IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v159y2021ics030142152100495x.html
   My bibliography  Save this article

Analysis and forecast of China's energy consumption structure

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030142152100495X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.enpol.2021.112630?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Bedi, Jatin & Toshniwal, Durga, 2019. "Deep learning framework to forecast electricity demand," Applied Energy, Elsevier, vol. 238(C), pages 1312-1326.
    13. 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.
    14. 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).
    15. 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.
    16. 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.
    17. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    18. 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.
    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. 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.
    21. 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.
    22. Pokharel, Shaligram, 2007. "An econometric analysis of energy consumption in Nepal," Energy Policy, Elsevier, vol. 35(1), pages 350-361, January.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. Papadimitriou, Theophilos & Gogas, Periklis & Stathakis, Efthimios, 2014. "Forecasting energy markets using support vector machines," Energy Economics, Elsevier, vol. 44(C), pages 135-142.
    28. 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).
    29. 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.
    30. Unknown, 2016. "Energy for Sustainable Development," Conference Proceedings 253270, Guru Arjan Dev Institute of Development Studies (IDSAsr).
    31. 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.
    32. 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.
    33. 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.
    34. 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).
    35. 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.
    36. 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.
    37. 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.
    38. 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.
    39. Ding, Yishan, 2018. "A novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting," Energy, Elsevier, vol. 154(C), pages 328-336.
    40. 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.
    41. 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.
    42. 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.
    43. 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.
    44. 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.
    45. repec:dau:papers:123456789/8180 is not listed on IDEAS
    46. 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.
    47. 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.
    48. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pang, Qinghua & Dong, Xianwei & Zhang, Lina & Chiu, Yung-ho, 2023. "Drivers and key pathways of the household energy consumption in the Yangtze river economic belt," Energy, Elsevier, vol. 262(PA).
    2. Song, Xiang & Wang, Dingyu & Zhang, Xuantao & He, Yuan & Wang, Yong, 2022. "A comparison of the operation of China's carbon trading market and energy market and their spillover effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    3. Zhang, Tianhu & Wang, Fuxi & Gao, Yi & Liu, Yuanjun & Guo, Qiang & Zhao, Qingxin, 2023. "Optimization of a solar-air source heat pump system in the high-cold and high-altitude area of China," Energy, Elsevier, vol. 268(C).
    4. Chi, Yuanying & Xu, Weiyue & Xiao, Meng & Wang, Zhengzao & Zhang, Xufeng & Chen, Yahui, 2023. "Fuel-cycle based environmental and economic assessment of hydrogen fuel cell vehicles in China," Energy, Elsevier, vol. 282(C).
    5. Di Zhu & Yinghong Wang & Fenglin Zhang, 2022. "Energy Price Prediction Integrated with Singular Spectrum Analysis and Long Short-Term Memory Network against the Background of Carbon Neutrality," Energies, MDPI, vol. 15(21), pages 1-20, October.
    6. Ding, Wangwang & Du, Juntao & Kazancoglu, Yigit & Mangla, Sachin Kumar & Song, Malin, 2023. "Financial development and the energy net-zero transformation potential," Energy Economics, Elsevier, vol. 125(C).
    7. Hu, Haisheng & Zhao, Laijun & Dong, Wanhao, 2023. "How to achieve the goal of carbon peaking by the energy policy? A simulation using the DCGE model for the case of Shanghai, China," Energy, Elsevier, vol. 278(PA).
    8. Lijing Zhang & Shuke Fu & Jiali Tian & Jiachao Peng, 2022. "A Review of Energy Industry Chain and Energy Supply Chain," Energies, MDPI, vol. 15(23), pages 1-21, December.
    9. Li, Jianling & Zhao, Ziwen & Xu, Dan & Li, Peiquan & Liu, Yong & Mahmud, Md Apel & Chen, Diyi, 2023. "The potential assessment of pump hydro energy storage to reduce renewable curtailment and CO2 emissions in Northwest China," Renewable Energy, Elsevier, vol. 212(C), pages 82-96.
    10. Hou, Fei & Zhong, Xiaoxing & Zanoni, Marco A.B. & Rashwan, Tarek L. & Torero, José L., 2024. "Multi-step scheme and thermal effects of coal smouldering under various oxygen-limited conditions," Energy, Elsevier, vol. 299(C).
    11. Ma, Shaoyue & Man, Hecheng & Li, Xiao & Xu, Xiangbo & Sun, Mingxing & Xie, Minghui & Zhang, Linxiu, 2023. "How nonfarm employment drives the households’ energy transition: Evidence from rural China," Energy, Elsevier, vol. 267(C).
    12. Li, Kai & Tan, Xiujie & Yan, Yaxue & Jiang, Dalin & Qi, Shaozhou, 2022. "Directing energy transition toward decarbonization: The China story," Energy, Elsevier, vol. 261(PA).
