A low-error carbon monitoring method base d on equipment status identification is proposed to solve the problem of insufficient accuracy in existing carbon monitoring methods for industrial users.Firstly，an equipment status identification model based on temporal convolutional network-gated recurrent unit （TCN-GRU ） is built to accurately identify the operating status of key carbon-emitting equipment for industrial users.Secondly，genetic algorithms （GAs） are introduced to dynamically optimize the parameters of the fully connected layer in the model，enhancing the classifier ’s identification capability for equipment with high carbon emissions.Finally，low-error carbon emission monitoring is achieved based on the optimized status identification results.Experiments conducted on the industrial dataset IAID demonstrate that the proposed method significantly reduces carbon monitoring errors，with the root mean square error （RMSE） decreasing by approximately 13%.Additionally，it outperforms existing methods in key metrics such as RMSE and R2，effectively improving the accuracy and reliability of carbon emission monitoring for industrial users.