Machine Learning Techniques for Environmental Sustainability Score Prediction

Abstract

This paper introduces a novel, hybrid machine learning framework for predicting comprehensive Environmental Sustainability Scores (ESS) for urban and industrial entities, a
task traditionally reliant on manual, resource-intensive audits. Departing from conventional single-model approaches or purely economic-environmental metrics, our methodology
uniquely integrates three disparate data modalities: high-resolution, multi-spectral satellite
imagery for land-use and vegetation analysis; unstructured textual data from corporate sustainability reports and regulatory filings, processed via a custom domain-adapted Natural
Language Processing (NLP) pipeline; and structured time-series data on resource consumption (energy, water, waste). The core innovation lies in a Hierarchical Attention Fusion
Network (HAFN), a bespoke neural architecture that dynamically learns and weights the
contribution of each data modality, mimicking a human expert’s integrative assessment. We
formulate the prediction not as a simple regression but as a structured output learning problem, simultaneously predicting the overall ESS and its constituent sub-scores (e.g., carbon efficiency, biodiversity impact, circular economy adherence). Trained and validated on a newly
curated dataset of 5,000 global entities, our model achieves a mean absolute error (MAE) of
4.2 points on a 0-100 ESS scale, significantly outperforming benchmark models like Gradient
Boosting (MAE: 7.8) and standard Multi-Layer Perceptrons (MAE: 9.1). More importantly,
the HAFN’s attention mechanisms provide unprecedented, actionable interpretability, identifying, for instance, that for manufacturing sectors, satellite-derived green space metrics are
the dominant predictive feature, whereas for financial services, the sentiment and specificity
of disclosure in narrative reports are paramount. This research demonstrates that a consciously designed, multi-modal ML system can transcend traditional analytical silos, offering
a scalable, transparent, and highly accurate tool for automated sustainability assessment,
with profound implications for investors, regulators, and policymakers seeking to accelerate
the transition to a sustainable economy.