Overview and recommendations for regionalized life cycle impact assessment

Environmental impact assessment of multi-source solid waste based on a life cycle assessment, principal component analysis, and random forest algorithm

As a national pilot city for solid waste disposal and resource reuse, Dongguan in Guangdong Province aims to vigorously promote the high-value utilization of solid waste and contribute to the sustainable development of the Greater Bay Area. In this study, life cycle assessment (LCA) coupled with principal component analysis (PCA) and the random forest (RF) algorithm was applied to assess the environmental impact of multi-source solid waste disposal technologies to guide the environmental protection direction. In order to improve the technical efficiency and reduce pollution emissions, some advanced technologies including carbothermal reduction‒oxygen-enriched side blowing, directional depolymerization‒flocculation demulsification, anaerobic digestion and incineration power generation, were applied for treating inorganic waste, organic waste, kitchen waste and household waste in the park. Based on the improved techniques, we proposed a cyclic model for multi-source solid waste disposal. Results of the combined LCA-PCA-RF calculation indicated that the key environmental load type was human toxicity potential (HTP), came from the technical units of carbothermal reduction and oxygen-enriched side blowing. Compared to the improved one, the cyclic model was proved to reduce material and energy inputs by 66%-85% and the pollution emissions by 15%-88%. To sum up, the environmental impact assessment and systematic comparison suggest a cyclic mode for multi-source solid waste treatments in the park, which could be promoted and contributed to the green and low-carbon development of the city.

Coupling optimization with territorial LCA to support agricultural land-use planning

The life cycle assessment framework was adapted to the territorial level (the "territorial LCA") to assess the environmental impacts and services of land-use planning scenarios. Given the various geographical conditions of the territory, the potential alternatives of land-use scenarios could be enormous. To prevent the iterative process of proposing and comparing alternative scenarios, this work aims to move one step further to automatically generate optimal planning scenarios by linking the novel territorial LCA with multi-objective optimization (MOO). A fuzzy optimization approach is adopted to deal with the trade-offs among objectives and to generate optimized scenarios, minimizing the environmental damages and maximizing the satisfaction level of the desired land-use functions subjected to constraints such as area availability and demand. Geographical Information System (GIS) is employed to manipulate geographic datasets for spatial assessment. An illustrative case study tests the novel integrated method (the territorial LCA, MOO, and GIS) on its ability to propose optimal land-use planning for bioenergy production in a region in Belgium. The study results reveal the competition of land uses for different energy products, the trade-offs among impact categories, and potential impacts on other territories if implementing optimal land planning for the territory under study. The optimization outcomes can help decision-making on the optimal locations for different crop types (i.e., miscanthus, willow, and maize in the case study) and utilizations (i.e., electricity, heat, biogas, and bioethanol in this study) complying with the objectives and constraints. This integrated tool holds the potential to assist policymakers when deciding on how to use the territory facing the global context of increasing demands for multiple uses of bio-based products, such as for food, feed, fuel, fiber, and chemicals. Limitations of the current method and its potential for real-world applications are discussed, such as expanding the scope to include life cycle sustainability assessment and taking farmers' behavior and crop rotation into account.

Global water consumption impacts on riverine fish species richness in Life Cycle Assessment

Reduced river discharge and flow regulation are significant threats to freshwater biodiversity. An accurate representation of potential damage of water consumption on freshwater biodiversity is required to quantify and compare the environmental impacts of global value chains. The effect of discharge reduction on fish species richness was previously modeled in life cycle impact assessment, but models were limited by the restricted geographical scope of underlying species-discharge relationships and the small number of species data. Here, we propose a model based on a novel regionalized species-discharge relationship (SDR). Our SDR-based model covers 88 % of the global landmass (2320 river basins worldwide excluding deserts and permanently frozen areas) and is based on a global dataset of 11,450 riverine fish species, simulated river discharge, elevation, and climate zones. We performed 10-fold cross-validation to select the best set of predictors and validated the obtained SDRs based on observed discharge data. Our model performed better than previous SDRs employed in life cycle impact assessment (Kling-Gupta efficiency coefficient about 4 times larger). We provide both marginal and average models with their uncertainty ranges for assessing scenarios of small and large-scale water consumption, respectively, and include regional and global species loss. We conducted an illustrative case study to showcase the method's applicability and highlight the differences with the currently used approach. Our models are useful for supporting sustainable water consumption and riverine fish biodiversity conservation decisions. They enable a more specific, reliable, and complete impact assessment by differentiating impacts on regional riverine fish species richness and irreversible global losses, including up-to-date species data, and providing spatially explicit values with high geographical coverage.

