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The effects of temperature on mental health: evidence from China

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Abstract

We examine the effects of ambient temperatures on mental health using a nationally representative longitudinal survey of Chinese individuals. We find that temperatures over \(30^\circ{\rm C}\) significantly increase the likelihood of depression. High temperatures have larger detrimental effects on the mental health of the middle-aged and elderly, females, the less-educated, and agricultural workers. We discuss two likely mechanisms for the mental health impact of high temperatures: raising the incidence of physical illness and reducing sleeping time. We find suggestive evidence of air conditioners moderating the adverse impacts of high temperatures and of adaptation to high temperatures in the long term. We reveal that without any government interventions or private adaptation, mental health will deteriorate by 3.1% in the medium term and 5.3% in the long term based on the Hadley GEM2-ES climate-change projection.

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Data Availability

The data that support the findings of this study are available from the first author, Yue Hua, upon reasonable request.

Notes

  1. The three studies all use the mental health data from BRFSS as their dependent variables. In BRFSS, respondents answered the following question: “Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?”.

  2. Two scientific studies examine the impacts of environmental factors on emotion or mental health of Chinese individuals. Wang et al. (2020) finds that extreme weather worsens emotional expressions on social media in China. Xue et al. (2019) estimates the impacts of long-term temperature level and temperature variability on mental health using the CFPS. We compare our results with Xue et al. (2019) in Sect. 3.2.

  3. According to Xie and Lu (2015) and Xie et al. (2017), the original target sample size was 16,000 households. A total of 8000 households were generated by oversampling with five independent sampling frames (called “large provinces”) of Shanghai, Liaoning, Henan, Gansu, and Guangdong. The “large provinces” were self-representative at the regional level, which could contribute to provincial population inferences and cross-region comparisons. Another 8000 households were from an independent sampling frame composed of 20 provinces (called “small provinces”). With second-stage sampling, the five “large provinces” together with the “small provinces” made up the overall sampling frame to be representative of the national population.

  4. Prefecture-level city (Dijishi) is the second-level administrative division in China that ranks below province-level division and above county-level division.

  5. For instance, if a person was surveyed on May 1st, 2010, then the temperature variables and weather characteristics during April 1–April 30 of 2010 of the city he/she lived are merged with his/her mental health variables.

  6. Most CFPS interviews are conducted during summer when student interviewers are during summer break, so the average number of days with average temperatures below freezing is small.

  7. The estimates in columns (1)–(4) suggest a consistent turning point range.

  8. We also estimate a model that adds the temperature-bin number-of-days variables in three weekly leads as a placebo test. Results are displayed in Appendix Table A2. Most of the variables since the lead two week are insignificant and do not represent a U relationship between temperature and mental health. Temperature variables in the lead one week exhibit a U relationship between temperature and mental health, but the number of days over \({30}^{o}C\) in the lead one week is not significantly different from any other number-of-days variable in the lead one week. Thus, we do not find a significant effect of high temperature in the lead one week on current mental health as well.

  9. Hukou is a system of household registration used in mainland China. A Hukou record officially identifies a person as a permanent resident of an area and includes information such as parents, spouse, date of birth, and residing location.

  10. This percentage change is calculated by dividing the coefficient on Female (1/0) (0.114) by the sum of the coefficient on Female (1/0) (0.114) and the coefficient on Number of days (AT ≥ 30℃) (0.111).

  11. China’s compulsory education law was enacted in 1986, which requires that all children should receive at least nine years of education (6 years in primary school plus three years in junior high school). Some respondents in our data set completed their school education before 1986, for whom the years of schooling could be lower than 9 years.

  12. This percentage change is calculated by dividing the coefficient on Lower education (1/0) (0.169) by the sum of the coefficient on the coefficient on lower education (1/0) (0.169) and the coefficient on number of days (at ≥ 30℃) (0.092).

