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Analysis and forecast of China's energy consumption structure

Author

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  • Zeng, Sheng
  • Su, Bin
  • Zhang, Minglong
  • Gao, Yuan
  • Liu, Jun
  • Luo, Song
  • Tao, Qingmei

Abstract

In the context of the practice of high-quality social development gradually deepening, the optimization of energy structure is an important link to promote high-quality economic development. We used China's historical data from 1980 to 2019, and identified 17 influencing factors of its energy consumption structure. From four dimensions (economy, structure, technology, population and policy), Copula function was employed to establish a multi-factor dynamic support vector machine model to predict the advanced index of energy consumption structure in 2020–2030. The results show that (a) China's energy consumption structure is being optimized. An up-trend is found in the advanced index of China's energy consumption structure, and the proportion of its coal consumption shows a downward trend, but the decline is gradually decreasing. (b) Energy price adjustment, increased rural income, industry structure improvement, higher R&D expenses contribute to energy consumption structure optimization in China. (c) China is able to meet the carbon emission target set for 2030 on schedule. China is expected to reach carbon emission peak in 2030, and non-fossil energy will account for about 21% in 2026. The carbon emission target per unit of GDP is expected to be completed ahead of schedule.

Suggested Citation

  • Zeng, Sheng & Su, Bin & Zhang, Minglong & Gao, Yuan & Liu, Jun & Luo, Song & Tao, Qingmei, 2021. "Analysis and forecast of China's energy consumption structure," Energy Policy, Elsevier, vol. 159(C).
  • Handle: RePEc:eee:enepol:v:159:y:2021:i:c:s030142152100495x
    DOI: 10.1016/j.enpol.2021.112630
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    as
    1. Wang, Jue & Zhou, Hao & Hong, Tao & Li, Xiang & Wang, Shouyang, 2020. "A multi-granularity heterogeneous combination approach to crude oil price forecasting," Energy Economics, Elsevier, vol. 91(C).
    2. Al-Ghandoor, A. & Al-Hinti, I. & Jaber, J.O. & Sawalha, S.A., 2008. "Electricity consumption and associated GHG emissions of the Jordanian industrial sector: Empirical analysis and future projection," Energy Policy, Elsevier, vol. 36(1), pages 258-267, January.
    3. Wang, Delu & Wang, Yadong & Song, Xuefeng & Liu, Yun, 2018. "Coal overcapacity in China: Multiscale analysis and prediction," Energy Economics, Elsevier, vol. 70(C), pages 244-257.
    4. Wang, Hongye & Su, Bin & Mu, Hailin & Li, Nan & Gui, Shusen & Duan, Ye & Jiang, Bo & Kong, Xue, 2020. "Optimal way to achieve renewable portfolio standard policy goals from the electricity generation, transmission, and trading perspectives in southern China," Energy Policy, Elsevier, vol. 139(C).
    5. Wang, Xiaoyu & Luo, Dongkun & Zhao, Xu & Sun, Zhu, 2018. "Estimates of energy consumption in China using a self-adaptive multi-verse optimizer-based support vector machine with rolling cross-validation," Energy, Elsevier, vol. 152(C), pages 539-548.
    6. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    7. Voyant, Cyril & Darras, Christophe & Muselli, Marc & Paoli, Christophe & Nivet, Marie-Laure & Poggi, Philippe, 2014. "Bayesian rules and stochastic models for high accuracy prediction of solar radiation," Applied Energy, Elsevier, vol. 114(C), pages 218-226.
    8. Bessec, Marie & Fouquau, Julien, 2008. "The non-linear link between electricity consumption and temperature in Europe: A threshold panel approach," Energy Economics, Elsevier, vol. 30(5), pages 2705-2721, September.
    9. L. Rachel Ngai & Christopher A. Pissarides, 2007. "Structural Change in a Multisector Model of Growth," American Economic Review, American Economic Association, vol. 97(1), pages 429-443, March.
