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

The overarching goal of this paper is to demonstrate the importance of carrying out midpoint climate impact assessment sensitivity analyses, particularly when assessing or comparing systems which generate large amount of SLCPs, with a secondary objective being to produce a framework for other researchers to adopt. In the case of agri-food systems, this largely means CH₄ arising from ruminant production as well as rice production, not covered herein. To validate the necessity of such an effort-intensive proposition (from a consultant's/practitioner's point of view), we generated a 9 × 10 full factorial virtual experiment whereby we tested the importance of CH₄ (in terms of Y_m, also known as CH₄ conversion factor: the ratio of gross energy intake to enteric CH₄ produced by an animal) vs. N₂O (in terms of IPCC's emission factor EF₁ + EF_3PRP, which represent amounts of N₂O lost from the application of inorganic and organic fertiliser, respectively) at the cradle-to-farmgate exit system level for 90 combinations of Y_m and EF₁ + EF₃ of a specialised (i.e. prime) pasture-based beef production system. EF₁ and EF₃ were combined as they represent a single process in the farming system: fertilisation of the soil. Y_m, was investigated in isolation as it has been shown to account for ∼50% or more (pending uncertainties) of total GWP₁₀₀ carbon footprints of grassland beef systems in the UK (McAuliffe et al 2018). Further, these three coefficients were chosen for exploration as they have previously been shown to be the most important drivers of emissions' uncertainty for beef production systems (Takahashi et al 2019).

The functional unit was set as 1 kg liveweight (LW) exiting the finishing-cattle farmgate. In line with the functional unit, as mentioned above, the system boundary covered the extraction of raw materials (cradle) to the farmgate exit (figure 1). All material inputs and outputs, as well as losses to nature (i.e. direct and indirect GHG emissions) were covered, including those arising from the suckler herd from which the finishing animals were sourced. The only exception to the inclusion of material inputs was farm infrastructure and veterinary medicine. Both of these inputs were excluded as they have negligible effects on system wide environmental footprints of grazing beef systems as per previously published research which the current study builds upon (McAuliffe et al 2018). As the permanent pasture had not been ploughed for almost 100 years in some fields, with a minimum of 25 years of ley in others, soil organic carbon stocks changes were assumed to be in equilibrium as is common in grassland beef studies (de Vries et al 2015).

Zoom In Zoom Out Reset image size

All foreground data were sourced from the UK Research and Innovation National Bioscience Research Infrastructure, the North Wyke Farm Platform (NWFP). Despite having a range of long-term system trials on the NWFP, for the purposes of this study, the permanent pasture system (50°46'10''N, 3°54'05''W) was deemed sufficient to elucidate the research goal detailed in section 2.1 (figure 1). The NWFP is one of the most instrumented farms in the world for assessing the environmental performance of farming systems (Orr et al 2016, Takahashi et al 2018). The data utilised in the current study covered the 2016 permanent pasture cattle grazing system, meaning animals for finishing were born in 2015, grazed in spring and summer of 2016, and typically finished towards the end of 2016 (table 1). The finishing farm occupies approximately 21 ha, and in 2016 the beef enterprise maintained 30 Charolais × Hereford–Friesian finishing cattle sourced from an adjacent suckler herd farm which is managed in the same manner as the NWFP permanent pasture system (e.g. fertiliser rates, stocking densities etc; tables 2 and 3). All GHG emissions for 2016 were calculated according to McAuliffe et al (2018) using a modified IPCC (2006) Tier 2 approach, which aligns with the majority of extant grassland LCA literature.

