AI-Powered Carbon Footprint Tracking Is Redefining Green Investment Decision-Making

Abstract

As climate change intensifies, investors need carbon data that are timely, comparable, and decision-grade—especially for Scope 3 emissions. Traditional reporting is fragmented, retrospective, and coarse. This paper examines how AI-powered carbon footprint tracking can reshape green investment decision-making by delivering near-real-time, high-resolution estimates of Scope 1–3 emissions. Methods are synthesized that combine NLP over disclosures and supply-chain records with IoT telemetry and satellite/remote sensing to improve accuracy, quantify uncertainty, and detect anomalies indicative of greenwashing. These capabilities are shown to strengthen transition-risk modeling, scenario analysis, and portfolio construction, enabling more responsive capital allocation. Critical challenges are also highlighted, including inconsistent standards and taxonomies, data provenance and auditability, algorithmic transparency and bias, and the environmental costs of training and deploying large models. To reconcile benefits and risks, a governance agenda is outlined, centered on interoperable data layers, independent assurance, and explainable models. Overall, AI is positioned as a powerful complement—not a substitute—for human judgment and regulatory oversight in sustainable finance.