GIS-based regionalized life cycle assessment: how big is small enough? Methodology and case study of electricity generation

Characterizing spatial dependence of boron, arsenic, and other trace elements for Permian groundwater in Northern Anhui plain coal mining area, China, using spatial autocorrelation index and geostatistics

Anthropogenic and geological factors play an essential role in the variability of groundwater quality, resulting in a weak spatial dependence of groundwater trace elements. Thus, it is an essential study to investigate the factors affecting groundwater quality and its spatial abundance of trace elements (including As, B, and other metalloids). In this study, samples are obtained from a Permian sandstone fracture aquifer in a coal mining area. A multivariate statistical analysis, hydrogeochemistry modeling, and spatial autocorrelation analysis were used to analyze the data. The results showed that Moran index was positive for all trace elements, which had good spatial autocorrelation. The Local indicators of spatial association (LISA) indicated that trace elements were clustered. The hydrogeochemical modeling results indicated that the precipitation and stability of iron-phase minerals, such as rhodochrosite and arsenic (As) absorption on the surface of iron-phase minerals in the aquifer, may limit concentrations in the southern region. The spatial autocorrelations of both As and Boron (B) were positive (high-high) in the western areas, indicating that As contamination occurred from both natural geological causes and human coal mining activities. In contrast, B contamination was mainly linked to the influence of human agricultural or industrial activities. Over 96% of the groundwater concentrations of As (10 μg/L) and B (300 μg/L) in the study area exceeded World Health Organization (WHO) limits. Overall, the results of this work could help decision-makers involved in regional water quality management visualize disperse zones where specific anthropogenic and geological processes may threaten groundwater quality.

Derivation and assessment of regional electricity generation emission factors in the USA

PURPOSE

Electricity production is one of the largest sources of environmental emissions-especially greenhouse gases (GHGs)-in the USA. Emission factors (EFs) vary from region to region, which requires the use of spatially relevant EF data for electricity production while performing life cycle assessments (LCAs). Uncertainty information, which is sought by LCA practitioners, is rarely supplied with available life cycle inventories (LCIs).

METHODS

To address these challenges, we present a method for collecting data from different sources for electricity generation and environmental emissions; discuss the challenges involved in agglomerating such data; provide relevant suggestions and solutions to merge the information; and calculate EFs for electricity generation processes from various fuel sources for different spatial regions and spatial resolutions. The EFs from the US 2016 Electricity Life Cycle Inventory (eLCI) are analyzed and explored in this study. We also explore the method of uncertainty information derivation for the EFs.

RESULTS AND DISCUSSION

We explore the EFs from different technologies across Emissions & Generation Resource Integrated Database (eGRID) regions in the USA. We find that for certain eGRID regions, the same electricity production technology may have worse emissions. This may be a result of the age of the plants in the region, the quality of fuel used, or other underlying factors. Region-wise life cycle impact assessment (LCIA) ISO 14040 impacts for total generation mix activities provide an overview of the total sustainability profile of electricity production in a particular region, rather than only global warming potential (GWP). We also find that, for different LCIA impacts, several eGRID regions are consistently worse than the US average LCIA impact for every unit of electricity generated.

CONCLUSION

This work describes the development of an electricity production LCI at different spatial resolutions by combining and harmonizing information from several databases. The inventory consists of emissions, fuel inputs, and electricity and steam outputs from different electricity production technologies located across various regions of the USA. This LCI for electricity production in the USA will prove to be an enormous resource for all LCA researchers-considering the detailed sources of the information and the breadth of emissions covered by it.

Hydrocarbon Bio-Jet Fuel from Bioconversion of Poplar Biomass: Life Cycle Assessment of Site-Specific Impacts

Hydrocarbon drop-in bio-jet fuels could help to reduce greenhouse gas emissions within the aviation sector. Large tracts of land will be required to grow biomass feedstock for this biofuel, and changes to the management of these lands could have substantial environmental impacts. This research uses spatial analysis and life cycle assessment methodologies to investigate potential environmental impacts associated with converting land to grow poplar trees for conversion to bio-jet fuel from different regions within the western United States. Results vary by region and are dependent on land availability, type of land converted, prior land management practices, and poplar growth yields. The conversion of intensively managed cropland to poplar production results in a decrease in fertilizer and a lower annual global warming potential (GWP) (Clarksburg CA region). Bringing unmanaged rangeland into production results in increases in fertilizers, chemical inputs, fuel use, and GWP (Jefferson OR region). Where poplar yields are predicted to be lower, more land is converted to growing poplar to meet feedstock demands (Hayden ID). An increased use of land leads to greater fuel use and GWP. Changes to land use and management practices will drive changes at the local level that need to be understood before developing a drop-in biofuels industry.

