An AI-Enabled IoT Framework for Carbon Emission Monitoring and Sustainable Environmental Management

Abstract

Rising carbon emissions and the increasing impact of climate change necessitate the development of intelligent and real-time environmental monitoring systems. The traditionally used methods for monitoring carbon emissions are mostly periodical, manual and fail to provide sufficient insights in fast changing environmental situations. For these reasons, an AI-supported Internet of Things (IoT) framework is proposed for real-time monitoring of carbon emissions and for sustainable environmental management in general. Distributed sensor networks of the IoT in industrial, urban and residential areas record environmental parameters like concentration of carbon dioxide (CO₂), temperature, humidity, as well as other emission indicators. Measured data is transmitted by reliable connections and, after processing by a structured data pipeline, analyzed by machine learning and deep learning networks for predicting emissions, for trend forecast, for detecting anomalies as well as for spatial clustering of pollution hotspots. The real-time registration of emission changes, the early detection of strong increases of emissions and a precise prognosis of future emission developments support the increased environmental awareness and facilitate fast decision making. In addition, the sustainable use of resources is promoted by the framework in the context of Smart City. It supports the implementation of environmental regulations and enables carbon management in line with global targets for net zero emissions.