The Emissions Database for Global Atmospheric Research (EDGAR) compiles
anthropogenic emissions data for greenhouse gases (GHGs), and for multiple air
pollutants, based on international statistics and emission factors. EDGAR data
provide quantitative support for atmospheric modelling and for mitigation
scenario and impact assessment analyses as well as for policy evaluation. The
new version (v4.3.2) of the EDGAR emission inventory provides global
estimates, broken down to IPCC-relevant source-sector levels, from 1970 (the
year of the European Union's first Air Quality Directive) to 2012 (the end
year of the first commitment period of the Kyoto Protocol, KP). Strengths of
EDGAR v4.3.2 include global geo-coverage (226 countries), continuity in time,
and comprehensiveness in activities. Emissions of multiple chemical
compounds, GHGs as well as air pollutants, from relevant sources (fossil fuel
activities but also, for example, fermentation processes in agricultural
activities) are compiled following a bottom-up (BU), transparent and IPCC-compliant methodology. This paper describes
EDGAR v4.3.2 developments with respect to three major long-lived GHGs (CO2,
CH4, and N2O) derived from a wide range of human activities
apart from the land-use, land-use change and forestry (LULUCF) sector and
apart from savannah burning; a companion paper quantifies and discusses
emissions of air pollutants. Detailed information is included for each of the
IPCC-relevant source sectors, leading to global totals for 2010 (in the
middle of the first KP commitment period) (with a 95 % confidence
interval in parentheses): 33.6(±5.9) Pg CO2 yr-1, 0.34(±0.16) Pg CH4 yr-1, and 7.2(±3.7) Tg N2O yr-1. We provide uncertainty factors in emissions
data for the different GHGs and for three different groups of countries: OECD
countries of 1990, countries with economies in transition in 1990, and the
remaining countries in development (the UNFCCC non-Annex I parties). We
document trends for the major emitting countries together with the European
Union in more detail, demonstrating that effects of fuel markets and
financial instability have had greater impacts on GHG trends than effects of
income or population. These data (10.5281/zenodo.2658138,
Janssens-Maenhout et al., 2019) are visualised with annual and monthly global emissions grid maps
of 0.1∘×0.1∘ for each source sector.
Historical evolution
An essential component of the UN
Framework Convention on Climate Change (UNFCCC, 1992) is the collection of
nationally reported inventories and information on these greenhouse gas (GHG) emission
inventory time series. At the time the UNFCCC was established, the 24 members of
the OECD in 1990 and 16 other European countries and Russia were considered
liable for “the largest share of historical and current global emissions of
GHG” and taken up in Annex I to the UNFCCC. These Annex I countries and the
European Union
This includes the 28 Member States of the European
Union (EU) as of 1 July 2013.
submit annually complete inventories of GHG
emissions from the 1990 base year
For some economies in transition,
another year such as 1988 or 1989 can be chosen under UNFCCC as the base
year. These GHG emissions are mainly sources, but also include carbon stock
sinks for which the human-induced part needs to be assessed with care (Grassi
et al., 2018).
until the latest year for which full accounting is completed
and reviewed (typically with a 2-year time lag), and these inventories are
all reviewed to ensure transparency, completeness, comparability, consistency
and accuracy
These five principles of a good reporting practice are
defined in the UNFCCC guidelines for national GHG inventory, e.g.
https://pdfs.semanticscholar.org/3c30/a1bd769dee5299746e0af825c7ab4ed55fba.pdf.
EDGAR uses the term “comprehensiveness” to summarise these principles.
.
This allows for most of these Annex I countries to track progress towards
their reduction targets committed under the Kyoto Protocol (UNFCCC, 1997).
Other (non-Annex I) countries are encouraged to submit their GHG inventories
as part of their National Communications and Biennial Update Reports (BURs).
The GHG inventories of non-Annex I countries were required to cover
CO2, CH4 and N2O emissions for 1 year (1990 or
1994), without specific documentation and only subject to a brief review.
However, the Paris Agreement (UNFCCC, 2015) requires submission every 2 years
of BURs
The first BUR submitted should cover the inventory for the
year no more than 4 years prior to the submission data, and subsequent BURs
should be submitted every 2 years, but flexibility is given to the least
developed countries and small island developing states.
, which are subject
to international consultation and analysis. Theoretically, UNFCCC should
receive at the latest after 2 years national emissions inventories from each
of the 197 countries, but as shown in Fig. 1a, not all countries did provide
a national inventory and 154 countries did not provide a completed (i.e. year-2) time series
of inventories. In addition, many countries lack a well-developed statistical
infrastructure, which is needed for an accurate bottom-up (BU) inventory. Figure 1b
presents the latest year that is covered with a national inventory, with
dates for quite a few countries more than 10 years ago: for most South-East
Asian countries this is between 2004 and 2007 and for most African countries
between 2000 and 2003.
(a) Inventory submission as received at UNFCCC (by January
2017) for all countries: expressed with the year of emission reporting in
which the latest national communication to UNFCCC took place.
(b) Inventory submission as received at UNFCCC (by January 2017) for
all countries expressed with the latest year of emission that is covered in
the inventory submitted to UNFCCC.
As such, the collection of national reports/communications does not provide a
complete, consistent and comparable global dataset which can be used to
understand the global budgets of the most important GHG emissions and their
impact on climate. Very few bottom-up inventories of global anthropogenic
emissions have been produced with continued effort for more than 2 decades.
The Carbon Dioxide Information Analysis Centre (CDIAC) (Boden et al., 2017;
Andres et al., 2014) and the Emissions Database for Global Atmospheric
Research (EDGAR) (Olivier and Janssens-Maenhout, 2016; Olivier et
al., 2016) provide global
totals, whereas the IEA provides CO2 estimates from fuel combustion
only and the FAO CH4 from agriculture only. While CDIAC ceased
operation in September 2017, the Open-source Data Inventory for Anthropogenic
CO2 (ODIAC) (Oda et al., 2018) continued to use the CDIAC data and
combined these with geospatial proxies (including night light satellite maps)
to provide CO2 grid maps, as EDGAR is also doing (using other
geospatial proxies). In addition, the new Community Emissions Data System
(CEDS) of Hoesly et al. (2018) builds upon existing inventories to provide a
new gridded dataset of all emission species for the Climate Model
Inter-comparison Programme CMIP6.
The scientific community started to bring together these anthropogenic BU
emissions with top-down estimates covering also the natural component to
obtain the Global Carbon Budget (GCB) (Le Quéré et al., 2018) and the
Global Methane Budget (Saunois et al., 2016). These budgets are important
input for the periodic global stocktake that the Paris Agreement envisages
from 2023 onwards (with the submitted inventories for 2021). Even though
significant progress in inventory compilation has been made, the overall
uncertainty of the global total has become larger over time because the share
of emissions from non-Annex I countries (with less developed statistical
infrastructure) increased from less than 40 % in 1990 to more than
60 % in 2012, as shown in Fig. 2.
Relative contribution of the Annex I and non-Annex I countries to
the global total GHG emissions. The red, brown and orange dashed parts of the
stack correspond to the non-Annex I share that increases from about 1/3 in
1990 to almost 2/3 in 2012.
To support both science and policy making with the monitoring and
verification of the GHG emissions, it is important that emissions are
estimated by using comparable methodologies, consistent source allocation and
comprehensive coverage of the globe. The EDGAR v4.3.2 global inventory
illustrates the result of a bottom-up technology-based compilation of
country- and sector-specific emission time series for 1970–2012.
Furthermore, the monthly resolution and global grid maps at a spatial
resolution of 0.1∘×0.1∘ allow direct use in
atmospheric models as well as in analyses of policy impacts. The first
version of the Emissions Database for Global Atmospheric Research (EDGAR v2)
answered the needs of the air quality community to map technological
parameters of air pollution sources and was published by Olivier et
al. (1996). Since then, several updated versions (Olivier, 2002) have been
released (EDGAR-HYDE, EDGAR v3.2, EDGAR 3.2 FT2000). Driven by the
development of scientific knowledge on emission generating processes and by
the availability of more recent information, the EDGAR v4 datasets were
constructed including new emission factors and additional end-of-pipe abatement measures. The
specification of the combustion technology and its end-of-pipe abatement is
more important for air pollutants and aerosols than for GHGs.
CO2 combustion emissions are fuel-determined and carbon capture and
storage are not yet implemented at an operational level and are not
considered here. However, abatement is considered for e.g. CH4
recovery of coal mines, and technology and end-of-pipe abatement are
important for both adipic and nitric acid plants. Finally, management of crop
cultivation (e.g. for rice) or of manure are accounted for by
technology-specific emission factors for CH4 and N2O.
Previous EDGAR versions v4.1 and v4.2 (available at
http://edgar.jrc.ec.europa.eu/index.php#) are interim frozen datasets
without peer-reviewed documentation, but are nevertheless extensively used by
modellers. Illustrative examples of the EDGAR v4 use are given in Table S5 in
the Supplement. The new online version of EDGAR v4.3.2 is the main reference
for the EDGAR v4 datasets, and is the subject of this paper. We wish to stress
that EDGAR v4.3.2 is the result of a steady improvement of the EDGAR v4
database over more than a decade, also thanks to the feedback of users. For
the main differences between EDGAR v4.3.2 and v 4.2, we refer the reader to the
Supplement of the paper, Sect. 3 and Table S5 with the findings of
atmospheric studies using EDGAR v4 as input. For the main differences between
EDGAR v4.2 and v4.1, we refer the reader to
http://edgar.jrc.ec.europa.eu/Main_differences_between_EDGARv42_and_v41.pdf.
In this paper we focus on the three key GHG emission components of
EDGAR v4.3.2, describing the methodology, emission sources, activity data,
emission factors and emission disaggregation (in space and time). For
CO2 we distinguish between (i) long-cycle carbon CO2 from
fossil fuel use and industrial processes (cement production, carbonate use of
limestone and dolomite, non-energy use of fuels and other combustion,
chemical and metal processes, solvents, agricultural liming and urea, waste
and fossil fuel fires) and (ii) short-cycle carbon CO2 from biofuel
use or short-cycle biomass burning (such as agricultural waste burning). The
non-CO2 GHG emissions are also provided to the IEA for the annual
publication of emissions from fuel combustion (Olivier and Janssens-Maenhout,
2016). The EDGAR v4.3.2 frozen dataset for 1970–2012 is used to produce the
updates from 2013 onwards, derived with a fast track (FT) approach (e.g.
EDGAR v4.3.2_FT2016). Under the FT update the activities are grouped into
five main source sectors and for each of the latter trends of the most recent
activity statistics are used. These are derived from data provided by the
latest IEA (2016) and BP (2017) statistics in terms of fuel trend indicators
that are applied to the fossil fuel combustion sector. For the other main
sectors we use most recent commodity statistics from the US Geological
Survey, the World Steel Association and the International Fertiliser
Association, as explained in more detail in Olivier et al. (2016). The
methodology and activity data are also used to estimate corresponding gaseous
and particulate air pollutant emissions, as part II of the EDGAR v4.3.2
release (Crippa et al., 2018). Other EDGAR v4 air pollutants inventories are
EDGAR v4.3.1 (Crippa et al., 2016a; Huang et al., 2017), EDGAR v4tox1
(Muntean et al., 2014) and EDGAR v4tox2 (Muntean et al., 2018).
MethodBottom-up emission calculation
Annual country-specific emissions are calculated using international activity
data and emission factors, updated according to the latest scientific
knowledge and following IPCC (2006a) methods. Emissions (EMs) from a given
sector i in a country C accumulated during a year t for a chemical
compound x are calculated with the country-specific activity data (AD),
quantifying the activity for sector i, with the mix of j technologies
(TECH) and with the mix of k (end-of-pipe) abatement measures (EOP)
installed with share k for each technology j, the emission rate with an
uncontrolled emission factor (EF) for each sector i and technology j and
relative reduction (RED) by abatement measure k, as summarised in the
following formula:
EMi(C,t,x)=∑j,k[ADi(C,t)⋅TECHi,j(C,t)1⋅EOPi,j,k(C,t)⋅EFi,j(C,t,x)⋅1-REDi,j,k(C,t,x)].
The activity data are very sector dependent and vary from fuel consumption in
energy units (TJ) of a particular fuel type, to the amount (ton) of products
manufactured, and to the number of animals or the area (hectares) and yield
(ton) of cultivated crops. The technology mixes,
(uncontrolled
Uncontrolled means without end-of-pipe abatement.
)
emission factors and end-of-pipe measures are determined at different levels:
country-specific, regional, country group (e.g. Annex I/non-Annex I), or
global. Technology-specific emission factors are used to allow a tier-2
approach, taking into account the different management/technology processes
or infrastructures (e.g. different distribution networks) under specific
“technologies”, and modelling explicitly abatements/reductions, e.g. the
CH4 recovery from coal mine gas at country level under the
“end-of-pipe measures”. Just as with national inventories, EDGAR v4.3.2
starts from accounting over a period of time, 1 calendar year, and over a
well-defined region, the country in which the emissions took place.
The sector-specific total emissions of substance x for country C in year
t are then distributed in time and space using sector- and even
technology-specific monthly shares m and spatial proxy datasets f. The
proxy datasets are expressed as a function of coordinates (longitude,
latitude) weighted at country level and with the Heaviside function equalling
1 when the grid cell belongs to the country area according to the following
formula:
emi,jlon,lat,t,x=EMi,jC,t,xmi,j,k(C)∑k=1,…12mi,j,k(C)⋅fi,jlon,lat,t∑lon,latfi,jlon,lat,t⋅HC,lon,lat,withHi,jC,lon,lat=fractionofgridcellwithinC.
While the monthly shares are more specified in a generic way (only varying
with the latitudinal band and with the sectors), the spatial proxy datasets
take into account point-source information at sub-sector level (facilities)
that can change from year to year.
Sector definition and data sources
Table 1a provides a structured overview of all the emission sources included
in the EDGA Rv4 database. The energy-related sector (with the largest share in
total GHG emissions) requires less detailed information on “technologies”
than the agriculture- and waste-related sectors require on the “practices”
applied
CO2 emissions depend on the total mass and carbon
content of the fuel and not much on the type of combustion technology, while
CH4 emissions depend strongly on the types of fermentation processes
in addition to the total mass and composition of the decomposing organic
matter.
. This imbalance of the requirements for a higher level of detail for
less important sources in terms of contribution to the national total is
against the normal expectation (and time efficiency) of
expending more efforts on those sources with
the largest impact on the national totals (Pulles, 2018).
