Assessment of the economic impacts of heat waves: A case study of Nanjing, China

Abstract

The southeast region of China is frequently affected by summer heat waves. Nanjing, a metropolitan city in Jiangsu Province, China, experienced an extreme 14-day heat wave in 2013. Extreme heat can not only induce health outcomes in terms of excess mortality and morbidity (hospital admissions) but can also cause productivity losses for self-paced indoor workers and capacity losses for outdoor workers due to occupational safety requirements. All of these effects can be translated into productive working time losses, thus creating a need to investigate the macroeconomic implications of heat waves on production supply chains. Indeed, industrial interdependencies are important for capturing the cascading effects of initial changes in factor inputs in a single sector on the remaining sectors and the economy. To consider these effects, this paper develops an interdisciplinary approach by combining meteorological, epidemiological and economic analyses to investigate the macroeconomic impacts of heat waves on the economy of Nanjing in 2013. By adopting a supply-driven input-output (IO) model, labour is perceived to be a key factor input, and any heat effect on human beings can be viewed as a degradation of productive time and human capital. Using this interdisciplinary tool, our study shows a total economic loss of 27.49 billion Yuan for Nanjing in 2013 due to the heat wave, which is equivalent to 3.43% of the city's gross value of production in 2013. The manufacturing sector sustained 63.1% of the total economic loss at 17.34 billion Yuan. Indeed, based on the ability of the IO model to capture indirect economic loss, our results further suggest that although the productive time losses in the manufacturing and service sectors have lower magnitudes than those in the agricultural and mining sectors, they can entail substantial indirect losses because of industrial interdependencies. This important conclusion highlights the importance of incorporating industrial interdependencies and indirect economic assessments in disaster risk studies

New approach to identifying proper thresholds for a heat warning system using health risk increments

“…In this work, all-cause mortality, hospital admissions and emergency visits of heat-related illness (such as heat stroke and heat exhaustion) are used in view of their different considerations in evaluating proper thresholds of the heat warning system. A warning system with the threshold identified using all-cause mortality aims at reducing overall mortality, while those with thresholds determined using hospital admissions and emergency visits target at reducing morbidity related to work productivity and/or human wellbeing (Xia et al, 2018). In reality, some countries may only have complete datasets for one of the health records presented here.…”

Linking city‐level input–output table to urban energy footprint: Construction framework and application

“…Although these databases have helped gain a comprehensive understanding of regional disparities and spillover effects in interregional and international trade, none of current MRIOs (national or provincial) offer insights in assessment at the local scale, particularly at the city level. This widens a massive gap between research resolution and government decision making, especially in the context of increasing significance of cities with regard to economic growth, energy consumption, resources management, and public health (Nair, George, Malano, Arora, & Nawarathna, ; Shan et al., ; Xia et al., ).…”

Assessing Urban Risk to Extreme Heat in China

“…Related studies use long-term observed temperature data from meteorological stations to explore the geographical patterns and temporal variations of extreme heat events at the national or regional level in China [26][27][28]. The second is assessing the impacts and potential risks of extreme heat on human health [29][30][31][32], agricultural production [33,34], economic development [7], psychological perception [35], etc. The third is constructing heat vulnerability index or heat risk index by integrating hazard, exposure, sensitivity, and adaptive capacity to assess extreme heat risk at urban or regional level [36][37][38].…”