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Author: Yousefpour, R. × Has files: Yes ×

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    Inconsistent recognition of uncertainty in studies of climate change impacts on forests
    (Bristol : IOP Publ., 2019) Petr, M.; Vacchiano, G.; Thom, D.; Mairota, P.; Kautz, M.; Goncalves, L.M.S.; Yousefpour, R.; Kaloudis, S.; Reyer, C.P.O.
    Background. Uncertainty about climate change impacts on forests can hinder mitigation and adaptation actions. Scientific enquiry typically involves assessments of uncertainties, yet different uncertainty components emerge in different studies. Consequently, inconsistent understanding of uncertainty among different climate impact studies (from the impact analysis to implementing solutions) can be an additional reason for delaying action. In this review we (a) expanded existing uncertainty assessment frameworks into one harmonised framework for characterizing uncertainty, (b) used this framework to identify and classify uncertainties in climate change impacts studies on forests, and (c) summarised the uncertainty assessment methods applied in those studies. Methods. We systematically reviewed climate change impact studies published between 1994 and 2016. We separated these studies into those generating information about climate change impacts on forests using models –'modelling studies', and those that used this information to design management actions—'decision-making studies'. We classified uncertainty across three dimensions: nature, level, and location, which can be further categorised into specific uncertainty types. Results. We found that different uncertainties prevail in modelling versus decision-making studies. Epistemic uncertainty is the most common nature of uncertainty covered by both types of studies, whereas ambiguity plays a pronounced role only in decision-making studies. Modelling studies equally investigate all levels of uncertainty, whereas decision-making studies mainly address scenario uncertainty and recognised ignorance. Finally, the main location of uncertainty for both modelling and decision-making studies is within the driving forces—representing, e.g. socioeconomic or policy changes. The most frequently used methods to assess uncertainty are expert elicitation, sensitivity and scenario analysis, but a full suite of methods exists that seems currently underutilized. Discussion & Synthesis. The misalignment of uncertainty types addressed by modelling and decision-making studies may complicate adaptation actions early in the implementation pathway. Furthermore, these differences can be a potential barrier for communicating research findings to decision-makers.
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    Realizing Mitigation Efficiency of European Commercial Forests by Climate Smart Forestry
    (London : Nature Publishing Group, 2018) Yousefpour, R.; Augustynczik, A.L.D.; Reyer, C.P.O.; Lasch-Born, P.; Suckow, F.; Hanewinkel, M.
    European temperate and boreal forests sequester up to 12% of Europe's annual carbon emissions. Forest carbon density can be manipulated through management to maximize its climate mitigation potential, and fast-growing tree species may contribute the most to Climate Smart Forestry (CSF) compared to slow-growing hardwoods. This type of CSF takes into account not only forest resource potentials in sequestering carbon, but also the economic impact of regional forest products and discounts both variables over time. We used the process-based forest model 4 C to simulate European commercial forests' growth conditions and coupled it with an optimization algorithm to simulate the implementation of CSF for 18 European countries encompassing 68.3 million ha of forest (42.4% of total EU-28 forest area). We found a European CSF policy that could sequester 7.3-11.1 billion tons of carbon, projected to be worth 103 to 141 billion euros in the 21st century. An efficient CSF policy would allocate carbon sequestration to European countries with a lower wood price, lower labor costs, high harvest costs, or a mixture thereof to increase its economic efficiency. This policy prioritized the allocation of mitigation efforts to northern, eastern and central European countries and favored fast growing conifers Picea abies and Pinus sylvestris to broadleaves Fagus sylvatica and Quercus species.