Freshwater ecotoxicity characterization factors for aluminum

Purpose

Aluminum (Al) is an abundant, non-essential element with complex geochemistry and aquatic toxicity. Considering its complex environmental behavior is critical for providing a reasonable estimate of its potential freshwater aquatic ecotoxicity in the context of Life Cycle Impact Assessment (LCIA).

Methods

Al characterization factors (CFs) are calculated using the following: (1) USEtox^? model version 2.1 for environmental fate, (2) MINEQL+ to estimate the distribution of Al between the solid phase precipitate and total dissolved Al, (3) WHAM 7 for Al speciation within the total dissolved phase, and (4) Biotic Ligand Model (BLM) and Free Ion Activity Model (FIAM) for ecotoxicity estimation for seven freshwater archetypes and default landscape properties for the European continent. The sensitivity of the CFs to aquatic chemistry parameters is calculated. New CFs are compared with Dong et al. (Chemosphere 112:26–33, 2014) and default CF calculated by USEtox 2.1.

Results and discussion

Al CFs vary over 5 orders of magnitude between the seven archetypes, with an arithmetic average CF_ave of 0.04 eq 1,4-DCB (recommended for use), geometric mean CF_geo of 0.0014 eq 1,4-DCB, and weighted average CF_wt of 0.026 eq 1,4-DCB. These values are lower (less toxic) than those for Cu, Ni, Zn, and Pb (with one exception). The effect factor (EF) contributed most to this variability followed by the bioavailability factor (BF), varying over 8 and 4 orders of magnitude, respectively. These revised CFs are 2–6 orders of magnitude lower than those presented by Dong et al. (Chemosphere 112:26–33, 2014) mainly because of consideration of Al precipitation.

Conclusions

Freshwater archetype-specific Al CFs for freshwater ecotoxicity that address the effect of Al speciation on bioavailability (BF) and ecotoxicity (EF) have been calculated, and a CF of 0.04 eq 1,4-DCB is recommended for use in generic LCA. For site-specific LCA, the choice of water chemistry and, in particular, pH, and consideration of metal precipitation could significantly influence results.

Practical implications

Incorporating estimates of metal speciation and its effect on aquatic toxicity is essential when conducting LCIA. Along with metal speciation estimates, the values derived from the definition of water chemistry parameters must also be included into LCIA. For site-generic assessments, we recommend using the arithmetic average of metal CFs. We also recommend using FIAM as a suitable alternative to BLM to estimate EF if the latter is not available. Consideration of metal speciation is essential for providing more realistic estimates of Al freshwater ecotoxicity in the context of LCIA.

     

Uncertainty in LCA case study due to allocation approaches and life cycle impact assessment methods

Purpose

Uncertainty is present in many forms in life cycle assessment (LCA). However, little attention has been paid to analyze the variability that methodological choices have on LCA outcomes. To address this variability, common practice is to conduct a sensitivity analysis, which is sometimes treated only at a qualitative level. Hence, the purpose of this paper was to evaluate the uncertainty and the sensitivity in the LCA of swine production due to two methodological choices: the allocation approach and the life cycle impact assessment (LCIA) method.

Methods

We used a comparative case study of swine production to address uncertainty due to methodological choices. First, scenario variation through a sensitivity analysis of the approaches used to address the multi-functionality problem was conducted for the main processes of the system product, followed by an impact assessment using five LCIA methods at the midpoint level. The results from the sensitivity analysis were used to generate 10,000 independent simulations using the Monte Carlo method and then compared using comparison indicators in histogram graphics.

Results and discussion

Regardless of the differences between the absolute values of the LCA obtained due to the allocation approach and LCIA methods used, the overall ranking of scenarios did not change. The use of the substitution method to address the multi-functional processes in swine production showed the highest values for almost all of the impact categories, except for freshwater ecotoxicity; therefore, this method introduced the greater variations into our analysis. Regarding the variation of the LCIA method, for acidification, eutrophication, and freshwater ecotoxicity, the results were very sensitive. The uncertainty analysis with the Monte Carlo simulations showed a wide range of results and an almost equal probability of all the scenarios be the preferable option to decrease the impacts on acidification, eutrophication, and freshwater ecotoxicity. Considering the aggregate result variation across allocation approaches and LCIA methods, the uncertainty is too high to identify a statistically significant alternative.

