Figure 1 shows the projected emission levels in the five scenarios in 2050 and 2100 compared to the emissions in 2010 (Section S2 in the Supplement shows the model-specific results, including time series of the five scenarios. These also indicate that the model difference in emissions in the base year is limited to 12%, in line with uncertainty found in AR5 (IPCC, 2013)). Significant cost-effective and non-climate policy related end-of-pipe reductions are already realized in the Reference case (resulting from methane recovery, safety, and air quality considerations). Virtually all these reductions occur in energy production (e.g., reduction of leakage in oil and gas production) and waste (e.g., CH4 collection from landfills) (within 2050, reductions of 26% and 13%, respectively), while reductions in agriculture are very small (Table S2.2 in the Supplement). Note that this applies to end-of-pipe measures. Structural changes, like increased feed efficiency, also reduce emissions somewhat. However, these have not been examined here, since any projected structural changes are included in both Fzn-CH4 and Reference, so do not show up as a difference in emissions between the two scenarios.
Despite the improvements in emission factors, CH4 emissions in the Reference case are projected to rise steadily by 24–47% compared to 2010 in 2050 and by 10–72% in 2100, in line with projections used for the IPCC 5th Assessment Report (Clarke et al., 2014). Although the projection for 2100 is obviously more uncertain, the general trend is that emissions in the 2050–2100 period stabilize, driven mostly by a stabilizing global population and a decreasing importance of fossil fuels.
In the 2 °C climate policy case (ClimPolicy), the emissions are projected to be reduced, relative to Reference emissions, by 58% (43–70%) and 68% (58–84%) in 2050 and 2100, respectively, indicating that most of the reductions are realized in the short term and that smaller additional reductions are expected in the second half of the century. Emission levels in 2100 are projected to reach 159 Mt CH4/year on average (range, 78–229 Mt), less than half the emissions in 2010.
All models show a larger mitigation effect resulting from measures that directly target CH4 emissions (in ClimPolicy-CH4 only) than indirectly from structural changes resulting from CO2 mitigation policy (in ClimPolicy-CO2 only); hence, the former can be considered more effective in bringing down CH4 emissions. This is in line with earlier work (Gambhir et al., 2017). In 2100, direct mitigation alone is projected to lead to a reduction of 50% to 74% compared to the reference case, compared with 22% to 48% reduction indirectly resulting from CO2 policy.
In terms of regional emissions, Asia has the largest contribution due to its high population and large size, both with and without climate policy (see Fig. 2). Emissions from the Middle East/Africa (MAF) region, however, show the strongest increase, which is also in line with the projected strong population growth in this region (KC and Lutz, 2017). With strong climate policy, ASIA and MAF are therefore projected to be the two regions with the largest residual emissions, 37% and 22% of total emissions in 2100 on average. Note that the growth in MAF could also partly be increased by a global shift of fossil fuel and agricultural production to the region. However, understanding the relative influence of global demand and trade of energy and agricultural products is outside the scope of this study. In per capita terms, the picture shifts. Here, regional differences are explained by how regions differ in terms of sectoral emissions. Across the models, sectoral abatement potentials are relatively similar across regions. Regional differences, therefore, arise mainly due to structural differences, such as the role of livestock, rice cultivation, and fossil resource extraction. In the Reference case, models consistently show that the highest per capita emissions are found in REF (Reforming economies of E. Europe/former Soviet Union; mostly because of CH4 emissions from gas extraction) followed by LAM (Latin America; mostly livestock sector), while ASIA and MAF emissions are the lowest (see section S3 in the Supplement for a sectoral breakdown of the per capita emissions in the world regions). With strong climate policy, residual per capita emissions originate predominantly from agriculture and are projected to be the highest in LAM, due to the relatively large share of livestock emissions. This source is characterized by high emission factors and low abatement potentials (note that although rice CH4 emissions constitute about a third of CH4 emissions in Asia, in per capita terms, this is much less emission-intensive than livestock).
Global sectoral breakdown
Fig. 3 shows the projected development of sectoral CH4 emissions in the Reference and ClimPolicy cases. In 2010, agriculture emissions (from livestock, rice, and other crops) are found to be the largest anthropogenic source (43–49% of total emissions), followed by energy supply and demand (32–39%), and waste (16–22%). Without climate policy, emissions from all three main aggregated sectors will continue to increase, with similar growth rates (i.e., their relative contribution to total emissions are not expected to change much in time, see Supplement S3). In ClimPolicy scenario, energy emissions are expected to decrease dramatically, due to a combination of direct mitigation and a reduction of fossil fuel. Waste emissions in 2100 are expected to decrease by half compared to 2010, while agriculture emissions are projected to only slightly decrease or even remain stable. This difference in sectoral mitigation potential makes agriculture CH4 emissions by far the largest source of remaining emissions under the climate policy scenario: 60–80% in of total emissions 2050 and 55–88% in 2100.