A large-scale sensitivity analysis capturing emission factor uncertainties was developed to assess the effect of climate impact interpretation of a typical lowland grazing beef system whilst also capturing coefficient uncertainties (i.e. Y_m, EF₁ and EF₃) which fall within the range of latest IPCC guidelines (IPCC 2019). As the permanent pasture system also supports sheep (75 ewes plus their offspring: typically twins), on-farm impacts of sheep grazing, both positive through lamb production and associated soil fertility via excreta deposition as well as negative through GHG emissions, were separated from the model using the economic allocation-based decomposition method outlined in McAuliffe et al (2018). Material inputs to the system are displayed in table 3. Emissions associated with background processes, such as field activities and the production of small quantities of supplementary feeds (rapeseed expeller in the current case), were sourced from the life cycle databases ecoinvent (Wernet et al 2016) and Agri-footprint (Blonk et al 2022). Embedded CO₂ emissions (e.g. energy consumed, fertiliser production, transportation, etc) were calculated using the aforementioned LCA databases. A 9 × 10 full factorial virtual experiment was designed to include various combinations of CH₄ (Y_m range = 4.5%–8.5%, in steps of 0.5%, with 6.5% being the default) and N₂O (EF₁ range = 0.2%–2.0%, in steps of 0.2%, with 1% being default, + EF_3PRP range = 0.4%–4.0%, in steps of 0.4% with 2% being default) emission factors. These stepwise changes were adopted to test decision-making surrounding EFs mathematically, but it is important to reiterate that our calculations remain within IPCC's novel recommended uncertainty ranges (i.e. 95% confidence intervals), particularly given the new system-specific seasonally- and feed-driven tailored CH₄-yield (MY; kg CH₄ per kg dry matter intake; DMI) calculations available from IPCC (2019).

Carbon footprints were calculated for each scenario under GWP₁₀₀ and GTP₁₀₀ using the IPCC 5th Assessment Report (IPCC 2013) characterisation values without climate-carbon feedbacks, as these are the most commonly used and agreed upon for UNFCCC reporting. It is worth noting that IPCC's AR6 report (IPCC 2021) has suggested changes in CO₂-eq characterisation factors whilst, as discussed briefly in section 2.2, IPCC (2019) provides more robust calculation frameworks for estimating agricultural GHG emissions; however, the majority of extant LCA studies (including previous LCA work carried out on the NWFP which this study builds upon) use 5th Assessment Report characterisation factors as provided above (and which have also been highlighted for use under the Paris Agreement reporting purposes); additionally, earlier emission factors are most commonly used in existing LCA literature (e.g. EF₁ and EF_3prp; prp = pasture, range, and paddock; IPCC 2006). All 90 scenarios were simulated in SimaPro 8.5.2 (PRé Sustainability) using parameterisation to run multiple scenarios simultaneously. For temporal visualisations using GWP* to demonstrate 'pulse' (i.e. GHG emissions arising from a single production cycle) and 'sustained' (considering on-going, business-as-usual, production of beef over a 100 years period) emissions, only five out the 90 scenarios were presented for ease of interpretation. These were the four extremes (i.e. highest vs. lowest CH₄ and N₂O) and the baseline scenario which adopted default IPCC factors (i.e. Y_m = 6.5%, EF₁ = 1%, and EF₃ = 2%, as detailed in section 2.2). Full calculation details are available in each figure's caption in section 3. GWP* was calculated according to Smith et al (2021). In addition, GWP* cumulative emissions at year 100 were calculated for each of the 90 scenarios under both pulse and sustained emissions to provide heatmaps for visual comparison with GWP₁₀₀ and GTP₁₀₀ as outlined in the next section.

2.3.1. GWP*

GWP* is a relatively novel emission reporting approach that aims to capture the dynamic differences in shorter- and longer-lived GHGs, as outlined in the introduction. We calculate 'CO₂-warming-equivalent' emissions under GWP* following the equation provided in Smith et al (2021):

$$\begin{equation*}{E^*}\left( t \right) = 128 \times {E_{{\text{C}}{{\text{H}}_{\text{4}}}}}\left( t \right) - 120 \times {E_{{\text{C}}{{\text{H}}_{\text{4}}}}}\left( {t - 20} \right).\end{equation*}$$

Our GWP* reported CH₄ emissions (E*) at year t are calculated as the difference between CH₄ emissions at year t multiplied by 128 and the CH₄ emissions rate of 20 years previously (t-20) multiplied by 120. These two constants, 128 and 120, can respectively be thought of as representing the high initial impact of a CH₄ emission (relative to CO₂), and then an approximation of how much of the impact is automatically reversed as the CH₄ naturally breaks down, ultimately derived from the principles of how radiative forcing responds to CO₂ emissions (Smith et al 2021).

