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A new multivariate grey prediction model for forecasting China’s regional energy consumption

Author

Listed:
  • Geng Wu

    (Chung Yuan Christian University)

  • Yi-Chung Hu

    (Chung Yuan Christian University)

  • Yu-Jing Chiu

    (Chung Yuan Christian University)

  • Shu-Ju Tsao

    (Chung Yuan Christian University)

Abstract

Predicting energy consumption is an essential part of energy planning and management. The reliable prediction of regional energy consumption is crucial for the authority in China to formulate policies by with respect to the dual control of its energy consumption and energy intensity. Given that energy consumption is affected by a number of factors, this study proposes a non-homogeneous, discrete, multivariate grey prediction model based on adjacent accumulation to predict the regional energy consumption in China. Interestingly regional GDP was selected by grey relational analysis as the independent variable in the proposed model. The results show that it can outperform the other multivariate grey models considered in terms of predicting regional energy consumption in China. Moreover, we found that economic development and energy consumption of each region in China remain closely related. In the post-COVID-19 period, regional economic development will continue to grow and increase energy consumption.

Suggested Citation

  • Geng Wu & Yi-Chung Hu & Yu-Jing Chiu & Shu-Ju Tsao, 2023. "A new multivariate grey prediction model for forecasting China’s regional energy consumption," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(5), pages 4173-4193, May.
    • Handle: RePEc:spr:endesu:v:25:y:2023:i:5:d:10.1007_s10668-022-02238-1
    DOI: 10.1007/s10668-022-02238-1
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