(untitled) (gjstudies, 2021)
This paper introduces a novel, hybrid machine learning framework for environmentalperformance rating that fundamentally departs from traditional index-based and checklistmethodologies. We propose the Synergistic Environmental Rating Network (SERN), whichintegrates three unconventional computational approaches: a Graph Convolutional Network(GCN) to model complex interdependencies between environmental indicators that are typically treated as independent; a Transformer-based attention mechanism to dynamicallyweight indicators based on contextual relevance to specific industrial sectors and geographical regions; and a bio-inspired optimization algorithm, derived from slime mold foragingbehavior, to identify non-linear threshold boundaries between rating categories. Traditionalrating systems suffer from rigidity, subjective weight assignments, and an inability to capture emergent, system-level environmental behaviors. SERN addresses these limitations bylearning the latent structure of environmental performance from multi-modal data, includingsatellite imagery, supply chain transaction records, self-reported disclosures, and real-timesensor feeds from industrial Internet of Things (IoT) deployments. Our methodology wasvalidated on a newly compiled dataset of 4,500 manufacturing and energy sector facilitiesacross 12 countries. Results demonstrate that SERN achieves a 34% improvement in predictive accuracy for regulatory compliance events compared to conventional LEED- and ISO14001-inspired scoring models, and uncovers 22 previously unrecognized indicator synergies that significantly influence overall performance. For instance, the model revealed thatinteractions between water recycling rates and particulate matter emissions are a strongerpredictor of long-term sustainability in textile manufacturing than either metric in isolation.This research contributes a new paradigm for environmental assessment that is adaptive,transparent in its learned relationships, and capable of evolving with new data, movingbeyond static benchmarks toward dynamic, intelligent evaluation systems