Decoding environmental impact with image-based CO2 emission analytics.

As per the regulations of Intergovernmental Panel on Climate Change (IPCC), the country's carbon emission data hold primacy. Nevertheless, establishing and utilizing a database pose a daunting challenge. This study presents the "Carbon Seer System," a novel software prototype designed to inform and educate users about the carbon footprint during their production and in daily life. The development process involved a three-stage user interview process, ensuring system's alignment with user needs and preferences. The system employs machine learning and visual recognition technology, including convolutional neural networks (CNNs), feature pyramid networks, and self-attention mechanisms, enabling users to analyze captured images for carbon emission factors. It automatically identifies the carbon emission factors of industrial products, energy producers, household producers, waste treatment, and transportation. A unique "group detection" method allows for the simultaneous analysis of multiple objects in a single image, enhancing user convenience. Additionally, the software features a carbon footprint tracker and a carbon sink dashboard, providing users with insights into their carbon emissions and the efforts needed for offsetting. The study concludes that the "Carbon Seer System" represents a significant step towards individual enablement at understanding and actively participating in a low-carbon lifestyle. [ABSTRACT FROM AUTHOR]