A low-carbon critical path and strategy generation model based on population genetic algorithm —— taking the chemical industry as an example
Lei Fang, Bingbing Chen, Feng Jin, Zongxuan Li, Kai Zhang, Guangliang Sun · 2024
As global climate change intensifies, low-carbon development has become a common goal for all countries. In order to achieve low-carbon transformation, it is necessary to analyze the carbon emissions of different industries, formulate reasonable carbon emission reduction plans, and find the optimal low-carbon critical path and strategy. In response to the low-carbon development needs of the chemical industry, a low-carbon critical path and strategy generation method based on population genetic algorithm is proposed. The first is to use carbon accounting technology to establish a carbon emission inventory for the chemical industry; the second is to use carbon emission reduction technology to formulate a carbon emission reduction plan; finally, the population genetic algorithm is used to optimize and determine the best low-carbon critical path and strategy, aiming to provide low-carbon solutions for the chemical industry. Transformation provides scientific guidance and decision-making support.