5. Anthropogenic methane sources, emissions and future projections
5.2 Global anthropogenic methane emissions in past years
5.2.3 Recent inventories of global anthropogenic methane emissions
Independent bottom-up inventories have been produced by the United States Environmental Protection Agency (USEPA 2006, 2012), the IIASA GAINS model (UNEP 2011a; ECLIPSE 2012, 2014; Höglund-Isaksson 2012) and the Emissions Database for Global Atmospheric Research (EDGAR 2010, 2013), which is an inventory compiled by the European Commission Joint Research Centre (EC-JRC) and Netherland’s Environmental Parameter Emission inventories of past emissions Projections of future emissions
Activity data • Factors in identification of emission source sectors
• Factors in activity data reported to statistical databases
• Handling of missing activity data and strategies chosen to bridge information gaps
• Factors in the geospatial allocation of activities and emissions to grids
• Factors in the representativeness of the proxy used for gridding
• Factors in the expected future development of key activity drivers, for example, affected by future economic growth, technological progress and structural changes in energy systems
• Factors in the spatial movement of point sources
• Factors in the change of spatial pattern of the proxy data used
Emission factors • Factors affecting the representativeness of a limited number of on-site emission measurements to emission characteristics for whole countries/regions
• Choice of methodology to derive emission factors based on information availability (i.e. Tier 1 to Tier 3 in IPCC’s terminology, see Sect. 5.2.2)
• Lack of information about country-specific factors affecting emissions
• Strategies chosen to bridge information gaps
• Use of default emission factors
• Uncertainty inherent in implied emission factors reported by countries to the UNFCCC
• All sources of uncertainty present in the derivation of historical emission factors will also be present in emission projections
Emission control • Factors in identification and effectiveness of existing control technology
• Representation of the effects on emissions of the implementation of existing emission regulations, determined, for example, by assumptions about removal efficiencies and applicability of technologies
• Factors in the future development of control technology
• Factors in the future penetration (uptake) of control technology
• Factors in the adoption and stringency of future climate policies
• Factors in the effectiveness of future policies in stimulating adoption of mitigation technology and strategies Table 5.1 Potential sources of uncertainty in global methane inventories of past anthropogenic emissions and emission projections.
Assessment Agency (PBL). A comparison is also made with the global emission inventory used as starting point for the future emission scenarios generated by the family of Integrated Assessment Models (IAMs) that contributed to the Representative Concentration Pathways (RCPs) feeding into scenarios used for the IPCC Fifth Assessment Report (Lamarque et al. 2010; IPCC 2014). These models are the Global Change Assessment Model (GCAM) of the Joint Global Change Research Institute, the Model of Energy Supply Systems and the General Environmental Impacts (MESSAGE) of IIASA, the Asia-Pacific Integrated Model (AIM), and the PBL Integrated Model to Assess the Greenhouse Effect (IMAGE), hereafter referred to as the ‘RCP models’. The RCP models were calibrated to global anthropogenic methane emissions in base year 2000 based on Lamarque et al. (2010), who combined the EDGAR v4.1 (2010) inventory with that of the UNFCCC and other sources for a comprehensive consistent global data set (van Vuuren et al. 2011a).
Table 5.2 provides an overview of the referenced inventories with indications of their level of aggregation, specification of source sectors and geographical regions, and their base year and timeframe. All inventories cover the major methane emission sources: fossil fuel production, transmission and distribution;
livestock (enteric fermentation and manure management);
rice cultivation; solid waste and wastewater. The USEPA and EDGAR inventories provide country-specific estimates of methane emissions for all countries of the world. The GAINS model produces country-specific estimates for Europe, North America, Asia, Australia and New Zealand, provincial estimates for 32 Chinese and 23 Indian provinces, and estimates for Latin America and Africa each aggregated to four regions. The RCP data archive (IIASA 2009) contains methane emissions with the world split by nine major regions, although the resolution of the individual RCP models is higher (see Table 5.2). Methane emissions are reported for 13 source sectors by the USEPA and 25 source sectors by EDGAR. The GAINS estimates of methane emissions can be aggregated to about 80 different sources for which emission factors are identified separately. (It should be
noted that the sector aggregation level for the reporting of emissions is different from the level of aggregation used in the estimation of emissions. For example, when counting the total number of individual source sectors identified and used in common for the estimation of emissions of a large number of air- and waterborne substances, the number of source sectors amount to over 4000 in the EDGAR inventory and over 2000 in the GAINS model. Only a subset of these source sectors has direct relevance for methane, and in the reporting of emissions they are further aggregated.) GCAM covers 54 separate source sectors for methane.
