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Evaluation and trend prediction of the relationship between carbon emissions, energy, and sustainable growth based on neural networks

Tingting Tan

International Journal of Environment and Pollution2026https://doi.org/10.1504/ijep.2026.151754article
ABDC C
Weight
0.50

What the paper says

This study investigates the relationship between carbon emissions (CE), energy, and sustainable growth using neural networks.Data from five regions -North America, South America, Europe, Asia Pacific, and Africawere analysed to model CE trends based on energy structure and consumption.A neural network model was trained and optimised to predict correlations among CE, energy use, and economic growth.Focusing on China, the study examines vehicle emissions, fuel-powered versus new energy vehicle sales, and their impact on CE and the economy.Results show a strong correlation between energy consumption and CE (R = 0.99), with energy efficiency and composition also influencing emissions.As new energy vehicle adoption rises, fossil fuel demand declines, helping curb total CE, support carbon neutrality, and promote sustainable development.The model demonstrates that optimising energy structure is key to balancing economic growth and environmental protection.

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https://doi.org/https://doi.org/10.1504/ijep.2026.151754

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@article{tingting2026,
  title        = {{Evaluation and trend prediction of the relationship between carbon emissions, energy, and sustainable growth based on neural networks}},
  author       = {Tingting Tan},
  journal      = {International Journal of Environment and Pollution},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1504/ijep.2026.151754},
}

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Evaluation and trend prediction of the relationship between carbon emissions, energy, and sustainable growth based on neural networks

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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.