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The Innovation Geoscience > 2025 Vol. 3 > No. 4 > 100172
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Decoding resources strategies: Food-energy-water nexus across varied development stages

  • 1.

    State Key Laboratory of Soil Pollution Control and Safety, Southern University of Science and Technology, Shenzhen 518055, China

  • 2.

    Guangdong-Hong Kong Joint Laboratory for Soil and Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China

  • 3.

    Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China

  • 4.

    Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China

  • 5.

    State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China

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The Innovation Geoscience  3(4),  https://doi.org/10.59717/j.xinn-geo.2025.100172  |  Cite this article
    GRAPHICAL ABSTRACT
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    • PUBLIC SUMMARY

    1. Multiple factors act upon the food-energy-water nexus and thus affect the security of the entire system.

      An enhancement in the accuracy rate was observed after the Bayesian network model was optimized.

      Validation and application of the model in varying development levels countries enables its broader promotion.

    • ABSTRACT
  • Globalization, climate change, and socioeconomic pressures intensify food-energy-water (FEW) nexus complexity and challenges, but existing models are constrained by regional biases and data gaps, limiting their generalization and adaptability for large-scale cross-regional analysis. This study develops a national-scale Bayesian network model using open-access datasets (e.g., FAO, World Bank) to ensure data availability across most countries, combined with an Expectation-Maximization (EM) algorithm to achieve 90% prediction accuracy under 10% data missingness, effectively overcoming regional and data constraints. Parameter optimization improves model accuracy by 18% compared to the lowest scenario, while it decreases accuracy by 2% compared to the highest scenario but improving prediction precision by 25%. Framework optimization extended the applicability of the model to multi-scenario application, including internal mechanism analysis, influencing factor analysis, and scenario analysis. Analyses of Greece, China, and Tajikistan indicate that the food system dominates FEW nexus in Greece and China, while the water system is dominant in Tajikistan. Socioeconomic development significantly influences all three countries. Greece faces severe energy risks (Energy dependence rate, EDR 74.9% - 81.3%), China has prominent water risks (Water stress, WS 39.7% - 42.0%), and Tajikistan confronts concurrent water and energy risks (WS 42.8% - 54.9%, EDR 33.5% - 43.7%). It is suggested that the dominant subsystems be given priority for control and management to enhance the regulatory efficiency, balance socioeconomic development with resource carrying capacity, address the cross-border water resources issue in Tajikistan and Greece’s dependence on energy imports through international cooperation to strengthen system resilience.
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    Shi Y., Wang Y., Liu S., et al. (2025). Decoding resources strategies: Food-energy-water nexus across varied development stages. The Innovation Geoscience 3:100172. https://doi.org/10.59717/j.xinn-geo.2025.100172
    Shi Y., Wang Y., Liu S., et al. (2025). Decoding resources strategies: Food-energy-water nexus across varied development stages. The Innovation Geoscience 3:100172. https://doi.org/10.59717/j.xinn-geo.2025.100172

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