Life Cycle Data Interoperability Improvements through Implementation of the Federal LCA Commons Elementary Flow List

As a fundamental component of data for life cycle assessment models, elementary flows have been demonstrated to be a key requirement of life cycle assessment data interoperability. However, existing elementary flow lists have been found to lack sufficient structure to enable improved interoperability between life cycle data sources. The Federal Life Cycle Assessment Commons Elementary Flow List provides a novel framework and structure for elementary flows, but the actual improvement this list provides to the interoperability of life cycle data has not been tested. The interoperability of ten elementary flow lists, two life cycle assessment databases, three life cycle impact assessment methods, and five life cycle assessment software sources is assessed with and without use of the Federal Life Cycle Assessment Commons Elementary Flow List as an intermediary in flow mapping. This analysis showed that only 25% of comparisons between these sources resulted in greater than 50% of flows being capable of automatic name-to-name matching between lists. This indicates that there is a low level of interoperability when using sources with their original elementary flow nomenclature, and elementary flow mapping is required to use these sources in combination. The mapping capabilities of the Federal Life Cycle Assessment Commons Elementary Flow List to sources were reviewed and revealed a notable increase in name-to-name matches. Overall, this novel framework is found to increase life cycle data source interoperability.

Towards a LCA Database for the Planning and Design of Zero-Emissions Neighborhoods

The integration of science-based knowledge on greenhouse gas (GHG) emissions into practice-based neighborhood design and planning is key to inform and implement climate mitigation strategies. LCA is a method that is commonly used to provide objective and science-based information on the environmental impacts of specified systems or products. To use a LCA methodology at neighborhood scale is in turn dependent on the development of a common structure for life cycle inventory data. Such a LCA database does not operate on its own, but functions as a structured source of relevant high-quality data inputs linked to other different analytical tools. The aim of this study is to analyze the needs and requirements and provide a foundation for a LCA database at neighborhood scale that can provide users with an interface to find and access life cycle data in the users’ preferred format. The result of this study is the outline of the foundations of a user-centric LCA database for neighborhoods, including several sub-systems (buildings, infrastructure, mobility, and energy supply). Recommendations are given in the Conclusions Section to provide harmonized decision support on reducing GHG emissions at local levels in the planning and design of urban development projects at the neighborhood scale.

Life cycle assessment (LCA): informing the development of a sustainable circular bioeconomy?

The role of life cycle assessment (LCA) in informing the development of a sustainable and circular bioeconomy is discussed. We analyse the critical challenges remaining in using LCA and propose improvements needed to resolve future development challenges. Biobased systems are often complex combinations of technologies and practices that are geographically dispersed over long distances and with heterogeneous and uncertain sets of indicators and impacts. Recent studies have provided methodological suggestions on how LCA can be improved for evaluating the sustainability of biobased systems with a new focus on emerging systems, helping to identify environmental and social opportunities prior to large R&D investments. However, accessing economies of scale and improved conversion efficiencies while maintaining compatibility across broad ranges of sustainability indicators and public acceptability remain key challenges for the bioeconomy. LCA can inform, but not by itself resolve this complex dimension of sustainability. Future policy interventions that aim to promote the bioeconomy and support strategic value chains will benefit from the systematic use of LCA. However, the LCA community needs to develop the mechanisms and tools needed to generate agreement and coordinate the standards and incentives that will underpin a successful biobased transition. Systematic stakeholder engagement and the use of multidisciplinary analysis in combination with LCA are essential components of emergent LCA methods. This article is part of the theme issue 'Bio-derived and bioinspired sustainable advanced materials for emerging technologies (part 1)'.

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.

Developing Conversion Factors of LCIA Methods for Comparison of LCA Results in the Construction Sector

The inconsistency caused by different life cycle impact assessment (LCIA) methods is a long-term challenge for the life cycle assessment (LCA) community. It is necessary to systematically analyze the differences caused by LCIA methods and facilitate the fair comparison of LCA results. This study proposes an effective method of conversion factors (CFs) for converting the results of 8 LCIA methods for 14 impact categories and then demonstrates its application in the construction sector. Correlation analyses of the datasets of construction materials are conducted to develop CFs for the impact categories. A set of conversion cards are devised to present the CFs and the associated correlation information for the LCIA methods. It is revealed that the differences between LCIA methods are largely caused by the characterization methods, rather than due to the metrics. A comparison based only on the same metrics but ignoring the underlying LCIA mechanisms is misleading. High correlations are observed for the impact categories of climate change, acidification, eutrophication, and resource depletion. The developed CFs and conversion cards can greatly help LCA practitioners in the fair comparison of LCA results from different LCIA methods. Case studies are conducted, and verify that by applying the CFs the seemingly incomparable results from different LCIA methods become comparable. The CF method addresses the inconsistency problem of LCIA methods in a practical manner and helps improve the comparability and reliability of LCA studies in the construction sector. Suggestions are provided for the further development of LCIA conversion factors.

Life Cycle Assessment of Biofuels

Life cycle assessment (LCA) assesses the environmental burdens or impacts of products from cradle to grave. It is also possible to assess such burdens or impacts for parts of the life cycle. A brief overview is given of LCA methodology. A number of choices have to be made in the goal and scope definition, inventory analysis, and impact assessment stages of life cycle assessments. Such choices can have substantial impacts on LCA outcomes. There are uncertainties in outcomes linked to inventory data and modeling. In the case that future biofuels and production processes are studied, assessment outcomes are characterized by relatively large uncertainties. Choices and uncertainties should be considered in the interpretation stage of life cycle assessments. Methodologies applied to several important environmentally relevant aspects of biofuel life cycles are discussed. These aspects are: emissions of substances impacting climate, depletion of virtually nonrenewable abiotic resources, primary energy demand, and water footprint. LCA can be useful in identifying life cycle stages and processes that are major contributors to environmental burdens, for determining the energetic return on energy invested in biofuels, for the identification of environmental trade-offs, and for comparing the life cycle environmental burdens of products.

Green Nanofabrication Opportunities in the Semiconductor Industry: A Life Cycle Perspective

The turn of the 21st century heralded in the semiconductor age alongside the Anthropocene epoch, characterised by the ever-increasing human impact on the environment. The ecological consequences of semiconductor chip manufacturing are the most predominant within the electronics industry. This is due to current reliance upon large amounts of solvents, acids and gases that have numerous toxicological impacts. Management and assessment of hazardous chemicals is complicated by trade secrets and continual rapid change in the electronic manufacturing process. Of the many subprocesses involved in chip manufacturing, lithographic processes are of particular concern. Current developments in bottom-up lithography, such as directed self-assembly (DSA) of block copolymers (BCPs), are being considered as a next-generation technology for semiconductor chip production. These nanofabrication techniques present a novel opportunity for improving the sustainability of lithography by reducing the number of processing steps, energy and chemical waste products involved. At present, to the extent of our knowledge, there is no published life cycle assessment (LCA) evaluating the environmental impact of new bottom-up lithography versus conventional lithographic techniques. Quantification of this impact is central to verifying whether these new nanofabrication routes can replace conventional deposition techniques in industry as a more environmentally friendly option.

Nitrogen in Life Cycle Assessment (LCA) of agricultural crop production systems: Comparative analysis of regionalization approaches

Assessing reactive nitrogen (N_r) flows resulting from agricultural crop production systems (ACPS) with LCA requires regionalization. However, methodological approaches for regionalized LCA of ACPS may not necessarily reflect a comprehensive picture of N_r compounds and their complex interaction with the environment. This study presents a comprehensive analysis of the consideration of N_r in methodological approaches for regionalized LCA applied to ACPS. We conducted a review comprehending scientific literature on regionalization approaches applied to ACPS and compared these with general requirements of LCA and the scientific background of the N-cycle following the LCA framework. Special focus was placed on the analysis of process-based models (PBM) and life cycle impact assessment (LCIA) methods. We derived key factors relevant for a regional assessment of N flows in LCA and compared these to 23 regionalization approaches applied to ACPS. Main obstacles identified to conduct a regionalized LCA for ACPS involved the consideration of different regional scales and thus a missing common regionalization approach suitable for ACPS. Although, N related key-factors were mainly considered by the analyzed approaches in the different LCA phases, critical points involved the consideration of N field emissions and N impact assessment. Based on these findings, practical recommendations were given. Our comprehensive review provides relevant requirements for an improved regional N assessment in regionalized LCA of ACPS and aims to present a realistic picture when comparing different alternatives considering N specific regional impacts.

Estimating greenhouse gas emissions from direct land use change due to crop production in multiple countries

Greenhouse gas (GHG) emissions from direct land use change (LUC) in GHG footprint studies of crops are often estimated using national land use change statistics, as in many cases the exact location of crop cultivation and land use history is unknown. As such, these studies neglect spatial variability in land use change (amount and configuration) at the sub-national level as well as spatial variability in natural carbon stocks. For this reason, a spatial approach that enables consistent implementation of LUC emissions of crop production at different locations is developed and applied in this study. The dataset of crop production covers 69 crops cultivated on 1885 farms in 33 countries, spanning North and South America, Asia, Australia and Oceania, Europe and Africa, in the year 2014. Of the 1885 farms, 33% (619 farms) were identified to have LUC emissions when estimated at the local scale. LUC emissions of farms, derived using local scale location information, were found to have little correlation with those estimated at coarser spatial scales (such as the province or country level) using the spatial approach in this study or estimated using accounting approaches based on national statistics. Analysis at coarser spatial scales typically overestimated the LUC emissions of crops, as LUC in other regions can heavily influence these estimates. Therefore, it is concluded that local scale LUC emissions better represent local LUC dynamics, thereby improving the reliability of GHG footprint studies.

Controlling biodiversity impacts of future global hydropower reservoirs by strategic site selection

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.

A Regionalised Life Cycle Assessment Model to Globally Assess the Environmental Implications of Soil Salinization in Irrigated Agriculture

We present a global, locally resolved life cycle assessment (LCA) model to assess the potential effects on soil quality due to the accumulation of water-soluble salts in the agricultural soil profile, allowing differentiation between agricultural practices. Using globally available soil and climate information and crop specific salt tolerances, the model quantifies the negative implications that salts in irrigation water have on soil quality, in terms of change in the soil electrical conductivity and the corresponding change in the amount of crops that can be grown at increasing soil salinity levels. To facilitate the use of the model, we provide a life cycle inventory tool with information on salts emitted with irrigation water per country and 160 crops. Global average soil susceptibility is 0.19 dS/m per grams of salt in 1 m³ of soil, and the average resulting relative crop diversity loss is 5.7 × 10^-2 per grams of salt in 1 m³ of soil. These average values vary tangibly as a function of the location. In most humid regions worldwide, the characterization factor is null, showing that in these cases soil salinization due to irrigation does not contribute to soil degradation. We displayed how to apply the model with a case study. The model serves for guiding decision-making processes toward improving the sustainability of irrigated agriculture.

LCA of tomato greenhouse production using spatially differentiated life cycle impact assessment indicators: an Albanian case study

The increasing attention to agricultural exports and sustainability issues is driving a surge of interest in the life cycle assessment (LCAs) of greenhouse crop production in Albania. Meanwhile, most of the reported agricultural LCAs tend to be generic without considering regionalized environmental sensitivities. In this study, ReCiPe 2016, covering 18 midpoint indicators and 3 endpoint indicators was used to generate a full-fledged cradle-to-farm gate LCA of greenhouse tomatoes in a typical Albanian farm including spatial differentiation and indicators not covered by contemporary LCAs. The most important midpoint categories per 1 ha identified from foreground-background analysis were global warming (2660.4 kg CO₂-eq), stratospheric ozone depletion (0.0308 kg CFC11-eq), particulate matter formation (7.99 kg PM2.5-eq), human health and ecosystem ozone formation (8.47 and 14.95 kg NO_x-eq), water consumption (2293.23 m³), and terrestrial acidification (42.28 kg SO₂-eq). The application of spatial differentiation resulted in higher impacts with about 21% for particulate matter formation, 12% for human health ozone formation, 134% for ecosystem ozone formation, 19% for terrestrial acidification, and 13% for water consumption. The impacts primarily originated from nitrogen-based fertilizer emissions and diesel fuel with the origin of the impact from nitrous oxide (N₂O), ammonia volatilization (NH₃), nitrogen oxides (NOx), and non-methane volatile organic compounds (NMVOCs). Water consumption was dominated by irrigation water use. Overall, at the endpoint level, 9% and 24% less cumulative damage to human health and ecosystem quality were calculated with respect to the site-generic analysis primarily from the cause-and-effect chain of water consumption (mainly lower water stress index). This affirms the importance of regional considerations in LCA calculations to reflect the impacts accordingly (i.e., the magnitude of impacts, the most relevant midpoint categories, and their relevance on endpoint level) and increase the possibility of making correct conclusions and sub-optimizations, i.e., increase the discriminating power of LCA.

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.