Following the recognition that the temperature response to CO₂ shows a simple linear correlation with cumulative emissions (IPCC 2021), the ambition behind GWP* is to provide 'CO₂ w.e.' of other gases that conform to this same relationship. Hence the cumulative CO₂-w.e. in any given year following any given emission can be considered as reporting the resulting temperature increase in that year in CO₂-eq terms. This approach provides temporal insight whilst avoiding the need for more complex time varying metrics (e.g. multiple GWP_h or GTP_h calculations for any and all h time-horizons). Further, GWP* reduces the necessity to make a subjective decision over what time-horizon should be used, given that the simple temporal approximations employed by GWP* hold reasonably well both within the 0–100 years period and beyond (as illustrated in Lynch et al 2020). For longer-lived gases such as N₂O, individual emissions still act sufficiently cumulatively (over timescales up to at least a couple of centuries), meaning that the warming response is well captured without having to imagine a subsequent CO₂-w.e. removal. The profile of N₂O's impacts over time decays sufficiently similarly to CO₂ over this period; consequently, its GWP₁₀₀ CO₂-eq can be treated as a 'CO₂-warming equivalent' quantity contributing cumulative additions to overall temperature change. When also reporting N₂O emissions in our GWP*-based figures, these simply use the AR5 GWP₁₀₀ CO₂-eq value of 265 based on the aforementioned linear cumulation. See Allen et al (2021) for further considerations, and a comparison with a more mathematically precise approach to this equivalence.

In essence, the study as a whole is a robust sensitivity and scenario analysis combined, making it a large-scale interpretation as recommended (albeit perhaps not to this extent, which is for scientific purposes rather than product declarations etc) by ISO 14044 (2006). Here, we first compare 'conventional' impact assessments, GWP₁₀₀, GTP₁₀₀, by generating comparative heatmaps in PANDAs, a statistical dataframe-based programming module in Python.

Then, to explore the potential of GWP* in agri-food based LCAs, we employ it to provide a shorthand illustration of how climate impacts vary over time. Here, the climate change impacts were reported as a single CO₂-w.e., a metric, as mentioned, that also generates a 'CO₂-eq' quantity, but with a direct correspondence to temperature evolution over time.

Following these analyses (all based on measured data from section 2.2), two theoretical intervention scenarios were proposed and examined: (1) whether mitigating CH₄ or N₂O after 30 years is more effective at reducing the cumulative impacts of pasture-based beef production systems, and (2) whether mitigation of CH₄ or N₂O first would lead to an overall reduction in emissions over 100 years if both gases were mitigated at separate timepoints (i.e. at 30 and 50 years). In all cases of hypothetical mitigation, we imagine a transition from the highest to the lowest emission factor 95% confidence interval in our range for the relevant gas. These ranges were determined by uncertainty values provided for N₂O in IPCC 2006 (which do not change in terms of statistical range in IPCC 2019), whilst also covering a wide range of Y_m values which overstretch the ±20% recommendation by IPCC, but is supported by unpublished research conducted at the NWFP using GreenFeed© technology to directly measure CH₄ emissions during respiration, suggesting that the IPCC's CH₄ uncertainty range may be underestimated in certain soil types and microclimates). Statistical differences between GWP₁₀₀ and GTP₁₀₀, as well as cumulative GWP* LCIA differences at 20 years vs. 100 years, were calculated in python using a paired sample t-test.

When CH₄ occupies a significant share of emissions (as in the grass-fed lowland beef system in this study), the choice of impact assessment plays a considerable role in LCIA (figure 2). Across the 90 scenarios considered (see section 2.2), when GWP₁₀₀ is adopted as an LCIA, the effect of enteric CH₄ (determined in this study by Y_m), a single source of GHG emissions, is comparable to the combined effect of N₂O's EF₁ + EF₃ which represent emissions arising from applied nitrogen inputs to soil (both organic and synthetic fertiliser) and deposited nitrogen from excreta (urine plus dung) and associated losses to nature, respectively (figure 2(A)). When CH₄ is assigned a lower characterisation factor (i.e. CO₂-eq), as per GTP₁₀₀, emissions associated with N₂O losses from fertiliser application become the dominant contributors to a lowland pasture-based beef system's cradle-to-farmgate exit emissions intensity (figure 2(B)). It is worth noting that the choice of LCIA plays a role in the total emissions intensities across both methods (i.e. GWP₁₀₀ and GTP₁₀₀). For instance, the maximum emissions intensity reported under GWP₁₀₀ is 29.8 kg CO₂-eq/kg LW, whilst the maximum value under GTP₁₀₀ is 16.1 kg CO₂-eq/kg LW. Similarly, the average value across the 90 scenarios for GWP₁₀₀ is 23.1 kg CO₂-eq/kg LW with the mean for GTP₁₀₀ being 12.3 kg CO₂-eq/kg LW (p < 0.001). Lastly, it is worth noting that under GWP₁₀₀, the total percentage contribution from CH₄ was 39.9% whilst under GTP₁₀₀ it was considerably lower at 9.14% under default IPCC (2013) AR5 values (i.e. Y_m = 6.5%; EF₁ = 1%; EF_3PRP = 2%).

