Carbon emissions scenario prediction of the thermal power industry in the Beijing-Tianjin-Hebei region based on a back propagation neural network optimized by an improved particle swarm optimization algorithm

• Autorzy: Zhou J. , Du S. , Shi Ji. , Guang F.
• Języki publikacji: EN

Abstrakty

EN

Rapid economic growth in the Beijing-Tianjin-Hebei region has been accompanied by a dramatic increase in carbon emissions. Therefore, a precise study of forecasting carbon emissions is important as regards curbing them. To identify the influence factors of carbon emissions and effectively predict carbon emissions under the three different GDP growth rate scenarios in the Beijing-Tianjin-Hebei thermal power industry, we employed a combination of the improved particle swarm optimization-back propagation algorithm (IPSO-BP) with scenario prediction. The results are as follows: 1) The influencing degree of carbon emissions factors from strong to weak are the installed capacity of thermal power, thermal power generation, urbanization rate, GDP, and utilization ratio of units (with grey correlation degrees of 0.9262, 0.9247, 0.8683, 0.8082, and 0.7704, respectively). 2) Compared with the BP neural network, it is testified that using the IPSO-BP neural network model with an annual average relative error of 2.53%, while the prediction precision of BP neural network is 5.07%. Besides, the number of iterations to achieve the optimal solution is approximately reduced by 33%. 3) GDP is the contributor to the increment of carbon emissions of the power industry, whereby GDP growth rate can be reduced appropriately to curb carbon emissions, avoiding excessive pursuit of economic growth.

• Wydawca: -
• Czasopismo: Polish Journal of Environmental Studies
• Rocznik: 2017
• Tom: 26
• Numer: 4
• Opis fizyczny: p.1895-1904,fig.,ref.

Twórcy

autor

Zhou J.

• Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071003, China

autor

Du S.

• Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071003, China

autor

Shi Ji.

• Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071003, China

autor

Guang F.

• Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071003, China

Bibliografia

• 1. CETIN M., ADIGUZEL F., KAYA O., SAHAP A. Mapping of bioclimatic comfort for potential planning using GIS in Aydin, Environment Development & Sustainability, 1, 2016.
• 2. CETIN M., SEVIK H. Assessing Potential Areas of Ecotourism through a Case Study in Ilgaz Mountain National Park, Tourism - From Empirical Research Towards Practical Application, 2016.
• 3. CETIN M. Sustainability of Urban Coastal Area Management: A Case Study on Cide. 35 (7), 527, 2016.
• 4. KAYA L.G., CETIN M., DOYGUN H. A Holistic Approach in Analyzing the Landscape Potential: Porsuk Dam Lake and Its Environs, Turkey, Fresenius Environmental Bulletin, 18 (8), 1525, 2009.
• 5. YI B.W., XU J. H, YING F. Determining factors and diverse scenarios of CO₂ emissions intensity reduction to achieve the 40-45% target by 2020 in China - a historical and prospective analysis for the period 2005-2020, Journal of Cleaner Production, 122, 87, 2016.
• 6. ZHAO A.W., LI D. Grey Forecast of China’s Carbon Dioxide Emissions, Mathematics in Practice and Theory, 42 (4), 61, 2012.
• 7. SHU Y., LAM N.S.N. Spatial disaggregation of carbon dioxide emissions from road traffic based on multiple linear regression model, Atmospheric Environment, 45 (3), 634, 2011.
• 8. NAIK M.K., PANDA R. A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition, Applied Soft Computing, 38 (C), 661, 2016.
• 9. CHOI K.H., ANG B.W. A time-series analysis of energy-related carbon emissions in Korea, Energy Policy, 29 (13), 1155, 2001.
• 10. SUN W., LIANG Y., XU Y. Application of carbon emissions prediction using least squares support vector machine based on grid search, Wseas Transactions on Systems & Control, 10 (1), 95, 2015.
• 11. JI G.Y., DEPARTMENT P.E. Application of BP neural network model in the prediction of China’s carbon emissions based on the grey correlation analysis, Mathematics in Practice & Theory, 14, 243, 2014.
• 12. WU Z.X., SHI J. The influencing factor analysis and trend forecasting of Beijjing energy carbon emission based on STIRPAT and GM(1,1) model’s, Chinese Journal of Management Science, (S2), 803, 2012.
• 13. 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.
• 14. ZHANG F., YIN X.Q., DONG H.Z. Application of combination grey model in carbon emissions prediction in Shan Dong province, Environmental Monitoring & Assessment, 33 (2), 147, 2015.
• 15. ZHAO C.B., MAO C.M. Forecast intensity of carbon emissions to China based on BP neural network and ARIMA combined model, Resources and Environment in the Yangtze Basin, 21 (6), 665, 2012.
• 16. PAO H.T., FU H.C., TSENG C.L. Forecasting of CO₂, emissions, energy consumption and economic growth in China using an improved grey model, Energy, 40 (1), 400, 2012.
• 17. DOUCOURE B., AGBOSSOU K., CARDENAS A. Time series prediction using artificial wavelet neural network and multi-resolution analysis: Application to wind speed data, Renewable Energy, 92, 202, 2016.
• 18. YANG L., LIN B. Carbon dioxide-emission in China’s power industry: Evidence and policy implications, Renewable & Sustainable Energy Reviews, 60, 258, 2016.
• 19. WEN L., CAO Y. Factor decomposition analysis of China’s energy-related CO₂ emissions using STIRPAT model, Polish Journal of Environmental Studies, 24 (5), 2015.
• 20. CETIN M. A Change in the Amount of CO₂ at the Center of the Examination Halls: Case Study of Turkey, Studies on Ethno-Medicine, 10 (2), 146, 2016.
• 21. CETIN M., SEVIK H. Measuring the Impact of Selected Plants on Indoor CO₂ Concentrations, Polish Journal of Environmental Studies, 25 (3), 973, 2016.
• 22. SEVIK H., ÇETIN M., BELKAYALI N. Effects of Forests on Amounts of CO₂: Case Study of Kastamonu and Ilgaz Mountain National Parks, Polish Journal of Environmental Studies, 24 (1), 253, 2015.
• 23. SEVIK H., CETIN M. Effects of Water Stress on Seed Germination for Select Landscape Plants, Polish Journal of Environmental Studies, 24 (2), 689, 2015.
• 24. YIN Y., MIZOKAMI S., AIKAWA K. Compact development and energy consumption: Scenario analysis of urban structures based on behavior simulation, Applied Energy, 159, 449, 2015.
• 25. TIAN L., GAO L., XU P. The Evolutional Prediction Model of Carbon Emissions in China Based on BP Neural Network, International Journal of Nonlinear Science, 10, 1749, 2010.
• 26. HOU J.C., SHI D. Driving factors for the evolution of carbon dioxide emissions from electricity sector in China, China Industrial Economics, (6), 44, 2014.
• 27. LI HONG, YA KUN. The study on relationship between industrial carbon emission and the development of economy, Macroeconomics, 11, 46, 2012.
• 28. IPCC (Intergovernmental Panel on Climate Change), 2006. In: EGGLESTON H.S., BUENDIA L., MIWA K., NGARA T., TANABE K. (Eds.), 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas Inventories Programme. IGES, Japan.
• 29. YANG Q., LIU H.J. Regional Difference Decomposition and Influence Factors of China’s Carbon Dioxide Emissions, The Journal of Quantitative & Technical Economics, 5, 36-49, 2012.
• 30. DU L.M. Impact Factors of China’s Carbon Dioxide Emissions: Provincial Panel Data Analysis, South China Journal of Economics, 11, 20, 2010.
• 31. XU S.C., HE Z.X., LONG R.Y., CHEN H., HAN H. M., ZAANG W.W. Comparative analysis of the regional contributions to carbon emissions in China, Journal of Cleaner Production, 127, 406, 2016.
• 32. DENG J.L. Introduction to Grey system theory, Journal of Grey System, 1 (1), 1, 1989.
• 33. REN C., AN N., WANG J., LI L., HU B. Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting, Knowledge-Based Systems, 56 (3), 226, 2014.
• 34. ZHANG G.Y., HU Z. Improved BP neural network model and its stability analysis, Journal of Central South University (Science and Technology), 42 (1), 115, 2011.
• 35. KENNEDY J., EBERHART R.C. A discrete binary version of the particle swarm algorithm, In Proceedings of the 1997 IEEE International Conference on Computational Cybernetics and Simulation, Systems, Man, and Cybernetics, Orlando, FL, USA, 12-15 October 1997.
• 36. LI J., SHI J., LI J. Exploring Reduction Potential of Carbon Intensity Based on Back Propagation Neural Network and Scenario Analysis: A Case of Beijing, China, Energies, 9 (8), 2016.
• 37. SAYED M., GHARGHORY S.M., KAMAL H.A. Gain tuning PI controllers for boiler turbine unit using a new hybrid jump PSO, Journal of Electrical Systems & Information Technology, 2 (1), 99, 2015.
• 38. SHI Y., EBERHART R.C. Parameter Selection in Particle Swarm Optimization, In International Conference on Evolutionary Programming Vii, Spring-Verlag, London, UK, Volume 1447, 591,1998.
• 39. SUN W., Y X. Using a back propagation neural network based on improved particle swarm optimization to study the influential factors of carbon dioxide emissions in Hebei Province, China, Journal of Cleaner Production, 112, 1282, 2016.
• 40. Yin Y., MIZOKAMI S., AIKAWA K. Compact development and energy consumption: Scenario analysis of urban structures based on behavior simulation, Applied Energy, 159, 449, 2015.
• 41. WEN L., LIU Y. The peak value of carbon emissions in the Beijing-Tianjin-Hebei Region based on the STIRPAT model and scenario design, Polish Journal of Environment Studies, 25, 823, 2016.
• 42. LIU S.F., CAI H., YANG Y.J., YING C. Research progress of grey relational analysis model, Systems Engineering - Theory & Practice, 33, 2041, 2013.
• 43. SONG J.K., ZHANG Y. Scene prediction of China’s carbon emissions based on BP neural network, Science technology and engineering, 11 (17), 4108, 2011.

Identyfikatory

DOI

10.15244/pjoes/68881