Understanding carbon resilience under public health emergencies: a synthetic difference-in-differences approach

Public health emergencies influence urban carbon emissions, yet an in-depth understanding of deviations between regional emissions under such emergencies and normal levels is lacking. Inspired by the concept of resilience, we introduce the concept of regional carbon resilience and propose four resilience indicators covering periods during and after emergencies. A synthetic difference-in-differences model is employed to compute these indicators, providing a more suitable approach than traditional methods assuming unchanged levels before and after emergencies. Using the COVID-19 pandemic in China as a case study, focusing on the power and industry sectors, we find that over 40% regions exhibit strong resilience (> 0.9). Average in-resilience (0.764 and 0.783) is higher than post-resilience (0.534 and 0.598) in both sectors, indicating lower resilience during than after emergencies. Significant differences in resilience performance exist across regions, with Hebei (0.93) and Hangzhou (0.92) as top performers, and Qinghai (0.29) and Guiyang (0.36) as the least resilient. Furthermore, a preliminary correlation analysis identifies 22 factors affecting carbon resilience; higher energy consumption, stronger industrial production, and a healthier regional economy positively contribute to resilience with coefficients over + 0.3, while pandemic severity negatively impacts resilience, with coefficients up to -0.58. These findings provide valuable references for policymaking to achieve carbon neutrality goals.