Driving Factors of Citizen Science Change in Japan during COVID-19

Abstract

Abstract Citizen science had been severely affected by COVID-19. However, changes of citizen science due to the pandemic in Asia and the driving factors underlying the changes have not been fully investigated. Based on a citizen science observation dataset for 8 cities of Japan from 2016 to 2021, we categorized the users into long-term and short-term users. The long-term users have higher observation number due to their persistent higher activity frequency. Then the changes of observation number were decomposed into user population effect, user structure effect, activity frequency effect, and observation intensity effect using the Logarithmic Mean Divisia Index (LMDI) model for each city resepectively. The user population effect is the largest contributor to observation number changes in the cities for most years, with positive impacts before the pandemic and negative after the pandemic. The following effects are the observation intensity effect, activity frequency effect, and user structure effect. The findings suggest that, to recover citizen science from pandemic, the policymakers, practitioners, and researchers should consider the reasons underlying the changes in more detail.