    10. Shine, P. & Scully, T. & Upton, J. & Murphy, M.D., 2019. "Annual electricity consumption prediction and future expansion analysis on dairy farms using a support vector machine," Applied Energy, Elsevier, vol. 250(C), pages 1110-1119.
    11. Kang, Jidong & Ng, Tsan Sheng & Su, Bin & Milovanoff, Alexandre, 2021. "Electrifying light-duty passenger transport for CO2 emissions reduction: A stochastic-robust input–output linear programming model," Energy Economics, Elsevier, vol. 104(C).
    12. Unknown, 2016. "Energy for Sustainable Development," Conference Proceedings 253270, Guru Arjan Dev Institute of Development Studies (IDSAsr).
    13. Panapakidis, Ioannis P. & Dagoumas, Athanasios S., 2016. "Day-ahead electricity price forecasting via the application of artificial neural network based models," Applied Energy, Elsevier, vol. 172(C), pages 132-151.
    14. Zhang, Chi & Su, Bin & Zhou, Kaile & Sun, Yuan, 2020. "A multi-dimensional analysis on microeconomic factors of China's industrial energy intensity (2000–2017)," Energy Policy, Elsevier, vol. 147(C).
    15. Shin, Ho-Chul & Park, Jin-Won & Kim, Ho-Seok & Shin, Eui-Soon, 2005. "Environmental and economic assessment of landfill gas electricity generation in Korea using LEAP model," Energy Policy, Elsevier, vol. 33(10), pages 1261-1270, July.
    16. Kavaklioglu, Kadir, 2011. "Modeling and prediction of Turkey's electricity consumption using Support Vector Regression," Applied Energy, Elsevier, vol. 88(1), pages 368-375, January.
    17. Lee, Chien-Chiang, 2005. "Energy consumption and GDP in developing countries: A cointegrated panel analysis," Energy Economics, Elsevier, vol. 27(3), pages 415-427, May.
    18. Zhang, Jin-Liang & Zhang, Yue-Jun & Zhang, Lu, 2015. "A novel hybrid method for crude oil price forecasting," Energy Economics, Elsevier, vol. 49(C), pages 649-659.
    19. Ziel, Florian & Weron, Rafał, 2018. "Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks," Energy Economics, Elsevier, vol. 70(C), pages 396-420.
    20. repec:dau:papers:123456789/8180 is not listed on IDEAS
    21. Li, Ke & Lin, Boqiang, 2015. "Impacts of urbanization and industrialization on energy consumption/CO2 emissions: Does the level of development matter?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1107-1122.
    22. Zhong, Hai & Wang, Jiajun & Jia, Hongjie & Mu, Yunfei & Lv, Shilei, 2019. "Vector field-based support vector regression for building energy consumption prediction," Applied Energy, Elsevier, vol. 242(C), pages 403-414.
    23. Ediger, Volkan S. & Akar, Sertac, 2007. "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, Elsevier, vol. 35(3), pages 1701-1708, March.
    24. Prasad, Ramendra & Ali, Mumtaz & Kwan, Paul & Khan, Huma, 2019. "Designing a multi-stage multivariate empirical mode decomposition coupled with ant colony optimization and random forest model to forecast monthly solar radiation," Applied Energy, Elsevier, vol. 236(C), pages 778-792.
    25. Frei, Christoph W., 2004. "The Kyoto protocol--a victim of supply security?: or: if Maslow were in energy politics," Energy Policy, Elsevier, vol. 32(11), pages 1253-1256, July.
    26. Jónsson, Tryggvi & Pinson, Pierre & Madsen, Henrik, 2010. "On the market impact of wind energy forecasts," Energy Economics, Elsevier, vol. 32(2), pages 313-320, March.
    27. Kais Saidi & Sami Hammami, 2016. "Economic growth, energy consumption and carbone dioxide emissions: recent evidence from panel data analysis for 58 countries," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(1), pages 361-383, January.
    28. Yang, Zhenbing & Shao, Shuai & Yang, Lili & Miao, Zhuang, 2018. "Improvement pathway of energy consumption structure in China's industrial sector: From the perspective of directed technical change," Energy Economics, Elsevier, vol. 72(C), pages 166-176.
