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ESTIMATING CO2 EMISSIONS BY USING ENERGY INTENSITY DATA OF OECD COUNTRIES

Year 2019, Volume: 61 Issue: 1, 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, Volume: 61 Issue: 1, 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 Volume: 61 Issue: 1

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.
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