A Commodity Supply Mix for More Regionalized Life Cycle Assessments

Supply chain information is invaluable to further regionalize product life cycle assessments (LCAs), but detailed information linking production and consumption centers is not always available. We introduce the commodity supply mix (CSM) defined as the trade-volume-weighted average representing the combined geographic areas for the production of a commodity exported to a given market with the goal of (1) enhancing the relevance of inventory and impact regionalization and (2) allocating these impacts to specific markets. We apply the CSM to the Brazilian soybean supply chain mapped by Trase to obtain the mix of ecoregions and river basins linked to domestic consumption and exports to China, EU, France, and the rest of the world, before quantifying damage to biodiversity, and water scarcity footprints. The EU had the lowest potential biodiversity damage but the largest water scarcity footprint following respective sourcing patterns in 12 ecoregions and 18 river basins. These results differed from the average impact scores obtained from Brazilian soybean production information alone. The CSM can be derived at different scales (subnationally, internationally) using existing supply chain information and constitutes an additional step toward greater regionalization in LCAs, particularly for impacts with greater spatial variability such as biodiversity and water scarcity.

Assessing the diverse environmental effects of biochar systems: An evaluation framework

Biochar has been recognised as a carbon dioxide removal (CDR) technology. Unlike other CDR technologies, biochar is expected to deliver various valuable effects in e.g. agriculture, animal husbandry, industrial processes, remediation activities and waste management. The diversity of biochar side effects to CDR makes the systematic environmental assessment of biochar projects challenging, and to date, there is no common framework for evaluating them. Our aim is to bridge the methodology gap for evaluating biochar systems from a life-cycle perspective. Using life cycle theory, actual biochar projects, and reviews of biochar research, we propose a general description of biochar systems, an overview of biochar effects, and an evaluation framework for biochar effects. The evaluation framework was applied to a case study, the Stockholm Biochar Project. In the framework, biochar effects are classified according to life cycle stage and life cycle effect type; and the biochar's end-of-life and the reference situations are made explicit. Three types of effects are easily included in life cycle theory: changes in biosphere exchanges, technosphere inputs, and technosphere outputs. For other effects, analysing the cause-effect chain may be helpful. Several biochar effects in agroecosystems can be modelled as future productivity increases against a reference situation. In practice, the complexity of agroecosystems can be bypassed by using empirical models. Existing biochar life cycle studies are often limited to carbon footprint calculations and quantify a limited amount of biochar effects, mainly carbon sequestration, energy displacements and fertiliser-related emissions. The methodological development in this study can be of benefit to the biochar and CDR research communities, as well as decision-makers in biochar practice and policy.

Grid-Scale Life Cycle Greenhouse Gas Implications of Renewable, Storage, and Carbon Pricing Options

Models that characterize life cycle greenhouse gases from electricity generation are limited in their capability to estimate emissions changes at scales that capture the grid-scale benefits of technologies and policies that enhance renewable systems integration. National assumptions about generation mixes are often applied at annual time steps, neglecting spatiotemporal resolutions that provide insights on impacts from time-variable emissions. Our grid-scale model incorporates details of transmission and generation planning that allows a geographically and temporally textured and more realistic assessment of the life cycle greenhouse gas emissions outcomes, using a case study of the Western Interconnection of North America. Results from a co-optimized model of generation, transmission, and operations-the Johns Hopkins Stochastic Multistage Integrated Network Expansion Model-provide a detailed characterization of twenty-one scenarios with different configurations of storage additions, new renewable capacity, and carbon prices. Life cycle results suggest that optimization models that focus on generation alone may underestimate emissions by 18-29% because only emissions from power generation are quantified (i.e., supply chain emissions are omitted) but also that carbon pricing is the predominant driver of reducing emissions in the scenarios we examine. Life cycle assessment of electricity generation should move beyond individual technologies toward capturing the influence of policies at the system level to better understand technology-policy dynamics for the grid.

Effects of Spatial Scale on Life Cycle Inventory Results

Efforts to compile life cycle inventory (LCI) data at more geographically refined scales or resolutions are growing. However, it remains poorly understood as to how the choice of spatial scale may affect LCI results. Here, we examine this question using U.S. corn as a case study. We compile corn production data at two spatial scales, state and county, and compare how their LCI results may differ for state and national level analyses. For greenhouse gas (GHG) emissions, estimates at the two scales are similar (<20% of difference) for most state-level analyses and are basically the same (<5%) for national level analysis. For blue water consumption, estimates at the two scales differ more. Our results suggest that state-level analyses may be an adequate spatial scale for national level GHG analysis and for most state-level GHG analyses of U.S. corn, but may fall short for water consumption, because of its large spatial variability. On the other hand, although county-based LCIs may be considered more accurate, they require substantially more effort to compile. Overall, our study suggests that the goal of a study, data requirements, and spatial variability are important factors to consider when deciding the appropriate spatial scale or pursuing more refined scales.