Conclusions

The uncertainty analysis showed that performing only a sensitivity analysis could mislead the decision-maker with respect to LCA results; our analysis with the Monte Carlo simulation indicates no significant difference between the alternatives compared. Although the uncertainty in the LCA outcomes could not be decreased due to the wide range of possible results, to some extent, the uncertainty analysis can lead to a less uncertain decision-making by demonstrating the uncertainties between the compared alternatives.

     

A generalized computational structure for regional life-cycle assessment

Purpose

Regional life-cycle assessment (LCA) is gaining an increasing attention among LCA scholars and practitioners. Here, we present a generalized computational structure for regional LCA, discuss in-depth the major challenges facing the field, and point to a direction in which we believe regional LCA should be headed.

Methods

Using an example, we first demonstrate that when there is regional heterogeneity (be it due to environmental conditions or technologies), average data would be inadequate for estimating the life-cycle impacts of a product produced in a specific region or even that of an average product produced in many regions. And when there is such regional heterogeneity, an understanding of how regions are connected through commodity flows is important to the accuracy of regional LCA estimates. Then, we present a generalized computational structure for regional LCA that takes into account interregional commodity flows, can evaluate various cases of regional differentiation, and can account for multiple impact categories simultaneously. In so doing, we show what kinds of data are required for this generalized framework of regional LCA.

Results and discussion

We discuss the major challenges facing regional LCA in terms of data requirements and computational complexity, and their implications for the choice of an optimal regional scale (i.e., the number of regions delineated within the geographic boundary studied).

Conclusions

We strongly recommend scholars from LCI and LCIA to work together and choose a spatial scale that not only adequately captures environmental characteristics but also allows inventory data to be reasonably compiled or estimated.

     

Interpretation of comparative LCAs: external normalization and a method of mutual differences

Purpose

Identification of environmentally preferable alternatives in a comparative life cycle assessment (LCA) can be challenging in the presence of multiple incommensurate indicators. To make the problem more manageable, some LCA practitioners apply external normalization to find those indicators that contribute the most to their respective environmental impact categories. However, in some cases, these results can be entirely driven by the normalization reference, rather than the comparative performance of the alternatives. This study evaluates the influence of normalization methods on interpretation of comparative LCA to facilitate the use of LCA in decision-driven applications and inform LCA practitioners of latent systematic biases. An alternative method based on significance of mutual differences is proposed instead.

Methods

This paper performs a systematic evaluation of external normalization and describes an alternative called the overlap area approach for the purpose of identifying relevant issues in a comparative LCA. The overlap area approach utilizes the probability distributions of characterized results to assess significant differences. This study evaluates the effects in three LCIA methods, through application of four comparative studies. For each application, we call attention to the category indicators highlighted by each interpretation approach.

Results and discussion

External normalization in the three LCIA methods suffers from a systematic bias that emphasizes the same impact categories regardless of the application. Consequently, comparative LCA studies that employ external normalization to guide a selection may result in recommendations dominated entirely by the normalization reference and insensitive to data uncertainty. Conversely, evaluation of mutual differences via the overlap area calls attention to the impact categories with the most significant differences between alternatives. The overlap area approach does not show a systematic bias across LCA applications because it does not depend on external references and it is sensitive to changes in uncertainty. Thus, decisions based on the overlap area approach will draw attention to tradeoffs between alternatives, highlight the role of stakeholder weights, and generate assessments that are responsive to uncertainty.

Conclusions

The solution to the issues of external normalization in comparative LCAs proposed in this study call for an entirely different algorithm capable of evaluating mutual differences and integrating uncertainty in the results.

