Technical potentials and costs for reducing global anthropogenic methane emissions in the 2050 timeframe –results from the GAINS model

The GAINS model estimates emissions bottom-up, i.e., quantifications of human activities contributing to emissions are multiplied by an emission factor representing the average emissions per unit of activity. Such estimates rely on a wealth of publicly available information to develop internally consistent emission factors across countries, sectors and technologies. The starting point for estimations of anthropogenic CH₄ is the methodology recommended in the IPCC (2006) guidelines, for most source sectors using country-specific information to allow for deriving country- and sector/technology- specific emission factors at a Tier 2 level. For some source sectors consistent methodologies were further developed, e.g., for oil and gas systems (Höglund-Isaksson 2017) and solid waste sectors (Gómez-Sanabria et al 2018). The resulting emission estimates are thereby well comparable across geographic and temporal scales and with a possibility to provide plausible explanations for deviations in past emissions. CH₄ emissions are estimated for 174 countries/regions, with the possibility to aggregate to a global emission estimate, and spanning a timeframe from 1990 to 2050 in five-year intervals. For the purpose of better evaluating historical CH₄ emissions, annual estimates for 1990–2015 were produced for this study. Following the general GAINS methodology (Amann et al 2011), emissions from source s in region i and year t are calculated as the activity data A_its times an emission factor ef_ism. If emissions are controlled through implementation of technology m, the fraction of the activity controlled is specified by Appl_itsm, i.e.,

$$\begin{eqnarray}&&{E}_{its}=\displaystyle \displaystyle \sum _{m}\left[{A}_{its}\,* \,e{f}_{ism}\,* \,App{l}_{itsm}\right],\end{eqnarray} \tag{ 1 }$$

where

$$\begin{eqnarray}&&\displaystyle \displaystyle \sum _{m}App{l}_{its}=1,\end{eqnarray} \tag{ 2 }$$

and where A_its is the activity (e.g., number of animals, tons of waste, PJ gas produced),

ef_ism is the emission factor for the fraction of the activity subject to control by technology m,

Appl_itsmis the application rate of technology m to activity s.

Hence, for each emission source sector, country-and year- specific sets of application rates for all the possible technologies (including no control) are defined such that application rates always sum to unity.

The GAINSv4 model structure covers all relevant source sectors for anthropogenic CH₄ emissions, for details see tables S1–1 in the supplement information (SI) is available online at stacks.iop.org/ERC/2/025004/mmedia. Activity drivers for macroeconomic development, energy supply and demand, and agricultural activities are entered externally in GAINS. For the baseline scenario presented here, the macroeconomic and energy sector activity drivers are consistent with the IEA World Energy Outlook 2018 New Policies Scenario (IEA-WEO 2018). Growth in global population, Gross Domestic Product (GDP) and GDP per capita are illustrated in figure 1. This energy scenario assumes that countries comply with the Intended National Determined Contributions (INDCs) to climate change mitigation they pledged in the lead-up to the UNFCCC's COP21 in Paris in 2015, however, it should be noted that these pledges fall short of the Paris Agreement of keeping the Earth's warming well below 2 °C above the pre-industrial average. How this energy scenario translates into global consumption of different types of fuels is illustrated in figure 2. Note that for the purpose of this study of improving the understanding of the technical mitigation potentials at the sectoral and regional level, only one baseline has been developed against which future emission reductions are assessed. To provide a full range of possible future developments of global anthropogenic methane emissions, a set of alternative activity scenarios would be required. This is however considered out of scope of this paper, as the relative technical mitigation potentials at the sector and regional level will be comparable irrespective of the baseline emission level.

Zoom In Zoom Out Reset image size

Zoom In Zoom Out Reset image size

Agricultural activity data are taken from FAOSTAT (2018) with projections aligned to the most recent forecast of FAO (Alexandratos and Bruisma 2012) and complemented with data from national sources e.g., reporting to UNFCCC (2018) and EUROSTAT (2016) for information about manure management practices, farm sizes etc. The historical and projected changes in global livestock numbers are illustrated in figure 3.

