Incorporating  Land  Use  Impacts  on  Biodiversity  into  LCA:    A  Comparative  Case  Study  of  Four  Patagonia  T-­‐Shirts  

 Project  Work  Plan  May  2013      Elena  Egorova,  Heather  Perry,  Louisa  Smythe,  Runsheng  Song,  Sarah  Sorensen                Faculty  Advisor:  Roland  Geyer    

2  

Table  of  Contents  Executive Summary  ....................................................................................................................................................................  3  

Project Objectives  ........................................................................................................................................................................  4  

Significance  ...................................................................................................................................................................................  4  

Background and Literature Review  .......................................................................................................................................  6  

Technical Approach for Solving the Problem  .................................................................................................................  12  

Data Catalog  ...............................................................................................................................................................................  17  

Project Deliverables  .................................................................................................................................................................  17  

Milestones  ...................................................................................................................................................................................  18  

Management Plan  .....................................................................................................................................................................  19  

Budget  ..........................................................................................................................................................................................  23  

References  ...................................................................................................................................................................................  24  

 

3  

Executive Summary Industry and agriculture across the globe are significantly affecting the quality of Earth’s ecosystems. Life Cycle Assessment (LCA) is one tool that environmentally-aware companies are using to measure the potential environmental impacts of a particular product system throughout its life cycle. While certain impacts, such as those to global warming, are readily incorporated into LCAs with standardized practices, methodologies for other impacts, such as those of land use, are still being developed. Because land use change can have major implications on ecosystem health it is critical to establish easily practicable standards for measuring these impacts. On this front, an international panel of LCA experts recently completed the Land Use Life Cycle Impact Assessment (LULCIA) project, which establishes preliminary methods for incorporating land use impacts on biodiversity and ecosystem services, two recognized indicators of ecosystem quality, into LCA.

Partnering with Patagonia, a pioneer in sustainable manufacturing, we will conduct a comparative analysis of four Patagonia t-shirts utilizing the LULCIA methodology to assess the biodiversity and ecosystem services impacts. Each shirt is of comparable weight and style, but made from a different raw material—wool, polyester, cotton, and Tencel®—so that we can examine the effectiveness of the methodology in differentiating between types of land use in apparel manufacturing. One medium sized t-shirt will serve as our functional unit, a key component of LCA, which quantifies the product’s function and serves as a common reference unit that enables comparison of results across product systems.

Building from the LULCIA framework, we have determined that at minimum we will evaluate biodiversity damage potential (BDP) using relative change in species richness as an indicator of BDP. We will use characterization factors, an element of an LCA, generated by de Baan et al. (2012) and Souza et al. (2013) to convert measures of land use into quantifiable impacts. Specifically we will evaluate land use occupation—the ongoing use of a piece of a land, which prevents its return to a natural state—using primary and secondary data such as area occupied by agricultural land and pastures, the type of local land cover, and the amount of time occupied. Land cover and land use type will be classified using a hierarchical system outlined by Koellner et al. (2012). Given sufficient time and resources, we will also assess the impacts to ecosystem services using methods described by Saad et al. (2013) and Brandao and Mila I Canals (2012). Where possible, we will also integrate other ecosystem services and biodiversity measurement tools that have been established outside of LCA to contrast their approach and discuss how such tools might complement one another.

In addition to evaluating an emerging LCA methodology, this pilot study should provide Patagonia with a meaningful comparison of the land use impacts of its common textiles, as well as a potential tool for future assessments. The study will continue to build Patagonia’s already extensive analysis of the environmental impacts of its products and supply chain and serve as a

4  

case study in the further development of incorporating land use into LCAs. The results of this project will include information about the impact of land use on biodiversity from Patagonia’s four t-shirts, and a critical review of the LULCIA methodology for incorporating land use impacts on biodiversity into LCAs, including recommendations for refining the methodologies and existing data gaps and limitations.

Project Objectives 1. Using the LULCIA methodology, conduct a comparative assessment of the potential land

use impacts on biodiversity of four Patagonia t-shirts, made of four different fiber types (wool, cotton, polyester, and Tencel®).

2. Provide a critical review of the LULCIA method for incorporating land use impacts on biodiversity and ecosystem services into Life Cycle Assessment (LCA). Identify potential areas of refinement in the methodology, as well as limitations of existing data.

3. Evaluate other established ecosystem services and biodiversity measurement tools outside of LCA to contrast their approach and discuss how such tools might complement one another.

Significance In order to better understand the significant impacts that industry and agriculture have on the quality of Earth’s ecosystems, companies and researchers are using Life Cycle Assessment (LCA) as a tool to measure the potential environmental impacts of a particular product system throughout its life cycle. While certain impacts, such as those to global warming, are readily incorporated into LCAs with standardized practices, methodologies for other impacts, such as those of land use, are still being developed.

Within the framework of the UNEP-SETAC International Life Cycle Initiative, a partnership formed by the United Nations Environment Programme (UNEP) and the Society for Environmental Toxicology and Chemistry (SETAC), an international panel of LCA experts has completed the Land Use Life Cycle Impact Assessment (LULCIA) project (Koellner et al., 2013). The LULCIA project establishes preliminary methods for incorporating land use impacts on biodiversity and ecosystem services, two recognized indicators of ecosystem quality into LCA. The work of the LULCIA team is published in the Global Land Use Impacts on Biodiversity and Ecosystem Services in LCA special issue of the International Journal of Life Cycle Assessment.

5  

Partnering with Patagonia, an outdoor apparel and gear company that has been a pioneer in sustainable manufacturing for more than 30 years, we will conduct a pilot study utilizing the LULCIA methodology to assess the biodiversity and ecosystem services impacts of four Patagonia t-shirts. Each shirt is made from a different raw material—wool, polyester, cotton, and lyocell (represented by Tencel®)—so that we can examine the effectiveness of the methodology in differentiating between types of land use in apparel manufacturing. Thus far, the methodology has undergone little testing through case studies, so our research will help to illuminate limitations of both the methodology and global data that can be improved upon in the future, as well as areas for refinement.

Patagonia’s innovative approach is rooted in its mission to “use business to inspire and implement solutions to the environmental crisis,” and its eagerness to create a socially responsible corporate culture has led other companies to follow suit. Although Patagonia has evaluated a range of environmental impacts of its products and supply chain, impacts of land use on biodiversity and ecosystem services from activities such as raw material extraction, processing, manufacturing, customer use, and end-of-life disposal have yet to be considered.

