A globally applicable and high-resolution method to assess land use impacts on biodiversity
Land use is a major driver of global biodiversity loss and therefore a relevant environmental concern that should be considered in Life cycle assessment (LCA) studies. However, largely due to underdeveloped LCA land use impact assessment methods, the aspect of land use is often not considered in LCA studies. Traditionally, LCA methods assessing biodiversity impacts at the product level relied on linear, static, and nonspatially explicit modeling, which is not well representing how biodiversity responds to pressures. In addition, most methods were developed for specific regions, mainly Europe (e.g.. Koellner & Scholz 2008; De Schryver et al. 2010), and results could not easily be transferred to other regions. Global coverage, however, is needed when land use impacts of globally distributed value chains are assessed.
Here, we present a novel method that assesses biodiversity impacts at high spatial resolution (900m grid cells) including species-specific information on conservation status and global rarity of mammal species. This method is based on habitat suitability models that have been developed by the global mammals assessment for nearly all terrestrial mammal species (Rondinini et al. 2011). Using simple land cover scenarios, we assess how past or future land use changes might reduce the habitat area of each species. The species loss is finally assessed per grid cell and weighted by the species threat level and global rarity. We illustrate the application of the novel method to the case study of three important export-crops (coffee, tea, and tobacco) cultivated across East Africa. In addition, we compare this method with two previously developed, globally applicable land use impact assessment methods (de Baan et al., 2013a, assessing local relative impacts per WWF biome and de Baan et al., 2013b, assessing absolute regional impacts per WWF ecoregion).
In the past 20 years, strong increases in tea, coffee, and tobacco cultivation were observed in East Africa. When assessing the impacts on biodiversity with the novel method, crops that were produced within the range of globally rare mammals had by far the highest impacts on biodiversity. Only very weak correlation was found between the results of the novel and the two previous methods (de Baan et al. 2013a,b). In LCA studies providing the necessary spatially detailed life cycle inventory data, the new method can directly highlight crop production regions that overlap with regions of high mammal extinction risks. We recommend applying the novel method in conjunction with the method from de Baan et al. (2013b), which assesses impacts on regional species extinction and thereby gives an earlier warning signal than global extinction. These novel land use impact assessment methods could help to better understand the drivers of biodiversity loss and to find solutions for the global biodiversity crisis.
de Baan L., Alkemade R., Koellner T. (2013a) Land use impacts on biodiversity in LCA: a global approach. The International Journal of Life Cycle Assessment 18, 1216-1230.
de Baan L., Mutel C.L., Curran M., Hellweg S., Koellner T. (2013b) Land use in Life Cycle Assessment: Global characterization factors based on regional and global potential species extinctions. Environmental Science & Technology 47, 9281–9290.
De Schryver A.M., Goedkoop M.J., Leuven R.S.E.W., Huijbregts M.A.J. (2010) Uncertainties in the application of the species area relationship for characterisation factors of land occupation in life cycle assessment. The International Journal of Life Cycle Assessment 15, 682-691.
Koellner T., Scholz R.W. (2008) Assessment of land use impacts on the natural environment. Part 2: Generic characterization factors for local species diversity in Central Europe. The International Journal of Life Cycle Assessment 13, 32-48.
Rondinini C., Di Marco M., Chiozza F. et al. (2011) Global habitat suitability models of terrestrial mammals. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences 366, 2633-2641.