Scholarly Work - Plant Sciences & Plant Pathology
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/8870
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Item Characterization Factors to Assess Land Use Impacts on Pollinator Abundance in Life Cycle Assessment(American Chemical Society, 2023-02) Alejandre, Elizabeth M.; Scherer, Laura; Guinée, Jeroen B.; Aizen, Marcelo A.; Albrecht, Matthias; Balzan, Mario V.; Bartomeus, Ignasi; Bevk, Danilo; Burkle, Laura A.; Clough, Yann; Cole, Lorna J.; Delphia, Casey M.; Dicks, Lynn V.; Garratt, Michael P.D.; Kleijn, David; Kovács-Hostyánszki, Anikó; Mandelik, Yael; Paxton, Robert J.; Petanidou, Theodora; Potts, Simon; Sárospataki, Miklós; Schulp, Catharina J.E.; Stavrinides, Menelaos; Stein, Katharina; Stout, Jane C.; Szentgyörgyi, Hajnalka; Varnava, Androulla I.; Woodcock, Ben A.; van Bodegom, Peter M.While wild pollinators play a key role in global food production, their assessment is currently missing from the most commonly used environmental impact assessment method, Life Cycle Assessment (LCA). This is mainly due to constraints in data availability and compatibility with LCA inventories. To target this gap, relative pollinator abundance estimates were obtained with the use of a Delphi assessment, during which 25 experts, covering 16 nationalities and 45 countries of expertise, provided scores for low, typical, and high expected abundance associated with 24 land use categories. Based on these estimates, this study presents a set of globally generic characterization factors (CFs) that allows translating land use into relative impacts to wild pollinator abundance. The associated uncertainty of the CFs is presented along with an illustrative case to demonstrate the applicability in LCA studies. The CFs based on estimates that reached consensus during the Delphi assessment are recommended as readily applicable and allow key differences among land use types to be distinguished. The resulting CFs are proposed as the first step for incorporating pollinator impacts in LCA studies, exemplifying the use of expert elicitation methods as a useful tool to fill data gaps that constrain the characterization of key environmental impacts.