In addition to evaluating an emerging LCA methodology, this pilot study should provide Patagonia with a meaningful comparison of the land use impacts of its common textiles, as well as a potential tool for future assessments. Because Patagonia does not own and operate its own manufacturing facilities, it is difficult to maintain complete visibility of its supply chain impact across the globe. By capturing data directly from the supply chain, this study will help increase Patagonia’s understanding of their global impacts, all the way down to their raw material suppliers, fabric vendors and manufacturers. With this knowledge, Patagonia can begin to outline the appropriate actions to take to minimize the impacts of its land use on biodiversity. Patagonia has proved that it can motivate industry-wide change in sustainability practices, so the significance of this project goes beyond assessing one company’s impacts. As the LULCIA methodology is refined and improved, other companies can apply the same techniques to assess their supply chains. Led by innovative companies such as Patagonia, the apparel industry is uniting through the Sustainable Apparel Coalition (SAC) to advance corporate sustainability efforts. The mission of the SAC is to create “a common approach for measuring and evaluating apparel and footwear product sustainability performance that will spotlight priorities for action and opportunities for technological innovation.” Establishing a consistent measure for land use impacts on biodiversity is a critical addition to that common approach. Patagonia can use the SAC as a forum to share the findings of this study, and enable other companies to evaluate and reduce their own impacts on biodiversity. Furthermore, NGOs can use such applicable methodologies to better understand what guidance they can provide to individual companies.

6  

Background and Literature Review

Background Life Cycle Assessment (LCA) is a valuable tool used to evaluate the potential environmental impacts of a product system throughout its life cycle. As outlined in International Standard 14040 established by the International Standard Organization (ISO), LCA consists of four phases: Goal and Scope Definition, Life Cycle Inventory Analysis (LCI), Life Cycle Impact Assessment (LCIA), and Interpretation. LCA is an iterative process, with interpretation being conducted throughout to identify necessary adjustments in the goal, scope, and subsequent phases.

At the core of an LCA is the functional unit, which quantifies the product(s) function(s) and serves as a common reference unit that enables comparison of results across product systems. In this study, the functional unit will allow us to compare the potential land use impacts of the four fiber types we will be evaluating, each represented by a specific Patagonia t-shirt. The functional unit, as well as the reference flow (the amount of product(s) required to fulfill the intended function defined by the functional unit) and the system boundary are established in the first phase. During the LCI phase, inputs and outputs to or from the environment, called elementary flows, of the product system are quantified through data collection (such as energy inputs, waste, emissions to air) and data calculation. In the LCIA, the results of the LCI are assigned to particular areas of environmental concern called impact categories, which are quantifiably represented by an impact category indicator. Characterization factors derived from a characterization model are used to convert the LCI results into indicator results summed for each impact category. In this way, the LCIA measures the potential environmental impacts of the specific inputs and outputs of the product system. For example, if CO2 is an output in the LCI, it could be assigned to the impact category “global warming,” which might be represented by the category indicator “infrared radiative forcing,” and converted using the characterization factor “Global Warming Potential” into a category indicator result represented in units of kg-CO2-equivalent (ISO, 2007).

As discussed in the Significance section, experts on the Land Use Life Cycle Impact Assessment (LULCIA) project have proposed methods for incorporating land use impacts into LCIA, and thus into LCA. In the following section we review the key features and divergent approaches to the new methods as described in the relevant literature thus far, which is published in the Global Land Use Impacts on Biodiversity and Ecosystem Services in LCA Special Issue of the International Journal of Life Cycle Assessment.

7  

Literature Review

The LULCIA methodology aims to integrate land use impacts on biodiversity and ecosystem services into LCIA in order to account for changes to ecosystem quality by land use in LCA. The LULCIA literature emphasizes establishing methods that can accommodate the global nature of product systems. The global evaluation can be supplemented by regional data for foreground systems. Koellner et al. (2013) propose considering two endpoint land use impact path ways in LCIA: ecosystem services damage potential and biodiversity damage potential. Methods for conducting an LCIA of the ecosystem services pathway, including potential impact categories and indicators, and development of characterization factors, are further described by Saad et al. (2013), Brandão and Milà i Canals (2013), and Müller-Wenk and Brandão (2010). De Baan et al. (2012) and Souza et al. (2013) provide similar methods for addressing biodiversity impacts. Each category and indicator offers unique advantages and limitations, which are further discussed below. Characterization factors for many of these impact categories have been developed, but techniques for calculation will not be discussed here. The Technical Approach section of this report describes how these factors were calculated for relevant indicators.

Inventory Modeling of Land Use

Before conducting an impact assessment in LCA, inventory data of elementary flows must be measured. According to the literature, three types of land use interventions should be accounted for as elementary flows in the LCI: land occupation, land transformation, and permanent impacts. Land transformation refers to a change in land use, such as conversion from a forest to agriculture, which is assumed to occur instantaneously, while occupation is the ongoing use of a piece of a land which prevents the return of the land to a natural state, such as a crop field (Koellner et al., 2013). Permanent impacts imply irreversible changes to the ecosystem. In order to establish occupation impacts on biodiversity, de Baan et al. (2012) consider the relative difference in biodiversity between the occupied land and a (semi-) natural reference situation. Calculation of land transformation and permanent impacts rests heavily on data describing regeneration success and times of ecosystems, or the length of time required after land is abandoned for all signs of human intervention to vegetation and soil to disappear (Koellner et al., 2013). Regeneration times are a function of the impact pathway, the type of transformation, and climatic region.

Reference Situation

In order to measure land use impacts in LCA, a reference situation must be established as a point of comparison to assess changes in land quality over time (Koellner et al., 2013). However, this reference could be a point in the past, present, or future (de Baan et al., 2012), and the goal of an LCA may lead to different value judgments as to which reference situation is best. Several potential reference situations are proposed in the existing literature. Milà i Canals et al. (2007) propose using the non-use of the area to define a dynamic reference situation, rather than

8  

referring to either a historical natural land state or the potential land state after regeneration, in order to avoid allocation issues resulting from alternative land uses.

Koellner et al. (2013) identify three options for establishing a reference situation. The first is Potential Natural Vegetation (PNV), which is described by Chiarucci et al. (2010) as the expected natural state of the ecosystem in the absence of human intervention (in Koellner et al., 2013). Another option is to use the present land use mix as the reference. However, Koellner et al. ultimately recommend using as a reference state “the (quasi-) natural land cover in each biome/ecoregion, i.e. the natural mix of forests, wetlands, shrubland, grassland, bare area, snow and ice, lakes and rivers.”

