Recovery of economic activities in China uncovered by remotely sensed nighttime light data under the pandemic new normal

Abstract

The systematic identification of economic recovery trends and their underlying drivers under pandemic new normal offers valuable insights for shaping recovery strategies for similar crises in future. Thus, using China as a case study, we utilized nighttime light (NTL) data as a comprehensive proxy for economic activity to analyze the trajectory of economic recovery during the pandemic new normal. The differences-in-differences (DID) model was used to assess the recovery in economic volume (EV) and economic growth rate (EGR). Then, the ‘leave-one-out’ method was used to analyze individual city responses, and the random forest (RF) algorithm was employed to identify the key demographic and economic factors driving the recovery of economic activities. Results showed that despite incomplete recovery in EV (−2.7%) and EGR (−8.2%), there were notable positive time-varying spillover effects with a 47% increase in EV and a 41% increase in EGR. Meanwhile, the recovery trajectories exhibited an inverted "N" shape, reflecting a rebound pattern during the pandemic. Overall, relatively positive trends in EV recovery occurred primarily in East China, while variability in EGR responses across individual cities remained relatively insignificant. The driving mechanisms of the two economic recovery indicators were heterogeneous, with demographic factors dominating EV recovery, whereas economic factors were the primary contributors to EGR recovery.