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Further reservoir-based hydropower development can contribute to the United Nations’ sustainable development goals (SDGs) on affordable and clean energy, and climate action. However, hydropower reservoir operation can lead to biodiversity impacts, thus interfering with the SDGs on clean water and life on land. We combine a high-resolution, location-specific, technical assessment with newly developed life cycle impact assessment models, to assess potential biodiversity impacts of possible future hydropower reservoirs, resulting from land occupation, water consumption and methane emissions. We show that careful selection of hydropower reservoirs has a large potential to limit biodiversity impacts, as for example, 0.3% of the global hydropower potential accounts for 25% of the terrestrial biodiversity impact. Local variations, e.g. species richness, are the dominant explanatory factors of the variance in the quantified biodiversity impact and not the mere amount of water consumed, or land occupied per kWh. The biodiversity impacts are mainly caused by land occupation and water consumption, with methane emissions being much less important. Further, we indicate a trade-off risk between terrestrial and aquatic biodiversity impacts, as due to the weak correlation between terrestrial and aquatic impacts, reservoirs with small aquatic biodiversity impacts tend to have larger terrestrial impacts and vice versa.

PurposeModels for quantifying impacts on biodiversity from renewable energy technologies are lacking within life cycle impact assessment (LCIA). We aim to provide an overview of the effects of wind energy on birds and bats, with a focus on quantitative methods. Furthermore, we investigate and provide the necessary background for how these can be integrated into new developments of LCIA models in future.MethodsWe reviewed available literature summarizing the effects of wind energy developments on birds and bats. We provide an overview of available quantitative assessment methods that have been employed outside of the LCIA framework to model the different impacts of wind energy developments on wildlife. Combining the acquired knowledge on impact pathways and associated quantitative methods, we propose possibilities for future approaches for a wind energy impact assessment methodology for LCIA.Results and discussionWind energy production has impacts on terrestrial biodiversity through three main pathways: collision, disturbance, and habitat alterations. Birds and bats are consistently considered the most affected taxonomic groups, with different responses to the before-mentioned impact pathways. Outside of the LCIA framework, current quantitative impact assessment prediction models include collision risk models, species distribution models, individual-based models, and population modeling approaches. Developed indices allow scaling of species-specific vulnerability to mortality, disturbance, and/or habitat alterations.ConclusionsAlthough insight into the causes behind collision risk, disturbance, and habitat alterations for bats and birds is still limited, the current knowledge base enables the development of a robust assessment tool. Modeling the impacts of habitat alterations, disturbance, and collisions within an LCIA framework is most appropriate using species distribution models as those enable the estimation of species’ occurrences across a region. Although local-scale developments may be more readily feasible, further up-scaling to global coverage is recommended to allow comparison across regions and technologies, and to assess cumulative impacts.

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. Data Deficient species are more likely to be at extinction risk than previously thought across multiple taxonomic groups.

PurposeGuidance is needed on best-suited indicators to quantify and monitor the man-made impacts on human health, biodiversity and resources. Therefore, the UNEP-SETAC Life Cycle Initiative initiated a global consensus process to agree on an updated overall life cycle impact assessment (LCIA) framework and to recommend a non-comprehensive list of environmental indicators and LCIA characterization factors for (1) climate change, (2) fine particulate matter impacts on human health, (3) water consumption impacts (both scarcity and human health) and 4) land use impacts on biodiversity.MethodsThe consensus building process involved more than 100 world-leading scientists in task forces via multiple workshops. Results were consolidated during a 1-week Pellston Workshop™ in January 2016 leading to the following recommendations.Results and discussionLCIA framework: The updated LCIA framework now distinguishes between intrinsic, instrumental and cultural values, with disability-adjusted life years (DALY) to characterize damages on human health and with measures of vulnerability included to assess biodiversity loss. Climate change impacts: Two complementary climate change impact categories are recommended: (a) The global warming potential 100 years (GWP 100) represents shorter term impacts associated with rate of change and adaptation capacity, and (b) the global temperature change potential 100 years (GTP 100) characterizes the century-scale long term impacts, both including climate-carbon cycle feedbacks for all climate forcers. Fine particulate matter (PM2.5) health impacts: Recommended characterization factors (CFs) for primary and secondary (interim) PM2.5 are established, distinguishing between indoor, urban and rural archetypes. Water consumption impacts: CFs are recommended, preferably on monthly and watershed levels, for two categories: (a) The water scarcity indicator “AWARE” characterizes the potential to deprive human and ecosystems users and quantifies the relative Available WAter REmaining per area once the demand of humans and aquatic ecosystems has been met, and (b) the impact of water consumption on human health assesses the DALYs from malnutrition caused by lack of water for irrigated food production. Land use impacts: CFs representing global potential species loss from land use are proposed as interim recommendation suitable to assess biodiversity loss due to land use and land use change in LCA hotspot analyses.ConclusionsThe recommended environmental indicators may be used to support the UN Sustainable Development Goals in order to quantify and monitor progress towards sustainable production and consumption. These indicators will be periodically updated, establishing a process for their stewardship.

