Carbon footprint of a pale lager packed in different formats: assessment and sensitivity analysis based on transparent data

Energy and water consumption, waste generation, and emissions to air are the main environmental issues of the brewing industry. Several strategies have been so far proposed to reduce its impact on the global climate. This study assessed the environmental impact of the industrial production and distribution of 1 hL of a pale lager, as packed in different formats by the Italian brewery Birra Peroni Srl (Rome, Italy) over the period April 2012-March 2013, in compliance with the Publicly Available Specification 2050 standard method. The estimated carbon footprint of 1 hL of lager beer packaged in 66-cL glass bottles, 33-cL glass bottles assembled in cardboards or cluster packs, 33-cL aluminum cans, or 30-L steel kegs was of the order of 57, 67, 74, 69, or 25 kg CO2e, respectively. Such a difference in the overall carbon footprint values was due to the diverse contributions of packaging materials and transportation. In particular, the impact of packaging materials was minimum in the case of kegs, in virtue of the high reuse coefficient, and maximum in the case of the 33-cL glass bottle cluster packs. The estimated carbon footprint values were considerably lower than those reported in the most recent literature, probably because of the large production scale and short distribution chain of Birra Peroni brewery, utilization of beer co-products as feed and anaerobic digestion of liquid wastewaters. Owing to the linearity of the mathematical model of the carbon footprint, its sensitivity to the change of one-emission factor-at-a-time allowed the main hot spots in the life cycle of beer (i.e., glass bottle production and barley cultivation) to be identified and targeted for mitigating the carbon footprint of any pale lager, both of them being not related to the brewery production scale examined here. The scientific value of this work relies on the choice of estimating the carbon footprint using wholly transparent data in order to allow its direct comparison to other estimates, as well as its straightforward re-calculation using better quality data, as available.