Are single global warming potential impact assessments adequate for carbon footprints of agri-food systems?

The vast majority of agri-food climate-based sustainability analyses use global warming potential (GWP100) as an impact assessment, usually in isolation; however, in recent years, discussions have criticised the ‘across-the-board’ application of GWP100 in Life Cycle Assessments (LCAs), particularly of food systems which generate large amounts of methane (CH4) and considered whether reporting additional and/or alternative metrics may be more applicable to certain circumstances or research questions (e.g. Global Temperature Change Potential (GTP)). This paper reports a largescale sensitivity analysis using a pasture-based beef production system (a high producer of CH4 emissions) as an exemplar to compare various climatatic impact assessments: CO2-equivalents using GWP100 and GTP100, and ‘CO2-warming-equivalents’ using ‘GWP Star’, or GWP*. The inventory for this system was compiled using data from the UK Research and Innovation National Capability, the North Wyke Farm Platform, in Devon, SW England. LCAs can have an important bearing on: (i) policymakers’ decisions; (ii) farmer management decisions; (iii) consumers’ purchasing habits; and (iv) wider perceptions of whether certain activities can be considered ‘sustainable’ or not; it is, therefore, the responsibility of LCA practitioners and scientists to ensure that subjective decisions are tested as robustly as possible through appropriate sensitivity and uncertainty analyses. We demonstrate herein that the choice of climate impact assessment has dramatic effects on interpretation, with GWP100 and GTP100 producing substantially different results due to their different treatments of CH4 in the context of carbon dioxide (CO2) equivalents. Given its dynamic nature and previously proven strong correspondence with climate models, out of the three assessments covered, GWP* provides the most complete coverage of the temporal evolution of temperature change for different greenhouse gas emissions. We extend previous discussions on the limitations of static emission metrics and encourage LCA practitioners to consider due care and attention where additional information or dynamic approaches may prove superior, scientifically speaking, particularly in cases of decision support.