De Baan et al. (2012) choose to use “the current, late-succession habitat stages as reference, which are widely used as target for restoration ecology and serve as a proxy for the Potential Natural Vegetation.” A nearly-natural land state is described as “(semi)-natural” to illustrate that the reference state may not precisely match pre-human habitat due to the different degrees of human disturbance in the past (e.g. certain European landscapes where essentially no undisturbed regions exist.)

Land Classification

Completing a comprehensive LCA of land use requires the ability to compare land cover at a global scale (ie. between continents), as well as regional specificity. The use of standardized methods will enable the development of relatable characterization factors and inventory data. To enable global comparison of land use impacts of product systems Koellner et al. (2012) identify the critical need for a standardized land use and land cover classification scheme. As such, Koellner et al. (2012) propose a four-tiered classification that relies on existing databases and modifies existing classification systems. The four narrowing tiers are defined as follows:

• Level 1 uses very general land use and land cover classes (from GLC 2000), • Level 2 refines the categories of level 1 (using mainly the classification of ecoinvent v2.0

and GLOBIO3), • Level 3 gives more information on the land management (e.g., irrigated versus non-

irrigated arable land), and • Level 4 mostly specifies the intensity of the land uses (extensive versus intensive land

use).

This four-tiered approach allows land use to be analyzed at different levels of detail, depending on what data are available and what results are necessary. The level of detail for land use classification will vary depending on the scope of the LCA and whether the practitioner is looking at the foreground (e.g. Australian pasture) or background land use flows (e.g. fertilizer production), in which a coarser level of differentiation may be used due to unknown data (Milà I

9  

Canals et al. 2013). Koellner et al. (2012) suggest that the year 2000 serve as a baseline for the normalization of land occupation in all regions of the world.

A method for regionalizing land use elementary flows that can be applied at differing levels of geographical detail depending on the LCA goals and available data is also necessary. For this purpose Koellner et al. (2012) propose the following levels of differentiation:

• Level 1: Differentiation between terrestrial biomes, freshwater biomes, coastal water, and shelf biomes (shallower than 200 m) and deep sea biomes

• Level 2: Climatic regions (tropical/subtropical, temperate, boreal, polar) • Level 3: Classification for terrestrial and freshwater biomes (n=16) by Olson et al. (2001)

and classification for marine biomes (n=3) based on the eight realms by Spalding et al. (2007).

• Level 4: Olson terrestrial and freshwater ecoregions (n=867 and n=238 priority regions) and Spalding coastal and shelf ecoregions (n=232); no differentiation for deep sea

• Level 5: Exact geo-referenced information of land use in grid cells of 1.23 km2 or less defined by degrees longitude and latitude with two decimals, which allows to derive elevation of land use (above and below sea level)

Using these two standardized classification systems, characterization factors can be assigned for any relevant combination of land use type and region (Koellner et al., 2012). Theoretically, any land in any geographic location (biome) can be transformed into any land use type, however only a small number of transformations are relevant. For example, transformation from coastal does not make sense in the middle of the desert. Any higher-level geographic information can be translated to a lower, coarser classification such as ecoregions to biome, if detailed spatial information is not required for the impact assessment. New LCIA methods must specify the level of differentiation of land use type as well as biogeographical region used (Koellner et al., 2013).

De Baan et al. (2012) develop regionalized characterization factors per biome for biodiversity damage potential per land use based on the Koellner scheme. Their analysis includes data from nine biomes, the spatial unit of biogeographical differentiation proposed by Koellner et al. (2012), as defined by the World Wide Fund for Nature (WWF) based on climate, flora, and fauna. Prior to publication of the Koellner et al. (2012) and de Baan et al. (2012) classifications, Schmidt (2008) provided an alternative classification typology more detailed than the biome level. His method distinguishes between arable land, raw material extraction, urban and sealed land, nature (non-productive) land, which are then further differentiated. Schmidt also analyzes the intensity of the land use and classified each as either high or low. High intensity land use is defined as arable land, permanent crops, and built on areas, roads, and barren land. Low intensity land use is defined as ‘forest and other wooded land.’

Impact Assessment of Land Use

10  

Quantifying the biodiversity or ecosystem services loss resulting from land use through an LCIA requires assigning LCI results to appropriate impact categories, and choosing indicators for these categories.

Ecosystem Services Pathway

Saad et al. (2013) focus on three ecosystem services impact categories: Erosion Regulation Potential (ERP), Freshwater Regulation Potential (FWRP), and Water Purification Potential (WPP). These impact categories represent the three ecosystem services identified by the Millennium Ecosystem Assessment (MEA, 2005) as most impacted by anthropogenic interventions. For each category, an impact indicator is suggested: erosion resistance for ERP, groundwater recharge for FWRP, and physiochemical filtration and mechanical filtration for WPP. These impact categories and indicators are summarized in Table 1.

Table  1.  Ecosystem  services  impact  categories  and  indicators.  

Impact Category

Erosion Regulation Potential (ERP)

Freshwater Regulation Potential (FWRP)

Water Purification Potential (WPP)

Ecosystem’s ability to resist erosion

Shows the soil’s capacity to regulate peak water flows

Soil’s ability to absorb dissolved soil particles (physiochemical) and clean the water entering the groundwater supply (mechanical)

Indicator Erosion resistance Groundwater recharge

Physiochemical filtration,

Mechanical filtration

Measured in (tons of soil eroded/(ha*yr)

Millimeters of water recharged into the water table per year

Centimoles of cation fixed/ kg soil

Rate of H20 passing through soil (cm/day)

Brandão and Milà i Canals (2012) add Biotic Production Potential (BPP) to the list of potential impact categories, which is describes as a system’s lasting ability to produce biological mass. Change in soil organic matter (SOM) is suggested as an indicator for the BPP impact category because it impacts most of the chemical, physical, and biological properties of soil, and therefore governs the conditions necessary for biological productivity. SOM is best measured by soil organic carbon (SOC) content, and is defined as the organic component in soils and acts to store nutrients and water in soil. Although it is limited in its ability to capture all aspects of biotic production potential, this indicator is closely related to many other soil quality indicators such as soil biota, structure, and cation-exchange capacity, making a valuable measurement of BPP. SOM has been widely used and cited outside of LCA methodology and therefore the data

11  

availability makes this a functional indicator (Brandão and Milà i Canals, 2012). Loss of SOM is attributed to anthropogenic actions such as soil erosion, land conversion, tillage and over grazing (Van-Camp et al., 2004). Impacts to BPP from land use are dependent on time, area, and change in SOM.

