunities and challenges for Russia. Post-Communist Economies. doi:10.1080/14631377.2021.1943918
   Web of Science ®Google Scholar
• Stolyarova, E. (2013). Carbon dioxide emissions, economic growth and energy mix: Empirical evidence from 93 countries. Conference Paper: EcoMod, Prague, Czech Republic. https://ecomod.net/system/files/CO2_Growth_Energy_Mix.pdf.accessed on 01.18.2018. (01 Feb 2022).
   Google Scholar
• Tamazian, A., & Rao, B. B. (2010). Do economic, financial and institutional developments matter for environmental degradation? Evidence from transitional economies. Energy Economics, 32(1), 137–145.
   Web of Science ®Google Scholar
• Wagner, M. (2008). The carbon Kuznets curve: A cloudy picture emitted by bad econometrics? Resour. Resource and Energy Economics, 30(3), 388–408.
   Web of Science ®Google Scholar
• Wall Street Journal (WSJ), (2022). Russia’s war in Ukraine could have environmental impact that lasts decades. https://www.wsj.com/articles/russias-war-in-ukraine-could-have-environmental-impact-that-lasts-decades-11650801603 (12.May.2022).
   Google Scholar
• Way, R., Ives, M. C., Mealy, P., & Farmer, J. D. (2022). Empirically grounded technology forecasts and the energy transition. Joule, 6(9), 1–26.
   Web of Science ®Google Scholar
• White, H. (1980). A heteroscedasticity consistent covariance matrix estimator and a direct test of heteroscedasticity. Econometrica, 48(4), 817–818.
   Web of Science ®Google Scholar
• World Meteorological Organization (WMO), (2022). State of the global climate 2021 (WMO-No. 1290). https://library.wmo.int/index.php?lvl=notice_display&id=22080 (20.May.2022).
   Google Scholar
• Yang, X., Lou, F., Sun, M., Wang, R., & Wang, Y. (2017). Study of the relationship between greenhouse gas emissions and the economic growth of Russia based on the environmental Kuznets curve. Applied Energy, 193, 162–173.
   Web of Science ®Google Scholar
• Zakarya, G. Y., Mostefa, B., Abbesa, S. M., & Seghir, G. M. (2015). Factors Affecting CO2 Emissions in the BRICS countries: A panel data analysis. Procedia Economics and Finance, 26, 114–125.
   Google Scholar