Besides being central for understanding both global biodiversity patterns and associated anthropogenic impacts, species range maps are currently only available for a small subset of global biodiversity. Here, we provide a set of assembled spatial data for terrestrial vascular plants listed at the global IUCN red list. The dataset consists of pre-defined native regions for 47,675 species, density of available native occurrence records for 30,906 species, and standardized, large-scale Maxent predictions for 27,208 species, highlighting environmentally suitable areas within species’ native regions. The data was generated in an automated approach consisting of data scraping and filtering, variable selection, model calibration and model selection. Generated Maxent predictions were validated by comparing a subset to available expert-drawn range maps from IUCN (n = 4,257), as well as by qualitatively inspecting predictions for randomly selected species. We expect this data to serve as a substitute whenever expert-drawn species range maps are not available for conducting large-scale analyses on biodiversity patterns and associated anthropogenic impacts. Measurement(s) species distributions Technology Type(s) machine learning Sample Characteristic - Organism Tracheophyta Sample Characteristic - Environment terrestrial natural environment

Purpose In this paper, we summarize the discussion and present the findings of an expert group effort under the umbrella of the United Nations Environment Programme (UNEP)/Society of Environmental Toxicology and Chemistry (SETAC) Life Cycle Initiative proposing natural resources as an Area of Protection (AoP) in Life Cycle Impact Assessment (LCIA). Methods As a first step, natural resources have been defined for the LCA context with reference to the overall UNEP/SETAC Life Cycle Impact Assessment (LCIA) framework. Second, existing LCIA methods have been reviewed and discussed. The reviewed methods have been evaluated according to the considered type of natural resources and their underlying principles followed (use-to-availability ratios, backup technology approaches, or thermodynamic accounting methods). Results and discussion There is currently no single LCIA method available that addresses impacts for all natural resource categories, nor do existing methods and models addressing different natural resource categories do so in a consistent way across categories. Exceptions are exergy and solar energy-related methods, which cover the widest range of resource categories. However, these methods do not link exergy consumption to changes in availability or provisioning capacity of a specific natural resource (e.g., mineral, water, land etc.). So far, there is no agreement in the scientific community on the most relevant type of future resource indicators (depletion, increased energy use or cost due to resource extraction, etc.). To address this challenge, a framework based on the concept of stock/fund/flow resources is proposed to identify, across natural resource categories, whether depletion/dissipation (of stocks and funds) or competition (for flows) is the main relevant aspect. Conclusions An LCIA method—or a set of methods—that consistently address all natural resource categories is needed in order to avoid burden shifting from the impact associated with one resource to the impact associated with another resource. This paper is an important basis for a step forward in the direction of consistently integrating the various natural resources as an Area of Protection into LCA.