A fifth potential impact category for the ecosystem services pathway is suggested by Müller-Wenk and Brandão (2010): the climatic impact of land use, labeled Climate Regulation Potential (CRP) in Koellner et al. (2013). This impact category compares a baseline of natural vegetation to land areas either transformed or kept in their non-natural state, and measures the potential mobilized carbon that may result from human land use. Impact to climate is expressed in tons of CO2 transferred per hectare (Müller-Wenk and Brandão, 2010).

Biodiversity Pathway

Regarding evaluating impacts to biodiversity, de Baan et al. (2012) use the indicator relative change in species richness to calculate characterization factors for biodiversity damage potential (BDP). Species richness (SR), also referred to as alpha diversity, is a measure of biodiversity that simply counts the number of species in a habitat. Relative species richness is the difference between the observed area and a (semi-) natural reference area (de Baan et al., 2012).

Although data availability is greater for species richness than for other biodiversity indicators (de Baan et al., 2012), it includes limited information on many aspects of biodiversity. For example, species richness does not account for species abundance and weights species equally regardless of their ecosystem function. As an indicator it also lacks differentiation on habitat diversity. Moreover, the results of this method are highly dependent on sampling effort, which leads to uncertainty regarding the robustness of results. Additional limitations reported in the literature of species richness as a measure of biodiversity include:

l The richness of species of one group cannot represent the richness of species in another group comprehensively. An overwhelming number of studies show no correlation between species richness in one taxonomic group and other taxonomic groups (Michelson, 2008).

l There may be a tremendous time lag between the extinction of species and the land use change. This is known as the ‘extinction debt’. Thus, the current situation of the number of species in a certain area cannot directly represent the real species richness for a given land use (Michelson, 2008).

l The function of the land may be disabled even if the species richness is ‘good,’ but the abundance falls under a certain level (the threshold). Species evenness should be taken into account to adjust for this loss (Michelson, 2008).

Acknowledging that these limitations might lead to an inaccurate evaluation of land use impacts to biodiversity, an inherently complex concept, de Baan et al. (2012) calculate four additional

12  

common indicators of species diversity to correct for limitations of the species richness indicator. These four indicators are Fisher’s α, Shannon’s H, Sorensen’s Ss, and mean species abundance (MSA). Based on a sensitivity analysis including these indicators, de Baan et al.(2012) conclude that relative species richness is a sufficient indicator, but that MSA or Sorenson’s Ss may be preferable in the future as research progresses.

As an alternative to species richness as an indicator of biodiversity impacts, Souza et al. (2013) propose Functional Diversity (FD), which considers the association between species traits and ecosystem functions. The study considers the three taxonomic groups of mammals, birds, and plants. Functional diversity reflects the range of services provided by different species, such as carbon storage, nutrient cycling, and biotic productivity.

Souza et al. find a significant difference between characterization factors calculated for species richness versus functional diversity across all taxa, illustrating the importance of FD as an additional impact category. The inclusion of mammals and birds alongside plants is an important addition to studies using other species richness measures. However, data collection on functional traits and species composition across land use types has proved challenging, and much of the information is currently unavailable on a global scale (Souza et al., 2013).

Preceding Case Study

In order to demonstrate the potential applicability of the methods developed by the LULCIA initiative, Milà i Canals, et al. (2012), completed a case study of the land use impacts on biodiversity and ecosystem services of margarine in the UK and Germany. This case study defined their system boundary to exclude the distribution process and any processes downstream of it. Milà i Canals et al. (2012) use seven different impact categories, including biodiversity damage potential, climate regulation potential, biotic production potential, freshwater regulation potential, erosion regulation potential and water purification potential. By using methodologies, which are referenced in the UNEP-SETAC guidelines, this case study was able to determine the weaknesses and limitations of these methods. It was determined that further research is needed on differentiating land use types, as clear gaps were identified in the use of broad classes of agricultural systems. This study also identified the need for further research on the usefulness of these selected impact categories and whether there is overlap between them (Milà i Canals 2012).

Technical Approach for Solving the Problem In order for companies such as Patagonia to improve the sustainability of their operations, they first must be able to analyze, evaluate, and compare their environmental impacts using tools such as LCA. At present, there is no standard, tested methodology for evaluating land use impacts on biodiversity or ecosystem services, which are both critical indicators of ecosystem quality. Our overall approach to help solve this problem is to build off the most recent and comprehensive

13  

framework developed thus far, the methodologies published by the LULCIA experts in the special issue of the International Journal of Life cycle Assessment, to conduct an assessment of Patagonia’s land use impacts on biodiversity. This  methodology  was  chosen  for  evaluation  because  the  initiative  is  led  by  experts  in  the  field  and  at  the  forefront  of  land  use  in  LCA,  and  it  is  the  most  comprehensive  land  use  LCA  method  that  we  found.  Review  of  the  method  is  especially  timely  as  the  Special  Issue  should  be  published  in  June  2013,  the  same  month  in  which  this  initial  work  plan  is  completed.  

Selecting Products for Comparison

As the first task in our study, we determined that a comparative LCIA of four fiber types (wool, polyester, cotton, and Tencel®), which can be expected to have markedly different biodiversity impacts due to their unique land use needs, would prove most useful. In collaboration with our project advisor and client, we selected four Patagonia products of comparable weight and similar style, that each represents one of the four fiber types considered in this case study. The selected products and initial product data are:

1. Women’s Merino 1 Silkweight T-Shirt (65% wool/35% recycled polyester) • Finished weight: 3.54 oz/yd2 • Country of Origin: Vietnam • Patagonia’s wool is supplied by global merino and comes from non-mulesed

Australian merino sheep (Patagonia, 2013). Generally, harvests for wool are made annually or bi-annually depending on conditions, after which the material is processed into yarn. The processing phase consists of washing the wool to eliminate any dirt or fats, then sorting the fibers, carding and combing the wool, and finally spinning it into fibers. Major inputs to the process of growing wool consist of water involved in the processing and pasture for the sheep (International Wool Textile Organization, 2013). Approximately 197.5 ounces of greasy wool are produced per head of sheep (FAO, 2013).

