Does the market value sustainable supply chain management? New evidence from the outbreak of COVID-19

This study aims to determine why a firm would ever engage in sustainable supply chain management (SSCM) during a turbulent era. Combining stakeholder theory, the resource-based view, and the literature on SSCM, we argue that SSCM can alleviate negative market reactions to severe external crises. Based on analyses of a sample of firms listed in China from 2019 to 2020, the results robustly display a positive relationship between SSCM and the abnormal returns surrounding the outbreak of COVID-19. The findings of this study extend the supply chain literature by constituting an important addition in external crisis times rather than only in normal times.JEL Classification: M14

Does the market value sustainable supply chain management? New evidence from the outbreak of COVID-19

This study aims to determine why a firm would ever engage in sustainable supply chain management (SSCM) during a turbulent era. Combining stakeholder theory, the resource-based view, and the literature on SSCM, we argue that SSCM can alleviate negative market reactions to severe external crises. Based on analyses of a sample of firms listed in China from 2019 to 2020, the results robustly display a positive relationship between SSCM and the abnormal returns surrounding the outbreak of COVID-19. The findings of this study extend the supply chain literature by constituting an important addition in external crisis times rather than only in normal times.JEL Classification: M14

The Impact of Weather and Air Pollution on Viral Infection and Disease Outcome Among Pediatric Pneumonia Patients in Chongqing, China, from 2009 to 2018: A Prospective Observational Study.

BACKGROUND: For pediatric pneumonia, the meteorological and air pollution indicators have been frequently investigated for their association with viral circulation but not for their impact on disease severity. METHODS: We performed a 10-year prospective, observational study in 1 hospital in Chongqing, China, to recruit children with pneumonia. Eight commonly seen respiratory viruses were tested. Autoregressive distributed lag (ADL) and random forest (RF) models were used to fit monthly detection rates of each virus at the population level and to predict the possibility of severe pneumonia at the individual level, respectively. RESULTS: Between 2009 and 2018, 6611 pediatric pneumonia patients were included, and 4846 (73.3%) tested positive for at least 1 respiratory virus. The patient median age was 9 months (interquartile range, 4‒20). ADL models demonstrated a decent fitting of detection rates of R2 > 0.7 for respiratory syncytial virus, human rhinovirus, parainfluenza virus, and human metapneumovirus. Based on the RF models, the area under the curve for host-related factors alone was 0.88 (95% confidence interval [CI], .87‒.89) and 0.86 (95% CI, .85‒.88) for meteorological and air pollution indicators alone and 0.62 (95% CI, .60‒.63) for viral infections alone. The final model indicated that 9 weather and air pollution indicators were important determinants of severe pneumonia, with a relative contribution of 62.53%, which is significantly higher than respiratory viral infections (7.36%). CONCLUSIONS: Meteorological and air pollution predictors contributed more to severe pneumonia in children than did respiratory viruses. These meteorological data could help predict times when children would be at increased risk for severe pneumonia and when interventions, such as reducing outdoor activities, may be warranted.