Paper Type
ERF
Abstract
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.
Paper Number
1622
Recommended Citation
Sadeghi, Sepide and Nan, Ning, "AI’s Sustainability Dilemma: How Personalized Recommendations Influence Carbon Footprints" (2025). AMCIS 2025 Proceedings. 7.
https://aisel.aisnet.org/amcis2025/sig_green/sig_green/7
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.
When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.
Comments
SIGGREEN