(a) Main category with all source/sink categories
conforming to the IPCC (1996a, 2006b) Guidelines.
Note that neither large-scale biomass burning nor land-use, land-use change
and forestry emissions are included, although we do include biofuel
combustion and agricultural activities (such as livestock and milk
production, crop and rice production, agricultural waste burning, field
burning, histosols and liming). (b) Data sources for activity
statistics and emission factors for the main categories of emission sources
defined in (a) (cf. references in the paper or the Supplement).
(a)Main category of emission sectorsEDGAR_codeEmission sectorsIPCC (1996a)IPCC (2006b)of data deliveryEnergy comprises the production, handling, transmissionENEPower industry1A1a1.A.1.aand combustion of fossil fuels and biofuels andis calculated with energy statistics. For CO2the short-cycle C is split off from the long-cycle C,because the short-cycle CO2 emitted from the combustionof biofuel is assumed to neutralise the CO2 uptake duringthe same year the biofuel was grown. Any disequilibriumof this balance needs to be taken up under the land-use,land-use change and forestry sectors.As such the long-cycle CO2 energy refers to fossil fuelcombustion only, the short-cycle CO2energy refers to the biofuel combustion. All othersubstances include fossil and biofuel combustion.INDCombustion for1A21.A.2manufacturingRCOEnergy for1A41.A.4+buildings1.A.5.a+1.A.5.b.i+1.A.5.b.iiREF_TRFOil refineries1A1b+1.A.1.b+and1A1c+1.B.2.a.iii.4+Transformation1A5b1+1.A.1.c+industry1B1b+1.A.5.b.iii+1B2a5+1.B.1.c+1B2a6+1.B.2.a.iii.6+1B2b5+1.B.2.b.iii.32C1bTNR_Aviation_CDSAviation1A3a_CDS1.A.3.a_CDSclimbingand descentTNR_Aviation_CRSAviation1A3a_CRS1.A.3.a_CRScruiseTNR_Aviation_LTOAviation landing1A3a_LTO1.A.3.a_LTOand takeoffTNR_Aviation_SPSAviation1A3a_SPS1.A.3.a_SPSsupersonicTNR_OtherRailways,1A3c+1.A.3.c+pipelines,1A3e1.A.3.eoff-roadtransportTNR_ShipShipping1A3d+1C21.A.3.dTRORoad1A3b1.A.3.btransportation
Continued.
(a)Fugitive refers mainly to gas flaring and ventingPROFuel1B1a+1.B.1.a+during oil and gas production, coalbed methane duringexploitation1B2a1+1.B.2.a.ii+underground or surface mining and CH4 distribution1B2a2+1.B.2.a.iii.2+losses and evaporation during transmission and1B2a3+1.B.2.a.iii.3+mainly distribution. This is based on fuel production1B2a4+1.B.2.b.ii+statistics, supplemented nightlight observations.1B2c1.B.2.b.iii.2+1.B.2.b.iii.4+1.B.2.b.iii.5+1.CIndustrial Processes refer to non-combustion emissionsChemical2B2.B.1+from either manufacturing of cement, lime, soda ash, carbides,processes2.B.2+ammonia, methanol, ethylene, methanol, adipic acid, nitric acid,2.B.3+caprolactam, glyoxal and other chemicals, or from production of2.B.4+metals and from the use of soda ash, limestone and dolomite,2.B.5+from production of ferrous and non-ferrous metals and from2.B.6+ 2.B.8non-energy use of lubricants and waxes. The emission estimatesuse the volume of industrial product produced (and traded)from the industry statistics.FOO_PAPFood and2D2.HPaperIROIron and2C1a+2.C.1+ 2.C.2steel2C1c+production2C1d+2C1e+2C1f+2C2NEUNon energy2G2.D.1+use of fuels2.D.2+2.D.4NFENon-ferrous2C3+2.C.3+metals2C4+2.C.4+production2C52.C.5+2.C.6+2.C.7NMMNon-metallic2A2.AmineralsproductionSolvents and products use includes CO2 from solvents in paint,PRU_SOLSolvents and32.B.9+degreasing and dry cleaning, chemical products and other product use,products use2E+as well as use of N2O as anaesthesia and in aerosol spray cans.2F+Estimates are based on a combination of population2G+2D3and solvents statistics.Agriculture comprises the application of urea and agricultural lime,AGSAgricultural4C+4D3.C.2+enteric fermentation, rice cultivation, enteric fermentation,soils3.C.3+manure management, fertiliser use (synthetic and manure),3.C.4+agricultural waste burning (in field) and is based on agricultural3.C.7statistics. Large-scale biomass burning from savannahis not included.AWBAgricultural4F3.C.1.bwasteburningENFEnteric4A3.A.1fermentationMNMManure4B3.A.2management
Continued.
(a)Waste comprises landfills and wastewater management,SWD_INCSolid waste6C4.Cand waste incineration that is not producing energyincineration(neither generation of electricity nor heat recovery,SWD_LDFSolid waste6A+6D4.A+4.Bbecause these are accounted in the energy sector(non-energy).landfillsEstimates are based on a combination of population and solidWWTWastewater6B4.Dand liquid waste product statistics.handlingOther refers to direct emissions from fossil fuel firesFFFFossil fuel fires7A5.B(coal fires and the Kuwait oil fires), N2O usage andIDEIndirect7C5.Aindirect emissions from atmospheric deposition of NOxEmissionsand NH3 from non-agricultural sources,N2OIndirect N2O from4D33.C.5+3.C.6for which other historical statistics are consulted.agriculture
Continued.
(b)Activity data (AD): data sourceActivity data (AD):Emission factors (EFs): data sourceEmission Factors (EF):data referencesdata referencesenergyfossilIEA energy balance statistics (version 2014, last year 2012)IEA (2014)IPCC (2006a)IPCC (2006c)balancefuelfor 138 OECD and non-OECD countries expressed in TJGuidelines (GL)statisticsfor the 64 fuel types and 94 activities.aBiofuelIEA final consumption (version 2014) of biogasolineIEA (2014),IPCC (2006a) GLIPCC (2006c)(bioethanol), biodiesel and other liquid biofuelEIA (2014),categories for OECD countries.bUS DA (2014)fossil fuelcoalWorld Coal Association data of 2016 for hardWorld CoalIPCC (2006a) GL, supplemented with EMEP/EEA (2013)IPCC (2006c),productioncoal and brown coal production data, separatedAssociation (2016)Guidebook EF (specified in function ofEMEP/EEA (2013)statisticsinto surface and underground mining.caverage depths of coal production).dgas andFor exploration: IEA (version 2014)Exploration: IEA (2014)Exploration: IPCC (2006a) GLIPCC (2006c)oilgas and oil production data.For transmission and distribution: the leakage rate isTransmission/distribution:Transmission/distribution: IPCC (2006a) GL, supplementedUNFCCC (2014, 2016),calculated in function of the length of the pipelines andEurogas (2010),with data of UNFCCC national inventory reports (NIR).Lelieveld et al. (2005)its construction material (grey cast iron, steel, polyethyleneMarcogaz (2013),Gas transmission through large pipelines: relatively smallor polyvinylchloride), with data from Eurogas (2010)UNFCCC (2014),country-specific emission factors of Lelieveld et al. (2005);report and Marcogaz (2013) technical sheet, UNFCCCCIA (2016)Gas distribution: large and material-dependentNational Inventory Reports (2014) and CIA (2016).leakage rates of IPCC (2006a) GLFor venting and flaring: the total amount of gas flaredVenting and flaring:Venting:CH4 EF are based on country-specificand vented is statistics (version 2014) supplementedIEA (2014),UNFCCC NIR data (and average valuewith trends from CDIAC (time series till 2014),Andres et al. (2014),as default for all other countries).EIA (2014) and counterchecked against UNFCCC 2014)EIA (2014),National Inventory Reports for most countries until 1994.UNFCCC (2014),For flaring: CO2 EF is taken fromThe share that isis flared is from 1994 onwards derivedElvidge et al. (2016)IPCC (2006a) GL (and excludes indirectwith NOAA satellite observation of the intensity of flaringemissions through gas venting).lights as DMSP data of US Air Force WeatherAgency updated with VIIRS data of NOAA-NGDC (2015)Industrialmetallic andProduction data for cement, iron and steel, non-ferrous metalsUN STATS (2014),CO2 from cement production is based on the tier-1IPCC (2006c),processesnon-metallicand various chemicals are based on Commodity Statistics ofUSGS (2014),emission factor for clinker production, whereas cementUSGS (2014),mineralsUN STATS (version 2014) often supplemented for recentWSA (2015), andclinker production is calculated from cementUNFCCC (2014, 2016),years by USGS (2014). Iron and steel production is furtherinternal reportsproduction reported by USGS (2014). The impliedWBCSD-CSI (2015)split into technologies (basic oxygen furnace, openbased onclinker to cement ratio is based on either clinkerhearth, electric arc furnace) using data of the World Steelproduction data from UNFCCC NIR (Annex I countries)Association – WSA (2015). For production of lime,IAI (2008),and the China Cement Almanac, or ratios fromsoda ash, ammonia, ferroalloys and non-ferrous metals,IMA (1999)the World Business Council for Sustainablewe combine USGS (2014) data and data reported toDevelopment – Cement Sustainabilitythe UNFCCC (2014). Primary aluminium productionInitiative – WBCSD-CSI (2015).statistics per country from UN are combined with smeltertypes (Horizontal and Vertical Stud Söderberg technologiesas well as Centre Work, Point Feed, and Side WorkPrebake technologies) characterised by the Aluminium Verlag(2007) and the International Aluminium Institute – IAI (2006).For primary magnesium production and die-casting globalconsumption was derived from production statistics fromthe US Geological Survey – USGS (2014) and the InternationalMagnesium Association – IMA (1999) and reportedcountry-specific die-casting companies. UN STATS (version 2014)
Continued.
(b)chemicalFor the CO2 sources from industrial productionUNFCCC (2014, 2016),For the N2O sources of nitric acid,IPCC (2006c),industryof silicon and calcium carbide, glyoxal and otherIFA (2015),adipic acid and caprolactam, productionUNFCCC (2014, 2016),chemical bulk products (acrylonitrile, black carbon,FAOSTAT (2014),as well as abatement data from 1990 onwardsinternal reportethylene, ethylene oxide, methanol, and vinyl chloride) forUSGS (2014),are based on UNFCCC NIR and SRI Consulting (2008).based on SRIfor which no international statistics were available,UN STATS (2014),For nitric acid production in 1970, only oldConsulting (2008)UNFCCC NIR is used, although limited to Annex I countries.FAO (2016c, d)technology is assumed, with a gradual changeInterpolations and extrapolations were only done to gap-fillin technology by 1990 into high pressure plantssingle years with missing reported data in the time seriesin non-Annex I countries and a mix of low and1970–2012, making use of the average of the previous andmedium pressure plants in Annex I countries,following years. Data of the International Fertilizerin line with reported emissions to UNFCCC NIR.Industry Organisation – IFA (2015) are used for ureaproduction, which accounts the fossil carbon in CO2 fromammonia production, following IPCC (2006a).Data of FAOSTAT (2014) are used for production of pulp, meatand poultry. Ammonia production data are taken from USGS (2014).UNSTATS (2014) Commodity Statistics are applied to estimatethe emissions from bread production, while for paper, wineand beer we use FAO (2016c, d) production data.SolventActivity data for paints, glues and adhesives,UN Comtrade (2016),For CO2, the national inventory reportsIPCC (2014, 2016),statisticsdegreasing products, pesticides and vegetalUN STATS (2014),of UNFCCC indicate a small amount of CO2UNFCCC (2014, 2016)oil are found in the UN Commodity statisticsUNFCCC (2014, 2016)emission per ton paint applied, or per tonand supplemented with the UN Comtrade (2016)degreasing and dry cleaning product or otherstatistics details. Activity data 1990–2012chemical product used. N2O use as an anaestheticfor other solvent use from UNFCCC NIR wasand in aerosol spray cans is assumed proportionalintegrated for Europe, USA, Australia andto the population. The average per capita N2O useNew Zealand and Japan and linearlyreported by Annex I countries to UNFCCC NIR wasextrapolated backwards in time.used as region-specific default.AgriculturecropFollowing IPCC (2006a) methodology we apply FAO crop andFAOSTAT (2014),IPCC (2006a) emission factors for CO2, CH4 and N2OIPCC (2006c),(excludinglivestock data, specified as livestock numbers for buffalo,IPCC (2006a)N2O emissions from the use of animal waste as fertiliserFAOSTAT (2014),rice)camels, dairy and non-dairy cattle, goats, horses, swine,are estimated taking into account both the loss of nitrogenIFA (2015)sheep, mules and asses and for poultry (turkeys, geese,that occurs from manure management systems before manurechickens and ducks). These all contribute to manure and tois applied to soils and the additional nitrogen introducedenteric fermentation, except poultry that only produce manure.by bedding material. N2O emissions from fertiliserHistoric data for countries of the former Soviet Union (1970–1990),use and CO2 from urea fertilisation are estimatedYugoslavia (1970–1992), Belgium and Luxembourg (1970–1999),based on IFA and FAO statistics. The N2O emissionCzechoslovakia (1970–1992) and Ethiopia and Eritrea (1970–2012)factor for direct soil emissions of N2O from theare split up, using the share in the first available year ofuse of synthetic fertilisers and from manure used asstatistics for the individual countries. Serbia and Montenegro datafertilisers and from crop residues is taken fromare merged from 2006 onwards, Sudan data are gap-filled forIPCC (2006a), that updated the default IPCC emission2012–2014 with data of 2011 and the chicken data for Switzerlandfactor in the IPCC (2000) Good Practice Guidancewere corrected in 2007. For enteric fermentation by cattle,with a 20 % lower value.country-specific methane emission factors are calculated followingIPCC (2006a) methodology using country-specific milk yield(dairy cattle) and carcass weight (other cattle) trends fromFAOSTAT (2014) to estimate the trends. For other animal types,regional emission factors from IPCC (2006a) are used.
Continued.
