Work

How incentive mechanisms affect the dynamic internalization of low-carbon behavior? A perspective from carbon inclusion platforms

Year: 2026

Type: article

Abstract: The carbon inclusion platform, as an important digital tool for promoting public low-carbon lifestyles, guides individuals to engage in low-carbon behavior through mechanisms such as point incentives, behavioral feedback, and social interaction, and plays an increasingly important role in low-carbon governance. Existing research has primarily focused on how platform incentives influence the initiation and short-term participation in low-carbon behavior, while longitudinal evidence on motivational internalization and the long-term maintenance of low-carbon behavior remains limited. To address these gaps, this study examines how incentive mechanisms embedded in carbon inclusion platforms influence the generation and sustained maintenance of individual low-carbon behavior. First, based on in-depth user interview data, a theoretical model of low-carbon behavior maintenance is developed through grounded theory. The model is then empirically tested using Dynamic Structural Equation Modeling (DSEM) with 10-day longitudinal diary data collected from 248 participants, yielding 2480 participant-day observations. The results indicate that: (1) platform choice architecture significantly activates extrinsic incentive, confirming the role of platform design as a behavioral activator; (2) extrinsic incentive predicts both self-efficacy and low-carbon behavior outcome, while self-efficacy contributes to the continued maintenance of low-carbon behavior; and (3) low-carbon behavior exhibits temporal persistence, whereas platform choice architecture does not directly predict later self-efficacy, suggesting that motivational internalization depends on repeated incentives and behavioral reinforcement rather than platform cues alone. These findings provide empirical evidence on the temporal maintenance mechanisms of low-carbon behavior and offer practical insights for optimizing platform incentive design.

Source: Technological Forecasting and Social Change

DOI: 10.1016/j.techfore.2026.124900