Stabilizing global temperature will require a shift to renewable or nuclear power from fossil power and the large-scale deployment of CO2 capture and storage (CCS) for remaining fossil fuel use. Non-climate co-benefits of low-carbon energy technologies, especially reduced mortalities from air pollution and decreased ecosystem damage, have been important arguments for policies to reduce CO2 emissions. Taking into account a wide range of environmental mechanisms and the complex interactions of the supply chains of different technologies, we conducted the first life cycle assessment of potential human health and ecological impacts of a global low-carbon electricity scenario. Our assessment indicates strong human health benefits of low-carbon electricity. For ecosystem quality, there is a significant trade-off between reduced pollution and climate impacts and potentially significant ecological impacts from land use associated with increased biopower utilization. Other renewables, nuclear power and CCS show clear ecological benefits, so that the climate mitigation scenario with a relatively low share of biopower has lower ecosystem impacts than the baseline scenario. Energy policy can maximize co-benefits by supporting other renewable and nuclear power and developing biomass supply from sources with low biodiversity impact.

The global shift towards renewable energy plays an important role in fighting climate change. To facilitate the global growth of renewable energy production, the expansion of the electric grid becomes inevitable. Yet further construction of power lines poses a risk to biodiversity. Power lines traverse natural habitats and can lead to habitat conversion, fragmentation, and loss. Moreover, due to collisions and electrocutions, power lines kill hundreds of millions of birds each year. These impacts, however, have so far not been incorporated into decision-making. Life cycle assessment (LCA) is a widely used framework to compare environmental impacts and support decision-makers in planning and promoting sustainable strategies. We adapted existing life cycle impact assessment (LCIA) models to quantify the three main impacts of power lines on biodiversity: collision, electrocution, and habitat conversion and fragmentation. Our models incorporated species-area and species-habitat relationships to assess the effects of power lines on the diversity of birds and non-flying mammals in Norway, as the country is currently committed to reducing its emissions by expanding its renewable energy capacity. Overall, habitat conversion and fragmentation had the highest impact across the three impact categories, particularly affecting mammal richness. Furthermore, distribution lines often affected species richness more than transmission lines. The effect of the three impact pathways varied among different species groups, highlighting the vulnerability of certain species to habitat change, collision, and electrocution. Integrating LCIA models that quantify the impacts of power lines on biodiversity into LCA can support decision-makers with tools to promote the development of the electric grid without overlooking its effect on species richness. In addition, our models set the stage for a comprehensive assessment of the effects of electricity generation and transmission on biodiversity.

Purpose Agriculture is a major water user worldwide, potentially depriving many ecosystems of water. Comprehensive global impact assessment methodologies are therefore required to assess impacts from water consumption on biodiversity. Since scarcity of water, as well as species richness, varies greatly between different world regions, a spatially differentiated approach is needed. Therefore, our aim is to enhance a previously published methodology in terms of spatial and species coverage. Methods We developed characterization factors for lifecycle impact assessment (LCIA) targeting biodiversity loss of various animal taxa (i.e., birds, reptiles, mammals, and amphibians) in wetlands. Data was collected for more than 22,000 wetlands worldwide, distinguishing between surface water- and groundwater-fed wetlands. Additionally, we account for a loss of vascular plant species in terrestrial ecosystems, based on precipitation. The characterization factors are expressed as global fractions of potential species extinctions (PDF) per cubic meter of water consumed annually and are developed with a spatial resolution of 0.05 arc degrees. Based on the geographic range of species, as well as their current threat level, as indicated by the International Union for Conservation of Nature (IUCN), we developed a vulnerability indicator that is included in the characterization factor. Results and discussion Characterization factors have maximal values in the order of magnitude of 10 −11 PDF·year/m 3 for animal taxa combined and 10 −12 PDF·year/m 3 for vascular plants. The application of the developed factors for global cultivation of wheat, maize, cotton, and rice highlights that the amount of water consumption alone is not sufficient to indicate the places of largest impacts but that species richness and vulnerability of species are indeed important factors to consider. Largest impacts are calculated for vascular plants in Madagascar, for maize, and for animal taxa; in Australia and the USA for surface water consumption (cotton); and in Algeria and Tunisia for groundwater consumption (cotton). Conclusions We developed a spatially differentiated approach to account for impacts from water consumption on a global level. We demonstrated its functionality with an application to a global case study of four different crops.