Zoom In Zoom Out Reset image size

Arguments have been made for and against the use of different pulse metrics and time-horizons thereof. There is no single emission metric that can be deemed appropriate for all purposes, and any static pulse emission metric may obscure temporal detail, as discussed below. Here, we highlight that the variation in Y_m, and hence CH₄ emissions, only has a significant proportional impact on total emission footprints using GWP₁₀₀ (figure 2(A)), whilst variation in EF₁ + EF₃, and hence N₂O emissions, has a major relative impact on total footprints for both GWP₁₀₀ and GTP₁₀₀ (figure 2(B)) footprints, suggesting that clarifying N₂O emissions and prioritising their mitigation may be suggested as a more universal priority, while the relative importance of CH₄ may be more dependent upon time-horizon and/or metric of interest.

To illustrate the operation of GWP* simply, we show how a single CH₄ emission is reported in figure 3(A), taking the CH₄ contribution for an intermediate Y_m (6.5%) in isolation. The initial emission, in year zero, is assigned a very large CO₂-w.e., but followed by a slightly smaller negative CO₂-w.e. (i.e. equivalent to a CO₂ removal resulting in a temporary 'cooling' effect, as expanded upon in detail by Allen et al 2023) in year 20. For a single pulse emission of 0.36 kg CH4, as shown in figure 2(B), this equates to 40.8 kg CO2-w.e. in the first 20 years and 2.55 kg CO2-w.e. thereafter. As noted, this simple approach approximates the initially very strong impact of a CH₄ emissions and also their automatic reversibility due to natural atmospheric removals, both of which may be obscured via conventional treatment of CH₄ using static emission metrics such as the GWP₁₀₀.

Zoom In Zoom Out Reset image size

It is cumulative emissions as reported using GWP* that have a direct correspondence with temperature change by design Cain et al (2019). This is because cumulative CO₂ emissions show a simple linear relationship to temperature increases (as noted above), and therefore cumulative GWP* CO₂-w.e. over time, as in figure 3(B), can be considered as a proxy for contributions to global temperature increase for the reported emissions at each individual year covered.

We expand on this simple illustration of the intermediate methane emission footprint to show the cumulative CO₂-w.e. profile for all three major gases, including the intermediate value and upper and lower extremes for CH₄ and N₂O in figure 4, which displays a single-season 'pulse' emission from producing 1 kg of LW leaving the farmgate broken down by individual GHG gases as described using GWP* for 100 years after the emission. Across the first 20 years, CH₄ is by far the dominant GHG contributing to climatic change, after which point it becomes much smaller, owing to its ∼10 years half-life, and the longer-term CO₂-w.e. from this CH₄ pulse is comparable to direct CO₂ emissions from the farming system, which are typically very small (excluding embedded emissions from, e.g. ammonium nitrate production; McAuliffe et al 2018). From years 1 to 100, N₂O and CO₂ are both assumed to have the same impacts per year, with N₂O being a considerable contributor across the entire timeframe. There is no reduction in N₂O and CO₂ emissions due to their atmospheric lifetimes extending beyond the 100 years time horizon covered in our study.