Spatialized freshwater ecosystem life cycle impact assessment of water consumption based on instream habitat change modeling

In this article a new characterization model and factors are proposed for the life cycle impact assessment (LCIA) of water consumption on instream freshwater ecosystems. Impact pathways of freshwater consumption leading to ecosystem damage are described and the alteration of instream physical habitat is identified as a critical midpoint for ecosystem quality. The LCIA characterization model aims to assess the change in habitat quantity due to consumptive water use. It is based on statistical, physical habitat simulation for benthic invertebrates, fish species and their size classes, and guilds of fish sharing common habitat preferences. A habitat change potential (HCP) midpoint, mechanistic indicator, is developed and computed on the French river network at the river reach scale (the river segment with variable length between the upstream and downstream nodes in the hydrographic network), for median annual discharges and dry seasons. Aggregated, multi-species HCPs at a river reach are proposed using various aggregation approaches. Subsequently, the characterization factors are spatially aggregated at watershed and sub-watershed scales. HCP is highly correlated with median and low flow discharges, which determine hydraulic characteristics of reaches. Aggregation of individual HCPs at reach scale is driven by the species most sensitive to water consumption. In spatially aggregated HCPs, consistently with their reduced smaller average discharge rate, small stream habitats determine the overall watershed characterization. The study is aimed primarily at life cycle assessment (LCA) practitioners and LCIA modelers. However, since it is the result of a productive cross-fertilization between the ecohydrology and LCA domains, it could be potentially useful for watershed management and risk assessment as well. At the moment, the proposed model is applicable in France. For a broader implementation, the development of global, high resolution river databases or the generalization of the model are needed. Our new factor represents nevertheless an advancement in freshwater ecosystems LCIA laying the basis for new metrics for biodiversity assessment.

Overview and recommendations for regionalized life cycle impact assessment

PURPOSE

Regionalized life cycle impact assessment (LCIA) has rapidly developed in the past decade, though its widespread application, robustness, and validity still faces multiple challenges. Under the umbrella of UNEP/SETAC Life Cycle Initiative, a dedicated cross-cutting working group on regionalized LCIA aims to provides an overview of the status of regionalization in LCIA methods. We give guidance and recommendations to harmonize and support regionalization in LCIA for developers of LCIA methods, LCI databases, and LCA software.

METHOD

A survey of current practice among regionalized LCIA method developers was conducted. The survey included questions on chosen method spatial resolution and scale, the spatial resolution of input parameters, choice of native spatial resolution and limitations, operationalization and alignment with life cycle inventory data, methods for spatial aggregation, the assessment of uncertainty from input parameters and model structure, and variability due to spatial aggregation. Recommendations are formulated based on the survey results and extensive discussion by the authors.

RESULTS AND DISCUSSION

Survey results indicate that majority of regionalized LCIA models have global coverage. Native spatial resolutions are generally chosen based on the availability of global input data. Annual modelled or measured elementary flow quantities are mostly used for aggregating characterization factors (CFs) to larger spatial scales, although some use proxies, such as population counts. Aggregated CFs are mostly available at the country level. Although uncertainty due to input parameter, model structure, and spatial aggregation are available for some LCIA methods, they are rarely implemented for LCA studies. So far, there is no agreement if a finer native spatial resolution is the best way to reduce overall uncertainty. When spatially differentiated models CFs are not easily available, archetype models are sometimes developed.

CONCLUSIONS

Regionalized LCIA methods should be provided as a transparent and consistent set of data and metadata using standardized data formats. Regionalized CFs should include both uncertainty and variability. In addition to the native-scale CFs, aggregated CFs should always be provided, and should be calculated as the weighted averages of constituent CFs using annual flow quantities as weights whenever available. This paper is an important step forward for increasing transparency, consistency and robustness in the development and application of regionalized LCIA methods.

Perspectives of Informed Citizen Panel on Low-Carbon Electricity Portfolios in Switzerland and Longer-Term Evaluation of Informational Materials

Low-carbon transition is gaining momentum, but relatively little is known about the public preferences for low- and zero-carbon electricity portfolios given their environmental, health, and economic impacts. Decision science literature argues that conventional opinion surveys are limited for making strategic decisions because the elicited opinions may be distorted by misconceptions and awareness gaps that prevail in the public. We created an informed citizen panel ( N = 46) in Switzerland using technology factsheets, interactive web-tool Riskmeter, and group discussions. We measured the evolution of the panel's knowledge and preferences from initial (uninformed) to informed and longer-term views 4 weeks after. In terms of energy transition, our elicited technology and portfolio preferences show strong support for the low-carbon electricity sector transition, especially relying on hydropower, solar power, electricity savings and efficiency, and other renewable sources. Since these informed preferences are structurally different from the futures considered by many energy experts, we argue that these preferences should also inform the Swiss Energy Strategy 2050s implementation. In terms of methodologies in decision science, our factsheets, Riskmeter, and group discussions all proved effective in forming the preferences and improving knowledge. But we also intriguingly found that in a longer run the participants tended to revert back to their initial opinions. The latter finding opens up multiple new research questions on the longer-term effectiveness of informational tools and stability of informed preferences.

