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Modeling a hybrid methodology for evaluating and forecasting regional energy efficiency in China

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  • Li, Ming-Jia
  • He, Ya-Ling
  • Tao, Wen-Quan

Abstract

This study proposes a new hybrid methodology for short-term prediction of energy efficiency. This new method consists of the stochastic frontier analysis-generalised autoregressive conditional heteroskedasticity (SFA-GARCH) model and the radial basis function neural (RBFN) model. The study finds that 30 regions (provinces and municipalities) in China have cluster-hetergeneity, and the different levels of industry structure, technology content and energy resources in the different regions lead to dissimilar energy saving quotas. In addition, through fair comparison between the traditional GARCH model and the new hybrid model, it is proved that the new hybrid model shows good performance and the results are reasonable. The energy efficiency indicators predicted by the hybrid model appear to be more reliable than the summation of the individual forecasts because it avoids the superposition of errors.

Suggested Citation

  • Li, Ming-Jia & He, Ya-Ling & Tao, Wen-Quan, 2017. "Modeling a hybrid methodology for evaluating and forecasting regional energy efficiency in China," Applied Energy, Elsevier, vol. 185(P2), pages 1769-1777.
    • Handle: RePEc:eee:appene:v:185:y:2017:i:p2:p:1769-1777
    DOI: 10.1016/j.apenergy.2015.11.082
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    8. Zhou, Yi-Peng & He, Ya-Ling & Tong, Zi-Xiang & Liu, Zhan-Bin, 2019. "Multi-physics coupling effects of nanostructure characteristics on the all-back-contact silicon solar cell performances," Applied Energy, Elsevier, vol. 236(C), pages 127-136.
    9. Wu, Shu & Ding, Song, 2021. "Efficiency improvement, structural change, and energy intensity reduction: Evidence from Chinese agricultural sector," Energy Economics, Elsevier, vol. 99(C).
    10. Li, Meng-Jie & Qiu, Yu & Li, Ming-Jia, 2018. "Cyclic thermal performance analysis of a traditional Single-Layered and of a novel Multi-Layered Packed-Bed molten salt Thermocline Tank," Renewable Energy, Elsevier, vol. 118(C), pages 565-578.
    11. Chang, Chun & Sciacovelli, Adriano & Wu, Zhiyong & Li, Xin & Li, Yongliang & Zhao, Mingzhi & Deng, Jie & Wang, Zhifeng & Ding, Yulong, 2018. "Enhanced heat transfer in a parabolic trough solar receiver by inserting rods and using molten salt as heat transfer fluid," Applied Energy, Elsevier, vol. 220(C), pages 337-350.
    12. Han, Hongyun & Zhou, Zinan, 2024. "The rebound effect of energy consumption and its determinants in China's agricultural production," Energy, Elsevier, vol. 290(C).
    13. Wang, Haotian & Hao, Liang & Wang, Weizheng & Chen, Xingyu, 2023. "Natural resources lineage, high technology exports and economic performance: RCEP economies perspective of human capital and energy resources efficiency," Resources Policy, Elsevier, vol. 87(PA).
    14. Guo, Jia-Qi & Li, Ming-Jia & Xu, Jin-Liang & Yan, Jun-Jie & Wang, Kun, 2019. "Thermodynamic performance analysis of different supercritical Brayton cycles using CO2-based binary mixtures in the molten salt solar power tower systems," Energy, Elsevier, vol. 173(C), pages 785-798.
    15. Huang, Junbing & Chen, Xiang, 2020. "Domestic R&D activities, technology absorption ability, and energy intensity in China," Energy Policy, Elsevier, vol. 138(C).
    16. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    17. Qiu, Yu & He, Ya-Ling & Li, Peiwen & Du, Bao-Cun, 2017. "A comprehensive model for analysis of real-time optical performance of a solar power tower with a multi-tube cavity receiver," Applied Energy, Elsevier, vol. 185(P1), pages 589-603.
    18. Li, Ming-Jia & Jin, Bo & Ma, Zhao & Yuan, Fan, 2018. "Experimental and numerical study on the performance of a new high-temperature packed-bed thermal energy storage system with macroencapsulation of molten salt phase change material," Applied Energy, Elsevier, vol. 221(C), pages 1-15.
    19. Li, Ming-Jia & Tao, Wen-Quan, 2017. "Review of methodologies and polices for evaluation of energy efficiency in high energy-consuming industry," Applied Energy, Elsevier, vol. 187(C), pages 203-215.
    20. Li, Hongkuan & He, Haiyan & Shan, Jiefei & Cai, Jingjing, 2019. "Innovation efficiency of semiconductor industry in China: A new framework based on generalized three-stage DEA analysis," Socio-Economic Planning Sciences, Elsevier, vol. 66(C), pages 136-148.
    21. Liu, Jie & Qian, Haoqi & Zhang, Qian & Lin, Zhiyan & Siano, Pierluigi, 2023. "Corruption induced energy inefficiencies: Evidence from China's energy investment projects," Energy Policy, Elsevier, vol. 183(C).
    22. Zhang, H. & Fan, L.W. & Zhou, P., 2020. "Handling heterogeneity in frontier modeling of city-level energy efficiency: The case of China," Applied Energy, Elsevier, vol. 279(C).

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