IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i14p11428-d1200673.html
   My bibliography  Save this article

Agent-Based Modeling for Water–Energy–Food Nexus and Its Application in Ningdong Energy and Chemical Base

Author

Listed:
  • Meilian Zhu

    (College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China)

  • Guoli Yang

    (College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China)

  • Yanan Jiang

    (College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China
    Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Northwest A&F University, Ministry of Education, Xianyang 712100, China)

  • Xiaojun Wang

    (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China
    Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, China)

Abstract

Water, Energy and Food (WEF) are coordinated and constrained by each other, constituting a multivariate coupled feed-forward dynamical system. Traditional modeling and simulation methods struggle to model and simulate complex interactions in the WEF nexus. Therefore, we proposed and developed an agent-based model, which is one of the most effective tools for simulating complex systems. It also has unique advantages in simulating WEF allocation, which is very helpful in improving regional WEF use efficiency. By taking Ningdong Energy and Chemical Base as the research area, an agent-based water–energy–food model based on MESA library was developed using Python 3.9 language, which includes six types of agents and can explore and simulate the complex dynamic interactions in the supply and demand process of WEF sectors. Different behavior rules were proposed to quantify the interactions between WEF sectors of Ningdong Energy and Chemical Base. Four different scenarios were set up, namely, the baseline scenario, the water conservation scenario, the new reservoir scenario and the integrated scenario, and the uncertain system evolution processes between departments and resources under the four different scenarios were analyzed in detail to quantitatively analyze the evolution of the water–energy–food complex system of Ningdong Energy and Chemical Base, which has proven the effectiveness of the proposed model. The results show that: water allocation, energy consumption and food consumption in the domestic sector have similar degrees of impact, because the natural population growth rate does not change under different scenarios; water allocation in the food sector shows a trend corresponding to changes in crop yields; water allocation in the energy management sector shows an upward trend, the water allocation in the actual years 2016–2020 is almost the same, and in the forecast years 2021–2025, the baseline scenario and the water conservation scenario can’t meet the demand volume of the energy management sector due to limited water sources, so the total allocated water is lower than that in the increased reservoir and comprehensive scenario; the water allocated to ecological sector has a balanced situation, and the annual growth of the ecological greening coverage area is also balanced; the total water allocation also shows a trend of annual growth; regarding the annual energy volume that can be delivered to the area outside the base, the curve first grows sharply with a growth rate of about 19.85%, and then becomes slowly with a growth rate of about 3.53%. The total volume is expected to increase to 4.96 × 10 7 tce by 2025; the total energy, consumed energy and output energy, in general, shows a growing trend, and with the development of the economy and technology, the total energy of the base will reach 7.96 × 10 7 tce by 2025.

