Bridging the Gaps
Preview
Real estate is a major contributor to global greenhouse gas emissions, but most current estimates rely on proxies that overlook building-level variations. As demand grows for transparent, asset-level climate reporting, especially where data is scarce, more accurate methods are needed.
This paper introduces a machine-learning (ML) framework that combines property features, utility data and contextual inputs (e.g., weather, grid intensity) to deliver more precise emissions estimates than traditional approaches.
Key findings
- ML models outperformed proxy methods, particularly when energy data was missing.
- Total energy usage was the most impactful variable for accuracy.
- Residual boosting offered a strong mix of interpretability and performance.
- Predicting energy use first helped only when those predictions were reliable.
The framework supports improved emissions reporting, risk assessments and regulatory compliance in data-limited environments.
Data as of June 2025. Source: MSCI ESG Research
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