In Fig. 4, the relatively large reductions in the energy sector are visualized by comparing Reference and the three mitigation scenarios in a primary energy use vs. emission factor plot for 2050 (see also Figure S3.5 in the Supplement for the projections for 2100, when the situation is comparable, but with deeper reductions). The figure shows that even in the energy supply sectors, both reduced fossil fuel use and end-of-pipe measures contribute significantly to reducing emissions. End-of-pipe measures alone (in ClimPolicy-CH4Only) or CO2 measures alone (reducing fossil fuel production, in ClimPolicy-CO2Only) would both bring global energy supply CH4 emissions from the 100–200 Mt/year range (in Reference) to the 50–100 Mt/year range. Residual emissions in ClimPolicy-CO2Only occur due to a lack of incentives for end-of-pipe measures to abate CH4 emissions from remaining fossil fuel use, while in ClimPolicy-CH4Only, the absence of CO2 pricing disincentivises a switch away from coal and gas towards non-fossil energy carriers. Only the combination of CH4 and CO2 pricing (in ClimPolicy) achieves deeper CH4 emission reductions towards about 25–50 Mt/year.
Diagnostic analysis and literature comparison
In the diagnostic exercise, the models have been driven by the same carbon price level, applied to all GHGs, to compare the elasticity of CH4 emissions to a greenhouse gas tax (note that all models participated, except ENV-Linkages, which was not part of the ADVANCE project where the analysis was performed and DNE21+, which included scenarios up until 2050). Figure 5 shows the models’ CH4 emission reduction under an increasing carbon price compared to a situation with no carbon price (Reference). The reductions shown are the combined effect of both end-of-pipe measures and indirect reductions due to CO2 mitigation, comparable to the case in ClimPolicy. The maximum carbon price in 2100 of 1500$(2005)/tCO2 is higher than any of the models’ projected carbon price in ClimPolicy and near the high end of the range of projected maximum carbon prices in other multi-model studies such as the SSPs (Riahi et al., 2017). Therefore, the projected reductions for 2100 can effectively be considered the maximum reduction potentials (MRPs).
There is a relatively good agreement in projected CH4 emission reductions relative to baseline (between 64% and 82%, with most models between 64% and 72%) despite large differences in sectoral reduction potentials (see next section). The highest reduction is seen in POLES, leading up to 82% in 2100. Mainly, this is the result of very optimistic assumptions on reductions in agriculture (up to 74%).Footnote 3 AIM/CGE also stands out with a relatively high MRP (up to 79% in 2100), mainly resulting from extrapolation of the underlying MAC data (Lucas et al., 2007), leading to relatively high sectoral reduction potentials.
Consistently across models, there is an increasing MRP as carbon prices increase to very high values. This is driven by three effects that vary in influence in the models: (1) increased potential in the MAC curves at high prices, (2) indirect mitigation from CO2 policy, and (3) economic feedbacks from high methane prices (i.e., lower demand for methane-intensive products). Regarding the first effect (1), POLES, AIM/CGE, DNE21+, ENV-Linkages, and MESSAGE-GLOBIOM make use of marginal abatement cost (MAC) curves that are (sometimes partly) based on extrapolation of underlying datasets, which allows for slowly increasing reduction levels under increasingly high carbon prices. The other models have assumed sectoral MRPs or marginal abatement cost curves that do not allow further reductions at a specific price (see Supplement S5 for model-specific MAC data and additional assumptions/modifications). The indirect mitigation effect from CO2 mitigation (2) is strongest in GCAM and IMAGE, followed by WITCH2016, DNE21+, and AIM (see Supplement S2). Economic feedbacks from methane prices on the demand of heavily methane-producing activities (3) (e.g., fossil fuel and livestock production) have been modeled in AIM/CGE, ENV-Linkages, GCAM, MESSAGE, and REMIND.
