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Optimisation of selection and placement of nature-based solutions for climate adaptation: a literature review on the modelling and resolution approaches

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  • Justin Capgras

    (Ecole des Ponts ParisTech
    Mitigrate)

  • Felicien Barhebwa Mushamuka

    (Mitigrate)

  • Laurent Feuilleaubois

    (Mitigrate)

Abstract

Nature-Based Solutions can be considered one of the best answers to the various consequences and problems caused by climate change, poor urbanisation and population growth. They are used not only as measures for the protection, sustainable management and restoration of natural and modified ecosystems but also as measures to mitigate certain natural disasters such as erosion, flooding, drought, storm surge and landslide. The benefit is for both biodiversity and human well-being. This paper reviews articles about optimising the selection and placement of Nature-Based Solutions. It presents several Operations Research approaches used in the context of climate adaptation. The analysis provided in this paper focuses on various case studies, state-of-the-art on Nature-Based Solutions, Operations Research algorithms, dissertations, and other papers dealing with infrastructure placement approaches in the context of climate adaptation.

Suggested Citation

  • Justin Capgras & Felicien Barhebwa Mushamuka & Laurent Feuilleaubois, 2023. "Optimisation of selection and placement of nature-based solutions for climate adaptation: a literature review on the modelling and resolution approaches," Environment Systems and Decisions, Springer, vol. 43(4), pages 577-598, December.
  • Handle: RePEc:spr:envsyd:v:43:y:2023:i:4:d:10.1007_s10669-023-09933-y
    DOI: 10.1007/s10669-023-09933-y
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    References listed on IDEAS

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    1. Cococcioni, Marco & Pappalardo, Massimo & Sergeyev, Yaroslav D., 2018. "Lexicographic multi-objective linear programming using grossone methodology: Theory and algorithm," Applied Mathematics and Computation, Elsevier, vol. 318(C), pages 298-311.
    2. Majid Montaseri & Mahdi Hesami Afshar & Omid Bozorg-Haddad, 2015. "Development of Simulation-Optimization Model (MUSIC-GA) for Urban Stormwater Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(13), pages 4649-4665, October.
    3. Richard Bellman, 1954. "Some Applications of the Theory of Dynamic Programming---A Review," Operations Research, INFORMS, vol. 2(3), pages 275-288, August.
    4. Peter C. Fishburn, 1967. "Letter to the Editor—Additive Utilities with Incomplete Product Sets: Application to Priorities and Assignments," Operations Research, INFORMS, vol. 15(3), pages 537-542, June.
    5. J. Yazdi, 2016. "Decomposition based Multi Objective Evolutionary Algorithms for Design of Large-Scale Water Distribution Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(8), pages 2749-2766, June.
    6. Khalil Amine, 2019. "Multiobjective Simulated Annealing: Principles and Algorithm Variants," Advances in Operations Research, Hindawi, vol. 2019, pages 1-13, May.
    7. Richard Bellman, 1954. "On some applications of the theory of dynamic programming to logistics," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 1(2), pages 141-153, June.
    8. Minakshi Kalra & Shobhit Tyagi & Vijay Kumar & Manjit Kaur & Wali Khan Mashwani & Habib Shah & Kamal Shah, 2021. "A Comprehensive Review on Scatter Search: Techniques, Applications, and Challenges," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-21, May.
    9. Mayrhofer, Jan P. & Gupta, Joyeeta, 2016. "The science and politics of co-benefits in climate policy," Environmental Science & Policy, Elsevier, vol. 57(C), pages 22-30.
    10. Fábio André Matos & Peter Roebeling, 2022. "Modelling Impacts of Nature-Based Solutions on Surface Water Quality: A Rapid Review," Sustainability, MDPI, vol. 14(12), pages 1-17, June.
    11. Beausoleil, Ricardo P., 2006. ""MOSS" multiobjective scatter search applied to non-linear multiple criteria optimization," European Journal of Operational Research, Elsevier, vol. 169(2), pages 426-449, March.
    12. Jaeggi, D.M. & Parks, G.T. & Kipouros, T. & Clarkson, P.J., 2008. "The development of a multi-objective Tabu Search algorithm for continuous optimisation problems," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1192-1212, March.
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