Machine-Learned Scarcity Numeraires for Water–Energy Risk and Sustainable Asset Valuation

Introduction
Financial valuation still relies largely on monetary numeraires that may not internalize bio-physical scarcity. This study aims to develop an original resource-based valuation framework in which water, energy, and related sustainability pressures enter the unit of account used to compare asset prices and risk exposures. The focus is not on replacing currencies, but on testing whether a scarcity-sensitive numeraire can provide more stable and informative representations of financial value under environmental constraints.

Methods
We seek to construct a panel of resource-scarcity indicators, including water stress, renewable and fossil energy dependence, climate anomalies, commodity prices, sector-level ESG disclosures, and institutional sustainability reports. Dimensionality reduction and clustering can be used to identify latent scarcity regimes. Supervised machine learning and regularized optimization can then estimate time-varying resource weights that minimize asset-price instability, inflation co-movement, and downside-risk measures. A complementary compact NLP/LLM module can extract water–energy risk signals from policy and corporate texts and tests whether these textual variables improve out-of-sample risk explanation.

Results
The expected output is a dynamic scarcity-adjusted numeraire and a set of empirical diagnostics linking resource pressures to sectoral valuation, commodity sensitivity, and market volatility. We expect water–energy interactions to be especially informative for utilities, agriculture, energy-intensive industries, and critical-mineral supply chains.

Conclusions
The study aims to contribute to AI-based risk modeling and the reconstruction of economic valuation by translating environmental scarcity into a testable financial metric. The framework is meant to offer a bridge between sustainable finance, resource economics, and machine learning, while preserving a cautious interpretation: the proposed numeraire is an empirical risk and valuation tool, not a deterministic pricing rule.