For past years, the USEPA (2012) adopts the emissions reported by countries to the UNFCCC. The GAINS model and the EDGAR inventory recognize that countries have used different methodological approaches to derive reported emissions.
Instead of adopting reported emissions as they are, GAINS and EDGAR produce independent estimates of historical emissions using a consistent approach for all countries (for example, when deriving country-specific emission factors).
Usually, this means making extensive use of country-specific information, and adopting IPCC default factors or implied emission factors reported to UNFCCC when sufficient country-specific information is unavailable (Höglund-Isaksson 2012;
Olivier et al. 2012). The USEPA, the GAINS model and the EDGAR inventory all take into account the effects on past emissions of abatement technology adopted in response to already implemented emission control policies.
Figure 5.1 shows global anthropogenic methane emissions in years 2000, 2005 and 2010 as estimated by the USEPA (2012), the GAINS model (ECLIPSE 2014), and EDGAR (2013). For the year 2000, there is close agreement between inventories that about 300 Tg CH4 was released globally from anthropogenic sources. Between years 2000 and 2010 total emissions are estimated to have increased by 13% and 14% in the USEPA and GAINS inventories, respectively, and by 21% in the EDGAR inventory. The largest increases in emissions are estimated from coal mining and oil and natural gas systems. In the EDGAR Table 5.2 Inventory databases and models of global anthropogenic methane emissions.
Source Approach No. of methane
source sectors No. of geographical
regions Period covered Home institute References to methane data and assessments
USEPA Integrated
emission model 13 (in reporting
format) 200 2000–2030
(10-yr interval) USEPA, USA USEPA 2006, 2012;
UNEP 2011a
GAINS Integrated
emission model ~80 with direct relevance for
methane
162 1990–2050
(5-yr interval) IIASA, Austria ECLIPSE 2012, 2014;
Höglund-Isaksson 2012;
Shindell et al. 2012 EDGAR v4.1 Emission
inventory 25 (in reporting
format) 234 1970–2005
(annual) JRC, EC; PBL EDGAR 2010
EDGAR
v4.2FT2010 Emission
inventory 25 (in reporting
format) 234 2000–2010
(annual) JRC, EC; PBL EDGAR 2013; Olivier et al. 2012
MESSAGE
(RCP8.5) Integrated
assessment model 9 11 1990–2100
(10-yr interval) IIASA, Austria IIASA 2009; Riahi et al. 2011
GCAM
(RCP4.5) Integrated
assessment model 54 14 1990–2100
(15-yr interval) Pacific Northwest National Laboratory and the University
of Maryland, USA
assessment model 21 24 1990–2100
(10-yr interval) National Institute for
Environmental Studies, Japan IIASA 2009; Masui et al. 2011 IMAGE
(RCP2.6) Integrated
assessment model n.a. 26 1990–2100
(10-yr interval) PBL, Netherlands IIASA 2009; van Vuuren et al. 2011b
inventory, emissions from these sources increase more rapidly than in the other two inventories. As statistics on fossil fuel production and consumption are relatively good and not likely to differ much between inventories, the differences can probably be attributed to variations in region-specific emission factors.
Despite relatively good agreement between the inventories on total emissions from year 2000 onwards, differences remain at the sector level. This points at high uncertainty in emission inventory estimates, as also discussed by the IPCC (2014).
To better understand the sector differences, Table 5.3 presents a detailed sector comparison of global anthropogenic methane emissions estimated in 2005, which is a common year for which several recently published inventories have detailed, sector-specific data available. The most recent estimates for year 2005 by the USEPA (2012), GAINS (ECLIPSE 2014) and EDGAR (2013) range from 321 to 349 Tg CH4 emitted globally from anthropogenic sources. Agricultural emissions from livestock and rice cultivation account for about 40% of global emissions in all inventories, with the exception of the 2006 version from the USEPA (USEPA 2006), where it accounts for 56%. Fossil fuel production and use account for between 24%
and 31% of emissions in the older estimates by the USEPA (2006) and the RCP models (IIASA 2009), while the more recent assessments from the USEPA (2012), GAINS (ECLIPSE 2014) and EDGAR (2013) suggest these sources to contribute between 34% and 43%. The upward revision of fossil fuel emissions appears to be the result of more measurements becoming available, in particular for fugitive emissions from oil and gas extraction. This is discussed in more detail in Sect.