(b)LivestockLivestock numbers of FAOSTAT (2014) are combined withFAOSTAT (2014),CO2 emissions from liming of soils:IPCC (2006c),estimates for animal waste per head to estimateLeip et al. (2011),from UNFCCC NIR and on the use of ammoniumFAO Geonetwork (2011),the total amount of animal waste produced.Bouwman et al. (2005),fertilisers for other countries from FAOSTAT (2014),Goldewijk et al. (2007),Nitrogen excretion rates for cattle, pigs andUNFCCC (2014, 2016),as liming is needed to balance the acidity causedFAOSTAT (2014),chicken in developed countries are derivedZhou et al. (2007),by ammonium fertilisers. Areas of cultivatedYevich and Logan (2003)from the CAPRI model (as specified in note e).IPCC (2006a)histosols are estimated by combining the FAOclimate and soil maps with the land-use map ofthe National Institute of Public Health andthe Environment – RIVM. Different N2O emissionfactors are applied to tropical and non-tropical regions.Nitrogen and dry matter content of agricultural residuesare estimated from the cultivation area and yield for24 crop types (2 types of beans, barley, cassava,cereals, 3 types of peas, lentils, maize, millet, oats,2 types of potatoes, pulses, roots and tubers, rice,rye, soybeans, sugar beet, sugar cane, sorghum, wheatand yams) from FAOSTAT (version 2014) and using emissionfactors of IPCC (2006a). The fraction of crop residuesremoved from and/or burned in the field is estimatedusing data of Yevich and Logan (2003)and UNFCCC NIR.RiceThe total area for rice cultivation, obtained fromFAOSTAT (2014),IPCC (2006a) guidelines,IPCC (2006c),FAOSTAT (2014), is split between the differentIRRI (2007),for China updated withPeng et al. (2016)agro-ecological land-use types (rain fed, irrigated,IIASA (2007)data of Peng et al. (2016)deep water and upland) using data from the InternationalRice Research Institute – IRRI (2007). Methane emissionfactors for the various production land-uses are takenfrom the GAINS database (version of 2007) ofthe International Institute for AppliedSystems Analysis – IIASA.
Continued.
(b)SolidlandfillsThe per capita MSW generation rate (for 2000) and the fractionIPCC (2006d),The MCF default is calculated as a linearIPCC (2006d),wasteMSW disposed, incinerated and and composted are based onUNFCCC (2014, 2016),variation between 0.4 and 1.0 with the urbanUNFCCC (2014,productthe specification for 75 countries by IPCC (2006a).UNEP Risøpopulation share and is corrected with reported2016),statisticsFor 151 other countries, the MSW generation rateCentre (2011)data from UNFCCC NIR for 1990–2012 and linearlyOonk (2010)and fraction disposed are assumed the same in comparableextrapolated backwards in time. The decompositioncountries of the same region (with the world divided intoconstant k is inversely proportional to thefour Asian regions, five African regions, four European regions,half-life value of the DOC and depends on climatictwo regions in Oceania, three American regions and the Caribbean)conditions, so that the exponential decayingand within the same (income class within the same GDP range).reaction varies between 0.96 and 0.67. The IPCCWaste Model specifies default k values for 4The IPCC Waste Model provides for these 19 regions the averageclimatic zones (dry temperate, wet temperate,weight fraction DOC under aerobic conditions, which hasexcept for the Annex I countries where the nationallybeen used as the default for all countries. For Annex Idry tropical and moist/wet tropical) are applied,countries these three parameters have been updated aftermeasured value is selected instead. As Oonk (2010)consultation of UNFCCC (2014) country-specific informationindicated, the k value of CH4 generationon the parameters (within the expected range). The nationalhalf-life or biodegradation rate is a lesstotal MSW reported to UNFCCC (2014) correlates best withsensitive parameter in the emissions calculation withtotal population (POP) for industrialised countriesthe FOD the k value of CH4 generationand with urban population in case of developing countries.half-life or biodegradation rate is a lessAs such the total MSW is calculated using the respectivesensitive parameter in the emissionscorrelation for each country according to whether it iscalculation with the FOD than the oxidationindustrialised or developing. The DOC fraction of the totalof CH4, for which data are missing.MSW landfilled is direct input to a first-order decay (FOD)OX, which depends in part on the top layer designmodel for CH4 generation, described according to IPCC (2006a)of the landfill and on climatic conditions,Guidelines and using default parametersis by default zero and is only updated tofor the methane correction factor (MCF), the decompositiona value between 3 % and 10 %constant (k) and the oxidation factor (OX).when reported in UNFCCC NIR.The MCF is characterised by the type of landfill: managed aerobicor anaerobic, unmanaged deep or shallow. Decomposition underanaerobic conditions is assumed to occur for 50 % in thecountries apart from 12 Annex I countries, which are corrected toa country-specific estimate based on their UNFCCC (2014) reports.The volumetric fraction of CH4 in generated landfill gasis assumed constant and equal to 50 % for all world countries.Finally, the amounts of recovered CH4 R (used or flared) aresubtracted from the gross CH4 emissions, when reported inUNFCCC NIR and for 23 non-Annex I countries with CDMprojects reported by UNEP Risø Centre. It is evidentthat these estimates are relatively uncertain,even though the source is declining considerably.IncinerationOther waste sources are incineration, with activity dataIPCC (2006d),IPCC (2006a)IPCC (2006c)from UNFCCC NIR and IPCC (2006a), extrapolated assumingUNFCCC (2014, 2016),for combustion ofa fixed ratio to landfilling, and secondly, composting,Gupta et al. (1998),fuel typesbased on UNFCCC NIR for Annex I countries,Sharholy et al. (2008)Gupta et al. (1998) for developing countriesand Sharholy et al. (2008) for India.
Continued.
(b)WastewaterThe effect wastewater discharges have on the receivingIPCC (2006d),For industrial CH4 emissions, on-site treatmentIPCC (2006c),statisticsenvironment depends on the oxygen required toFAO (2016a, b, c),in WWTP, sewer with and without city WWTP,Doorn et al. (1997),oxidise soluble and particulate organic matterUN SD (2016),and raw discharge are distinguished withFAO (2016a, b)in the water and as such the chemical oxygenRenewable Fuelsshares and regional emission factors fromdemand (COD) and biochemical oxygen demand (BOD)Association –Doorn et al. (1997). For N2O, nitrogenare used to characterise the quality of industrialRFA (2016),in the effluent discharged to aquaticand domestic wastewater. The total organicallyUN DP (2014, 2015),environments (N-effluent) was calculated followingdegradable material in wastewater for industryUN HABITAT (2016a, b),IPCC (2006a) GL for each country, as a function of(TOWi) is estimated as kg COD yr-1 withWorld Bank (2016),the human population and annual per capita proteincountry-specific data of FAO on meat, sugar, pulp, andVan Drecht et al. (2009),consumption data from FAO. Other parameters areof the Renewable Fuels Association on ethyl alcoholDoorn and Liles (1999)kept constant: the nitrogen fraction for proteinand 31 organic chemicals production. IPCC (2006d)is 0.16 kg N kg-1 protein (IPCC, 2006a,default values for wastewater generationdefault values), the factor for non-consumedand CODs are used to derive TOWs for each industryprotein entering wastewater is 1.25 for the USAtype. The annual total population (both sexes) byand 1.1 for all other countries, and the factorcountry is obtained from the yearly revised UN worldfor industrial and commercial co-dischargedpopulation prospects provides consistentprotein into the sewer system is 0.25 fortime series 1950–2015 (used from 1970 onwards)industrial N-effluent and 1.00 for domesticfor 228 world countries. With the country-specificand commercial N-effluent.percentage of population at mid-year residing in urbanareas from the world urbanisation prospects, twoadditional time series, one for urban population andone for the counterpart of rural population werederived for each of the 228 countries.For domestic and commercial organically degradable materialin wastewater (TOWd) we used default values for kg BOD yr-1for rural and urban (low and high-income) areas and UNHABITATcountry-specific shares of low-income and high-income urbanpopulation. Different wastewater treatments are specified withtechnology-specific CH4 emission factors. For domesticwastewater the sewer to waste water treatment plants (WWTP),sewer to raw discharge, bucket latrine, improved latrine,and UNHABITAT country-specific shares of low-income andhigh-income urban population. Different wastewater treatmentsare specifiedwith technology-specific CH4 emission factors.For domestic wastewater the sewer to waste water treatmentplants (WWTP), sewer to raw discharge, bucket latrine,improved latrine, public or open pit and septic tank aredistinguished. Regional or country-specific shares for 2000are by default from IPCC (2006a) and supplemented withdata of improved sanitation over time fromVan Drecht et al. (2009) and Doorn and Liles (1999).
Continued.
(b)IndirectIndirect N2O emissions from leaching andIPCC (2006a),The fraction of nitrogen lost through leachingIPCC (2006c),N2Orunoff of nitrate are estimated from nitrogenFAO Geonetwork (2011)and runoff is based on the study ofVan Drecht etinput to agricultural soils as described above.Van Drecht et al. (2003). The updated emissional. (2003)factor for indirect N2O emissionsIndirect N2O emissions from atmosphericfrom nitrogen leaching and run-off fromdeposition of nitrogen of NOx andthe IPCC (2006a) GL is selected, while notingNH3 emissions from non-agricultural sources,that it is 70 % lower than the meanmainly fossil fuel combustion, are estimatedvalue of the IPCC (1996a) GL and the IPCC Goodusing nitrogen in NOx and NH3Practice Guidance (IPCC, 1997, 2000).from these sources as activity data, based onEDGAR v4.3.2 data for these gases. The sameemission factor from IPCC (2006a) is used forindirect N2O from atmospheric deposition ofnitrogen from NH3 and NOx emissions,as for agricultural emissions.Fossil fuelFossil fuel fires include the Kuwait oil andHusain (1994),IPCC (2006a)IPCC (2006c)firesgas fires with the amount of fuel burntVan Dijk etguidelinesevaluated by Husain (1994) andal. (2009)the underground coal mine fires evaluatedby Van Dijk et al. (2009),mainly for China and India.
a We note that (1) hard coal and brown coal data for
1970–1978 were split using the 1979 shares of the fuel types. (2) For the
countries of the former Soviet Union and former Yugoslavia, the pre-1990 data
was allocated to the countries using the same sector-specific country shares
of the new countries from 1990. (3) We used “Serbia-Montenegro” in the
dataset, which includes Kosovo and Montenegro. (4) For the lumped sum IEA
regions “Other America”, “Other Africa” and “Other Asia”, the sector-
and fuel-specific activity data have been disaggregated following the IEA
definition of these regions and using the total production and consumption
figures per country of coal, gas and oil from energy statistics reported by
the US Energy Information Administration (EIA, 2014).
b We note that (1) for Iceland, Israel and Mexico this is
supplemented with the biofuel consumption reported by EIA (2014). (2) For Japan, Argentina, Brazil, China, India, Indonesia,
Malaysia, Peru, Philippines and Thailand the biofuel data are supplemented
with the data from US DA (2014).
c We note that (1) abandoned and closed mines are taken up with
very different shares to the total CH4 emissions in 2012 from coal
mining in UK (with 24 %), Romania, China, USA, Czech Republic, Germany
and Ukraine (0.3 %). (2) CH4 recovery from coal mining was
estimated following IPCC (2006a) for the 11 countries with largest coal
mining in the past. These are in decreasing order of the share of the total
CH4 emission from this sector (with absolute CH4 recovery in
2012): Czech Republic (60 % with 41.1 kt CH4 yr-1), Spain
(36 % with 5.0 kt yr-1), Poland (33 % with
157.0 kt yr-1), USA (29 % with 739.9 kt yr-1), UK (25 %
with 19.1 kt yr-1), Germany (24 % with 45.0 kt yr-1),
Ukraine (16 % with 97.7 kt yr-1), Australia (15 % with
186.0 kt yr-1), China (9 % with 1974.1 kt yr-1), Russia
(3 % with 75.5 kton yr-1), Kazakhstan (2 % with
10.0 kt yr-1). d According to Peng et al. (2016)
and Liu et al. (2015), Chinese underground coal mines are characterised by
low quality coal and, as such, low EF, corresponding to the lower end of the
range of EFs recommended by EMEP/EEA for coal mines in Europe.
e The Common Agriculture Policy Regional Impact model
(https://www.capri-model.org/dokuwiki/doku.php?id=start) provided
tier-3 input to derive “technological parameters” and implied emission
factors, representing country-specific practices.
All the sources of Table 1a defined under the sectors and codes used in the
IPCC (1996a, 2006b) guidelines,
chap. 1 of vol. 1 Reporting Instructions and converted into the new
IPCC (2006b) guidelines, chap. 8 of vol. 1 Guidance and Reporting, are
considered, except the Land-Use, Land-Use Change and Forestry (LULUCF)
sector.
EDGAR includes autoproducer emissions in 1A1a and not in the
industrial sector where they are generated.
In contrast to the other
sectors, LULUCF is not covered by annual, statistical assessments of the
goods (“trees”), but needs geostatistical and/or remote sensing information
as AD. For the emission sources and sinks related to carbon stock changes in
the subcategory “Forest-land-remaining-forest-land”, we refer the reader to
Petrescu et al. (2012), and for the large-scale biomass burning (including
forest fires, savannah burning, grassland and woodland fires), we refer the
reader to GFED (van der Werf et al., 2010), GFAS (Kaiser et al., 2012) or
FINN (Wiedinmyer et al., 2011).
Most AD for EDGAR v4.3.2 are taken from international statistics and screened
for completeness and consistency by EDGAR routines, removing outliers
(clerical errors, wrong units) and gaps in time (missing single year) with a
linear interpolation of the previous and following years. Preference is given
to international statistics such as those of IEA (2014) and FAOSTAT (2014)
over regional offices, such as EuroStat or national statistical bureaux, in
order to profit from international definitions (e.g. for fuel types by IEA),
inter-comparability amongst countries and the data quality and control by IEA
or FAO. For China and the USA, national data from the Chinese Bureau for
statistics and the US Energy Industry Administration respectively are
consulted to assess and fill possible gaps in AD with consumption of fuels
(fossil and bio) and of products (mainly metals and non-metallic minerals
such as cement, chemicals, or solvents). For EU28, the biofuel statistics of
EuroStat are used as they are updated more quickly than the IEA fuel
statistics.
Where possible, GHG emission factors are selected from the IPCC (2006c) to
ensure consistent and complete time series which are comparable across
countries. The representativeness of default emission factors and the
effectiveness of implemented control measures for the different regions are
assessed based on expert judgement and by consulting annual Inventory Reports
of Annex I countries to the UNFCCC (2014, 2016) or
National Communications and Update Reports from some of the most important
non-Annex I countries to UNFCCC (2014, 2012, 2017). Clean Development
Mechanism projects (UNEP DTU, 2011) are taken into account in non-Annex I countries to account for abatement measures of CH4 and N2O emissions via
CH4 recovery from coal mining and landfills and N2O reduction
in nitric and adipic acid production.
