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Estimating energy-related CO2 emissions using a novel multivariable fuzzy grey model with time-delay and interaction effect characteristics

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  • Ding, Qi
  • Xiao, Xinping
  • Kong, Dekai

Abstract

An objective and accurate forecast of carbon emissions can provide the government with an important baseline for the implementation of the Green Economic Development Strategy. This paper considers the time-lag effect and the interaction effect of the influencing factors on carbon emissions simultaneously and establishes a new grey multivariate coupled model (CTGM(1,N)) for carbon emission projection by introducing the Choquet fuzzy integral and grey multivariate delay model. To further promote the prediction performance, the time-lag number of each influencing factor is determined by time-delay grey correlation analysis, and the whale optimization algorithm is designed to acquire the optimal parameters and accuracy of the model. The new model is designed to fitting carbon emissions data in three countries and compare it to six reference models. The performance test shows that the CTGM(1,N) model has high stability. The results of the forecasts show that China's carbon emissions are expected to rise by 1.17% by 2025 from 2020 levels. Meanwhile, emissions will decrease by 5.08% (US) and 0.88% (Japan). The prediction results were consistent with the development status of the three countries. According to the results, we can grasp the development trend of carbon emissions and formulate targeted strategies to achieve sustainable development.

Suggested Citation

  • Ding, Qi & Xiao, Xinping & Kong, Dekai, 2023. "Estimating energy-related CO2 emissions using a novel multivariable fuzzy grey model with time-delay and interaction effect characteristics," Energy, Elsevier, vol. 263(PE).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pe:s0360544222028912
    DOI: 10.1016/j.energy.2022.126005
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