Zoom In Zoom Out Reset image size

Activity data for the waste and wastewater sectors are derived in GAINSv4 using the methodology described in the Supplement of Gómez-Sanabria et al (2018). Drivers for the generation of municipal solid waste (MSW) are GDP per capita and urbanization rate, here in consistency with macroeconomic assumptions of the IEA-WEO2018 (see figure 1). Elasticities for MSW generation by income group are estimated from historical data and reflect the relative increase in average per capita waste generated in response to a relative increase in the average per capita income and urbanization rate. As shown in Gómez-Sanabria et al higher waste generation elasticity estimates are found for countries with higher incomes. At lower income levels, households primarily generate food waste, while at higher average income levels it is primarily the generation of non-food waste that increase with income. Figure 3 illustrates the global gross generation of waste (i.e., before disposal through scattering, landfill, recycling, incineration or other treatment) for the period 1970–2050 as estimated within the GAINSv4 model. Because of slow decomposition of organic waste in landfills, we account for a time-lag of up to 20 years between disposal of waste to a landfill and the release of CH₄ emissions. To estimate emissions from the year 1990 onwards, it is therefore necessary to estimate waste generation already from the year 1970. As shown in figure 4, the growth rate for the generation of global municipal solid waste is estimated to increase after 2010, with global amounts growing by 4.5 percent between 2005–2010 and by 14 percent between 2010–2015. Note that for the waste sector the baseyear for projections is 2010 and the 2015 estimate is a model result. The strong increase in global MSW generation between 2010 and 2015 is mainly driven by an expected 20 percent increase in MSW generation in China and India, which follows from the application of a higher MSW generation elasticity as several provinces move into higher average income segments between 2010 and 2015. Although a model result in GAINSv4, the higher growth rate for China after 2010 is confirmed empirically by Chayy et al (2018) who find that collected and transported MSW in China increased by 1.5 percent between 2005 and 2010 and by 21 percent between 2010 and 2015.

Zoom In Zoom Out Reset image size

Sector-specific emission factors are identified both for a no control case and for each control technology applicable to the specific sector in a country. Emission factors are adopted from country-specific information and/or derived in a consistent manner across countries from information on factors determining the country-specific emission factors. Table 2 presents a selection of the most important information sources for CH₄ emission factors in GAINSv4 with a focus on updates made after the publication of Höglund-Isaksson (2012). In addition, a wealth of national information has been fed into individual emission factor estimates, as documented in Höglund-Isaksson (2012, 2017), Höglund-Isaksson et al (2015, 2018), and Gómez-Sanabria et al (2018). More sector details are available in section S6 of the SI.

An implicit assumption in the development of the baseline scenario is that it considers effects on current and future CH₄ emissions from regulations and legislation already adopted as of Dec 2018. Tables S4–1 in the SI presents a list of implemented national and regional legislation with direct or indirect impacts on CH₄ emissions that have been considered in the GAINSv4 baseline scenario. Note that future mitigation potentials and associated costs are always assessed as additive to the baseline. Emission reductions and costs incurred by abatement options adopted already in the baseline are not reflected in the estimation of future mitigation potentials and costs.

The mitigation potential assessed in the marginal abatement cost curves of the GAINSv4 model refers to feasible reductions in emissions through adoption of technologies defined as installations or applications of physical equipment or material, or modifications in physical parameters affecting emissions. In the short-run, immediate adoption of control technology is assumed constrained by lock-in of investments into existing technology, with successive phase in of new technology modelled by sector over the period 2020–2035 and with full effect on emissions from implementation of maximum technically feasible reductions (MFR) only achievable from 2040 onwards. The GAINSv4 baseline scenario assumes no effects on costs and removal efficiencies from technological development as it is assumed that any incentives to adopt (and therefore further develop) emission control technology rely heavily on the existence and stringency of policies directly addressing CH₄ emissions. Hence, without further policy incentives, there are assumed to be no further driver for technological development, which means emission factors for a given technology remain constant over time in the baseline. An exception could be technologies that simultaneously reduce CH₄ emissions and recover/save gas that can be utilized for energy purposes. Adoption of such technologies may arise spontaneously if the future price of gas become high enough to make gas recovery profitable. As the development in future fuel prices is highly uncertain, such technology uptake is not reflected in the baseline scenario, but treated as a future mitigation potential available at a negative cost. In contrast to the baseline scenario, GAINSv4 mitigation scenarios for CH₄ assume additional policy incentives are indeed put in place to stimulate both uptake and further development of CH₄ abatement technology. Assumptions in GAINSv4 about the effects of technological development on removal efficiency and costs for CH₄ mitigation options are presented in tables S5–1 of the SI. Justifications for these assumptions are based on empirical findings of observed developments in control technology following introductions of NOx and SO₂ regulations in the US (Popp 2003), Japan (Matsuno et al 2010) and Sweden (Höglund-Isaksson and Sterner 2010) in the 1990s, as presented in section 2.5.1 of Höglund-Isaksson et al (2018).