Industrial process emissions have been calculated with the mineral production
statistics of the US Geological Survey (USGS, 2014). For the agricultural
activities we consulted the EU's Common Agricultural Policy Regionalised
Impact (CAPRI) model and derived implied (weighted) emission factors which
represent country-specific technologies and practices. For the waste sector
we applied the IPCC First Order Decay (FOD) model of the IPCC (2006d) that is
driven by the annual per capita generated municipal solid waste, the fraction
deposited in landfills, and the fraction degradable organic carbon for the
solid waste disposal emissions, whereas the chemical and biochemical oxygen
demands are used to calculate the wastewater emissions.
Table 1b details the applied sector-specific and, where needed,
region-specific data sources (activity statistics and emission factor with
model parameters) on fuel balances, traded industrial products, crops,
livestock and waste. For the agriculture and waste, a more detailed
description with the model parameters is given under the “emission factor”
heading. EDGAR v4.3.2 aims to collect all underlying human activity
statistics and not to model the emissions directly as a function of the
income, population or other proxy data. Table 1b uses the same main
categories of Table 1a: energy, fugitive, industrial processes, solvents and
products use, agriculture, waste and other (indirect N2O emissions
and fossil fuel fires). All emissions data can be downloaded also at
subcategory level and are unambiguously identified with the IPCC
(1996a) code.
Temporal profiles for the monthly distribution of the annual
emissions
The legal reporting obligations under UNFCCC require time series of annual
inventories, in line with the output of most national statistics
infrastructures with accurate, annual accounting. For the atmospheric models,
a higher temporal resolution is essential. Temporal profiles in EDGAR v4 were
developed in 2010 under the European Commission's 6th Framework Programme
Research projects CIRCE (Climate change and Impact Research: the
Mediterranean
Environment)
https://www.cmcc.it/projects/circe-climate-change-and-impact-research-the-mediterranean-environment,
dataset document under Pozzer et al. (2012)
, because the global air quality
models needed monthly disaggregated air pollutant emission grid maps as
input. The temporal profiles are a bottom-up estimate of the monthly
variations for major sectors, based on the insights of regional air quality
models. Recently, the temporal profiles have been revised and extended, as
documented by Crippa et al. (2019).
Table S4a summarises the sector-specific monthly profiles applied to the
aggregated sectors for each GHG in the Northern Hemisphere. The largest
variation is found in the temporal profiles for the agricultural sector (see
Fig. S2a in the Supplement), then in the emissions from residential heating,
and the smallest variation is present for the road transport and power
generation sector. Covering regions from all over the world, a reverse
profile is applied to the Southern Hemisphere, reflecting the opposite
seasonality. No seasonal pattern is used for the equatorial region, defined
within the range of [30∘ S, 30∘ N] latitude. For more
refined time profiles (hourly) and in-depth analysis of the temporal
distribution, we refer the reader to Crippa et al. (2019). Comparison of the
EDGAR v4.3.2 monthly profiles and those used for other global emission
products (Andres et al., 2011; Hoesly et al., 2018; Janssens-Maenhout et
al., 2015) is given in Fig. S2b.
Proxy data for the spatial distribution of the country total
emissions
For visualisation and as an input to atmospheric chemistry transport and
climate models, the EDGAR v4.3.2 database distributes anthropogenic pollutant
emissions over a uniform, global 0.1∘×0.1∘ grid
defined with lower left coordinates. In emission inventories the emissions
can be emitted either from a single point source or distributed over a line
source (e.g. roads) or over an area source (e.g. agricultural fields),
depending on the source sector or subsector. The line and area sources are
distributed over the grid cells with the proxy data covering the globe
entirely or partially, whereas the point sources are allocated to individual
grid cells and reported as the area average of the sum of the point sources
for that grid cell.
The proxy datasets that are used to grid different sector-specific sources
are given in Table S4b. A detailed description is
available in the EDGAR gridding manual (Janssens-Maenhout et al., 2013).
The spatial grid maps are graphical representations of the country totals,
making use of spatial proxy data. EDGAR tries to allocate as much as possible
the human activity to the places where it is likely located: to the place of
the industrial facilities (several point source databases), or using the road
network or the housing. Alternatives can be night light satellite data, as
used by Oda et al. (2018) for those emission sources that were not yet
covered with point sources (such as power plants) or the population data as
proposed by Andres et al. (2016). We feel our proxy data to be more in line
with our BU approach of allocating the sectoral emissions to the place of the
emission source. We do not recommend an uncertainty analysis of the proxy
data themselves, but a sensitivity assessment of the representativeness of
the selected proxy data using atmospheric transport modelling. EDGAR v4.3.2
grid-map uncertainties are currently the subject of scrutiny and are being
further investigated under European (Horizon 2020) research projects
CO2 Human Emissions (CHE, https://www.che-project.eu/) and the
Observation-based System for monitoring and verification of greenhouse gases
(VERIFY, http://verify.lsce.ipsl.fr/).
Uncertainty assessment of the greenhouse gas emissions
Uncertainties associated with emission of greenhouse gases stem from several
sources, broadly described in vol. 1, chapt. 3, Sect. 3.1.5 of the IPCC
(2006a) Guidelines. The uncertainties in this section are those caused by
“statistical random sampling error”, which can primarily be thought of
having an epistemic nature (lack of knowledge, thus reducible by gathering
more data) but also including an aleatory component (uncertainty due to
intrinsic randomness, and therefore uncompressible) (see e.g.
Beven, 2016). As already
pointed out by e.g. Gütschow et al. (2016), the heterogeneity of
reporting, lack of documentation, differences, ranges of uncertainties, and
sector aggregation all factor to make it difficult to compare, compile, and
combine the multiple sources of information and to convey to a robust,
coherent, estimate of uncertainty.
This section presents an analysis of the relative uncertainty per country
grouping and gas, calculated using Eq. (1) and the parameters reported in
Table 2, which also identifies a few countries as examples of GHG emissions
reporting. (expressed in CO2eq)
Following the standards of
the European Commission's policy documents and using the GPW-100 values of 25 for CH4 and 298 for N2O
:
σGHG=3σCO2EMiCO22+σCH425EMiCH42+σN2O298EMiN2O2EMiCO2+25EMiCH4+298EMi(N2O).
In accordance
with the IPCC tiered approach to infer uncertainties to emission factors as
well as to activity data, the analysis here assumes that countries belonging
to the 24 member countries of the OECD in 1990 (24OECD90)
Australia,
Austria, Belgium, Canada, Switzerland, Germany, Denmark, Spain, Finland,
France, United Kingdom, Greece, Ireland, Iceland, Italy, Japan, Luxembourg,
the Netherlands, Norway, New Zealand, Portugal, Sweden, Turkey, USA
were
economically stable and that they would already have, or be able to
build, a good statistical infrastructure and have the lowest uncertainties in
their inventories. On the same line, the 16 countries with Economies in
Transition of 1990 (16EIT90)
have experienced greater
economic instability, and their inventories are more uncertain than those of
the 24OECD90 but less uncertain than those from the other remaining
non-Annex I countries. Exceptions to the country grouping are made for the
following new or historic trading nations, China, Russia and India, because
of global proliferation of emission-regulated goods, as Crippa et al. (2016b)
analysed for air pollution.
Relative uncertainty of the GHG inventory for countries/country
types (a) with the uncertainties per gas (b).
All uncertainties are reported in Tables 3, 4 and 5 within twice the standard
deviation (±2σ) of the mean value, corresponding to a 95 %
confidence interval of the sample. This is a larger uncertainty range than
the ±1σ selected by the Global Carbon Budget 2017 (Le Quéré
et al., 2018), but is in
line with IPCC recommendations. For comparative shares and trends in biofuel
or non-CO2 GHG emissions, data on gases and sources are much more
uncertain than for fossil fuel CO2. While Denier van der Gon et
al. (2015) indicate that the biofuel combustion activity (and corresponding
short-cycle carbon CO2) is difficult to estimate for the different
countries in Europe, Tian (2015) estimate the large uncertainties in CH4 and N2O
budgets. The uncertainties in these emissions are caused by the scarcity and
limited accuracy of the corresponding international activity statistics
combined with the use of less representative country-wide emission factors
(Olivier, 2002; Olivier et al., 2010). Using Eq. (1), the uncertainty
estimate in the global total anthropogenic CO2 emissions is ±9.0 %, that is, slightly higher than the estimate of 8.4 % by Andres
et al. (2014), most probably since EDGAR v4.3.2 also includes the highly
uncertain waste incineration, urea and liming activities (IPCC, 2006b,
reports an uncertainty associated with the default emission factors for
CO2 of 40 %, for waste incineration), which are not part of the
analysis by Andres et al. (2014).
Intercomparison of eight global CO2 datasets (GCP, Le
Quéré et al., 2016; PKU-FUEL, Wang et al., 2013; ODIAC2016, Oda et
al., 2018; CDIAC, Andres et al., 2014; EIA, 2014; IEA, 2014;
BP, 2017) with regard to their spatial and temporal coverage
and their estimate of the global total Pg CO2 per source for 2010 (and
2007 for PKU-FUEL).
CO2totals inEDGAR v4.3.2GCPaPKU-FUEL (-CO2)ODIAC2016aPg yr-1for 2010Time series1970–2012, fast1959–201520072000–2016track to 2015spatial resolution0.1∘×0.1∘0.1∘×0.1∘1km×1kmtemporal resolutionMonthlyAnnualAnnualMonthlyGeo-coverage226 countriesGlobal223 countriesglobalactivity split150 activities,5 main sectors,64 fuel types6 data inputs (based on42 fossil and42 fuel typesnighttime light,15 bio fuels)CARMA and CDIAC)fossil fuel combustion30.5 (±5.3) 95 %CIBottom-up estimate: 34.528.7133.4non-combustion3.1 (±1.6) 95 %CI(Top-down estimate: 35.6)1.6CO2totals inCDIACEIAIEAbBPbPg yr-1for 2010Time series1751–20141980–20111971–20141965–2015temporal resolutionannualAnnualannualAnnualGeo-coverage224 countries224 countries137 countries, 3 macro-regions67 countries, 5 regionsactivity5 main sectors,6 main sectors,64 activities, 42 fossil8 activities, 3 fossil andsplit-up42 fuel types42 fuel typesand 15 bio fuels)3 other fuel typesfossil fuel combustion32.731.631.033.5non-combustion1.6
a GCP, ODIAC and BP have used more recent energy
statistics than EDGAR and IEA (2014), which explains the major difference in
global CO2 emissions between them. b The difference in
the calculation for IEA and EDGAR are mainly the different carbon factors
used: IPCC (1996a) for IEA and IPCC (2006a)
for EDGAR. In addition, EDGAR v4 supplements the charcoal production activity
with fuelwood data of FAO, the venting and flaring activity with satellite
data and the fossil fuel mine gas recovery with UNFCCC data, and EDGAR v4
calculates the transformation losses which IEA neglects. The main difference
meanwhile disappeared as IEA updated the carbon factors with IPCC (2006a)
values.
Intercomparison of the global total Pg CH4 in 2010 by
EDGAR v4.3.2 and by four other global emission inventories: USEPA (2012),
GAINS-ECLIPSEv5 CH4 of Höglund-Isaksson et
al. (2015), Kirschke et al. (2013) and the global methane budget of Saunois
et al. (2016). Note that the sector-specific global total is given in
Tg CH4 yr-1 for 2010 and in brackets for 2000. The USEPA 2010 value
is projected. For Kirschke et al. (2013), instead of 2010 (2000) we used the maximum (minimum) of the
2000–2009 range. For Saunois we used instead of 2010 (2000) the 2012 value
(mean value of the 2000–2009 range). The 2010 values are bold, and the 2000
values are in italics.
Intercomparison of the global (EU) total Tg N2O in 2005
by EDGAR v4.3.2 and by other European and global inventories: the European N
Assessment of Leip et al. (2011) for EU27, GAINS Europe of
Höglund-Isaksson et al. (2010) and GAINS global of Winiwarter et
al. (2018), global total of US EPA (2012). The global values are bold, and
the European values are given in italics between brackets.
N2O totals in Tg yr-1EDGAR v4.3.2N budgetGAINS globalUS EPA (2012)for 2005 global (EU)global (EU27)(EU27)(EU27)Globaltime series1970–20122000–20071990–20151990–2005(projected to 2030)(projected to 2030)spatial resolution0.1∘×0.1∘1km×1kmtemporal resolutionMonthlyAnnual5-yearlyAnnual226 countries172 countries/regionsGeo-coverage(27 countries in(27 countries in(27 countries inglobalEurope in 2005)Europe in 2005)Europe in 2005)Agriculture4.63 (±3.6)5.711.95(0.43 (±0.23))(0.68)(0.87)Non-agriculture2.54 (±2.5)1.978.91(0.37 (±0.35))(0.31)(0.44)Total7.16 (±6.7)7.6810.86(0.80 (±0.45))(1.08)(1.30)
CO2 uncertainty can vary significantly among countries (Marland et
al., 1999; Olivier et al., 2014). Larger uncertainties of about ±15 % are obtained for non-Annex I countries, whereas uncertainties of
less than ±5 % are obtained for the 24OECD90 countries for the time
series from 1990 (Olivier et al., 2015) reported to UNFCCC. For emissions of
CH4 and N2O, we estimate uncertainties of ±32 %
and ±42 % respectively for 24OECD90 countries and ±57 % and
±93 % for the other countries. These are based on the default
uncertainty estimates of IPCC (2006a) and are in line with Bun et al. (2010).
These are higher than the estimates of ±25 % and ±30 % by
UNEP (2012) but justified by the large uncertainties reported by Tubiello et
al. (2015) for the FAO activity statistics of ±30 % and ±50 % for crop and livestock.
As for the uncertainty of the emission grid maps, Fig. 9 of Andres et
al. (2016)
Note that Andres et al. (2016) limited the result by
saying Case for CDIAC.
reports a population map's uncertainty in excess of
150 % for Europe, the western USA, China, etc. Such uncertainty, when
propagated into the emission calculations, will likely outweigh the combined
uncertainty of activity data and emission factors, especially for
CO2. Also according to Hogue et al. (2016) is the uncertainty on CO2
mapping that “with 1∘×1∘ grid cells for the United
States is typically on the order of ±150 %”. In light of the high
impact of spatial proxies on the overall uncertainty, the authors wanted to
focus on a complete uncertainty assessment of the emission grid maps in
collaboration with the atmospheric modelling community, evaluating carefully
a useful covariance matrix, and refer to the ongoing sensitivity assessments
in the CHE project mentioned in Sect. 2.4. Observation-based verification of
European CH4 and N2O emissions using inverse modelling
(e.g. Bergamaschi et al., 2015, 2018) indicates that
the relatively low uncertainty estimates for some countries are not
consistent with the relatively large uncertainty estimates of others, and for
CH4 a common uncertainty band in the upper range is considered more
appropriate.
