Potential of shifting work hours for reducing heat-related loss and regional disparities in China: a modelling analysis

Background

As climate change intensifies, the economic losses caused by heat-related labour productivity loss are gaining increasing attention. Shifting work hours has become a prevalent practice to reduce outdoor workers' heat exposure. However, both the potential of this adaptation measure for reducing labour productivity and economic loss and how this potential will change in the future remain unclear. Answers to these questions at the subnational level are important for decision makers to promote the implementation of adaptations and the development of comprehensive strategies to tackle the residual consequences of climate change. This study aimed to model the potential of shifting work hours for reducing labour productivity and economic loss at the national and provincial level in China.

Methods

We did a modelling study to estimate the potential of shifting work hours for reducing heat-related labour productivity loss in China under different climate change scenarios. We used the China Hybrid Energy and Economic Research model, a dynamic multiregional computable general equilibrium model, to quantify the economic impacts of heat-related labour productivity loss from 2020 to 2100, with an exposure–response function between heat stress and labour productivity loss and bias-corrected climate change projections from the US National Aeronautics and Space Administration Earth Exchange Global Daily Downscaled Projections dataset conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6). We used nine different scenarios: three climate change scenarios consistent with the shared socioeconomic pathway (SSP)–representative concentration pathway scenarios used in CMIP6 (SSP1–2·6, SSP2–4·5, and SSP5–8·5); three adaptation scenarios (SSP1–2·6_shift, SSP2–4·5_shift, and SSP5–8·5_shift); and three counterfactual scenarios (SSP1–2·6cf, SSP2–4·5cf, and SSP5–8·5cf). SSP1–2·6 is a scenario with less than 2°C warming by 2100 and low carbon emissions. SSP2–4·5 is a middle scenario with a 2·7°C rise in global mean temperature, representing current emission trends. SSP5–8·5 is an extreme scenario, with a 4·4°C rise in global mean temperature and high carbon emissions. The climate change scenarios and adaptation scenarios considered heat-related labour productivity loss caused by climate change in the future, whereas the counterfactual scenarios held loss constant at the 2020 level. The adaptation scenarios considered the impact of shifting work hours earlier when estimating labour productivity loss. We assumed that outdoor work hours could maximally be rescheduled to sunrise time. The economic growth pathways in the SSP1–2·6cf, SSP2–4·5cf, and SSP5–8·5cf scenarios were derived from SSP1, SSP2, and SSP5, respectively. We compared results for the different adaptation and climate change scenarios to evaluate the reduction potential of the adaptation measure. By comparing the climate, adaptation, and counterfactual scenarios separately, we also estimated the economic loss caused by heat-related labour productivity loss and economic loss. We did not consider specific mitigation measures but rather reflected the influence of mitigation efforts by comparing results under different climate change scenarios.

Findings

Shifting work hours could substantially reduce the impact of heat on labour productivity and economic development in China. The potential of this adaptation strategy for reducing loss was projected to increase with lower levels of temperature rise (ie, under improving mitigation efforts). Compared with the SSP2–4·5 climate change scenario, shifting work hours under the SSP2–4·5_shift scenario was projected to reduce up to 26·2% (uncertainty range 24·8–28·5) of national outdoor labour productivity loss in 2100, leading to a decrease in residual GDP loss from 4·3% to 3·8%. The potential for reducing labour productivity loss was projected to increase to 31·0% (uncertainty range 30·1–34·1) in 2100 under the SSP1–2·6_shift scenario. Considering this synergy between shifting work hours and mitigation measures, our results suggest that only simultaneous implementation of adaptation and mitigation measures could achieve the maximum reduction in residual economic loss. However, even with the implementation of ambitious mitigation measures and the most robust implementation of this adaptation measure, the residual damage resulting from heat-related labour productivity loss could not be completely avoided in our modelling results. Under the most optimistic SSP1–2·6_shift scenario, the residual GDP loss in 2100 was projected to be reduced to 2·0%, equivalent to 54% of the expenditure of China's basic medical insurance fund in 2020 (approximately US$303 billion). Moreover, our results suggested that shifting work hours might reduce development disparities among provinces (this measure cannot change the distribution patterns of economic loss). The largest avoided economic loss was projected in low-income provinces with large agricultural populations, including Guangxi, Guizhou, Hainan, and Jiangxi, whereas high-income regions, including Beijing and Shanghai, were projected to see low proportions of avoided economic loss. In 2100, the reduced economic loss was projected to be 9·4% of GDP loss in Beijing and 7·7% of GDP loss in Guangdong, compared with 42·3% of GDP loss in Guizhou and 19·2% of GDP loss in Sichuan under the SSP2–4·5_shift scenario.

Interpretation

This modelling study suggests that shifting work hours could substantially reduce heat-related labour productivity and economic loss and further reduce development disparities among regions in China. This study contributes to the broader discussion in the literature around the synergistic relationships and trade-offs that exist between climate change adaptation and mitigation measures. Our results show that there are important synergies between shifting work hours (ie, an adaptation measure) and mitigation measures. The effectiveness of this adaptation measure increases with escalating mitigation efforts. However, this single adaptation measure cannot eliminate economic losses entirely. To minimise residual economic loss, local governments will need to implement targeted policies that promote flexible work hours for different regions and develop an integrated adaptation strategy. Moreover, more aggressive mitigation efforts should be pursued together with adaptation measures to minimise residual economic loss.