Predictive analysis of CO2 emissions and the role of environmental technology, energy use and economic output: evidence from emerging economies

Abstract

In 2019. the BICS (Brazil, India, China and South Africa) group remained not only one of the largest global CO2 emitter, but also most of the projected carbon emissions in the future and highly vulnerable to negative impacts of climate are associated with them. However, the recent literature finds number of studies having contradictory numbers about present and future carbon emissions in the BICS. It does not only affect the existing environmental regulations in the BICS but also mislead the future policy framework for the climate change. Thus, this study develops comprehensive forecasting model using Grey system theory approach for policy analysis. The variables (i.e. CO2 emissions, environmental related technological change, fossil fuel and renewable energy consumption, and economic output) are used to predict CO2 emissions. The results find that the emissions intensity will continue to rise in BICS region. However, the significant progress at environmental technology front can reduce the CO2 emissions intensity while achieving the economic growth targets. The results also find that the fossil fuels will remain the significant source of energy mix in BICS countries. The use of renewable energy is expected to increase during the projection period. The results are useful for BICS countries in shaping future environmental policy.

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References

  1. Ahmed K, Ahmed S (2018) A predictive analysis of CO 2 emissions, environmental policy stringency, and economic growth in China. Environ Sci Pollut Res 25(16):16091–16100

    CAS  Article  Google Scholar 

  2. Ahmed K, Long W (2013) Climate change and trade policy: from legal complications to time factor. J Int Trade Law Policy

  3. Ahmed K, Bhattacharya M, Shaikh Z, Ramzan M, Ozturk I (2017) Emission intensive growth and trade in the era of the Association of Southeast Asian Nations (ASEAN) integration: An empirical investigation from ASEAN-8. J Clean Prod 154:530– 540

    Article  Google Scholar 

  4. AlFarra HJ, Abu-Hijleh B (2012) The potential role of nuclear energy in mitigating CO2 emissions in the United Arab Emirates. Energy Policy 42:272–285

    Article  Google Scholar 

  5. Ang JB (2007) CO2 Emissions, energy consumption, and output in France. Energy policy 35 (10):4772–4778

    Article  Google Scholar 

  6. Apergis N, Payne JE, Menyah K, Wolde-Rufael Y (2010) On the causal dynamics between emissions, nuclear energy, renewable energy, and economic growth. Ecol Econ 69(11):2255– 2260

    Article  Google Scholar 

  7. Cui J, Liu SF, Zeng B, Xie NM (2013) Parameters characteristics of grey Verhulst prediction model under multiple transformation. Control and decision 28(4):605–608

    Google Scholar 

  8. Cui J, Li B, Ma H, Yuan C, Liu S, Zhang H, Song X (2016) Properties of NGM (1, 1, v) and its optimized model with multiple transformation. J Grey Syst 28(4)

  9. da Silva Soito JL, Freitas MAV (2011) Amazon and the expansion of hydropower in Brazil:, Vulnerability, impacts and possibilities for adaptation to global climate change. Renew Sust Energ Rev 15(6):3165–3177

    Article  Google Scholar 

  10. Deng JL (1982) A new modelling approach to insect reproduction with same-shape reproduction distribution and rate summation: with particular reference to Russian wheat aphid. Syst Control Lett 5:288–294

    Google Scholar 

  11. Fadel M, Zohri ANA, Makawy M, Hsona MS, Abdel-Aziz AM (2014) Recycling of vinasse in ethanol fermentation and application in Egyptian distillery factories. Afr J Biotechnol 13(47)

  12. Fei Q, Rasiah R, Shen LJ (2014) The clean energy-growth nexus with CO2 emissions and technological innovation in Norway and New Zealand. Energy Environ 25(8):1323–1344

    Article  Google Scholar 

  13. Hatzigeorgiou E, Polatidis H, Haralambopoulos D (2011) CO2 Emissions, GDP and energy intensity: a multivariate cointegration and causality analysis for Greece, 1977–2007. Appl Energy 88(4):1377–1385

    Article  Google Scholar 

  14. Hochstetler K, Viola E (2011) Brazil and the multiscalar politics of climate change. In: Colorado conference on earth systems governance, Colorado State University, Fort Collins, Colorado, pp 17–20

  15. Hu Z, Zhang CH (2019) A national analysis of the geographic aspects and ecological correlates of PM 2.5 in China based on ground observational data. Air Qual Atmos Health 12(4):425– 434

    CAS  Article  Google Scholar 

  16. IEA (2019). https://www.epa.gov/ghgemissions/global-greenhouse-gas-emissions-data [Accessed 25.02.2020]

  17. IEA (2009). https://www.iea.org/data-and-statistics?country=WORLD&fuel=Energy%20supply&indicator=Coal%20production%20by%20type [Accessed 25.02.2020]