     

Human health characterization factors of nano-TiO<Subscript>2</Subscript> for indoor and outdoor environments

Purpose

The increasing use of engineered nanomaterials (ENMs) in industrial applications and consumer products is leading to an inevitable release of these materials into the environment. This makes it necessary to assess the potential risks that these new materials pose to human health and the environment. Life cycle assessment (LCA) methodology has been recognized as a key tool for assessing the environmental performance of nanoproducts. Until now, the impacts of ENMs could not be included in LCA studies due to a lack of characterization factors (CFs). This paper provides a methodological framework for identifying human health CFs for ENMs.

Methods

The USEtox? model was used to identify CFs for assessing the potential carcinogenic and non-carcinogenic effects on human health caused by ENM emissions in both indoor (occupational settings) and outdoor environments. Nano-titanium dioxide (nano-TiO₂) was selected for defining the CFs in this study, as it is one of the most commonly used ENMs. For the carcinogenic effect assessment, a conservative approach was adopted; indeed, a critical dose estimate for pulmonary inflammation was assumed.

Results and discussion

We propose CFs for nano-TiO₂ from 5.5E?09 to 1.43E?02 cases/kg_emitted for both indoor and outdoor environments and for carcinogenic and non-carcinogenic effects.

Conclusions

These human health CFs for nano-TiO₂ are an important step toward the comprehensive application of LCA methodology in the field of nanomaterial technology.

     

Towards harmonizing natural resources as an area of protection in life cycle impact assessment

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.

     

Life cycle assessment of a steam thermolysis process to recover carbon fibers from carbon fiber-reinforced polymer waste

Purpose

Carbon fibers have been widely used in composite materials, such as carbon fiber-reinforced polymer (CFRP). Therefore, a considerable amount of CFRP waste has been generated. Different recycling technologies have been proposed to treat the CFRP waste and recover carbon fibers for reuse in other applications. This study aims to perform a life cycle assessment (LCA) to evaluate the environmental impacts of recycling carbon fibers from CFRP waste by steam thermolysis, which is a recycling process developed in France.

Methods

The LCA is performed by comparing a scenario where the CFRP waste is recycled by steam-thermolysis with other where the CFRP waste is directly disposed in landfill and incineration. The functional unit set for this study is 2 kg of composite. The inventory analysis is established for the different phases of the two scenarios considered in the study, such as the manufacturing phase, the recycling phase, and the end-of-life phase. The input and output flows associated with each elementary process are standardized to the functional unit. The life cycle impact assessment (LCIA) is performed using the SimaPro software and the Ecoinvent 3 database by the implementation of the CML-IA baseline LCIA method and the ILCD 2011 midpoint LCIA method.

Results and discussion

Despite that the addition of recycling phase produces non-negligible environmental impacts, the impact assessment shows that, overall, the scenario with recycling is less impactful on the environment than the scenario without recycling. The recycling of CFRP waste reduces between 25 and 30% of the impacts and requires about 25% less energy. The two LCIA methods used, CML-IA baseline and ILCD 2011 midpoint, lead to similar results, allowing the verification of the robustness and reliability of the LCIA results.

Conclusions

The recycling of composite materials with recovery of carbon fibers brings evident advantages from an environmental point of view. Although this study presents some limitations, the LCA conducted allows the evaluation of potential environmental impacts of steam thermolysis recycling process in comparison with a scenario where the composites are directly sent to final disposal. The proposed approach can be scaled up to be used in other life cycle assessments, such as in industrial scales, and furthermore to compare the steam thermolysis to other recycling processes.

     

Impacts from hydropower production on biodiversity in an LCA framework—review and recommendations

Purpose

Expanding renewable energy production is widely accepted as a promising strategy in climate change mitigation. However, even renewable energy production has some environmental impacts, some of which are not (yet) covered in life cycle impact assessment (LCIA). We aim to identify the most important cause-effect pathways related to hydropower production on biodiversity, as one of the most common renewable energy sources, and to provide recommendations for future characterization factor (CF) development.

