Article
AI-Enabled Carbon Tagging Systems (AI-CTS): An Intelligent Architecture for Real-Time Carbon Accountability and Sustainable Decision-Making
The growing urgency of climate action has intensified the need for transparent, scalable and decision-relevant carbon accountability systems. While existing carbon accounting and ESG disclosure mechanisms have improved organizational reporting, they remain largely static, resource-intensive and disconnected from real-time operational and consumer decision contexts. Building on the foundational concept of the Carbon Tagging System (CTS), this paper proposes an advanced AI-enabled Carbon Tagging System (AI-CTS) that operationalizes product- and service-level carbon transparency through intelligent automation. The study develops a multi-layer socio-technical framework that integrates artificial intelligence, Internet of Things (IoT) and blockchain to enable real-time carbon estimation, dynamic tag generation, behavioral personalization and automated verification. Grounded in stakeholder theory, institutional theory and behavioral economics, the AI-CTS framework extends prior sustainability disclosure models by embedding machine learning-driven lifecycle assessment, explainable AI for tag transparency and predictive governance dashboards within a unified architecture. The paper advances the emerging domain of Carbon Information Systems (CIS) by demonstrating how AI can transform carbon tagging from a static reporting tool into an adaptive decision intelligence infrastructure. Conceptual propositions are developed to guide empirical testing of consumer trust, purchase behavior, greenwashing detection and firm-level ESG performance under AI-enabled tagging conditions. The study contributes to sustainability, information systems and ESG scholarship in three ways: (1) it introduces an intelligent automation pathway for scalable product-level carbon disclosure; (2) it integrates behavioral nudging with AI-driven environmental analytics; and (3) it provides a practical implementation blueprint for firms, policymakers and digital platform providers. The paper concludes by outlining validation strategies, governance considerations and research directions necessary to operationalize AI-CTS in both developed and emerging economies, positioning it as a critical infrastructure for real-time carbon accountability and low-carbon market transformation.