Fairness-aware multi-objective reinforcement learning for integrated WEFE Nexus governance

A holistic Water-Energy-Food-Ecosystems (WEFE) nexus approach requires advanced decision-support systems capable of navigating complex interdependencies and optimizing trade-offs among multiple, often conflicting objectives. Moreover, the WEFE Nexus aims not only to promote efficiency in cross-sectoral interactions but also to ensure fairness by distributing resources in a fair and just manner. However, decision-making is further complicated by uncertainty in stakeholder preferences, as diverse actors often hold conflicting priorities and ambiguous visions of what constitutes an acceptable outcome. To address these challenges, this study presents a fairness-aware Multi-Objective Reinforcement Learning (MORL) framework based on Pareto Q-Learning, designed to support policy-making in high-dimensional objective spaces under preference uncertainty. The framework integrates a hybrid utility function combining efficiency and fairness with the real-time response of a learning approach, where a combination of a weighted prioritization mechanism with a targeted standard deviation regularization term enables dynamic balancing of competing objectives while preserving Pareto-optimality. We apply our methodology to the Inkomati-Usuthu river basin, a transboundary region shared by South Africa, Eswatini, and Mozambique. This basin plays a critical role in regional energy production, agricultural development, and water security, while also supporting ecologically sensitive ecosystems. Building on system dynamics modelling, the framework generates AI-driven recommendations for policy packages tailored to stakeholder-defined preferences, supports efficient many-objective optimization, and enables transparent identification of cross-sectoral impacts, synergies, and trade-offs across the WEFE Nexus. Our findings contribute to the growing body of research on AI-enabled governance, demonstrating how fairness-aware MORL can enhance real-time many-objective policy optimization, foster cross-sectoral coordination, improve resilience, and strengthen transboundary basin governance.