Methods

We start with a comprehensive review of cause-effect chains related to hydropower production for both aquatic and terrestrial biodiversity. Next, we explore contemporary coverage of impacts on biodiversity from hydropower production in LCA. Further, we select cause-effect pathways displaying some degree of consistency with existing LCA frameworks for method development recommendations. For this, we compare and contrast different hydrologic models and discuss how existing LCIA methodologies might be modified or combined to improve the assessment of biodiversity impacts from hydropower production.

Results and discussion

Hydropower impacts were categorized into three overarching impact pathways: (1) freshwater habitat alteration, (2) water quality degradation, and (3) land use change. Impacts included within these pathways are flow alteration, geomorphological alteration to habitats, changes in water quality, habitat fragmentation, and land use transformation. For the majority of these impacts, no operational methodology exists currently. Furthermore, the seasonal nature of river dynamics requires a level of temporal resolution currently beyond LCIA modeling capabilities. State-of-the-art LCIA methods covering biodiversity impacts exist for land use and impacts from consumptive water use that can potentially be adapted to cases involving hydropower production, while other impact pathways need novel development.

Conclusions

In the short term, coverage of biodiversity impacts from hydropower could be significantly improved by adding a time step representing seasonal ecological water demands to existing LCIA methods. In the long term, LCIA should focus on ecological response curves based on multiple hydrologic indices to capture the spatiotemporal aspects of river flow, by using models based on the “ecological limits to hydrologic alteration” (ELOHA) approach. This approach is based on hydrologic alteration-ecological response curves, including site-specific environmental impact data. Though data-intensive, ELOHA represents the potential to build a global impact assessment framework covering multiple ecological indicators from local impacts. Further, we recommend LCIA methods based on degree of regulation for geomorphologic alteration and a fragmentation index based on dam density for “freshwater habitat alteration,” which our review identified as significant unquantified threats to aquatic biodiversity.

     

Ecosystem quality in LCIA: status quo,harmonization, and suggestions for the way forward

Purpose

Life cycle impact assessment (LCIA) results are used to assess potential environmental impacts of different products and services. As part of the UNEP-SETAC life cycle initiative flagship project that aims to harmonize indicators of potential environmental impacts, we provide a consensus viewpoint and recommendations for future developments in LCIA related to the ecosystem quality area of protection (AoP). Through our recommendations, we aim to encourage LCIA developments that improve the usefulness and global acceptability of LCIA results.

Methods

We analyze current ecosystem quality metrics and provide recommendations to the LCIA research community for achieving further developments towards comparable and more ecologically relevant metrics addressing ecosystem quality.

Results and discussion

We recommend that LCIA development for ecosystem quality should tend towards species-richness-related metrics, with efforts made towards improved inclusion of ecosystem complexity. Impact indicators—which result from a range of modeling approaches that differ, for example, according to spatial and temporal scale, taxonomic coverage, and whether the indicator produces a relative or absolute measure of loss—should be framed to facilitate their final expression in a single, aggregated metric. This would also improve comparability with other LCIA damage-level indicators. Furthermore, to allow for a broader inclusion of ecosystem quality perspectives, the development of an additional indicator related to ecosystem function is recommended. Having two complementary metrics would give a broader coverage of ecosystem attributes while remaining simple enough to enable an intuitive interpretation of the results.

Conclusions

We call for the LCIA research community to make progress towards enabling harmonization of damage-level indicators within the ecosystem quality AoP and, further, to improve the ecological relevance of impact indicators.

     

The EcoSpold 2 format—why a new format?

Purpose

This paper introduces the new EcoSpold data format for life cycle inventory (LCI).

Methods

A short historical retrospect on data formats in the life cycle assessment (LCA) field is given. The guiding principles for the revision and implementation are explained. Some technical basics of the data format are described, and changes to the previous data format are explained.