Unit costs for mitigation of CH₄ per unit of activity are in GAINSv4 calculated as the sum of investment costs, labour costs, non-labour operation and maintenance costs, cost-savings due to recovery or saving of electricity, heat or gas, and non-energy cost savings like avoidance of landfill fees. Unit costs are expressed in constant 2010 Euros per unit of activity. Country and sector specific annual average wages for the agricultural and manufacturing industry sectors are taken from LABORSTA (ILO 2010) for historical years. Growth in average future wages is proportional to the expected future development in GDP per capita with sector adjustments consistent with growth in sector value added as provided by IEA-WEO (2018). The cost-saving of energy recovery from biogas production or reduced leakage of natural gas during production, transmission and distribution is set equal to the expected future electricity or gas consumer price in industry as taken from the IEA-WEO (2018) New Policies Scenario. Gas recovery refers to the recovery of gas of an upgraded quality of 97 percent CH₄. For some mitigation options, e.g., when biogas is recovered from large-scale anaerobic digestion of food and organic waste, upgrading from 60 to 97 percent CH₄ is necessary for supplying the gas to the grid (Persson 2003). Costs for upgrading gas have in these cases been included in investment costs.

The total mitigation cost in sector s, country i and year t is defined for sets of application combinations of the possible technologies applicable to the sector. For a given country, year and sector, a technology setting is defined such that the sum of all application rates Appl_itsm of possible technologies m (including the no control option) is always unity. The total cost of each technology setting is defined as:

$$\begin{eqnarray}&&T{C}_{its}=\displaystyle \displaystyle \sum _{m}\left[{A}_{its}\,* \,{C}_{itm}\,* \,App{l}_{itsm}\right],\end{eqnarray} \tag{ 3 }$$

where A_its is the activity level, C_itm is the cost per unit of activity and $\displaystyle {\sum }_{m}App{l}_{itsm}=1.$

The country- and year- specific average cost per unit of reduced emissions is first calculated for each technology available by dividing the unit cost with the difference between the technology emission factor and the no control emission factor, such that:

$$\begin{eqnarray}&&A{C}_{itm}=\displaystyle \frac{{C}_{itm}}{e{f}_{it}^{No\_control}-e{f}_{itm}}.\end{eqnarray} \tag{ 4 }$$

Within a sector, the available technologies are first sorted by increasing average cost. The technology with the lowest average cost is ranked the first-best technology and assumed adopted to its maximum applicability in a given sector. The second-best technology has the second lowest average cost and is assumed available for adoption provided it can achieve an emission factor that is lower than the first-best technology. The marginal cost of the second-best technology when implemented in the marginal abatement cost curve (MACC) is the unit cost divided by the additional emission reduction still available for a given sector, i.e.

$$\begin{eqnarray}&&M{C}_{it2}=\displaystyle \frac{{C}_{it2}-{C}_{it1}}{e{f}_{it1}-e{f}_{it2}}.\end{eqnarray} \tag{ 5 }$$

In a similar manner, each additional technology available in a sector is added on top of the next best available technology. The result is a MACC built up technology-wise by sector, country and year. Note that if most of the technical abatement potential is exhausted with the first-bejst technology, the marginal cost of subsequent technologies becomes very high due to the limited additional emission reduction potential. Note also that a technology with both a higher average cost and a higher emission factor than another technology available to a sector will not be adopted at all, since it is both less effective in reducing emissions and comes at a higher cost than other available technologies. Finally, abatement technologies are not always additive, but can also be partly complementary. This is the case e.g., for measures addressing emissions from rice cultivation and enteric fermentation in cattle. For these sectors, we have constructed 'combined technologies', which reflect the overall effect on emissions and costs when more than one measure are implemented simultaneously. For rice cultivation, the first-best technology is improved water management by extending the periods fields are dried out. The second-best technology is improved water management combined with low-CH₄ hybrids and use of soil enhancing amendments. For enteric fermentation in cattle, the first-best technology is breeding for enhanced productivity and animal health and fertility, while the second-best option is to combine breeding with different animal feed changes.