    29. Godarzi, Ali Abbasi & Amiri, Rohollah Madadi & Talaei, Alireza & Jamasb, Tooraj, 2014. "Predicting oil price movements: A dynamic Artificial Neural Network approach," Energy Policy, Elsevier, vol. 68(C), pages 371-382.
    30. Bedi, Jatin & Toshniwal, Durga, 2019. "Deep learning framework to forecast electricity demand," Applied Energy, Elsevier, vol. 238(C), pages 1312-1326.
    31. Jammazi, Rania & Aloui, Chaker, 2012. "Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling," Energy Economics, Elsevier, vol. 34(3), pages 828-841.
    32. Huang, Yophy & Bor, Yunchang Jeffrey & Peng, Chieh-Yu, 2011. "The long-term forecast of Taiwan’s energy supply and demand: LEAP model application," Energy Policy, Elsevier, vol. 39(11), pages 6790-6803.
    33. Arsenault, E. & Bernard, J. -T. & Carr, C. W. & Genest-Laplante, E., 1995. "A total energy demand model of Quebec : Forecasting properties," Energy Economics, Elsevier, vol. 17(2), pages 163-171, April.
    34. Pokharel, Shaligram, 2007. "An econometric analysis of energy consumption in Nepal," Energy Policy, Elsevier, vol. 35(1), pages 350-361, January.
    35. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
    36. Keles, Dogan & Scelle, Jonathan & Paraschiv, Florentina & Fichtner, Wolf, 2016. "Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks," Applied Energy, Elsevier, vol. 162(C), pages 218-230.
    37. Ferreira Neto, Amir B. & Perobelli, Fernando S. & Bastos, Suzana Q.A., 2014. "Comparing energy use structures: An input–output decomposition analysis of large economies," Energy Economics, Elsevier, vol. 43(C), pages 102-113.
    38. Papadimitriou, Theophilos & Gogas, Periklis & Stathakis, Efthimios, 2014. "Forecasting energy markets using support vector machines," Energy Economics, Elsevier, vol. 44(C), pages 135-142.
    39. Tang, Ling & Yu, Lean & Wang, Shuai & Li, Jianping & Wang, Shouyang, 2012. "A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 93(C), pages 432-443.
    40. Rallapalli, Srinivasa Rao & Ghosh, Sajal, 2012. "Forecasting monthly peak demand of electricity in India—A critique," Energy Policy, Elsevier, vol. 45(C), pages 516-520.
    41. Ozturk, Murat & Yuksel, Yunus Emre, 2016. "Energy structure of Turkey for sustainable development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1259-1272.
    42. Feng, Taiwen & Sun, Linyan & Zhang, Ying, 2009. "The relationship between energy consumption structure, economic structure and energy intensity in China," Energy Policy, Elsevier, vol. 37(12), pages 5475-5483, December.
    43. Oh, Wankeun & Lee, Kihoon, 2004. "Causal relationship between energy consumption and GDP revisited: the case of Korea 1970-1999," Energy Economics, Elsevier, vol. 26(1), pages 51-59, January.
    44. Ding, Yishan, 2018. "A novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting," Energy, Elsevier, vol. 154(C), pages 328-336.
    45. Kim, Sung Hyun & Kim, Tae Heon & Kim, Youngduk & Na, In-Gang, 2001. "Korean energy demand in the new millenium: outlook and policy implications, 2000-2005," Energy Policy, Elsevier, vol. 29(11), pages 899-910, September.
    46. Beyca, Omer Faruk & Ervural, Beyzanur Cayir & Tatoglu, Ekrem & Ozuyar, Pinar Gokcin & Zaim, Selim, 2019. "Using machine learning tools for forecasting natural gas consumption in the province of Istanbul," Energy Economics, Elsevier, vol. 80(C), pages 937-949.
    47. Agrawal, Rahul Kumar & Muchahary, Frankle & Tripathi, Madan Mohan, 2019. "Ensemble of relevance vector machines and boosted trees for electricity price forecasting," Applied Energy, Elsevier, vol. 250(C), pages 540-548.
    48. Zhu, Bangzhu & Han, Dong & Wang, Ping & Wu, Zhanchi & Zhang, Tao & Wei, Yi-Ming, 2017. "Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression," Applied Energy, Elsevier, vol. 191(C), pages 521-530.
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