Impact of morphological parameters on carbon emission intensity in cold-region university campus clusters: Simulation and optimization

Abstract

Urban morphology significantly influences the energy efficiency and carbon performance of building clusters. Although previous studies have investigated the effects of urban morphological parameters on either energy use or solar energy generation separately, few studies have explored their combined effects on carbon emission intensity (CEI). Moreover, their focus has often been on residential and office buildings, neglecting university campuses, particularly those in cold climates. This study addresses this gap by analyzing the CEI of university campus clusters in cold regions. A simulation framework was developed to combine energy consumption and solar energy generation and assess the impact of functional and sub-climatic zone differences on CEI. Key urban morphological parameters, such as shape factor, facade roof area ratio, standard deviation of building height, floor area, average building height, and building height-to-depth ratio, were identified using correlation and regression analyses. Machine learning models were applied, with the artificial neural network (ANN) achieving 95.4 % accuracy in predicting emissions. Coupled with optimization algorithms, the ANN model enabled emission reductions of 81.05–137.61 kg/m²/y across six case studies, offering valuable insights for sustainable campus planning in cold climates.