Zoom In Zoom Out Reset image size

Figure 5, like figure 4, displays a single pulse emission from the system under investigation. However, figure 5 represents an overall carbon footprint by summing individual gases into a single combined total (w.e./kg LW). We can see how overall warming in figure 5 follows the trends revealed from the individual gas pulse emissions displayed in figure 4, with initially significant CH₄-dominated warming over the first 20 years, after which most of its impacts are modelled as being reversed, by a removal of 'CO₂-w.e.' (Cain et al 2019, Lynch et al 2020). From here on, the overall CO₂-w.e. declines, with N₂O now the dominant GHG in terms of total CO₂-w.e. emissions associated with the lowland permanent pasture-based beef system. Whilst exploring a single pulse emission is interesting from the perspective of examining the behaviour of individual gases, and is the basis of most standard emission metrics, it does not tell us very much about sustained production which is expected to be required of most agri-food systems to feed an ever-growing population (Gerber et al 2013); nor does it provide a more realistic scenario of time-series emissions.

Zoom In Zoom Out Reset image size

To understand how ongoing emissions would be reported under GWP* as an LCIA, we modelled beef production under constant (steady state) production (figure 6). As shown in figures 4 and 5, the most rapid increase in reported emissions intensity occurs during the first 20 years when the impact of establishing the new CH₄ source is highlighted as leading to a significant rate of warming and is consequently the key driver of the overall GWP* footprint. From year 20 onwards, the trajectory of reported emissions reduces and, future increases are driven predominately by N₂O and CO₂. The sustained emissions (figure 6) also show a cross-over between the blue (high Y_m & low EF₁ + EF₃) and red (low Y_m & high EF₁ + EF₃) lines at around year 60 indicating the total impacts of the long-lived, accumulating N₂O emissions starts to exceed those of SLCPs, i.e. non-accumulating CH₄ emissions. This dynamic occurs despite emissions of both gases continuing at the same rates each year: hence the temporal change in relative significance would not be revealed through static metrics such as the GWP₁₀₀ or GTP₁₀₀ that are defined by the impacts across or at 100 years only.

Zoom In Zoom Out Reset image size

The heatmaps in figure 7 reveal some of the links between GWP* and pulse-emission metrics aided by generating results for 100 years from figures 4 and 5 across the full range of all virtual scenarios. Figure 7(B) shows the cumulative GWP* CO₂-w.e. reported for all virtual scenarios occurring in year 100 following a pulse emission of the GHG footprint: i.e. the same CO₂-w.e. in figure 5 at year 100; the five values shown in figure 5 correspond to the four corners and central value of the heatmap in figure 7(B). Figure 8(B) is very similar to the GTP₁₀₀ footprint in figure 3(B), as expected, as they convey the same information: CO₂-eq temperature change contribution at a certain number of years following a set of pulse emissions.

Zoom In Zoom Out Reset image size

Zoom In Zoom Out Reset image size

As shown in figure 6, GWP* provides a shorthand approximation of how these change over time, rather than having to calculate a full temporal evolution of GTP for every year x. Figure 7(A) shows the GWP* CO₂-w.e. from all virtual scenarios occurring in year 100 following sustained emissions of the GHG footprint: i.e. the same CO₂-w.e. in figure 7 at year 100. Figure 7(A) shows a similar pattern to the GWP₁₀₀ footprints in figure 2(A) but increased by around two orders of magnitude. This broad increase can be intuited straightforwardly as expressing CO₂-eq impacts of 100 years' worth of recurrent emissions vs a single annual pulse. A similar relative valuation across different scenarios is also as expected, recognising some further connections between different emission metrics: as employed here, GWP* is essentially providing a shorthand equivalent to the 100 years sustained sGTP—the relative contribution to temperature change resulting from emissions sustained at the same rate for the defined number of years, which results in a similar ratio to the GWP (as observed in Azar and Johannson 2012), and thus we see a similar spread in results when the same time-horizon (100 years) is used for both. The utility of GWP* is to provide a simple and intuitive way to explore time-varying temperature impacts, with a greater universality than GTP_h , sGTP_h or GWP_h for a given time-horizon. Moreover, GWP* is applicable for any range of emission scenarios, not just the simple pulses and sustained emissions as illustrated here.

