Analyzing and predicting CO2 emissions in China based on the LMDI and GA-SVM model

• Autorzy: Zhou J. , Yu X. , Guang F. , Li W.
• Języki publikacji: EN

Abstrakty

EN

With the effect of CO₂ emissions being the primary cause of the greenhouse effect, a selection and analysis study of driving factors of CO₂ emissions is vital to controlling growth from the source. This paper decomposes CO₂ emissions based on the logarithmic mean division index (LMDI) from three industries and residential consumption in China during the period 2000-14. A genetic algorithm-support vector machine (GA-SVM) was established. The eight driving factors as input variables have been innovated to apply the forecasting model. In the case study, the data set of driving factors from 2000 to 2009 is selected as training samples, and the other data set of driving factors from 2010 to 2014 is regarded as test samples. The results show that the factor decomposed based on the LMDI method of CO₂ emissions is very rational and can greatly improve forecast accuracy. The effectiveness of the GA-SVM model has been proven by the final simulation, which indicates that the proposed model outperforms a back propagation neural network (BPNN) model and a single SVM model in forecasting CO₂ emissions.

• Wydawca: -
• Czasopismo: Polish Journal of Environmental Studies
• Rocznik: 2018
• Tom: 27
• Numer: 2
• Opis fizyczny: P.927-938,fig.,ref.

Twórcy

autor

Zhou J.

• Department of Economics and Management, North China Electric Power University, Baoding 071000, China

autor

Yu X.

• Department of Economics and Management, North China Electric Power University, Baoding 071000, China

autor

Guang F.

• School of Economics and Management, North China Electric Power University, Beijing 102206, China

autor

Li W.

• Department of Economics and Management, North China Electric Power University, Baoding 071000, China

