Method to assess the carbon footprint at product level in the dairy industry
Introduction
Companies and organisations are, to an increasing extent, assessing their contribution to global warming as well as different mitigation options. Reliable models to estimate greenhouse gas (GHG) emissions are therefore essential and there is a growing need for tools that can assist companies in tracking their impacts over time. Many companies calculate their corporate carbon footprint (CF) according to, e.g., the Greenhouse Gas Protocol (WRI & WBCSD, 2004). However, the total emissions of a company can be a poor indicator on the actual efficiency, as it does not account for variation in total production volumes between years. It can therefore be relevant to also be able to follow improvements and account for the amount and ‘types’ of products delivered.
To track the improvements at product level, total emissions need to be allocated between the different product groups. Several studies have analysed the GHG emissions of different dairy products (Berlin, 2002, Flysjö, 2011, Høgaas Eide, 2002, Sheane et al., 2011, van Middelaar et al., 2011), using different allocation methods (e.g., based on economic value or milk solids content). Feitz, Lundie, Dennien, Morian, & Jones (2007) suggested an allocation matrix designed specifically for dairy products, which takes into account different allocation factors for raw milk, energy, water, etc. This allocation matrix is also recommended in the guidelines on how to calculate the carbon footprint (CF) of dairy products developed by the International Dairy Federation (IDF, 2010). Carbon Trust (2010) makes the same recommendations in their guidelines for carbon footprinting of dairy products. The allocation matrix implies that the same allocation factors are used for all milk solids, which might not be ideal. For example, in the dairy company Arla Foods the farmer is paid a different price for different milk solids. Apart from this, there are other issues regarding definition of product categories that makes the allocation matrix somewhat difficult to apply in practice. The method developed by Feitz et al. (2007) is based on dairies in Australia, while Arla Foods primarily has production in northern Europe, which means that the product categories might be difficult to match in some cases.
The purpose of the present study was to develop a method that enables a complex dairy business with a wide range of products (like Arla Foods) to track the CF at a product group level and which at the same time is manageable in terms of data requirements. In the present paper, the model is applied on a corporate level, but the model is intended to be implemented at site level to follow improvements over time.
Section snippets
Scope and product groups
The present study analyses the GHG emissions of dairy products from farm to customer (wholesale or retail), including all significant emissions from the system (Fig. 1) using the production structure and data from the dairy company Arla Foods. The applied method is termed life cycle assessment (LCA), which is a standardised method for assessing the environmental impacts of products in a life cycle perspective (ISO., 2006a, ISO., 2006b). Conducting an LCA but only focussing on the impact
Carbon footprint per kg final product
The CF per kg final product is presented in Fig. 2, together with the relative contribution from each life cycle stage. BSM is the product group with the highest CF (8.1 kg CO2e per kg) followed by powder and protein products (7.4 kg CO2e per kg), cheese (6.5 kg CO2e per kg), other (1.2 kg CO2e per kg) and FDP (1.1 kg CO2e per kg). Raw milk, including purchased dairy products, represents the largest share of the CF for all final products. BSM has a higher than average share of other non-dairy
Conclusions
The present study presents a model for calculating the CF for specific dairy products and groups of dairy products. The suggested model is simple to use and communicate, which are important when applying in a company context. All significant activities contributing to the CF are identified and relevant allocation factors are suggested. The allocation factors are chosen so they, to the extent possible, reflect the underlying causal relationship between emissions and final products. The
Acknowledgement
The study was performed with funding from the Danish Agency for Sciences, Technology and Innovation.
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