5.2.4. Waste and wastewater sectors account for about 20% of global methane emissions in all reviewed inventories, while the contribution from incomplete combustion of biomass varies between 3% and 13%. The latter difference appears to derive from variations in sector inclusion. While the GAINS model (UNEP 2011a; ECLIPSE 2012, 2014; Höglund-Isaksson 2012) only accounts for methane from open burning of agricultural field residues, USEPA (2012), EDGAR (2013)
and GCAM (2009) also include emissions from large-scale biomass burning (forest, savannah, grassland and peat fires).
The reason for the exclusion of these sources in the GAINS model is the difficulty of distinguishing the origin of forest and grassland fires as anthropogenic or natural (Höglund-Isaksson 2012).
Kirschke et al. (2013) published a review of estimates of global methane emissions in the period 1980 to 2009 (see also Sect. 2.2) using results from both top-down inverse models and bottom-up emission inventories. The results shown for the estimates of anthropogenic emissions, indicate that global estimates of bottom-up inventories tend to be lower than the estimates following from top-down inverse model results based on direct measurements of methane concentration in the atmosphere.
This is particularly true for the period 1980-2000, when top-down estimates of global anethropogenic methane emissions are 13% to 19% higher than bottom-up estimates. Agreement between top-down and bottom-up estimates improves for the years after 2000, which is displayed in the far right columns of Table 5.3. The inventories referenced by Kirschke et al.
(2013) are a draft inventory by USEPA (2011) and EDGAR version 4.2 (EDGAR 2012). These versions are very similar to the inventories for 2005 presented in the final version of the inventory by the USEPA (USEPA 2012) and in the updated EDGAR version 4.2FT2010 (EDGAR 2013). The estimates of global anthropogenic methane emissions from 2012 or later by the USEPA, GAINS and EDGAR, fall within the ranges given by Kirschke et al. (2013), however, the mean contribution of 96 Tg CH4 or 29% from the fossil fuel sector presented for 2000 to 2009 by Kirschke et al. (2013) appears on the low side compared to the range of 34% to 43% estimated for year 2005 in the more recent inventories.
In a recent article by Nisbet et al. (2014), the trend in year-to-year variation for methane concentration in the atmosphere shows a relatively steep increase from about 1630 ppb in 1985 to about 1775 ppb in 2000, then remaining relatively constant at around 1775 ppb until 2008, when the methane
Wastewater
Industrial processes & other Solid waste
Combustion fuels
Gas production, transmission
& distribution Petroleum systems Oil & natural gas systems Coal mining
Rice cultivation Livestock
Agricultural field burning, grassland & forest fires
0 50 100
2000
USEP A (2012)
GAINS (2014) EDGAR (2013)
USEP A (2012)
GAINS (2014) EDGAR (2013)
USEP A (2012)
GAINS (2014) EDGAR (2013)
2005 2010
150 200 250 300 350 400 Tg CH4
Fig. 5.1 Estimates of global anthropogenic methane emissions 2000 to 2010. Sources: USEPA (2012), GAINS (ECLIPSE 2014), EDGAR (2013).
Table 5.3 Inventories of global anthropogenic methane emissions estimated for year 2005. Data in Tg CH4 in year 2005.
Livestock 114 101 96 96 96 108 108 89 94
Not available in more detail Rice
Agriculture 174 124 123 123 123 142 143 126 136 133 136 134
209
concentration again increases reaching almost 1825 ppb in 2013. This variation in atmospheric methane concentration over the past few decades is not explained by inverse model results using existing inventories of anthropogenic and natural methane emissions. Nisbet et al. (2014) concluded that more data and measurements are needed to improve existing emission inventories in order to resolve the current divergence between top-down and bottom-up estimates of global methane emissions.
5.2.4