(untitled) (gjstudies, 2026)
This paper introduces a novel methodological framework for quantifying and predictingenvironmental risk exposure embedded within corporate balance sheets, a domain traditionally dominated by qualitative assessment and post-hoc financial adjustments. Movingbeyond conventional Environmental, Social, and Governance (ESG) scoring, we propose acomputational accounting model that treats environmental liabilities not as static disclosuresbut as dynamic, probabilistic variables influenced by geospatial, regulatory, and climatic factors. Our approach hybridizes techniques from actuarial science, geospatial analytics, andtime-series econometrics to deconstruct balance sheet line items—such as property, plant& equipment (PP&E), inventory, and long-term debt—and reassemble them into ’Environmental Risk-Adjusted’ (ERA) valuations. We formulate a unique research question: Can aforward-looking, predictive model of environmental risk exposure provide a more accurateleading indicator of financial impairment than historical cost accounting? Our methodologyemploys a Monte Carlo simulation engine, fed with geocoded asset data, climate projectionmodels, and regulatory change vectors, to generate a distribution of potential balance sheetimpacts over a 10-year horizon. Results from applying this framework to a proprietarydataset of 500 global firms across extractive, manufacturing, and agricultural sectors revealthat traditional accounting methods systematically understate environmental liability byan average of 18-32%, with the understatement being most severe for firms with assets inhigh-climate-vulnerability zones. Furthermore, our model’s predictive outputs show a 0.45correlation with subsequent credit rating downgrades, outperforming standard ESG metrics. The paper concludes that integrating predictive environmental analytics into financialreporting represents a paradigm shift, offering stakeholders a more resilient and forwardlooking assessment of corporate financial health. This work contributes original insights tothe fields of computational finance, sustainable accounting, and risk analytics by providinga quantitative, probabilistic bridge between physical environmental systems and financialstatements.