Using the product environmental footprint for supply chain management: lessons learned from a case study on pork

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Int J Life Cycle Assess (2017) 22: DOI /s LCI METHODOLOGY AND DATABASES Using the product environmental footprint for supply chain management: lessons learned from a case
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Int J Life Cycle Assess (2017) 22: DOI /s LCI METHODOLOGY AND DATABASES Using the product environmental footprint for supply chain management: lessons learned from a case study on pork Lasse Six 1 Bruno De Wilde 1 Frederic Vermeiren 2 Steven Van Hemelryck 2 Mieke Vercaeren 2 Alessandra Zamagni 3 Paolo Masoni 4 Jo Dewulf 5 Steven De Meester 6 Received: 1 December 2015 /Accepted: 8 December 2016 /Published online: 22 January 2017 # The Author(s) This article is published with open access at Springerlink.com Abstract Purpose The purpose of this study was to test the chainorganization environmental footprint (chain-oef) approach by applying it to part of a pork production chain in Belgium. The approach is supposed to provide insight into the environmental impact of a specific production chain in an efficient manner by applying pragmatic data collection throughout the chain. This is achieved by allocating the environmental impact of each of the production sites to the product of interest using straightforward allocation rules. Methods The cradle-to-gate (up to retail) environmental impact of pork was determined by life cycle assessment (LCA), in line with the product and organisation environmental footprint guidelines (PEF and OEF; European Commission 2013b). Foreground data was gathered at a feed production site, two farmers, a slaughterhouse and a meat processing site. All foreground operations are part of the same pork Responsible editor: Adriana Del Borghi * Lasse Six OWS, Dok Noord 5, 9000 Ghent, Belgium Colruyt Group, Edingsesteenweg 196, 1500 Halle, Belgium Ecoinnovazione, Via Guido Rossa 26, Padova, Ponte San Nicolò, Italy ENEA, via Martiri di Monte Sole 4, Bologna, Italy Department of environmental organic chemistry and technology, Ghent University, Coupure Links 653, 9000 Ghent, Belgium Department of industrial biological sciences, Ghent University Campus Kortrijk, Graaf Karel de Goedelaan 5, 8500 Kortrijk, Belgium production chain in Belgium. The chain was completed using background data from Ecoinvent v3.01 (Wernet et al. 2016), Agri-Footprint v1.0 (Blonk 2014), European Life Cycle Database v3.0, LCA Food Database (Nielsen et al. 2003) and OEF Sector Rules Retail (Humbert et al. 2015b). The impact was quantified using the international reference life cycle data system (ILCD) midpoint method for 14 impact categories, but focussing on climate change. Results and discussion The total carbon footprint of the cradle-to-gate pork production system equals 0.46 kg CO 2 -eq. (100 g pork) 1. This result is quite similar to that of earlier studies analysing the pork production chain: 0.58and0.57kgCO 2 -eq. (100 g pork) 1 (Bracquené et al. 2011, Agri-Footprint2014). Most of the carbon footprint was caused by feed production and more specifically, by the feed ingredients and their transport. Grains, soy and palm oil have the largest impact contributions. The farms are responsible for most of the remaining impact. N 2 OandCH 4 emissions are the largest cause of greenhouse gas emissions at the farms. Also, in the other 13 considered impact categories, feed production and farming are responsible for more than half of the total impact, mostly followed by meat processing. Conclusions Applying the chain-oef approach in this study has shown that a chain LCA can be performed successfully and pragmatic data collection allows obtaining LCA results relatively fast, especially for small or medium-sized enterprises (SMEs). Whereas data availability was not such an issue, the main bottlenecks identified are data management and the link of LCA to other disciplines such as engineering, policy, etc. which could increase the added value of LCA studies. Keywords Chain-OEF. Pork. Product environmental footprint. Supply chain management Int J Life Cycle Assess (2017) 22: Abbreviations CV Coefficient of variation DQR Data quality rating HFCs Hydrofluorocarbons ILCD International reference life cycle data system LCA Life cycle assessment LCI Life cycle inventory NACE Nomenclature statistique des Activités économiques dans la Communauté Européenne OEF Organisation environmental footprint PM Particulate matter PEF Product environmental footprint SME Small or medium-sized enterprise WWTP Wastewater treatment plant 1 Introduction With a growing world population, the demand for a sustainable food supply is increasing (Ilbery and Maye, 2005). It is therefore essential to be able to analyse and benchmark the environmental sustainability of different food products. To do so, life cycle assessment (LCA) is recommended as the most appropriate method (Finnveden and Moberg, 2005, Guinée et al. 2011). However, LCA as defined by ISO 14040/44 is only a framework leaving many