Uncertainty is prevalent along many different dimensions both in the estimations of emissions, abatement potentials and costs. When constructing global bottom-up emission inventories at a detailed country and source level, it is inevitable that some information gaps will be bridged using default assumptions. As it is difficult to speculate about how such sources of uncertainty affect resulting historical and future emission estimates, we instead address uncertainty in historical emissions by making comparisons to estimates by other publicly available and independently developed bottom-up inventories, i.e., EDGARv4.3.2 (2018) and CEDS-CMIP6 (2017), and various top-down estimates consistent with atmospheric measurements and inverse model results (e.g., Saunois et al 2016). Comparisons of global historical CH₄ emission estimates are presented in section 3.1 and by World region in section S2 of the SI. The bottom-up inventories adhere to the recommended guidelines of the IPCC (2006), however the flexibility in the recommended methodologies is large as it depends on the availability and quality of the gathered source information. There is accordingly a wide range of possible sources of uncertainty built into estimations in these comprehensive efforts. Having a pool of independently developed inventories, each with its own strengths and weaknesses, can improve the understanding of the scope for uncertainty in these estimates.

Regarding uncertainty in emission projections and as already discussed in section 2.2, we only produce one baseline scenario, which is consistent with the economic and energy sector developments of the IEA-WEO (2018) New Policies Scenario. Providing a range of baselines describing different future developments in the activity drivers is out of scope of this study as the intention here is to focus on the relative technical mitigation potentials and costs for reducing emissions at the region, sector and technology level.

Uncertainty in cost estimations is generally high. This is partly a feature of the many dimensions along which uncertainty enters into cost estimates and partly a general lack of detailed information on abatement costs in the literature. There are some uncertainty features that are more systematic than other as they derive from more general assumptions about how investors make decisions about adoption of control technologies. To account for the uncertainty range caused by these particular assumptions, we estimate a range for the marginal abatement cost curves (MACCs). The upper range limit represents the most pessimistic case in the sense that we assume no further technological development and that marginal abatement costs reflect a private investor perspective. Private investors are assumed to operate with a ten percent interest rate on fixed investments, a maximum investment perspective limited to ten years, and no speculation about an expected future increase in energy prices but only considering current (here referring to projected 2020) energy prices when deciding on investments. The lower range limit of the MACC represents the most optimistic case assuming the cost perspective of a social planner and with improving removal efficiencies and declining abatement costs over time due to technological development. A social planner is assumed to take decisions based on a four percent interest rate for fixed investments, considering the entire expected lifetime of the technology, and a future increase in energy prices as expected in the projections of the IEA-WEO (2018) New Energy Policies scenario. Why is it of interest from a climate policy point of view to consider both private investor and social planner perspectives on future abatement costs ? The reason is that a social planner, when looking to balance the costs and benefits of climate change mitigation against those of other areas of public spending, e.g., health and education, will need to make such trade-offs on the basis of a low discounting of future values in order to secure opportunities for decent lives also for coming generations. Hence, the social planner's MACCs are suitable for taking decisions about targets for emission reductions that will optimize social welfare. When considering implementation of policies that will actually achieve the socially optimal emission reduction targets, policy maker ought to rely on MACCs estimated from the private investor perspective. These reflect better the higher marginal abatement costs (and higher carbon price levels) needed for private investors to find it profitable to invest in abatement at a level that meets the desired emission reduction targets (Baumol and Oates 1971).

For a good understanding of future emissions, we must first understand the current level and source attribution of emissions. We therefore develop a global inventory of annual CH₄ emissions 1990–2015 and compare it to other global bottom-up inventories as well as to top-down inverse model results. GAINSv4 bottom-up estimates of global anthropogenic CH₄ emissions 1990–2015 are presented in figure 5. GAINSv4 does not include estimates of emissions from forest fires and savannah burning due to a lack of detailed country-specific information. For the purpose of illustrating total anthropogenic CH₄ emissions in figure 5, the GAINSv4 estimate of all other CH₄ sources has been complemented with the global estimates of emissions from forest fires and savannah burning from the GFEDv4.0 database (Randerson et al 2018).

