%0 Journal Article %1 Yuan2016Chinas %A Yuan, Xiao-Chen %A Sun, Xun %A Lall, Upmanu %A Mi, Zhi-Fu %A He, Jun %A Wei, Yi-Ming %B Climatic Change %D 2016 %I Springer Netherlands %K climatechange statistics extremes flooding risk China %P 1--13 %R 10.1007/s10584-016-1749-3 %T China's socioeconomic risk from extreme events in a changing climate: a hierarchical Bayesian model %U http://dx.doi.org/10.1007/s10584-016-1749-3 %X 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.

@article{Yuan2016Chinas, abstract = {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.}, added-at = {2018-06-18T21:23:34.000+0200}, author = {Yuan, Xiao-Chen and Sun, Xun and Lall, Upmanu and Mi, Zhi-Fu and He, Jun and Wei, Yi-Ming}, biburl = {https://www.bibsonomy.org/bibtex/2fec98cf7af7893e73d5e209b3a7189e2/pbett}, booktitle = {Climatic Change}, citeulike-article-id = {14144341}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/s10584-016-1749-3}, citeulike-linkout-1 = {http://link.springer.com/article/10.1007/s10584-016-1749-3}, doi = {10.1007/s10584-016-1749-3}, interhash = {00a2224afd83fe3969c8eb5c3bdc31d0}, intrahash = {fec98cf7af7893e73d5e209b3a7189e2}, keywords = {climatechange statistics extremes flooding risk China}, pages = {1--13}, posted-at = {2016-09-22 13:14:02}, priority = {2}, publisher = {Springer Netherlands}, timestamp = {2018-08-22T09:32:01.000+0200}, title = {China's socioeconomic risk from extreme events in a changing climate: a hierarchical Bayesian model}, url = {http://dx.doi.org/10.1007/s10584-016-1749-3}, year = 2016 }