Application of a Decision-Making Model to Reduce CO2 Emissions in Iran (Case Study: CHP-CCS technology and renewable energy)

Abokyi, E., Appiah-Konadu, P., Abokyi, F. and Oteng-Abayie, E. F. (2019). Industrial growth and emissions of CO2 in Ghana: the role of financial development and fossil fuel consumption. Energy Rep., 5, 1339-1353. Adeleke, O. and Josue, M. (2019). Poverty and green economy in South Africa: What is the nexus? Cogent Econ. Finance, 7(1), 1646847.
Ahamada, I. and Kirat, D. (2018). Non-linear Pass-Through of the CO2 Emission-Allowance Price onto Wholesale Electricity Prices. Environ. Model. Assess, 23(5), 497.
Aldakhil, A. M., Zaheer, A., Younas, S., Nassani, A. A., Abro, M. M. Q. and Zaman, K. (2019). Efficiently managing green information and communication technologies, high-technology exports, and research and development expenditures: A case study. J. Clean. Prod., 240, 118164.
Pollution, 6(4): 893-908, Autumn 2020
907
Allen, M. R., Frame, D. J., Huntingford, C., Jones, C. D., Lowe, J. A., Meinshausen, M. and Meinshausen, N. (2009). Warming caused by cumulative carbon emissions towards the trillionth tonne. Nature, 458(7242), 1163-1166.
Anenberg, S. C., Achakulwisut, P., Brauer, M., Moran, D., Apte, J. S. and Henze, D. K. (2019). Particulate matter-attributable mortality and relationships with carbon dioxide in 250 urban areas worldwide. Sci. Rep., 9(1), 1.
Anwar, A., Sarwar, S., Amin, W. and Arshed, N. (2019). Agricultural practices and quality of environment: evidence for global perspective. Environ. Sci. Pollut. Res., 26(15), 15617-15630.
Azadeh, A., Jafari-Marandi, R., Abdollahi, M. and Roudi, E. (2017). A novel benchmark methodology for estimating industrial electricity demand considering unsteady socio-economic conditions. Int. J. Bus. Perform. Manage. 18(2), 196-215.
Azam, M. and Khan, A. Q. (2017). Growth-corruption-health triaca and environmental degradation: empirical evidence from Indonesia, Malaysia, and Thailand. Environ. .Sci. Pollut. Res., 24(19), 16407-16417.
Bakhtiar, A., Aslani, A. and Hosseini, S. M. (2020). Challenges of diffusion and commercialization of bioenergy in developing countries. Renewable energy, 145, 1780-1798.
Behrang, M. A., Assareh, E., Assari, M. R. and Ghanbarzadeh, A. (2011). Using bees algorithm and artificial neural network to forecast world carbon dioxide emission. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 33(19), 1747-1759. Carlowicz, M. (2010). World of change: global temperatures: feature articles.
Davoudpour, H. and Ahadi, M. S. (2006). The potential for greenhouse gases mitigation in household sector of Iran: cases of price reform/efficiency improvement and scenario for 2000–2010. Energy Policy, 34(1), 40-49.
Dogan, H. G. and Kan, M. (2018). The nexus of CO2 emission, population, agricultural area size, GDP and energy use in Turkey. Fresenius Environ. Bull. (FEB), 27(10), 6812-6823.
Dudley, B. (2018). BP statistical review of world energy. BP Statistical Review, London, UK, accessed Aug, 6, 2018.
Energy, R. (2018). Energy Efficiency Organization (SATBA). Renewable Energy in Iran. Retrieved May, 15.
Fang, D., Zhang, X., Yu, Q., Jin, T. C. and Tian, L. (2018). A novel method for carbon dioxide emission forecasting based on improved Gaussian processes regression. J. Clean. Product. 173, 143-150.
Fang, D., Zhang, X., Yu, Q., Jin, T. C. and Tian, L. (2018). A novel method for carbon dioxide emission forecasting based on improved Gaussian processes regression. J. Clean Product, 173, 143-150.
Fotros, M. H., Ferdosi, M. and Mehrpeyma, H. (2012). An examination of energy intensity and urbanization effect on environmental degradation in Iran (a cointegration analysis).
Ghanem, S. K. (2018). The relationship between population and the environment and its impact on sustainable development in Egypt using a multi-equation model. Environ. Dev. Sustain., 20(1), 305-342.
Gokmenoglu, K. K., Olasehinde-Williams, G. O. and Taspinar, N. (2019). Testing the environmental Kuznets curve hypothesis: the role of deforestation. In Energy and Environmental Strategies in the Era of Globalization (pp. 61-83). Springer, Cham.
Grand, M. C. and D'Elia, V. V. (2017). Relationship between CO2 emissions and GDP functional form and decoupling. Int. J. Green Econ., 11(2), 83-106.
Guo, D., Chen, H. and Long, R. (2018). Can China fulfill its commitment to reducing carbon dioxide emissions in the Intended Nationally Determined Contributions (INDCs)? Analysis based on a back-propagation neural network. Environ. Sci. Pollut. Res., 25(27), 27451-27462.
Gupta, V. and Yadav, U. (2016). Combining indicators of energy consumption and CO2 emissions: EKC in India. Int. J. Eco. Econom. Sta., 37(2), 56-74.
Heydari, A., Garcia, D. A., Keynia, F., Bisegna, F. and De Santoli, L. (2019). Renewable energies generation and carbon dioxide emission forecasting in microgrids and national grids using GRNN-GWO methodology. Energy Procedia, 159, 154-159.
Hosseini, S. M., Saifoddin, A., Shirmohammadi, R. and Aslani, A. (2019). Forecasting of CO2 emissions in Iran based on time series and regression analysis. Energy Rep., 5, 619-631.