Bottom-up versus top-down results
The atmospheric composition can be addressed either top-down (TD), using
atmospheric composition (and measurements, such as total column measurements
by satellite imagery), or bottom-up (BU), summing up the different emissions
released by the different activities. Both are needed and are complementary
to each other: BU estimates relate to drivers and are of prime interest to
policy makers, whereas TD estimates relate to observations. The two
approaches have atmospheric transport models in common as link and allow us
to cross-check the consistency between the two approaches. Several
assessments have been carried out: in the air quality community (e.g. Solazzo
and Galmarini, 2015) as well as in the carbon cycle community under the
Global Carbon Project with the GCB of Le Quéré et al. (2018) and the
global methane budget of Saunois et al. (2016). EDGAR v4.3.2 only focuses on
the BU calculated anthropogenic part of the emissions and gets only posterior
feedback on the use of the datasets by atmospheric modellers on the grid maps
of which examples are listed in Table S5. Although the posterior feedback on
the prior emission grid maps is very useful, it remains limited because of
the uncertainties related to the transport model, the atmospheric chemistry
model, the meteorology input and the in situ or space-borne observations.
However, the use of the emission grid maps indicated the sensitivity of the
emission grid maps to the choice of spatial proxy data. The spatial
representativeness needs to be checked by measured data, such as from remote
sensing (e.g. Yu et al., 2017). This was so far most successful for air
pollutants: NOx (Ding et al., 2017), SO2 (Liu et
al., 2018), CO (Hooghiemstra et al., 2011) and CH4
(Bergamaschi et al., 2015; Saunois et al., 2017). In the 2018 the GCB used
the spatial patterns of the EDGAR grid maps and might give feedback in the
future.
The global CO2 budget
Table 3 summarises the main features of eight global CO2 atlases
and/or inventories, EDGAR v4.3.2, GCB (Le Quéré et al., 2016, 2018),
the PKU-FUEL (Wang et al., 2013) and the ODIAC2016 (Oda et al., 2018; Oda and
Maksyutov, 2011), CDIAC (Andres et al., 2014), EIA (2014), IEA (2014) and BP
(2017) in temporal and spatial characteristics, sector break-down,
methodology and CO2 totals for major source categories in 2010
(which, for PKU-FUEL, was approximated by the latest available year, 2007).
Despite the substantially different levels of detail for the fuel use
calculations, the global totals are relatively similar. At global level the
differences in CO2 emissions between IEA (2014) and EDGAR v4.3.2 are
around 4 %, which can be explained largely by the difference in overall
emission factors used (differences due to different default values for the
carbon content and oxidation factors in IPCC, 2006a, and IPCC,
1996a). This yields 2 %, 1 % and
0.5 % higher CO2 emissions from coal, oil and gas combustion
respectively and increases overall fossil fuel emissions by about 1.3 %.
In addition, the latest IEA statistics for recent years show more updated
values for fuel consumption than for years further in the past. Marland et
al. (1999) compared for the first time the EDGAR and CDIAC datasets. Andres
et al. (2012) followed this further with a more detailed analysis of the
differences between the global CO2 datasets available in 2012,
including the 2012 version of CDIAC, IEA, EIA and EDGAR v4.2 (EC-JRC/PBL,
2011). One of the remaining differences is that the flaring in EDGAR v4.3.2
is twice as high as in CDIAC and EIA, which is explained by the different
estimation method for the activity data (reported energy statistics in CDIAC
and EIA versus satellite night lights of flaring from NOAA-NGDC (2015) and
Elvidge et al. (2016) in EDGAR). Although the different EDGAR datasets
deviate by less than 0.5 % for Annex I countries, this deviation becomes
3.4 % for non-Annex I countries (see Fig. S3).
Larger differences are seen for the non-combustion CO2 emissions.
Figure 6a examines the most important ones comparing process emissions of the
non-metallic sector (cement, lime, dolomite limestone, ceramics and glass
production) of EDGAR v4.3.2, Le Quéré et al. (2016) and Xi et
al. (2016). CO2 from cement production in EDGAR v4.3.2 is 13 %
(19 %) lower than in Xi et al. (2016) (based on CDIAC) because of the
correction for the fraction of clinker in the cement produced. The
EDGAR v4.3.2 data provide cement production emission estimates very close to
the estimates of Andrew (2018) as reported in Figs. 3 and 4. A further large
difference is found for developing countries, especially those with emerging
economies. Figure 6b zooms in with the total CO2 emissions regionally
on China and compares EDGAR v4.3.2 estimates per sector with those of Guan et
al. (2012) and Liu et al. (2015), who brought a large underestimation or
overestimation in the Chinese CO2 inventory to the broad attention of
scientists and the media. Guan et al. (2012) indicated the 1.4 Gt CO2
gap in the national total compared to the sum of the provincial statistics
and proposed 9.1 Gt CO2 in 2010. The EDGAR v.4.3.2 estimate of
8.8 t CO2 for 2010 differs only by -3 %, which is composed of a
difference of -19 % for the fossil fuel combustion emissions and of
+27 % for the process emissions. In 2015 China revised its coal
statistics with lower coal carbon content and the energy consumption was
considerably decreased (for coal power plants with -12 %). Liu et
al. (2015) published coal carbon content for 4200 Chinese mines and analysed
the impact on the total CO2 from combustion in China. EDGAR v4.3.2
revised the activity data for 1990–2012 and obtained for 2010 an emission
reduction for power generation of -8 % and for the CO2 total of
-2 % only. Although Liu et al. (2015) reported 14 % lower emissions
compared to EDGAR, this is effectively only 6 % (below the uncertainty
range for China's CO2 emissions) when correcting for the flaring,
coke production, chemical production and limestone which were not accounted
for in their study. This illustrates the importance of clearly documented
datasets for data comparisons and further understanding the sources of
discrepancies. The higher estimate of Liu et al. (2015) can be understood by
his 3 % lower average net calorific value
This difference in
average net calorific value results from a 8 % difference in
non-oxidation fraction and a 2 % difference in energy-specific carbon
content.
than the default of IPCC (2006a) used by EDGAR.
Time series 1970–2012 of fossil fuel CO2, CH4 and
N2O global emissions from human activities excluding the LULUCF
sector with global total (a) and European total (b). The
stacked bars use AR4 GWP-100 values, whereas the dashed line and full line
indicate the total CO2eq of the three gases in the case where the
SAR and AR5 GWP-100 values are respectively used.
Annual greenhouse gas time series 1970–2012 of EDGAR v4.3.2 with
periodic error bar indication for the different types of countries with top
emitters: (i) non-Annex I countries with China, India, Brazil and the rest of
the non-Annex I countries, (ii) 24OECD90 countries with USA, EU15 and the
remaining eight OECD countries of 1990, and (iii) 16EIT90 countries with
Russia, EU13 and the remaining two newly independent Eurasian states. For the
figures per gas we refer the reader to Fig. S4a–c.
The global CH4 budget
Table 4 compares the EDGAR v4.3.2 global CH4 estimates of 0.34(±0.16) Pg CH4 yr-1 with four other global datasets (the
bottom-up inventories of US EPA, 2012, and GAINS
Greenhouse Gas–Air
Pollution Interactions and Synergies (GAINS) project of IIASA under
http://gains.iiasa.ac.at/models/
Eclipse v5 of Höglund-Isaksson,
2012); and the global
budgets of Kirschke et al. (2013) and Saunois et al. (2016). Even though the
global total CH4 emissions for the bottom-up inventories vary by less
than 4 %, global annual emissions from the agricultural and fossil fuel
production sectors vary with ±22 % and ±17 % respectively.
The top-down inventory estimates are 16 %–29 % larger than the
bottom-up ones.
Figure 7a illustrates the origin of the large variations in the estimated
fugitive emissions of oil and gas production (including extraction,
transmission and distribution). Large uncertainties in CH4 from
venting and flaring at oil and gas extraction facilities have
been reported by e.g. Lyon et al. (2015) or Peischl et al. (2015). The
CH4 venting of oil and gas extraction facilities is, in particular
during the times of the Soviet Union, now believed to be larger than
previously thought (e.g. in EDGAR v4.2 or US EPA), after Höglund-Isaksson
(2017) used ethane–methane ratios as indicators. Additionally, gas
distribution is a relatively large source of uncertainty, in particular in
countries with old gas distribution city networks using steel pipes now
distributing dry rather than wet gas, with potentially more leakages. Based
on IPCC (2006a), EMEP/EEA (2009, 2013) and Marcogaz (2013), the emission
factors for steel and grey cast iron pipelines vary in the range of
0.1–7 t km-1 yr-1, whereas this is
a factor of 2 lower for PVC and polyethylene pipelines. The difference in
composition of the gas distribution networks is taken into account in EDGAR
v4.3.2 with country-specific variations in emission factors. The high
CH4 emissions during the natural gas transmission in the Russian
reporting to UNFCCC (2016) might also account for all or part of accidental
CH4 releases, which are not negligible according to
Höglund-Isaksson (2017). These are not included in the EDGAR datasets.
China is currently the top emitter of CH4 because it has become the
largest coal producer and it is a major rice cultivator. While the fugitive
CH4 emissions from coal production in China are increasing, emissions
from rice cultivation are decreasing, as shown in Fig. 7b. The emission
factor CH4 ha-1 yr-1 for irrigated rice fields has been
reduced from 1970 to 2000 by ∼1/3 by changing farming practices, as
reported by Li et al. (2002), resulting in
0.47 kg CH4 ha-1 yr-1 for the last decade. A comparison
with Peng et al. (2016) illustrates the large range of emission factors used:
the emission factor in EDGAR v4.3.2 for rice cultivation is twice as high as
in Peng et al. (2016). Also for the coal mining the CH4 emission
factor for China in EDGAR v4.3.2 is 9 % higher than in Peng et al. (2016).
EDGAR v4.3.2 revised emission factors for coal mining with local data from
Peng et al. (2016), weighted by coal mine activity per province. These
emission factors are at the lower end of IPCC (2006c) recommendations and
yield EDGAR v4.3.2 estimates of 17.2 Tg in 2008 and 21.2 Tg in 2012, which
are comparable to estimates of Peng et al. (2016) within ±2 Tg.
Total CH4 emissions in EDGAR v4.3.2 in 2005 are 2 % (3 %)
lower than in the v4.2 (4.1) version, which has been used in global inverse
modelling studies of Monteil et al. (2011), Bergamaschi et al. (2013, 2015,
2018), Ganesan et al. (2015), Kort et al. (2008),
and Miller et al. (2013). Except for the Chinese coal mining, no other major
shortcomings to v4.2 were indicated in these global studies. More regional
inverse modelling studies are nowadays able to “verify”
The term
“verify” is selected in consultation with the EC policymakers for Climate
and refers to the detection of biases in emission inventories.
the
CH4 emissions better (such as Henne et al., 2016, for Switzerland),
and first atmospheric model runs with EDGAR v4.3.2 CH4 emissions
started recently. Total emissions have not changed significantly for either
EU28 or the USA, but there are changes in the patterns of emissions: the
-2.5 % (-0.2 %) change in the EU28 estimates of v4.3.2 compared
to those of v4.2 (v4.1) is still within the range of the inverse model
simulations of Bergamaschi et al. (2018), while the -4.7 %
(-3.4 %) change in the USA in EDGAR v4.3.2 compared to v4.2(v4.1) is
not in line with the suggested +50 %–70 % higher anthropogenic
emissions based on the inverse modelling study of Miller et al. (2013). The
latter might be explained on the emissions side by delayed reporting of
statistics on fracking for shale gas and oil and the not well-characterised
and highly uncertain emission factors as indicated by the US EPA (2015) and
on the modelling side by large uncertainties in inverse models and the
potential contribution of natural sources. For China the EDGAR v4.3.2
estimate for fugitive emissions from coal mining yields a 38 % lower
CH4 emissions total in 2008, which is in line with Saunois et
al. (2016), Brandt et al. (2014) and Kirschke et al. (2013), suggesting lower
CH4 emissions in particular in northern China where coal mining takes
place.
The global N2O budget
An overview of the global N2O budget is not yet available as for
CO2 and CH4. Recent efforts from the modelling community to
provide input for the global N2O budget by Tian et al. (2018)
report anthropogenic emission estimates for 2006 of
10.8 Tg N2O yr-1, confirming the 2005 global total by US EPA
(2012) of 10.9, but a full overview of the global nitrous oxide budget is
still forthcoming. The bottom-up estimate of EDGAR v4.3.2 of 7.2(±3.7) Tg N2O yr-1 for 2005 differs from this with 34 %,
which is still within the uncertainty range. The bottom-up estimate of GAINS
by Winiwarter et al. (2018) differs in a similar way by 29 %. It is noted
that the differences within each source category remain very large (see
Table 5). A comparison at European level between EDGAR v4.3.2 and the
N-budget of Leip et al. (2011) shows relative moderate discrepancies also at
sector-specific level for the total and agricultural sectors, 26 % or
37 % smaller estimates by EDGAR compared to Leip et al. (2011), but for
the non-agricultural sectors, 19 % larger estimates. Höglund-Isaksson
et al. (2010) provided GAINS estimates for EU27 that are respectively
28 %, 42 % and 20 % larger than the total, agriculture and
non-agricultural sector estimates of Leip et al. (2011).
Although in EDGAR v4.3.2 the agricultural sector contributes most to the
anthropogenic direct and indirect N2O emissions, the production of
chemicals, such as nitric acid, glyoxal, caprolactam and adipic acid, and its
use as anaesthesia or for aerosol spray cans also play an important role. In
1970 the chemicals sector contributed 20 % to the total, but this has
been significantly reduced to less than 8 % because of technological
developments. Figure 8 shows the impact of technological developments from
old plants to higher-pressure plants or plants with non-selective catalytic
reduction, reducing the N2O emissions by factors of 2 and 10
respectively. The N2O emissions of nitric and adipic acid plant
facilities which EDGAR v4.3.2 estimated are in line with the estimates of US
EPA (2012) and by GAINS for the year 2005. However, a discrepancy evolves
when looking at the 2010 values, because of the relatively large reduction
between 2007 and 2010 in EDGAR and the relative constant trend in GAINS.