methodological choices open. This results in differentiation of results for similar products, causes confusion and eases greenwashing. To improve harmonisation and consistency of environmental claims, the European Commission created the Single Market for Green Products Initiative with rules for a product environmental footprint (PEF) and organisation environmental footprint (OEF; European Commission, 2013a, b, c; Galatola and Pant 2014). Apart from these general rules, more stringent standards are defined per product category or per sector during a pilot phase. The initiative aims at lowering the remaining confusion among consumers (TNS Opinion and Social 2014) and to aid producers making reliable green claims. It opens the door for more large-scale application of LCA in industry, e.g. for communication or comparisons, and the new standards are thus welcomed. Nevertheless, a major constraint of the strict PEF and OEF rules is that the LCA procedure is very much generalized in this initiative and typical processes and organisations are used to model the background data instead of investigating the actual situation and choosing the most appropriate model. Often, these typical processes are modelled using generic data and as such, substitute more specific data from the studied product life cycle (Finkbeiner 2014; Lehmann et al. 2015). This is in contrast to the supply chain feature of LCA which should be aimed at optimizing specific processes and supply chain choices. One of the main positive properties of LCA is the detailed analysis of and interaction with specific suppliers, and stimulation of green procurement and life cycle thinking resulting in innovative business models and positive feedback loops. Although the PEF/OEF initiative may also aim for supply chain optimisation, it seems to put most attention on the sector specific harmonisation, which causes cross sectoral issues such as data management and positive supply chain interaction to receive less attention. The aim of this paper is to address the above-mentioned concerns and test the practical applicability of the PEF and OEF guidelines in a case study with value chain interactions: the pork supply chain of the Colruyt Group, a large Belgian retailer. Pork production was selected for the following reasons: meat production is an important source of global greenhouse gas emissions (FAO 2014) and plays an important role in the impact associated with retail products (Humbert 2015a), pork is an important contributor to global environmental impacts (Carlsson-Kanyama 1998), it represents an important share of the product portfolio of retailers (De Schryver et al. 2012) and is, by far, the most produced type of meat in Belgium, a net exporter of the product (VLAM 2014). Furthermore, it is also an important in-house product of the Colruyt Group and was previously analysed in an OEF screening study (Quantis 2015). For these reasons, it is interesting to analyse the chain in depth to be able to improve its environmental performance and manage sustainability issues in the supply chain. 2 Methods 2.1 Chain-OEF approach The chain-oef initiative was started to support the PEF/OEF harmonisation effort. It is complementary to the OEF retail pilot and is aimed at testing alternative approaches in the EU pilot testing phase ( ). The two main ingredients of the chain-oef initiative are linking actors in the supply chain and collecting primary data in a pragmatic way (Fig. 1). The linking throughout the supply chain is done in order to limit the use of generic market data as much as possible (Pedrazzini et al. 2014) so that suppliers or entire production chains can be differentiated from one another. This allows the selection of the most environment-friendly supply chain or the identification of the most important improvement potentials. Furthermore, the linking of actors is intended to initiate and strengthen communication and collaboration throughout the supply chain in order to allow the effective reduction of the most important environmental impacts. Pragmatic data collection in this context differs from more detailed, individual unit operation-focussed LCA data collection by treating combined unit operations (i.e. entire processing plants) as black box models, without internal details but with known in- and outputs (e.g. based on accounting balances), 1356 Int J Life Cycle Assess (2017) 22: Fig. 1 Chain-OEF approach (background system data is derived from databases or literature and is generally based on sector averages; foreground system data is gathered on-site; solid arrows indicate material and energy flows; dotted arrows indicate interactions on sustainability within the supply chain) instead of going into detail and investigating all the processes and internal exchanges that form the large units. As such, the ease and efficiency of data collection are improved allowing the relatively rough assessment of supply chains with similar effort as detailed assessments of single supply chain actors. This pragmatic data collection is not novel as such; similar approaches have