Machine Learning Techniques for Environmental Sustainability Score Prediction (gjstudies, 2023)
This paper introduces a novel, hybrid machine learning framework for predicting comprehensive Environmental Sustainability Scores (ESS) for urban and industrial entities, atask traditionally reliant on manual, resource-intensive audits. Departing from conventional single-model approaches or purely economic-environmental metrics, our methodologyuniquely integrates three disparate data modalities: high-resolution, multi-spectral satelliteimagery for land-use and vegetation analysis; unstructured textual data from corporate sustainability reports and regulatory filings, processed via a custom domain-adapted NaturalLanguage Processing (NLP) pipeline; and structured time-series data on resource consumption (energy, water, waste). The core innovation lies in a Hierarchical Attention FusionNetwork (HAFN), a bespoke neural architecture that dynamically learns and weights thecontribution of each data modality, mimicking a human expert’s integrative assessment. Weformulate 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 newlycurated dataset of 5,000 global entities, our model achieves a mean absolute error (MAE) of4.2 points on a 0-100 ESS scale, significantly outperforming benchmark models like GradientBoosting (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 arethe dominant predictive feature, whereas for financial services, the sentiment and specificityof disclosure in narrative reports are paramount. This research demonstrates that a consciously designed, multi-modal ML system can transcend traditional analytical silos, offeringa scalable, transparent, and highly accurate tool for automated sustainability assessment,with profound implications for investors, regulators, and policymakers seeking to acceleratethe transition to a sustainable economy.