Bibliografia

• 1. SPECHT E., REDEMANN T., LORENZ N. Simplified mathematical model for calculating global warming through anthropogenic CO₂. Int. J. Therm. Sci, 102, 1, 2016.
• 2. ZHANG B., PENG S., XU X., WANG L. Embodiment analysis for greenhouse gas emissions by Chinese economy based on global thermodynamic potentials. Energies, 4 (11), 1897, 2011.
• 3. LIU Z., GUAN D., WEI W., Davis S.J., CIAIS P., BAI J. Reduced carbon emission estimates from fossil fuel combustion and cement production in china. Nature, 524 (7565), 335, 2015.
• 4. YAN Z., ZHANG J., YANG Z., LI S. Regional differences in the factors that influence China’s energy-related CO₂ emissions, and potential mitigation strategies. Energy Policy, 39 (12), 7712, 2011.
• 5. DENG M. X., LI W., YAN H. Decomposing industrial energy-related CO₂ emissions in Yunnan province, China: switching to low-carbon economic growth. Energies, 9, 23, 2016.
• 6. IEA, 2007a. World Energy Outlook 2007: China and India Insights. International Energy Agency: Paris, 2007.
• 7. CHENG K., PAN G., SMITH P., LUO T., LI L, ZHENG J. Carbon footprint of china's crop production – an estimation using agro-statistics data over 1993-2007.Agriculture Ecosystems & Environment, 142 (3-4), 231, 2011.
• 8. Strengthening the Response to Climate Change – China National Autonomous Contribution. Available online: http://www.scio.gov.cn/xwf bh/xwbf bh/wqf bh/2015/20151119/xgbd33811/Document/1455864/1455864.htm (accessed on 19 November 2015).
• 9. Revolutionary strategy of energy production and consumption. Available online: http://www.ndrc.gov.cn/fzgggz/fzgh/ghwb/gjjgh/201705/t20170517_847664.html. (accessed on 25 April 2016).
• 10. The State Council of the People’s Republic of China (SCPRC), The 12th Five-Year Plan Outline of National Economy and Social Development of People’s Republic of China, 2011.
• 11. WANG Y., ZHAO H., LI L., LIU Z., LIANG S. Carbon dioxide emission drivers for a typical metropolis using input – output structural decomposition analysis. Energy Policy, 58 (9), 312, 2013.
• 12. CHANG Y.F., LEWIS C., LIN S.J. Comprehensive evaluation of industrial CO₂, emission (1989-2004) in Taiwan by input – output structural decomposition. Energy Policy, 36 (7), 2471, 2008,
• 13. XU X.Y., ANG B.W. Analysing residential energy consumption using index decomposition analysis. Applied Energy, 113 (1), 342, 2014.
• 14. ANG B.W., ZHANG F.Q. A survey of index decomposition analysis in energy and environmental studies. Energy, 25 (12), 11496, 2000.
• 15. ANG B.W., ZHANG F.Q., CHOI K.H. Factorizing changes in energy and environmental indicators through decomposition. Energy, 23 (6), 489, 1998.
• 16. WU L., KANEKO S., MATSUOKA S. Driving forces behind the stagnancy of China’s energy-related CO₂, emissions from 1996 to 1999: the relative importance of structural change, intensity change and scale change. Energy Policy, 3 (3), 319, 2005.
• 17. LEE K., OH W. Analysis of CO₂ emissions in APCE countries: a time-series and a cross-sectional decomposition using the log mean divisia method. Energy Policy, 34 (17), 2779, 2006.
• 18. LIU L. C., FAN Y., WU G., WEI Y. M. Using lmdi method to analyze the change of China's industrial CO₂, emissions from final fuel use: an empirical analysis. Energy Policy, 35 (11), 5892, 2007.
• 19. FAN Y., LIU L.C., WU G., TSAI H.T., WEI Y.M. Changes in carbon intensity in China: empirical findings from 1980-2003. Ecological Economics, 62 (3-4), 683, 2007
• 20. TIAN L., TANG J., HANG L., WANG B. Decomposition analysis of CO₂ emission intensity of Jilin industry using lmdi. Ecological Economy, 2014.
• 21. Zhang W., LI K., ZHOU D., ZHANG W., GAO H. Decomposition of intensity of energy-related CO₂ emission in Chinese provinces using the lmdi method. Energy Policy, 2, 369, 2016.
• 22. WANG Z., DANG Y. Research on carbon emission prediction in Jiangsu Province based on an improved GM (1, 1) model. IEEE International Conference on Grey Systems and Intelligent Services, 93-97, 2013.
• 23. SONG J.K. China's CO₂ emissions prediction model based on support vector regression. Journal of China University of Petroleum, 36 (1), 182, 2012.
• 24. ZHOU J.G., ZHANG X.G. Projections about Chinese CO₂ emissions based on rough sets and gray support vector machine. China Environmental Science, 33 (12), 2157, 2013.
• 25. SUN W., XU Y. Using a back propagation neural network based on improved particle swarm optimization to study the influential factors of CO₂ emissions in Hebei province, China. Journal of Cleaner Production, 112, 1282, 2016.
• 26. SUN W., LIU M. Prediction and analysis of the three major industries and residential consumption CO₂ emissions based on least squares support vector machine in China. Journal of Cleaner Production, 122, 144, 2016.
• 27. KAYA Y. Impact of CO₂ emissions control on GNP growth: interpretation of proposed scenarios response strategies. Paris: Working Group, 1990.
• 28. ANG B.W., LIU N. Handling zero values in the logarithmic mean divisia index decomposition approach. Energy Policy, 35 (1), 238-, 2007.
• 29. ANG B.W., LIU N. Negative-value problems of the logarithmic mean divisia index decomposition approach. Energy Policy, 35 (1), 739, 2007.
• 30. CORTES C., VAPNIK V. Support-vector networks. Machine Learning, 20 (3), 273. 1995.
• 31. CHANG C.C., LIN C.J. LIBSVM: A library for support vector machines. ACM, 2011.
• 32. RABAOUI A., KADRI, H., LACHIRI, Z., ELLOUZE, N. One-class SVMs challenges in Audio Detection and Classification Applications. Eurasip Journal on Advances in Signal Processing, 1-14. 2008.
• 33. BRERETON R.G., LLOYED G.R. Support vector machines for classification and regression. Analyst, 2010.
• 34. WHITLEY D. A genetic algorithm tutorial. Statistics & Computing, 4 (2), 65, 1994.
• 35. GOLDBERG D.E., GOLDBERG D.M., GOLDBERG D.E. Genetic algorithm is search optimization and machine learning. xiii (7), 2104, 1989.
• 36. TIMOTHY B., EDWARD M., HARRY B., MICHAEL, ARLENE H., DENNIS M. Application of GA optimization for automatic generation control design in an interconnected power system. Energy Conversion & Management, 52 (5), 2247, 2011.
• 37. ODA T., ELMAZI D., BAROLLI A., SAKAMOTO S., BAROLLI L., XHAFA F. A genetic algorithmbased system for wireless mesh networks: analysis of system data considering different routing protocols and architectures. Soft Computing, 20 (7), 2627, 2016.
• 38. WEN J., YANG H., TONG X., LI K., WANG S., LI Y. Optimization investigation on configuration parameters of serrated fin in plate-fin heat exchanger using genetic algorithm. International Journal of Thermal Sciences, 101, 116, 2016.

Identyfikatory

DOI

10.15244/pjoes/76407