Figure 6 gives an overview of the models’ source-specific MRPs (and in the case of POLES, AIM, and MESSAGE, the reduction potential at 1500$/tCO2) compared to reduction potentials found in recent bottom-up studies of CH4 MAC curves (Harmsen et al. 2019; Höglund-Isaksson, 2012, 2017; 2015) (see Supplement S6 for results from Harmsen et al. 2019). It can be seen that MRP assumptions differ substantially across models, especially in 2050. Even when using the same data source, differences can be substantial (see fig. S5.1 in the Supplement), indicating that models add additional assumptions to MAC curve data. In 2100, the MRPs for the fossil fuel sectors and for landfills/solid waste are generally higher than 90% (except in POLES and GCAM, based on GECS 2002 and US-EPA 2013). There is large uncertainty about the MRP of wastewater and of the three main agriculture sources: enteric fermentation, rice, and manure. For the agricultural sources, the MRPs are generally also much lower in all models (between 25% and 60% in 2100).
The maximum MRP values (i.e., the highest found among the models) are higher (so more ambitious) than any of the reduction potentials found in recent literature, and for several sources, the average MRPs can be considered on the high end of what is found in literature (see right panel). The higher MRPs in the models can partly be explained by the optimistic estimates (for coal, natural gas, and wetland rice) from Lucas et al. (2007) (used by AIM/CGE, IMAGE, and REMIND) as well as extrapolations of reduction potentials at higher carbon prices (e.g., for enteric fermentation). Note, however, that models do not always reach their MRP due to too low carbon prices or inertia in the speed of emission reduction (e.g., in IMAGE and POLES). In addition, Höglund-Isaksson et al., (2012, 2015) used as a comparison here is based on the technical potential of currently known technologies and can be considered relatively conservative. However, when considering the maximum MRPs, it can be concluded that some models assume that technology improvements and global implementations go beyond what is found in recent literature.
Figure 7 shows yearly emissions per source in 2050 and 2100 in ClimPolicy-CH4 only and ClimPolicy (see also Fig. S5.2 in the Supplement for the relative reductions by source, including a comparison with the models MRPs). The projected emission ranges are large, indicating large uncertainties for all sources. There are no drastic changes between 2050 and 2100. However, the models’ MRPs are generally not fully reached in 2050, while in 2100, this is usually almost the case (then ClimPolicy-CH4 only is almost MRP, with less than 5% difference in 95% of the cases). In ClimPolicy, reduction is clearly higher than in ClimPolicy-CH4 only, particularly in the coal/oil/natural gas sectors, due to a reduction in the use of fossil fuels. With strong mitigation in 2100, the fossil fuel sectors are responsible for 5–28 Mt/year, or 3.5–18% of total CH4.
All models agree that enteric fermentation in ruminants can by far be considered the largest remaining mitigation bottleneck in a strong climate policy case. In ClimPolicy in 2100, emissions are projected to be an average 90 Mt/year (range, 31–137 Mt) or 58% (40–87%) of total emissions. These reductions are to a large extent realized by end-of-pipe measures and not by any human dietary changes, since this is not incorporated in the models’ MAC curves (note that most models do include price feedbacks, where ruminant meat and dairy demand is lowered somewhat as a result of higher CH4 prices). The average reduction in 2100 (55%) is comparable with the MRP estimated in Harmsen et al. (2019): 52%. This could be realized if all existing technologies (e.g., breeding through genetic selection, food supplements) would further develop and be optimally introduced worldwide. Höglund-Isaksson (2012) estimates the technical reduction potential at 50%, but only if there would be a large diet shift away from ruminant meat consumption. Due to physiological limitations, it seems very unlikely that much higher reductions (> 60%) are feasible without a global change in diets and/or a large-scale introduction of cultivated meat products.
For the remaining emission sources, the average model projections are generally in line with existing literature, although options for improvements exist and ranges in model outcomes are large, indicating substantial uncertainty (see Table S5.2 in the Supplement). Sewage and wastewater emissions are projected to be the second largest remaining emission source (with very large uncertainty). If all countries would reach the same per capita emissions as in OECD Europe, emission reductions would be 90% (Harmsen et al., 2019; US-EPA, 2013). This would require a large-scale implementation of centralized aerobic wastewater treatment systems, improvement of the CH4 recovery rate of existing plants, and additional abatement options for anaerobic treatment. With a reduction of 8% to 94% in ClimPolicy 2100 (average: 52%), some of the model projections can be considered conservative.
Projected emission reductions for rice also differ considerably across the models (in ClimPolicy, 29–87% in 2100) as do estimates from literature: In 2100, 31% according to Höglund-Isaksson (2012) and 77% according to Harmsen et al. (2019). The latter study assumes implementation of extra measures (see Supplement S6), maximized implementation potential, and technological improvements. Even in such a case, residual global emissions around 10 Mt/year may be unavoidable.