Integrating groundwater stress in life-cycle assessments - An evaluation of water abstraction

Understanding groundwater abstraction effects is vital for holistic impact assessments in areas depending on groundwater resources. The objective of our study was to modify the state-of-the-art AWaRe (available water remaining), freshwater impact assessment specifically for use in LCAs in areas dependent on groundwater resources. The new method, called "AGWaRe" (available groundwater remaining), reflects groundwater availability, based on a fraction of available groundwater remaining locally relative to a reference. Furthermore, our method increases spatial resolution beyond 1770 km² grid cells and adjusts demarcations in order to improve the representation of the heterogeneity of groundwater catchments. The applicability of AGWaRe was demonstrated on three groundwater systems producing 5 million m³ water for the city of Copenhagen, namely Advanced Treatment of Groundwater, Simple Treatment of Groundwater and Infiltration of Reclaimed water. Results were normalised to compare with other effects of supplying water to an average Danish person. The normalised impacts for drinking water for one person ranged between 0.1 and 39 PE (person equivalent) for the three systems, which indicates that effects on groundwater resources differ substantially between systems. A comparative LCA of these groundwater systems shows that other impact categories range between 0 and 1 PE/person. Advanced Treatment of Groundwater generally has the lowest effect, for example <50% of the other groundwater systems in Global Warming Potential. The AGWaRe results indicate that freshwater impacts from Simple Treatment of Groundwater are up to 100 times greater than for Infiltration of Reclaimed water. Furthermore, AGWaRe exposes differences between the groundwater systems that AWaRe cannot evaluate, because one AWaRe cell covers two of the systems in question. These improvements are crucial for groundwater managers looking to include sustainability considerations in their analysis and decision-making.

Data analytics approach to create waste generation profiles for waste management and collection

Extensive monitoring data on waste generation is increasingly collected in order to implement cost-efficient and sustainable waste management operations. In addition, geospatial data from different registries of the society are opening for free usage. Novel data analytics approaches can be built on the top of the data to produce more detailed, and in-time waste generation information for the basis of waste management and collection. In this paper, a data-based approach based on the self-organizing map (SOM) and the k-means algorithm is developed for creating a set of waste generation type profiles. The approach is demonstrated using the extensive container-level waste weighting data collected in the metropolitan area of Helsinki, Finland. The results obtained highlight the potential of advanced data analytic approaches in producing more detailed waste generation information e.g. for the basis of tailored feedback services for waste producers and the planning and optimization of waste collection and recycling.

Modeling Net Land Occupation of Hydropower Reservoirs in Norway for Use 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 m²·yr/kWh of Norwegian storage hydropower plants for the life cycle inventory. Further, we calculated an adjusted average land occupation of 0.007 m²·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.

Exploring the relevance of spatial scale to life cycle inventory results using environmentally-extended input-output models of the United States

The accuracy of direct and indirect resource use and emissions of products as quantified in life cycle models depends in part upon the geographical and technological representativeness of the production models. Production conditions vary not just between nations, but also within national boundaries. Understanding the level of geographic resolution within large industrial nations needed to reach acceptable accuracy has not been well-tested across the broad spectrum of goods and services consumed. Using an aggregate 15-industryenvironmentally-extended input-output model of the US along with detailed interstate commodity flow data, we test the accuracy of regionalizing the national model into two-regions (state - rest of US) versus 51 regions (all US states + DC). Our findings show the two-region form predicts life cycle emissions and resources used within 10-20% of the more detailed 51-region form for most of the environmental flows studied. The two-region form is less accurate when higher variability exists in production conditions for a product.

Emerging role of Geographical Information System (GIS), Life Cycle Assessment (LCA) and spatial LCA (GIS-LCA) in sustainable bioenergy planning

Sustainability of a bioenergy project depends on precise assessment of biomass resource, planning of cost-effective logistics and evaluation of possible environmental implications. In this context, this paper reviews the role and applications of geo-spatial tool such as Geographical Information System (GIS) for precise agro-residue resource assessment, biomass logistic and power plant design. Further, application of Life Cycle Assessment (LCA) in understanding the potential impact of agro-residue bioenergy generation on different ecosystem services has also been reviewed and limitations associated with LCA variability and uncertainty were discussed. Usefulness of integration of GIS into LCA (i.e. spatial LCA) to overcome the limitations of conventional LCA and to produce a holistic evaluation of the environmental benefits and concerns of bioenergy is also reviewed. Application of GIS, LCA and spatial LCA can help alleviate the challenges faced by ambitious bioenergy projects by addressing both economics and environmental goals.