  13. This percentage change is calculated by dividing the coefficient on Working in the agricultural sector (1/0) (0.127) by the sum of the coefficient on Working in the agricultural sector (1/0) (0.127) and the coefficient on Number of Days (AT ≥ 30℃) (0.219). Note that the number of observations used for this regression is smaller than the whole sample because some respondents did not report their job types. Appendix Table A5 shows the effects of temperature on CES-D score using the subsample of respondents whose job sectors are unknown, in which we do not find significant effects of exposure to high temperatures on mental health.

    We re-estimate this regression using the subsamples of rice producing and wheat producing provinces, respectively. Results are in Appendix Table A5. In rice producing provinces, compared with non-agricultural workers, the mental health of agricultural workers is significantly better (worse) than that of nonagricultural workers by an additional day with mean temperature in the \(25-{30}^{o}C\) bin (over \({30}^{o}C\)) in the previous month relative to a \(20-25^\circ{\rm C}\) day. There is no significant difference in the impact of the \(15-{20}^{o}C\) bin. The results are consistent with the fact that the optimum temperature for germination of rice seeds, heading and flowering, and grouting of rice is approximately \({25-30}^{o}C\) (China Agricultural Information Network 2022). In wheat producing provinces, the mental health of agricultural workers is significantly better (worse but insignificant) than that of nonagricultural workers by an additional day with mean temperature in the \(15-{20}^{o}C\) bin (over \({25}^{o}C\)) in the previous month relative to a \(20-25^\circ{\rm C}\) day. The results are consistent with the fact that the optimum temperature for germination and emergence of wheat seeds, root growth, and tiller growth of wheat is approximately \({15-20}^{o}C\) (China Agricultural Information Network 2022).

  14. In China, whether an area is urban or rural is defined at the county level, which means a prefecture-level city (synonym for prefecture-level administrative unit) can have both rural and urban area. Table 1 suggests that 53.7% of the respondents hold a rural Hukou, whereas the urbanization rate based on permanent residency is over 50%. This is because massive number of rural–urban migrant workers (280 million in 2020) live in cities but are not officially registered as urban residents.

  15. For example, a rural individual may cancel the farming activities on the day of being noticed and the interview day, which could prevent her mental health from being influenced by immediate high temperatures.

  16. We also estimate the impacts of the interview-day minimum temperature. Results are presented in Appendix Table A6. Compared with a 20‒25℃ day, daily minimum temperature over 30℃ increases the CES-D score by 1.69 (53.8% of sample mean). A possible reason is that daily minimum temperature over 30℃ indicates a larger daily average or maximum temperature. Most of the other temperature-bin variables are insignificant no matter which temperature variable is used.

  17. Data source: https://cera-www.dkrz.de/WDCC/ui/cerasearch/q?query=CMIP5&page=0&rows=15.

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Acknowledgements

We are grateful to editor Oded Galor, Teng Li, Sen Xue, and three anonymous referees for their helpful comments and suggestions.

Funding

Hua received the support from the National Science Foundation of China (No.71803043), National Science Foundation of Hunan Province (No. 2022JJ40103), and the Academic Enhancement Plan of Hunan University. Qiu received support from the National Science Foundation of China (No.72203078) and the 111 Project of China (Grant No. B18026).

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Authors and Affiliations

  1. School of Economics and Trade, Hunan University, Yuelu District, 109 Shijiachong Rd, Changsha, 410079, China

    Yue Hua

  2. Institute for Economic and Social Research, Jinan University, 601 W. Huangpu Ave., Guangzhou, 510632, China

    Yun Qiu

  3. Institute for Global Health and Development, Peking University, 5 Yiheyuan Rd., Beijing, 100871, China

    Yun Qiu

  4. School of Economics and Trade, Hunan University, Yuelu District, 109 Shijiachong Rd, Changsha, 410079, China

    Xiaoqing Tan

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Correspondence to Yun Qiu.

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Hua, Y., Qiu, Y. & Tan, X. The effects of temperature on mental health: evidence from China. J Popul Econ 36, 1293–1332 (2023). https://doi.org/10.1007/s00148-022-00932-y

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