While EDGAR assumes abatement technologies for nitric and adipic acid plants
in China following the reporting under the Clean Development Mechanism,
Schneider et al. (2010) assume that abatement was not used at least for the
new adipic acid plants. The latter assumption was followed by Winiwarter et
al. (2018) and explains the differences in the global nitric and adipic acid
N2O emission estimates between GAINS and EDGAR.
Discussion of the trendsGlobal greenhouse gases 1970–2012
A country-based statistical analysis including 4 decades of GHG emissions
(EDGAR v4.2) and GDP (Purchasing Power Parity data of the Penn World Tables 7
of Feenstra et al., 2013) was carried out to investigate the possible
causality between emissions and income. The results, summarised in Paruolo et
al. (2015), showed that no presence of causality could be statistically
proven. This reflects a complex link between the very heterogeneous economic
activities (ranging from manufacturing to services) and emissions, and
justifies the meticulous bottom-up inventory compilation using statistics
instead of modelling.
Figure 3 shows the global trend of GHG emissions in CO2 equivalent
(100-year time horizon), using the GWP-100 values of AR4 (IPCC,
2007)
In the latest UNFCCC revision of the reporting guidelines
adopted by COP (2014), it was decided to use for the reporting from 2015
onwards the global warming potential coefficients (GWP-100) from AR4 (IPCC,
2007) with 25 for CH4 and 298 for N2O.
. The GHG total is
composed of all sources (excluding LULUCF) of CH4 and N2O
but only CO2 from long-cycle C fossil sources and excluding the
short-cycle
The IPCC (2006a) methodology for CO2 accounts
for the emissions from short-cycle C (released by combusting biofuels,
agricultural waste burning or field burning) under the Agriculture, Forestry
and Land Use (AFOLU) sector (see IPCC, 2006a, vol. 2, sector 2.3.3.4 related
to biomass combustion and methodologies for harvested wood products).
C for
the CO2 accounting, conforming to IPCC (2006a). The estimated global
total GHG in 2010 of 44.7 Pg CO2eq was shown to be 0.7 %
lower than the estimates for the 2010 global total (without LULUCF) in the
UNEP (2012, 2015) Emission Gap reports. The share of each gas to the total
GHG is relatively stable and yields for CO2 76.8 % (+2.1 pp,
-1.2 pp), for CH4 18.1 % (-2.5 pp, +1.9 pp) and for
N2O 5.1 % (+0.4 pp, -0.6 pp), where in between brackets
the percent point impact of the evolution of the GWP-100 value from the SAR
(IPCC, 1996b)
In SAR (IPCC, 1996b): GWP-100 of CH4=21
and GWP-100 of N2O=310.
to the AR5 (IPCC, 2014)
In
AR5 (IPCC, 2014): GWP-100 of CH4=28 and GWP-100 of
N2O=265.
is given.
In the global GHG emissions time series, the trend was shown to be dominated
by CO2 as it has the largest share and the largest increase. In the
1970s N2O increased at the same rate as CO2
(2.6 % yr-1), while CH4 was half as fast. In the 1980s and
1990s, N2O and CH4 increases were very small, while
CO2 continued albeit at a slower rate (1.6 % yr-1). In the
last decade, 2002–2012, CO2 and CH4 growth rates increased
with respectively 3.2 % yr-1 and 2.0 % yr-1. While over
the 4 decades (1970–2012) the global total GHG increased in line with global
population (91 % versus 88 %), the inter-annual and regional emission
variations do not always reflect the rates in population increase, but are
instead better explained by the global fuel markets and economy, the 1973 and
1979 oil crises, the dissolution of the Soviet Union (1989–1991), the growth
of the Chinese economy, after they joined the World Trade Organisation in
2002, and the 2008 global financial crisis.
Greenhouse gas trend analysis for regions and top emitting
countries
Figure 4 shows the GHG trends for the major regions: 24OECD90 (split into
USA, EU15 and the rest), 16EIT90 (with Russia and EU13 and the rest) and
non-Annex I (for which China, India and Brazil are shown separately). The
gas-specific GHG trend is also available per country in Janssens-Maenhout et
al. (2017) and is downloadable from
https://edgar.jrc.ec.europa.eu/overview.php?v=CO2andGHG1970-2016. To
understand the trends of the total GHG (in CO2eq), the
decomposition with the trends of CO2, CH4 and N2O
for the same regions is given in Fig. S4a, b and c respectively and with a
discussion per country group in the Supplement.
Focussing on the top four emitting countries and regions, Fig. 5 compares the
reported UNFCCC (2004, 2012, 2014, 2016, 2017) emissions
of China, USA, EU28, and Russia and the emission estimates of EDGAR v4.3.2.
There is a very good agreement between the UNFCCC-reported values and the
EDGAR v4.3.2 estimates for the EU28, whereas for USA and Russia the EDGAR
v4.3.2 estimates are lower than those reported by UNFCCC (2016). For the USA
this is explained by lower N2O emissions in EDGAR v4.3.2, although
N2O emissions reported by USA to UNFCCC (2014, 2016) are within the
large uncertainty range for the EDGAR v4.3.2 estimates. For Russia
CH4 emissions reported to UNFCCC (2016) are 37 % higher than
those estimated by EDGAR v4.3.2, but this is also within the uncertainty
range. The largest difference is found in the estimation of gas pipeline
transmission emissions, which are 4 times higher in the UNFCCC inventory of
Russia than in EDGAR v4.3.2. The relatively low emission factor for gas
pipelines, used by EDGAR, is in line with the recommendations of Lelieveld et
al. (2005). For China, a very good agreement between the EDGAR v4.3.2
estimate and the UNFCCC (2004, 2012, 2017) reported values is obtained,
taking into account the importance of the coal statistics revision. In order
to evaluate the latter effect, two time series of emission are calculated by
EDGAR, with and without coal statistics revision. The revision includes a
decrease in the 2010–2012 values and yields an increase for the 1990–2009
values of about +3 % for 2005 and 1994. It is evident that the previous
estimates of the UNFCCC inventory in 2005 and 1994 would need to be revised
in order to evaluate the emissions change from 2005 to 2012. Even if relative
uncertainty in EDGAR estimates for China could be reduced, it is evident that
the size of the Chinese inventory has a large impact on the global absolute
uncertainty.
GHG emissions of the largest emitting countries and regions (USA,
EU28, Russia, China) of EDGAR v4.3.2 (solid line) with their uncertainty band
compared to the reported UNFCCC time series of 2016 (dotted line). For China,
two inventories were reported by national communications (1994, 2005), and a
biennial update in 2017 added a new inventory value for 2012. The dashed
yellow line gives the EDGAR v4.3.1 estimate of the Chinese GHG emissions using
the energy statistics before the Coal Statistics Abstract (CSA) revision of
October 2015.
Intercomparison of CO2 emissions trends estimated by EDGAR
and by others with (a) details for cement process emissions globally
with data of Le Quéré et al. (2016) and Xi et al. (2016), and
(b) details for China's sector-specific emissions with data of Guan
et al. (2012) and Liu et al. (2015). The total is for all datasets subdivided
into fossil fuel combustion and industrial process emissions (i.e.
non-combustion industrial emissions, including cement).
Discussion of the grid maps
In this section, the gridded EDGAR datasets at 0.1∘×0.1∘ are further screened to identify hot spots and to check for
anomalies. An overview of the region-specific totals and their
sector-specific composition for the year 2012 is given in Figs. 9, 12 and 15
for the different substances. The sector-specific country totals are provided
in the overview Table 6a per region and Table 6b per sector for 2012.
(a) Global and regional GHG emissions (in Gg and tons
per person) for the year 2012. CO2eq emissions have been calculated
including only CO2 from long-cycle carbon only, CH4 and
N2O. (b) Global sector-specific GHG emissions for the year
2012 (in ktons and tons per person). CO2eq emissions have been
calculated including only CO2 from long-cycle carbon only,
CH4 and N2O.
(a) Year 2012 Gg CO2Gg CO2Gg CH4Gg N2OGg CO2eqGg CO2eqGg CO2eqton CO2eq per capitalong-cycle Cshort-cycle C(AR5)(AR4)(SAR)(AR4)Canada 5.64E+055.33E+044.68E+031.23E+027.28E+057.18E+057.00E+0520.6USA 5.20E+063.10E+052.58E+049.44E+026.18E+066.13E+066.04E+0619.5Mexico 4.84E+055.23E+045.20E+033.73E+027.29E+057.26E+057.09E+055.9Rest central America 1.71E+059.63E+043.60E+038.54E+012.95E+052.87E+052.73E+053.3Brazil 4.73E+055.20E+051.92E+045.63E+021.16E+061.12E+061.05E+065.5Rest South America 6.61E+051.59E+051.62E+044.07E+021.22E+061.19E+061.13E+065.8Northern Africa 4.87E+051.68E+047.20E+031.40E+027.25E+057.08E+056.81E+054.1Western Africa 1.71E+059.14E+051.57E+042.77E+026.83E+056.45E+055.86E+051.5Eastern Africa 5.51E+045.53E+051.15E+043.33E+024.65E+054.42E+054.00E+051.6Southern Africa 4.49E+053.95E+058.21E+031.94E+027.30E+057.12E+056.82E+053.5OECD Europe 3.08E+063.74E+051.83E+047.10E+023.78E+063.75E+063.68E+069.1Central Europe 8.51E+051.08E+056.41E+032.39E+021.09E+061.08E+061.06E+068.7Turkey 3.40E+053.37E+043.76E+031.56E+024.87E+054.80E+054.67E+056.4Ukraine +3.93E+052.45E+043.46E+031.61E+025.32E+055.27E+055.15E+059.0Asia-Stan 4.52E+056.00E+037.75E+031.12E+026.99E+056.79E+056.50E+0510.6Russia +1.82E+063.29E+041.84E+042.35E+022.39E+062.35E+062.28E+0614.7Middle_East 1.84E+068.65E+032.05E+042.17E+022.48E+062.42E+062.34E+0610.7India +2.34E+061.19E+064.70E+041.10E+033.95E+063.85E+063.67E+062.3Korea 6.61E+051.08E+042.41E+035.26E+017.43E+057.37E+057.28E+059.9China +1.03E+078.50E+056.76E+041.78E+031.26E+071.25E+071.22E+079.0South-East Asia 8.03E+055.43E+051.93E+042.97E+021.42E+061.37E+061.30E+063.8Indonesia +4.53E+053.28E+051.21E+042.58E+028.61E+058.33E+057.88E+053.3Japan 1.30E+065.36E+041.85E+037.56E+011.37E+061.37E+061.36E+0610.8Oceania 4.67E+054.90E+046.49E+032.07E+027.04E+056.91E+056.68E+0522.7Internat. shipping 6.09E+051.49E+024.92E+028.44E+016.45E+056.46E+056.45E+050.1Internat. aviation 4.83E+053.38E+002.36E+014.89E+054.90E+054.90E+050.1Totals 3.49E+076.68E+063.53E+059.15E+034.72E+074.64E+074.51E+076.5
Continued.
(b) EDGAR sectorDescriptionGg CO2Gg CO2Gg CH4Gg N2OGg CO2eqGg CO2eqGg CO2eqton CO2eq perlong-cycle Cshort-cycle C(AR5)(AR4)(SAR)capita (AR4)AGSAgricultural soils1.6E+053.8E+045.0E+032.5E+062.6E+062.5E+060.36AWBAgricultural waste burning1.0E+061.8E+034.6E+016.2E+045.9E+045.2E+040.01CHEChemical processes6.8E+052.8E+026.9E+028.7E+058.9E+059.0E+050.13ENEPower industry1.4E+074.9E+053.8E+022.8E+021.4E+071.4E+071.4E+071.95ENFEnteric fermentation1.0E+052.9E+062.6E+062.2E+060.37FFFFossil fuel fires4.7E+041.5E+027.5E-015.2E+045.1E+045.1E+040.01FOO_PAPFood and paper0.00INDCombustion for manufacturing5.5E+067.4E+055.6E+027.6E+015.6E+065.6E+065.6E+060.79IROIron and steel production2.2E+055.2E+012.2E+052.2E+052.2E+050.03MNMManure management1.2E+043.4E+024.2E+054.0E+053.5E+050.06NEUNon energy use of fuels2.5E+042.5E+042.5E+042.5E+040.003NFENon-ferrous metals production8.1E+048.1E+048.1E+048.1E+040.01NMMNon-metallic minerals production1.7E+061.7E+061.7E+061.7E+060.24PROFuel exploitation2.2E+051.1E+053.3E+003.2E+062.9E+062.5E+060.41PRU_SOLSolvents and products use1.7E+058.6E+011.9E+051.9E+052.0E+050.03RCOEnergy for buildings3.3E+063.4E+061.4E+042.7E+023.7E+063.7E+063.6E+060.52REF_TRFOil refineries and transformation industry1.8E+068.7E+056.0E+032.1E+012.0E+061.9E+061.9E+060.27SWD_INCSolid waste incineration1.1E+041.5E+041.3E+034.0E+004.9E+044.5E+044.0E+040.01SWD_LDFSolid waste landfills2.9E+041.1E+018.2E+057.3E+056.2E+050.10TNR_Aviation_CDSAviation climbing and descent2.9E+052.0E+008.1E+002.9E+052.9E+052.9E+050.04TNR_Aviation_CRSAviation cruise3.9E+052.7E+001.1E+013.9E+053.9E+053.9E+050.06TNR_Aviation_LTOAviation landing and takeoff9.3E+046.5E-012.6E+009.4E+049.4E+049.4E+040.01TNR_Aviation_SPS*Aviation supersonicTNR_OtherRailways, pipelines, off-road transport2.6E+057.5E+028.7E+003.8E+012.7E+052.7E+052.7E+050.04TNR_ShipShipping7.8E+051.6E+027.1E+012.0E+017.9E+057.9E+057.9E+050.11TRORoad transportation5.4E+061.7E+058.0E+022.3E+025.5E+065.5E+065.5E+060.78WWTWaste water handling3.8E+043.5E+021.2E+061.1E+069.1E+050.15IDEIndirect emissions6.2E+021.6E+051.8E+051.9E+050.03N2OIndirect N2O emissions1.1E+032.8E+053.2E+053.3E+050.04
* Note that emissions from the Supersonic aviation are
available only till the year 2003, when the Concorde airplanes stopped
flying.