Karali, N., Xu, T. and Sathaye, J. (2016). Developing long-term strategies to reduce energy use and CO 2 emissions—analysis of three mitigation scenarios for iron and steel production in China. Mitig. Adapt. Strat. Gl., 21(5), 699-719.
Kim, S. (2019). CO2 Emissions, Foreign Direct Investments, Energy Consumption, and GDP in Developing Countries: A More Comprehensive
Alinejad, H.R. et al.
Pollution is licensed under a "Creative Commons Attribution 4.0 International (CC-BY 4.0)"
908
Study using Panel Vector Error Correction Model. Korean. Econ. Rev., 35, 5-24.
Köne, A. Ç. and Büke, T. (2010). Forecasting of CO2 emissions from fuel combustion using trend analysis. Renew. Sust. Energ. Rev., 14(9), 2906-2915.
Li, C. (2016). A gentle introduction to gradient boosting. URL: http://www. ccs. Neu. Edu/home/vip/teach/MLcourse/4_ boosting/slides/gradient boosting. Pdf.
Lotfalipour, M. R., Falahi, M. A. and Bastam, M. (2013). Prediction of CO2 emissions in Iran using grey and ARIMA models. Int. J. Energy. Econ. Policy, 3(3), 229.
Magazzino, C. and Cerulli, G. (2019). The determinants of CO2 emissions in MENA countries: a responsiveness scores approach. Int. J. Sustainable Dev. World Ecol., 26(6), 522-534.
Meng, X. and Han, J. (2018). Roads, economy, population density, and CO2: A city-scaled causality analysis. Resou. Conserv. Recycl., 128, 508-515.
Moreno, B., López, A. J. and García-Álvarez, M. T. (2012). The electricity prices in the European Union. The role of renewable energies and regulatory electric market reforms. Energy, 48(1), 307-313.
Niu, D., Wang, K., Wu, J., Sun, L., Liang, Y., Xu, X. and Yang, X. (2020). Can China achieve its 2030 carbon emissions commitment? Scenario analysis based on an improved general regression neural network. J. Clean. Prod., 243, 118558.
Njoke, M. L., Wu, Z. and Tamba, J. G. (2019). Empirical Analysis of Electricity Consumption, CO2 Emissions and Economic Growth: Evidence from Cameroon. Int. J. Energy Econ. Policy, 9(5), 63.
Pachauri, R. K., Allen, M. R., Barros, V. R., Broome, J., Cramer, W., Christ, R. and Dubash, N. K. (2014). Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change (p. 151). Ipcc.
Pata, U. K. (2018). The influence of coal and non-carbohydrate energy consumption on CO2 emissions: revisiting the environmental Kuznets curve hypothesis for Turkey. Energy, 160, 1115-1123.
Pata, U. K. (2019). Environmental Kuznets curve and trade openness in Turkey: bootstrap ARDL approach with a structural break. Environ. Sci. Pollut. Res., 26(20), 20264-20276.
Pham, H. (2019). A New Criterion for Model Selection. Mathematics, 7(12), 1215.
Rasmussen, C. E. (2006). CKI Williams Gaussian processes for machine learning mit press. Cambridge, MA.
Rasmussen, C. E. and Nickisch, H. (2010). Gaussian processes for machine learning (GPML) toolbox. J. mach. Learn. Res., 11(Nov), 3011-3015.
Riti, J. S., Song, D., Shu, Y., Kamah, M. and Atabani, A. A. (2018). Does renewable energy ensure environmental quality in favour of economic growth? Empirical evidence from China’s renewable development. Qual. Quant., 52(5), 2007-2030.
Rüstemoğlu, H. and Uğural, S. (2017). CO2 emissions in Iran for 1990-2010: A decomposition analysis. Appl. Ecol. Environ. Res., 15(4), 1833-1846.
Seeger, M. (2004). Gaussian processes for machine learning. Int. J. Neural. Sys. 14(02), 69-106.
Shafiezadeh,M.A.,Tavanpour,M,.Amini,F,.Ghahramani,N,.Saberfattahi,L,.Soleimanpour,P,.Energy Balances, Iran and World Energy Facts and Figures, (2019) Power and Energy planning Department Ministry of I.R.IRAN.
Sun, W., Ye, M. and Xu, Y. (2016). Study of carbon dioxide emissions prediction in Hebei province, China using a BPNN based on GA. J. Renew. Sustain. Ener. 8(4), 043101.
Tokimatsu, K., Yasuoka, R. and Nishio, M. (2017). Global zero emissions scenarios: The role of biomass energy with carbon capture and storage by forested land use. Appl. Energy, 185, 1899-1906.
Usman, M., Ma, Z., Wasif Zafar, M., Haseeb, A. and Ashraf, R. U. (2019). Is air pollution, economic and non-economic factors associated with per capita health expenditures? Evidence from emerging economies. Int. J. Environ. Res. Pub. Health, 16(11), 1967.
Vogel, T., Oeljeklaus, G. and Görner, K. (2017, June). Study on integration potential of gas turbines and gas engines into parabolic trough power plants. In AIP Conference Proceedings (Vol. 1850, No. 1, p. 060006). AIP Publishing LLC.
Wang, X., Bai, M. and Xie, C. (2019). Investigating CO2 mitigation potentials and the impact of oil price distortion in China's transport sector. Energy Policy, 130, 320-327.
Xue, Y., Ren, J. and Bi, X. (2019). Impact of Influencing Factors on CO2 Emissions in the Yangtze River Delta, Urban. Sustain. 11(15), 4183.