Dynamic Nature Reserve Project Effectiveness Assessment and Environmental Sensitivity Analysis Based on Genetic Algorithm

This study takes the dynamic nature reserve project as the research object, and utilizes genetic algorithm and other computer technologies to assess the effect of the project through horizontal comparison and vertical prediction, and to conduct environmental sensitivity analysis. Cross-sectional comparisons analyzed data from 28 regions and countries using COX regression to verify the effectiveness of the project's wildlife conservation interventions. Longitudinal prediction combined with legislation time and nature reserve area to infer the effectiveness of project implementation.The results of COX regression model analysis showed that factors such as nature reserve area, legislation time, and economic level had a significant impact on the project effectiveness.VAR analysis revealed the sustained contribution of the project implementation to the control of illegal wildlife trade. Likelihood analysis was conducted by genetic algorithm, and the maximum likelihood was 1.243 and the minimum likelihood was 0.983, which predicted that illegal wildlife trade is expected to be reduced by 20% after the project implementation. Sensitivity analysis showed that the model has good adaptability to environmental changes. The study provides theoretical support and decision-making reference for the effective implementation of the dynamic nature reserve project, and at the same time highlights the application value of computer technology in the field of environmental protection.