We expand our analysis and highlight the potential for GWP* to reveal temporal dynamics and trade-offs by exploring potential mitigation strategies. Figure 8 shows trajectories of CO₂-w.e./kg LW if hypothetical emission reduction interventions were introduced at year 30 for CH₄ or N₂O. In both cases our imagined mitigations involved moving from the upper to the lower ends of the emissions uncertainty range, to indicate a realistic range in mitigation potentials (i.e. the CH₄ mitigation moves from high Y_m to Low Y_m, while the N₂O mitigation moves from high EF₁ + EF₃ to low EF₁ + EF₃). When the CH₄ mitigation scenario is adopted (orange line), compared to no mitigation (blue), we observe a large initial drop in CO₂-w.e., as due to CH₄'s short-lived nature, most warming it causes is rapidly reversed (i.e. there is a temporary 'cooling') once emissions cease. Meanwhile, for N₂O mitigation (green line), reducing emissions slows the rate of warming, but due to its long-atmospheric lifetime, we do not achieve a reversal of the warming caused by past emissions (at least within the time-horizon explored here, and the simplifications employed by this CO₂-warming-equivalent approach). Nevertheless, if production continued as-is following either single intervention, thus leaving emissions of the other gas to be continued unabated, eventually—from around year 100 onwards—we would be better off having mitigated N₂O instead of CH₄, with the accumulating benefits of reducing the long-lived, cumulative gas outweighing the initial advantage shown for reducing CH₄. However, it is worth reiterating that this is a virtual experiment and in reality, multiple mitigation strategies will, or should, be deployed together and importantly, those would need to consider potential co-benefits and trade-offs for other environmental consequences of agri-food systems, beyond GHG emissions alone.

Another hypothetical experiment explores interventions carried out at years 30 and 50, with either CH₄ or N₂O being reduced first, followed by the other gas 20 years later (figure 9). Again, we observe the greater initial temperature decrease when acting upon CH₄ first (orange), but if we abate N₂O first (green), we see more greatly reduced warning for all periods beyond this initial short-term window, it was better to prevent the accumulation of N₂O emissions first, rather than prioritising emission reductions for non-accumulating CH₄, as most of the temperature-reversal benefits of reducing CH₄ emissions are still achieved even if CH₄ emission reductions are delayed. The wider context of these or other potential mitigation decisions should be interpreted with caution however, since we do not consider the broader consequential impacts that these hypothetical mitigations may be associated with in real life; for example, land use to facilitate carbon uptake such as via agroforestry/woodland expansion or other land cover transitions resulting from agricultural system change are not accounted for in the current study. Nevertheless, the results presented in figure 9 are consistent with observations made in Lynch et al (2020) which compared CH₄ with CO₂ rather than N₂O.

Zoom In Zoom Out Reset image size

As shown above, this subjective decision (i.e. LCIA choice) has a considerable effect on the interpretation of a given study and demonstrates the necessity for LCIA sensitivity analyses. The simplistic use of single, static CO₂-eq valuations of CH₄ inherent in these assessments suggests that environmental scientists and systems analysts need to work diligently to be better-able to account for and report the complexities of individual gases in the atmosphere. Given the influence of environmental issues to consumer decision-making, the importance of these calculations cannot be underestimated. Recent high-profile studies (e.g. Poore and Nemecek 2018a) have taken a marked first step in standardising global LCAs (Poore and Nemecek 2018b); however, the true climate response is not sufficiently tractable due to aggregation of different gases with simple, static metrics, and we argue more could be done to address some of these complexities by the LCA community (e.g. through emission factor and LCIA sensitivity analyses as described above). GWP*, as outlined with simple examples in section 3.2, further provides a simple but effective tool to break the impact of each GHG down across any timeframe by providing a simple 'warming-equivalent' approach.

It should also be noted that these GWP* examples essentially show the warming that would result from our hypothetical emission scenarios being introduced with no prior emissions. In reality, any emitters' contribution to global warming, and overall global temperature change, are also a function of past emissions. For long-lived gases this is broadly a simple function of cumulative emissions that result in long-term warming, but for SLCPs, the warming behaviour is more dynamic, as past emissions are continuously removed, and climate impacts depend more on the current flow of emissions. The year-on-year temperature change resulting from a certain quantity of CH₄ differs markedly depending on whether this is a newly established source, or an existing flow of emissions being maintained, for which the pronounced initial temperature increase is already experienced.

