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A non-linear systematic grey model for forecasting the industrial economy-energy-environment system

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  • Wang, Zheng-Xin
  • Jv, Yue-Qi

Abstract

To estimate the internal structure and forecast dynamic trends in the industrial economy-energy-environment (3E) system, a non-linear systematic grey model (NSGM(1, m)) is proposed. This model is derived from a non-linear grey Bernoulli model (NGBM(1, 1)) and consisted of multiple non-linear equations in an embedded manner. To minimise the mean absolute percentage error (MAPE), a particle swarm optimisation (PSO) algorithm is used to optimise the parameters of the model. The empirical analysis based on a dataset of Zhejiang Province, China from 2000 to 2018 shows that the NSGM(1, m) model can identify a non-linear relationship among industrial economic growth, energy consumption, and pollutant emissions. The MAPEs of training sets of three industrial 3E systems are 2.95%, 4.90%, and 3.75%, while those of test sets are 1.36%, 6.94%, and 1.72%, respectively. From 2019 to 2023, it is predicted that, with constant industrial and economic growth, industrial energy consumption and discharge of exhaust and solid waste will continue to increase while industrial wastewater discharge will decline. Moreover, efficiency indices indicate that the economic efficiency of the industrial 3E systems in Zhejiang Province will increase.

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  • Wang, Zheng-Xin & Jv, Yue-Qi, 2021. "A non-linear systematic grey model for forecasting the industrial economy-energy-environment system," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    • Handle: RePEc:eee:tefoso:v:167:y:2021:i:c:s0040162521001396
    DOI: 10.1016/j.techfore.2021.120707
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    6. Ye, Li & Yang, Deling & Dang, Yaoguo & Wang, Junjie, 2022. "An enhanced multivariable dynamic time-delay discrete grey forecasting model for predicting China's carbon emissions," Energy, Elsevier, vol. 249(C).
    7. Ye, Li & Dang, Yaoguo & Fang, Liping & Wang, Junjie, 2023. "A nonlinear interactive grey multivariable model based on dynamic compensation for forecasting the economy-energy-environment system," Applied Energy, Elsevier, vol. 331(C).
    8. Michal Pavlicko & Mária Vojteková & Oľga Blažeková, 2022. "Forecasting of Electrical Energy Consumption in Slovakia," Mathematics, MDPI, vol. 10(4), pages 1-20, February.
    9. Xin Ma & Yubin Cai & Hong Yuan & Yanqiao Deng, 2023. "Partially Linear Component Support Vector Machine for Primary Energy Consumption Forecasting of the Electric Power Sector in the United States," Sustainability, MDPI, vol. 15(9), pages 1-26, April.
    10. Li, Xuemei & Wu, Xinran & Zhao, Yufeng, 2023. "Research and application of multi-variable grey optimization model with interactive effects in marine emerging industries prediction," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    11. Du, Pei & Guo, Ju'e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2022. "A novel two-stage seasonal grey model for residential electricity consumption forecasting," Energy, Elsevier, vol. 258(C).

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