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Spatiotemporal grey evolution in the dual control of the energy consumption

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  • Zhao, Kai
  • Shi, Kaihe
  • Wu, Lifeng

Abstract

Against the backdrop of the intensifying global climate change and energy governance pressures, China, as the world’s largest energy consumer, is confronted with dual challenges of the reducing carbon emissions while sustaining economic growth. Existing models have limitations in analyzing spatiotemporal interactions and heterogeneity. To address this, this study developed a novel spatial disturbance grey model. The integration of the spatial non-equal accumulation parameters and a multidimensional information fusion mechanism was implemented to strengthen spatiotemporal coupling capabilities. The methodology adheres to First Law of Geography and core principles of the grey system theory. And it has been successfully applied to the dual control analysis of energy consumption in 30 provinces in China. Empirical analysis revealed the dual effects of the positive synergy-negative inhibition in regional energy consumption during the 14th Five-Year Plan period. Policy continuity is projected to exacerbate regional disparities in the 15th Five-Year Plan period, with intensified spatial heterogeneity and spillover effects of energy consumption. By 2030, the energy intensity is expected to exhibit a unique diagonal bounded clustering pattern between Heilongjiang and Guizhou provinces. These findings provide theoretical foundations and actionable pathways for provincial energy governance planning in China.

Suggested Citation

  • Zhao, Kai & Shi, Kaihe & Wu, Lifeng, 2025. "Spatiotemporal grey evolution in the dual control of the energy consumption," Energy, Elsevier, vol. 340(C).
  • Handle: RePEc:eee:energy:v:340:y:2025:i:c:s0360544225050236
    DOI: 10.1016/j.energy.2025.139381
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    1. Wu, Wenqing & Ma, Xin & Zeng, Bo & Wang, Yong & Cai, Wei, 2018. "Application of the novel fractional grey model FAGMO(1,1,k) to predict China's nuclear energy consumption," Energy, Elsevier, vol. 165(PB), pages 223-234.
    2. Chen, Yan & Lifeng, Wu & Lianyi, Liu & Kai, Zhang, 2020. "Fractional Hausdorff grey model and its properties," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    3. Gao, Mingyun & Yang, Honglin & Xiao, Qinzi & Goh, Mark, 2022. "COVID-19 lockdowns and air quality: Evidence from grey spatiotemporal forecasts," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    4. Yan, Junna & Su, Bin, 2020. "What drive the changes in China's energy consumption and intensity during 12th Five-Year Plan period?," Energy Policy, Elsevier, vol. 140(C).
    5. Du, Xiaoyi & Wu, Dongdong & Yan, Yabo, 2023. "Prediction of electricity consumption based on GM(1,Nr) model in Jiangsu province, China," Energy, Elsevier, vol. 262(PA).
    6. Somu, Nivethitha & Raman M R, Gauthama & Ramamritham, Krithi, 2021. "A deep learning framework for building energy consumption forecast," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    7. Wang, Zheng-Xin & Li, Qin & Pei, Ling-Ling, 2018. "A seasonal GM(1,1) model for forecasting the electricity consumption of the primary economic sectors," Energy, Elsevier, vol. 154(C), pages 522-534.
    8. Wu, Lifeng & Gao, Xiaohui & Xiao, Yanli & Yang, Yingjie & Chen, Xiangnan, 2018. "Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China," Energy, Elsevier, vol. 157(C), pages 327-335.
    9. Zuo, Ziyue & Xiao, Xinping & Gao, Mingyun & Rao, Congjun, 2025. "Mixed-frequency fusion grey panel model for spatiotemporal prediction of photovoltaic power generation," Renewable Energy, Elsevier, vol. 248(C).
    10. Wang, Na & Fu, Xiaodong & Wang, Shaobin, 2022. "Spatial-temporal variation and coupling analysis of residential energy consumption and economic growth in China," Applied Energy, Elsevier, vol. 309(C).
    11. Xiao, Hongwei & Ma, Zhongyu & Mi, Zhifu & Kelsey, John & Zheng, Jiali & Yin, Weihua & Yan, Min, 2018. "Spatio-temporal simulation of energy consumption in China's provinces based on satellite night-time light data," Applied Energy, Elsevier, vol. 231(C), pages 1070-1078.
    12. Hayes, M. Cordey & Wilson, A. G., 1971. "Spatial interaction," Socio-Economic Planning Sciences, Elsevier, vol. 5(1), pages 73-95, February.
    13. Zhao, Kai & Wu, Lifeng, 2025. "Heterogeneity grey model and its prediction of energy consumption under the shared socioeconomic pathways," Energy, Elsevier, vol. 319(C).
    14. Zeng, Bo & Duan, Huiming & Bai, Yun & Meng, Wei, 2018. "Forecasting the output of shale gas in China using an unbiased grey model and weakening buffer operator," Energy, Elsevier, vol. 151(C), pages 238-249.
    15. Shimei Wu & Xinye Zheng & Chu Wei, 2017. "Measurement of inequality using household energy consumption data in rural China," Nature Energy, Nature, vol. 2(10), pages 795-803, October.
    16. Chen, Chaoyi & Pinar, Mehmet & Stengos, Thanasis, 2020. "Renewable energy consumption and economic growth nexus: Evidence from a threshold model," Energy Policy, Elsevier, vol. 139(C).
    17. Ben Liu & Peng Wang & Jin Zhou & Yang Guo & Shijun Ma & Wei-Qiang Chen & Jiashuo Li & Victor W.-C. Chang, 2025. "Refocusing on effectiveness over expansion in urban waste–energy–carbon development in China," Nature Energy, Nature, vol. 10(2), pages 215-225, February.
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