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Integrating stochastic programming and decision tree techniques in land conversion problems

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  • Vincenzina Messina
  • Valentina Bosetti

Abstract

This paper is concerned with gradual land conversion problems, placing the main focus on the interaction between time and uncertainty. This aspect is extremely relevant since most decisions made in the field of natural resources and sustainable development are irreversible decisions. In particular, we discuss and develop a scenario-based multi-stage stochastic programming model in order to determine the optimal land portfolio in time, given uncertainty affecting the market. The approach is then integrated in a decision tree framework in order to account for domain specific (environmental) uncertainty that, diversely from market uncertainty, may depend on the decision taken. Although, the designed methodology has many general applications, in the present work we focus on a particular case study, concerning a semi-degraded natural park located in northern Italy. Copyright Springer Science + Business Media, Inc. 2006

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  • Vincenzina Messina & Valentina Bosetti, 2006. "Integrating stochastic programming and decision tree techniques in land conversion problems," Annals of Operations Research, Springer, vol. 142(1), pages 243-258, February.
    • Handle: RePEc:spr:annopr:v:142:y:2006:i:1:p:243-258:10.1007/s10479-006-6170-2
    DOI: 10.1007/s10479-006-6170-2
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    Cited by:

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    2. Tahvonen, Olli & Quaas, Martin F. & Voss, Rüdiger, 2018. "Harvesting selectivity and stochastic recruitment in economic models of age-structured fisheries," Journal of Environmental Economics and Management, Elsevier, vol. 92(C), pages 659-676.
    3. Trigeorgis, Lenos & Tsekrekos, Andrianos E., 2018. "Real Options in Operations Research: A Review," European Journal of Operational Research, Elsevier, vol. 270(1), pages 1-24.
    4. Xiangling Hu & Charles Munson & Stergios Fotopoulos, 2012. "Purchasing decisions under stochastic prices: Approximate solutions for order time, order quantity and supplier selection," Annals of Operations Research, Springer, vol. 201(1), pages 287-305, December.
    5. Milford, James & Henrion, Max & Hunter, Chad & Newes, Emily & Hughes, Caroline & Baldwin, Samuel F., 2022. "Energy sector portfolio analysis with uncertainty," Applied Energy, Elsevier, vol. 306(PA).

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