This behaviour is revealed through GWP*: CH₄ emissions, even if emitted at the same annual rate (as in figure 6, for example), are reported as 'CO₂ warming-equivalent' emissions that change over time so they would result in approximately the same dynamic temperature changes as the methane emissions themselves would cause. However, as individual methane emissions are reported as different 'CO₂ warming-equivalents' according to temporal context, Rogelj and Schleussner (2019) argue GWP* may lead to 'unintentional unfairness' due to the valuation of methane in a given year being contingent on past emission rates. Cain et al (2021) and Allen et al (2021) counter that this context-dependence is necessary to facilitate true 'warming-equivalent' comparisons and hence can itself reveal relevant equity concerns, and that a more transparent way of overcoming concerns about equitable entitlements of methane emissions themselves is simply to report and compare methane emissions directly, which can be done independently of any CO₂-eq metric. Alternatively, the framing presented here is not only applicable for considering the establishment of a new emission source (e.g. a new farm resulting from land use change) in year 0, but also represents the 'marginal' future warming that could potentially be avoided from contemporary and future emissions (or reductions thereof), following the terminology employed by Reisinger et al (2021). This 'marginal' approach (employing GWP* with a zero-emission baseline) shows future impacts independently of any past emissions and warming they may have caused, and thus methane emissions from different emitters would always be reported in the same way independently of past context.

These dynamics have wider implications on how overall warming contribution is perceived and what might be necessary for different emitters to achieve climate-related 'sustainability', or to meet certain temperature targets (Lynch et al 2021). On the other hand, it has also been argued that, despite the advantages of GWP* in more accurately reflecting the temperature impacts of CH₄ emission pathways over time, the metric would not provide a full alignment between the Paris Agreement's mitigation mechanisms (a metric defined as a 'balance between anthropogenic emissions [...] and removals') and long-term temperature goals (Schleussner et al 2019). Allen et al (2022a, 2022b) explore some of the context for different 'net-zero' definitions and challenges in trying to align conventional emission metrics with temperature-based goals.

Full discussion of these points is beyond the scope of this paper, however, as we (a) focus on providing a stepwise approach to calculating GWP* in agricultural circumstances, and more scientifically speaking, (b) demonstrate more applied comparison of the key gas dynamics and simple cases of how they are reported in emission metrics; yet, we highlight some of the broader considerations that become apparent following more detailed interrogation of how different emissions operate than would be possible using static pulse-emission metrics. A CO₂-w.e. approach, such as GWP*, provides a straightforward means of exploring these issues and could be used to compare different framings, but implications and most appropriate context are still questions that cannot be resolved by metric selection in and of itself (in other words, not one metric suits all research questions, hence our recommendation that LCIAs should be rigorously tested for sensitivity and reported accordingly).

As above (section 4.1), we argue that using a single static impact assessment (LCIA in the context of LCA) is insufficient to elucidate the complexities of how agri-food systems contribute to climate change. Instead, consideration should be given to representing these complexities by providing a range of metrics. This may include reporting individual GHG emissions independently of each other for a reader's ease of interpretation thereby enabling them to undertake their own climate impact interrogations (Lynch 2019) which adequately demonstrate the trade-offs associated with different approaches. For instance, long versus short-term impacts and/or potential benefits of mitigation interventions such as novel 'sustainable' fertilisers (e.g. biochar; Kammann et al 2018) and methane inhibitors including 3NOP (e.g. Lopes et al 2016). Given the importance of farming to international food security, there is no doubt that the sector needs to optimise productivity (McAuliffe et al 2018, Lee et al 2021) whilst minimising energy-intensive inputs such as inorganic fertiliser (McAuliffe et al 2020b). This importance is demonstrated by the range of GHG impact efficiencies across the globe under various production systems, some of which can be quite inefficient (Poore and Nemecek 2018a, 2018b). However, the evidence presented above suggests that much of the information currently being communicated to stakeholders and laypeople alike may provide an incomplete or, potentially even misleading, representation of the impact of agriculture towards climate change (section 4.3).

As a result, the first major recommendation resulting from this study is that LCA practitioners, national inventory compilers, and other sustainability scientists calculating environmental burdens of agri-food systems need to test the robustness of assumptions by adopting multiple sensitivity analyses (e.g. in terms of LCIA: GWP₁₀₀, GTP₁₀₀, and GWP*; figures 2 and 7, respectively), whilst also reporting GHG emissions individually (Lynch 2019). At a minimum, CO₂-eq emissions must be reported separately for long- and short-lived GHGs, as it is widely acknowledged that without this disaggregation it is not possible to infer temperature outcomes, and hence stymies comprehensive communication surrounding the implications of any emissions mitigation measures for global climate targets (Allen et al 2022b).

