Prediction of Building Carbon Emissions in Campus Areas Based on Building a Carbon Emission Correlation Factor

This study introduces a new method for predicting carbon emissions from campus buildings, which is crucial to achieving low-carbon campuses in higher education and meeting “Carbon Peaking and Carbon Neutrality Goals”. The method begins with manually classifying buildings and introducing a carbon emission correlation factor, linking each building type’s emissions to the total category emissions. Using this factor, three models—Seasonal Autoregressive Integrated Moving Average (SARIMA), Long Short-Term Memory (LSTM), and Random Forest (RF)—were developed to predict emissions. The results show improved accuracy after adding the correlation factor: 17.23%, 6.159%, and 3.949% for the SARIMA model in Categories A, B, and C, respectively; 2.76%, 12.636%, and 3.370% for LSTM; and 3.61%, 10.893%, and 4.776% for Random Forest. These results demonstrate the value of using carbon emission correlation factors to improve prediction accuracy and promote sustainable campus development.