Results

The EcoSpold 2 data format caters for new requirements that have arisen in the LCA field in recent years.

Conclusions

The new data format is the basis for the Ecoinvent v3 database, but since it is an open data format, it is expected to be adopted by other LCI databases. Several new concepts used in the new EcoSpold 2 data format open the way for new possibilities for the LCA practitioners and to expand the application of the datasets in other fields beyond LCA (e.g., Material Flow Analysis, Energy Balancing).

     

Bridge processes: a solution for LCI datasets independent of background databases

Introduction

New platforms are emerging that enable more data providers to publish life cycle inventory data.

Background

Providing datasets that are not complete LCA models results in fragments that are difficult for practitioners to integrate and use for LCA modeling. Additionally, when proxies are used to provide a technosphere input to a process that was not originally intended by the process authors, in most LCA software, this requires modifying the original process.

Results

The use of a bridge process, which is a process created to link two existing processes, is proposed as a solution.

Discussion

Benefits to bridge processes include increasing model transparency, facilitating dataset sharing and integration without compromising original dataset integrity and independence, providing a structure with which to make the data quality associated with process linkages explicit, and increasing model flexibility in the case that multiple bridges are provided. A drawback is that they add additional processes to existing LCA models which will increase their size.

Conclusions

Bridge processes can be an enabler in allowing users to integrate new datasets without modifying them to link to background databases or other processes they have available. They may not be the ideal long-term solution but provide a solution that works within the existing LCA data model.

     

The Glasgow consensus on the delineation between pesticide emission inventory and impact assessment for LCA

Purpose

Pesticides are applied to agricultural fields to optimise crop yield and their global use is substantial. Their consideration in life cycle assessment (LCA) is affected by important inconsistencies between the emission inventory and impact assessment phases of LCA. A clear definition of the delineation between the product system model (life cycle inventory—LCI, technosphere) and the natural environment (life cycle impact assessment—LCIA, ecosphere) is missing and could be established via consensus building.

Methods

A workshop held in 2013 in Glasgow, UK, had the goal of establishing consensus and creating clear guidelines in the following topics: (1) boundary between emission inventory and impact characterisation model, (2) spatial dimensions and the time periods assumed for the application of substances to open agricultural fields or in greenhouses and (3) emissions to the natural environment and their potential impacts. More than 30 specialists in agrifood LCI, LCIA, risk assessment and ecotoxicology, representing industry, government and academia from 15 countries and four continents, met to discuss and reach consensus. The resulting guidelines target LCA practitioners, data (base) and characterisation method developers, and decision makers.

Results and discussion

The focus was on defining a clear interface between LCI and LCIA, capable of supporting any goal and scope requirements while avoiding double counting or exclusion of important emission flows/impacts. Consensus was reached accordingly on distinct sets of recommendations for LCI and LCIA, respectively, recommending, for example, that buffer zones should be considered as part of the crop production system and the change in yield be considered. While the spatial dimensions of the field were not fixed, the temporal boundary between dynamic LCI fate modelling and steady-state LCIA fate modelling needs to be defined.

Conclusions and recommendations

For pesticide application, the inventory should report pesticide identification, crop, mass applied per active ingredient, application method or formulation type, presence of buffer zones, location/country, application time before harvest and crop growth stage during application, adherence with Good Agricultural Practice, and whether the field is considered part of the technosphere or the ecosphere. Additionally, emission fractions to environmental media on-field and off-field should be reported. For LCIA, the directly concerned impact categories and a list of relevant fate and exposure processes were identified. Next steps were identified: (1) establishing default emission fractions to environmental media for integration into LCI databases and (2) interaction among impact model developers to extend current methods with new elements/processes mentioned in the recommendations.