The models project an emission reduction of 20–74% of CH4 emissions from manure in ClimPolicy in 2100. Anaerobic digesters, centralized or farm scale, combined with efficient and enclosed manure collection and processing could bring down emissions by 75% or more if further developed (Graus et al., 2004). However, it is unlikely that this will ever be viable and implemented in the least developed regions, in particular for non-confined ruminant livestock. With very high carbon prices enforced worldwide, and maximized free trade, emission reductions could still be considerable, by reallocation of livestock production towards more efficient and less GHG intensive systems (Havlík et al., 2014).
For landfills and solid waste CH4 mitigation, a whole range of options exist that can bring down emissions, varying from waste diversion (recycling and reuse), to CH4 utilization and flaring, and all studies agree that high MRPs (80–90%) are feasible. The models project reductions of 37–99% (average 79%) in ClimPolicy in 2100, resulting in 7.5 Mt CH4/year residual emissions on average.
Figure 8 gives an overview of CH4 radiative forcing (RF) and total global mean temperature (GMT) effects in the five scenarios for 2050 and 2100 (present-day values are provided in the caption). The upper left panel shows that when unabated (in Reference), direct CH4 RF increases from 0.59 W/m2 today to 0.81 W/m2 (range, 0.69–1.0 W/m2) in 2100, whereas with strong climate policy (in ClimPolicy), present-day RF can almost be halved to 0.34 W/m2 (range, 0.26–0.44 W/m2). The total forcing difference between Reference and ClimPolicy resulting from the difference in CH4 emissions is in fact even larger, as CH4 contributes to tropospheric ozone (O3) forcing (as an O3 precursor), stratospheric H2O forcing, and HFC forcing (by affecting HFC atmospheric lifetimes).
The upper left panel also shows that the direct CH4 forcing in ClimPolicy_CH4 only is very close to the direct forcing in ClimPolicy (much closer than the CH4 emissions in the two scenarios; see Fig. 1). This is the result of a shorter CH4 lifetime in ClimPolicy_CH4 only, since the lifetime of CH4 is indirectly dependent on NOx, CO, and VOC concentrations, through its relation with tropospheric OH (Meinshausen et al., 2011).
Note in the upper right panel that in both Reference and ClimPolicy, the relative contribution of direct CH4 to total forcing decreases in time, as it is short-lived and does not accumulate in the atmosphere as much as CO2 and N2O (see Supplement S7 for forcer specific RF). Despite this decrease, CH4 is still projected as the most influential GHG second to CO2. This is clearly the case if no direct CH4 regulation would be enforced (in ClimPolicy-CO2 only in 2100, direct CH4 forcing constitutes 19% of total forcing on average), but also with maximum mitigation in a 2-degree scenario (on average, 13% of total forcing comes from CH4 in ClimPolicy in 2100), especially when considering the additional indirect contribution of CH4 to total forcing.
The lower panel shows the temperature differences between the five scenarios in 2050 and 2100, thus indicating the relevance of CH4 reducing measures. In 2100, cost-effective and non-climate policy-related CH4 reductions in the Reference case would reduce GMT 0.15 °C on average compared to Fzn-CH4, while strong CH4 mitigation policy (in ClimPolicy_CH4 only) can further bring down GMT by 0.46 °C (or 12% of total GMT change), in line with a similar assessment by Rogelj et al. (2015). The temperature difference between ClimPolicy-CO2 only and ClimPolicy represents the effect of including non-CO2 emissions in a 2 °C climate strategy, which amounts to 0.33 °C (or 16.5% of total GMT change) in 2100 on average. Although the exact value cannot be determined based on this assessment, more than half this difference can likely be attributed to CH4 mitigation (consistent with the estimate by Rogelj et al. 2015; 0.25 °C). Roughly half of the forcing difference between the two scenarios (47% on average in 2100) results from differences in direct CH4 forcing, and the total temperature increase from CH4 is estimated at 140% of the temperature increase from direct forcing alone (Smith et al., 2012). Note also that, particularly in the long term, some models have substantial indirect CO2 forcing reductions in ClimPolicy_CH4 only, due to increased fossil fuel prices resulting in CO2 concentration decreases (see discussion elsewhere in this special issue (Smith, 2018)).
Note that the results might be influenced by recent studies that could change the understanding of the climatic role of CH4. According to Etminan et al. (2016), the present-day RF of CH4 has been historically underestimated and should be increased by 25%. Additionally, Modak et al. (2018) suggest that efficacy (effective impact of RF on GMT) for CH4 should be reduced (17% lower than earlier assumed). In terms of impact on GMT, these factors may partially compensate each other.