Machine Learning for Leadership in Energy and Environmental Design Credit Targeting: Project Attributes and Climate Analysis Toward Sustainability

Achieving Leadership in Energy and Environmental Design (LEED) certification is a key objective for sustainable building projects, yet targeting LEED credit attainment remains a challenge influenced by multiple factors. This study applies machine learning (ML) models to analyze the relationship between project attributes, climate conditions, and LEED certification outcomes. A structured framework was implemented, beginning with data collection from the USGBC (LEED-certified projects) and US NCEI (climate data), followed by preprocessing steps. Three ML models—Decision Tree (DT), Support Vector Regression (SVR), and XGBoost—were evaluated, with XGBoost emerging as the most effective due to its ability to handle large datasets, manage missing values, and provide interpretable feature importance scores. The results highlight the strong influence of the LEED version and project type, demonstrating how certification criteria and project-specific characteristics shape sustainability outcomes. Additionally, climate factors, particularly cooling degree days (CDD) and precipitation (PRCP), play a crucial role in determining LEED credit attainment, underscoring the importance of regional environmental conditions. By leveraging ML techniques, this research offers a data-driven approach to optimizing sustainability strategies and enhancing the LEED certification process. These insights pave the way for more informed decision-making in green building design and policy, with future opportunities to refine predictive models for even greater accuracy and impact.