     

Developing characterisation factors for land fragmentation impacts on biodiversity in LCA: key learnings from a sugarcane case study

Purpose

Habitat change was identified by the Millennium Ecosystem Assessment as the main direct driver of biodiversity loss. However, while habitat loss is already implemented in Life Cycle Impact Assessment (LCIA) methods, the additional impact on biodiversity due to habitat fragmentation is not assessed yet. Thus, the goal of this study was to include fragmentation effects from land occupation and transformation at both midpoint and endpoint levels in LCIA.

Methods

One promising metric, combining the landscape spatial configuration with species characteristics, is the metapopulation capacity λ, which can be used to rank landscapes in terms of their capacity to support viable populations spatially structured. A methodology to derive worldwide regionalised fragmentation indexes based on λ was used and combined with the Species Fragmented-Area Relationship (SFAR), which relies on λ to assess a species loss due to fragmentation. We adapted both developments to assess fragmentation impacts due to land occupation and transformation at both midpoint and endpoint levels in LCIA. An application to sugarcane production occurring in different geographical areas, more or less sensitive to land fragmentation, was performed.

Results and discussion

The comparison to other existing LCIA indicators highlighted its great potential for complementing current assessments through fragmentation effect inclusion. Last, both models were discussed through the evaluation grid used by the UNEP-SETAC land use LCIA working group for biodiversity impact assessment models.

Conclusions

Midpoint and endpoint characterisation factors were successfully developed to include the impacts of habitat fragmentation on species in LCIA. For now, they are provided for bird species in all forest ecoregions belonging to the biodiversity hotspots. Further work is required to develop characterisation factors for all taxa and all terrestrial ecoregions.

     

Characterization factors for zinc terrestrial ecotoxicity including speciation

Purpose

Ignoring metal speciation in the determination of characterization factors (CFs) in life cycle assessment (LCA) could significantly alter the validity of LCA results since toxicity is directly linked to bioavailability.

Methods

Zinc terrestrial ecotoxicity CFs are obtained using modified USEtox fate factors, WHAM 6.0-derived bioavailable factors, and effect factors calculated using the assessment of mean impact (AMI) method with available terrestrial ecotoxicity data. Soil archetypes created using influent soil properties on Zn speciation (soil texture, pH, cation exchange capacity, organic matter and carbonate contents) are used to group soils of the world into a more manageable spatial resolution for LCA. An aggregated global CF value is obtained using population density as a Zn emission proxy. Results are presented in a world map to facilitate use.

Results and discussion

When using soluble Zn as the bioavailable fraction, CF values vary over 1.76 orders of magnitude, indicating that a single aggregated value could reasonably be used for the world. When using true solution Zn, CFs cover 14 orders of magnitude. To represent this variability, 518 archetypes and 13 groups of archetypes were created. Aggregated global default values are 4.58 potentially affected fraction of species (PAF) m³·day kg^?1 for soluble Zn and 1.45 PAF m³·day kg^?1 for true solution Zn. These values are respectively 28 and 88 times lower than the Zn terrestrial CF in IMPACT 2002 (128 PAF m³·day kg^?1).

Conclusions

The CFs obtained for Zn, except for soluble Zn, are at least 2 orders of magnitude lower than current CFs. However, they must be tested in case studies to measure the impact of including Zn speciation in the CF definition of terrestrial ecotoxicity.

     

Salinisation impacts in life cycle assessment: a review of challenges and options towards their consistent integration

Purpose

Salinisation is a threat not only to arable land but also to freshwater resources. Nevertheless, salinisation impacts have been rarely and only partially included in life cycle assessment (LCA) so far. The objectives of this review paper were to give a comprehensive overview of salinisation mechanisms due to human interventions, analyse the completeness, relevance and scientific robustness of existing published methods addressing salinisation in LCA and provide recommendations towards a comprehensive integration of salinisation within the impact modelling frameworks in LCA.

Methods

First, with the support of salinisation experts and related literature, we highlighted multiple causes of soil and water salinisation and presented induced effects on human health, ecosystems and resources. Second, existing life cycle impact assessment (LCIA) methods addressing salinisation were analysed against the International Reference Life Cycle Data System analysis grid of the European Commission. Third, adopting a holistic approach, the modelling options for salinisation impacts were analysed in agreement with up-to-date LCIA frameworks and models.

