Evaluation of Climate Change Risk Perception in Baoji City Based on AHP-Bayesian Network

Abstract

To analyze the gap between the Baoji population’s climate change risk perception and the scientifically measured intensity, danger degree, vulnerability, and exposure of climate change risk based on the basic elements of risk assessment, this paper combines analytic hierarchy process and the Bayesian network to evaluate the climate change risk perception intensity in Baoji City, aiming at simulating climate change risk scenarios and improving the objectivity of assessment results. Specifically, the simulation of climate change risk scenarios is carried out through the measurement of such basic elements as risk, vulnerability, and exposure perceptions, and an objective evaluation of the public climate change risk perception intensity in Baoji City is made, thereby systematically assessing local people’s perception of climate change risk. The model weights the indices of risk perception, vulnerability perception, and exposure perception by analytic hierarchy process, constructs the Bayesian network according to the causal relationship among the risk perception assessment elements, and calculates the risk perception probability at each level by combining the Bayesian network to get the system perception intensity. The perceived intensity of climate change risk was 0.497, being at a medium level. The result has different reference value in terms of the response to and management of different climate change risk categories, so it needs to be adjusted according to the actual situation of Baoji City. The main factors that affect the risk perception intensity in Baoji City are gender, climate change perception trend, ecological environment deterioration degree, and disaster severity degree. Therefore, the decision-makers can make risk management plans accordingly, which plays an important role in regulating and narrowing the gap between people’s perception of climate change risk and the results of scientific measurement.