Reductions in Labor Capacity from Intensified Heat Stress in China under Future Climate Change

Abstract

Heat stress would be intensified under global warming and become a key issue of occupational health for labor force working outdoors. The changes in labor force would affect regional socioeconomic development. So far, changes in labor force due to heat stress are not well documented in China. In this study, heat stress based on wet-bulb globe temperature (WBGT), which combines the thermal effects on the human body of both temperature and humidity, is projected for the near future (2021–2050) and the end of the century (2071–2099). Changes in labor capacity are then estimated for heavy and light work based on the relationships between labor capacity and the WBGT. Low and high emission scenarios, namely Representative Concentration Pathway (RCP) 2.6 and RCP8.5, are considered for the future projections in the hottest two months (July and August) in China. Results suggest that the WBGT would increase by more than 3–5 °C by the end of the century. The labor capacity would decrease by more than 40% for both heavy and light work in considerable areas such as South and East China, where there is a large population and developed economy. This indicates that labor force would reduce significantly due to intensified heat stress. This study calls for special attention to the impact of heat stress on occupational health and the labor force in China in the future.

Explicit Calculations of Wet‐Bulb Globe Temperature Compared With Approximations and Why It Matters for Labor Productivity

“…Particularly, sWBGT has been frequently adopted for estimating heat stress‐induced labor productivity reduction both globally (Chavaillaz et al., 2019; Kjellstrom et al., 2009; Knittel et al., 2020) and regionally (Altinsoy & Yildirim, 2014; Liu, 2020; Zhang & Shindell, 2021; Zhu et al., 2021). For instance, under the RCP8.5 scenario labor productivity for heavy outdoor work was predicted to decrease by 38% in Southeast Asia and the Middle East by 2050 (Knittel et al., 2020) and more than 40% in South and East China by the end of this century (Liu, 2020); in the U.S., around 1.8 billion and 4.4 billion workforce hours were predicted to be lost annually by the 2050 and 2100 s under RCP8.5 scenario (Zhang & Shindell, 2021). Such estimates have been applied to informing adaptation strategies (Zhu et al., 2021) or feed into economic models for assessing the downstream socioeconomic impact (Chavaillaz et al., 2019; DARA, 2012; Zhang & Shindell, 2021).…”

“…However, it is widely applied because of its simplicity (Chen et al., 2020; Cooper et al., 2016; Kakamu et al., 2017; Lee & Min, 2018; Matthews et al., 2017; Schwingshackl et al., 2021; Smith et al., 2018; Willett & Sherwood, 2010). Particularly, sWBGT has been frequently adopted for estimating heat stress‐induced labor productivity reduction both globally (Chavaillaz et al., 2019; Kjellstrom et al., 2009; Knittel et al., 2020) and regionally (Altinsoy & Yildirim, 2014; Liu, 2020; Zhang & Shindell, 2021; Zhu et al., 2021). For instance, under the RCP8.5 scenario labor productivity for heavy outdoor work was predicted to decrease by 38% in Southeast Asia and the Middle East by 2050 (Knittel et al., 2020) and more than 40% in South and East China by the end of this century (Liu, 2020); in the U.S., around 1.8 billion and 4.4 billion workforce hours were predicted to be lost annually by the 2050 and 2100 s under RCP8.5 scenario (Zhang & Shindell, 2021).…”

“…In this analysis, we have estimated annual labor productivity from hourly data, which may be not available in most archives such as CMIP and CORDEX. It is common to see studies using subdaily (Buzan & Huber, 2020; De Lima et al., 2021), daily (Altinsoy & Yildirim, 2014; Kjellstrom et al., 2018; Liu, 2020; Orlov et al., 2020; Zhu et al., 2021), or even monthly output (Dunne et al., 2013) for a similar purpose. Although not the focus of this article, it is useful to quantify the potential error introduced thereby.…”

“…sWBGT (ABM, 2010) is an approximate form requiring only temperature and humidity and explicitly assuming fixed moderately high solar radiation and low wind speeds, which implies potential positive or negative biases when these assumptions are not met. sWBGT has been widely used because of its simplicity for assessing heat stress and the implication on athletes and labor (Altinsoy & Yildirim, 2014; Cooper et al., 2016; Kakamu et al., 2017; Kjellstrom et al., 2009; Lee & Min, 2018; Liu, 2020; Smith et al., 2018; Willett & Sherwood, 2010; Zhu et al., 2021). ESI was constructed via a multiple regression with WBGT being the dependent variable and temperature, humidity, solar radiation, and their interaction terms being independent variables (D. Moran et al., 2001).…”

Spatial correlation length of summer extreme heat stress over eastern China

“…With global warming and rapid urbanization, China has experienced frequent serious heat events, especially in the highly urbanized and densely populated areas (Sun et al ., 2014; Lu and Chen, 2016). For example, in the face of continually intensifying heat stress, reductions in labour capacity of more than 40% could occur in considerable areas of south and east China by the end of the 21st century (Liu, 2020). It is well known that the spatiotemporal characteristics of heat stress are the basis for understanding heat stress development and its impacts.…”