Zoom In Zoom Out Reset image size

GAINSv4 estimates a decline in global CH₄ emissions in the first half of the 1990s, primarily a consequence of the collapse of the Soviet Union and the associated general decline in production levels in agriculture and fossil fuels (see Regional emission illustrations in figures S2–1 of the SI). In addition, as described by Evans and Roshchanka (2014) and assumed in Höglund-Isaksson (2017), venting of associated petroleum gas declined significantly in Russia due to an increase in flaring. It is unclear why this happened, but a possible explanation could be that the privatization of oil production in this period meant that the new private owners were less willing to take the security risks of venting and invested in flaring devices to avoid potential production disruptions. This hypothesis is however yet to be confirmed. Global CH₄ emissions are estimated to remain relatively constant in the second half of the 1990s, but then start to increase in the first few years of the new millennia. This time the primary drivers for growth in emissions are a mix of sources; increased coal mining in China, increased oil and/or gas production in Russia and Africa, rapidly expanding cattle rearing in Latin America, and increased generation of waste and wastewater in China, India and the rest of South-East Asia. The latter driven by population and rapid economic growth. Between 2008 and 2010 there is a brief downturn in emissions following a general decline in economic activity in response to the global financial crisis. After 2010 emissions increase again with principal drivers being; rapidly growing extraction of unconventional gas in North America, increased coal mining in Indonesia, and accentuated growth in waste and wastewater emissions in all rapidly developing regions of the world, including China, India, the rest of South-East Asia, Latin America, and Africa. The latter development would offer a possible explanation to observed increases in atmospheric CH₄ from biogenic sources in tropical regions (Nisbet et al 2014, 2019). It should however be noted that there is also a small but steady increase in global emissions from livestock, in particular beef and dairy. Emissions from pigs have however seen a slight decline in the last decade due to an expansion in the use of biogas digesters in Europe for treatment of pig manure.

In figure 5, the GAINSv4 bottom-up estimates are compared with the average top-down estimates of anthropogenic emissions following from inverse model results reconciling bottom-up with top-down measurements of the CH₄ concentration in the atmosphere. Saunois et al (2016) provide such estimates for three time periods: 2000–2009, 2003–2012, and 2012. As shown, these estimates align quite well with the GAINSv4 bottom-up estimates. Figure 6 illustrates the average and full uncertainty ranges for top-down estimates of emissions by groups of CH₄ isotopic signatures identifiable in the atmosphere and mentioned e.g., in Saunois et al (2016) and Dlugokencky et al (2011). The isotopic signatures make it possible to distinguish between atmospheric CH₄ from biogenic (agriculture and waste) sources, fossil fuel sources, and burning of biomass sources. GAINSv4 estimates fall within the uncertainty ranges of the atmospheric measurements for all three CH₄ isotopic signature groups. For the biogenic sources presented in figure 6(a), GAINSv4 estimates are close to those by CEDS-CMIP6 (2017) and lower than those by EDGARv4.3.2 (2018). The higher CH₄ emissions from biogenic sources in EDGARv4.3.2 can primarily be attributed to higher annual emissions from wastewater sources than in GAINSv4 (see table 5.3 in Höglund-Isaksson et al 2015), in particular for Africa and South-East Asia where GAINSv4 assumes poor conditions for CH₄ formation in areas lacking proper infrastructure for centralized wastewater collection. For fossil fuel sources presented in figure 6(b), the average top-down estimate of CH₄ by Saunois et al is somewhat lower than the GAINSv4 estimate from year 2000 onwards and considerably lower than the CEDS-CMIP6 estimate for the later years, as discussed in detail below. For emissions from burning of biomass and biofuels presented in figure 6(c), the sum of the GAINSv4 estimate of CH₄ emissions from burning of agricultural waste residuals and the GFEDv4.0 estimate of global CH₄ emissions from forest fires and savannah burning, reveals that the GAINSv4 estimate for these sources falls somewhat short of the average top-down estimate.

