AI-Driven Carbon Monitoring: Enhancing Environmental Accountability Through Intelligent Systems

Abstract

The escalating urgency of climate change demands more accurate and timely methods for carbon footprint monitoring. This study investigates the transformative role of Artificial Intelligence (AI) in enhancing environmental accountability through intelligent carbon tracking systems. Leveraging machine learning, natural language processing, and IoT integration, AI-driven solutions offer superior accuracy, real-time responsiveness, and predictive capabilities compared to traditional carbon accounting methods. A mixed-methods analysis across 60 organizations revealed that firms employing AI-based monitoring achieved an average 22.9% reduction in carbon emissions over five years, significantly outperforming those using conventional approaches. The findings emphasize AI's potential to operationalize real-time environmental accountability while highlighting challenges related to data quality, algorithmic transparency, and ethical deployment. This research contributes to the growing discourse on digital solutions for climate resilience and offers strategic insights for policymakers, sustainability leaders, and technologists committed to achieving net-zero goals.