Integrated Management of Land-use Systems under Systemic Risks and Food-(bio)energy-water-environmental Security Targets: A Stochastic Global Biosphere Management Model

Ermolieva, T., Havlik, P. ORCID: https://orcid.org/0000-0001-5551-5085, Yermoliev, Y., Mosnier, A., Obersteiner, M. ORCID: https://orcid.org/0000-0001-6981-2769, Leclère, D., Khabarov, N. ORCID: https://orcid.org/0000-0001-5372-4668, Valin, H. ORCID: https://orcid.org/0000-0002-0618-773X, & Reuter, W. (2015). Integrated Management of Land-use Systems under Systemic Risks and Food-(bio)energy-water-environmental Security Targets: A Stochastic Global Biosphere Management Model. In: Systems Analysis 2015 - A Conference in Celebration of Howard Raiffa, 11 -13 November, 2015, Laxenburg, Austria.

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Abstract

Interdependencies among land-use systems resemble a complex network connected through demand–supply relations, and disruption of the network may catalyze systemic risks affecting food, energy, water, and environmental security (FEWES) worldwide. This paper describes the conceptual development, expansion, and practical application of a stochastic version of the Global Biosphere Management Model (GLOBIOM), a model that is used to assess competition for land use between agriculture, bioenergy, and forestry at regional and global scales. In the stochastic version of the model, systemic risks of various kinds are explicitly covered and can be analyzed and mitigated in all their interactions. While traditional deterministic scenario analysis produces sets of often contradictory outcomes, stochastic GLOBIOM explicitly derives robust decisions that leave the systems better off, independently of what scenario occurs. Stochastic GLOBIOM is formulated as a stochastic optimization model that is central for evaluating portfolios of robust interdependent decisions: ex ante strategic decisions (production allocation, storage capacities) and ex post adaptive (demand, trading, storage control) decisions. For example, the model is applied to the case of increased storage facilities, which can be viewed as catastrophe pools to buffer production shortfalls and fulfill regional and global FEWES requirements when extreme events occur. Expected shortfalls and storage capacities have a close relation with Value-at-Risk and Conditional Value-at-Risk risk measures. The Value of Stochastic Solutions is calculated to present the benefits of the stochastic over the deterministic model.

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