Zoom In Zoom Out Reset image size

Figure 7 displays the estimates of CH₄ emissions from fossil fuel sources by hydrocarbon source and global bottom-up inventory (for further details see section S3.3 of the SI). In panel 7a, GAINSv4 shows fairly constant estimates of annual emissions of about 80 Tg CH₄ from global oil and gas systems between 1995–2015. Looking closer we see that this seemingly stable emission level is the result of steadily increasing emissions from natural gas extraction, driven by increased gas production in general and shale gas production in particular, and a simultaneous steady decline in emissions from oil extraction. The latter is referred to increased recovery rates for associated petroleum gas, particularly in Russia and parts of Africa (Höglund-Isaksson 2017). Emissions from oil and gas systems are in the CEDS-CMIP6 and EDGAR v4.3.2 inventories reported as aggregates and it is therefore difficult to know whether the same developments in oil and gas production emissions, respectively, are prevalent also in these inventories. Panel 7b shows how global emissions from coal mining (including from abandoned coalmines) develop over time in the different bottom-up inventories. While GAINSv4 and EDGARv4.3.2 agree quite well, CEDS-CMIP6 estimates considerably higher emissions from this source, in particular for China in the period post-2005. The basis for the higher emissions from coal mining in China in CEDS-CMIP6 is not clear, however, consistent with higher emissions from this source in previous versions of EDGAR (see table 5.3 in Höglund-Isaksson et al 2015). Recent results of inverse models (Miller et al 2019, Sheng et al 2019) find considerably lower CH₄ emissions from coal mining in China, indicating that also estimates by GAINSv4 and EDGAR 4.3.2 may be on the higher side.

Zoom In Zoom Out Reset image size

A global projection of baseline anthropogenic CH₄ emissions to 2050 consistent with the energy sector developments of the IEA-WEO (2018) New Policies Scenario, is presented in the left panel in figure 8 in five-year intervals. Baseline emissions are expected to increase close to linearly by about 3 Tg CH₄ per year or 30 percent between 2015 and 2050. Global emission increases are primarily driven by an expected increase in solid waste generation as population grows and countries become richer and by an expected increased extraction of unconventional natural gas. The latter is partly a reflection of a substitution of coal with natural gas and renewables projected in the IEA-WEO (2018) New Policies Scenario and goes together with a decline in emissions from coal mining in the period post-2030 in that particular energy scenario.

Zoom In Zoom Out Reset image size

Baseline emission developments at a regional level are presented in figures S3–1 in the SI. For China, baseline CH₄ emissions are expected to continue growing to 2040, but then level off at an annual emission level of about 65 Tg CH₄ due to a decline in coal mining. A strong increase in CH₄ emissions from shale gas production in North America is expected to continue until 2045, when emissions decline due to a projected drop in gas demand in the IEA-WEO2018 New Policies scenario. Due to already adopted climate policy strategies, the European Union is expected to be on track for a decline in CH₄ emissions by about 20 percent between 2015 and 2030, however, further reductions will need implementation of additional policy incentives. Continued growth in population and income are expected to drive increases in waste and wastewater CH₄ emissions in Africa, India & South-East Asia. A continued increase in demand for beef is expected to be the prime driver for increased CH₄ emissions in Latin & Central America, while a continued demand for oil drives emission increases in the Middle East. An expected rapid growth in natural gas production in Australia coupled with no phase-out of coal mining, translate into a steady increase in emissions in Oceanian OECD (Australia, New Zealand and Japan) in the period leading up to 2050.

The maximum technically feasible reduction (MFR) of global anthropogenic CH₄ in year 2050 is estimated at 54 percent below baseline emissions of that year. This corresponds to a global emission level that is 40 percent below the 2015 level and reflects that baseline emissions are expected to grow by 30 percent between 2015 and 2050 (see right panel of figure 8). The MFR for fossil fuel sources is assessed at 74 percent below baseline in 2050 (see table 3), assuming full implementation worldwide of at least 98 percent recovery of associated petroleum gas and, in addition, leakage detection and repair (LDAR) programs to reduce unintended leakage during extraction, transmission and distribution of natural gas. Investments into control of fossil fuel emissions would of course become redundant should the World decide on a massive phase-out of fossil fuel use in the next few decades. High technical abatement potentials at about 80 percent below baseline emissions in 2050 are considered feasible for CH₄ emissions from solid waste management. This assumes it possible in a twenty years perspective to extend the infrastructure for source separation, recycling and energy recovery schemes globally, including a ban on all landfill of organic waste and allowing for useful utilization of the carbon content of the waste (Gómez-Sanabria et al 2018).

