Halting current rates of biodiversity loss will be a defining challenge of the 21st century. To assess the effectiveness of strategies to achieve this goal, indicators and tools are required that monitor the driving forces of biodiversity loss, the changing state of biodiversity, and evaluate the effectiveness of policy responses. Here, we review the use of indicators and approaches to model biodiversity loss in Life Cycle Assessment (LCA), a methodology used to evaluate the cradle-to-grave environmental impacts of products. We find serious conceptual shortcomings in the way models are constructed, with scale considerations largely absent. Further, there is a disproportionate focus on indicators that reflect changes in compositional aspects of biodiversity, mainly changes in species richness. Functional and structural attributes of biodiversity are largely neglected. Taxonomic and geographic coverage remains problematic, with the majority of models restricted to one or a few taxonomic groups and geographic regions. On a more general level, three of the five drivers of biodiversity loss as identified by the Millennium Ecosystem Assessment are represented in current impact categories (habitat change, climate change and pollution), while two are missing (invasive species and overexploitation). However, methods across all drivers can be greatly improved. We discuss these issues and make recommendations for future research to better reflect biodiversity loss in LCA.

Human and ecosystem health damage due to greenhouse gas (GHG) emissions is generally poorly quantified in the life cycle assessment of products, preventing an integrated comparison of the importance of GHGs with other stressor types, such as ozone depletion and acidifying emissions. In this study, we derived new characterization factors for 63 GHGs that quantify the impact of an emission change on human and ecosystem health damage. For human health damage, the Disability Adjusted Life Years (DALYs) per unit emission related to malaria, diarrhea, malnutrition, drowning, and cardiovascular diseases were quantified. For ecosystem health damage, the Potentially Disappeared Fraction (PDF) over space and time of various species groups, including plants, butterflies, birds, and mammals, per unit emission was calculated. The influence of value choices in the modeling procedure was analyzed by defining three coherent scenarios, based on Cultural theory perspectives. It was found that the characterization factor for human health damage by carbon dioxide (CO 2 ) ranges from 1.1 × 10 -2 to 1.8 × 10 +1 DALY per kton of emission, while the characterization factor for ecosystem damage by CO 2 ranges from 5.4 × 10 -2 to 1.2 × 10 +1 disappeared fraction of species over space and time ((km 2 • year)/kton), depending on the scenario chosen. The characterization factor of a GHG can change up to 4 orders of magnitude, depending on the scenario. The scenario-specific differences are mainly explained by the choice for a specific time horizon and stresses the importance of dealing with value choices in the life cycle impact assessment of GHG emissions.

Purpose Uncertainties in land use damage modeling are recognized, but hardly quantified in life cycle assessment (LCA). The objective of this study is to analyze the influence of various key assumptions and uncertainties within the development of characterisation factors (CFs) for land use in LCA. We assessed the influence on land use CFs of (1) parameter uncertainty and (2) the choice for a constant or land use-specific species accumulation factor z and including or excluding regional effects. Methods A model framework was developed to analyze the uncertainties of CFs for six land use types and three agricultural practices. The CFs are expressed as potential disappeared fraction (PDF) of vascular plant species based on the species area relationship (S=c.A z ). The species area relationship describes the relation between the species number and area size, with help of the species accumulation factor z and the species richness factor c. A dataset representative for Great Britain was used to quantify both modeling choices and parameter uncertainty. Modeling choices were analyzed by defining three coherent scenarios, based on cultural theory perspectives. The parameter uncertainties of average species number and species accumulation factor z were quantified using Monte Carlo simulation.Results and discussion Pair-wise comparison of the CFs shows that 68-85% of the CFs significantly differ from each other within each perspective. It is found that the ranking of organic, less intensive, and intensive land practices of each land use type is unaltered by the chosen model scenario. However, the absolute values of the CFs can change from negative to positive scores with an average difference of 0.8 PDF between the two extreme perspectives, i.e., individualistic and egalitarian. The difference between these scenarios is for 40% explained by the choice in z and for 60% by the choice in including regional effects. Within the egalitarian and hierarchist perspective the species accumulation factor z is for more than 80% responsible for the parameter uncertainty. Conclusions Modeling choices and uncertainties within the species area relationship hardly change the ranking of the different land practices but largely influence the absolute value of the CFs for land use. The absolute change in the land use CFs can change the interpretation of land use impacts compared with other stressors such as climate change.

Increasing CO2 atmospheric levels lead to increasing ocean acidification, thereby enhancing calcium carbonate dissolution of calcifying species. We gathered peer-reviewed experimental data on the effects of acidified seawater on calcifying species growth, reproduction, and survival. The data were used to derive species-specific median effective concentrations, i.e., pH50, and pH10, via logistic regression. Subsequently, we developed species sensitivity distributions (SSDs) to assess the potentially affected fraction (PAF) of species exposed to pH declines. Effects on species growth were observed at higher pH than those on species reproduction (mean pH10 was 7.73 vs 7.63 and mean pH50 was 7.28 vs 7.11 for the two life processes, respectively) and the variability in the sensitivity of species increased with increasing number of species available for the PAF (pH10 standard deviation was 0.20, 0.21, and 0.33 for survival, reproduction, and growth, respectively). The SSDs were then applied to two climate change scenarios to estimate the increase in PAF (ΔPAF) by future ocean acidification. In a high CO2 emission scenario, ΔPAF was 3 to 10% (for pH50) and 21 to 32% (for pH10). In a low emission scenario, ΔPAF was 1 to 4% (for pH50) and 7 to 12% (for pH10). Our SSDs developed for the effect of decreasing ocean pH on calcifying marine species assemblages can also be used for comparison with other environmental stressors.