To address the monitoring needs for carbon emissions during the construction phase, this paper proposes an intelligent analysis method based on construction activities. Fine-grained monitoring of construction carbon emissions is achieved through the collaborative application of a carbon emission quantification model, a digital twin monitoring model, and a Long Short-Term Memory (LSTM) prediction model. Firstly, based on three dimensions—time, space, and elements—the method constructs a quantification model for construction carbon emissions grounded in construction activities. This model accurately captures the dynamic relationships between material transportation losses, construction machinery usage, and carbon emissions. Secondly, leveraging digital twin technology, an integrated monitoring model is established, unifying three dimensions: element information, temporal processes, and model hierarchy. This model enables continuous data acquisition during the construction period. Finally, a Long Short-Term Memory (LSTM) neural network is introduced to enhance the accuracy of carbon emission predictions. Using a public building in Beijing as a case study, the research demonstrates that, with the traditional inventory method as a baseline (which exhibited a deviation of 18–25% from actual emissions verified through post-construction reconciliation), the proposed activity-based model reduced the calculation deviation for core division works to within 3.2%, an absolute reduction of approximately 15–22% points. The LSTM prediction model achieves an overall short-term prediction accuracy of 89%, with the Mean Absolute Percentage Error (MAPE) reaching approximately 11% across the full validation set. For a representative two-week forecasting case, the model yields a MAPE of 4.3%, with a deviation of 23 tCO2e between predicted and actual emissions. This provides a viable technical pathway for carbon emission monitoring during the construction phase of building projects.
Wang et al. (Thu,) studied this question.
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