    13. Su, Xing & Xu, Zehan & Tian, Shaochen & Chen, Chaoyang & Huang, Yixiang & Geng, Yining & Chen, Junfeng, 2023. "Life cycle assessment of three typical solar energy utilization systems in different regions of China," Energy, Elsevier, vol. 278(C).
    14. Kai-Hua Wang & Jia-Min Kan & Cui-Feng Jiang & Chi-Wei Su, 2022. "Is Geopolitical Risk Powerful Enough to Affect Carbon Dioxide Emissions? Evidence from China," Sustainability, MDPI, vol. 14(13), pages 1-16, June.
    15. He, Weijun & Li, Wanyu & Wang, Chun & Wang, Siyuan & Yang, Yuantao, 2024. "Does energy resource misallocation affect energy utilization efficiency? Evidence from Chinese provincial panel data," Energy, Elsevier, vol. 288(C).
    16. Wu, Liangpeng & Xu, Chengzhen & Zhu, Qingyuan & Zhou, Dequn, 2024. "Multiple energy price distortions and improvement of potential energy consumption structure in the energy transition," Applied Energy, Elsevier, vol. 362(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    2. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    3. Qin, Quande & Xie, Kangqiang & He, Huangda & Li, Li & Chu, Xianghua & Wei, Yi-Ming & Wu, Teresa, 2019. "An effective and robust decomposition-ensemble energy price forecasting paradigm with local linear prediction," Energy Economics, Elsevier, vol. 83(C), pages 402-414.
    4. Manickavasagam, Jeevananthan & Visalakshmi, S. & Apergis, Nicholas, 2020. "A novel hybrid approach to forecast crude oil futures using intraday data," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    5. Li, Zheng & Zhou, Bo & Hensher, David A., 2022. "Forecasting automobile gasoline demand in Australia using machine learning-based regression," Energy, Elsevier, vol. 239(PD).
    6. Cheng, Fangzheng & Li, Tian & Wei, Yi-ming & Fan, Tijun, 2019. "The VEC-NAR model for short-term forecasting of oil prices," Energy Economics, Elsevier, vol. 78(C), pages 656-667.
    7. Özen, Kadir & Yıldırım, Dilem, 2021. "Application of bagging in day-ahead electricity price forecasting and factor augmentation," Energy Economics, Elsevier, vol. 103(C).
    8. Shao, Zhen & Zheng, Qingru & Yang, Shanlin & Gao, Fei & Cheng, Manli & Zhang, Qiang & Liu, Chen, 2020. "Modeling and forecasting the electricity clearing price: A novel BELM based pattern classification framework and a comparative analytic study on multi-layer BELM and LSTM," Energy Economics, Elsevier, vol. 86(C).
    9. Umut Ugurlu & Ilkay Oksuz & Oktay Tas, 2018. "Electricity Price Forecasting Using Recurrent Neural Networks," Energies, MDPI, vol. 11(5), pages 1-23, May.
    10. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    11. Wang, Bin & Wang, Jun, 2020. "Energy futures and spots prices forecasting by hybrid SW-GRU with EMD and error evaluation," Energy Economics, Elsevier, vol. 90(C).
    12. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
    13. Ding, Yishan, 2018. "A novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting," Energy, Elsevier, vol. 154(C), pages 328-336.
    14. F. Cordoni, 2020. "A comparison of modern deep neural network architectures for energy spot price forecasting," Digital Finance, Springer, vol. 2(3), pages 189-210, December.
    15. Gulay, Emrah & Duru, Okan, 2020. "Hybrid modeling in the predictive analytics of energy systems and prices," Applied Energy, Elsevier, vol. 268(C).
    16. Elmore, Clay T. & Dowling, Alexander W., 2021. "Learning spatiotemporal dynamics in wholesale energy markets with dynamic mode decomposition," Energy, Elsevier, vol. 232(C).
    17. Zhao, Zhengling & Sun, Shaolong & Sun, Jingyun & Wang, Shouyang, 2024. "A novel hybrid model with two-layer multivariate decomposition for crude oil price forecasting," Energy, Elsevier, vol. 288(C).
    18. Simon Pezzutto & Gianluca Grilli & Stefano Zambotti & Stefan Dunjic, 2018. "Forecasting Electricity Market Price for End Users in EU28 until 2020—Main Factors of Influence," Energies, MDPI, vol. 11(6), pages 1-18, June.
    19. Grzegorz Marcjasz & Tomasz Serafin & Rafał Weron, 2018. "Selection of Calibration Windows for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 11(9), pages 1-20, September.
    20. Mwampashi, Muthe Mathias & Nikitopoulos, Christina Sklibosios & Konstandatos, Otto & Rai, Alan, 2021. "Wind generation and the dynamics of electricity prices in Australia," Energy Economics, Elsevier, vol. 103(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:enepol:v:159:y:2021:i:c:s030142152100495x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/enpol .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.