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Related Experiment Videos

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

Lluís Echeverria1, Chaymaa Dkouk1, Nuria Nievas1

  • 1Eurecat, Centre Tecnològic de Catalunya, Unit of Applied Artificial Intelligence, Lleida, 25003, Spain.

Journal of Environmental Management
|June 4, 2026
PubMed
Summary

This study introduces a fairness-aware AI framework for the Water-Energy-Food-Ecosystems nexus, optimizing resource allocation under uncertainty. It enhances policy-making for equitable and efficient outcomes in transboundary river basins.

Keywords:
Fair Nexus policy-makingMulti-Objective Reinforcement LearningStakeholder preference uncertaintyWater–Energy–Food–Ecosystem Nexus

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Area of Science:

  • Environmental Science and Policy
  • Artificial Intelligence
  • Water Resource Management

Background:

  • Holistic Water-Energy-Food-Ecosystems (WEFE) nexus approaches require advanced decision-support systems.
  • Decision-making in WEFE is complicated by complex interdependencies, conflicting objectives, and uncertain stakeholder preferences.
  • Ensuring fairness in resource distribution is a key goal of the WEFE Nexus.

Purpose of the Study:

  • To present a fairness-aware Multi-Objective Reinforcement Learning (MORL) framework based on Pareto Q-Learning.
  • To support policy-making in high-dimensional objective spaces under preference uncertainty.
  • To integrate efficiency and fairness in WEFE nexus decision-making.

Main Methods:

  • Developed a fairness-aware MORL framework using Pareto Q-Learning.
  • Integrated a hybrid utility function combining efficiency and fairness with a weighted prioritization mechanism and standard deviation regularization.
  • Applied the framework to the Inkomati-Usuthu river basin using system dynamics modelling.

Main Results:

  • Generated AI-driven recommendations for policy packages tailored to stakeholder preferences.
  • Achieved efficient many-objective optimization and transparent identification of cross-sectoral impacts, synergies, and trade-offs.
  • Demonstrated dynamic balancing of competing objectives while preserving Pareto-optimality.

Conclusions:

  • Fairness-aware MORL enhances AI-enabled governance for WEFE nexus challenges.
  • The framework improves real-time many-objective policy optimization, cross-sectoral coordination, and resilience.
  • The study strengthens transboundary basin governance through equitable and efficient resource management.