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Research on green trade data prediction under global economic shock based on ConvLSTM model oriented towards reducing carbon emissions

Nianjie Shang et al.

International Journal of Environment and Pollution2026https://doi.org/10.1504/ijep.2026.151758article
ABDC C
Weight
0.50

What the paper says

Existing green trade data prediction models focus only on the temporal characteristics of the data, while ignoring the spatial relationships of the data, resulting in large prediction errors for trade volume (M and X).This paper takes Sino-Korean trade as the main research object, and uses the convolutional long short-term memory (ConvLSTM) model to predict trade volume (M and X) data by combining the advantages of spatiotemporal features.This paper first collects and preprocesses relevant green trade data, then constructs a ConvLSTM model, and finally uses the model to output the predicted values of trade volumes M and X for the next year and compares them with the actual data.Experimental results show that the RMSE and MAE of the ConvLSTM model are 16,300 and 20,500, respectively, which are 1900 and 2300 lower than those of the LSTM model.

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https://doi.org/https://doi.org/10.1504/ijep.2026.151758

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@article{nianjie2026,
  title        = {{Research on green trade data prediction under global economic shock based on ConvLSTM model oriented towards reducing carbon emissions}},
  author       = {Nianjie Shang et al.},
  journal      = {International Journal of Environment and Pollution},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1504/ijep.2026.151758},
}

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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

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.