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Extreme temperature and mortality: evidence from China

Abstract

The frequency, intensity, and duration of extreme temperature events are expected to rise in the future and increase the related health risks of human beings. Using a novel, nationwide dataset that links extreme temperature and mortality, we estimated the short-term and long-term effects of extreme temperature on mortality in China during 2002–2013. Both extreme hot and extreme cold had immediate and long-term effects on all-cause mortality. Annual deaths per 100,000 people due to extreme hot and cold in the long term were considerably larger compared to the short term. The change in cold spell duration indicator exhibited the greatest effects on annual deaths per 100,000 people among a set of extreme weather indicators. Furthermore, cities with low economic development levels were more vulnerable to extreme temperature, compared to cities with high economic development levels. Our results offer important policy implications for developing a regional-specific extreme weather plan to handle extreme temperature events in China.

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Notes

  1. Our PCA analysis results show that the first and second primary components have an eigenvalue higher than 1, while the third primary component has an eigenvalue smaller than 1. The calculated Kaiser-Mayer-Olkin (KMO) index is above 70%. The relative weights of each component are (1) extreme hot indices = 0.211 × change rate of tropical nights + 0.390 × change rate of summer days + 0.466 × change rate of warm nights + 0.571 × change rate of warm days + 0.510 × change rate of warm spell duration indicator, and (2) extreme cold indices = 0.195 × change rate of frost days + 0.309 × change rate of ice days + 0.509 × change rate of cool nights + 0.553 × change rate of cool days + 0.549 × change rate of cold spell duration indicator.

  2. Annual deaths per 100,000 people = coefficient of change in extreme weather × mean of change in extreme weather / 1000 × 100,000; monetary loss per 100,000 people = annual deaths per 100,000 people × average value of statistical life. Following Wang and He (2010), the value estimates were constructed and converted to the US dollars (average value of statistical life = 128.367 thousand US dollar pre person) based on the average exchange rate in 2013 (1 US dollar = 6.193 RMB).

Abbreviations

GDP:

Gross domestic product

WMO:

World Meteorological Organization

PCA:

Principal component analysis

KMO:

Kaiser-Mayer-Olkin

IDL:

Infinite distributed lag

LLC:

Levin-Lin-Chu

IPS:

Im-Pesaran-Shin

GMM:

Generalized moment method

RMB:

Renminbi

US:

United States

NHFPC:

National Health and Family Planning Commission

IPCC:

Intergovernmental Panel on Climate Change

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Funding

This paper was supported by the National Natural Science Foundation of China (No. 71503059; 71673022; 71704010; 71704009); China Postdoctoral Science Foundation (No. 2018m630284); Fundamental Research Funds for the Central Universities (No. FRF-TP-16-050A1; FRF-BR-17-005B; DUT17RC(4)24); Beijing Social Science Foundation (No. 17LJB004).

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Correspondence to Qing Wang.

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Zhiming Yang and Qing Wang are joint first authors

Appendix

Appendix

Table 8 Threshold and values for daily maximum temperatures and daily minimum temperature
Table 9 Regression results on the extreme temperature and mortality: high-GDP and low-GDP groups (robustness checks)
Table 10 City classification

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Yang, Z., Wang, Q. & Liu, P. Extreme temperature and mortality: evidence from China. Int J Biometeorol 63, 29–50 (2019). https://doi.org/10.1007/s00484-018-1635-y

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Keywords

  • Extreme temperature
  • Mortality
  • China
  • Climate change
  • Infinite distributed lag model