Based on the precise measurement of carbon emissions in the Qinshan Zero Carbon Future City Industrial Park, the prediction of carbon emission trends, and the planning of carbon emission reduction paths, this paper establishes a carbon emission management cloud platform, and uses the data middle platform and Apache Druid to efficiently and low-cost acquire and manage energy big data and carbon emission related data. Secondly, based on the internationally accepted carbon emission accounting method, the carbon emissions of micro-entities are automatically calculated, a carbon account is established, and information such as carbon emissions and carbon emission quotas are recorded. Next, the "dual carbon" monitoring service platform will be used to dynamically monitor the carbon emissions of various regions, industries, parks, and micro-entities, and to assess the effectiveness of emission reduction work. In addition, a localized energy-carbon emission prediction model is constructed to scientifically predict carbon emission trends, carbon absorption potential, social and economic development trends, etc. After the above steps, the model prediction accuracy increases from 95.1% to 99.7%, proving the positive role of path planning in reducing carbon emissions, especially in February, March and April. These achievements provide new perspectives and solutions for carbon footprint management in industrial parks, further promoting global emission reduction efforts and the implementation of climate change response strategies.
包建飞 et al. (Thu,) studied this question.
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