Projections of heat-related excess mortality in China due to climate change, population and aging

Abstract

● Four scenarios were used to project heat-related excess mortality in China.

● Decomposed the impacts of climate change, population, and aging.

● Quantified the economic burden of heat-related premature mortality.

Climate change is one of the biggest health threats of the 21st century. Although China is the biggest developing country, with a large population and different climate types, its projections of large-scale heat-related excess mortality remain understudied. In particular, the effects of climate change on aging populations have not been well studied, and may result in significantly underestimation of heat effects. In this study, we took four climate change scenarios of Tier-1 in CMIP6, which were combinations of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). We used the exposure-response functions derived from previous studies combined with baseline age-specific non-accidental mortality rates to project heat-related excess mortality. Then, we employed the Logarithmic Mean Divisia Index (LMDI) method to decompose the impacts of climate change, population growth, and aging on heat-related excess mortality. Finally, we multiplied the heat-related Years of Life Lost (YLL) with the Value of a Statistical Life Year (VSLY) to quantify the economic burden of premature mortality. We found that the heat-related excess mortality would be concentrated in central China and in the densely populated south-eastern coastal regions. When aging is considered, heat-related excess mortality will become 2.8–6.7 times than that without considering aging in 2081–2100 under different scenarios. The contribution analysis showed that the effect of aging on heat-related deaths would be much higher than that of climate change. Our findings highlighted that aging would lead to a severe increase of heat-related deaths and suggesting that regional-specific policies should be formulated in response to heat-related risks.

Graphical abstract

Keywords

Heat-related excess mortality / LMDI / Aging / YLL / VSLY

References

[1] Ang B W. (2015). LMDI decomposition approach: a guide for implementation. Energy Policy, 86: 233–238

[2] Ang B W, Choi K H. (1997). Decomposition of aggregate energy and gas emission intensities for industry: a refined divisia index method. Energy Journal, 18(3): 59–73

[3] Basu R, Ostro B D. (2008). A multicounty analysis identifying the populations vulnerable to mortality associated with high ambient temperature in California. American Journal of Epidemiology, 168(6): 632–637

[4] Chen Y, Guo F, Wang J, Cai W, Wang C, Wang K. (2020). Provincial and gridded population projection for China under shared socioeconomic pathways from 2010 to 2100. Scientific Data, 7(1): 83

[5] Chung J Y, Hsu C C, Chen J H, Chen W L, Lin H J, Guo H R, Huang C C. (2018). Geriatric influenza death (GID) score: a new tool for predicting mortality in older people with influenza in the emergency department. Scientific Reports, 8(1): 9312

[6] Fischer T, Gemmer M, Liu L, Su B (2012). Change-points in climate extremes in the Zhujiang River Basin, South China, 1961–2007. Climatic Change, 110(3−4): 3−4

[7] Gu S, Zhang L, Sun S, Wang X, Lu B, Han H, Yang J, Wang A. (2020). Projections of temperature-related cause-specific mortality under climate change scenarios in a coastal city of China. Environment International, 143(10): 105889

[8] Huang Y, Zhang T, Lou J, Wang P, Huang L. (2022). Effective interventions on health effects of Chinese rural elderly under heat exposure. Frontiers of Environmental Science and Engineering, 16(5): 66

[9] IPCC (2018). Global Warming of 1.5 °C: an IPCC Special Report on the Impacts of Global Warming of 1.5 °C above Pre-Industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate. Masson-Delmotte V, et al. eds. Geneva: IPCC Spec Rep 2 (October)

[10] Jackson J E, Yost M G, Karr C, Fitzpatrick C, Lamb B K, Chung S H, Chen J, Avise J, Rosenblatt R A, Fenske R A (2010). Public health impacts of climate change in Washington State: projected mortality risks due to heat events and air pollution. Climatic Change, 102(1−2): 1−2

[11] Johnson H, Kovats S, McGregor G, Stedman J, Gibbs M, Walton H. (2005). The impact of the 2003 heat wave on daily mortality in England and Wales and the use of rapid weekly mortality estimates. Eurosurveillance, 10(7): 15–16

[12] Kim T N, Park M S, Lee E J, Chung H S, Yoo H J, Kang H J, Song W, Baik S H, Choi K M. (2017). Comparisons of three different methods for defining sarcopenia: an aspect of cardiometabolic risk. Scientific Reports, 7(1): 6491

[13] Lee J Y, Kim H. (2016). Projection of future temperature-related mortality due to climate and demographic changes. Environment International, 94: 489–494

[14] Li G, Zeng Q, Pan X. (2016). Disease burden of ischaemic heart disease from short-term outdoor air pollution exposure in Tianjin, 2002–2006. European Journal of Preventive Cardiology, 23(16): 1774–1782

[15] Li T, Gao Y, Wei Z, Wang J, Guo Y, Liu F, Liu Z, Cheng Y. (2012). Assessing heat-related mortality risks in Beijing, China. Biomedical and Environmental Sciences, 25(4): 458–464

[16] Li Y, Ren T, Kinney P L, Joyner A, Zhang W. (2018). Projecting future climate change impacts on heat-related mortality in large urban province in China. Environmental Research, 163(2): 171–185

