Öz
It is discussed that
economic development has an essential effect on the country’s CO2 emission
which plays an important role in global warming. In this research well-known
machine learning algorithm Extreme Learning Machine, ELM, is used to
investigate the relationship between CO2
emission and energy intensity for countries in OECD. The results indicate a
strong correlation and the method perform well for estimation.
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I. Ozturk and A. Caravci, CO2 emissions, energy consumption and economic growth in Turkey. Renew Sustain Energy Rev 2010;14:3220–5.
BP. BP statistical review of world energy June 2016; 2016. http://www.bp.com/ statisticalreview.
SJ. Davis, K. Caldeira and HD. Matthews, Future CO2 emissions and climate change from existing energy infrastructure. Science 2010;329:1330–3.
P.R. Ehrlich and J.P. Holdren, Impact of population growth. Science 1971, 3977, 1212–1217.
A. Shi, The impact of population pressure on global carbon dioxide emissions, 1975–1996: Evidence from pooled cross-country data. Ecol. Econ. 2003, 1, 29–42.
M. Wang and C. Feng, Decomposition of energy-related CO2 emissions in China: an empirical analysis based on provincial panel data of three sectors. Appl Energy 2017;190:772–87.
B. Lin and H. Liu, CO2 emissions of China’s commercial and residential buildings: Evidence and reduction policy. Build Environ 2015;92:418–31.
www.worlddatabank.com
J. Long, L. Shuai, H. Bin and L. Mei, A survey on projection neural networks and their applications Applied Soft Computing, Volume 76, 2019, Pages 533-544
G.B. Huang, Q.Y. Zhu and C.K. Siew, Extreme learning machine:Theory and applications, Neurocomputing 70 (2006a) 489501
S. Gang and D. Qun, A novel double deep ELMs ensemble system for time series forecasting , Knowledge-Based Systems, Volume 134, 2017, Pages 31-49.
K. Marius, Y. Yang, L. Caihong, C. Yanhua and L. Lian Mixed kernel based extreme learning machine for electric load forecasting Neurocomputing, Volume 312, 2018, Pages 90-106