Intelligent models for carbon footprint management: deep learning and sustainable development

Abstract

Achieving a carbon-neutral strategy requires effective carbon footprint management, an area where deep learning technology shows significant promise. However, current methods for managing carbon footprints face challenges related to accuracy and scalability. To address these limitations, we propose an intelligent model that leverages deep learning to enhance the efficiency and precision of carbon footprint measurement and management. Our model integrates diverse data sources, including meteorological data, athlete running data, and other emissions-related information. By employing deep learning, the model can automatically identify patterns and trends in carbon emissions, facilitating more informed decision-making toward carbon neutrality. Through a series of experiments, our model has demonstrated notable performance advantages. It provides more accurate measurements of carbon footprints and offers personalized, carbon-neutral recommendations for long-distance runners, such as optimizing training routes or schedules to reduce emissions. This study introduces deep learning into the carbon footprint management field, enhancing measurement accuracy and the intelligence of management practices.