CO2 emissions and urban hot spots
The 2012 grid map of CO2 emissions from both long-cycle and
short-cycle carbon in Fig. 9 with the relative sectorial breakdown for
selected world regions (Europe, North America, Latin America, Africa, Middle
East, Oceania, Russia and China) clearly shows the fossil fuel combustion
activities, representing 90.6 % of the total CO2 emissions. In
this section we include for completeness biofuel emissions, which were
omitted from the comparisons with UNFCCC reporting, because UNFCCC assumes
carbon neutrality for all agricultural and biofuel CO2 emissions in a
country for any individual year. In the 24OECD90 countries 75.2 % of
CO2 emissions are produced by the power, road transport and
residential sectors, while these sectors represent only 60.9 % in
non-Annex I countries. The share of the industrial combustion and production
sectors (mining/manufacturing) of non-Annex I countries reaches 36.8 %.
The CO2 shares of the fuel combustion in the power generation, road
transport, buildings and manufacturing sectors vary for the different regions
from 16 % to 50 %, from 5 % to 27 %, from 6 % to 39 %
and from 9 % to 22 % of total emissions (see Table 6a and b)
respectively. Interestingly, agricultural waste burning
Note that
the agricultural waste burning does not include the savannah burning.
represents 10 % of CO2 emissions in Latin America (mainly due to
sugarcane crop residue burning), and 22 % of CO2 emissions in
Africa derives from the transformation industry (charcoal production using as
input primary solid biomass). Industrial emissions are distributed at the
point-source locations of the power/heat plants or industrial facilities
(e.g. cement factories) using the capacity of the plants or facilities as a
weighting factor.
Intercomparison of CH4 emissions trends estimated by different versions of EDGAR
and by others with (a) details for the CH4 venting for oil
and gas extraction, transmission and distribution with data of
Höglund-Isaksson (2017) and (b) details for China's
sector-specific emissions with data of Peng et al. (2016).
Global N2O emissions trends for chemical processes, which
mainly originate from nitric and adipic acid production (apart from smaller
contributions from glyoxal and caprolactam
production). The coloured area illustrates the penetration of technology for
nitric acid production (with high-pressure plants, medium-pressure plants,
low-pressure plants, plants with non-selective catalytic reduction and old
plants) to reduce the emissions.
In the grid maps hot spots are particularly visible over cities, of which the
top four emit 2.75 % of the global total
At a rate of more than
125 Mt (0.5∘×0.5∘)-1
and coincide with the
cities of Shanghai, Huangshi, Shenyuang and Moscow. In fact, 5 % of the
0.1∘×0.1∘ grid cells emit more than
5 Mt (0.5∘×0.5∘)-1 yr-1 and account for
34.08 % of the global total. It is therefore interesting to look at the
contribution of the various sectors in megacities, as shown in Fig. 10.
Emissions from the road transport sector (Fig. 10a) for the 20 selected
cities seem to be more important in suburban areas than in the centre of the
megacity. For power plants more heterogeneity was found (Fig. 10b), with
larger power plants typically located on the periphery of the city in the
24OECD90 countries, while for major cities of the 16EIT90 and non-Annex I
countries, several larger power plants are located within the central city
areas. The remaining share of CO2 emissions was shown to be mainly
from the building sectors and the industrial manufacturing emissions.
CO2 emission grid map and relative contribution of EDGAR
sectors in world regions (pie charts) for 2012. The legend for the PIE charts
relates to the EDGAR sectors defined in Table S3: AGS: agricultural soils,
AWB: agricultural waste burning, MNM: manure management, ENF: enteric
fermentation, ENE: power industry, PRO: fuel production, PRU: production and
use of products, REF: oil refineries, TRF: transformation industry,
RCO: residential, TRO: road transport, TNR: non-road transport, WWT: waste
water, SWD: solid waste disposal, FFF: fossil fuel fires, IND: manufacturing
industry, IRO: iron and steel, CHE: chemicals, NEU: non-energy use,
NFE: non-ferrous metals, NMM: non-metallic minerals, SOL: solvents,
IDE: indirect emissions. The represented CO2 emissions also include
those from short-cycle carbon (i.e. of e.g. biofuel combustion and
agricultural waste burning).
(a) Zoom of CO2 emission grid maps over cities,
representing the share of the road transport within the cities. The
represented CO2 emissions also include those from short-cycle carbon.
(b) Zoom of CO2 emission grid maps over cities, representing
the share of the power plants within the cities. The represented CO2
emissions also include those from short-cycle carbon.
(a) Difference in CO2 emissions from buildings
between 2012 and 1970. The represented CO2 emissions also include
those from short-cycle carbon. The figures for the long-cycle and short-cycle
carbon separately are taken up in Fig. S5. (b) Difference in
CO2 emissions from transport between 2012 and 1970. The represented
CO2 emissions also include those from short-cycle carbon. The figures
for the long-cycle and short-cycle carbon separately are taken up in
Fig. S5.
CH4 emission grid map and relative contribution of EDGAR
sectors in world regions (pie charts) for 2012. The legend for the PIE charts
relates to the EDGAR sectors defined in Table S3: AGS: agricultural soils,
AWB: agricultural waste burning, MNM: manure management, ENF: enteric
fermentation, ENE: power industry, PRO: fuel production, PRU: production and
use of products, REF: oil refineries, TRF: transformation industry,
RCO: residential, TRO: road transport, TNR: non-road transport, WWT: waste
water, SWD: solid waste disposal, FFF: fossil fuel fires, IND: manufacturing
industry, IRO: iron and steel, CHE: chemicals, NEU: non-energy use,
NFE: non-ferrous metals, NMM: non-metallic minerals, SOL: solvents,
IDE: indirect emissions.
The evolution over time from 1970 to 2012 shows a different pattern for the
residential sector than for the road transport sector. Figure 11a shows that
while the residential sector decreased over these 4 decades in America and
Europe, it increased in Asia and Africa. The difference in CO2
emissions from the road transport sector meanwhile presents in Fig. 11b a
more homogeneous picture with increases from 1970 to 2012 in almost all
regions. Please note that Fig. 11 includes both long-cycle and short-cycle
carbon fuel use, but Fig. S5a–d presents these separately and shows e.g. the
use of the vegetal waste and dung for residential heating in India and the
biofuel use for car transport in Brazil.
CH4 emission maps
Because CH4 is mainly released from fermentation processes (enteric,
manure, landfills or rice) or diffusion processes (coal mine leakage or gas
distribution losses), the 2012 CH4 emission grid map with sector
contributions for major world regions (Fig. 12) does not mirror the same
human activities as the CO2 map. The CH4 shares for enteric
fermentation, fossil fuel production and transmission, and solid and water
waste treatment range from 9 % to 59 %, from 8 % to 68 % and
from 11 % to 37 % of the global total respectively, depending on the
region. For 24OECD90 countries enteric fermentation (with 31.1 % share),
fossil fuel production (28.1 %) and landfills (21.4 %) are the three
dominant sectors, whereas in the 16EIT90 countries, CH4 emissions are
dominated by fossil fuel production (49.4 % share). The non-Annex I
countries show a similar high share of enteric fermentation and fossil fuel
production to the 24OECD90 countries, but rice cultivation and domestic
wastewater together give much higher emissions than solid waste disposal.
Rice cultivation was shown to contribute significantly to the total
CH4 inventory of China (21.5 % or 14.2 Tg in 2012), which is
almost 11 times the CH4 emissions of rice cultivation in India
(3.8 Tg), despite the larger area for rice fields in India than in China
(425 000 compared to 303 000 km2). This is explained by the fact that India typically has
one harvest per year from 1/3 rain-fed fields and 2/3 irrigated fields,
whereas China has multiple harvests per year from irrigated rice fields.
Rain-fed rice fields in India are modelled with a 5 times lower emission
factor than the irrigated fields in China. Figure 13a and b show the opposing
trends with mainly positive 2012–1970 increments in enteric fermentation
(mainly cattle) (a) and mainly negative increments in CH4 emissions
from rice cultivation (b). The CH4 trend from rice cultivation in
Asia was shown to be remarkably stable, with the exception of Thailand, where
increased activity is noticed. The remaining non-Annex I countries of Africa
and Latin America show similar high contributions from enteric fermentation
(25.8 Tg versus 20.9 Tg respectively in 2012). However, the total
CH4 emissions from the African continent are higher than those of
Latin America because of the 3.5 times larger CH4 emissions from
fossil fuel production (gas and oil production). Interestingly, both
continents show significant CH4 emissions from charcoal production,
which compares to 16 % (Africa) and 15 % (Latin America) of their gas
and oil production emissions of CH4.
(a) Difference in CH4 emissions from enteric
fermentation between 2012 and 1970. (b) Difference in CH4
emissions from rice cultivation between 2012 and 1970.
CH4 emissions from fossil fuel production in 2012 with zoom
on areas with intense coal mining (within green frame) and gas and oil
production activities with venting (within blue circle). The shipping lines
are representing the CH4 leakage during transmission of oil tanker
transport as fugitive emissions from the fuel and not as combustion emissions
from the tanker.
Hot spots of CH4 are estimated for fossil fuel production, typically
at gas and oil production facilities or at coal mines, as shown in Fig. 14.
In North America a shift over the period 2005–2012 from coal mining in the
north-east (-21 %) to gas and oil production in particular in North
Dakota, Montana and Texas (+65 %) took place. The USA is nowadays the
largest producer of both shale gas and tight oil, which make up almost half
of total US gas and oil production (EIA, 2015). In Europe a much larger
decrease of -87 % in coal production happened earlier, while gas
production increased by 30 %. Consequently the EU28 needed to rely on oil
and gas imports and expanded its transmission and gas distribution network,
with a corresponding increase in CH4 leakages. Apart from the USA,
the Middle East was also shown to be a global world player on the oil and gas
market, shifting from oil production (with a decrease of 71 % over the
period 1976–1985) to gas production (with a 9.3-fold increase from 1985 to
2012), mainly driven by Iran, Saudi Arabia and Qatar. The African countries
with the highest CH4 emissions from fossil fuel production were in
decreasing order of importance Algeria and Nigeria (for oil and gas
production) and South Africa (for coal mining). Similarly to Nigeria, which
showed an approximate doubling of CH4 emissions from oil (and gas)
production over the last 4 decades, Mexico and Venezuela also showed similar
levels of CH4 emissions from oil and gas production (increasing by a
factor of 1.6 over the 4 decades). For gas production, Russia has shown the
largest CH4 venting and leakage, overtaking the USA in 1985.
Coal mining has become important for China, which since 1982 has been the
largest bituminous coal producer in the world, overtaking the USA. Moreover,
China was shown to also be the largest coal importer since 2011 (overtaking
Japan), as domestic coal produced in mainly the western and northern inland
provinces of China faced a bottleneck in transportation, lacking southbound
rail lines (Tu, 2012) towards the southern coast that has the highest coal
demand. Not only did EDGAR v4.3.2 revise the country-specific coal mining
emission factors, but the spatial distribution was also considerably updated
with hot spots at the location of the mining activity. For coal mine
activities in China (split into brown and hard coal), the coal mine database
of Liu et al. (2015) provided over 4200 coal mine locations, which is 10
times more than that available for EDGAR v4.2. For Europe, the closure of
mines since the 1990s has been taken into account using the European
Pollutant Release Transfer Register (EPRTR, 2012).
N2O emission grid map and relative contribution of EDGAR
sectors in world regions (pie charts) for 2012. The legend for the PIE charts
relates to the EDGAR sectors, defined in Table S3: AGS: agricultural soils,
AWB: agricultural waste burning, MNM: manure management, ENF: enteric
fermentation, ENE: power industry, PRO: fuel production, PRU: production and
use of products, REF: oil refineries, TRF: transformation industry,
RCO: residential, TRO: road transport, TNR: non-road transport, WWT: waste
water, SWD: solid waste disposal, FFF: fossil fuel fires, IND: manufacturing
industry, IRO: iron and steel, CHE: chemicals, NEU: non-energy use,
NFE: non-ferrous metals, NMM: non-metallic minerals, SOL: solvents,
IDE: indirect emissions.
Difference between 2012 and 1970 in N2O emissions from
fertiliser use on agricultural soils.
N2O emissions including indirect sources
Unlike the CO2 and CH4 grid maps, the gridded N2O
emissions for the year 2012 in Fig. 15 with the share of different sectors
for world regions showed a quite uniform global coverage distribution, due to
the predominance of soil emissions and indirect emissions (distributed with
the N-deposition map of Dentener et al., 2006), also from the sea's surface.
Over land, most N2O is emitted from the agricultural soils (the use
of animal manure as fertiliser, the application of N-containing fertilisers
and cattle in pasture), representing 35 % to 86 % of total
N2O emissions depending on the region. Fertilising farmland with
pasture or animal waste as fertiliser or crop residues has not increased so
much as the use of nitrogen fertilisers. Figure 16 shows the increased use
(by the difference 2012–1970) of nitrogen fertiliser, in particular in Asia.
Data availability
Annual grid maps for all GHGs and sectors covering the
years 1970–2012 are available as txt (expressed in the unit ton substance
per grid cell) and NetCDF (expressed in the unit kg per
substance m-2 s-1) with 0.1∘×0.1∘ spatial
resolution, available under 10.5281/zenodo.2658138 (Janssens-Maenhout
et al., 2019). (This is the
GHG part with CO2, – long-cycle carbon, CO2 – short-cycle
carbon, CH4 and N2O grid maps and time series of the EDGAR
dataset with PID:
http://data.jrc.ec.europa.eu/dataset/jrc-edgar-edgar_v432_ghg_gridmaps.)
In addition, monthly GHG global grid maps are produced for 2010 and are
available per sector and substance. The main features of the grid maps are
described (Sect. 5) while focusing on the year 2012, although analogous
considerations also pertain to previous years.
Conclusion and outlook
In line with the ESSD guidelines of
Carlson and Oda (2018), we aim with this publication not only for free open
access to all calculated data (with their uncertainty), but also for a
complete documentation of the EDGAR v4 products that has been compiled in a
transparent way to the extent possible.
Strengths and applications of EDGAR v4.3.2
The EDGAR v4.3.2 scientific global emission inventory database provides a
comprehensive dataset of anthropogenic emissions of CO2, CH4
and N2O in time series 1970–2012 (with monthly resolution) and
spatially disaggregated grid maps with 0.1∘×0.1∘
resolution. An advantage of EDGAR v4.3.2 is that the bottom-up emissions
calculation methodology is applied to all countries and the results are
available with regular updates based on a robust statistical data
infrastructure and provide direct information to policy makers in the
standard structure as used for the Annex I countries. EDGAR v4.3.2 may
provide useful information to countries with less strong statistical data
infrastructure for their future inventory requirement. In particular, the
time series of EDGAR v4.3.2 can complete the emission trends for non-Annex I
countries, as illustrated for the case of China, where the coal statistics
revision also impacts the 2005 and 1994 inventory with +3 %.
