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

Abstract

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.

Keywords: agriculture; climate change; greenhouse gas emissions; life cycle assessment; sensitivity analysis; uncertainty.

Figure 1.

Schematic system boundary of the beef system assessed in the current study.

Figure 2.

Heatmaps of all 90 scenarios described in section 2.2. Please note that the x-axis only displays EF_3prp emission factors (EF); however, EF₁ ranges are also included (0.2% to 2.0% in steps of 0.2%) to represent total N applied and deposited on pasture but are not displayed for simplicity. Figure 2(A) displays impacts under GWP₁₀₀ whilst figure 2(B) reports impacts under GTP₁₀₀. All impacts are reported as kg CO₂-eq/kg liveweight (LW) leaving the finishing farmgate, as calculated using the respective emission metric. prp = pasture range and paddock; Y _m = CH₄ conversion factor; EF1 = percentage of N₂O lost from applied nitrogen fertiliser; EF_3prp (PRP: pasture range and paddock) = total amount of nitrogen lost as N₂O from grazing animal excreta deposited on grassland.

Figure 3.

An individual pulse emission of CH₄ for the intermediate Y_m footprint reported as (a) annual and (b) cumulative CO₂-warming equivalent (CO₂-w.e.) using GWP* for the lowland permanent pasture-based beef production system at the North Wyke Farm Platform (NWFP).

Figure 4.

Gas-by-gas impact of a pulse emission over a 100 years time horizon calculated under GWP* for the lowland permanent pasture-based beef production system at the North Wyke Farm Platform. Y_m is the CH₄ conversion factor (i.e. the proportion of gross energy lost as CH₄ through methanogenic biohydrogenation) whilst EF₁ represents the percentage of N₂O lost from applied nitrogen fertiliser and EF_3prp (PRP: pasture range and paddock) represents the total amount of nitrogen lost as N₂O from grazing animal excreta deposited on grassland in the current system. EF = emission factor; eq. = equivalent.

Figure 5.

Combined greenhouse gas emissions (i.e. cradle-to-farmgate carbon footprints) for the four most extreme scenarios and the baseline scenario calculated under GWP* (section 2.3 provides more information on scenario analysis under GWP*). See figure 2 for a description of ‘Y _m’, ‘EF₁/EF_3prp’ and ‘eq.’ SC = scenario; SC1 & SC10 are the top left and top right scenarios as per figures 2 and 7, respectively, whilst SC81 & SC90 are the bottom left and bottom right scenarios as per figures 2 and 7, respectively. SC45 reflects the central ‘cell’ in figures 2 and 7 and can be considered the ‘default’ emissions according to IPCC (2006).

Figure 6.

Cumulative emissions calculated for the five scenarios addressed under GWP* (section 2.3). This analysis assumes a steady-state level of beef production emissions efficiency under each scenario. SC = scenario; SC1 & SC10 are the top left and top right scenarios as per figures 2 and 7, respectively, whilst SC81 & SC90 are the bottom left and bottom right scenarios as per figures 2 and 7, respectively. SC45 reflects the central ‘cell’ in figures 2 and 7 and can be considered the ‘default’ emissions according to IPCC (2006).

Figure 7.

Heatmaps of all 90 scenarios, as in figure 3, but with two sets of GWP*-reported emissions. Figure 8(A) shows cumulative GWP* CO₂-w.e. in year 100, following sustained emissions (as determined by each square’s Y _m and EF₁ + EF₃ combination) at the same rate every year from years 0 to 100. Figure 8(B) shows cumulative GWP* CO₂-w.e. in year 100, following a pulse emission (as determined by each square’s Y _m and EF₁ + EF₃ combination) in year 0, and no subsequent emissions. All impacts are reported per kg liveweight (LW) leaving the finishing farmgate in the respective scalar legends.

Figure 8.

Hypothetical virtual experiment to determine which GHG (i.e. CH₄ or N₂O) would be more appropriate to mitigate at year 30 over a 100 years time horizon. w.e. = warming equivalent.

Figure 9.

Virtual experiment to investigate whether it is more prudent to hypothetically mitigate CH₄ or N₂O first. Hypothetical interventions are introduced at years 30 and 50. w.e. = warming equivalent.