Expanding Kenya's protected areas under the Convention on Biological Diversity to maximize coverage of plant diversity

Biodiversity is highly valuable and critically threatened by anthropogenic degradation of the natural environment. In response, governments have pledged enhanced protected-area coverage, which requires scarce biological data to identify conservation priorities. To assist this effort, we mapped conservation priorities in Kenya based on maximizing alpha (species richness) and beta diversity (species turnover) of plant communities while minimizing economic costs. We used plant-cover percentages from vegetation surveys of over 2000 plots to build separate models for each type of diversity. Opportunity and management costs were based on literature data and interviews with conservation organizations. Species richness was predicted to be highest in a belt from Lake Turkana through Mount Kenya and in a belt parallel to the coast, and species turnover was predicted to be highest in western Kenya and along the coast. Our results suggest the expanding reserve network should focus on the coast and northeastern provinces of Kenya, where new biological surveys would also fill biological data gaps. Meeting the Convention on Biological Diversity target of 17% terrestrial coverage by 2020 would increase representation of Kenya's plant communities by 75%. However, this would require about 50 times more funds than Kenya has received thus far from the Global Environment Facility.

Spatial Variability and Uncertainty of Water Use Impacts from U.S. Feed and Milk Production

This paper addresses water use impacts of agriculture, developing a spatially explicit approach tracing the location of water use and water scarcity related to feed production, transport, and livestock, tracking uncertainties and illustrating the approach with a case study on dairy production in the United States. This approach was developed as a step to bring spatially variable production and impacts into a process-based life cycle assessment (LCA) context. As water resources and demands are spatially variable, it is critical to take into account the location of activities to properly understand the impacts of water use, accounting for each of the main feeds for milk production. At the crop production level, the example of corn grain shows that 59% of water stress associated with corn grain production in the United States is located in Nebraska, a state with moderate water stress and moderate corn production (11%). At the level of milk production, four watersheds account for 78% of the national water stress impact, as these areas have high milk production and relatively high water stress; it is the production of local silage and hay crops that drives water consumption in these areas. By considering uncertainty in both inventory data and impact characterization factors, we demonstrate that spatial variability may be larger than uncertainty, and that not systematically accounting for the two can lead to artificially high uncertainty. Using a nonspatial approach in a spatially variable setting can result in a significant underestimation or overestimation of water impacts. The approach demonstrated here could be applied to other spatially variable processes.

Assessing the environmental impacts of freshwater thermal pollution from global power generation in LCA

Freshwater heat emissions from power plants with once-through cooling systems constitute one of many environmental pressures related to the thermoelectric power industry. The objective of this work was to obtain high resolution, operational characterization factors (CF) for the impact of heat emissions on ecosystem quality, and carry out a comprehensive, spatially, temporally and technologically differentiated damage-based environmental assessment of global freshwater thermal pollution. The aggregation of CFs on a watershed level results in 12.5% lower annual impacts globally and even smaller differences for the most crucial watersheds and months, so watershed level CFs are recommended when the exact emission site within the basin is unknown. Long-range impacts account for almost 90% of the total global impacts. The Great Lakes, several Mississippi subbasins, the Danube, and the Yangtze are among the most thermally impacted watersheds globally, receiving heat emissions from predominantly coal-fuelled and nuclear power plants. Globally, over 80% of the global annual impacts come from power plants constructed during or before the 1980s. While the impact-weighted mean age of the power plants in the Mississippi ranges from 38 to 51years, in Chinese watersheds including the Yangtze, the equivalent range is only 15 to 22years, reflecting a stark contrast in thermal pollution mitigation approaches. With relatively high shares of total capacity from power plants with once-through freshwater cooling, and tracing a large part of the Danube, 1kWh of net electricity mix is the most impactful in Hungary, Bulgaria and Serbia. Monthly CFs are provided on a grid cell level and on a watershed level for use in Life Cycle Assessment. The impacts per generating unit are also provided, as part of our effort to make available a global dataset of thermoelectric power plant emissions and impacts.

Spatially Explicit Analysis of Biodiversity Loss Due to Global Agriculture, Pasture and Forest Land Use from a Producer and Consumer Perspective

Anthropogenic land use to produce commodities for human consumption is the major driver of global biodiversity loss. Synergistic collaboration between producers and consumers in needed to halt this trend. In this study, we calculate species loss on 5 min × 5 min grid level and per country due to global agriculture, pasture and forestry by combining high-resolution land use data with countryside species area relationship for mammals, birds, amphibians, and reptiles. Results show that pasture was the primary driver of biodiversity loss in Madagascar, China and Brazil, while forest land use contributed the most to species loss in DR Congo and Indonesia. Combined with the yield data, we quantified the biodiversity impacts of 1 m(3) of roundwood produced in 139 countries, concluding that tropical countries with low timber yield and a large presence of vulnerable species suffer the highest impact. We also calculated impacts per kg for 160 crops grown in different countries and linked it with FAO food trade data to assess the biodiversity impacts embodied in Swiss food imports. We found that more than 95% of Swiss consumption impacts rest abroad with cocoa, coffee and palm oil imports being responsible for majority of damage.

