Carbon emission risk prediction based on improved genetic algorithm

Jun Sun; Yong Tian; Tenglong Jiang; Chunyan Zhou

doi:10.1117/12.2667397

10 February 2023 Carbon emission risk prediction based on improved genetic algorithm

Jun Sun, Yong Tian, Tenglong Jiang, Chunyan Zhou

Proceedings Volume 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022); 125522V (2023) https://doi.org/10.1117/12.2667397
Event: International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 2022, Kunming, China

Abstract

The issue of environmental risks caused by carbon emissions has become an important content of attention. The multi-objective optimization problem of minimizing the incremental cost of input and maximizing the incremental benefit obtained is related to the development of carbon emissions to the environment. Aiming at this problem, this paper proposes a carbon emission optimization scheme based on an improved genetic algorithm to maximize environmental value in multi-objective problems. First, in order to better reflect the efficiency of pollution caused by carbon emissions, the improved genetic algorithm is used to obtain multiple pareto solutions under the combined optimization of different transformation schemes; Secondly, the gain obtained by unit incremental cost is used as the evaluation index; Finally, through experimental verification, it can be seen that the algorithm proposed in this study has certain advantages. At the same time, in terms of environmental risks caused by carbon emissions, it is not necessary to select all the transformation schemes. Better results can be obtained by optimizing them according to actual conditions.

Citation Download Citation

Jun Sun, Yong Tian, Tenglong Jiang, and Chunyan Zhou "Carbon emission risk prediction based on improved genetic algorithm", Proc. SPIE 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 125522V (10 February 2023); https://doi.org/10.1117/12.2667397

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