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

doi:10.1007/s11783-023-1732-y

Front. Environ. Sci. Eng.

2023, Vol. 17

Issue (11) : 132 https://doi.org/10.1007/s11783-023-1732-y

RESEARCH ARTICLE

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

Zhao Liu^1,², Si Gao³, Wenjia Cai²(

), Zongyi Li³, Can Wang⁴, Xing Chen⁵, Zhiyuan Ma⁵, Zijian Zhao⁵

¹. School of Airport Economics and Management, Beijing Institute of Economics and Management, Beijing 100102, China
². Department of Earth System Science, Institute for Global Change Studies, Ministry of Education Ecological Field Station for East Asian Migratory Birds, Tsinghua University, Beijing 100084, China
³. School of Land Science and Technology, China University of Geosciences (Beijing), Beijing 100083, China
⁴. State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), and School of Environment, Tsinghua University, Beijing 100084, China
⁵. Global Energy Interconnection Development and Cooperation Organization (GEIDCO), Beijing 100052, China

Download: PDF(2158 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

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.

Keywords Heat-related excess mortality LMDI Aging YLL VSLY

Corresponding Author(s): Wenjia Cai

About author:

* Both are co-first authors.

Issue Date: 15 November 2023

Cite this article:

Zhao Liu,Si Gao,Wenjia Cai, et al. Projections of heat-related excess mortality in China due to climate change, population and aging[J]. Front. Environ. Sci. Eng., 2023, 17(11): 132.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-023-1732-y
https://academic.hep.com.cn/fese/EN/Y2023/V17/I11/132

Tab.1 Geographical regions of China

Tab.2 Heat-related mortality in each area considering aging in 2010 and 2081–2100 under SSP1-2.6 and SSP2-4.5 scenarios

Fig.1 Chinese heat-related excess mortality under the four scenarios considering aging or not (The colored area illustrate the maximal and minimum range of projected excess mortality using maximal and minimum β, minimum, and maximal MMT).

Fig.2 Annual heat-related excess mortality in different geographical regions under SSP2-4.5 considering population aging.

Fig.3 Decomposition of Chinese projected change in heat-related excess mortality from 2010 to 2100 under four scenarios.

Fig.4 Decomposition of annual geographical region projected change in heat-related excess mortality in the future (2081–2100) under the different scenarios (S1, S2, S3 and S4 refers to SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenario, respectively; Total (gray) refers to the total heat-related excess mortality change from 2010 to 2081–2100; C-only (yellow) refers to the change in excess mortality related to temperature increase only; P-only (orange) refers to the change in excess mortality related to population growth without climate change and aging; C × age and P × age refer to the change in excess mortality related to temperature increase (light green) and population growth (dark green) when considering aging; the aging effect is the sum of these two columns (green).)

Fig.5 Quantifying and predicting the loss of life due to heat-related excess mortality. (a) YLL of heat-related excess mortality under four scenarios; (b) YLL per death of heat-related excess mortality under four scenarios.

