Hybrid forecasting of carbon emission price in China based on multimodal data feature fusion and multiscale decomposition strategy

Xizhen Xu et al.

Sustainable Futures2026https://doi.org/10.1016/j.sftr.2026.101847article
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https://doi.org/https://doi.org/10.1016/j.sftr.2026.101847

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@article{xizhen2026,
  title        = {{Hybrid forecasting of carbon emission price in China based on multimodal data feature fusion and multiscale decomposition strategy}},
  author       = {Xizhen Xu et al.},
  journal      = {Sustainable Futures},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.sftr.2026.101847},
}

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Hybrid forecasting of carbon emission price in China based on multimodal data feature fusion and multiscale decomposition strategy

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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