The IUCN Red List of Threatened Species is essential for practical and theoretical efforts to protect biodiversity. However, species classified as “Data Deficient” (DD) regularly mislead practitioners due to their uncertain extinction risk. Here we present machine learning-derived probabilities of being threatened by extinction for 7699 DD species, comprising 17% of the entire IUCN spatial datasets. Our predictions suggest that DD species as a group may in fact be more threatened than data-sufficient species. We found that 85% of DD amphibians are likely to be threatened by extinction, as well as more than half of DD species in many other taxonomic groups, such as mammals and reptiles. Consequently, our predictions indicate that, amongst others, the conservation relevance of biodiversity hotspots in South America may be boosted by up to 20% if DD species were acknowledged. The predicted probabilities for DD species are highly variable across taxa and regions, implying current Red List-derived indices and priorities may be biased.

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Compared to conventional energy technologies, hydropower has the lowest carbon emissions per kWh. Therefore, hydropower electricity production can contribute to combat climate change challenges. However, hydropower electricity production may at the same time still contribute to environmental impacts and has been characterized as a large water consumer with impacts on aquatic biodiversity. However, Life Cycle Assessment is not yet able to assess the biodiversity impact of water consumption from hydropower electricity production on a global scale. The first step to assess these biodiversity impacts in Life Cycle Assessment is to quantify the water consumption per kWh energy produced. We calculated catchment-specific net water consumption values for Norway ranging between 0 and 0.012 m 3 /kWh. Further, we developed the first Characterization Factors (CF) for quantifying the aquatic biodiversity impacts of water consumption in a post-glaciated region. We apply of our approach to quantify the biodiversity impact per kWh Norwegian hydropower electricity. Our result varying over six orders of magnitude, highlight the importance of our spatiality-explicitly approach. This study contributes to assessing the biodiversity impacts of water consumption globally in Life Cycle Assessment.

Increasing hydropower electricity production constitutes a unique opportunity to mitigate climate change impacts. However, hydropower electricity production also impacts aquatic and terrestrial biodiversity through freshwater habitat alteration, water quality degradation, and land use and land use change (LULUC). Today, no operational model exists that covers any of these cause-effect pathways within life cycle assessment (LCA). This paper contributes to the assessment of LULUC impacts of hydropower electricity production in Norway in LCA. We quantified the inundated land area associated with 107 hydropower reservoirs with remote sensing data and related it to yearly electricity production. Therewith, we calculated an average net land occupation of 0.027 m·yr/kWh of Norwegian storage hydropower plants for the life cycle inventory. Further, we calculated an adjusted average land occupation of 0.007 m·yr/kWh, accounting for an underestimation of water area in the performed maximum likelihood classification. The calculated land occupation values are the basis to support the development of methods for assessing the land occupation impacts of hydropower on biodiversity in LCA at a damage level.