1	B W Ang. (2015). LMDI decomposition approach: a guide for implementation. Energy Policy, 86: 233–238 https://doi.org/10.1016/j.enpol.2015.07.007
2	B W Ang, K H Choi. (1997). Decomposition of aggregate energy and gas emission intensities for industry: a refined divisia index method. Energy Journal, 18(3): 59–73
3	R Basu, B D Ostro. (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 https://doi.org/10.1093/aje/kwn170
4	Y Chen, F Guo, J Wang, W Cai, C Wang, K Wang. (2020). Provincial and gridded population projection for China under shared socioeconomic pathways from 2010 to 2100. Scientific Data, 7(1): 83 https://doi.org/10.1038/s41597-020-0421-y
5	J Y Chung, C C Hsu, J H Chen, W L Chen, H J Lin, H R Guo, C C Huang. (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 https://doi.org/10.1038/s41598-018-27694-6
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	S Gu, L Zhang, S Sun, X Wang, B Lu, H Han, J Yang, A Wang. (2020). Projections of temperature-related cause-specific mortality under climate change scenarios in a coastal city of China. Environment International, 143(10): 105889 https://doi.org/10.1016/j.envint.2020.105889
8	Y Huang, T Zhang, J Lou, P Wang, L Huang. (2022). Effective interventions on health effects of Chinese rural elderly under heat exposure. Frontiers of Environmental Science and Engineering, 16(5): 66 https://doi.org/10.1007/s11783-022-1545-4
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	H Johnson, S Kovats, G McGregor, J Stedman, M Gibbs, H Walton. (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 https://doi.org/10.2807/esm.10.07.00558-en
12	T N Kim, M S Park, E J Lee, H S Chung, H J Yoo, H J Kang, W Song, S H Baik, K M Choi. (2017). Comparisons of three different methods for defining sarcopenia: an aspect of cardiometabolic risk. Scientific Reports, 7(1): 6491 https://doi.org/10.1038/s41598-017-06831-7
13	J Y Lee, H Kim. (2016). Projection of future temperature-related mortality due to climate and demographic changes. Environment International, 94: 489–494 https://doi.org/10.1016/j.envint.2016.06.007
14	G Li, Q Zeng, X Pan. (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 https://doi.org/10.1177/2047487316651352
15	T Li, Y Gao, Z Wei, J Wang, Y Guo, F Liu, Z Liu, Y Cheng. (2012). Assessing heat-related mortality risks in Beijing, China. Biomedical and Environmental Sciences, 25(4): 458–464 https://doi.org/10.3967/0895-3988.2012.04.011
16	Y Li, T Ren, P L Kinney, A Joyner, W Zhang. (2018). Projecting future climate change impacts on heat-related mortality in large urban province in China. Environmental Research, 163(2): 171–185 https://doi.org/10.1016/j.envres.2018.01.047
17	Z Liu, B Anderson, K Yan, W Dong, H Liao, P Shi. (2017). Global and regional changes in exposure to extreme heat and the relative contributions of climate and population change. Scientific Reports, 7(1): 43909 https://doi.org/10.1038/srep43909
18	J Lü, W Fu, Y Liu. (2016). Physical activity and cognitive function among older adults in China: a systematic review. Journal of Sport and Health Science, 5(3): 287–296 https://doi.org/10.1016/j.jshs.2016.07.003
19	W Ma, R Chen, H Kan. (2014). Temperature-related mortality in 17 large Chinese cities: how heat and cold affect mortality in China. Environmental Research, 134: 127–133 https://doi.org/10.1016/j.envres.2014.07.007
20	W Ma, L Wang, H Lin, T Liu, Y Zhang, S Rutherford, Y Luo, W Zeng, Y Zhang, X Wang. et al.. (2015). The temperature–mortality relationship in China: an analysis from 66 Chinese communities. Environmental Research, 137: 72–77 https://doi.org/10.1016/j.envres.2014.11.016
21	Y Niu, J Yang, Q Zhao, Y Gao, T Xue, Q Yin, P Yin, J Wang, M Zhou, Q Liu. (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 https://doi.org/10.1007/s11783-023-1681-5
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	B N Patenaude, I Semali, J Killewo, T Bärnighausen. (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 https://doi.org/10.1016/j.vhri.2019.07.009
24	S Piao, P Ciais, Y Huang, Z Shen, S Peng, J Li, L Zhou, H Liu, Y Ma, Y Ding. et al.. (2010). The impacts of climate change on water resources and agriculture in China. Nature, 467(7311): 43–51 https://doi.org/10.1038/nature09364
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	NC SwartJNS ColeV V KharinM LazareJF Scinocca NP GillettJ AnsteyV AroraJR ChristianY Jiao, et al.. (2019). CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP SSP126. Earth System Grid Federation, DOI: 10.22033/ESCF/CMIP6.4025
28	K Takahashi, Y Honda, S Emori. (2007). Assessing mortality risk from heat stress due to global warming. Journal of Risk Research, 10(3): 339–354 https://doi.org/10.1080/13669870701217375
29	P Vaneckova, P J Beggs, R J de Dear, K W J McCracken. (2008). Effect of temperature on mortality during the six warmer months in Sydney, Australia, between 1993 and 2004. Environmental Research, 108(3): 361–369 https://doi.org/10.1016/j.envres.2008.07.015
30	A Voldoire (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	Y Wang, A Wang, J Zhai, H Tao, T Jiang, B Su, J Yang, G Wang, Q Liu, C Gao. et al.. (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 https://doi.org/10.1038/s41467-019-11283-w
32	N Watts, M Amann, N Arnell, S Ayeb-Karlsson, K Belesova, H Berry, T Bouley, M Boykoff, P Byass, W Cai. et al.. (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 https://doi.org/10.1016/S0140-6736(18)32594-7
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	W Wu, Y Xiao, G Li, W Zeng, H Lin, S Rutherford, Y Xu, Y Luo, X Xu, C Chu, W Ma. (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 https://doi.org/10.1016/j.scitotenv.2013.01.090
35	Z Xu, Y Tang, T Connor, D Li, Y Li, J Liu. (2017). Climate variability and trends at a national scale. Scientific Reports, 7(1): 3258 https://doi.org/10.1038/s41598-017-03297-5
36	J Yang, P Yin, M Zhou, C Q Ou, Y Guo, A Gasparrini, Y Liu, Y Yue, S Gu, S Sang. et al.. (2015). Cardiovascular mortality risk attributable to ambient temperature in China. Heart (British Cardiac Society), 101(24): 1966–1972 https://doi.org/10.1136/heartjnl-2015-308062
37	J Yang, M Zhou, Z Ren, M Li, B Wang, D L Liu, C Q Ou, P Yin, J Sun, S Tong. et al.. (2021). Projecting heat-related excess mortality under climate change scenarios in China. Nature Communications, 12(1): https://doi.org/1039doi:10.1038/s41467-021-21305-1

