A novel target design and path planning model for regional peak carbon dioxide emissions

Qiang Yu; Run Huang; Shuming Zhou; Huiji Wang; Deping Ke; Famei Ma

doi:10.1117/12.3024986

13 May 2024 A novel target design and path planning model for regional peak carbon dioxide emissions

Qiang Yu, Run Huang, Shuming Zhou, Huiji Wang, Deping Ke, Famei Ma

Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 13159A5 (2024) https://doi.org/10.1117/12.3024986
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China

Abstract

The premise of achieving carbon neutrality is to reach the peak carbon dioxide emissions. Therefore, this paper proposes a novel target design and path planning model for regional peak carbon dioxide emissions. First considering the attributes of influence factors on regional carbon emissions, and based on the logistic equation and grey theory, a highly adaptive forecasting method of influence factors of regional carbon emission is proposed. Subsequently, a regional carbon emissions prediction model is established based on multivariable convolutional neural network (CNN). Then, based on the target of peak carbon dioxide emissions, a scenario design method based on spatial mapping is proposed, and a target design and path planning model for regional peak carbon dioxide emissions based on genetic algorithm (GA) and multivariable CNN is established. Finally, the effectiveness of the proposed methods and models are verified in a practical case in a region along the southeast coast of China.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qiang Yu, Run Huang, Shuming Zhou, Huiji Wang, Deping Ke, and Famei Ma "A novel target design and path planning model for regional peak carbon dioxide emissions", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 13159A5 (13 May 2024); https://doi.org/10.1117/12.3024986

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KEYWORDS

Carbon

Design

Data modeling

Education and training

Convolutional neural networks

Genetic algorithms

Forestry

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