This study has been structured following ISO 14040-compliant and ISO 14044-compliant LCA methodology (ISO 2006a, b). These standards provide an internationally agreed method of conducting LCA, but leave significant degrees of flexibility in methodology to customize individual projects.
Goal and scope of the study
The main goal of this work was to equip US cheese industry stakeholders with timely, defensible, and relevant information to support the incorporation of environmental performance into decision-making and support the development of innovative products, processes, and services. The study will provide cheese manufacturers an opportunity to benchmark their individual performance against a 2009 industry average, which is reported in this paper.
The scope of the project was a cradle-to-grave assessment with particular emphasis on the unit operations under direct control of a typical cheese-processing plant. In particular, these unit operations were transport of raw milk to the plant, cheese and whey manufacture, and delivery of cheese and whey products to the first customer.
Because cheese is produced with variable moisture content, the results are presented on a moisture-free basis. Three relevant functional units were defined:
One ton (1,000 kg) of cheddar cheese consumed (dry weight basis);
One ton of mozzarella cheese consumed (dry weight basis);
One ton of dry whey delivered (dry weight basis).
System boundaries and cutoff criteria
System boundaries encompass production of raw milk (feed production and on-farm), cheese manufacturing, packaging, transport, retail, consumption, and end-of-life (Fig. 1). We also analyzed the gate-to-grave system to increase resolution of the manufacturing and use phases. The boundary for whey does not include retail or consumption due to lack of data. We did not include in the inventory processes activities such as employee commuting; air travel; and veterinary, accounting, or legal services.
In determining whether to expend project resources to collect data for the inclusion of specific inputs, a 1 % cutoff threshold for mass and energy was adopted. Although the study is intended to be comprehensive in consideration of impacts resulting from cheese supply chains, it is not a detailed engineering analysis of specific unit operations within the manufacturing sector. Thus, for example, we did not assign a specific energy requirement for cheese-making vats, cleaning in place, or starter culture operations, rather, we used the information available at the manufacturing plant scale, coupled with allocation of burdens to multiple plant products, to define the burden assigned to cheese, whey, and other coproducts. For this reason, it is important to state that all operations, as well as facility overhead (computers, heating, lights, etc.), are accounted for in this work.
Milk is the most significant input in the manufacturing of cheese, and milk solids (4.9 % lactose, 3.4 % fat, 3.3 % protein, and 0.7 % minerals) represent the important fraction of raw milk (87.7 % water and 12.3 % solids) in terms of cheese production. The production burden (at the dairy farm gate) for milk can be wholly assigned to the solids without differentiation (i.e., protein and fat assigned the same farm gate burden—water is considered only as a carrier), and the solids flow can be conceptually separated and treated as distinct inputs to the manufacturing system, allowing the solids content to be used as the mechanism for assigning the incoming milk burden to each coproduct (Feitz et al. 2007; Aguirre-Villegas et al. 2012). Allocation of the incoming milk solid burdens associated across the multiple coproducts based on milk solid distribution among the coproducts was our default approach (Fig. 2). In the plant survey, we requested each manufacturing facility operator to estimate the allocation of common utilities (electricity, natural gas, steam, etc.) to different operations within the plant boundary. Where this information was provided, we used it for plant-specific allocation of these inputs. For facilities that produce several types of cheese but have inputs without clearly identified fraction, the revenue associated with these sales were used to allocate the burdens among the cheeses.
Life cycle inventory
This LCA is comprehensive and includes all inputs to the dairy industry, from crop farming to the final disposition of the packaging at the end of the supply chain. However, the primary focus of this study was on processes within the control of cheese-manufacturing plants. For each participating plant, processing companies were asked to complete a spreadsheet-based survey to facilitate incorporation of the data. During 2010, data from 2009 operations were collected from a total of 17 processing plants, including 10 cheddar manufacturing facilities (0.55 million tons of cumulative production) and 6 mozzarella manufacturing facilities (0.35 million tons of cumulative production). The industry average life cycle inventory (LCI) data are available in the Electronic supplementary material of this paper. Based on US production estimates of 1.45 million tons/year of cheddar and 1.47 million tons/year of mozzarella (IDFA 2010), the study has a sample representing 38 and 24 % of production, respectively. A variety of plant sizes are represented, with production ranging from 0.014 to 0.14 million tons of cheese/year. The survey requested facility-level data regarding purchases (materials and energy), production (cheese and other products), and emissions (solid and liquid waste streams). Previous work conducted by the investigators for the production of fluid milk to the farm gate was used as background for milk production (Thoma et al. 2012a, b). Data collected from primary sources were checked for validity by ensuring consistency of units for reporting and conversion as well as material balances to insure that all incoming milk solids are accounted for in products leaving the manufacturing facility. The ecoinvent pedigree matrix approach to assigning uncertainty of inputs was applied to unit processes generated from primary data. Secondary data were taken from the ecoinvent v2.2 database. The data quality pedigree provided by the ecoinvent center for these data was adopted without revision. If secondary data are not available, input–output LCI datasets from the Open IO database were used as a proxy (TSC, Open IO) (TSC 2012). SimaPro© 7.3 (PRé Consultants, The Netherlands 2012) was used as the primary modeling software; the ecoinvent database, modified to account for US electricity, provided information on the “upstream” burdens associated with materials such as fuels and plant chemicals.
