Research on Enterprise Carbon Emission Optimization Management Based on an Electricity-Carbon Coupling Model

As energy consumption and resource demand continue to rise, the manufacturing industry has become a major source of greenhouse gas emissions, making low-carbon manufacturing essential for sustainable development. This paper proposes a cloud-edge-end architecture based on an electro-carbon coupling model, aimed at promoting low-carbon transformation in manufacturing through optimized carbon emission management. Using edge computing technology, the architecture enables real-time collection and preprocessing of energy consumption data—including electricity, natural gas, compressed air, and refrigerants—before conducting in-depth analysis in the cloud. This approach establishes an electro-carbon coupling model to calculate corporate carbon emissions and total energy consumption. Drawing on energy and carbon emission data from Dongguan City from 2020 to 2023, the study introduces an accurate carbon emission forecasting method with a prediction accuracy within 5%. Additionally, leveraging carbon emission profiling, the paper proposes measures such as optimizing heating start temperatures, reducing equipment operating time, and adjusting fresh air volume to enhance corporate carbon efficiency, identify high-emission areas, and implement energy-saving improvements. This research provides scientific insights to support energy conservation and emission reduction in manufacturing, advancing green manufacturing and sustainable development.