Results and discussion

We proposed a categorisation of salinisation processes in four main types based on salinisation determinism: land use change, irrigation, brine disposal and overuse of a water body. For each salinisation type, key human management and biophysical factors involved were identified. Although the existing methods addressing salinisation in LCA are important and relevant contributions, they are often incomplete with regards to both the salinisation pathways they address and their geographical validity. Thus, there is a lack of a consistent framework for salinisation impact assessment in LCA. In analysing existing LCIA models, we discussed the inventory and impact assessment boundary options. The land use/land use change framework represents a good basis for the integration of salinisation impacts due to a land use change but should be completed to account for off-site impacts. Conversely, the land use/land use change framework is not appropriate to model salinisation due to irrigation, overuse of a water body and brine disposal. For all salinisation pathways, a bottom-up approach describing the environmental mechanisms (fate, exposure and effect) is recommended rather than an empirical or top-down approach because (i) salts and water are mobile and theirs effects are interconnected; (ii) water and soil characteristics vary greatly spatially; (iii) this approach allows the evaluation of both on- and off-site impacts and (iv) it is the best way to discriminate systems and support a reliable eco-design.

Conclusions

This paper highlights the importance of including salinisation impacts in LCA. Much research effort is still required to include salinisation impacts in a global, consistent and operational manner in LCA, and this paper provides the basis for future methodological developments.

     

Impacts of onshore wind energy production on birds and bats: recommendations for future life cycle impact assessment developments

Purpose

Models 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.

Methods

We 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 discussion

Wind 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.

Conclusions

Although 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.

     

Uncertainty analysis in LCA using precalculated aggregated datasets

Purpose

Some LCA software tools use precalculated aggregated datasets because they make LCA calculations much quicker. However, these datasets pose problems for uncertainty analysis. Even when aggregated dataset parameters are expressed as probability distributions, each dataset is sampled independently. This paper explores why independent sampling is incorrect and proposes two techniques to account for dependence in uncertainty analysis. The first is based on an analytical approach, while the other uses precalculated results sampled dependently.

Methods

The algorithm for generating arrays of dependently presampled aggregated inventories and their LCA scores is described. These arrays are used to calculate the correlation across all pairs of aggregated datasets in two ecoinvent LCI databases (2.2, 3.3 cutoff). The arrays are also used in the dependently presampled approach. The uncertainty of LCA results is calculated under different assumptions and using four different techniques and compared for two case studies: a simple water bottle LCA and an LCA of burger recipes.

Results and discussion

The meta-analysis of two LCI databases shows that there is no single correct approximation of correlation between aggregated datasets. The case studies show that the uncertainty of single-product LCA using aggregated datasets is usually underestimated when the correlation across datasets is ignored and that the magnitude of the underestimation is dependent on the system being analysed and the LCIA method chosen. Comparative LCA results show that independent sampling of aggregated datasets drastically overestimates the uncertainty of comparative metrics. The approach based on dependently presampled results yields results functionally identical to those obtained by Monte Carlo analysis using unit process datasets with a negligible computation time.

Conclusions

Independent sampling should not be used for comparative LCA. Moreover, the use of a one-size-fits-all correction factor to correct the calculated variability under independent sampling, as proposed elsewhere, is generally inadequate. The proposed approximate analytical approach is useful to estimate the importance of the covariance of aggregated datasets but not for comparative LCA. The approach based on dependently presampled results provides quick and correct results and has been implemented in EcodEX, a streamlined LCA software used by Nestlé. Dependently presampled results can be used for streamlined LCA software tools. Both presampling and analytical solutions require a preliminary one-time calculation of dependent samples for all aggregated datasets, which could be centrally done by database providers. The dependent presampling approach can be applied to other aspects of the LCA calculation chain.