Cheese and whey plant data collection
The plants within the study combined cheese and whey production. The survey requested information at the subfacility scale; however, in many cases, only facility-level data were available. For example, most plants reported a single annual electrical energy use. We requested engineering estimates for separate material and/or energy flows (inputs and outputs) associated solely with cheese or whey products. This information was used in the algorithm that allocated material and energy flows between the coproducts of cheese and whey (see Fig. 2). For this study, each output product of each plant was classified as one of the following: main cheese, other cheese, dry whey, wet whey, and other coproducts. Protection of confidential business information requires an aggregation of the data that were acquired from the manufacturing facilities that participated in this project. Representative average production LCI data were generated using the allocated LCI data for each plant, which was totaled to create a generic inventory for each of the five potential coproducts at each facility. The resultant inventory is a production-weighted dataset because each product’s reference flow was the sum of production from all reporting facilities.
Transportation: farm to manufacturing and manufacturing to retail
The survey included information on transportation distances from farm to manufacturing facility and also for distribution to retail (or in the case of whey, to the first customer). These data were used to determine the impacts of these stages within the cheese supply chain. The baseline vehicle was considered to be an insulated tanker truck and a refrigerated truck for raw milk and finished product, respectively. For a refrigerated truck transport, we modified ecoinvent unit process by adding refrigerant loss (Nutter et al. 2012). Empty kilometers (during return) were also included. Allocation of transportation of the raw milk to different products was based on milk solids. Post-manufacturing transport was directly assigned to the product being transported.
Contribution to environmental impacts from the retail sector was assessed from information previously requested from the project sponsor, who provided data regarding shelf space occupied in retail grocery outlets coupled with publicly available data for energy consumption in the building sector (Energy Information Administration (EIA) 2003; Energy Star 2008). Disposal of secondary packaging was accounted for through recycling rates of the materials commonly recycled (corrugated packaging and pallets). After distribution from the processor to the retail gate, cheese is displayed for consumer purchase. During this phase, there are three distinct emissions streams: refrigerant leakage, refrigeration electricity, and overhead electricity. For the purposes of this LCA, cheese sales channels were divided into two primary channels: supermarkets and mass merchandisers. Estimates of the sales volume, space occupancy, and energy demands were used to determine the burden of this supply chain stage.
According to the US Department of Transportation, Bureau of Transportation Statistics, Research and Innovative Technology Administration’s 2001 National Household Travel Survey (NHTS), the average household makes 88.4 trips annually of 10.8 km roundtrip for shopping (NHTS 2009). Allocation of impacts from this activity to cheese is 1.16 % (all cheese, 11.9 % of dairy (USDA 2010); all dairy, 9.8 % of grocery sales (Food Marketing Institute 2010)) resulting in 0.15 km/kg cheese. The average number of people per household is assumed to be 2.6 (US Census Bureau 2009), and the per capita annual consumption of cheese is estimated to be 3.69 and 3.95 kg for cheddar and mozzarella, respectively. Therefore, the total annual household consumption was estimated to be 9.59 and 10.26 kg for cheddar and mozzarella, respectively. Considering all cheeses (excluding ricotta and cottage cheeses), annual household consumption is calculated at 28.9 kg, resulting in 33.2 and 35.5 % of all cheese consumption for cheddar and mozzarella, respectively. The transportation distances, then allocated to cheddar and mozzarella, are thus 0.050 and 0.054 km/kg cheese purchased, respectively.
The average fuel economy for passenger cars and other four-wheel vehicles (pickup truck, sport utility vehicles) was determined from the NHTS (2009) to be 9.61 and 7.70 km/L, respectively. It was assumed that all personal vehicles are powered by gasoline. The National Automobile Dealers Association (NADA) State of the Industry Report (NADA 2011) reports a 50:50 market share ratio of passenger cars to other four-wheel vehicles. Therefore, a weighted average of 8.65 km/L was assumed as the average fuel economy of personal vehicles. As the LCI datasets for personal transport in ecoinvent do not exactly match this fuel economy, we adjusted the number of kilometers of operation to ensure that the estimated fuel consumed, based on average US fuel economy, was properly calculated.
