A hierarchical Food-Energy-Water Nexus (FEW-N) decision-making approach for Land Use Optimization

Area of Science:

• Environmental Science
• Operations Research
• Economics

Background:

• Land use allocation is critical for sustainable development, impacting food, energy, and water resources.
• Conflicting objectives among stakeholders (industry, agriculture, government) pose a major challenge to land use optimization.
• Existing methods struggle to address the complexity of multi-stakeholder land use decisions.

Purpose of the Study:

• To develop a hierarchical FEW-N (Food, Energy, Water-Natural Resources) approach for land use optimization.
• To facilitate decision-making and reduce resource competition for sustainable land development.
• To address the challenge of conflicting stakeholder objectives in land use planning.

Main Methods:

• Formulated the land use problem as a Stackelberg duopoly game, with government as leader and producers/developers as followers.
• Developed a bi-level mixed-integer programming problem to model the leader-follower dynamics.
• Utilized ARGONAUT, a hybrid optimization framework, to solve the complex bi-level optimization problem.

Main Results:

• The data-driven approach successfully provided feasible solutions for complex bi-level optimization problems.
• The hierarchical FEW-N model effectively balanced competing stakeholder interests.
• Demonstrated the capability of the novel algorithm to solve problems previously intractable with deterministic methods.

Conclusions:

• The proposed hierarchical FEW-N approach offers a viable solution for complex land use optimization problems.
• This method effectively minimizes resource competition and promotes sustainable land development.
• The ARGONAUT framework provides a powerful tool for solving high-dimensional, constrained grey-box optimization problems.