Suggested Citation

  • Meilian Zhu & Guoli Yang & Yanan Jiang & Xiaojun Wang, 2023. "Agent-Based Modeling for Water–Energy–Food Nexus and Its Application in Ningdong Energy and Chemical Base," Sustainability, MDPI, vol. 15(14), pages 1-25, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:11428-:d:1200673
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/14/11428/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/14/11428/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. An, Li, 2012. "Modeling human decisions in coupled human and natural systems: Review of agent-based models," Ecological Modelling, Elsevier, vol. 229(C), pages 25-36.
    2. Bieber, Niclas & Ker, Jen Ho & Wang, Xiaonan & Triantafyllidis, Charalampos & van Dam, Koen H. & Koppelaar, Rembrandt H.E.M. & Shah, Nilay, 2018. "Sustainable planning of the energy-water-food nexus using decision making tools," Energy Policy, Elsevier, vol. 113(C), pages 584-607.
    3. Lubega, William N. & Farid, Amro M., 2014. "Quantitative engineering systems modeling and analysis of the energy–water nexus," Applied Energy, Elsevier, vol. 135(C), pages 142-157.
    4. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
    5. Kaegi, M. & Mock, R. & Kröger, W., 2009. "Analyzing maintenance strategies by agent-based simulations: A feasibility study," Reliability Engineering and System Safety, Elsevier, vol. 94(9), pages 1416-1421.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ulfia A. Lenfers & Julius Weyl & Thomas Clemen, 2018. "Firewood Collection in South Africa: Adaptive Behavior in Social-Ecological Models," Land, MDPI, vol. 7(3), pages 1-17, August.
    2. Pacilly, Francine C.A. & Hofstede, Gert Jan & Lammerts van Bueren, Edith T. & Kessel, Geert J.T. & Groot, Jeroen C.J., 2018. "Simulating crop-disease interactions in agricultural landscapes to analyse the effectiveness of host resistance in disease control: The case of potato late blight," Ecological Modelling, Elsevier, vol. 378(C), pages 1-12.
    3. Leigh Tesfatsion, 2017. "Elements of Dynamic Economic Modeling: Presentation and Analysis," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 43(2), pages 192-216, March.
    4. Anshuka Anshuka & Floris F. Ogtrop & David Sanderson & Simone Z. Leao, 2022. "A systematic review of agent-based model for flood risk management and assessment using the ODD protocol," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(3), pages 2739-2771, July.
    5. Huber, Robert & Bakker, Martha & Balmann, Alfons & Berger, Thomas & Bithell, Mike & Brown, Calum & Grêt-Regamey, Adrienne & Xiong, Hang & Le, Quang Bao & Mack, Gabriele & Meyfroidt, Patrick & Millingt, 2018. "Representation of decision-making in European agricultural agent-based models," Agricultural Systems, Elsevier, vol. 167(C), pages 143-160.
    6. Noeldeke, Beatrice & Winter, Etti & Ntawuhiganayo, Elisée Bahati, 2022. "Representing human decision-making in agent-based simulation models: Agroforestry adoption in rural Rwanda," Ecological Economics, Elsevier, vol. 200(C).
    7. Tesfatsion, Leigh, 2017. "Modeling Economic Systems as Locally-Constructive Sequential Games," ISU General Staff Papers 201704300700001022, Iowa State University, Department of Economics.
    8. Nicholas R. Magliocca, 2020. "Agent-Based Modeling for Integrating Human Behavior into the Food–Energy–Water Nexus," Land, MDPI, vol. 9(12), pages 1-25, December.
    9. Jonas Friege & Georg Holtz & Emile Chappin, 2016. "Exploring Homeowners’ Insulation Activity," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(1), pages 1-4.
    10. Leigh Tesfatsion, 2017. "Modeling economic systems as locally-constructive sequential games," Journal of Economic Methodology, Taylor & Francis Journals, vol. 24(4), pages 384-409, October.
    11. Egger, Claudine & Plutzar, Christoph & Mayer, Andreas & Dullinger, Iwona & Dullinger, Stefan & Essl, Franz & Gattringer, Andreas & Bohner, Andreas & Haberl, Helmut & Gaube, Veronika, 2022. "Using the SECLAND model to project future land-use until 2050 under climate and socioeconomic change in the LTSER region Eisenwurzen (Austria)," Ecological Economics, Elsevier, vol. 201(C).
    12. Tesfatsion, Leigh, 2017. "Modeling Economic Systems as Locally-Constructive Sequential Games," ISU General Staff Papers 201703280700001022, Iowa State University, Department of Economics.
    13. Enda O’Connell, 2017. "Towards Adaptation of Water Resource Systems to Climatic and Socio-Economic Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(10), pages 2965-2984, August.
    14. Giacomo Ravaioli & Tiago Domingos & Ricardo F. M. Teixeira, 2023. "A Framework for Data-Driven Agent-Based Modelling of Agricultural Land Use," Land, MDPI, vol. 12(4), pages 1-17, March.
    15. J. Farmer & Cameron Hepburn & Penny Mealy & Alexander Teytelboym, 2015. "A Third Wave in the Economics of Climate Change," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 62(2), pages 329-357, October.
    16. Li, Feixue & Li, Zhifeng & Chen, Honghua & Chen, Zhenjie & Li, Manchun, 2020. "An agent-based learning-embedded model (ABM-learning) for urban land use planning: A case study of residential land growth simulation in Shenzhen, China," Land Use Policy, Elsevier, vol. 95(C).
    17. An, Li & Grimm, Volker & Sullivan, Abigail & Turner II, B.L. & Malleson, Nicolas & Heppenstall, Alison & Vincenot, Christian & Robinson, Derek & Ye, Xinyue & Liu, Jianguo & Lindkvist, Emilie & Tang, W, 2021. "Challenges, tasks, and opportunities in modeling agent-based complex systems," Ecological Modelling, Elsevier, vol. 457(C).
    18. Wallentin, Gudrun, 2017. "Spatial simulation: A spatial perspective on individual-based ecology—a review," Ecological Modelling, Elsevier, vol. 350(C), pages 30-41.
    19. King, Elizabeth G. & Franz, Trenton E., 2016. "Combining ecohydrologic and transition probability-based modeling to simulate vegetation dynamics in a semi-arid rangeland," Ecological Modelling, Elsevier, vol. 329(C), pages 41-63.
    20. Fetta, Angelico & Harper, Paul & Knight, Vincent & Williams, Janet, 2018. "Predicting adolescent social networks to stop smoking in secondary schools," European Journal of Operational Research, Elsevier, vol. 265(1), pages 263-276.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:11428-:d:1200673. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.