The technical abatement potential for agricultural sources is assessed at 21 percent below baseline emissions in year 2050. This includes relatively limited abatement potentials for livestock of 12 percent due to applicability limitations (see section S3.4. in the SI for details). Large farms with more than 100 LSU contribute about a third of global CH₄ emissions from livestock and for this group we find it technically feasible to reduce emissions by just over 30 percent below baseline emissions in year 2050 (see figures S6–2 in the SI). The available options include reduction of enteric fermentation emissions through animal feed changes (Gerber et al 2013, Hristov et al 2013) combined with implementation of breeding schemes that simultaneously target genetic traits for improved productivity and enhanced animal health/longevity and fertility. Increased productivity reduces system emissions by enabling the production of the same amount of milk using fewer animals. The dual objective in breeding schemes is important as a one-eyed focus on increased productivity leads to deteriorating animal health and fertility and a risk that system emissions increase due to a need to keep a larger fraction of unproductive replacement animals in the stock (Lovett et al 2006, Berglund 2008, Bell et al 2011). The enteric fermentation options are considered economically feasible for commercial/industrial farms with more than 100 LSU but not for smaller- and medium- sized farms. Breeding schemes are assumed to deliver impacts on emissions only after 20 years and feed changes are assumed applicable only while animals are housed indoor. Emissions from manure management can be reduced through treatment of manure in anaerobic digesters (ADs) with biogas recovery. To be efficient from both an economic and environmental point of view, a certain scale is needed to accommodate both the fixed investment of the AD plant and the time farmers spend carefully attending to and maintaining the process (for details see section 3.3.1.3 in Höglund-Isaksson et al 2018). About a third of global livestock CH₄ emissions can be attributed to smallholder farmers particularly prevalent in Africa and South-East Asia. These livestock typically have low productivity and emissions per head and are well adapted genetically to local conditions. We do not consider any technical abatement potential for this group of farmers, because enhanced productivity may not be of primary interest when considering that livestock often fills a dual purpose; beside providing milk and meat it also functions as a mean to store assets and manage risks over time (FAO 2008, Udo et al 2011). In absence of access to credit markets and publicly provided health care, the robustness of indigenous breeds may become more important than the increased production that can be achieved by introducing highly productive breeds from abroad. Hence, control of these emissions are closely linked to more general institutional and economic reforms. For CH₄ emissions from rice cultivation, a halving of global emissions is considered possible through improved water management that shorten the period of continuous flooding of fields, combined with a use of low-CH₄ generating hybrids and different soil amendments (see section S6.5 of the SI for details).

Due to locked in capital of existing technology in the short-run, the cumulative emissions in the MFR scenario is assessed at 38 percent below baseline between 2020 and 2050 (see table 3). This leaves 7.7 Pg CH₄ or 216 Pg CO₂eq using GWP₁₀₀ from AR5 (IPCC 2013) released globally between today and 2050 that will likely be difficult to remove through technical solutions. In 2050, MFR leaves 5.7 Pg CO₂eq of CH₄ still released. This is a lot if we consider that to stay at 1.5 degrees warming, IPCC (2018) estimates we must not exceed 10 Pg CO₂eq for all greenhouse gases in 2050 (and be at zero net emissions around 2075). In addition to technical solutions, this calls for widespread implementation in the 2050 timeframe of behavioural options, e.g., human diet changes that reduce meat and milk consumption (e.g, Springmann et al 2016, Clune et al 2017, Willett et al 2019) and general institutional and social reforms indirectly mitigating greenhouse gas emissions in developing countries (Evans and Steven 2009).

Figure 9 illustrates the technical CH₄ abatement potentials 2020–2050 by major World region. As expected, the technical abatement potentials are highly region-specific with the largest relative reduction potentials possible in major fossil fuel supplying regions like Russia and the Middle East. Significantly lower reduction potentials are found for regions where agricultural sources dominate CH₄ emissions, i.e., India, Latin America, Oceanian OECD and South-East Asia.

