Climatic Change

, Volume 139, Issue 2, pp 169–181 | Cite as

China’s socioeconomic risk from extreme events in a changing climate: a hierarchical Bayesian model

  • Xiao-Chen Yuan
  • Xun Sun
  • Upmanu Lall
  • Zhi-Fu Mi
  • Jun He
  • Yi-Ming Wei


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.


Gross Domestic Product Economic Loss Extreme Event Adaptive Capacity Hierarchical Bayesian Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors are grateful for the financial support from the National Natural Science Foundation of China (NSFC) (Nos. 71521002 and 71020107026), National Key R&D Program (2016YFA0602603), and the China Scholarship Council. For their roles in producing, coordinating, and making available the ISI-MIP model output, we acknowledge the modeling groups (HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, GFDL-ESM2M, and NorESM1-M) and the ISI-MIP coordination team. We thank all colleagues from Center for Energy & Environmental Policy Research, Beijing Institute of Technology, for providing helpful suggestions. We also appreciate the anonymous reviewers and the editor for their insightful and constructive comments that substantially improved the manuscript.

Supplementary material

10584_2016_1749_MOESM1_ESM.pdf (2.2 mb)
ESM 1 (PDF 2.17 MB)


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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Xiao-Chen Yuan
    • 1
    • 2
  • Xun Sun
    • 3
  • Upmanu Lall
    • 3
    • 4
  • Zhi-Fu Mi
    • 1
    • 2
  • Jun He
    • 5
  • Yi-Ming Wei
    • 1
    • 2
  1. 1.Center for Energy and Environmental Policy ResearchBeijing Institute of Technology (BIT)BeijingChina
  2. 2.School of Management and EconomicsBeijing Institute of TechnologyBeijingChina
  3. 3.Columbia Water Center, Earth InstituteColumbia UniversityNew YorkUSA
  4. 4.Department of Earth and Environmental EngineeringColumbia UniversityNew YorkUSA
  5. 5.Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina

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