2. Women’s Short-Sleeved Fore Runner Shirt (100% virgin polyester) • Finished weight: 3.5/yd2 • Country of Origin: Vietnam • Virgin polyester is a synthetic fiber derived from petroleum. Extraction and transport

of raw materials such as petroleum is necessary. Polyester production has large energy requirements for and high greenhouse gas emissions. The water inputs for production of virgin polyester products are much lower than for natural fibers such as cotton (NRDC, 2011).

3. Women’s Astrid Two-Way Tunic (45% Tencel®/55% organic cotton)

• Finished weight: 4.0/yd2

14  

• Country of Origin: Vietnam • For the purpose of this project we use Tencel® to represent lyocell. Tencel® is a

brand of lyocell, a cellulosic fiber that is part of the rayon family, produced by the company Lenzing. Tencel® is primarily made from the pulp of eucalyptus trees grown in South Africa. A limited amount of beach wood from Europe and pinewood from North America is also used. The wood is pulverized into a pulp that is then converted to a fiber, then to yarn, and ultimately into fabric to make clothing. The fiber production is a closed loop cycle in which 99.7% of the chemicals used are recycled. Eucalyptus is considered to be an environmentally friendly input due to its minimal growth requirements. Eucalyptus grows quickly on low-grade land, requiring very little water and no irrigation. Production of one ton of Tencel® requires just one half acre of eucalyptus trees. For these reasons, it is considered a revolutionary and eco-friendly fiber. (NRDC, 2013).

4. Men’s GPIW™ Axe T-Shirt (100% organic cotton)

• Finished weight: 3.5/yd2 • Country of Origin: Mexico • Patagonia uses only 100% organic cotton in all of their product lines (Patagonia,

2013). Cotton is one of the main agricultural products of the world, and a key source for the textile industry. Production is characterized by high productivity, low cost and low initial investments, and is widely spread across the world. The main producing areas are the U.S, China, India and Uzbekistan. The life cycle of cotton production consists of the following phases: cotton cultivation, manufacturing, and use. Cotton cultivation requires irrigation and fertilizer inputs, as well as land occupation (Cotton Incorporated, 2012).

In addition to the information provided above, we have collected the following data for each product: BUD (business unit director), product developer and leader, sourcing manager, production manager, factory name, fabric supplier, fabric developer, and raw material supplier. Our Project Intern, Elena Egorova, will use these contacts over Summer 2013 to collect primary data required for the project, which is discussed below.

System Boundary

To satisfy the goal and scope of our study, we chose to exclude the use and disposal phases of the life cycle of these products. Because our focus is land use impacts, we include land used for agriculture, forestry, and other inputs to material production, as well as land use for manufacturing and processing facilities. Land use designated for transportation may be assessed in this study depending on data availability.

Functional Unit and Reference Flows

15  

To allow for the comparison of land use impacts of our four fiber types (wool, lyocell, cotton, and polyester) represented by Patagonia products, we defined the functional unit of this study as one medium sized t-shirt. We have also defined the following reference flows (the amount of product required to fulfill the functional unit) for each product:

1. Wool – 3.5 oz./yd2 2. Polyester – 3.5 oz./yd2 3. Lyocell – 4.0 oz/yd2 4. Cotton – 5.4 oz/yd2

Developing an LCIA Methodology

The next undertaking of our project is to determine how we will apply the LULCIA methodologies to our product case study. At a minimum, we will evaluate biodiversity damage potential (BDP) as an impact pathway using relative change in species richness as the impact indicator. To conduct our study we will rely on the methodology presented by de Baan et al. (2012), which is based on the guidelines developed by the LULCIA initiative and reported by Koellner et al. (2013), including the developed characterization factors. As de Baan et al. (2012) report, results are highly sensitive to the indictor used, and thus we will also want to consider the other presented indicators (Fisher’s α, Shannon’s H, Sorensen’s Ss, and MSA), which offer particular adjustments in measuring species diversity. To further understand the relationship between land use and biodiversity loss in our analysis, we will ideally assess Functional Diversity as an additional impact category using the methodology outlined by Souza et al. (2013). However, as Souza (2013) explains, FD is limited for global application and necessary data may not be available.

Within both the de Baan and Souza methodologies, characterization factors for only land occupation are provided due to data limitations (de Baan et al., 2012; Souza et al., 2013). As it is unlikely that the required data will be fully developed in time for our case study, we will restrict our analysis to the land use impacts of occupation, and exclude transformation. Although Koellner et al. (2013) provide strategies for careful estimation of regeneration times; these calculations are beyond the scope of our study, particularly due to the high degree of uncertainty of such estimations. Our case study will rely on characterization factors that have been previously developed and reported.

In order to better evaluate the methodology established by the “Special Issue,” and to provide a more robust analysis of the land use impacts of Patagonia’s products, we would ideally be able to consider the Ecosystem Services Damage Potential Pathway. To do so we would likely refer to Saad et al.’s (2013) proposed methodology, which uses FWRP, WPP, and ERP as impact categories, as well as Brandão and Milà i Canals’ (2012) recommendation for considering BPP as an impact category. We would use the indicators and developed characterization factors recommended by those studies for each impact category as the methodology for our case study.

16  

Inventory Modeling

In order to apply these methodologies to the Patagonia products we need to complete a life cycle inventory (LCI) to measure land occupation and land transformation as elementary flows. As reported by Koellner et al. (2012), the LCI should report the type of land use, the spatial extent, the temporal extent, and the geographical location (Milà i Canals et al., 2007). The elementary flows of land use are thus defined (Koellner et al., 2012) as:

Occupation: square meter X years, land use type i and region k

Transformation: square meter, initial land use type i à final land use type j, and region k

The data we will require to evaluate the elementary flows include:

A. Area occupied by the agricultural land, pastures, forestry, and facilities, and possibly roads for transportation, required to produce these products

B. Area of land converted from a natural state to a land use state C. The amount of time that land has been occupied D. Specific location of land use, including ecosystem and habitat information for

those areas in use E. Reference situations for distinct land types F. Data for reference situations of the different land use types

Where possible, primary data will be used to complete our assessment, which will be supplemented with secondary data from literature and other sources as needed.

Classification system

Although we will be using developed characterization factors, we need to establish a classification system for measuring our inventory flows. In choosing a land cover classification system, Koellner et al. (2012) identify three important features: 1) large, accepted, well-maintained databases, 2) inclusion of land use intensity and environmentally relevant classes, and 3) allow for characterization factors to be applied across land use type and location. Based on the available data and the characterization factors we will be using, it will be logical to use the same classification system outlined by de Baan et al. (2012), which is based off Koellner et al. (2012).