Zoom In Zoom Out Reset image size

The estimated range for the global MACC for CH₄ in year 2050 is presented in figure 10. The lower range limit of the MACC corresponds to a social planner's perspective and include impacts of technological development, while the upper range limit corresponds to a private investor's perspective and excluding impacts from technological development (see section 2.5). Starting from a baseline emission level of 450 Tg CH₄ in 2050, a 35 percent reduction is estimated as possible at a zero or negative marginal cost (i.e., at a net profit) at the lower range limit of the MACC, while the same relative reduction would only be possible with the introduction of an additional policy incentive equivalent to 82 €/t CO₂eq at the upper range limit of the MACC. At the lower range limit it is considered possible to almost halve baseline emissions in 2050 at a marginal cost below 20 €/t CO₂eq, while at the upper range limit three quarters of the full baseline emissions are expected to remain at the same marginal cost level. Hence, the marginal abatement costs are highly sensitive to the time and opportunity cost perspective of the investor and to the potential impact from technological development on costs and removal efficiencies. Although policy makers must have a social planner's perspective when determining the optimal allocation of resources to emission abatement in relation to other public goods, they must let a higher MACC guide the setting of carbon price levels to provide enough incentives for private investors to achieve the desired emission reductions in various sectors and regions.

Zoom In Zoom Out Reset image size

The ranges for the MACCs differ significantly between major source sectors both at a global scale (see figure 11) and across World regions (see figure 12). At the lower range limit, more than 85 percent of the global MFR is found attainable at a marginal cost below 20 €/t CO₂eq for all three major source sectors Energy, Agriculture and Waste. At the upper range limit, however, a policy incentive equivalent to the same carbon price level achieves the more modest emission reductions of 57, 71 and 50 percent, respectively. It is evident from the regional analysis that extensive potentials to reduce CH₄ emissions at low costs exist in the fossil fuel production sectors in Russia and the Middle East. Targeting these two sources alone could remove more than 10 percent of global baseline emissions in 2050. An additional almost 10 percent of baseline emissions in 2050 could be removed at a marginal cost below 20 €/t CO₂eq by implementing proper waste and wastewater handling in China, India and the rest of South-East Asia. This would likely come with considerable co-benefits in the form of reduced air and water pollution.

Zoom In Zoom Out Reset image size

Zoom In Zoom Out Reset image size

The long-run technical abatement potential for global CH₄ emissions in year 2050 has been assessed by Lucas et al (2007) and Harmsen et al (2019). Figure 13 illustrates the MFR in total and by sector as estimated in these two studies in comparison to GAINSv4. The different assessments agree fairly well on the long-run technical abatement potential in non-agricultural sectors. Lucas et al appears generally to be more optimistic than both Harmsen et al and GAINSv4. The most notable difference is in the assessment of the technical abatement potential for the agricultural sector. Table 3 presents recent estimates from four different studies of global CH₄ mitigation potentials in 2030 and 2050 for this sector. GAINSv4 is slightly more conservative than Beach et al (2015) in the estimate for 2030, but well within the range estimated in Frank et al (2018). In the 2050 timeframe, the maximum technically feasible reduction of about 1 Pg CO₂eq in GAINS v4 appears as a middle estimate between the Frank et al estimate of 0.52 and the Harmsen et al estimate of 1.7 Pg CO₂eq. The discrepancy can mainly be referred to differences in livestock sector mitigation potentials, where GAINSv4 estimates maximum 12 percent reductions in global manure management and enteric fermentation emissions, respectively. Harmsen et al estimates 55 and 41 percent reductions for the respective sources and Lucas et al 50 percent for both sources. This difference can be referred to the applicability limitations introduced in GAINSv4 on the basis of farm size and intensive/extensive systems as discussed in section 3.3 and sections S6–4 in the SI. Harmsen et al and Lucas et al assume almost the same applicability rates for livestock mitigation options across different World regions and no applicability constraints for implementation of enteric fermentation (breeding and animal feed changes) options to the about one third of livestock emissions attributable to smallholder farmers in developing countries. Such applicability constraints apply in GAINSv4 due to the important role livestock herds play in the management of risks for smallholder farmers in Africa and South-East Asia (see section S6.4 in the SI). GAINSv4 is however considerably more optimistic than Frank et al about the mitigation potentials of breeding and animal feed changes in year 2050.

Zoom In Zoom Out Reset image size