RESEARCH ON EVALUATION ALGORITHMS OF CARBON ENVIRONMENT DEVELOPMENT IN SCENIC SPOTS.

Taking the Sanshan scenic spot as the research object, this study analysed the main influencing factors of CO2 emission, and compared the sensitivity of the genetic algorithm (GA), particle swarm optimisation (PSO) algorithm and PSO-GA to changes in parameters. The change in the adjusted Rand index (ARI) value of each algorithm under different types of data sets was examined, the best prediction model of CO₂ emissions was optimised, and the development level of the carbon environment at the Sanshan scenic spot was evaluated. The results showed that for the correlation of the CO₂ emission structure, transportation, accommodation, and catering services were the main influencing factors of carbon emissions in the three mountainous scenic spots. In the sensitivity test, the PSO-GA had the highest sensitivity (9.86), GA was second with an average sensitivity of 8.68, and the PSO algorithm had the lowest sensitivity (8.59). After 160 iterations, the ARI value of the PSO-GA was the highest, reaching 1. The predicted and measured values of CO₂ emissions under this algorithm were the best, and was the best algorithm for evaluating carbon environment development at scenic spots. The PSO-GA was used to calculate the carbon emissions of three mountainous scenic spots over the next five years, and the medium and high carbon levels should change to a medium carbon development stage by 2024. [ABSTRACT FROM AUTHOR]