Investigating the Energy-Water Usage Efficiency of the Reuse of Treated Municipal Wastewater for Artificial Groundwater Recharge

This project investigates the energy-water usage efficiency of large scale civil infrastructure projects involving the artificial recharge of subsurface groundwater aquifers via the reuse of treated municipal wastewater. A modeling framework is introduced which explores the various ways in which spatially heterogeneous variables such as topography, landuse, and subsurface infiltration capacity combine to determine the physical layout of proposed reuse system components and their associated process energy-water demands. This framework is applied to the planning and evaluation of the energy-water usage efficiency of hypothetical reuse systems in five case study regions within the State of California. Findings from these case study analyses suggest that, in certain geographic contexts, the water requirements attributable to the process energy consumption of a reuse system can exceed the volume of water that it is able to recover by as much as an order of magnitude.

Assessment of Life Cycle Impacts on Ecosystem Services: Promise, Problems, and Prospects

The analysis of ecosystem services (ES) is becoming a key-factor to implement policies on sustainable technologies. Accordingly, life cycle impact assessment (LCIA) methods are more and more oriented toward the development of harmonized characterization models to address impacts on ES. However, such efforts are relatively recent and have not reached full consensus yet. We investigate here on the transdisciplinary pillars related to the modeling of LCIA on ES by conducting a critical review and comparison of the state-of-the-art in both LCIA and ES domains. We observe that current LCIA practices to assess impacts on "ES provision" suffer from incompleteness in modeling the cause-effect chains; the multifunctionality of ecosystems is omitted; and the "flow" nature of ES is not considered. Furthermore, ES modeling in LCIA is limited by its static calculation framework, and the valuation of ES also experiences some limitations. The conceptualization of land use (changes) as the main impact driver on ES, and the corresponding approaches to retrieve characterization factors, eventually embody several methodological shortcomings, such as the lack of time-dependency and interrelationships between elements in the cause-effect chains. We conclude that future LCIA modeling of ES could benefit from the harmonization with existing integrated multiscale dynamic integrated approaches.

Life cycle impact assessment modeling for particulate matter: A new approach based on physico-chemical particle properties

Particulate matter (PM) causes severe damage to human health globally. Airborne PM is a mixture of solid and liquid droplets suspended in air. It consists of organic and inorganic components, and the particles of concern range in size from a few nanometers to approximately 10μm. The complexity of PM is considered to be the reason for the poor understanding of PM and may also be the reason why PM in environmental impact assessment is poorly defined. Currently, life cycle impact assessment is unable to differentiate highly toxic soot particles from relatively harmless sea salt. The aim of this article is to present a new impact assessment for PM where the impact of PM is modeled based on particle physico-chemical properties. With the new method, 2781 characterization factors that account for particle mass, particle number concentration, particle size, chemical composition and solubility were calculated. Because particle sizes vary over four orders of magnitudes, a sound assessment of PM requires that the exposure model includes deposition of particles in the lungs and that the fate model includes coagulation as a removal mechanism for ultrafine particles. The effects model combines effects from particle size, solubility and chemical composition. The first results from case studies suggest that PM that stems from emissions generally assumed to be highly toxic (e.g. biomass combustion and fossil fuel combustion) might lead to results that are similar compared with an assessment of PM using established methods. However, if harmless PM emissions are emitted, established methods enormously overestimate the damage. The new impact assessment allows a high resolution of the damage allocatable to different size fractions or chemical components. This feature supports a more efficient optimization of processes and products when combating air pollution.

Integration of life cycle assessment and regional emission information in agricultural systems

BACKGROUND

Life cycle assessment (LCA) is a compilation and evaluation of the input energy and materials, output emissions and the potential environmental impacts of a product, service or system throughout its life cycle. While methodological issues of LCA are still being developed, much research is being conducted worldwide in order to improve them. One of the important advances in LCA is a regionalised LCA, i.e. the development of regionalised databases, inventories, and impact assessment methods and models.

RESULTS

Regional emission information (REI) was developed and integrated with the characterisation results in LCA of an agricultural product in the study area. Comparison of outcomes obtained with LCA characterisation results that did not include REI shows that the characterisation results taking REI into account are much higher as regards human toxicity, from 0.02% to 0.18%, freshwater ecotoxicity from 89% to 99% and terrestrial ecotoxicity from 8.006% to 26.177%.

CONCLUSION

Results of current LCA studies on agricultural products and systems that do not include REI are under-estimating the life cycle environmental impact. For the LCA of agricultural products and systems, the REI as well as regionalised life cycle inventory data should be developed and integrated into the current LCA approach.