[17] Liu Z, Anderson B, Yan K, Dong W, Liao H, Shi P. (2017). Global and regional changes in exposure to extreme heat and the relative contributions of climate and population change. Scientific Reports, 7(1): 43909

[18] Lü J, Fu W, Liu Y. (2016). Physical activity and cognitive function among older adults in China: a systematic review. Journal of Sport and Health Science, 5(3): 287–296

[19] Ma W, Chen R, Kan H. (2014). Temperature-related mortality in 17 large Chinese cities: how heat and cold affect mortality in China. Environmental Research, 134: 127–133

[20] Ma W, Wang L, Lin H, Liu T, Zhang Y, Rutherford S, Luo Y, Zeng W, Zhang Y, Wang X. . (2015). The temperature–mortality relationship in China: an analysis from 66 Chinese communities. Environmental Research, 137: 72–77

[21] Niu Y, Yang J, Zhao Q, Gao Y, Xue T, Yin Q, Yin P, Wang J, Zhou M, Liu Q. (2023). The main and added effects of heat on mortality in 33 Chinese cities from 2007 to 2013. Frontiers of Environmental Science & Engineering, 17(7): 81

[22] O’Neill B C, Tebaldi C, van Vuuren D P, Eyring V, Friedlingstein P, Hurtt G, Knutti R, Kriegler E, Lamarque J F, Lowe J, et al. (2016). The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geoscientific Model Development, 9(9): 3461−3482

[23] Patenaude B N, Semali I, Killewo J, Bärnighausen T. (2019). The value of a statistical life-year in sub-Saharan Africa: Evidence from a large population-based survey in Tanzania. Value in Health Regional Issues, 19: 151–156

[24] Piao S, Ciais P, Huang Y, Shen Z, Peng S, Li J, Zhou L, Liu H, Ma Y, Ding Y. . (2010). The impacts of climate change on water resources and agriculture in China. Nature, 467(7311): 43–51

[25] Qin X, Liu Y, Li L (2010). The value of Life and its regional variations: estimates based on national population sample surveys. China Industrial Economics, 10(1): 33−43 (in Chinese)

[26] Seferian R, Nabat P, Michou M, Saint-Martin D, Voldoire A, Colin J, Decharme B, Delire C, Berthet S, Chevallier M, et al. (2019). Evaluation of CNRM Earth System Model, CNRM-ESM2-1: role of earth system processes in present-day and future climate. Journal of Advances in Modeling Earth Systems, 11(12), 4182–4227

[27] SwartNCCole JNSKharinV VLazareMScinoccaJF GillettNPAnstey JAroraVChristianJRJiaoY, . (2019). CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP SSP126. Earth System Grid Federation, DOI: 10.22033/ESCF/CMIP6.4025

[28] Takahashi K, Honda Y, Emori S. (2007). Assessing mortality risk from heat stress due to global warming. Journal of Risk Research, 10(3): 339–354

[29] Vaneckova P, Beggs P J, de Dear R J, McCracken K W J. (2008). Effect of temperature on mortality during the six warmer months in Sydney, Australia, between 1993 and 2004. Environmental Research, 108(3): 361–369

[30] VoldoireA (2019). CNRM-CERFACS CNRM-CM6–1 model output prepared for CMIP6 HighResMIP highres-future. Earth System Grid Federation, DOI: 10.22033/ESGF/CMIP6.4025

[31] Wang Y, Wang A, Zhai J, Tao H, Jiang T, Su B, Yang J, Wang G, Liu Q, Gao C. . (2019). Tens of thousands additional deaths annually in cities of China between 1.5 °C and 2.0 °C warming. Nature Communications, 10(1): 3376

[32] Watts N, Amann M, Arnell N, Ayeb-Karlsson S, Belesova K, Berry H, Bouley T, Boykoff M, Byass P, Cai W. . (2018). The 2018 report of the Lancet Countdown on health and climate change: shaping the health of nations for centuries to come. Lancet, 392(10163): 2479–2514

[33] Wu T, Chu M, Dong M, Fang Y, Jie W, Li J, Li W, Liu Q, Shi X, Xin X, et al. (2018). BCC-CSM2MR model output prepared for CMIP6 CMIP 1pctCO₂. Earth System Grid Federation, DOI: 10.22033/ESGF/CMIP6.2833

[34] Wu W, Xiao Y, Li G, Zeng W, Lin H, Rutherford S, Xu Y, Luo Y, Xu X, Chu C, Ma W. (2013). Temperature–mortality relationship in four subtropical Chinese cities: a time-series study using a distributed lag non-linear model. Science of the Total Environment, 449: 355–362

[35] Xu Z, Tang Y, Connor T, Li D, Li Y, Liu J. (2017). Climate variability and trends at a national scale. Scientific Reports, 7(1): 3258

[36] Yang J, Yin P, Zhou M, Ou C Q, Guo Y, Gasparrini A, Liu Y, Yue Y, Gu S, Sang S. . (2015). Cardiovascular mortality risk attributable to ambient temperature in China. Heart (British Cardiac Society), 101(24): 1966–1972

[37] Yang J, Zhou M, Ren Z, Li M, Wang B, Liu D L, Ou C Q, Yin P, Sun J, Tong S. . (2021). Projecting heat-related excess mortality under climate change scenarios in China. Nature Communications, 12(1):

RIGHTS & PERMISSIONS

Higher Education Press