been applied successfully in other LCA studies, evaluating e.g. average product impacts, which concluded that the obtained results are in line with those of earlier research (Dalgaard et al. 2007; Djekic et al. 2015; Winkler et al. 2016). It is, however, an important feature of chain-oef as it reduces the assessment cost for each of the chain actors, which is crucial for adoption beyond the testing phase. This paper assesses the real life application of the chain- OEF approach for detailed supply chain analysis with the aim of identifying major environmental hotspots and improvement potentials and specifically addresses the following two challenges. First of all, the level of data gathering is an important aspect to be studied. To convince all actors to join an LCA of their complete supply chain, resources (costs and time) should be minimized while an acceptable balance between accuracy and data availability should be achieved. Most companies and organizations dispose of data and information at an organizational level (annual environmental reports, balances, etc.). Environment-related data are much less readily available at the product level. Therefore, the data availability at different levels of a company must be taken into account. It is also interesting to analyse the allocation method for these different levels of data to products and choosing a functional unit that allows a certain grouping of products within one functional unit in such a way that not each and every product requires a separate LCA study. Secondly, the supply chain actors need to be brought together and must discuss how they perceive an LCA study and how steps towards collaboration and improvement can be taken. 2.2 Goal On the one hand, the goal of this case study is to calculate the specific PEF of pork produced within the supply chain of one of the largest Belgian retailers, Colruyt Laagste Prijzen (Colruyt Lowest Prices), so as to identify environmental hotspots and to define strategies for the optimisation of the pork supply chain. On the other hand, the study is also intended as a test of pragmatic data collection throughout the supply chain and testing possible supply chain interactions (chain-oef), using primary, high-quality data from several actors within the production chain: an animal feed production facility, two pig farms, a slaughterhouse and a meat processing site, supplemented downstream with literature models for a local distribution centre and retail stores. 2.3 Scope In line with the declared goal and the chain-oef approach, the scope is limited to one specific cradle-to-gate production system of pork. This limitation of the scope is allowed by the PEF guide as long as it is appropriate for the application of the study. As this study aims at identifying environmental impacts up until the retailer for supply chain management, inclusion of the use and end-of-life phases would not be appropriate and lower the assessment efficiency. The study is not intended to be representative for pork production in general. The study has taken into account the most up-to-date position papers on key methodological issues in the framework of the pilot test when applying PEF/OEF methods, i.e. for electricity modelling, functional unit definition for meat products, and the principles set by the Cattle Model Working Group, in particular for economic allocation Functional unit Pork products are made in many different forms depending on market demand, consumer habits and dietary patterns: steaks, sausages, ribs, Orloff roast, etc. Considering that the purpose of the study is neither to evaluate the potential environmental impact of pork consumption nor of a specific pork form, but instead to look for optimisation in the supply chain, the analysis is limited to pure, fresh pork products. The functional unit is 100 g fresh pig meat, including inedible parts such as bone, presented to consumers in retail packaging. Only meat with an average storage time is considered, excluding frozen pork. It is classified under Nomenclature statistique des Activités économiques dans la Int J Life Cycle Assess (2017) 22: Communauté Européenne (NACE) code G Retail sale of meat and meat products in specialized stores (Humbert et al. 2015b; European Commission 2010) System boundaries and cutoff criteria The system boundaries include all production chain steps until the purchase by consumers in the retail store (Fig. 2). The use and end-of-life phases are not included since pork has many use pathways depending on consumer preferences and it is not in line with the goal and scope of the study. A distinction is made between the foreground system for which primary data is collected or specific calculation models are applied, and the background system for which life cycle inventory (LCI) data from databases is used (Tables 1, 2, 3, 4, 5, 6, 7, 8, 9; Ecoinvent v3.01 (Wernet et al. 2016); Agri- Footprint v1.0, Blonk 2014; European Life Cycle Database v3.0; LCA Food Database, Nielsen et al. 2003; and OEF Sector Rules Retail, Humbert et al. 2015b). The general rules followed are: The mass balance of the main product flow should fit to 100 %. Consumption goods are included for as far as data were readily available at plant level (e.g. utilities, diesel, feed ingredients, packaging, seeds, fertilizer, etc.). Major emissions are included (e.g. flue gases, agricultural emissions, hydrofluorocarbons (HFCs), unused gases) Waste treatment is included for solid wastes as well as wastewater. Capital goods (building infrastructure) and land use are included. Table 1 Background datasets used for modelling feed ingredients production Transported barley grain {GLO} (Colruytketen) Oat grain, consumption mix, at feed compound plant/ie economic Maize grain, organic {BE} Renaat Moors at feed compound Aveve (Colruytketen) Wheat grain Renaat Moors, at feed compound plant Aveve (Colruytketen) Maize germ, dried, from wet milling (germ drying), at plant/fr Economic Transported soybean {GLO} (Colruytketen) Sunflower seed {GLO} market for Alloc Def, U Maize bran, from wet milling (drying), at plant/fr Economic Sugar beet pulp, dried, consumption mix, at feed compound plant/nl Economic Molasses, from sugar beet {GLO} market for Alloc Def, U Crude palm oil, from crude palm oil production, at plant/id Economic Crude maize germ oil, from wet milling (germ oil production, pressing), at plant/de Economic Monocalciumphosphate Sodium chloride, powder {GLO} market for Alloc Def, U Wheat bran, consumption mix, at feed compound plant/nl Economic Rape meal {GLO} market for Alloc Def, U Crude soybean oil, from crushing (pressing), at plant/nl Economic Rye grain {GLO} market for Alloc Def, U Triticale, consumption mix, at feed compound plant/nl Economic Palm kernel meal {GLO} market for Alloc Def, U Calcium carbonate 63 μm, production, at plant EU-27S Distiller s dried grains with solubles {GLO} market for Alloc Def, U Soybean hulls, consumption mix, at feed compound plant/nl Economic Byproduct (fish oil) Coconut oil, crude {GLO} market for Alloc Def, U Magnesium oxide {GLO} market for Alloc Def, U Fig. 2 System boundary diagram of the studied pork production chain (transport between foreground system units is also included; WWTP waste water treatment plant, HFCs hydrofluorocarbons) 1358 Int J Life Cycle Assess (2017) 22: Table 2 Background datasets used for modelling packed feed production Transformation, from industrial area, built up Transformation, to industrial area, built up Occupation, industrial area, built up Electricity, medium voltage {BE} market for Alloc Def, U Natural gas, burned in gas motor, for storage {NL} processing Alloc Def, U Tap water, at user {CH} market for Alloc Def, U Diesel, burned in building machine {GLO} market for Alloc Def, U Printed paper {GLO} market for Alloc Def, U Packaging film, low density polyethylene {GLO} market for Alloc Def, U Emissions to air Carbon dioxide Municipal solid waste (waste treatment) {BE} treatment of municipal solid waste, incineration Alloc Def, U Table 3 Background datasets used for modelling bulk feed production Transformation, from industrial area, built up Occupation, industrial area, built up Transformation, to industrial area, built up Electricity, medium voltage {BE} market for Alloc Def, U Natural gas, burned in gas motor, for storage {NL} processing Alloc Def, U Tap water, at user {CH} market for Alloc Def, U Diesel, burned in building machine {GLO} market for Alloc Def, U Building, hall, steel construction {GLO} market for Alloc Def, U Table 4 Background datasets used for modelling farm A Water, well, in ground, BE Transformation, from agriculture Transformation, to agriculture Occupation, agriculture Electricity, low voltage {BE} market for Alloc Def, U Diesel, burned in building machine {GLO} market for Alloc Def, U Sulfuric acid {GLO} market for Alloc Def, U Transport, freight, lorry metric ton, EURO4 {RER} transport, freight, lorry metric ton, EURO4 Alloc Def, U Building, hall, steel construction {GLO} market for Alloc Def, U Table 5 Background datasets used for modelling farm B Water, well, in ground, BE Transformation, from agriculture Transformation, to agriculture Occupation, agriculture Diesel, burned in building machine {GLO} market for Alloc Def, U Sulfuric acid {GLO} market for Alloc Def, U Transport, freight, lorry metric ton, EURO4 {RER} transport, freight, lorry metric ton, EURO4 Alloc Def, U Electricity, low voltage {BE} market for Alloc Def, U Building, hall, steel construction {GLO} market for Alloc Def, U Table 6 Background datasets used for modelling the slaughterhouse Water, well, in ground, BE Water, rain Transformation, from industrial area, built up Occupation, industrial area, built up Transformation, to industrial area, built up Tap water, at user {Europe without Switzerland} market for Alloc Def, U Carbon dioxide, liquid {GLO} market for Alloc Def, U Transport, freight, lorry metric ton, EURO4 {RER} transport, freight, lorry metric ton, EURO4 Alloc Def, U Building, hall, steel construction {GLO} market for Alloc Def, U Electricity, low voltage {BE} market for Alloc Def, U Heat, c
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