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Synergy assessment and optimization for water-energy-food nexus: Modeling and application

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  • Zhang, Tong
  • Tan, Qian
  • Yu, Xiaoning
  • Zhang, Shan

Abstract

Water, energy, and food have complex and dynamic interactions, forming water-energy-food nexus (WEFN) systems with tradeoffs and synergies. To fill the gap in quantitatively analyzing the synergies of WEFN, coupled assessment-optimization models for synergies were developed. First, based on investigations over the key variables and interconnections among different subsystems, an assessment model based on the synergy theory and information entropy was developed for measuring the synergies of WEFN systems. Then a synergy evolution model with the aid of a logistic model and an improved constrained genetic algorithm was developed to analyze and search the steady states of WEFN systems. To validate the developed assessment-optimization methodology, it has been applied to analyze WEFN in a case of China. The results indicated that water supplies might be the most critical factor affecting the stability of the study system. Moreover, the order degrees of water, energy, and food subsystems could be improved by conducting integrated water-fertilizer irrigation, adjusting the structure of energy supplies, improving the efficiency of energy utilization, stabilizing food prices, and adjusting crop planting structure. Also, the results indicated that coordinated, dependent, and competitive relationships could exist within WEFN systems. The development of renewable energies would greatly alleviate water conflicts and contribute to guiding WEFN systems towards the steady state. Without loss of generality, the coupled assessment-optimization methodology is also applicable to analyze other complex systems across the world that involve multiple sectors.

Suggested Citation

  • Zhang, Tong & Tan, Qian & Yu, Xiaoning & Zhang, Shan, 2020. "Synergy assessment and optimization for water-energy-food nexus: Modeling and application," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    • Handle: RePEc:eee:rensus:v:134:y:2020:i:c:s1364032120303506
    DOI: 10.1016/j.rser.2020.110059
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