Reference Situation

The reference situation that will be defined for our study will be based off of the reference situation set up by de Baan et al. (2012). Because the reference is used within calculating the characterization factors, and we will be using factors calculated within their study, it is unnecessary for us to obtain our own reference situation to begin our assessment.

17  

Data Catalog To  address  the  objectives  of  our  study,  we  will  be  collecting  a  combination  of  primary  and  secondary  data.  Primary  data  will  be  collected  through  the  sources  provided  by  Patagonia,  including  the  different  vendors  and  suppliers.  From  these  sources,  we  will  collect  the  land  area  (acreage)  occupied  by  their  facilities  or  practices,  the  amount  of  time  that  Patagonia  has  spent  working  with  those  facilities,  and  the  land  use  type,  for  example,  pasture  or  industrial  uses.  At  times  where  primary  data  is  not  available  such  as  the  acreage  used  by  sheep  to  produce  wool  for  Patagonia,  we  will  used  secondary  data  sets  provided  by  sources  such  as  the  Australian  Bureau  of  Agriculture.  In  the  case  of  classifying  land  types  we  will  also  use  secondary  sources  such  as  GLOBIO3.  Once  data  has  been  gathered,  it  will  be  incorporated  into  calculating  the  impacts  to  each  of  our  impact  categories.  The  summer  intern  has  developed  a  database  for  the  collection  of  data.    

Project Deliverables

Deliverables for client

1. Literature review of an existing methodology used to evaluate impacts of land use on biodiversity and ecosystem services in LCA.

2. Detailed comparative assessment of the life cycle impacts on biodiversity and ecosystem services of land use for four Patagonia products:

a. Women’s Merino 1 Silkweight T-Shirt (65% wool/35% recycled polyester) - #36355

b. Women’s Short-Sleeved Fore Runner Shirt (100% virgin polyester) -#23661 c. Women’s Astrid Two-Way Tunic (45% Tencel®/55% organic cotton) -#54515 d. Men’s GPIW™ Axe T-Shirt (100% organic cotton) - #59578

3. Critical review of the effectiveness of the methodology for incorporating land use impacts on biodiversity and ecosystem services into LCA.

4. Overview of other established ecosystem services and biodiversity measurement tools, and recommendations for using such tools such that they are applicable, useful, and reliable for the apparel industry to apply to their supply chains.

Deliverables for Bren

1. Group website

2. Defense presentation

3. Final report

4. Project brief

18  

5. Project poster

6. Final presentation

Milestones

Spring Quarter 2013 Mon May 13 Draft Work Plan due to Faculty Advisor(s) Mon May 20 Feedback from Faculty Advisor(s) due back to students Fri May 24 Send revised Work Plan to Faculty Advisor(s), client and external advisors by

May 24 or one week prior to work plan review meeting By Jun 7 Host work plan review meeting with Faculty Advisor(s), client and external

advisors by this date Fri Jun 7 Send web link for GP website to GP Coordinator By Fri Jun 14 Submit 1-page summary of work plan review meeting to Faculty Advisor(s) Fri Jun 14 Submit final work plan to Faculty Advisor, client and external advisors Fri Jun 14 Submit Self/Peer Evaluation to Faculty Advisor(s) and GP Coordinator Fall Quarter 2013

By Fri Nov 15 Host fall review meeting with Faculty Advisor(s), client, and external advisors by this date

By Wed Nov 20 Submit 1-page summary of fall review meeting to Faculty Advisor(s) Fri Dec 13 Written Progress Report due to Faculty Advisor(s) Fri Dec 13 Submit Self/Peer Evaluation to Faculty Advisor(s) & GP Coordinator Winter Quarter 2014 Fri Feb 21 Draft of Final Report due to Faculty Advisor(s) Fri Feb 21 & 28 Group Project Defenses Fri Mar 7 Submit Final Presentation Program Abstract to GP Coordinator (Template sent

out by GP Coordinator 2 weeks prior) Fri Mar 7 Draft Project Brief due to Faculty Advisor(s) Fri Mar 7 Draft Project Poster due to Faculty Advisor(s) Fri Mar 21 Final Report (.pdf version) due to Faculty Advisor(s) and GP Coordinator Fri Mar 21 Submit Self/Peer Evaluation to Faculty Advisor(s) & GP Coordinator Fri Mar 21 Submit Faculty Advisor Evaluation to GP Coordinator Spring Quarter 2014 Fri Apr 4 Final Project Brief and Project Poster (.pdf version) due to Faulty Advisor(s),

GP Coordinator and posted on GP website

19  

1-2 weeks before Final Presentation

Print Final Poster and Project Briefs

1-2 weeks before Final Presentation

Take group photo w/ Faculty Advisor(s) to use as the first slide in the Final Presentation

1-2 weeks before Final Presentation

Submit draft Final Presentation to Faculty Advisor(s) for review

Apr 7-10 Practice and videotaping of Final Presentations Fri Apr 11 Master’s Project Final Presentations (hard copy poster will be collected by GP

Coordinator after Final Presentations)

Management Plan

Group structure and management

The Biodiversity team consists of six student members: Elena Egorova, Heather Perry, Louisa Smythe, Runsheng (Ray) Song, and Sarah Sorensen. Roland Geyer will serve as the faculty advisor, and Thomas Koellner, David Tilman, and Lee Hannah will serve as external advisors. The roles of Project Manager (PM), Data/Computing Manager (DM), Financial Manager (FM), Web Manager (WM), Client Liaison, and Advisor Liaison have been assigned to individuals who are responsible for ensuring the timely completion of work and appropriate delegation of tasks within their project area. The role of Project Secretary will rotate on a quarterly basis. A rotating system for writing and editing of all work will be implemented. Writing tasks will be divided amongst the group members, and when someone has completed a section, they will send to another group member to edit. All drafts will be edited by at least one member, and all team members will sign-off on all final documents, and then a designated Editor will conduct a final review for consistency. All team members are expected to contribute equally to the project. The responsibilities of the described roles are assigned as follows: Student Responsibilities

Project Manager: Sarah Sorensen

• Oversee milestones to confirm that responsibilities are appropriately delegated, and ensure timely completion of all project components

