时间：2017年11月9日（周四）11：50 - 12：50
报告题目：China’s socioeconomic risk from extreme events in a changing climate: a hierarchical Bayesian model
China has a large economic and demographic exposure to extreme events that is increasing rapidly due to its fast development, and climate change may further aggravate the situation. This paper investigates China’s socioeconomic risk from extreme events under climate change over the next few decades with a focus on sub-national heterogeneity. The empirical relationships between socioeconomic damages and their determinants are identified using a hierarchical Bayesian approach, and are used to estimate future damages as well as associated uncertainty bounds given specified climate and development scenarios. Considering projected changes in exposure we find that the southwest and central regions and Hainan Island of China are likely to have a larger percentage of population at risk, while most of the southwest and central regions could generally have higher economic losses. Finally, the analysis suggests that increasing income can significantly decrease the number of people affected by extremes.
China is vulnerable to climate change impacts, and this study investigates its potential socioeconomic damages from weather-related events under future climate conditions. A two-part model incorporating hierarchical Bayesian approach is employed to explore the effect of climate on human and economic damages. On the basis of the identified relationships, the changes in socioeconomic damages under RCPs are presented at the regional and national levels. It shows that China would have an increase in socioeconomic damages from rainfall-related events under RCP2.6 and RCP4.5, and the higher increments mainly appear in the central and southwestern areas. Future climate may dramatically raise national damages from drought events under RCP8.5. Those in some northern and southeastern provinces could double by 2081-2090. The national human damage from cold-related events is almost unchanged in most climate scenarios, but the downtrends are found for economic damage due to the extensive decrements across the country.