Improvement of agricultural life cycle assessment studies through spatial differentiation and new impact categories: case study on greenhouse tomato production

This paper presents the inclusion of new, relevant impact categories for agriculture life cycle assessments. We performed a specific case study with a focus on the applicability of spatially explicit characterization factors. The main goals were to provide a detailed evaluation of these new impact category methods, compare the results with commonly used methods (ReCiPe and USEtox) and demonstrate how these new methods can help improve environmental assessment in agriculture. As an overall conclusion, the newly developed impact categories helped fill the most important gaps related to land use, water consumption, pesticide toxicity, and nontoxic emissions linked to fertilizer use. We also found that including biodiversity damage due to land use and the effect of water consumption on wetlands represented a scientific advance toward more realistic environmental assessment of agricultural practices. Likewise, the dynamic crop model for assessing human toxicity from pesticide residue in food can lead to better practice in pesticide application. In further life cycle assessment (LCA) method developments, common end point units and normalization units should be agreed upon to make it possible to compare different impacts and methods. In addition, the application of site-specific characterization factors allowed us to be more accurate regarding inventory data and to identify precisely where background flows acquire high relevance.

Emerging approaches, challenges and opportunities in life cycle assessment

In the modern economy, international value chains--production, use, and disposal of goods--have global environmental impacts. Life Cycle Assessment (LCA) aims to track these impacts and assess them from a systems perspective, identifying strategies for improvement without burden shifting. We review recent developments in LCA, including existing and emerging applications aimed at supporting environmentally informed decisions in policy-making, product development and procurement, and consumer choices. LCA constitutes a viable screening tool that can pinpoint environmental hotspots in complex value chains, but we also caution that completeness in scope comes at the price of simplifications and uncertainties. Future advances of LCA in enhancing regional detail and accuracy as well as broadening the assessment to economic and social aspects will make it more relevant for producers and consumers alike.

Quantifying the environmental impact of an integrated human/industrial-natural system using life cycle assessment; a case study on a forest and wood processing chain

Life Cycle Assessment (LCA) is a tool to assess the environmental sustainability of a product; it quantifies the environmental impact of a product's life cycle. In conventional LCAs, the boundaries of a product's life cycle are limited to the human/industrial system, the technosphere. Ecosystems, which provide resources to and take up emissions from the technosphere, are not included in those boundaries. However, similar to the technosphere, ecosystems also have an impact on their (surrounding) environment through their resource usage (e.g., nutrients) and emissions (e.g., CH4). We therefore propose a LCA framework to assess the impact of integrated Techno-Ecological Systems (TES), comprising relevant ecosystems and the technosphere. In our framework, ecosystems are accounted for in the same manner as technosphere compartments. Also, the remediating effect of uptake of pollutants, an ecosystem service, is considered. A case study was performed on a TES of sawn timber production encompassing wood growth in an intensively managed forest ecosystem and further industrial processing. Results show that the managed forest accounted for almost all resource usage and biodiversity loss through land occupation but also for a remediating effect on human health, mostly via capture of airborne fine particles. These findings illustrate the potential relevance of including ecosystems in the product's life cycle of a LCA, though further research is needed to better quantify the environmental impact of TES.

Estimating water consumption of potential natural vegetation on global dry lands: building an LCA framework for green water flows

This study aimed to provide a framework for assessing direct soil-water consumption, also termed green water in the literature, in life cycle assessment (LCA). This was an issue that LCA had not tackled before. The approach, which is applied during the life cycle inventory phase (LCI), consists of quantifying the net change in the evapo(transpi)ration of the production system compared to the natural reference situation. Potential natural vegetation (PNV) is used as the natural reference situation. In order to apply the method, we estimated PNV evapotranspiration adapted to local biogeographic conditions, on global dry lands, where soil-water consumption impacts can be critical. Values are reported at different spatial aggregation levels: 10-arcmin global grid, ecoregions (501 units), biomes (14 units), countries (124 units), continents, and a global average, to facilitate the assessment for different spatial information detail levels available in the LCI. The method is intended to be used in rain-fed agriculture and rainwater harvesting contexts, which includes direct soil moisture uptake by plants and rainwater harvested and then reused in production systems. The paper provides the necessary LCI method and data for further development of impact assessment models and characterization factors to evaluate the environmental effects of the net change in evapo(transpi)ration.

Quantifying area changes of internationally important wetlands due to water consumption in LCA

Wetlands harbor diverse species assemblages but are among the world's most threatened ecosystems. Half of their global area was lost during the last century. No approach currently exists in life cycle impact assessment that acknowledges the vulnerability and importance of wetlands globally and provides fate factors for water consumption. We use data from 1184 inland wetlands, all designated as sites of international importance under the Ramsar Convention, to develop regionalized fate factors (FF) for consumptive water use. FFs quantify the change of wetland area caused per m(3)/yr water consumed. We distinguish between surface water-fed and groundwater-fed wetlands and develop FFs for surface water and groundwater consumption. FFs vary over 8 (surface water-fed) and 6 (groundwater-fed) orders of magnitude as a function of the site characteristics, showing the importance of local conditions. Largest FFs for surface water-fed wetlands generally occur in hyper-arid zones and smallest in humid zones, highlighting the dependency on available surface water flows. FFs for groundwater-fed wetlands depend on hydrogeological conditions and vary largely with the total amount of water consumed from the aquifer. Our FFs translate water consumption into wetland area loss and thus become compatible with life cycle assessment methodologies of land use.