• Maintain project binder and calendar • Ensure fair delegation and distribution of tasks • Serve as project point of contact (excluding with advisors and client) • Submit deliverables as required

Web Manager & Client Liaison: Louisa Smythe

20  

• Design and maintain the project website • Serve as point of contact for all communication with the client

Data Manager: Ray Song

• Manage the group computer, including downloading software and controlling user access

• Organize and maintain project data • Resolve technical difficulties and data management issues

Financial Manager & Project Intern: Elena Egorova

• Set and manage project budget, and justification for budget • Serve as summer intern to Patagonia; collect data from the supply chain

Project Secretary (Spring 2013) & Advisor Liaison: Heather Perry

• Maintain agendas, meeting minutes, and weekly task lists • Compile and distribute minutes and/or summary of special meetings • Serve as point of contact for all communication with project advisors

Advisor Responsibilities

Faculty Advisor: Roland Geyer

• Meet weekly with the team to provide guidance and expertise as needed • Participate in spring and fall review meetings • Review and provide feedback on the work plan, defense presentation, final report,

project brief, poster and final presentation • Assign grades to students for ESM 401A, B and C

External Advisors: Thomas Koellner, David Tilman, and Lee Hannah

• Provide guidance and expertise as needed • Participate in spring and fall review meetings

Client: Patagonia (represented by Elissa Loughman & Jill Dumain)

• Elissa Loughman will serve as the primary point of contact • Provide guidance for project objectives and product selection • Support the project intern in collecting data • Participate in spring and fall review meetings

21  

Meeting structure

The Biodiversity team will meet on a regular basis to discuss and advance our progress toward project objectives. Meetings for the 2013 Spring Quarter will be held Tuesdays from 10:00-11:30 with our faculty advisor joining us for the last hour; the Bonsai room is reserved for that time period for the remainder of the Spring quarter. Thursdays from 10:00-11:30 are also reserved for group meetings, though it will be determined at the Tuesday meeting if a second weekly meeting is necessary. The Project Manager will schedule any additional meetings in Corporate Time and cancel unnecessary Thursday meeting slots.

The Project Secretary is responsible for providing meeting agendas, taking and distributing minutes, and compiling the weekly task list. The process for recording meeting minutes is described in section V below.

At minimum, the Biodiversity team will meet with the faculty advisor, client and external advisory committee once in Spring quarter and once in Fall quarter to evaluate progress and receive feedback. When possible, meetings with the client will be held in person and scheduled at least one week in advance. Meetings with the external advisory committee will be held via Skype or conference call. A draft agenda will be circulated, finalized, and distributed before all meetings.

Guidelines for interacting with faculty advisors, clients, and external advisors

The Client Liaison will serve as the point of contact with the client, while the Advisor Liaison will serve as the point of contact with the faculty advisor and external advisors. The client will be kept informed of our progress through meetings and email updates when milestones are achieved. Meetings or phone calls will be conducted when obstacles requiring the client’s input arise. All team members are expected to use appropriate professional standards when interacting with the faculty advisor, client and external advisors.

Systems to ensure that critical tasks are completed on time

The Project Manager is ultimately responsible for ensuring that all milestones are completed on time and that any deliverables or other requirements are submitted according to the Guidelines. However, the FM, WM, and DM are primarily responsible for ensuring completion of milestones within their purview. All milestones will be tracked in a shared Excel spreadsheet, which will include fields for Milestone, Responsible Person, Date Due, Date for Review, and Date Completed. The Project Secretary is responsible for ensuring that all milestones are added to the Weekly Meeting Document (see below) beginning in the week when work for the milestone should begin and are discussed in the appropriate meeting. Major milestones are also recorded in a Google calendar.

22  

Procedures for documenting, cataloging, and archiving information The following documents will be stored in the group Dropbox, and backed up by the Data Manager to the group computer in order to ensure that information is reliably documented, catalogued, and archived.

Documents & Library Spreadsheet

Research-related documents are saved to the Library folder of the group Dropbox. Any person downloading a document is responsible for immediately recording the document in the Literature Library spreadsheet, which includes fields for document title, author, year, citation, topic, and notes, as well as a “Read By” column for each individual to indicate who has completed the reading.

Contact information

Contact information, including e-mail and phone number for all group members, advisors, client representatives, and the group project coordinator are saved in an Excel spreadsheet in the group Dropbox.

Weekly Meeting Document (Agenda, Minutes, Tasks)

Within one document an Agenda Template and Task List have been created for every anticipated meeting this quarter and are shared via Dropbox. Before each meeting the Project Secretary is responsible for filling out and distributing the agenda for the upcoming meeting, recording meeting minutes on the group agenda, and following up with a list of tasks for each group member to complete before the next meeting. Any group member may add to the agenda, and the task list is reviewed by the group at the end of each meeting. The agenda, minutes, and task list for every meeting will be available to all group members in the Dropbox document.

Website

The website will be hosted at ____ and developed using Weebly.

Budget Projections and Expenditures

Budget projects and expenditures are prepared by the Financial Manager and reviewed by the group. They are also stored in the group Dropbox.

Overall expectations of group members and faculty advisors

Using the tools described above, the team is expected to meet all deadlines for deliverables and drafts to be distributed to advisors for review. The biodiversity team will allow a minimum of

23  

one week for feedback from all advisors regarding work plans and drafts of major milestones. Open and regular correspondence between the group and advisors will be maintained through email and scheduled in-person meetings, Skype, or conference calls. The biodiversity team will seek regular advice from external advisors as needed, and is expected to equally divide and contribute to work. If students are not meeting the expectations of the group or advisor, individual meetings will be arranged to resolve the issue.

Conflict resolution process

Open communication will be a priority of the Biodiversity team in order to avoid and resolve conflicts. Similarly, maintaining the procedures and tracking documents described above will help prevent conflict over workload, uneven distribution of work, or low quality work. As issues arise concerned group members should add them to the weekly agenda, so that the group can address them as quickly as possible. If a resolution cannot be reached using these tools, then the group will engage the faculty advisor or Bren School administration.

Budget  

 

Costs presented in this budget are preliminary and may need to be adjusted as the project progresses. Currently we have posted expected costs such as phone calls, printing, presentation supplies, and printing our final poster. In addition, we have added $300 for travel expenses such as gas, as we will need to travel to Ventura for select meetings.

Expense Cost*($)Calls%($1/month%+$10%start%up) 22Copies 20Final%Poster% 250Presentation%Expenses% 50Administrative%Supplies 50Parking%for%client 50Travel 300Final%Report%Printouts% 100

Total 842Additional*ExpensesPrinting 200

24  

References Alkemade, R., Oorschot, M., Miles, L., Nellemann, C., Bakkenes, M., & ten, B. B. (2009). GLOBIO3: A

framework to investigate options for reducing global terrestrial biodiversity loss. Ecosystems, 12(3), 374-390.

Beck T., Bos U., Wittstock B., Baitz M., Fischer M., Sedlbauer K. (2010). LANCA—Land use indicator value calculation in life cycle assessment. Fraunhofer, Stuttgart.

Brandão, M., & i Canals, L. M. (2012). Global characterisation factors to assess land use impacts on biotic production. The International Journal of Life Cycle Assessment. DOI:10.1007/s11367-012-0381-3.

Chiarucci, A., Araújo, M. B., Decocq, G., Beierkuhnlein, C., & Fernández-Palacios, J. M. (2010). The concept of potential natural vegetation: an epitaph?. Journal of Vegetation Science, 21(6), 1172-1178.

Cotton Incorporated (2012). The Life Cycle Inventory & Life Cycle Assessment of Cotton Fiber & Fabric, Executive Summary. Retrieved May 24, 2013 from http://www.cottoninc.com.

Curran, M., de, B. L., De, S. A. M., Van, Z. R., Hellweg, S., Koellner, T., Sonnemann, G., ... Huijbregts, M. A. (2011). Toward meaningful end points of biodiversity in life cycle assessment. Environmental Science & Technology, 45(1), 70-9.

de Baan, L., Alkemade, R., & Koellner, T. (2012). Land use impacts on biodiversity in LCA: a global approach. The International Journal of Life Cycle Assessment. DOI:10.1007/s11367-012-0412-0.

Food and Agriculture Organization of the United Nations (FAO). n.d. Statistics Division. Retrieved May 23, 2013 from http://faostat.fao.org/.

Fisher, R. A., Corbet, A. S., & Williams, C. B. (1943). The relation between the number of species and

the number of individuals in a random sample of an animal population. Journal of Animal Ecology, 12(1), 42-58.

He, F., & Hubbell, S. P. (2011). Species-area relationships always overestimate extinction rates from habitat loss. Nature, 473(7347), 368-71.

International Organization for Standardization. (1997). Environmental management - Life cycle assessment: Principles and framework. Geneva: ISO.

International Wool Textile Organisation (2013). Wool Factsheet. Retrieved May 23, 2013 from http://www.iwto.org/wool/factsheet/.

Koellner, T., de Baan, L., Beck, T., Brandão, M., Civit, B., Goedkoop, M., Margni, M., Milà i Canals, L.,

Müller-Wenk, R., Weidema, B., Wittstock, B. (2012). Principles for life cycle inventories of land use on a global scale. The International Journal of Life Cycle Assessment. DOI:10.1007/ s11367-012-0392-0.

25  

Koellner, T., de Baan, L., Beck, T., Brandão, M., Civit, B.,Margni, M., Milà i Canals, L., Saad, R., de Souza, DM., Müller-Wenk, R. (2013). UNEP- SETAC guideline on global land use impacts on biodiversity and ecosystem services in LCA. The International Journal of Life Cycle Assessment. DOI: 10.1007/s11367-013-0579-z.

Koellner, T., & Geyer, R. (2013a). Global land use impact assessment on biodiversity and ecosystem services in LCA. The International Journal of Life Cycle Assessment. DOI: 10.1007/s11367-013-0580-6.

Michelsen, O. (2008). Assessment of land use impact on biodiversity. The International Journal of Life Cycle Assessment, 13(1), 22-31.

Milà i Canals, L., Rigarlsford, G., Sim, S. (2012). Land use impact assessment of margarine. The International Journal of Life Cycle Assessment. DOI:10.1007/s11367- 012-0380-4.

Milà i Canals, L., Bauer, C., Depestele, J., Dubreuil, A., Freiermuth Knuchel, R., Gaillard, G., Michelsen, O., Müller-Wenk, R., Rydgren, B. (2007). Key elements in a framework for land use impact assessment within LCA. The International Journal of Life Cycle Assessment, 12 (1), 5–15.

Millennium Ecosystem Assessment (MEA) (2005). Ecosystems and human well-being: synthesis. Island, Washington, DC.

Müller-Wenk, R., Brandão, M. (2010). Climatic impact of land use in LCA – carbon transfers between vegetation, soil and air. The International Journal of Life Cycle Assessment, 15, 172-182.

National Resources Defense Council (NRDC) (2012). Choosing Between Organic Cotton and Tencel. Retrieved May 8, 2013 from http://www.nrdc.org/living/stuff/choosing-between-organic-and-cotton-tencel.asp.

National Resources Defense Council (NRDC) (2011). Clean by Design: Polyester. National Resources Defense Council. Retrieved May 24, 2013 from http://www.nrdc.org/international/cleanbydesign/materials.asp

Patagonia. (2013). Retrieved May 24, 2013 from http://www.patagonia.com/.

Saad, R., Koellner, T., Margni, M. (2013). Land use impacts on freshwater regulation, erosion regulation and water purification: a spatial approach for a global scale. The International Journal of Life Cycle Assessment. DOI: 10.1007/s11367-013-0577-1.

Schenck, R. C. (2001). Land Use and Biodiversity Indicators for Life Cycle Impact Assessment. International Journal of Life Cycle Assessment, 6, 114-117.

Schmidt, J. H. (2008). Development of LCIA characterisation factors for land use impacts on biodiversity. Journal of Cleaner Production, 16(18), 1929-1942.

26  

Souza, DM., Flynn, D., Rosenbaum, RK., de Melo Lisboa, H., Koellner, T. (2013). Land use impacts on biodiversity: proposal of characterization factors based on functional diversity. The International Journal of Life Cycle Assessment. DOI: 10.1007/s11367-013-0578-0.

Sustainable Apparel Coalition. (2012). Retrieved May 24, 2013 from http://www.apparelcoalition.org/.

Van-Camp, L., Bujarrabal, B., Gentile, A-R., Jones, RJA., Montanarella, L., Olazabal, C., Selvaradjou, SK. (2004). Reports of the Technical Working Groups Established under the Thematic Strategy for Soil Protection. EUR 21319 EN/1. Luxembourg, Office for Official Publications of the European Communities.