Machine learning models for predicting rural residential carbon emissions and optimising spatial forms.

ABSTRACT: Global warming is exacerbating its effects on ecosystems and human populations, with carbon emissions identified as the primary cause. As a crucial aspect of urban development, spatial form significantly impacts energy efficiency and carbon emissions. However, research on rural areas has been limited compared to that on metropolitan and central cities. This study investigates carbon emissions and spatial form in rural residential areas. It employs Random Forest, XGBoost, and BP Neural Network methodologies to predict carbon emissions and optimise spatial form. The findings indicate that (1) spatial form factors, including floor area ratio, number of floors, and building orientation, exhibit a strong correlation with carbon emissions; (2) the XGBoost model demonstrates superior prediction a

SUBMITTER: Cui X 

PROVIDER: S-EPMC12474996 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature