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A Commodity Supply Mix for More Regionalized Life Cycle Assessments

  • Michael J. Lathuillière*
    Michael J. Lathuillière
    Stockholm Environment Institute, Linnégatan 87D, Box 24218, 104 51 Stockholm, Sweden
    *Email: [email protected]
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    Orcidhttps://orcid.org/0000-0001-6315-454X
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  • Laure Patouillard
    Laure Patouillard
    CIRAIG, Polytechnique Montreal, 3333 Queen Mary Rd suite 310, Montreal, Quebec H3V 1A2, Canada
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  • Manuele Margni
    Manuele Margni
    CIRAIG, Polytechnique Montreal, 3333 Queen Mary Rd suite 310, Montreal, Quebec H3V 1A2, Canada
    HES−SO, University of Applied Sciences and Arts Western Switzerland, Institute of Sustainable Energy, School of Engineering, Rue de l’Industrie 23, 1950 Sion, Switzerland
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  • Ben Ayre
    Ben Ayre
    Global Canopy, 3 Frewin Court, Oxford OX1 3HZ, U.K.
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  • Pernilla Löfgren
    Pernilla Löfgren
    Stockholm Environment Institute, Linnégatan 87D, Box 24218, 104 51 Stockholm, Sweden
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  • Vivian Ribeiro
    Vivian Ribeiro
    Stockholm Environment Institute, Linnégatan 87D, Box 24218, 104 51 Stockholm, Sweden
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  • Chris West
    Chris West
    Department of Environment and Geography, Environment Building, Stockholm Environment Institute York, Wentworth Way, University of York, York YO10 5NG, U.K.
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  • Toby A. Gardner
    Toby A. Gardner
    Stockholm Environment Institute, Linnégatan 87D, Box 24218, 104 51 Stockholm, Sweden
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  • Clément Suavet
    Clément Suavet
    Stockholm Environment Institute, 400 F Street, Davis, California 95616, United States
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Cite this: Environ. Sci. Technol. 2021, 55, 17, 12054–12065
Publication Date (Web):August 10, 2021
https://doi.org/10.1021/acs.est.1c03060
Copyright © 2021 The Authors. Published by American Chemical Society
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Abstract

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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.

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Synopsis

The commodity supply mix can improve estimates of product impacts to biodiversity and water scarcity following existing UN Environmental life cycle assessment guidelines.

Introduction

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Life cycle assessment (LCA) is an approach that allows for the quantification of potential impacts of production and consumption processes throughout the life cycle of a product or service. (1) LCAs follow a four-step process according to the ISO 14044 standard: (1) (1) goal and scope definition, (2) life cycle inventory (LCI), (3) life cycle impact assessment (LCIA), and (4) normalization and interpretation. LCAs originally presented average potential impacts using generic site conditions, until the 1990s when the benefits of site-specific approaches became an important topic of discussion. (2) Since then, developments have highlighted the importance of representing site-specific conditions not only to account for differences in processes across multiple sites (e.g., electricity generation) but also because emissions (to soil, air, water) in different environmental settings and locations may lead to different impacts (e.g., terrestrial acidification, eutrophication). (2,3) The inclusion of site-specific information can, in turn, affect the conclusions of LCAs that would only be based on site-generic data. (3)
Regionalization in LCA refers to the efforts to move from site-generic to more site-specific information and involves (1) providing more regionally relevant information that is applicable to both LCI (i.e., “inventory regionalization”) and LCIA phases (i.e., “impact regionalization”) and (2) accounting for spatial variability, particularly in elementary flows (or “inventory spatialization”). (3) Ideally, site-specific elementary flows would match the spatial resolution of existing characterization factors, but practitioners can be limited by the availability of data or the resolution of impact assessment models while also facing trade-offs between selecting finer spatial resolution data and reducing uncertainty in the results. (4) Efforts to further regionalize LCAs have accelerated in recent years, with the inclusion of land and water use impact categories, leading up to the UN Environment’s Life Cycle Initiative Global Guidance on Environmental Life Cycle Assessment Indicators (or Global Guidance, henceforth). (5) Land occupation and transformation impacts require detailed information on biophysical conditions and spatial heterogeneity to quantify potential damage to biodiversity and ecosystem services. (6,7) Similarly, water consumption and degradation impacts are known to vary across river basins due to variability in human and ecosystem demand (8,9) or background chemical concentrations (10) affecting human health (11) or natural resources. (12) More systematic regionalization in LCAs has been accompanied by methods to reduce uncertainty from spatial variability in both elementary flows and characterization factors, (13−17) methods for prioritizing regionalization, (18−20) software solutions, (3,21) and standardization of spatial boundaries. (22)
Regionalization has been a key consideration for the increasing number of LCAs of food products. (23−29) These LCAs require high-resolution information on regional practices, soil and water conditions for production, as well as environmental conditions for emissions to soil, air, and water to derive more regionalized impacts. (30) Impact spatialization, for instance, through finer-resolution LCI, can lead to different impact scores, particularly in cases of spatially variable characterization factors. (29) This finer regionalization can also reduce uncertainty in impact assessment results, (19) which can be an important step for agricultural products. (31) For example, Yang et al. show that a finer-scale LCI of corn production in the United States using county boundaries was beneficial in differentiating variability in water use impacts, while state boundaries were enough for greenhouse gas emissions. (29)
Three important challenges remain, however, to allow for a widespread application of finer-resolution LCI to further regionalize LCAs: (1) many food products are made using ingredients derived from agricultural commodities, which are aggregated and shipped in bulk, thereby increasing the challenge in deriving a spatially explicit LCI from “farm-to-fork”; (2) products are delivered to consumers through complex supply chains that obscure the true source region(s) and therefore the information required to regionalize LCAs; (3) when performing an LCA of a more complex consumer product (milk replacements, packaged meat product, etc.), practitioners may have limited information on the production process of the raw materials (e.g., soy or corn production), thus limiting the ability to estimate potential damage to biodiversity, ecosystem services, and water use, which are more regionally significant. To overcome the above challenges, much work to date has focused on mapping international supply chains using either physical trade models, (32) regionalized input–output models, (33,34) or data-driven subnational trade flow networks. (26,35,36) In many cases, a transformed commodity (e.g., livestock feed) is tracked in terms of its primary agricultural commodity (e.g., soy, corn) as a means to link producers with consumers in LCA. (28,32) These studies, however, do not typically include further industrialization of these final products and often miss consumer-facing life cycle phases (e.g., distribution, product use, and end-of-life).
Several options are available to practitioners to increase the regionalization of an LCA study, all of which implicitly attempt to detail the supply chain of the production system under consideration: (1) regional downscaling through field surveys or regional output percentages, (2) testing production and supply scenarios, or (3) supply mixes. Regional output percentages allow for spatialization of the LCI based on, for example, the production output to capture both inter- and intraregional movements of a commodity or product. (15) Production and supply scenario testing can provide insights into a set of production and supply conditions among different regions with distinct environmental settings (e.g., land, water use, climate) or supply chains. For example, Castanheira and Freire (37) tested land use change and transportation scenarios for Brazilian and Argentinean soybean exported to Europe. Consumption or supply mixes are now available in ecoinvent version 3 through market data sets, (38) where the LCI is available from the global supply and consumption of products. This last option is particularly convenient for practitioners with little or no information on where production activities occur; however, the data relies on global production and consumption statistics and will miss important subnational spatial variability for commodity production processes.
In this paper, we introduce the commodity supply mix (CSM) derived from a systematic approach to improve regionalization in LCA using existing commodity supply chain mapping at the subnational level but applicable to other scales (subregional to international). Similar to existing approaches exemplified by electricity (39) and water mixes, (40) the CSM aims to generate regionalized LCI for LCA practitioners even in cases where information may be lacking on the production of a commodity that is used as an ingredient into a more complex product. First, we describe the concept of the CSM for improving regionalization in LCAs before applying the method using Trase (36) to a case study of land and water uses for Brazilian soybean consumed domestically and exported to China (Mainland), the EU, France, and the rest of the world (RoW). We expect the CSM to be a much needed option for practitioners seeking to improve regionalization in LCAs of complex agrifood products, particularly for the quantification of potential impacts to biodiversity and water use. (5)

Methods

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Commodity Supply Mix

Weidema et al. (41) define a production mix as a combination of suppliers of a product to areas of production and define a supply mix as the additional link between this production mix and specific consumers. Following this definition, we describe the CSM as a trade-volume-weighted average representing the combined geographic areas for the production of a commodity (or source regions) that are destined to specific consumer markets for that commodity. The CSM relies on the mapping of commodity supply chains, either nationally or internationally, to determine (1) the spatially explicit location of potential source regions that are associated with elementary flows, (2) at a scale adaptable to regional characterization factors that (3) can be linked to consumers. The CSM relies on supply chain nodes where commodities are combined in bulk before being transported to the next node(s) (hereafter named “trade hubs”). The nodes could be whole countries when considering re-exports of commodities within the context of international trade but also grain storage facilities, processing plants, distribution centers, etc., when looking specifically at in-country processes. For instance, within a producing country A (Figure 1), a CSM can be derived subnationally considering source regions that act as both regions of production and consumption (regions 1–5) through trade hubs (TH1, TH2, TH3) within the country. A CSM can also be derived at the country level considering international trade partners. For example, country B (Figure 1) may import a commodity from both countries A and C before the commodity is re-exported to country D (interpreted as a “market”). In this international case, country C can be considered an international trade hub. The CSM therefore links production to a place of consumption (or market); it can be applied at any scale following the needs and availability of both elementary flow data and characterization factors defined in the LCA study’s goal and scope, a coarse resolution being defined through country-to-country international trade, and the finest resolution being the multicountry subnational sourcing-to-country connections. The strength of the CSM for more regionalized LCAs is to match the region(s) (Figure 1) to a resolution that can match existing characterization factors at a finer resolution than a country’s borders.

Figure 1

Figure 1. Supply chain of a commodity produced and exported from country A (exporter) to country B (importer) before being re-exported to country D. The commodity is sourced from several subregions within country A (regions 1–5) before supplying country B through the trade hub networks (TH1, TH2, TH3). Each region and country can both produce, supply (s), and consume (c) the commodity. Numbers in bold are those used in Table 1 to calculate the commodity supply mix.

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In LCA, potential impacts are calculated following eq 1 (15,16,42)(1)where Il is the potential impact for impact category l, mis are the LCI elementary flows of emissions or resources extracted s in region i, and CFisl are the characterization factors for the impact category l identified by each elementary flow s for each region i. Current guidelines recommend that impact categories include potential damage to biodiversity and impacts to water scarcity, (5) for which the elementary flows are to be constructed from land and water uses (as s in eq 1) in ecoregions and river basins (as i). Using information on the commodity supply chain, the CSM distributes both elementary flows and characterization factors according to individual source regions linked to specific consumer markets j, as shown in eqs 2 and 3(2)(3)where CSMicj (unitless) is the supply- or trade-volume-weighted share of source region i of commodity c for market j obtained by deriving the fraction of supply of the commodity among source regions Sicj that supply the commodity to market j. We apply eq 3 to a theoretical example (Table 1) to derive the CSM for a commodity from source regions in country A and exported to country B (Figure 1). The CSM is compatible with existing definitions of supply mix in ecoinvent (38) such that results may be integrated in ecoinvent’s market data sets.
Table 1. Theoretical Example of Calculation of the Commodity Supply Mix (CSM) of Country B Importing a Commodity from Country A Whose Supply Is Concentrated in Source Regions 1–5 (Figure 1)
regionscountry A supply from sourcestrade hubscountry A total supply to trade hubsproportion traded to country B from trade hubs of country ACSM: proportion traded to country B from country A regions
12TH1110.7860.143
230.214
360.429
41TH230.2140.071
520.143

Case Study

We apply the CSM to a regionalized cradle-to-market attributional LCA of Brazilian soybean (as whole bean) focused on potential biodiversity damage from land occupation and impacts of water consumption from irrigation. The functional unit is 1 tonne of soybean produced in Brazil in 2017 that is destined to the main markets considering both the domestic market and Brazil’s main international trading partners: China (Mainland), the EU (including the United Kingdom), France, and the RoW. The CSM is applied using the soybean supply chain map from the Transparency for Sustainable Economies (Trase) initiative (36) (see below) to obtain the share of soybean supplied from different municipalities classified within Brazilian ecoregions and river basins. These supply chains are then combined with land and water use information to derive the elementary flows for each regionally specific characterization factor. Potential damage to biodiversity from land occupation was derived following eq 4 (7)(4)where Ic,occ (PDF yr tonne–1) is the potential damage to biodiversity from commodity c considering the LCI as the product of the area of land occupation in Brazilian ecoregion i, Aic,occ (m2 tonne–1), and the occupation time (tc,occ assumed to be 0.30–0.38 yr according to Flach et al. (43)) (see Table S1 in the Supporting Information for the full list of Brazilian ecoregions and Figure S1 for the map). Values of CFic,occ (PDF m–2) are the ecoregion-specific characterization factors for land occupation from Chaudhary and Brooks (44) (crop-intensive) (Table S1) based on the ecoregions from The Nature Conservancy. (45) Values of Aic,occ were derived from the 2017 inverse yield of each Brazilian municipality. (46)
Impacts of water consumption were expressed as a water scarcity footprint (WSF, m3 tonne–1) following Boulay et al. (9) using characterization factors specific to irrigation and following eq 5(5)where WFIbc (m3 tonne–1) is the water footprint inventory of commodity c in each river basin b assuming a constant water consumption of 90 mm of irrigation per crop cycle (converted to m3 tonne–1) across the river basins following early planting practices in Brazil assumed to be widespread (47) for the purposes of the case study. Values of CFbc are the AWARE characterization factors for irrigation, as defined in Boulay et al. (Table S2 and Figure S2). (9)
We used Trase (trase.earth) for Brazilian soybean (v.2.5.0) (48) in 2017 to derive the CSM for the soybean supply chain and obtain the potential impacts of land occupation and water consumption from eqs 4 and 5. Trase is based on an improved supply chain map previously described by Godar et al., (49) which uses soybean exports and per-shipment trade data to link Brazilian municipalities of soybean production to import countries. In a first step, per-shipment trade records are used to identify the municipalities of taxation containing either farms, silos, or wholesale retailing (i.e., trader assets) by combining information from trade records (i.e., country of destination, cargo owner (trader), Brazilian state of production), tax records, and a list of trader assets. In a second step, the municipality of soybean production is identified by the minimum cost flow analysis using linear programming (50) that is optimized considering a combination of a trader’s asset location (with silos identified using information on economic activity linked to the tax information), soybean demand (domestic and export demand), and transportation costs. The result is a Brazilian subnational supply chain map linking a municipality of production to a country of destination and the trader/port combination for each transaction. Municipalities acting as sources for our markets of interest in 2017 (Brazil, China, the EU, France, and the RoW) were then classified into source ecoregions and river basins following two classification methods (see the Uncertainty Analysis section) prior to deriving the CSM for each market based on the supply volume of soybean exported.

Uncertainty Analysis

We accounted for three sources of uncertainty in the case study. First, we considered uncertainty in the classification of each municipality into ecoregions and river basins to ensure the results were not affected by zoning (also described as the “modifiable areal unit problem”). (14) The soybean supply chain mapped in Trase links a Brazilian municipality of production to the market of interest; as a result, a classification of each municipality within each ecoregion and river basin was necessary to select the appropriate characterization factors. However, a problem arises when attempting to classify a municipality located at the border of two adjacent ecoregions or river basins, leading to the selection of one characterization factor over another. We therefore considered two classification schemes following (1) the majority soybean area (labeled MS hereafter) and (2) the majority municipality area (MA). In the MS classification method, municipalities were part of an ecoregion or river basin if the majority of the soybean cropland area in the municipality (as determined using soybean crop maps from Song et al. (51) and available in Trase) were within that boundary. In the MA classification method, municipalities were part of an ecoregion or a river basin if the majority of the municipality’s area was within their limits. Second, we accounted for uncertainty in tc,occ from eq 4 (mean = 0.34 yr (125 days), upper bound = 0.38 yr (149 days), and lower bound = 0.30 yr (110 days)) following Flach et al. (43) to represent the share of soybean occupation time in the annual crop cycle, as many regions of Brazil harvest more than one crop per year (soybean is often followed by corn). Finally, we accounted for the uncertainty in the characterization factors for land occupation (eq 4) as Chaudhary and Brooks (44) provide a distribution (mean, upper, and lower 95% confidence intervals) for each of their characterization factors. Impact assessment results assume no uncertainty in the inverse yield for land occupation (Aic,occ) nor any uncertainty in the water consumption for irrigation (WFIbc and CFbc).
The Brazilian soybean supply chain map is based on trade data and a complete list of silos identified with company ownership that can be linked to exporters in the trade data. In addition, the links between the trade hubs and the municipalities of production represent those with the lowest cost of transportation, therefore illustrating the highest probable supply chain based on available information. In cases where these connections could not be made due to lack of information in the combined data sets (e.g., lack of information on the Brazilian state of production, missing trader, or ownership information), the soybean was labeled as having “unknown origin”. In those cases, impacts were calculated using an LCI derived from the national mean yield. This means that the soybean of unknown origin was assigned the impact of the Brazilian soybean production mix.

Interpretation of Results

Impact assessment results are presented as a weighted average and as a probability density of impact scores from the spatial variability across ecoregions and river basins. The weighted average is obtained by taking the sum of the impact scores for each municipality supplying soybean to each market, weighted by their individual CSM. We interpreted these results as the impacts of the average tonne of soybean exported to each market. They are compared against the Brazilian soybean production mix or the weighted average obtained by summing the impact scores for all municipalities that produced soybean in 2017, (46) weighted by the contribution of each municipality to total production.
We also present the probability density of impact scores following the spatial variability of results for each municipality that produced and supplied markets with soybean, according to five cases. These cases are meant to represent different options following a gradual increase in information available on commodity sourcing:

  • “Production” case (P) for which impact scores are derived assuming an equal probability of soybean supply to markets. In mathematical terms, the values of the LCI (mis in eq 1) are obtained in each of the Brazilian municipalities and then divided by the number of municipalities (2275). This case can be interpreted as a typical option available for an analyst who would only have information on Brazilian soybean production.

  • “Production mix” (PM) for which the probability of impact scores is distributed according to the contribution of each municipality to the total Brazilian soybean production. This case is similar to the P case, but for which a weighting factor accounts for the variability in soybean production across Brazil, assuming that markets have a greater probability of sourcing soybean from municipalities with greater production. This can also be interpreted as a typical option available for an analyst seeking to include more regional variability in the LCI.

  • “Production mix to market” (PMM) is the same as the PM case but is qualitatively augmented by the information provided by the trade connections between municipalities of soybean production and each market (i.e., trade volumes are not considered). In other words, the impact scores are obtained using information on municipal soybean production but reduced to those municipalities identified as suppliers of each of the markets. This case can be interpreted as an option resulting from in-country research or specific supply chain research carried out for the LCA.

  • “Commodity supply mix” (CSM) for which the probability of impact scores follows the CSM.

  • “Consumption boundary” (B) for which the probability of impact scores is derived assuming an equal probability of sourcing from the ecoregions or river basin boundaries identified in Trase. This case can be interpreted as an option resulting from general tendencies of supply focused specifically on ecoregion or river basin boundaries without additional knowledge on the amount of soybean sourced from these geographic boundaries.

In short, the above cases are meant to represent different options available for the LCA study considering limited information on regional production (as P, PM, B) or some information on supply chains (PMM, CSM).
Analysis was carried out, and graphs were generated using R statistical software (v.4.0.5) (52) in R Studio (v.1.4.1103), (53) with the tidyverse, (54)rgdal, (55)sf, (56) and patchwork (57) packages.

Results

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Commodity Supply Mix and Life Cycle Inventory

Brazil supplied 98.9 Mtonnes of soybean (as whole bean) in 2017, 93% of which was traced back to a municipality of origin (the remaining 7% could not be linked to a municipality of origin). The CSM varied for each market: spanning 12 ecoregions and 17 river basins for domestically consumed soybean, 11 ecoregions (Figure 2) and 19 river basins for exports to China, 12 ecoregions and 18 river basins (Figure 3) for exports to the EU (including France), 7 ecoregions and 9 river basins for France (Table 2), and 12 ecoregions and 20 river basins for exports to the RoW (values reported considering CSM ≥ 0.001; Tables S3 and S4 and Figures S3–S10). The Cerrado was the ecoregion that typically provided the majority of soybean to all markets with CSM values approaching 0.50. When combined with the Araucaria moist forests, Chiquitano dry forest, Parana-Paranaiba interior forests, Mato Grosso tropical dry forests, and the Uruguayan savanna, the sum of CSM values reached between 0.77 (France) and 0.98 (Brazil) (Tables 2 and S3). Similarly, the Amazon, São Francisco, and the La Plata were key source river basins for soybean, with combined CSM values ranging between 0.69 (China) and 0.91 (France) (Tables 2 and S4).

Figure 2

Figure 2. Spatially explicit life cycle inventory for land occupation (m2 yr tonne–1) for Brazilian soybean exported to China in 2017. Values shown are specific to the majority soybean classification of municipalities into the ecoregions (37) and mean land occupation time. (35) Results for Brazil’s domestic consumption and exports to the EU, France, and the RoW are shown in Figures S3–S6.

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Figure 3

Figure 3. Spatially explicit water footprint inventory per river basin (m3 tonne–1) for Brazilian soybean exported to the EU in 2017. Values shown are specific to the majority soybean classification of municipalities into river basins. (9) Results for Brazil’s domestic consumption and exports to China, France, and the RoW are shown in Figures S7–S10.

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Table 2. Commodity Supply Mix (CSM, Unitless) of Ecoregions and River Basins for Brazilian Soybean Destined to Each Market in 2017 (for Full Tables, See Tables S3 and S4 in the Supporting Information)a
 BrazilChinaEUFranceRoW
MS-MAMS-MAMS-MAMS-MAMS-MA
Ecoregions
Araucaria moist forests0.178–0.1790.131–0.1320.023–0.0230.097–0.1100.087–0.086
Cerrado0.489–0.4820.414–0.4040.490–0.4820.485–0.4770.474–0.466
Chiquitano dry forest0.005–0.0070.002–0.0010.039–0.0380.121–0.1190.010–0.007
Parana-Paraiba interior forests0.189–0.1920.125–0.1380.026–0.0260.071–0.0710.117–0.116
Mato Grosso tropical dry forests0.048–0.0490.061–0.0690.239–0.2490.097–0.1100.132–0.144
Uruguayan savanna0.074–0.0700.121–0.1130.001–0.0010–00.053–0.052
other0.017–0.0190.145–0.1440.182–0.1810.227–0.2230.128–0.128
River Basins (Number of Sub-basins)
Amazon0.109–0.113 (8)0.156–0.158 (7–6)0.552–0.560 (5–4)0.705–0.706 (5–4)0.313–0.315 (8–7)
La Plata0.641–0.653 (3)0.478–0.480 (3)0.086–0.083 (3)0.071–0.071 (2–1)0.415–0.411 (3)
Sao Francisco0.054–0.054 (1)0.055–0.066 (1)0.083–0.091 (1)0.137–0.137 (1)0.039–0.052 (1)
other0.196–0.180 (15)0.311–0.296 (20)0.280–0.266 (16)0.087–0.087 (3–2)0.232–0.222 (18)
a

Ranges represent the values obtained for the CSM following the majority soybean area (MS) and majority municipality area (MA) classification methods into ecoregions and river basins.

The land occupation LCI was 884–1120 m2 yr tonne–1 for Brazilian domestic consumption, 885–1120 m2 yr tonne–1 for soybean exported to China, 929–1176 m2 yr tonne–1 to the EU, 919–1041 m2 yr tonne–1 to France, and 900–1140 m2 yr tonne–1 to the RoW (Figure 4, left) (ranges represent the uncertainty from and land occupation times as per Flach et al.). (43) The water footprint inventory was 254 m3 tonne–1 for Brazil (domestic market), 265 m3 tonne–1 for exports to China, 279 m3 tonne–1 to the EU, 276 m3 tonne–1 to France, and 270 m3 tonne–1 to RoW (Figure 5, left). These values can be compared to the results obtained for the Brazilian soybean production mix, which was 888–1125 m2 yr tonne–1 (for land occupation) and 266 m3 tonne–1 (for water consumption) (Figures 4 and 5, left; production mix are represented by dashed lines).

Figure 4

Figure 4. Life cycle inventory and potential damage to biodiversity for 1 tonne of soybean domestically consumed in Brazil and exported to China, the EU, France, and the rest of the world (RoW) in 2017. Results are broken down into the source ecoregions and compared to the Brazilian soybean production mix (dashed line) considering the mean characterization factors for Brazilian ecoregions (36) and mean land occupation time. (35) Values shown are specific to the majority soybean area classification of municipalities into ecoregions. (37) Ecoregions hosting less than 4% of total life cycle inventory or damage to biodiversity were grouped into “Other”. The ecoregion “Unknown” refers to trade data for which no known source ecoregion could be determined.

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Figure 5

Figure 5. Water footprint inventory and water scarcity footprint for 90 mm of irrigation used in the crop development cycle for 1 tonne of soybean domestically consumed in Brazil and exported to China, the EU, France, and the rest of the world (RoW) in 2017. Results are broken down into source river basins and compared to the production mix (dashed line) considering the characterization factors for Brazilian river basins. (9) Sub-basins were aggregated into larger basins for clarity (Table S2). The river basin Unknown refers to trade data for which no known source ecoregion could be determined.

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Life Cycle Impact Assessment

Spatially explicit land occupation LCI and water footprint inventory weighted by trade volume through the CSM were matched with the ecoregion and river basin characterization factors to derive the potential biodiversity damage from land occupation (Figure 4, right) and the water scarcity footprint (Figure 5, right) for each market. These results represent a weighted average potential biodiversity damage of land occupation or water scarcity footprint of soybean sourced from different Brazilian ecoregions and river basins and destined to the respective markets. The potential biodiversity damage of Brazil’s soybean production mix due to land occupation was 2.08 × 10–10 PDF yr tonne–1, a value that was greater than the damage for soybean exported to the EU (1.48 × 10–10 PDF yr tonne–1), France (1.61 × 10–10 PDF yr tonne–1), and the RoW (1.91 × 10–10 PDF yr tonne–1) but lower when compared to Brazil’s domestic market (2.28 × 10–10 PDF yr tonne–1) and exports to China (2.10 × 10–10 PDF yr tonne–1) (all reported results consider both MS classification of municipalities into ecoregions, mean characterization factors, (44) and land occupation time) (43) (Figure 4, right, and Table S5).
Differences in impact scores were mostly due to less sourcing in the Araucaria moist forests and the Parana-Paraiba interior forests for both the EU and France, as well as a lower percentage of potential damage attributed to unknown sources in the soybean supply chain for the EU (5.6%) and France (7.3%) compared to that of China (12.9%) and the RoW (8.9%) and for which a national characterization factor was used. The water scarcity footprint of Brazil’s soybean production mix was 292 m3 tonne–1. This value was greater than the scores obtained for soybean consumed in Brazil (250 m3 tonne–1) and soybean exported to France (259 m3 tonne–1) and the EU (421 m3 tonne–1) but lower than soybean exported to China (301 m3 tonne–1) and the RoW (329 m3 tonne–1) (range representing estimates from the MS classification into river basins) (Figure 5, right, and Table S7). The water scarcity footprint was larger for soybean exported to the EU due to greater sourcing in the Paranaiba river basin in the southwest of the country.

Uncertainty and Probability Density

There was no statistically significant difference between impact scores when comparing distributions from the MS or MA classification of municipalities into ecoregions or river basins (t-test, p > 0.05 for all markets). When accounting for uncertainty in the characterization factors for biodiversity and the land occupation times in the LCI, the impact scores showed different ranges based on the market of interest (Tables S5 and S6). The EU’s weighted average potential biodiversity damage was 1.10–2.01 × 10–10 PDF yr tonne–1, which was lower than any other market: 1.73–3.03 × 10–10 PDF yr tonne–1 for soybean consumed in Brazil, 1.20–2.18 × 10–10 PDF yr tonne–1 for the EU, 1.43–2.56 × 10–10 PDF yr tonne–1 for the RoW, and 1.58–2.80 × 10–10 PDF yr tonne–1 for China (values representing the range obtained with MS classification).
The probability of impacts from production (P) to boundary (B) cases showed differences in the ranges of impacts for each market (Figures S11–S19), but particularly for France where the probability of potential biodiversity damage and mean impact scores changed between PM, PMM, and CSM cases (Figure 6). Statistically significant differences in mean impact scores were observed when comparing the production mix (PM) and the CSM cases for potential biodiversity damage (p < 0.05 for Brazil, China, and France) and water scarcity footprint (p < 0.05 for Brazil, EU, and France). The water scarcity footprint for soybean consumed domestically was the only market that had a significant difference in the mean impact score between the production mix for market (PMM) and CSM cases (Table S8).

Figure 6

Figure 6. Probability density of potential biodiversity damage (median (line) and mean (point)) for 1 tonne of soybean exported to France in 2017 following distributions obtained in five cases: production (P), production mix (PM), production mix to market (PMM), commodity supply mix (CSM), and boundary (B) as ecoregion. Values shown are specific to the majority soybean classification of municipalities into ecoregions (37) considering mean characterization factors (36) and land occupation time. (35) Probability densities for other markets and water scarcity footprints are available in the Supporting Information (Figures S11–S19).

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Discussion

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Improving Regionalization in Life Cycle Assessments

The Global Guidance to include potential impacts to biodiversity and water use in LCA (16) implies a reliable level of regionalization that can differentiate spatial variability in ecoregion land occupation and river basin water use. Results from the CSM can facilitate the operationalization of the Global Guidance recommendations and improve LCI spatialization by applying the most regionally relevant characterization factors. Previous research has shown the importance of scale in representing the variability of LCI and its effects on impact assessment results for corn produced and traded within the United States. (26,28,29) While spatial variability may not be an important factor to consider for some impact categories (e.g., CO2 emissions as in Yang (29)), other impact categories such as water scarcity (28,29) or biodiversity (this study) can benefit from further inventory spatialization. This spatial variability in the LCI is particularly relevant in the LCAs of more complex food products that rely on ingredients made from agricultural commodities such as soybean. The production and supply of these ingredients may be considered as a background process in the LCA of a food product for which information on spatial variability is often not available in online databases, (29) thus affecting the land and water use impact assessment results. Accurate information on exact sourcing of commodities can be challenging and costly to obtain. As a result, practitioners may need to make assumptions, consider different scenarios on the sourcing of ingredients, or may need to consult with experts to construct the supply chain of a specific product to include biodiversity and water use impacts (e.g., as in Milà i Canals et al. (23)). If widely applied, the CSM can help facilitate input data for LCAs using, for instance, the weighted average derived from the spatial variability to be included in the LCA of a product using Brazilian soybean as an ingredient.
The CSM relies on knowledge of the supply chain whose resolution (beyond country–country trade) can further improve LCI regionalization. It partially resembles what Yang (15) called the regional output percentage (ROP) approach in LCA for which a percentage output is allocated to different source regions. We see the use of a CSM to be adequate in cases where information is limited on the exact sourcing of a commodity, as is commonly the case for agricultural commodities. The CSM is applicable at the country level when considering international trade information, such as from COMTRADE (comtrade.un.org), from input–output, (33,34) or other physical trade methods, (32) but is more informative at a subnational sourcing level when information is available on source regions. In this study, we used Trase, which allowed for LCI regionalization considering the variability in soybean yields among the Brazilian municipalities, but other subnational supply chain maps may be used (e.g., as in Smith et al. (26) or Lin et al. (35)). Beyond applications for land occupation and water use, the CSM could also be complementary to identifying the source location of elementary flows for other impact categories. For instance, the results of this study could be complemented with production practices more generally (e.g., fertilizer application, management practices) to derive greenhouse gas emissions (58) or information relevant to social LCA as long as the information is available at the Brazilian municipality level.
Results obtained with the CSM show differences in biodiversity damage and water scarcity footprint among the different markets, while they also, in some cases, show differences with the results that would have been obtained using other assumptions about the soybean supply chain (e.g., production (P), production mix (PM), and consumption boundary (B)). The CSM could therefore be used as a method for prioritizing regionalization efforts in LCA such as that described by Patouillard et al. (18) using additional supply chain information in cases where spatial variability in the LCI is expected to have an important effect on the impact assessment results. For some markets, the impact scores were different when using a production mix compared to that of a CSM, highlighting the benefit to further spatialize the LCI while also allowing for a geographic hotspot analysis (see below). The uncertainty in the results of our case study came from the uncertainty in both the LCI and LCIA phases, considering differences in the occupation time (LCI) and the confidence interval of the characterization factors (LCIA). Uncertainty in the occupation time depends on either environmental factors, such as the photoperiod and accumulated rainfall, (59) or farming practices (e.g., double cropping), while uncertainty in the characterization factors depends on geographic variability within the same region. The lack of further information on farming practices (e.g., to resolve the occupation time per municipality) or the spatial variability in the characterization factors from Chaudhary and Brooks (44) shows a potential limit to further regionalization in our study based on existing information to resolve biodiversity damage across markets. Moreover, the results obtained from the CSM are only as good as the supply chain that is used to derive it. While Trase relies on detailed per-shipment trade data, the Brazilian soybean supply chain represents an average picture of relationships between municipalities of production and market in any given year with a certain level of stickiness, (60) keeping in mind that a small portion of these relationships could not be resolved due to missing information in the data.

Toward More Targeted Regional Hotspots

Different markets showed different sourcing patterns specific to their own soybean supply chain, which resulted in distinct compositions of the land occupation and water use elementary flows and impact assessment results. The results in this study for Brazilian soybean complement existing soybean LCAs with cradle-to-farm gate system boundaries (61−64) by expanding boundaries to include markets. The regionalization of impacts also highlighted geographic hotspots to address potential biodiversity and water use impacts in geographic areas of concern. For instance, the Cerrado ecoregion was a source of soybean for all markets and is an important biodiversity hotspot, which has experienced rampant deforestation these past 20 years. (65) Similar to our case study, Green et al. (66) used Trase to assess the impacts of soybean exported from the Cerrado biome on endemic species as a means to highlight the role of importing countries on biodiversity. Here, we used Trase as an input into a CSM that considers all of Brazil’s ecoregions and river basins to provide a more regionalized LCI and derive potential impacts from land and water uses following the methods recommended in the Global Guidance. (5) A geographic hotspot analysis in LCA can complement the current life cycle hotspot analysis, which has been the general objective of studies linking Brazilian soybean production to consumption centers, (37,58,67) and may be a bridge to combining both the territorial and supply chain perspectives in LCA. General efforts to improve transparency of supply chains can also underscore roles and responsibilities of countries and companies as supply chain actors, as expressed in recent commitments to zero deforestation in supply chains. (68)
In this paper, we introduced the CSM as a method to facilitate further regionalization in LCAs while at the same time allowing for an allocation of impacts to markets. This proposed approach is relevant for analysts with limited visibility on production and export of commodities with complex supply chains seeking to apply recent guidelines on land and water uses in LCA. The case study of the Brazilian soybean production and export revealed a variability in potential impacts on biodiversity and water scarcity based on the main ecoregions and river basins, and the markets identified in the soybean supply chain. It also revealed how increasing information on the supply chain can help redistribute impact scores based on increasing information from the consumption perspective. Further examples of the CSM require greater understanding of national and international supply chains to provide a new set of LCI information for more regionally relevant LCAs using detailed and enhanced material flow analysis or input–output tables. Finally, the application of the CSM should also be tested in the context of consequential LCA and territorial LCA to assess its effect on decision-making.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.1c03060.

  • Ecoregion and river basin maps of Brazil; spatially explicit life cycle inventory for ecoregion land use and water footprint inventory per river basin; probability density of potential damage to biodiversity and water scarcity footprint (PDF)

  • Comparison of impact scores from probability density curves (t-test) (XLSX)

  • Life cycle inventory results from the case study (XLSX)

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

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  • Corresponding Author
  • Authors
    • Laure Patouillard - CIRAIG, Polytechnique Montreal, 3333 Queen Mary Rd suite 310, Montreal, Quebec H3V 1A2, Canada
    • Manuele Margni - CIRAIG, Polytechnique Montreal, 3333 Queen Mary Rd suite 310, Montreal, Quebec H3V 1A2, Canada;  HES−SO, University of Applied Sciences and Arts Western Switzerland, Institute of Sustainable Energy, School of Engineering, Rue de l’Industrie 23, 1950 Sion, Switzerland
    • Ben Ayre - Global Canopy, 3 Frewin Court, Oxford OX1 3HZ, U.K.
    • Pernilla Löfgren - Stockholm Environment Institute, Linnégatan 87D, Box 24218, 104 51 Stockholm, Sweden
    • Vivian Ribeiro - Stockholm Environment Institute, Linnégatan 87D, Box 24218, 104 51 Stockholm, Sweden
    • Chris West - Department of Environment and Geography, Environment Building, Stockholm Environment Institute York, Wentworth Way, University of York, York YO10 5NG, U.K.
    • Toby A. Gardner - Stockholm Environment Institute, Linnégatan 87D, Box 24218, 104 51 Stockholm, Sweden
    • Clément Suavet - Stockholm Environment Institute, 400 F Street, Davis, California 95616, United States
  • Author Contributions

    The initial draft of the manuscript was written by M.J.L. with input from all authors. Conceptualization was done by M.J.L., L.P., M.M., and C.W. Data organization and access were by B.A., P.L., and C.S. GIS work was done by P.L. and V.R. All authors have given approval to the final version of the manuscript.

  • Funding

    This research was supported by the Gordon and Betty Moore Foundation (no. 7703), Norway’s International Climate and Forest Initiative (no. 18/3871), and the UKRI Global Challenges Research Fund Trade, Development and the Environmental Hub project (ES/S008160/1).

  • Notes
    The authors declare no competing financial interest.

Acknowledgments

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The authors thank Matthew Hansen from the University of Maryland (Global Land Analysis and Discovery) for sharing soybean crop maps, as well as Bart Wickel, Toby Reid, Andrew Feierman, and Harry Biddle of the Stockholm Environment Institute (Sweden and USA) for technical support. The authors especially thank Erasmus zu Ermgassen for feedback on an earlier version of the manuscript, as well as three anonymous reviewers for their comments and valuable input in the peer-review process.

Abbreviations

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B

consumption boundary case

CSM

commodity supply mix (and case)

LCA

life cycle assessment

LCI

life cycle inventory

LCIA

life cycle impact assessment

MA

majority area classification

MS

majority soybean classification

P

production case

PM

production mix case

PMM

production mix to market case

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    Environmental Science & Technology (2009), 43 (15), 5797-5803CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    Life cycle assessment (LCA) studies showed that site-dependent impact assessment for categories like acidification and eutrophication give more accurate, realistic results than site-generic assessments. To date, existing geog.-specific or regionalized impact assessment factors have not been applied to LCA databases and software tools. A simple, generic method to couple existing regionalized characterization factors with large life cycle inventory databases is discussed. This approach allows for detailed geog. life cycle impact assessment results. Case study results for European country-specific electricity mixes were calcd. using the Ecoinvent 2.01 database and the EDIP 2003 and Accumulated Exceedance impact assessment methods and CASES project external energy cost characterization factors. In most cases, regionalization showed different total scores, different processes of high importance, and varying geog. distribution of environmental impacts. Since the method requires no addnl. input other than geog. information already in existing LCA databases, it can be used routinely. Better, more consistent geog. information in life cycle inventory databases and impact assessment methods, tailored to specific spatial ranges of all environmental effects considered, would be beneficial.
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    Mutel, C. L.; Pfister, S.; Hellweg, S. GIS-Based Regionalized Life Cycle Assessment: How Big Is Small Enough? Methodology and Case Study of Electricity Generation. Environ. Sci. Technol. 2012, 46, 1096– 1103,  DOI: 10.1021/es203117z
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    GIS-Based Regionalized Life Cycle Assessment: How Big Is Small Enough? Methodology and Case Study of Electricity Generation
    Mutel, Christopher L.; Pfister, Stephan; Hellweg, Stefanie
    Environmental Science & Technology (2012), 46 (2), 1096-1103CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    New methodol. is described for performing regionalized life cycle assessment and systematically choosing the spatial scale of regionalized impact assessment methods. Std. matrix-based calcns. are extended to include matrixes that describe the mapping from inventory to impact assessment spatial supports. Uncertainty in inventory spatial data is modeled using a discrete spatial distribution function, which in a case study is derived from empirical data. The minimization of global spatial autocorrelation is used to choose the optimal spatial scale of impact assessment methods. These techniques are demonstrated on electricity prodn. in the United States, using regionalized impact assessment methods for air emissions and freshwater consumption. Case study results show important differences between site-generic and regionalized calcns., and provide specific guidance for future improvements of inventory data sets and impact assessment methods.
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    Yang, Y. Toward a More Accurate Regionalized Life Cycle Inventory. J. Cleaner Prod. 2016, 112, 308– 315,  DOI: 10.1016/j.jclepro.2015.08.091
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    Bulle, C.; Margni, M.; Patouillard, L.; Boulay, A.-M.; Bourgault, G.; De Bruille, V.; Cao, V.; Hauschild, M.; Henderson, A.; Humbert, S.; Kashef-Haghighi, S.; Kounina, A.; Laurent, A.; Levasseur, A.; Liard, G.; Rosenbaum, R. K.; Roy, P.-O.; Shaked, S.; Fantke, P.; Jolliet, O. IMPACT World+: A Globally Regionalized Life Cycle Impact Assessment Method. Int. J. Life Cycle Assess. 2019, 24, 1653– 1674,  DOI: 10.1007/s11367-019-01583-0
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    IMPACT World+: a globally regionalized life cycle impact assessment method
    Bulle, Cecile; Margni, Manuele; Patouillard, Laure; Boulay, Anne-Marie; Bourgault, Guillaume; De Bruille, Vincent; Cao, Viet; Hauschild, Michael; Henderson, Andrew; Humbert, Sebastien; Kashef-Haghighi, Sormeh; Kounina, Anna; Laurent, Alexis; Levasseur, Annie; Liard, Gladys; Rosenbaum, Ralph K.; Roy, Pierre-Olivier; Shaked, Shanna; Fantke, Peter; Jolliet, Olivier
    International Journal of Life Cycle Assessment (2019), 24 (9), 1653-1674CODEN: IJLCFF; ISSN:0948-3349. (Springer)
    Purpose: This paper addresses the need for a globally regionalized method for life cycle impact assessment (LCIA), integrating multiple state-of-the-art developments as well as damages on water and carbon areas of concern within a consistent LCIA framework. This method, named IMPACT World+. Methods: With IMPACT World+, we propose a midpoint-damage framework with four distinct complementary viewpoints to present an LCIA profile: (1) midpoint impacts, (2) damage impacts, (3) damages on human health, ecosystem quality, and resources. Results and discussion: We analyze the magnitude of global potential damages for each impact indicator, based on an estn. of the total annual anthropogenic emissions and extns. at the global scale (i.e., "doing the LCA of the world"). Similarly with ReCiPe and IMPACT 2002+, IMPACT World+ finds that (a) climate change and impacts of particulate matter formation have a dominant contribution to global human health impacts whereas ionizing radiation, ozone layer depletion, and photochem. Conclusions: IMPACT World+ provides characterization factors within a consistent impact assessment framework for all regionalized impacts at four complementary resolns.: global default, continental, country, and native (i.e., original and non-aggregated) resolns. IMPACT World+ enables the practitioner to parsimoniously account for spatial variability and to identify the elementary flows to be regionalized in priority to increase the discriminating power of LCA.
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    Faragò, M.; Benini, L.; Sala, S.; Secchi, M.; Laurent, A. National Inventories of Land Occupation and Transformation Flows in the World for Land Use Impact Assessment. Int. J. Life Cycle Assess. 2019, 24, 1333– 1347,  DOI: 10.1007/s11367-018-01581-8
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    Patouillard, L.; Bulle, C.; Margni, M. Ready-to-Use and Advanced Methodologies to Prioritise the Regionalisation Effort in LCA. Matériaux Tech. 2016, 104, 105  DOI: 10.1051/mattech/2016002
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    Patouillard, L.; Collet, P.; Lesage, P.; Tirado Seco, P.; Bulle, C.; Margni, M. Prioritizing Regionalization Efforts in Life Cycle Assessment through Global Sensitivity Analysis: A Sector Meta-Analysis Based on Ecoinvent V3. Int. J. Life Cycle Assess. 2019, 24, 2238– 2254,  DOI: 10.1007/s11367-019-01635-5
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    Patouillard, L.; Lorne, D.; Collet, P.; Bulle, C.; Margni, M. Prioritizing Regionalization to Enhance Interpretation in Consequential Life Cycle Assessment: Application to Alternative Transportation Scenarios Using Partial Equilibrium Economic Modeling. Int. J. Life Cycle Assess. 2020, 25, 2325  DOI: 10.1007/s11367-020-01785-x
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    Prioritizing regionalization to enhance interpretation in consequential life cycle assessment: application to alternative transportation scenarios using partial equilibrium economic modeling
    Patouillard, Laure; Lorne, Daphne; Collet, Pierre; Bulle, Cecile; Margni, Manuele
    International Journal of Life Cycle Assessment (2020), 25 (12), 2325-2341CODEN: IJLCFF; ISSN:0948-3349. (Springer)
    The main purpose of this article is to prioritize regionalization efforts to enhance interpretation in C-LCA by assessing the spatial uncertainty of a case study building on a partial equil. economic model. Results show that the implementation of alternative transport scenarios in compliance with public policy for the energy transition in France is beneficial for some impact categories (ICs) (global warming, marine acidification, marine eutrophication, terrestrial acidification, thermally polluted water, photochem. oxidant formation, and particulate matter formation), with a confidence level of 95%. For other ICs, uncertainty redn. is required to det. conclusions with a similar level of confidence. Input variables with spatial variability from the partial equil. economic model are significant contributors to the C-LCA spatial uncertainty and should be prioritized for spatial uncertainty redn. In addn., characterization factors are significant contributors to the spatial uncertainty results for all regionalized ICs (except land occupation IC). Ways to reduce the spatial uncertainty from economic modeling should be explored. Uncertainty redn. to enhance the interpretation phase and the decision-making should be prioritized depending on the goal and scope of the LCA study. In addn., using regionalized CFs in C-LCA seems to be relevant, and C-LCA calcn. tools should be adapted accordingly.
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    Mutel, C. Brightway: An Open Source Framework for Life Cycle Assessment. J. Open Source Software 2017, 2, 236  DOI: 10.21105/joss.00236
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    Pfister, S.; Oberschelp, C.; Sonderegger, T. Regionalized LCA in Practice: The Need for a Universal Shapefile to Match LCI and LCIA. Int. J. Life Cycle Assess. 2020, 25, 1867– 1871,  DOI: 10.1007/s11367-020-01816-7
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    Milà i Canals, L.; Rigarlsford, G.; Sim, S. Land Use Impact Assessment of Margarine. Int. J. Life Cycle Assess. 2013, 18, 1265– 1277,  DOI: 10.1007/s11367-012-0380-4
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    Land use impact assessment of margarine
    Mila i Canals, Llorenc; Rigarlsford, Giles; Sim, Sarah
    International Journal of Life Cycle Assessment (2013), 18 (6), 1265-1277CODEN: IJLCFF; ISSN:0948-3349. (Springer)
    Purpose This paper presents a case study of margarine, demonstrating the application of new characterization factors (CF) for land use and a no. of land use change impacts relating to biodiversity and ecosystem services. The objectives of this study were to generate insights as to the ease of applying these new factors and to assess their value in describing a no. of environmental impacts from land use and land use change relating to the margarine product system. Methods This case study is a partial descriptive life cycle assessment (LCA) of margarine. The functional unit of the study is 500 g of packaged margarine used as a spread in the UK and Germany. The life cycle stages included were: agricultural prodn., oil processing, margarine manuf. and transportation to regional European distribution centers. Essential for the application of the new CF was the identification and quantification of the inventory flows for land occupation (land use) and land transformation (land use change) flows. A variety of methods have been applied to det. the inventory flows for the agricultural and industrial stages in the life cycle. These flows were then assessed using the new CF and land use-related environmental impact categories recommended in this special issue. Results and discussion Land occupation was the major determinant for all the new impact categories with the exception of the water purifn. potential. Many of the impact categories followed a similar pattern and therefore, the inventory result for land occupation in this case study explains a large share of most of the impacts. Where land occupation alone is not a suitable proxy for environmental impacts (i.e. for freshwater regulation potential), differentiation at the level of biomes has proven relevant. In addn., the land use types distinguished so far were found to be useful in highlighting likely hotspots in the life cycle, although further differentiation of agricultural land' is suggested to account for the differences between annual and permanent crops. Conclusions The new land use impact assessment methods applied help to identify hotspots in the life cycle of margarines, with different proportions and sources of vegetable oils. The specific impacts of each vegetable oil are detd. mainly by the yield (and thus land occupation), but also by the type of agriculture (annual vs. permanent crops) and the sourcing location (and thus the sensitivity of biomes and occurrence of land use change). More research is needed to understand the usefulness of the various impact categories. For land use types, further refinement is required to describe different agricultural systems consistently across impact categories (e.g. annual vs. permanent cropping). In addn., the conceptual basis for the CFs applied in this case study (i.e. use of a potential ref. for occupation and transformation) has limitations for some types of decisions normally supported by LCA.
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    Quinteiro, P.; Dias, A. C.; Pina, L.; Neto, B.; Ridoutt, B. G.; Arroja, L. Addressing the Freshwater Use of a Portuguese Wine (’vinho Verde’) Using Different LCA Methods. J. Cleaner Prod. 2014, 68, 46– 55,  DOI: 10.1016/j.jclepro.2014.01.017
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    Lathuillière Michael, J.; Miranda, E. J.; Bulle, C.; Couto, E. G.; Johnson, M. S. Land Occupation and Transformation Impacts of Soybean Production in Southern Amazonia, Brazil. J. Cleaner Prod. 2017, 149, 680– 689,  DOI: 10.1016/j.jclepro.2017.02.120
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    Smith, T. M.; Goodkind, A. L.; Kim, T.; Pelton, R. E. O.; Suh, K.; Schmitt, J. Subnational Mobility and Consumption-Based Environmental Accounting of US Corn in Animal Protein and Ethanol Supply Chains. Proc. Natl. Acad. Sci. U.S.A. 2017, 114, E7891– E7899,  DOI: 10.1073/pnas.1703793114
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    Sub-national mobility and consumption-based environmental accounting of US corn in animal protein and ethanol supply chains
    Smith, Timothy M.; Goodkind, Andrew L.; Kim, Taegon; Pelton, Rylie E. O.; Suh, Kyo; Schmitt, Jennifer
    Proceedings of the National Academy of Sciences of the United States of America (2017), 114 (38), E7891-E7899CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
    Corn prodn. and its assocd. inputs are a relatively large source of greenhouse gas emissions and use significant amts. of water and land, thereby contributing to climate change, fossil fuel depletion, local air pollutants, and local water scarcity. As large consumers of this corn, corporations in ethanol and animal protein industries are increasingly assessing and reporting sustainability impacts across their supply chains to identify, prioritize, and communicate sustainability risks and opportunities material to their operations. In so doing, many discovered the direct impacts of their owned operations are dwarfed by those upstream in the supply chain, requiring transparency and knowledge about environmental impacts along the supply chains. Life cycle assessments (LCA) identified environmental impact hotspots at national levels, yet these provide little sub-national information necessary to guide firm-specific supply networks. This work used the Food System Supply-Chain Sustainability (FoodS3) model to connect spatial, firm-specific demand of corn purchasers with upstream corn prodn. in the US using a cost minimization transport model. This provided the means to link US county-level corn prodn. to firm-specific demand locations assocd. with downstream processing facilities. Model substantially improved current LCA assessment efforts which are confined to broad national or state level impacts. In detg. sub-national environmental impact levels which occur over heterogeneous areas and aggregating these landscape impacts by specific supply networks, targeted opportunities for improved supply chain sustainability performance were identified.
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    Payen, S.; Falconer, S.; Ledgard, S. F. Water Scarcity Footprint of Dairy Milk Production in New Zealand – A Comparison of Methods and Spatio-Temporal Resolution. Sci. Total Environ. 2018, 639, 504– 515,  DOI: 10.1016/j.scitotenv.2018.05.125
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    Water scarcity footprint of dairy milk production in New Zealand - A comparison of methods and spatio-temporal resolution
    Payen, Sandra; Falconer, Shelley; Ledgard, Stewart F.
    Science of the Total Environment (2018), 639 (), 504-515CODEN: STENDL; ISSN:0048-9697. (Elsevier B.V.)
    Water scarcity footprinting now has a consensual life cycle impact assessment indicator recommended by the UNEP/SETAC Life Cycle Initiative called AWaRe. It was used in this study to calc. the water scarcity footprint of New Zealand (NZ) milk produced in two contrasting regions; "non-irrigated moderate rainfall" (Waikato) and "irrigated low rainfall" (Canterbury). Two different spatial and temporal resolns. for the inventory flows and characterization factors (CFs) were tested and compared: country and annual vs. regional and monthly resoln. An inventory of all the consumed water flows was carried out from cradle to farm-gate, i.e. from the prodn. of dairy farm inputs to the milk and meat leaving the dairy farm, including all water uses on-farm such as irrigation water, cow drinking water and cleaning water. The results clearly showed the potential overestimation of a water scarcity footprint when using aggregated CFs. Impacts decreased by 74% (Waikato) and 33% (Canterbury) when regional and monthly CFs were used instead of country and annual CFs. The water scarcity footprint calcd. at the regional and monthly resoln. was 22Lworld eq/kg FPCM (Fat Protein Cor. Milk) for Waikato milk, and 1118Lworld eq/kg FPCM for Canterbury milk. The contribution of background processes dominated for milk from non-irrigated pasture, but was negligible for milk from irrigated pasture, where irrigation dominated the impacts. Results were also compared with the previously widely-used Pfister method (Pfister et al., 2009) and showed very similar ranking in terms of contribution anal. An endpoint indicator was evaluated and showed damages to human health of 7.66×10-5 DALY/kg FPCM for Waikato and 2.05×10-3 DALY/kg FPCM for Canterbury, but the relevance of this indicator for food prodn. needs reviewing. To conclude, this study highlighted the importance of using high-resoln. CFs rather than aggregated CFs.
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    Brauman, K. A.; Goodkind, A. L.; Kim, T.; Pelton, R. E. O.; Schmitt, J.; Smith, T. M. Unique Water Scarcity Footprints and Water Risks in US Meat and Ethanol Supply Chains Identified via Subnational Commodity Flows. Environ. Res. Lett. 2020, 15, 105018  DOI: 10.1088/1748-9326/ab9a6a
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    Unique water scarcity footprints and water risks in US meat andethanol supply chains identified via subnational commodity flows
    Brauman, Kate A.; Goodkind, Andrew L.; Kim, Taegon; Pelton, Rylie E. O.; Schmitt, Jennifer; Smith, Timothy M.
    Environmental Research Letters (2020), 15 (10), 105018CODEN: ERLNAL; ISSN:1748-9326. (IOP Publishing Ltd.)
    Within the US, supply chains aggregate agricultural prodn. and assocd. environmental impacts in specific downstream products and companies. This is particularly important for meat and ethanol, which consume nearly half of global crop prodn. as feed and feedstocks. However, lack of data has thus far limited the ability to trace inputs and impacts of commodity crops through domestic supply chains. For the first time, we use a commodity-flow model to link spatially distributed water resource impacts of corn and soy to individual meat and ethanol processing facilities. This creates transparency in the supply chains, illuminating substantial variation in embedded irrigation water and water scarcity footprints among meat and ethanol processed at different facilities. By calcg. unique blue water scarcity footprints for end-products, we show that beef processed in Iowa or Illinois, for example, has fewer water impacts than chicken processed in California and pork processed in Oklahoma. We find that over 75% of irrigated feed embedded in meat is consolidated in six companies and 39% of irrigated feedstock for ethanol is consolidated in five companies, with potentially neg. impacts to supply costs and risk management. This subnational variation and consolidation of impacts in key supply chains creates opportunities for producers and consumers of agriculture-based products to make management, investment, and sustainability decisions about those products.
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    Yang, Y.; Pelton, R. E. O.; Kim, T.; Smith, T. M. Effects of Spatial Scale on Life Cycle Inventory Results. Environ. Sci. Technol. 2020, 54, 1293– 1303,  DOI: 10.1021/acs.est.9b03441
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    Effects of Spatial Scale on Life Cycle Inventory Results
    Yang, Yi; Pelton, Rylie E. O.; Kim, Taegon; Smith, Timothy M.
    Environmental Science & Technology (2020), 54 (3), 1293-1303CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
    Efforts to compile life cycle inventory (LCI) data at more geog. refined scales or resolns. are growing; however, how the choice of spatial scale may affect LCI results is poorly understood. This work examd. this question using US corn as a case study. Corn prodn. data were compiled at two spatial scales (state and county) to compare how their LCI results may differ for state and national level analyses. For greenhouse gas (GHG) emissions, ests. at the two scales were similar (<20% difference) for most state-level analyses and were basically the same (<5%) for national level anal. For blue water consumption, ests. at the two scales differ more. Results suggested state-level analyses may be an adequate spatial scale for national level GHG anal. and for most state-level GHG analyses of US corn, but may fall short for water consumption due to its large spatial variability. Although county-based LCI may be considered more accurate, they require substantially more effort to compile. Overall, results suggested that study goals, data requirements, and spatial variability are important factors to consider when deciding appropriate spatial scale or pursuing more refined scales.
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    Poore, J.; Nemecek, T. Reducing Food’s Environmental Impacts through Producers and Consumers. Science 2018, 360, 987– 992,  DOI: 10.1126/science.aaq0216
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    Reducing food's environmental impacts through producers and consumers
    Poore, J.; Nemecek, T.
    Science (Washington, DC, United States) (2018), 360 (6392), 987-992CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
    Food's environmental impacts are created by millions of diverse producers. To identify solns. that are effective under this heterogeneity, we consolidated data covering five environmental indicators; 38,700 farms; and 1600 processors, packaging types, and retailers. Impact can vary 50-fold among producers of the same product, creating substantial mitigation opportunities. However, mitigation is complicated by trade-offs, multiple ways for producers to achieve low impacts, and interactions throughout the supply chain. Producers have limits on how far they can reduce impacts. Most strikingly, impacts of the lowest-impact animal products typically exceed those of vegetable substitutes, providing new evidence for the importance of dietary change. Cumulatively, our findings support an approach where producers monitor their own impacts, flexibly meet environmental targets by choosing from multiple practices, and communicate their impacts to consumers.
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    Yang, Y.; Tao, M.; Suh, S. Geographic Variability of Agriculture Requires Sector-Specific Uncertainty Characterization. Int. J. Life Cycle Assess. 2018, 23, 1581– 1589,  DOI: 10.1007/s11367-017-1388-6
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    Sun, Z.; Scherer, L.; Tukker, A.; Behrens, P. Linking Global Crop and Livestock Consumption to Local Production Hotspots. Global Food Secur. 2020, 25, 100323  DOI: 10.1016/j.gfs.2019.09.008
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    Castanheira, E. G.; Freire, F. Greenhouse Gas Assessment of Soybean Production: Implications of Land Use Change and Different Cultivation Systems. J. Cleaner Prod. 2013, 54, 49– 60,  DOI: 10.1016/j.jclepro.2013.05.026
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    Greenhouse gas assessment of soybean production: implications of land use change and different cultivation systems
    Castanheira, Erica Geraldes; Freire, Fausto
    Journal of Cleaner Production (2013), 54 (), 49-60CODEN: JCROE8; ISSN:0959-6526. (Elsevier Ltd.)
    The increase in soybean prodn. as a source of protein and oil is being stimulated by the growing demand for livestock feed, food and numerous other applications. Significant greenhouse gas (GHG) emissions can result from land use change due to the expansion and cultivation of soybean. However, this is complex to assess and the results can vary widely. The main goal of this article is to investigate the life-cycle GHG balance for soybean produced in Latin America, assessing the implications of direct land use change emissions and different cultivation systems. A life-cycle model, including inventories for soybean produced in three different climate regions, was developed, addressing land use change, cultivation and transport to Europe. A comprehensive evaluation of alternative land use change scenarios (conversion of tropical forest, forest plantations, perennial crop plantations, savannah and grasslands), cultivation (tillage, reduced tillage and no-tillage) and soybean transportation systems was undertaken. The main results show the importance of land use change in soybean GHG emissions, but significant differences were obsd. for the alternative scenarios, namely 0.1-17.8 kg CO2eq kg-1 soybean. The original land choice is a crit. issue in ensuring the lowest soybean GHG balance and degraded grassland should preferably be used for soybean cultivation. The highest GHG emissions were calcd. for tropical moist regions when rainforest is converted into soybean plantations (tillage system). When land use change is not considered, the GHG intensity varies from 0.3 to 0.6 kg CO2eq kg-1 soybean. It was calcd. that all tillage systems have higher GHG emissions than the corresponding no-tillage and reduced tillage systems. The results also show that N2O emissions play a major role in the GHG emissions from cultivation, although N2O emission calcns. are very sensitive to the parameters and emission factors adopted.
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    Wernet, G.; Bauer, C.; Steubing, B.; Reinhard, J.; Moreno-Ruiz, E.; Weidema, B. The Ecoinvent Database Version 3 (Part I): Overview and Methodology. Int. J. Life Cycle Assess. 2016, 21, 1218– 1230,  DOI: 10.1007/s11367-016-1087-8
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  • Abstract

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    Figure 1

    Figure 1. Supply chain of a commodity produced and exported from country A (exporter) to country B (importer) before being re-exported to country D. The commodity is sourced from several subregions within country A (regions 1–5) before supplying country B through the trade hub networks (TH1, TH2, TH3). Each region and country can both produce, supply (s), and consume (c) the commodity. Numbers in bold are those used in Table 1 to calculate the commodity supply mix.

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    Figure 2

    Figure 2. Spatially explicit life cycle inventory for land occupation (m2 yr tonne–1) for Brazilian soybean exported to China in 2017. Values shown are specific to the majority soybean classification of municipalities into the ecoregions (37) and mean land occupation time. (35) Results for Brazil’s domestic consumption and exports to the EU, France, and the RoW are shown in Figures S3–S6.

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    Figure 3

    Figure 3. Spatially explicit water footprint inventory per river basin (m3 tonne–1) for Brazilian soybean exported to the EU in 2017. Values shown are specific to the majority soybean classification of municipalities into river basins. (9) Results for Brazil’s domestic consumption and exports to China, France, and the RoW are shown in Figures S7–S10.

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    Figure 4

    Figure 4. Life cycle inventory and potential damage to biodiversity for 1 tonne of soybean domestically consumed in Brazil and exported to China, the EU, France, and the rest of the world (RoW) in 2017. Results are broken down into the source ecoregions and compared to the Brazilian soybean production mix (dashed line) considering the mean characterization factors for Brazilian ecoregions (36) and mean land occupation time. (35) Values shown are specific to the majority soybean area classification of municipalities into ecoregions. (37) Ecoregions hosting less than 4% of total life cycle inventory or damage to biodiversity were grouped into “Other”. The ecoregion “Unknown” refers to trade data for which no known source ecoregion could be determined.

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    Figure 5

    Figure 5. Water footprint inventory and water scarcity footprint for 90 mm of irrigation used in the crop development cycle for 1 tonne of soybean domestically consumed in Brazil and exported to China, the EU, France, and the rest of the world (RoW) in 2017. Results are broken down into source river basins and compared to the production mix (dashed line) considering the characterization factors for Brazilian river basins. (9) Sub-basins were aggregated into larger basins for clarity (Table S2). The river basin Unknown refers to trade data for which no known source ecoregion could be determined.

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    Figure 6

    Figure 6. Probability density of potential biodiversity damage (median (line) and mean (point)) for 1 tonne of soybean exported to France in 2017 following distributions obtained in five cases: production (P), production mix (PM), production mix to market (PMM), commodity supply mix (CSM), and boundary (B) as ecoregion. Values shown are specific to the majority soybean classification of municipalities into ecoregions (37) considering mean characterization factors (36) and land occupation time. (35) Probability densities for other markets and water scarcity footprints are available in the Supporting Information (Figures S11–S19).

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      Jolliet, O.; Antón, A.; Boulay, A.-M.; Cherubini, F.; Fantke, P.; Levasseur, A.; McKone, T. E.; Michelsen, O.; Milà i Canals, L.; Motoshita, M.; Pfister, S.; Verones, F.; Vigon, B.; Frischknecht, R. Global Guidance on Environmental Life Cycle Impact Assessment Indicators: Impacts of Climate Change, Fine Particulate Matter Formation, Water Consumption and Land Use. Int. J. Life Cycle Assess. 2018, 23, 2189– 2207,  DOI: 10.1007/s11367-018-1443-y
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      5
      Global guidance on environmental life cycle impact assessment indicators: impacts of climate change, fine particulate matter formation, water consumption and land use
      Jolliet, Olivier; Anton, Assumpcio; Boulay, Anne-Marie; Cherubini, Francesco; Fantke, Peter; Levasseur, Annie; McKone, Thomas E.; Michelsen, Ottar; Mila i Canals, Llorenc; Motoshita, Masaharu; Pfister, Stephan; Verones, Francesca; Vigon, Bruce; Frischknecht, Rolf
      International Journal of Life Cycle Assessment (2018), 23 (11), 2189-2207CODEN: IJLCFF; ISSN:0948-3349. (Springer)
      Purpose: Guidance is needed on best-suited indicators to quantify and monitor the man-made impacts on human health, biodiversity and resources. Therefore, the UNEP-SETAC Life Cycle Initiative initiated a global consensus process to agree on an updated overall life cycle impact assessment (LCIA) framework and to recommend a non-comprehensive list of environmental indicators and LCIA characterization factors for (1) climate change, (2) fine particulate matter impacts on human health, (3) water consumption impacts (both scarcity and human health) and 4) land use impacts on biodiversity. Methods: The consensus building process involved more than 100 world-leading scientists in task forces via multiple workshops. Results were consolidated during a 1-wk Pellston Workshop in Jan. 2016 leading to the following recommendations. Results and discussion: LCIA framework: The updated LCIA framework now distinguishes between intrinsic, instrumental and cultural values, with disability-adjusted life years (DALY) to characterize damages on human health and with measures of vulnerability included to assess biodiversity loss. Climate change impacts: Two complementary climate change impact categories are recommended: (a) The global warming potential 100 years (GWP 100) represents shorter term impacts assocd. with rate of change and adaptation capacity, and (b) the global temp. change potential 100 years (GTP 100) characterizes the century-scale long term impacts, both including climate-carbon cycle feedbacks for all climate forcers. Fine particulate matter (PM2.5) health impacts: Recommended characterization factors (CFs) for primary and secondary (interim) PM2.5 are established, distinguishing between indoor, urban and rural archetypes. Water consumption impacts: CFs are recommended, preferably on monthly and watershed levels, for two categories: (a) The water scarcity indicator "AWARE" characterizes the potential to deprive human and ecosystems users and quantifies the relative Available WAter REmaining per area once the demand of humans and aquatic ecosystems has been met, and (b) the impact of water consumption on human health assesses the DALYs from malnutrition caused by lack of water for irrigated food prodn. Land use impacts: CFs representing global potential species loss from land use are proposed as interim recommendation suitable to assess biodiversity loss due to land use and land use change in LCA hotspot analyses. Conclusions: The recommended environmental indicators may be used to support the UN Sustainable Development Goals in order to quantify and monitor progress towards sustainable prodn. and consumption. These indicators will be periodically updated, establishing a process for their stewardship.
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    6. 6
      Koellner, T.; de Baan, L.; Beck, T.; Brandão, M.; Civit, B.; Goedkoop, M.; Margni, M.; i Canals, L. M.; Müller-Wenk, R.; Weidema, B.; Wittstock, B. Principles for Life Cycle Inventories of Land Use on a Global Scale. Int. J. Life Cycle Assess. 2013, 18, 1203– 1215,  DOI: 10.1007/s11367-012-0392-0
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    7. 7
      Koellner, T.; de Baan, L.; Beck, T.; Brandão, M.; Civit, B.; Margni, M.; i Canals, L. M.; Saad, R.; de Souza, D. M.; Müller-Wenk, R. UNEP-SETAC Guideline on Global Land Use Impact Assessment on Biodiversity and Ecosystem Services in LCA. Int. J. Life Cycle Assess. 2013, 18, 1188– 1202,  DOI: 10.1007/s11367-013-0579-z
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    8. 8
      Berger, M.; Eisner, S.; van der Ent, R.; Flörke, M.; Link, A.; Poligkeit, J.; Bach, V.; Finkbeiner, M. Enhancing the Water Accounting and Vulnerability Evaluation Model: WAVE+. Environ. Sci. Technol. 2018, 52, 10757– 10766,  DOI: 10.1021/acs.est.7b05164
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      8
      Enhancing the Water Accounting and Vulnerability Evaluation Model: WAVE+
      Berger, Markus; Eisner, Stephanie; van der Ent, Ruud; Floerke, Martina; Link, Andreas; Poligkeit, Joseph; Bach, Vanessa; Finkbeiner, Matthias
      Environmental Science & Technology (2018), 52 (18), 10757-10766CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      Due to the increasing relevance of analyzing water consumption along product life cycles, the water accounting and vulnerability evaluation model (WAVE) has been updated and methodol. enhanced. Recent data from the atm. moisture tracking model WAM2-layers is used to update the basin internal evapn. recycling (BIER) ratio, which denotes atm. moisture recycling within drainage basins. Potential local impacts resulting from water consumption are quantified by means of the water deprivation index (WDI). Based on the hydrol. model WaterGAP3, WDI is updated and methodol. refined to express a basin's vulnerability to freshwater deprivation resulting from the relative scarcity and abs. shortage of water. Compared to the predecessor version, BIER and WDI are provided on an increased spatial and temporal (monthly) resoln. Differences compared to annual avs. are relevant in semiarid and arid basins characterized by a high seasonal variation of water consumption and availability. In order to support applicability in water footprinting and life cycle assessment, BIER and WDI are combined to an integrated WAVE+ factor, which is provided on different temporal and spatial resolns. The applicability of the WAVE+ method is proven in a case study on sugar cane, and results are compared to those obtained by other impact assessment methods.
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    9. 9
      Boulay, A.; Bare, J.; Benini, L.; Berger, M.; Lathuillière, M. J.; Manzardo, A.; Margni, M.; Motoshita, M.; Núñez, M.; Pastor, A. V.; Ridoutt, B.; Oki, T.; Worbe, S.; Pfister, S. The WULCA Consensus Characterization Model for Water Scarcity Footprints: Assessing Impacts of Water Consumption Based on Available Water Remaining (AWARE). Int. J. Life Cycle Assess. 2018, 23, 368– 378,  DOI: 10.1007/s11367-017-1333-8
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    10. 10
      Boulay, A. M.; Bouchard, C.; Bulle, C.; Deschênes, L.; Margni, M. Categorizing Water for LCA Inventory. Int. J. Life Cycle Assess. 2011, 16, 639– 651,  DOI: 10.1007/s11367-011-0300-z
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      Categorizing water for LCA inventory
      Boulay, Anne-Marie; Bouchard, Christian; Bulle, Cecile; Deschenes, Louise; Margni, Manuele
      International Journal of Life Cycle Assessment (2011), 16 (7), 639-651CODEN: IJLCFF; ISSN:0948-3349. (Springer)
      Purpose As impact assessment methods for water use in LCA evolve, so must inventory methods. Water categories that consider water quality must be defined within life cycle inventory. The method presented here aims to establish water categories by source, quality parameter and user. Materials and methods Water users were first identified based on their water quality requirements. A list of parameters was then defined, and thresholds for these parameters were detd. for each user. The thresholds were based on international stds., country regulations, recommendations and industry stds. Three different water sources were selected: surface water (including seawater), groundwater and rainwater. Based on the quality and water sources, categories were created by grouping user requirements according to the level of microbial or toxic contamination that the user can tolerate (high, medium or low). Results and discussion Seventeen water categories were created: eight for surface water, eight for groundwater and one for rainwater. Each category was defined according to 136 quality parameters (11 conventional parameters, 38 specific inorg. contaminants and 87 specific org. contaminants) and the users for which it can be of use. Conclusions A set of elementary flows is proposed in order to support a water inventory method oriented towards functionality. This can be used to assess potential water use impacts caused by a loss of functionality for human users.
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    11. 11
      Boulay, A.-M.; Motoshita, M.; Pfister, S.; Bulle, C.; Muñoz, I.; Franceschini, H.; Margni, M. Analysis of Water Use Impact Assessment Methods (Part A): Evaluation of Modeling Choices Based on a Quantitative Comparison of Scarcity and Human Health Indicators. Int. J. Life Cycle Assess. 2015, 20, 139– 160,  DOI: 10.1007/s11367-014-0814-2
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    12. 12
      Pradinaud, C.; Northey, S.; Amor, B.; Bare, J.; Benini, L.; Berger, M.; Boulay, A.-M.; Junqua, G.; Lathuillière, M. J.; Margni, M.; Motoshita, M.; Niblick, B.; Payen, S.; Pfister, S.; Quinteiro, P.; Sonderegger, T.; Rosenbaum, R. K. Defining Freshwater as a Natural Resource: A Framework Linking Water Use to the Area of Protection Natural Resources. Int. J. Life Cycle Assess. 2019, 24, 960– 974,  DOI: 10.1007/s11367-018-1543-8
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      12
      Defining freshwater as a natural resource: A framework linking water use to the area of protection natural resources
      Pradinaud Charlotte; Rosenbaum Ralph K; Pradinaud Charlotte; Junqua Guillaume; Northey Stephen; Amor Ben; Boulay Anne-Marie; Bare Jane; Niblick Briana; Benini Lorenzo; Berger Markus; Boulay Anne-Marie; Margni Manuele; Lathuilliere Michael J; Lathuilliere Michael J; Motoshita Masaharu; Payen Sandra; Pfister Stephan; Sonderegger Thomas; Quinteiro Paula
      The international journal of life cycle assessment (2019), 24 (5), 960-974 ISSN:0948-3349.
      Purpose: While many examples have shown unsustainable use of freshwater resources, existing LCIA methods for water use do not comprehensively address impacts to natural resources for future generations. This framework aims to (1) define freshwater resource as an item to protect within the Area of Protection (AoP) natural resources, (2) identify relevant impact pathways affecting freshwater resources, and (3) outline methodological choices for impact characterization model development. Method: Considering the current scope of the AoP natural resources, the complex nature of freshwater resources and its important dimensions to safeguard safe future supply, a definition of freshwater resource is proposed, including water quality aspects. In order to clearly define what is to be protected, the freshwater resource is put in perspective through the lens of the three main safeguard subjects defined by Dewulf et al. (2015). In addition, an extensive literature review identifies a wide range of possible impact pathways to freshwater resources, establishing the link between different inventory elementary flows (water consumption, emissions and land use) and their potential to cause long-term freshwater depletion or degradation. Results and discussion: Freshwater as a resource has a particular status in LCA resource assessment. First, it exists in the form of three types of resources: flow, fund, or stock. Then, in addition to being a resource for human economic activities (e.g. hydropower), it is above all a non-substitutable support for life that can be affected by both consumption (source function) and pollution (sink function). Therefore, both types of elementary flows (water consumption and emissions) should be linked to a damage indicator for freshwater as a resource. Land use is also identified as a potential stressor to freshwater resources by altering runoff, infiltration and erosion processes as well as evapotranspiration. It is suggested to use the concept of recovery period to operationalize this framework: when the recovery period lasts longer than a given period of time, impacts are considered to be irreversible and fall into the concern of freshwater resources protection (i.e. affecting future generations), while short-term impacts effect the AoP ecosystem quality and human health directly. It is shown that it is relevant to include this concept in the impact assessment stage in order to discriminate the long-term from the short-term impacts, as some dynamic fate models already do. Conclusion: This framework provides a solid basis for the consistent development of future LCIA methods for freshwater resources, thereby capturing the potential long-term impacts that could warn decision makers about potential safe water supply issues in the future.
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    13. 13
      Mutel, C. L.; Hellweg, S. Regionalized Life Cycle Assessment: Computational Methodology and Application to Inventory Databases. Environ. Sci. Technol. 2009, 43, 5797– 5803,  DOI: 10.1021/es803002j
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      13
      Regionalized Life Cycle Assessment: Computational Methodology and Application to Inventory Databases
      Mutel, Christopher L.; Hellweg, Stefanie
      Environmental Science & Technology (2009), 43 (15), 5797-5803CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      Life cycle assessment (LCA) studies showed that site-dependent impact assessment for categories like acidification and eutrophication give more accurate, realistic results than site-generic assessments. To date, existing geog.-specific or regionalized impact assessment factors have not been applied to LCA databases and software tools. A simple, generic method to couple existing regionalized characterization factors with large life cycle inventory databases is discussed. This approach allows for detailed geog. life cycle impact assessment results. Case study results for European country-specific electricity mixes were calcd. using the Ecoinvent 2.01 database and the EDIP 2003 and Accumulated Exceedance impact assessment methods and CASES project external energy cost characterization factors. In most cases, regionalization showed different total scores, different processes of high importance, and varying geog. distribution of environmental impacts. Since the method requires no addnl. input other than geog. information already in existing LCA databases, it can be used routinely. Better, more consistent geog. information in life cycle inventory databases and impact assessment methods, tailored to specific spatial ranges of all environmental effects considered, would be beneficial.
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    14. 14
      Mutel, C. L.; Pfister, S.; Hellweg, S. GIS-Based Regionalized Life Cycle Assessment: How Big Is Small Enough? Methodology and Case Study of Electricity Generation. Environ. Sci. Technol. 2012, 46, 1096– 1103,  DOI: 10.1021/es203117z
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      14
      GIS-Based Regionalized Life Cycle Assessment: How Big Is Small Enough? Methodology and Case Study of Electricity Generation
      Mutel, Christopher L.; Pfister, Stephan; Hellweg, Stefanie
      Environmental Science & Technology (2012), 46 (2), 1096-1103CODEN: ESTHAG; ISSN:0013-936X. (American Chemical Society)
      New methodol. is described for performing regionalized life cycle assessment and systematically choosing the spatial scale of regionalized impact assessment methods. Std. matrix-based calcns. are extended to include matrixes that describe the mapping from inventory to impact assessment spatial supports. Uncertainty in inventory spatial data is modeled using a discrete spatial distribution function, which in a case study is derived from empirical data. The minimization of global spatial autocorrelation is used to choose the optimal spatial scale of impact assessment methods. These techniques are demonstrated on electricity prodn. in the United States, using regionalized impact assessment methods for air emissions and freshwater consumption. Case study results show important differences between site-generic and regionalized calcns., and provide specific guidance for future improvements of inventory data sets and impact assessment methods.
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      Yang, Y. Toward a More Accurate Regionalized Life Cycle Inventory. J. Cleaner Prod. 2016, 112, 308– 315,  DOI: 10.1016/j.jclepro.2015.08.091
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      Bulle, C.; Margni, M.; Patouillard, L.; Boulay, A.-M.; Bourgault, G.; De Bruille, V.; Cao, V.; Hauschild, M.; Henderson, A.; Humbert, S.; Kashef-Haghighi, S.; Kounina, A.; Laurent, A.; Levasseur, A.; Liard, G.; Rosenbaum, R. K.; Roy, P.-O.; Shaked, S.; Fantke, P.; Jolliet, O. IMPACT World+: A Globally Regionalized Life Cycle Impact Assessment Method. Int. J. Life Cycle Assess. 2019, 24, 1653– 1674,  DOI: 10.1007/s11367-019-01583-0
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      16
      IMPACT World+: a globally regionalized life cycle impact assessment method
      Bulle, Cecile; Margni, Manuele; Patouillard, Laure; Boulay, Anne-Marie; Bourgault, Guillaume; De Bruille, Vincent; Cao, Viet; Hauschild, Michael; Henderson, Andrew; Humbert, Sebastien; Kashef-Haghighi, Sormeh; Kounina, Anna; Laurent, Alexis; Levasseur, Annie; Liard, Gladys; Rosenbaum, Ralph K.; Roy, Pierre-Olivier; Shaked, Shanna; Fantke, Peter; Jolliet, Olivier
      International Journal of Life Cycle Assessment (2019), 24 (9), 1653-1674CODEN: IJLCFF; ISSN:0948-3349. (Springer)
      Purpose: This paper addresses the need for a globally regionalized method for life cycle impact assessment (LCIA), integrating multiple state-of-the-art developments as well as damages on water and carbon areas of concern within a consistent LCIA framework. This method, named IMPACT World+. Methods: With IMPACT World+, we propose a midpoint-damage framework with four distinct complementary viewpoints to present an LCIA profile: (1) midpoint impacts, (2) damage impacts, (3) damages on human health, ecosystem quality, and resources. Results and discussion: We analyze the magnitude of global potential damages for each impact indicator, based on an estn. of the total annual anthropogenic emissions and extns. at the global scale (i.e., "doing the LCA of the world"). Similarly with ReCiPe and IMPACT 2002+, IMPACT World+ finds that (a) climate change and impacts of particulate matter formation have a dominant contribution to global human health impacts whereas ionizing radiation, ozone layer depletion, and photochem. Conclusions: IMPACT World+ provides characterization factors within a consistent impact assessment framework for all regionalized impacts at four complementary resolns.: global default, continental, country, and native (i.e., original and non-aggregated) resolns. IMPACT World+ enables the practitioner to parsimoniously account for spatial variability and to identify the elementary flows to be regionalized in priority to increase the discriminating power of LCA.
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      Faragò, M.; Benini, L.; Sala, S.; Secchi, M.; Laurent, A. National Inventories of Land Occupation and Transformation Flows in the World for Land Use Impact Assessment. Int. J. Life Cycle Assess. 2019, 24, 1333– 1347,  DOI: 10.1007/s11367-018-01581-8
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      Patouillard, L.; Bulle, C.; Margni, M. Ready-to-Use and Advanced Methodologies to Prioritise the Regionalisation Effort in LCA. Matériaux Tech. 2016, 104, 105  DOI: 10.1051/mattech/2016002
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      Patouillard, L.; Collet, P.; Lesage, P.; Tirado Seco, P.; Bulle, C.; Margni, M. Prioritizing Regionalization Efforts in Life Cycle Assessment through Global Sensitivity Analysis: A Sector Meta-Analysis Based on Ecoinvent V3. Int. J. Life Cycle Assess. 2019, 24, 2238– 2254,  DOI: 10.1007/s11367-019-01635-5
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      Patouillard, L.; Lorne, D.; Collet, P.; Bulle, C.; Margni, M. Prioritizing Regionalization to Enhance Interpretation in Consequential Life Cycle Assessment: Application to Alternative Transportation Scenarios Using Partial Equilibrium Economic Modeling. Int. J. Life Cycle Assess. 2020, 25, 2325  DOI: 10.1007/s11367-020-01785-x
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      20
      Prioritizing regionalization to enhance interpretation in consequential life cycle assessment: application to alternative transportation scenarios using partial equilibrium economic modeling
      Patouillard, Laure; Lorne, Daphne; Collet, Pierre; Bulle, Cecile; Margni, Manuele
      International Journal of Life Cycle Assessment (2020), 25 (12), 2325-2341CODEN: IJLCFF; ISSN:0948-3349. (Springer)
      The main purpose of this article is to prioritize regionalization efforts to enhance interpretation in C-LCA by assessing the spatial uncertainty of a case study building on a partial equil. economic model. Results show that the implementation of alternative transport scenarios in compliance with public policy for the energy transition in France is beneficial for some impact categories (ICs) (global warming, marine acidification, marine eutrophication, terrestrial acidification, thermally polluted water, photochem. oxidant formation, and particulate matter formation), with a confidence level of 95%. For other ICs, uncertainty redn. is required to det. conclusions with a similar level of confidence. Input variables with spatial variability from the partial equil. economic model are significant contributors to the C-LCA spatial uncertainty and should be prioritized for spatial uncertainty redn. In addn., characterization factors are significant contributors to the spatial uncertainty results for all regionalized ICs (except land occupation IC). Ways to reduce the spatial uncertainty from economic modeling should be explored. Uncertainty redn. to enhance the interpretation phase and the decision-making should be prioritized depending on the goal and scope of the LCA study. In addn., using regionalized CFs in C-LCA seems to be relevant, and C-LCA calcn. tools should be adapted accordingly.
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      Mutel, C. Brightway: An Open Source Framework for Life Cycle Assessment. J. Open Source Software 2017, 2, 236  DOI: 10.21105/joss.00236
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      Pfister, S.; Oberschelp, C.; Sonderegger, T. Regionalized LCA in Practice: The Need for a Universal Shapefile to Match LCI and LCIA. Int. J. Life Cycle Assess. 2020, 25, 1867– 1871,  DOI: 10.1007/s11367-020-01816-7
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      Milà i Canals, L.; Rigarlsford, G.; Sim, S. Land Use Impact Assessment of Margarine. Int. J. Life Cycle Assess. 2013, 18, 1265– 1277,  DOI: 10.1007/s11367-012-0380-4
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      23
      Land use impact assessment of margarine
      Mila i Canals, Llorenc; Rigarlsford, Giles; Sim, Sarah
      International Journal of Life Cycle Assessment (2013), 18 (6), 1265-1277CODEN: IJLCFF; ISSN:0948-3349. (Springer)
      Purpose This paper presents a case study of margarine, demonstrating the application of new characterization factors (CF) for land use and a no. of land use change impacts relating to biodiversity and ecosystem services. The objectives of this study were to generate insights as to the ease of applying these new factors and to assess their value in describing a no. of environmental impacts from land use and land use change relating to the margarine product system. Methods This case study is a partial descriptive life cycle assessment (LCA) of margarine. The functional unit of the study is 500 g of packaged margarine used as a spread in the UK and Germany. The life cycle stages included were: agricultural prodn., oil processing, margarine manuf. and transportation to regional European distribution centers. Essential for the application of the new CF was the identification and quantification of the inventory flows for land occupation (land use) and land transformation (land use change) flows. A variety of methods have been applied to det. the inventory flows for the agricultural and industrial stages in the life cycle. These flows were then assessed using the new CF and land use-related environmental impact categories recommended in this special issue. Results and discussion Land occupation was the major determinant for all the new impact categories with the exception of the water purifn. potential. Many of the impact categories followed a similar pattern and therefore, the inventory result for land occupation in this case study explains a large share of most of the impacts. Where land occupation alone is not a suitable proxy for environmental impacts (i.e. for freshwater regulation potential), differentiation at the level of biomes has proven relevant. In addn., the land use types distinguished so far were found to be useful in highlighting likely hotspots in the life cycle, although further differentiation of agricultural land' is suggested to account for the differences between annual and permanent crops. Conclusions The new land use impact assessment methods applied help to identify hotspots in the life cycle of margarines, with different proportions and sources of vegetable oils. The specific impacts of each vegetable oil are detd. mainly by the yield (and thus land occupation), but also by the type of agriculture (annual vs. permanent crops) and the sourcing location (and thus the sensitivity of biomes and occurrence of land use change). More research is needed to understand the usefulness of the various impact categories. For land use types, further refinement is required to describe different agricultural systems consistently across impact categories (e.g. annual vs. permanent cropping). In addn., the conceptual basis for the CFs applied in this case study (i.e. use of a potential ref. for occupation and transformation) has limitations for some types of decisions normally supported by LCA.
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      Quinteiro, P.; Dias, A. C.; Pina, L.; Neto, B.; Ridoutt, B. G.; Arroja, L. Addressing the Freshwater Use of a Portuguese Wine (’vinho Verde’) Using Different LCA Methods. J. Cleaner Prod. 2014, 68, 46– 55,  DOI: 10.1016/j.jclepro.2014.01.017
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      Lathuillière Michael, J.; Miranda, E. J.; Bulle, C.; Couto, E. G.; Johnson, M. S. Land Occupation and Transformation Impacts of Soybean Production in Southern Amazonia, Brazil. J. Cleaner Prod. 2017, 149, 680– 689,  DOI: 10.1016/j.jclepro.2017.02.120
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      Smith, T. M.; Goodkind, A. L.; Kim, T.; Pelton, R. E. O.; Suh, K.; Schmitt, J. Subnational Mobility and Consumption-Based Environmental Accounting of US Corn in Animal Protein and Ethanol Supply Chains. Proc. Natl. Acad. Sci. U.S.A. 2017, 114, E7891– E7899,  DOI: 10.1073/pnas.1703793114
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      26
      Sub-national mobility and consumption-based environmental accounting of US corn in animal protein and ethanol supply chains
      Smith, Timothy M.; Goodkind, Andrew L.; Kim, Taegon; Pelton, Rylie E. O.; Suh, Kyo; Schmitt, Jennifer
      Proceedings of the National Academy of Sciences of the United States of America (2017), 114 (38), E7891-E7899CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
      Corn prodn. and its assocd. inputs are a relatively large source of greenhouse gas emissions and use significant amts. of water and land, thereby contributing to climate change, fossil fuel depletion, local air pollutants, and local water scarcity. As large consumers of this corn, corporations in ethanol and animal protein industries are increasingly assessing and reporting sustainability impacts across their supply chains to identify, prioritize, and communicate sustainability risks and opportunities material to their operations. In so doing, many discovered the direct impacts of their owned operations are dwarfed by those upstream in the supply chain, requiring transparency and knowledge about environmental impacts along the supply chains. Life cycle assessments (LCA) identified environmental impact hotspots at national levels, yet these provide little sub-national information necessary to guide firm-specific supply networks. This work used the Food System Supply-Chain Sustainability (FoodS3) model to connect spatial, firm-specific demand of corn purchasers with upstream corn prodn. in the US using a cost minimization transport model. This provided the means to link US county-level corn prodn. to firm-specific demand locations assocd. with downstream processing facilities. Model substantially improved current LCA assessment efforts which are confined to broad national or state level impacts. In detg. sub-national environmental impact levels which occur over heterogeneous areas and aggregating these landscape impacts by specific supply networks, targeted opportunities for improved supply chain sustainability performance were identified.
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      Payen, S.; Falconer, S.; Ledgard, S. F. Water Scarcity Footprint of Dairy Milk Production in New Zealand – A Comparison of Methods and Spatio-Temporal Resolution. Sci. Total Environ. 2018, 639, 504– 515,  DOI: 10.1016/j.scitotenv.2018.05.125
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      Water scarcity footprint of dairy milk production in New Zealand - A comparison of methods and spatio-temporal resolution
      Payen, Sandra; Falconer, Shelley; Ledgard, Stewart F.
      Science of the Total Environment (2018), 639 (), 504-515CODEN: STENDL; ISSN:0048-9697. (Elsevier B.V.)
      Water scarcity footprinting now has a consensual life cycle impact assessment indicator recommended by the UNEP/SETAC Life Cycle Initiative called AWaRe. It was used in this study to calc. the water scarcity footprint of New Zealand (NZ) milk produced in two contrasting regions; "non-irrigated moderate rainfall" (Waikato) and "irrigated low rainfall" (Canterbury). Two different spatial and temporal resolns. for the inventory flows and characterization factors (CFs) were tested and compared: country and annual vs. regional and monthly resoln. An inventory of all the consumed water flows was carried out from cradle to farm-gate, i.e. from the prodn. of dairy farm inputs to the milk and meat leaving the dairy farm, including all water uses on-farm such as irrigation water, cow drinking water and cleaning water. The results clearly showed the potential overestimation of a water scarcity footprint when using aggregated CFs. Impacts decreased by 74% (Waikato) and 33% (Canterbury) when regional and monthly CFs were used instead of country and annual CFs. The water scarcity footprint calcd. at the regional and monthly resoln. was 22Lworld eq/kg FPCM (Fat Protein Cor. Milk) for Waikato milk, and 1118Lworld eq/kg FPCM for Canterbury milk. The contribution of background processes dominated for milk from non-irrigated pasture, but was negligible for milk from irrigated pasture, where irrigation dominated the impacts. Results were also compared with the previously widely-used Pfister method (Pfister et al., 2009) and showed very similar ranking in terms of contribution anal. An endpoint indicator was evaluated and showed damages to human health of 7.66×10-5 DALY/kg FPCM for Waikato and 2.05×10-3 DALY/kg FPCM for Canterbury, but the relevance of this indicator for food prodn. needs reviewing. To conclude, this study highlighted the importance of using high-resoln. CFs rather than aggregated CFs.
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      Brauman, K. A.; Goodkind, A. L.; Kim, T.; Pelton, R. E. O.; Schmitt, J.; Smith, T. M. Unique Water Scarcity Footprints and Water Risks in US Meat and Ethanol Supply Chains Identified via Subnational Commodity Flows. Environ. Res. Lett. 2020, 15, 105018  DOI: 10.1088/1748-9326/ab9a6a
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      Environmental Research Letters (2020), 15 (10), 105018CODEN: ERLNAL; ISSN:1748-9326. (IOP Publishing Ltd.)
      Within the US, supply chains aggregate agricultural prodn. and assocd. environmental impacts in specific downstream products and companies. This is particularly important for meat and ethanol, which consume nearly half of global crop prodn. as feed and feedstocks. However, lack of data has thus far limited the ability to trace inputs and impacts of commodity crops through domestic supply chains. For the first time, we use a commodity-flow model to link spatially distributed water resource impacts of corn and soy to individual meat and ethanol processing facilities. This creates transparency in the supply chains, illuminating substantial variation in embedded irrigation water and water scarcity footprints among meat and ethanol processed at different facilities. By calcg. unique blue water scarcity footprints for end-products, we show that beef processed in Iowa or Illinois, for example, has fewer water impacts than chicken processed in California and pork processed in Oklahoma. We find that over 75% of irrigated feed embedded in meat is consolidated in six companies and 39% of irrigated feedstock for ethanol is consolidated in five companies, with potentially neg. impacts to supply costs and risk management. This subnational variation and consolidation of impacts in key supply chains creates opportunities for producers and consumers of agriculture-based products to make management, investment, and sustainability decisions about those products.
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      Yang, Y.; Pelton, R. E. O.; Kim, T.; Smith, T. M. Effects of Spatial Scale on Life Cycle Inventory Results. Environ. Sci. Technol. 2020, 54, 1293– 1303,  DOI: 10.1021/acs.est.9b03441
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      Effects of Spatial Scale on Life Cycle Inventory Results
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      Efforts to compile life cycle inventory (LCI) data at more geog. refined scales or resolns. are growing; however, how the choice of spatial scale may affect LCI results is poorly understood. This work examd. this question using US corn as a case study. Corn prodn. data were compiled at two spatial scales (state and county) to compare how their LCI results may differ for state and national level analyses. For greenhouse gas (GHG) emissions, ests. at the two scales were similar (<20% difference) for most state-level analyses and were basically the same (<5%) for national level anal. For blue water consumption, ests. at the two scales differ more. Results suggested state-level analyses may be an adequate spatial scale for national level GHG anal. and for most state-level GHG analyses of US corn, but may fall short for water consumption due to its large spatial variability. Although county-based LCI may be considered more accurate, they require substantially more effort to compile. Overall, results suggested that study goals, data requirements, and spatial variability are important factors to consider when deciding appropriate spatial scale or pursuing more refined scales.
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      The increase in soybean prodn. as a source of protein and oil is being stimulated by the growing demand for livestock feed, food and numerous other applications. Significant greenhouse gas (GHG) emissions can result from land use change due to the expansion and cultivation of soybean. However, this is complex to assess and the results can vary widely. The main goal of this article is to investigate the life-cycle GHG balance for soybean produced in Latin America, assessing the implications of direct land use change emissions and different cultivation systems. A life-cycle model, including inventories for soybean produced in three different climate regions, was developed, addressing land use change, cultivation and transport to Europe. A comprehensive evaluation of alternative land use change scenarios (conversion of tropical forest, forest plantations, perennial crop plantations, savannah and grasslands), cultivation (tillage, reduced tillage and no-tillage) and soybean transportation systems was undertaken. The main results show the importance of land use change in soybean GHG emissions, but significant differences were obsd. for the alternative scenarios, namely 0.1-17.8 kg CO2eq kg-1 soybean. The original land choice is a crit. issue in ensuring the lowest soybean GHG balance and degraded grassland should preferably be used for soybean cultivation. The highest GHG emissions were calcd. for tropical moist regions when rainforest is converted into soybean plantations (tillage system). When land use change is not considered, the GHG intensity varies from 0.3 to 0.6 kg CO2eq kg-1 soybean. It was calcd. that all tillage systems have higher GHG emissions than the corresponding no-tillage and reduced tillage systems. The results also show that N2O emissions play a major role in the GHG emissions from cultivation, although N2O emission calcns. are very sensitive to the parameters and emission factors adopted.
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      The UNEP-SETAC life cycle initiative recently recommended use of the countryside species-area relationship (SAR) model to calc. the characterization factors (CFs; potential species loss per m2) for projecting the biodiversity impact of land use assocd. with a products' life cycle. However, CFs based on this approach are to date available for only six broad land use types without differentiating between their management intensities and have large uncertainties that limit their practical applicability. Here we derive updated CFs for projecting potential species losses of five taxa resulting from five broad land use types (managed forests, plantations, pasture, cropland, urban) under three intensity levels (minimal, light, and intense use) in each of the 804 terrestrial ecoregions. We utilize recent global land use intensity maps and International Union for Conservation of Nature (IUCN) habitat classification scheme to parametrize the SAR model. As a case study, we compare the biodiversity impacts of 1 m3 of wood produced under four different forest management regimes in India and demonstrate that the new land use intensity-specific CFs have smaller uncertainty intervals and are able to discern the impacts of intensively managed land uses from the low intensity regimes, which has not been possible through previous CFs.
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      In recent years, the debate about environmental impacts and the sustainability of agricultural products has increased. Environmental impact indicators are increasingly being demanded for policy and decision-making processes. Consumers are more and more concerned about the quality of food products and now looking for those with a low environmental impact, with a particular attention to greenhouse gas (GHG) emissions. There are few studies regarding the GHG emissions assocd. with the Brazilian soybean prodn. The aim of this study was to evaluate the main sources of GHG in soybean prodn. in the State of Mato Grosso, Brazil. Our anal. considered the Life Cycle Assessment (LCA) from cradle to farm gate. We evaluated 55 farms in the crop years of 2007/08, 2008/09 and 2009/10, accounting for 180,000 ha of soybean cultivation area and totaling 114 individual situations. The results indicated that the largest source of GHG in the soybean prodn. is the decompn. of crop residues (36%), followed by fuel use (19%), fertilizer application (16%), liming (13%), pesticides (7%), seeds (8%) and electricity consumed at the farms (<1%). The av. GHG emissions considering the three crop years were 0.186 kg of CO2eq kg-1 of soybean produced. We also categorized the results based on land use intensity and prodn. areas. This study contributed to identify the main sources of GHG in the soybean prodn. and indicate mitigation priorities assocd. to the soybean cultivation in Brazil. Further studies, including field expts., should contribute to a better understanding of the profile of emissions from crop residues in Brazil.
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      Greenhouse gases assessment of soybean cultivation steps in southern Brazil
      Maciel, Vinicius Goncalves; Zortea, Rafael Batista; Grillo, Igor Barden; Lie Ugaya, Cassia Maria; Einloft, Sandra; Seferin, Marcus
      Journal of Cleaner Production (2016), 131 (), 747-753CODEN: JCROE8; ISSN:0959-6526. (Elsevier Ltd.)
      This study presents a greenhouse gases emissions assessment of soybean cultivation in southern Brazil based on life cycle inventory. Although there are currently some studies on this topic, it is focused in the country level. Nevertheless, there are differences among the producing regions and it's estd. that for each 20 kg of soybeans produced in Brazil, one is produced in Rio Grande do Sul state. In a previous study, a life cycle inventory of soybean cultivated in this Brazilian state was developed, nevertheless, the influence of land use change along the life cycle was not taken into account. Therefore, the current study discusses the influence of direct land use change over the greenhouse gas emissions. The functional unit (FU) employed was 1 kg of soybean harvested for a cradle to gate study. For the soybean cultivation, in the scenario related to no land use change (scenario 1), 0.352 kg CO2/FU was emitted. This value increases up to 205% in scenario 2 (in this case, the actual scenario was that 15.4% of soybean cropland area replaced grassland) and 892% in scenario 3 (all land transformation was over forest). In scenario 1, soybean cultivation was responsible for the higher share of the greenhouse gases emissions (42%). The highest contributions in soybean cultivation for greenhouse gases emissions were: liming (37%), fertilization (19%) and seeding (9%).
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    63. 63
      Matsuura, M. I. S. F.; Dias, F. R. T.; Picoli, J. F.; Lucas, K. R. G.; de Castro, C.; Hirakuri, M. H. Life-Cycle Assessment of the Soybean-Sunflower Production System in the Brazilian Cerrado. Int. J. Life Cycle Assess. 2017, 22, 492– 501,  DOI: 10.1007/s11367-016-1089-6
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      Life-cycle assessment of the soybean-sunflower production system in the Brazilian Cerrado
      Matsuura, Marilia I. S. Folegatti; Dias, Fernando R. T.; Picoli, Juliana F.; Lucas, Kassio R. Garcia; de Castro, Cesar; Hirakuri, Marcelo H.
      International Journal of Life Cycle Assessment (2017), 22 (4), 492-501CODEN: IJLCFF; ISSN:0948-3349. (Springer)
      Purpose: In the "Cerrado" (Brazilian savanna), sunflower comes mostly from a cropping system where its seeding follows soybean harvest. Soybean has a much higher economic value, but this assocn. with sunflower reduces the environmental impacts from both crops by sharing resources. This study performed a life-cycle assessment (LCA) of the soybean-sunflower cropping system, identified its hotspots, and compared its environmental performance with two hypothetical monocultures, in order to corroborate its benefits. Methods: Soybean-sunflower cropping system inventory used data from farms of the Parecis region, consolidated by experts. Inventories for soybean and sunflower monocultures were estd. from the cropping system inventory. LUC (land-use changes) were calcd. from CONAB (2015), FAOSTAT (2012), and Macedo et al. (P Natl Acad Sci USA 109:1341-1346, 2012). Emissions estn. followed Nemecek and Schnetzer (2011), Mila´ i Canals (2003), and EC (2010). Land occupation, land-use changes, and liming were allocated by occupation time, but a sensitivity anal. was performed for yield and gross margin as allocation criteria. ReCiPe Midpoint (H) v1.12/World ReCiPe H was the impact assessment method, and some categories were disregarded as not relevant. We used pedigree matrix to est. uncertainties for inventory and Monte Carlo method for impact uncertainty anal. as in Goedkoop et al. (2008). We used SimaPro 8.0.5.13. Results and discussion: The soybean-sunflower cropping system generate relevant human toxicity, freshwater toxicity, freshwater eutrophication, climate change, and terrestrial acidification impacts, related to emissions derived from nitrogen and phosphate fertilizers and emissions generated by LUC. Sunflower-soybean cropping system has better environmental performance when compared to the combination of monocultures because of a no. of synergies made possible by sharing land use and other resources. Changing the allocation criteria altered the relative performance of some categories, but in all categories the environmental impacts of the cropping system were lower than those of the corresponding monoculture impacts, regardless of the allocation criteria implemented. Conclusions: We concluded that the environmental performance of the soybean-sunflower cropping system can be improved by optimizing the use of chem. fertilizers. Climate change impacts, which are mostly driven by LUC, could be reduced by prodn. intensification, preventing the clearing of native vegetation for agricultural purposes. This study confirmed the environmental benefits of cropping systems when compared to monocultures and the advantages of assocn. of nitrogen-fixing legumes with other plant species in a prodn. system.
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      Zortea, R. B.; Maciel, V. G.; Passuello, A. Sustainability Assessment of Soybean Production in Southern Brazil: A Life Cycle Approach. Sustainable Prod. Consumption 2018, 13, 102– 112,  DOI: 10.1016/j.spc.2017.11.002
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      Strassburg, B. B. N.; Brooks, T.; Feltran-Barbieri, R.; Iribarrem, A.; Crouzeilles, R.; Loyola, R.; Latawiec, A. E.; Oliveira Filho, F. J. B.; Scaramuzza, C. A.; Scarano, F. R.; Soares-Filho, B.; Balmford, A. Moment of Truth for the Cerrado Hotspot. Nat. Ecol. Evol. 2017, 1, 0099  DOI: 10.1038/s41559-017-0099
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      Moment of truth for the Cerrado hotspot
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      There is no expanded citation for this reference.
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      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC1cfotVeitg%253D%253D&md5=7b087cf7065a9041ec916d02ce82a4b6
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      Green, J. M. H.; Croft, S. A.; Durán, A. P.; Balmford, A. P.; Burgess, N. D.; Fick, S.; Gardner, T. A.; Godar, J.; Suavet, C.; Virah-Sawmy, M.; Young, L. E.; West, C. D. Linking Global Drivers of Agricultural Trade to On-the-Ground Impacts on Biodiversity. Proc. Natl. Acad. Sci. U.S.A. 2019, 116, 26085– 26086,  DOI: 10.1073/pnas.1920142116
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      Linking global drivers of agricultural trade to on-the-ground impacts on biodiversity
      Green, Jonathan M. H.; Croft, Simon A.; Duran, America P.; Balmford, Andrew P.; Burgess, Neil D.; Fick, Steve; Gardner, Toby A.; Godar, Javier; Suavet, Clement; Virah-Sawmy, Malika; Young, Lucy E.; West, Christopher D.
      Proceedings of the National Academy of Sciences of the United States of America (2019), 116 (51), 26085-26086CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
      There is no expanded citation for this reference.
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      Prudêncio da Silva, V.; van der Werf, H. M. G.; Spies, A.; Soares, S. R. Variability in Environmental Impacts of Brazilian Soybean According to Crop Production and Transport Scenarios. J. Environ. Manage. 2010, 91, 1831– 1839,  DOI: 10.1016/j.jenvman.2010.04.001
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      zu Ermgassen, E. K. H. J.; Ayre, B.; Godar, J.; Bastos Lima, M. G.; Bauch, S.; Garrett, R.; Green, J.; Lathuillière, M. J.; Löfgren, P.; MacFarquhar, C.; Meyfroidt, P.; Suavet, C.; West, C.; Gardner, T. Using Supply Chain Data to Monitor Zero Deforestation Commitments: An Assessment of Progress in the Brazilian Soy Sector. Environ. Res. Lett. 2020, 15, 035003  DOI: 10.1088/1748-9326/ab6497
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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.1c03060.

    • Ecoregion and river basin maps of Brazil; spatially explicit life cycle inventory for ecoregion land use and water footprint inventory per river basin; probability density of potential damage to biodiversity and water scarcity footprint (PDF)

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