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Methane emissions forecasting in American energy sector based on a grey jump modeling framework

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  • Shi, Kaihe
  • Gu, Haolei

Abstract

COVID-19 epidemic shock enhanced forecasting methane emission trend uncertainty. Traditional grey model is unsatisfied with best linear unbiased estimator property. It fails to reflect structural changes in the energy sector. This research constructed a modified fractional grey model named(JFGM(r,s,ad,1)) including jump shock term and shock attenuation parameter. The model fully considered the characteristics of small samples, nonlinear perturbation and time heterogeneity. The model's effectiveness is validated through comparison experiment and sensitivity analyze. The empirical study has separately modelled trends in total methane emissions and main sources. The result showed that the baseline model FGM(r,1) has excellent forecasting performance in small sample scenarios. Average MAPE is 1.19 %. The model accuracy is significantly improved after introduced external shock background value. Average MAPE is reduced to 0.65 %. Shock attenuation parameter sensitivity analyze revealed COVID-19 epidemic shock has temporal heterogeneity and nonlinear attenuation trend characteristics. Shock attenuation parameter further improved model performance. Methane emissions decreased rapidly with increased attenuation coefficient. Finally, the implications based on the empirical results are proposed.

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  • Shi, Kaihe & Gu, Haolei, 2025. "Methane emissions forecasting in American energy sector based on a grey jump modeling framework," Energy, Elsevier, vol. 331(C).
    • Handle: RePEc:eee:energy:v:331:y:2025:i:c:s0360544225027409
    DOI: 10.1016/j.energy.2025.137098
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