Elevating urban sustainability: An intelligent framework for optimizing water-energy-food nexus synergies in metabolic landscapes

Yanlai Zhou, Fi-John Chang, Li-Chiu Chang and Edwin Herricks

Applied Energy, 2024, vol. 360, issue C, No S0306261924002320

Abstract: As global urbanization accelerates, harmonizing water, energy, and food (WEF) resources within urban contexts is pivotal for sustainable development. This study introduces the Intelligent Urban Metabolism Framework (IUMF) for synergizing WEF dynamics, with a focus on socio-technological linkages and environmental concerns arising from climate change. Through a pioneering fusion of system dynamics simulation, machine learning surrogate, metaheuristic optimization, and multi-criteria decision making techniques, IUMF offers a transformative approach to resource management under climate uncertainty. Leveraging comprehensive data sourced from Taipei, Taiwan, this study demonstrates noteworthy enhancements in WEF nexus synergies, including a 9% boost in water supply, an 8% rise in energy benefits, and a significant 13.8% increase in food production. The cases corresponding to the best solutions under the scenario depicting a wet year and high solar radiation intensity would attain the largest benefits: 873 million m3 of water supply (water sector), 90.3 million USD of power benefits (energy sector), and 79 million kg of food production (food sector). These advancements are achieved while reducing computational runtime from 20 h to 30 min. By fostering a user-friendly interface and embracing an intelligent framework, IUMF catalyzes urban sustainability efforts. Our study highlights the potential of intelligent frameworks in addressing complex urban challenges and guiding the evolution of resource-efficient systems and offers a blueprint for a more resilient and sustainable urban future.

Keywords: Nexus optimization; Water-energy-food (WEF); Renewable energy; Urban metabolism; Artificial intelligence (AI) (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2024.122849

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