Article: Evaluation and trend prediction of the relationship between carbon emissions, energy, and sustainable growth based on neural networks Journal: International Journal of Environment and Pollution (IJEP) 2026 Vol.76 No.1/2 pp.1 - 19 Abstract: 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 Africa - were 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. Inderscience Publishers - linking academia, business and industry through research

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

Authors: Tingting Tan

Addresses: Post-Doctoral Scientific Research Workstation, Harbin Bank Co., Ltd., Harbin, 150010, Heilongjiang, China; School of Management, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China; Party School of HeiLongJiang Provincial Committee of C.P.C (Heilongjiang Academy of Governance), Harbin, 150080, Heilongjiang, China

Abstract: 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 Africa - were 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.

Keywords: carbon emissions; neural networks; energy mix; energy consumption; data analysis; sustainable development; climate change.

DOI: 10.1504/IJEP.2026.151754

International Journal of Environment and Pollution, 2026 Vol.76 No.1/2, pp.1 - 19

Received: 28 Mar 2025
Accepted: 21 Jul 2025
Published online: 18 Feb 2026 *

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

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