The EIA Residential Energy Consumption Survey estimates annual energy use for home refrigeration to be approximately 1,350 kWh (EIA 2005). The cheese portion of the total refrigerated products, 2.57 % (cheese, 11.9 % of all dairy (USDA 2010); dairy, 21.7 % of refrigerated sales (Food Marketing Institute 2010)), is used to calculate the home refrigeration attributable to cheese, which results in 34.7 kWh. Note that the refrigerated shelf space allocation at home is expected to be an overestimate: in-home shelf space occupied by cheese is likely smaller than at the store, since the fraction of shelf space occupied in-home is likely decreased due to items purchased at the store unrefrigerated that need refrigeration upon opening (e.g., ketchup). With these caveats, refrigeration energy per kilogram of all cheese at household is then estimated to be 1.2 kWh/kg, and thus, 0.40 and 0.43 kWh/kg for cheddar and mozzarella, respectively, based on their market share.
Water and energy burdens for dishwashing were taken from the Energy Star criteria for a standard-sized dishwasher model (Energy Star 2009). A standard-sized model is considered to use 1.51 kWh and 22 L of water/cycle. It has a capacity of eight place settings and six serving pieces. A “place setting” is assumed to be comprised of two plates, one bowl, six utensils, and three glasses. Therefore, each cycle is assumed to wash 36 utensils (6 utensils × 6 serving pieces) and 48 non-utensils (6 non-utensils × 8 place settings). It is assumed that 10 % of water and energy is allocated to the utensil rack and 90 % to the non-utensil pieces. We assumed the dishwashing burden for utensils and plates for cheese consumption to be 5 % of a dishwasher load/kg cheese consumed. This assumption was based on an estimate of the mean number of plates and utensils used for cheese and the capacity of a typical dishwasher.
Postconsumer solid waste
We model waste disposal in SimaPro© with unit processes from ecoinvent for consumer disposal of packaging material. Franklin Associates (2008) report that an estimated 14 % of postconsumer waste is incinerated with energy recovery. We modeled the incineration of these materials but did not account for energy recovery, as it fell below the 1 % cutoff criterion.
Scenario analysis of cheddar aging
The bulk of cheddar cheese sold in the USA is aged approximately 70 days, but specialty cheddar can be aged five or more years. In 2009, 1.45 million tons of cheddar cheese was produced in the USA (IDFA 2010). Cold holding reports for cheddar cheese were examined and a typical inventory of 0.28 million tons was reported (NASS 2010). Using a simple first in–first out assumption, the US inventory of cheddar cheese turns over 5.17 times a year (1.45 / 0.28 = 5.17), implying that the typical age of cheddar cheese at retail is 70.6 days (365 days / 5.17 = 70.6 days). Based on EIA (2003) survey data, refrigerated warehouses consume an average of 307 kWh/m2/year of electricity and 338 MJ/m2/year of natural gas. We assumed that pallets were stored on shelves up to six pallets high (typical warehouse height = ∼9 m) and used an industry estimate of the number of 18.1 kg (40-lb) blocks in 45 blocks per pallet. Ammonia is used for refrigeration in large warehouses used for cheese storage. We used an emission factor of 13.6 kg NH3/employee/year coupled with Industrial Assessment Center (IAC 2009) data on employees and warehouse size to arrive at an estimated emission of 0.013 kg NH3/m2/day of storage. Mozzarella is distributed for retail as rapidly as possible, but typically, needs to be held for 2 weeks before unwrapping to smaller pieces and repackaging to retail sizes. It should be noted that a large fraction of mozzarella is used in food service applications where it is frozen and stored for some time prior to being used. We did not include this branch of the supply chain as our focus was on cheese directly purchased by the end consumer.
Life cycle impact assessment
The intention of this study was to provide a comprehensive environmental life cycle impact assessment (LCIA) of cheese production and consumption, which stems from all phases of cheese production and delivery systems. These environmental impacts include climate change, cumulative energy demand, freshwater depletion, marine and freshwater eutrophication, photochemical oxidant formation, impacts to ecosystems and human toxicity, and ecotoxicity (Hertwich et al. 1998; Huijbregts et al. 2000; Jolliet et al. 2003; Goedkoop et al. 2009; Hischier and Weidema 2010). We chose impact categories relevant to the dairy industry: IPCC GWP 100a, Cumulative Energy Demand, ReCiPe Midpoint, ReCiPe Endpoint, and USEtox (Table 1).