Ecological Risk Assessment and Environmental Policy Analysis Based on Bayesian Networks

Abstract

At present, ecological and environmental problems are becoming increasingly serious. Traditional risk assessment methods are difficult to fully integrate the complex relationships among multiple variables and lack the ability to dynamically analyze uncertainty and conditional dependence, which affects the scientific nature of environmental policies. To address this problem, this paper proposes an ecological risk assessment and environmental policy analysis framework based on Bayesian networks, aiming to effectively quantify ecological risks and provide support for policy decisions. In terms of methodology, first, a Bayesian network structure is constructed with ecosystem elements as nodes and causal relationships as edges; second, the conditional probability table is determined by combining expert knowledge with historical data; then, node sensitivity analysis and scenario simulation are carried out to identify key risk factors and sources of uncertainty; finally, the regulatory effect of policy intervention on risks is evaluated. The study took a typical river basin as an example, and the results showed that driven by pollutant concentrations and land use changes, the river basin ecosystem risk index reached 0.72, among which water quality deterioration and biodiversity decline were the main sources of risk; policy simulation showed that by increasing sewage treatment rate and vegetation coverage rate, the risk index could be reduced to 0.48, a reduction of 33.3%. Studies have shown that Bayesian networks can effectively reveal causal relationships and the impact of policy interventions in complex ecosystems, providing a scientific basis for the precise formulation of environmental policies.