AI-Enhanced Carbon Footprint Tracking Using Satellite and IoT-Based Emission Data
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Abstract
Carbon footprint assessment has become a critical component of global climate governance, environmental sustainability, and emission mitigation planning. Conventional carbon accounting approaches often suffer from limited spatial coverage, delayed reporting, and insufficient real-time monitoring capabilities. This study proposes an AI-enhanced carbon footprint tracking framework that integrates satellite-based remote sensing data with Internet of Things (IoT)-enabled emission sensing networks to achieve accurate, scalable, and continuous monitoring of greenhouse gas emissions. The framework utilizes multisource data fusion techniques to combine atmospheric observations, land-use information, industrial activity indicators, and ground-level emission measurements. Advanced artificial intelligence models, including deep learning and predictive analytics, are employed for emission estimation, anomaly detection, trend forecasting, and carbon hotspot identification. The proposed system supports dynamic carbon mapping, automated decision-making, and improved transparency in environmental reporting. Furthermore, the integration of real-time IoT streams with high-resolution satellite observations enables enhanced spatial-temporal characterization of emission patterns across urban, industrial, and ecological regions. The research contributes toward intelligent environmental monitoring systems capable of supporting carbon neutrality initiatives, sustainable development goals, and evidence-based climate policy formulation..
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