  18. IPCC (2014). https://www.ipcc.ch/report/ar5/wg3/ [Accessed 25.02.2020]

  19. Katani EK (2019) Forecasting the total energy consumption in Ghana using grey models. Grey systems: theory and application

  20. Ma X, Liu Z, Wei Y, Kong X (2016) A novel kernel regularized nonlinear GMC (1, n) model and its application. J Grey Syst-UK 28(3):97

    Google Scholar 

  21. Meng D, Zhicun X, Wu L, Yang Y (2020) Predict the particulate matter concentrations in 128 cities of China. Air Qual Atmos Health :1-9

  22. Menyah K, Wolde-Rufael Y (2010) Energy consumption, pollutant emissions and economic growth in South Africa. Energy Econ 32(6):1374–1382

    Article  Google Scholar 

  23. Pao HT, Tsai CM (2010) CO2 Emissions, energy consumption and economic growth in BRIC countries. Energy Policy 38(12):7850–7860

    Article  Google Scholar 

  24. Qi X, Han Y (2020) Andamp; Kou P Population urbanization, trade openness and carbon emissions: an empirical analysis based on China. Air Qual Atmos Health :1–10

  25. Sadorsky P (2009) Renewable energy consumption and income in emerging economies. Energy Policy 37(10):4021–4028

    Article  Google Scholar 

  26. Salahuddin M, Gow J (2014) Economic growth, energy consumption and CO2 emissions in Gulf Cooperation Council countries. Energy 73:44–58

    Article  Google Scholar 

  27. Salim RA, Shafiei S (2014) Urbanization and renewable and non-renewable energy consumption in OECD countries: an empirical analysis. Econ Model 38:581–591

    Article  Google Scholar 

  28. Sbia R, Shahbaz M, Hamdi H (2014) A contribution of foreign direct investment, clean energy, trade openness, carbon emissions and economic growth to energy demand in UAE. Econ Model 36:191–197

    Article  Google Scholar 

  29. Sohag K, Begum RA, Abdullah SMS, Jaafar M (2015) Dynamics of energy use, technological innovation, economic growth and trade openness in Malaysia. Energy 90:1497–1507

    Article  Google Scholar 

  30. Sohag K, Al Mamun M, Uddin GS, Ahmed AM (2017) Sectoral output, energy use, and CO 2 emission in middle-income countries. Environ Sci Pollut Res 24(10):9754–9764

    CAS  Article  Google Scholar 

  31. Soytas U, Sari R, Ewing BT (2007) Energy consumption, income, and carbon emissions in the United States. Ecol Econ 62(3-4):482–489

    Article  Google Scholar 

  32. Statista (2019) https://www.statista.com/statistics/264699/worldwide-CO_2-emissions/ [Accessed25.02.2020]

  33. Tang R, Wang J (2013) A note on multiple reflections of radiation within CPCs and its effect on calculations of energy collection. Renew Energ 57:490–496

    Article  Google Scholar 

  34. Tien TL (2012) A research on the grey prediction model GM (1, n). Appl Math Comput 218 (9):4903–4916

    Google Scholar 

  35. Vapnik VN (1998) Communications and control. Stat Learn Theory 2:1–740

    Google Scholar 

  36. Wang ZX, Wang YY (2014) Evaluation of the provincial competitiveness of the Chinese high-tech industry using an improved TOPSIS method. Expert Syst Appl 41(6):2824–2831

    Article  Google Scholar 

  37. Wolf Jr C, Dalal S, DaVanzo J, Larson EV, Akhmedjonov A, Dogo H, Huang M, Montoya S (2011) China and India, 2025: A comparative assessment. RAND NATIONAL DEFENSE RESEARCH INST SANTA MONICA CA

  38. Xie NM, Liu SF (2008) Research on the affine properties of discrete grey model. Control and decision 23(2):200

    Google Scholar 

  39. Xie NM, Liu SF (2009) Discrete grey forecasting model and its optimization. Appl Math Model 33(2):1173–1186

    Article  Google Scholar 

  40. Zhou P, Poh KL, Ang BW (2016) Data envelopment analysis for measuring environmental performance. In: Handbook of operations analytics using data envelopment analysis. Springer, Boston, pp 31–49

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Correspondence to Sidrah Ahmed.

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Ahmed, S., Ahmed, K. & Ismail, M. Predictive analysis of CO2 emissions and the role of environmental technology, energy use and economic output: evidence from emerging economies. Air Qual Atmos Health 13, 1035–1044 (2020). https://doi.org/10.1007/s11869-020-00855-1

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Keywords

  • BICS
  • Environmental technology
  • GDP
  • Grey system theory
  • Renewable energy