User Activity in Tradable Credit Schemes: Experimental Evidence

Abstract

This study analyzes user activity in a five-week field trial of a Tradable Credit Scheme (TCS) designed to reduce commuting-related CO2 emissions at a university. Participants in the treatment group used a MobilityCoin app to trade credits, while commuting behavior was tracked with a GPS-based mobility app. We first analyze which users actively engage with the system using logistic regression models for app logins and manual trading. We then estimate how activity relates to emission reductions with fixed-effects models and instrumental-variable regressions to address the endogeneity of login behavior. The results show substantial inactivity, but also strong heterogeneity in engagement. Older participants and women were less likely to log in, while a more rational decision-making style was associated with manual trading. Most importantly, early app engagement, especially in the first treatment week, is linked to stronger emission reductions. The findings highlight the importance of simple and user-friendly TCS design.