AI’s Sustainability Dilemma: How Personalized Recommendations Influence Carbon Footprints

Mobile applications leveraging AI-driven personalization have emerged as powerful tools for promoting sustainability by tailoring user experiences and nudging behaviors toward eco-friendly choices. However, the broader impact of personalization on sustainability remains uncertain, particularly concerning second-order effects such as the rebound effect. While personalization enhances engagement, it may also lead to increased consumption, potentially undermining environmental benefits. This study employs Agent-Based Modeling (ABM) to examine how different levels of personalization in sustainability apps influence macro-level carbon reduction. By simulating individual decision-making and emergent system-wide effects, we analyze the trade-offs between AI-driven personalization and environmental outcomes. This research contributes to a deeper understanding of AI’s role in shaping sustainable digital ecosystems and provides actionable insights for designing more effective sustainability-focused mobile applications.