GWP* offers a complementary approach to calculate GHG emissions over time; however, further standardised applications of GWP* are required, and its widespread uptake is unlikely in the short term. In the meantime, in addition to reporting GWP₁₀₀ and GTP₁₀₀ simultaneously, scientists including inventory compilers should consider calculating emissions over various time-horizons (e.g. GWP₂₀ and GWP₅₀₀). Whilst this undoubtedly adds an additional layer of complexity to the interpretation of such studies, by focussing solely on GWP over 100 years, the manner in which the relative impacts of CH₄ vs CO₂ and N₂O change over time is currently unaccounted for. One aspect of this is that using a 100 years horizon alone also fails to reveal the full significance of the short-term gains in terms of reduced planetary warming of targeted mitigation of CH₄ as recommended in the final comunicae of the COP26 meeting in Glasgow (UN 2021).

In recent decades, LCA, and in particular related LCIs, have gained scientific robustness and sophistication (e.g. via increased awareness and consideration of uncertainty throughout supply chains); additionally, the potential for wider system boundaries (i.e. broader supply chain coverage) is made possible through the evolution of existing LCA databases such as ecoinvent and development of newer, agriculture-specific databases (e.g. Agri-footprint and Agribalyse). Further, as discussed earlier, there is a growing consensus that sustainability assessments of agri-food products should account for wider components of global burdens, not least societal issues (e.g. Costa et al 2020, McAuliffe et al 2020a). Stepping beyond the boundaries of the current study for a moment, considerable advancements have been made when tackling complex issues such as allocation in LCA-based agri-food systems which produce multiple products (e.g. milk and meat in the case of dairy production; Thoma et al 2013, Rice et al 2017, March et al 2021). Further, whilst in its infancy, nutritional- and health-based LCA (known as nLCA) has the potential to account for human health. This is often achieved indirectly through assessing the variability of nutritional quality of food commodities (Sonesson et al 2017, McAuliffe et al 2023), in addition to direct human health impacts arising from environmental burdens simultaneously via trade-off assessments (e.g. Stylianou et al 2016, Sonesson et al 2019).

Bearing the above advances in mind and given that, according to the FAO, ∼38% of global terrestrial land excluding icecaps is dedicated to agricultural activities, there is an urgent need to generate up-to-date environmental, economic, and social sustainability assessments of the UK's major agricultural commodities, particularly as around 70% of land in the UK is used for agriculture (CIEL 2020). The methodological case study provided herein demonstrates one such advancement in agri-food environmental sustainability by providing better insights into GHG emissions' behaviour in the atmosphere. Perhaps of equal importance, we encourage LCA scientists to test their subjective choices more rigorously including impact assessments and reporting LCIA sensitivity analyses in future studies, particularly when SLCPs are significant system-wide GHGs; for instance, the ultimate potential for 'sustainable' ruminant livestock systems and rice paddies may be viewed differently through the adoption of GWP* during LCIA when compared to GWP₁₀₀ or indeed GTP₁₀₀, thus providing ample scope for applied GWP*-based LCAs on global food items (i.e. national staple food commodities and emerging alternatives such as plant-based proteins).

The importance of testing LCA modelling subjectivity (which, for clarity, cannot be avoided but can be assessed and reported transparently) through robust sensitivity analyses and simulation-based statistics such as Monte Carlo cannot be underestimated as, demonstrated presently, these choices have drastic effects on LCA interpretation and subsequent communication (e.g. through 'eco-labelling'), which often misleads consumers (Steenis et al 2017) and policymakers (Cederberg et al 2011) due to modelling inconsistencies and a focus on single impact categories, primarily climate-related impacts (Nemecek et al 2016). Finally, a lack of attention to broader sustainability issues (Costa et al 2019) such as those discussed above in the current section and covered in detail by McLaren et al (2021), e.g. human health, agricultural resilience, nutritional complexities, and ultimately, and global food security requires LCA practitioners to be aware of, and take better care in, communicating limitations of their studies, which are often overlooked or unreported.