An integrated decision support system for the urban food-water-energy nexus

Methodology, modification, and model formulation

Bibliographic Data

ID21478665
AuthorsMaryam Ghodsvali (0000-0003-4436-1708, Eindhoven University of Technology, corresponding author), Gamze Dane (0000-0001-6107-2727, Eindhoven University of Technology), Bauke De Vries (0000-0002-7885-8221, Eindhoven University of Technology)
Year2023
Volume100
Pages101940
Publication date2023-03-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueComputers Environment and Urban Systems (JOURNAL)
Journal identifiersISSN: 0198-9715 • E-ISSN: 1873-7587
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.compenvurbsys.2023.101940
OpenAlexW3189376628
LanguageEN
Citations received2
References cited25

Making cities more sustainable relies on opportunities to optimally integrate and manage food, water, and energy resources, among other essential requirements for the thriving of every society, in a synergistic manner. By means of decision support tools and the development of policy scenarios, cities can better understand how sustainability may be achieved by the optimal integration of the natural resources. Although increasingly employed, the need remains for an integrative decision-making methodology and tool that supports the incorporation of food, water, and energy sectors and the corresponding environmental and social footprints into a general framework, and quantitatively investigating the complicated synergies to optimize nexus strategies from a holistic point of view. This research develops an integrated decision-support system by means of a spatial optimization game model that searches for optimal resource management solutions through a cooperative scenario-building environment. The design of the proposed system relies on an innovative combination of methods capable of navigating decision-making through complex systems modeling and planning. This includes multi-objective optimization and cooperative game theory in the frame of a spatial serious gaming environment for real-world implementation. Relying on such an algorithmic framework, this research provides the foundation for a spatial serious game that enables forecasting the impact of policy interventions based on socio-economic drivers of the demand for the resources, environmental carrying capacity, land management, and primary climate change drivers. The outcomes serve as strategic guidelines for policymakers and encourage effective decision-making related to maximizing socio-economic targets and minimizing environmental burdens

Decision support system · Economics · Environmental economics · Environmental resource management · Management science · Sustainability · Computer Science · Engineering · Smart Grid Energy Management · Water resources management and optimization · Water-Energy-Food Nexus Studies · Artificial Intelligence · Ecology

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