Land use in life cycle assessment: global characterization factors based on regional and global potential species extinction

Land use is one of the main drivers of biodiversity loss. However, many life cycle assessment studies do not yet assess this effect because of the lack of reliable and operational methods. Here, we present an approach to modeling the impacts of regional land use on plants, mammals, birds, amphibians, and reptiles. Our global analysis calculates the total potential damage caused by all land uses within each WWF ecoregion and allocates this total damage to different types of land use per ecoregion. We use an adapted (matrix-calibrated) species-area relationship to model the potential regional extinction of nonendemic species caused by reversible land use and land use change impacts. The potential global extinction of endemic species is used to assess irreversible, permanent impacts. Model uncertainty is assessed using Monte Carlo simulations. The impacts of land use on biodiversity varied strongly across ecoregions, showing the highest values in regions where most natural habitat had been converted in the past. The approach is thus retrospective and was able to highlight the impacts in highly disturbed regions. However, we also illustrate how it can be applied to prospective assessments using scenarios of future land use. Uncertainties, modeling choices, and validity are discussed.

Two-step sensitivity testing of parametrized and regionalized life cycle assessments: methodology and case study

Comprehensive sensitivity analysis is a significant tool to interpret and improve life cycle assessment (LCA) models, but is rarely performed. Sensitivity analysis will increase in importance as inventory databases become regionalized, increasing the number of system parameters, and parametrized, adding complexity through variables and nonlinear formulas. We propose and implement a new two-step approach to sensitivity analysis. First, we identify parameters with high global sensitivities for further examination and analysis with a screening step, the method of elementary effects. Second, the more computationally intensive contribution to variance test is used to quantify the relative importance of these parameters. The two-step sensitivity test is illustrated on a regionalized, nonlinear case study of the biodiversity impacts from land use of cocoa production, including a worldwide cocoa products trade model. Our simplified trade model can be used for transformable commodities where one is assessing market shares that vary over time. In the case study, the highly uncertain characterization factors for the Ivory Coast and Ghana contributed more than 50% of variance for almost all countries and years examined. The two-step sensitivity test allows for the interpretation, understanding, and improvement of large, complex, and nonlinear LCA systems.

Effects of consumptive water use on biodiversity in wetlands of international importance

Wetlands are complex ecosystems that harbor a large diversity of species. Wetlands are among the most threatened ecosystems on our planet, due to human influences such as conversion and drainage. We assessed impacts from water consumption on the species richness of waterbirds, nonresidential birds, water-dependent mammals, reptiles and amphibians in wetlands, considering a larger number of taxa than previous life cycle impact assessment methods. Effect factors (EF) were derived for 1184 wetlands of international importance. EFs quantify the number of global species-equivalents lost per m(2) of wetland area loss. Vulnerability and range size of species were included to reflect conservation values. Further, we derived spatially explicit characterization factors (CFs) that distinguish between surface water and groundwater consumption. All relevant watershed areas that are contributing to feeding the respective wetlands were determined for CF applications. In an example of rose production, we compared damages of water consumption in Kenya and The Netherlands. In both cases, the impact was largest for waterbirds. The total impact from water consumption in Kenya was 67 times larger than in The Netherlands, due to larger species richness and species' vulnerability in Kenya, as well as more arid conditions and larger amounts of water consumed.

A spatially and temporally explicit life cycle inventory of air pollutants from gasoline and ethanol in the United States

The environmental health impacts of transportation depend in part on where and when emissions occur during fuel production and combustion. Here we describe spatially and temporally explicit life cycle inventories (LCI) of air pollutants from gasoline, ethanol derived from corn grain, and ethanol from corn stover. Previous modeling for the U.S. by Argonne National Laboratory (GREET: Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation) suggested that life cycle emissions are generally higher for ethanol from corn grain or corn stover than for gasoline. Our results show that for ethanol, emissions are concentrated in the Midwestern "Corn Belt". We find that life cycle emissions from ethanol exhibit different temporal patterns than from gasoline, reflecting seasonal aspects of farming activities. Enhanced chemical speciation beyond current GREET model capabilities is also described. Life cycle fine particulate matter emissions are higher for ethanol from corn grain than for ethanol from corn stover; for black carbon, the reverse holds. Overall, our results add to existing state-of-the-science transportation fuel LCI by providing spatial and temporal disaggregation and enhanced chemical speciation, thereby offering greater understanding of the impacts of transportation fuels on human health and opening the door to advanced air dispersion modeling of fuel life cycles.