ESTIMATING CO2 EMISSIONS BY USING ENERGY INTENSITY DATA OF OECD COUNTRIES

Semra Gunduc; Recep Eryıgıt

doi:10.33769/aupse.525368

Research Article

ESTIMATING CO2 EMISSIONS BY USING ENERGY INTENSITY DATA OF OECD COUNTRIES

Year 2019, , 68 - 75, 30.06.2019

Semra Gunduc , Recep Eryıgıt

https://doi.org/10.33769/aupse.525368

Abstract

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.

Keywords

Extreme Learning Machine (ELM), Energy Intensity, CO2 emission

References

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

Year 2019, , 68 - 75, 30.06.2019

Semra Gunduc , Recep Eryıgıt

https://doi.org/10.33769/aupse.525368

Abstract

References

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

There are 12 citations in total.

Details

Primary Language	English
Journal Section	Review Articles
Authors	Semra Gunduc 0000-0002-3811-9547 Recep Eryıgıt This is me 0000-0002-4282-6340
Publication Date	June 30, 2019
Submission Date	February 11, 2019
Acceptance Date	May 24, 2019
Published in Issue	Year 2019

Cite

APA	Gunduc, S., & Eryıgıt, R. (2019). ESTIMATING CO2 EMISSIONS BY USING ENERGY INTENSITY DATA OF OECD COUNTRIES. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, 61(1), 68-75. https://doi.org/10.33769/aupse.525368
AMA	Gunduc S, Eryıgıt R. ESTIMATING CO2 EMISSIONS BY USING ENERGY INTENSITY DATA OF OECD COUNTRIES. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. June 2019;61(1):68-75. doi:10.33769/aupse.525368
Chicago	Gunduc, Semra, and Recep Eryıgıt. “ESTIMATING CO2 EMISSIONS BY USING ENERGY INTENSITY DATA OF OECD COUNTRIES”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 61, no. 1 (June 2019): 68-75. https://doi.org/10.33769/aupse.525368.
EndNote	Gunduc S, Eryıgıt R (June 1, 2019) ESTIMATING CO2 EMISSIONS BY USING ENERGY INTENSITY DATA OF OECD COUNTRIES. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 61 1 68–75.
IEEE	S. Gunduc and R. Eryıgıt, “ESTIMATING CO2 EMISSIONS BY USING ENERGY INTENSITY DATA OF OECD COUNTRIES”, Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng., vol. 61, no. 1, pp. 68–75, 2019, doi: 10.33769/aupse.525368.
ISNAD	Gunduc, Semra - Eryıgıt, Recep. “ESTIMATING CO2 EMISSIONS BY USING ENERGY INTENSITY DATA OF OECD COUNTRIES”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 61/1 (June 2019), 68-75. https://doi.org/10.33769/aupse.525368.
JAMA	Gunduc S, Eryıgıt R. ESTIMATING CO2 EMISSIONS BY USING ENERGY INTENSITY DATA OF OECD COUNTRIES. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 2019;61:68–75.
MLA	Gunduc, Semra and Recep Eryıgıt. “ESTIMATING CO2 EMISSIONS BY USING ENERGY INTENSITY DATA OF OECD COUNTRIES”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, vol. 61, no. 1, 2019, pp. 68-75, doi:10.33769/aupse.525368.
Vancouver	Gunduc S, Eryıgıt R. ESTIMATING CO2 EMISSIONS BY USING ENERGY INTENSITY DATA OF OECD COUNTRIES. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 2019;61(1):68-75.