The economics of circularity in wastewater valorization: harnessing ai and dynamic modeling for optimal investment, pricing, and resource allocation

Abstract

Wastewater valorization offers an important pathway for circular resource management, but investment decisions are constrained by uncertainty in resource prices, operating conditions, and market dynamics. Conventional Net Present Value approaches may not adequately capture managerial flexibility under such uncertainty. This study develops an AI-Enhanced Real Options Theory (A-ROT) framework integrating Long Short-Term Memory (LSTM) forecasting with Real Options Theory, Monte Carlo simulation, and explainable AI to evaluate investment timing, project valuation, and risk in wastewater valorization. A simulated biorefinery case study is used to compare conventional NPV, ROT, and A-ROT approaches. The A-ROT framework improves project valuation by approximately $4.0 million relative to static approaches and produces an Extended NPV of +$2.8 million. The LSTM model achieves a MAPE of 4.2% and contributes to a 42% reduction in Value-at-Risk. The analysis also indicates that data-integrity failures can reduce project value by approximately 15%. The findings demonstrate that integrating predictive AI with real options valuation can improve investment timing, risk management, and the economic viability of wastewater resource recovery. The framework provides a decision-support basis for dynamic financing, pricing, and policy design for circular economy infrastructure.