Construction and Application of Air Pollutants Emission Accounting Model for Typical Polluting Enterprises Based on Power Big Data

Atmospheric pollution exacerbates climate change and ecosystem degradation. The accurate and timely calculation of emissions from various pollution sources is crucial for effective source control. This study is based on multi-source heterogeneous data from typical polluting industries, including electricity consumption, production load, and pollution emission data. It systematically analyzes multi-dimensional features and dynamic association mechanisms and constructs an Electricity–Production–Pollution recursive accounting model to quantify the response relationship between electricity consumption and pollutant emissions. The model establishes a theoretical framework and technical pathway for precise pollution source regulation driven by power big data. Using the emission accounting model, the annual PM_2.5 emission totals for cement, coking, brick, and ceramic industries in the pilot city were calculated. The relative error range compared to the urban emission inventory was −17.55% to 1.07%, and the emission calculation errors for individual companies were also within an ideal range (−19.31% to 15.63%). The model can perform real-time calculations of air pollutant emissions, such as daily emission changes, by monitoring an enterprise’s electricity consumption, thereby improving the precision of pollution source emission control.