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

Yuan, Xiyue; Sun, Xun; Lall, Upmanu; Mi, Zhifu; He, Jun; Wei, Yi‐Ming

doi:10.1007/s10584-016-1749-3

Cited by 12 publications

(9 citation statements)

References 42 publications

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“…To accomplish this, global GHG emissions need to be reduced to 30-50 GtCO2eq by 2030(IPCC, 2014. However, China's CO2 emissions from fuel combustion were 8.5 Gt in 2013, accounting for a quarter of global emissions (Liu et al, 2016;Liu et al, 2015b;Yuan et al, 2016). In fact, China's carbon emissions have shown exponential growth over the past several decades and accounted for more than half of the increase in global CO2 emissions from 1990 to 2012 (Feng et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Socioeconomic impact assessment of China's CO2 emissions peak prior to 2030

Wei

Wang

et al. 2017

Journal of Cleaner Production

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372

116

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Abstract:China is the largest emitter of carbon emissions in the world. In this paper, we present an Integrated Model of Economy and Climate (IMEC), an optimization model based on the input-output model. The model is designed to assess the tradeoff between emission deceleration and economic growth. Given that China's projected average growth rate will exceed 5% over the next two decades, we find that China may reach its peak CO2 emissions levels by 2026. According to this scenario, China's carbon emissions will peak at 11.20 Gt in 2026 and will then decline to 10.84 Gt in 2030.Accordingly, approximately 22 Gt of CO2 will be removed from 2015 to 2035 relative to the scenario wherein China's CO2 emissions peak in 2030. While this earlier peaking of carbon emissions will result in a decline in China's GDP, several sectors, such as Machinery and Education, will benefit. In order to reach peak CO2 emissions by 2026, China needs to reduce its annual GDP growth rate to less than 4.5% by 2030 and decrease energy and carbon intensity levels by 43% and 45%, respectively, from 2015 to 2030.

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Section: Introductionmentioning

confidence: 99%

Socioeconomic impact assessment of China's CO2 emissions peak prior to 2030

Wei

Wang

et al. 2017

Journal of Cleaner Production

Self Cite

372

116

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“…Here, we further assess the spread of covariate effects across provinces with a multilevel model. A multivariate normal distribution is considered for the regression coefficients β s (Yuan et al 2016).…”

Section: Two-part Hierarchical Bayesian Modelmentioning

confidence: 99%

“…Lee et al (2017) suggest that annual precipitation has significantly positive effects on the damages from weather-related disasters in Korea. In addition, the frequency (Lloyd et al 2016;Yuan et al 2016) and intensity (Fankhauser and McDermott 2014) of extreme events are normally taken as the primary determinants for damages. Socioeconomic factors reflecting adaptive capacity also have important effects.…”

Section: Introductionmentioning

confidence: 99%

Climate change impacts on socioeconomic damages from weather-related events in China

Yuan

Sun

2019

Nat Hazards

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China is vulnerable to climate change impacts, and this study investigates the potential socioeconomic damages to China from weather-related events under future climate conditions. A two-part model incorporating a hierarchical Bayesian approach is employed to explore the effects of climate on human damage (the share of affected people in a total population) and economic damage (the share of economic losses in gross domestic product). Based on these relationships, the relative changes in socioeconomic damages under representative concentration pathways (RCPs) are presented at the regional and national levels. Our results show that China would experience 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 conditions may greatly increase national damages from drought events under RCP8.5. Damages in some northern and southeastern provinces could double by 2081-2090. The national damage to humans from cold-related events is almost unchanged in most climate scenarios; however, the associated economic damage has downtrends.

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“…A Hierarchical Bayesian (HB) model has been investigated more recently (Kwon et al, 2008;Yuan et al, 2016) primarily because it relaxes the assumption of scale invariance and may better quantify both parameter and model uncertainties. Through 15 a HB model, at-site information and regional dependence are considered together, and flood quantiles for different return periods can be estimated simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Local and regional flood frequency analysis based on hierarchical Bayesian model: application to annual maximum streamflow for the Huaihe River basin

Lall

Lima

et al. 2018

Preprint

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Abstract. We develop a hierarchical, multilevel Bayesian model for reducing uncertainties in local (at-site) and regional 10(ungauged or short data sites) flood frequency analysis. This model is applied to the annual maximum streamflow of 17 gauged sites in the Huaihe River basin, China. A Generalized Extreme Value (GEV) distribution is considered for each site, and its location and scale parameters depend on the site's drainage area. We assume the hyper-parameters come from Non-informative (independent, uniform) prior distribution and sample values from posterior distribution by the MCMC method using Gibbs sampling. For comparison, the ordinary GEV fitting by Maximum Likelihood Estimate (MLE) and index flood method fitted 15 by L-moments are also applied. The local simulation results show that for most sites the 95% credible interval simulated by the Hierarchical Bayesian model are narrower than the at site GEV outputs thus reducing uncertainty. By comparison, the homogeneity assumption of the index flood method often leads to large deviations from the empirical flood frequency curve.Cross validated flood quantiles and associated uncertainty intervals are also derived. These results show that the proposed model can better estimate the flood quantiles and their uncertainty than the index flood method.

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

Cited by 12 publications

References 42 publications

Socioeconomic impact assessment of China's CO2 emissions peak prior to 2030

Socioeconomic impact assessment of China's CO2 emissions peak prior to 2030

Climate change impacts on socioeconomic damages from weather-related events in China

Local and regional flood frequency analysis based on hierarchical Bayesian model: application to annual maximum streamflow for the Huaihe River basin

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