[1]

FSE-23025-OF-LZ_suppl_1

Download

[1]	Yiwei Liu, Kaili Gu, Jinhua Zhang, Jinxiang Li, Jieshu Qian, Jinyou Shen, Xiaohong Guan. Partial aging can counter-intuitively couple with sulfidation to improve the reactive durability of zerovalent iron[J]. Front. Environ. Sci. Eng., 2024, 18(2): 14-.
[2]	Qinwei Lu, Yi Zhou, Qian Sui, Yanbo Zhou. Mechanism and characterization of microplastic aging process: A review[J]. Front. Environ. Sci. Eng., 2023, 17(8): 100-.
[3]	Zhongyao Liang, Yaoyang Xu, Gang Zhao, Wentao Lu, Zhenghui Fu, Shuhang Wang, Tyler Wagner. Approaching the upper boundary of driver-response relationships: identifying factors using a novel framework integrating quantile regression with interpretable machine learning[J]. Front. Environ. Sci. Eng., 2023, 17(6): 76-.
[4]	Weiyi Liu, Ting Pan, Hang Liu, Mengyun Jiang, Tingting Zhang. Adsorption behavior of imidacloprid pesticide on polar microplastics under environmental conditions: critical role of photo-aging[J]. Front. Environ. Sci. Eng., 2023, 17(4): 41-.
[5]	Ying Yu, Xinna Liu, Yong Liu, Jia Liu, Yang Li. Photoaging mechanism of microplastics: a perspective on the effect of dissolved organic matter in natural water[J]. Front. Environ. Sci. Eng., 2023, 17(11): 143-.
[6]	Joana C. Prata, Ana L. Patrício Silva, Armando C. Duarte, Teresa Rocha-Santos. The road to sustainable use and waste management of plastics in Portugal[J]. Front. Environ. Sci. Eng., 2022, 16(1): 5-.
[7]	Zuyin Chen, Lihua Li, Lichong Hao, Yu Hong, Wencai Wang. *Hormesis-like growth and photosynthetic physiology of marine diatom Phaeodactylum tricornutum* Bohlin exposed to polystyrene microplastics**[J]. Front. Environ. Sci. Eng., 2022, 16(1): 2-.
[8]	Juntao HUO,Xiaohui LU,Xinning WANG,Hong CHEN,Xingnan YE,Song Gao,Deborah S. Gross,Jianmin CHEN,Xin YANG. Online single particle analysis of chemical composition and mixing state of crop straw burning particles: from laboratory study to field measurement[J]. Front. Environ. Sci. Eng., 2016, 10(2): 244-252.
[9]	Tingting MA, Ying TENG, Peter CHRISTIE, Yongming LUO. Phytotoxicity in seven higher plant species exposed to di-n-butyl phthalate or bis (2-ethylhexyl) phthalate[J]. Front. Environ. Sci. Eng., 2015, 9(2): 259-268.
[10]	RZYMSKI Piotr,PONIEDZIALEK Barbara,NIEDZIELSKI Przemysław,TABACZEWSKI Piotr,WIKTOROWICZ Krzysztof. Cadmium and lead toxicity and bioaccumulation in Microcystis aeruginosa[J]. Front.Environ.Sci.Eng., 2014, 8(3): 427-432.
[11]	Wenqiang SUN, Jiuju CAI, Hai YU, Lei DAI. Decomposition analysis of energy-related carbon dioxide emissions in the iron and steel industry in China[J]. Front Envir Sci Eng, 2012, 6(2): 265-270.
[12]	Jiying NING, Gang GANG, Zhihui BAI, Qing HU, Hongyan QI, Anzhou MA, Xuliang ZHUAN, Guoqiang ZHUANG. In situ enhanced bioremediation of dichlorvos by a phyllosphere Flavobacterium strain[J]. Front Envir Sci Eng, 2012, 6(2): 231-237.
[13]	Zhe LI , Jinsong GUO , Man LONG , Fang FANG , Jinping SHENG , Hong ZHOU , . Seasonal variation of nitrogen and phosphorus in Xiaojiang River—A tributary of the Three Gorges Reservoir[J]. Front.Environ.Sci.Eng., 2009, 3(3): 334-340.

Viewed

Full text

Abstract

Cited

Shared

Discussed