Addressing Climate Change Uncertainty In Forest Planning: A Multi-Stage Optimization Model With Multiple Futures And Price Sensitive Demands

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Addressing Climate Change Uncertainty In Forest Planning: A Multi-Stage Optimization Model With Multiple Futures And Price Sensitive Demands

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2020-12

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Forest ecosystems are complex, supplying numerous ecosystem services. Multiple ecological, economic, social, and political facets influence forest-planning decisions. Decision models have been widely used in forest management planning, but most are deterministic models, assuming the future is known. However, uncertainty about the future is often a concern because of long planning horizons, multiple stakeholders with different objectives, and many complexities of biological systems involving mixed-species stands and critical habitat for wildlife species. Addressing uncertainties surrounding the impacts of climate change on forests is considered a major challenge in forest planning. This research develops a stochastic forest planning model to recognize uncertainty surrounding the growth and yield estimates, allowing estimates of future growth and yield to depend on how climate changes. The intent is to help identify forest management actions for today that will perform well over a range of plausible climate change scenarios (futures). The stages of the model address how uncertainty about the future might unfold, with model solutions providing proposed immediate management actions plus detailed contingency (recourse) plans for each future. The use of specialized decomposition methods of operations research has allowed for testing the model using a very detailed statewide application in Minnesota, USA. For this case study, results show that planning for an average deterministic case produces a misleading solution, under-estimating the potential impact of climate change. On the other hand, planning for a worst-case scenario results in unsustainable harvest levels or unrealistic prices for timber in the long term. Test cases were also expanded to consider the use of downward sloping demand curves, recognizing that the marginal value of timber products and forest conditions vary with production level. Several demand curves are used and compared for aspen timber and hardwood old forest. Results demonstrate the potential for downward-sloping demand curves to help mitigate the infeasibilities and fluctuations in the marginal cost of production (shadow price) produced under the test cases with a fixed production target. Overall, these findings advance our understanding on the implications of including forest-wide uncertainty in the forest management process and highlights important tradeoffs when considering management options in a real-world application.

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University of Minnesota Ph.D. dissertation. December 2020. Major: Natural Resources Science and Management. Advisors: Howard Hoganson, Marcella Windmuller-Campione. 1 computer file (PDF); ix, 105 pages.

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De Pellegrin Llorente, Irene. (2020). Addressing Climate Change Uncertainty In Forest Planning: A Multi-Stage Optimization Model With Multiple Futures And Price Sensitive Demands. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/218727.

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