For the atmospheric modelling community EDGAR v4.3.2 enables models to use
historical emission grid maps for a top-down assessment of the total budget,
making use of in situ and remote sensing atmospheric observation records. The
results of inverse atmospheric models provide an evaluation of the nationally
collected emission data with regard to their uncertainty and as such support
the scientific review and updates of emission inventory methodologies. For
recent years (e.g. 2010) total anthropogenic budgets of 33.6(±5.9) Pg CO2 yr-1, 0.34(±0.16) Pg CH4 yr-1
and 7.2(±3.7) Tg N2O yr-1 are obtained. The current
evaluation capacity of inverse models using atmospheric measurements remains
limited where the models struggle with an accurate separation of the natural
emissions component from the total. Although modelling uncertainties and the
uncertainties of natural emissions remain large, the atmospheric models
provide observationally constrained top-down input, and it is expected that
inverse models will increasingly contribute to the independent verification
of the total fluxes. Moreover, the impact of updates of recommended tiered
emission factors (such as from IPCC, 1996a, 2006a, the refinement of 2019, or selected region-specific data) on
the resulting emissions can be assessed at global scale. EDGAR v4.2 evaluated
the impact of the update of the N2O emission factor for direct soil
emissions from the use of fertilisers (synthetic or manure or crop residues)
by IPCC (2006c) with a 20 % lower value than what the IPCC (2000) Good
Practice Guidance provided as a default. The update of the EDGAR v4.2 version
to v4.3.2 demonstrated e.g. the necessity to take up region-specific emission
factors for fugitive coal mining emissions in China, which are considerably
lower than the IPCC lower tier-1 default values (e.g. Peng et al., 2016;
Saunois et al., 2016).
With the 42-year long time series of EDGAR v4.3.2 we provide an important
input to the analysis of global GHG trends. We find an accelerated increase
in GHG emissions since the beginning of the 21st century compared to the 3
decades before, mainly driven by the increase in CO2 emissions from
countries with emerging economies. For the EU-28 the trend is determined by a
rather stable share of CO2 and a smooth but continuously decreasing
CH4 contribution, resulting in an overall reduction of total GHG
emissions. Even though the uncertainty of global total emissions has
increased mainly because of the increasing share of GHG emissions from
emerging economy countries, on a European scale the uncertainty has decreased
because of the progress in inventory compilation and the decrease in some
sectors with more uncertain CH4 emissions.
Overall the EDGAR v4.3.2 database aims at providing useful information for
both the scientific and policy communities involved in understanding GHG
emissions and budget, e.g. for the compilation of national inventories, the
UNFCCC periodic global stocktake, analysis of co-benefits between air
pollution and GHG emission mitigation strategies, interpretation of in
situ or space-borne Earth observation data, or understanding and
reducing of uncertainties.
Use, evaluation and limitation of the EDGAR v4.3.2 dataset
EDGAR v4.3.2 provides a global picture of GHG emissions using a tier-1–2
approach following IPCC (2006a) guidelines and allowing comparison of
country- and sector-specific sources. This global completeness comes at the
expense of lacking or less accurate information at (i) higher resolution or
subnational focus and (ii) detailed modelling of subsector emissions beyond
tiers 1–2.
Therefore, the potential users of the EDGAR v4.3.2 dataset are recommended to
carefully consider these limitations when
applying it for region-specific, subnational or urban case studies, for
which more detailed inventories should be used or constructed, using
bottom-up and local information. EDGAR v4.3.2 only uses national data, and any
subnational level is the result of a spatial distribution (top-down) making
use of proxy data. In general large differences can be expected between the
top-down spatially downscaled national emissions with proxy data and the
bottom-up inventory with local data, sometimes not strictly reporting
emissions that occur solely inside the small territory, as Gately and Hutyra (2017) demonstrate. The
EDGAR v4.3.2 can be used to gap-fill between other regional inventories (e.g.
in HTAPv2.2 of Janssens-Maenhout et al., 2015) or to bridge the gap between
point sources and the national inventory (e.g. Theloke et al., 2011). These
gap fillings come at the expense of losing consistency within the reported
emissions as an inventory;
applying the dataset outside the period 1970–2012 can only be recommended
when taking into account the fast track update from 2013 onwards based on
recent statistics, for which we refer the reader to the annual publication on
the hyperlink http://edgar.jrc.ec.europa.eu/index.php#. For the years
before 1970 we refer the reader to the HYDE dataset. We would refrain from
any linear extrapolation based on short-term trends of the emission time
series or on emission drivers, for which the causality proof for these short
time series is missing; and
comparing with other gridded datasets at grid-cell level, especially when
using EDGAR v4.3.2 disaggregated subsector emissions data. Several assumptions
about the technological evolution and the spatial distribution flow into the
EDGAR v4.3.2 subsector emissions. The difference between two grid maps cannot
be unambiguously attributed to missing activity data, the selected
region-specific emission factor, or the assumed technology share or the
spatial distribution proxy data. In particular, the latter factor is
important, in particular for very localised sources or point sources (such as
industrial complexes or urban areas). Moreover, the strength of point sources
is very sensitive to the choice of the characteristic parameter (such as
designed capacity or averaged annual emission estimate or given annual
throughput) in the proxy dataset and can vary strongly over time. As Hogue et
al. (2016) indicate, the largest uncertainty contribution in gridded emission
datasets comes from the proxy data used for spatial disaggregration of
national emissions. The subtraction of the sum of all the point sources from
the sector-specific country total leaves a remaining emission that is
composed of smaller sources and that is typically distributed with e.g. a
population density proxy, as information is lacking. The uncertainties for
the point sources and for the remaining smaller sources are highly different
and larger than the uncertainty of the sector-specific country total.
Information on the representativeness of the selected characteristic
parameter for point sources is most critical and needs to be evaluated with
measurements (such as in situ atmospheric measurements of co-emitted
pollutants), but would require an in-depth analysis beyond the scope of this
paper.
EDGAR v4.3.2 is the result of continuous improvement of previous datasets,
which have been used by modellers in inverse modelling studies to verify the
level and distribution of the emissions. Feedback has been taken into account
(e.g. Saunois et al., 2017, for CH4). The evaluation of the dataset
with a more advanced uncertainty assessment has not taken place yet.
Future perspective
EDGAR v4.3.2 demonstrates that inventories can be developed for all
countries within the limitations of the quality of the available statistical
data in order to contribute to the comprehensive picture needed for the
UNFCCC periodic global stocktakes. In 2023 a first global stocktake is
foreseen to track the progress of the collective efforts to reduce the
emissions as promised under the Nationally Determined Contributions. Comprehensive information on emissions
for all world countries can help to assess and build trust in the
effectiveness of the NDCs. In particular, the country estimates of EDGAR
v4.3.2 can help countries with less developed statistical infrastructures to
compile their inventories and complete time series.
EDGAR v4.3.2 yields grid maps not only for all greenhouse gases, but also for
air pollutants, representing multi-pollutant sources as single-point sources
with realistic ratios of the different pollutant emission rates. To analyse
the co-benefits and trade-offs of integrated approaches towards climate and
energy as well as air quality policies, it is of key importance to use the
transparency framework of “measuring–reporting–verifying” for a
world-wide evaluation of the emissions. A bridge between the inventory
compilers and satellite community can yield more dynamic emission databases.
So far the interpretation of satellite data has been more successful with air
pollutants, NOx, SO2, and CO, but also
CH4. For interpreting CO2, Berezin et al. (2013) demonstrated
a new methodology using ratios of NO2:CO2 to reveal the fossil
fuel component of CO2.
Emissions provided by the EDGAR database cannot always be considered the best
country or region-specific estimate. The use of a common denominator as
technology-based methodology across the world implies for some regions the
loss of more detailed knowledge and differences from the local inventories.
However, the comprehensiveness of the EDGAR v4.3.2 grid maps allows us to
generate per grid cell the emission ratios of different GHGs and air
pollutant gases or the sector-specific shares, as additional information for
interpreting satellite retrievals measuring column-averaged dry-air mole
fractions of total CO2 or CH4.
The supplement related to this article is available online at: https://doi.org/10.5194/essd-11-959-2019-supplement.
Author contributions
GJM prepared the manuscript to document the work of the EDGAR team over more than a decade with the EDGAR v4.3.2 dataset as the final result. JGJO and JvA were crucial for the setup of the EDGAR v4 database and the data compilation. GJM took over from JvA in 2011 with the scientific support of FD. SM, UD, JAHWP, AMRP helped populate the DB with activity data and emission factors, which was taken over by MM, MC, ES and GDO. VP helped with the IT and the spatial distribution, which was taken over by DG. DG contributed substantially to the maps of the paper (Figs. 10 to 16), while MC contributed with Table 6. PB provided early feedback and helped correct errors in the database.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
The EDGAR database compilation was initiated by Jos G. J. Olivier (PBL), but in 2008 was handed over to
JRC, where a new version v4 was developed with a relative large turnover of
personnel. The EDGAR v4 development profited from the substantial contribution
of the non-JRC/non-PBL authors when they were affiliated with JRC. The
authors are grateful to the IEA, Karen Treanton and Roberta Quadrelli for the
collaboration and data exchange of the energy-related statistics and to the
JRC colleague, Julian Wilson, for the thorough review and English proofreading.
Last but not least our thanks go to David Carlson, who significantly improved
the readability of the manuscript by his thorough revision and suggestions
for abstract, structure, shortening and working with tables. We believe that
his efforts also helped to bridge different communities: on carbon modelling
and on GHG inventory compiling.
Review statement
This paper was edited by David Carlson and reviewed by David
Carlson, Tomohiro Oda, and Robbie Andrew.
References
Andres, R. J., Gregg, J. S., Losey, L., Marland, G., and Boden, T. A.:
Monthly, global emissions of carbon dioxide from fossil fuel consumption,
Tellus B, 63, 309–327, 2011.Andres, R. J., Boden, T. A., Bréon, F.-M., Ciais, P., Davis, S.,
Erickson, D., Gregg, J. S., Jacobson, A., Marland, G., Miller, J., Oda, T.,
Olivier, J. G. J., Raupach, M. R., Rayner, P., and Treanton, K.: A synthesis
of carbon dioxide emissions from fossil-fuel combustion, Biogeosciences, 9,
1845–1871, 10.5194/bg-9-1845-2012, 2012.Andres, R. J., Boden, T. A., and Highdon, D.: A new evaluation of the
uncertainty associated with CDIAC estimates of fossil fuel carbon dioxide
emission, Tellus B, 66, 1–15, 10.3402/tellusb.v66.23616, 2014.Andres, R. J., Boden, T. A., and Higdon, D. M.: Gridded uncertainty in fossil
fuel carbon dioxide emission maps, a CDIAC example, Atmos. Chem. Phys., 16,
14979–14995, 10.5194/acp-16-14979-2016, 2016.Andrew, R. M.: Global CO2 emissions from cement production, Earth
Syst. Sci. Data, 10, 195–217, 10.5194/essd-10-195-2018, 2018.Berezin, E. V., Konovalov, I. B., Ciais, P., Richter, A., Tao, S.,
Janssens-Maenhout, G., Beekmann, M., and Schulze, E.-D.: Multiannual changes
of CO2 emissions in China: indirect estimates derived from satellite
measurements of tropospheric NO2 columns, Atmos. Chem. Phys., 13,
9415–9438, 10.5194/acp-13-9415-2013, 2013.Bergamaschi, P., Houweling, S., Segers, A., Krol, M., Frankenberg, C.,
Scheepmaker, R., Dlugokencky, E., Wofsy, S., Kort, E., and Sweeney, C.:
Atmospheric CH4 in the first decade of the 21st century: Inverse
modelling analysis using SCIAMACHY satellite retrievals and NOAA surface
measurements, J. Geophys. Res.-Atmos., 118, 7350–7369, 2013.Bergamaschi, P., Corazza, M., Karstens, U., Athanassiadou, M., Thompson, R.
L., Pison, I., Manning, A. J., Bousquet, P., Segers, A., Vermeulen, A. T.,
Janssens-Maenhout, G., Schmidt, M., Ramonet, M., Meinhardt, F., Aalto, T.,
Haszpra, L., Moncrieff, J., Popa, M. E., Lowry, D., Steinbacher, M., Jordan,
A., O'Doherty, S., Piacentino, S., and Dlugokencky, E.: Top-down estimates of
European CH4 and N2O emissions based on four different
inverse models, Atmos. Chem. Phys., 15, 715–736,
10.5194/acp-15-715-2015, 2015.Bergamaschi, P., Karstens, U., Manning, A. J., Saunois, M., Tsuruta, A.,
Berchet, A., Vermeulen, A. T., Arnold, T., Janssens-Maenhout, G., Hammer, S.,
Levin, I., Schmidt, M., Ramonet, M., Lopez, M., Lavric, J., Aalto, T., Chen,
H., Feist, D. G., Gerbig, C., Haszpra, L., Hermansen, O., Manca, G.,
Moncrieff, J., Meinhardt, F., Necki, J., Galkowski, M., O'Doherty, S.,
Paramonova, N., Scheeren, H. A., Steinbacher, M., and Dlugokencky, E.:
Inverse modelling of European CH4 emissions during 2006–2012 using
different inverse models and reassessed atmospheric observations, Atmos.
Chem. Phys., 18, 901–920, 10.5194/acp-18-901-2018, 2018.Beven, K.: Facets of uncertainty: epistemic uncertainty, non-stationarity,
likelihood, hypothesis testing, and communication, Hydrolog. Sci. J., 61,
1652–1665, 10.1080/02626667.2015.1031761, 2016.Boden, T. A., Marland, G., and Andres, R. J.: Global, Regional, and National
Fossil-Fuel CO2 Emissions, Carbon Dioxide Information Analysis
Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge,
TN, USA, 10.3334/CDIAC/00001_V2017, 2017.
Bouwman, A. F., Van der Hoek, K. W., Eickhout, B., and Soenario, I.: Exploring changes in world ruminant production systems, Agr. Syst., 84, 121–153, 2005.BP: BP Statistical Review of World Energy 2016, available at:
http://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html
last access: 8 June 2017.
Brandt, A. R., Heath, G. A., Kort, E. A., O'Sullivan, F., Petron, G.,
Jordaan, S. M., Tans, P., Wilcox, J., Gopstein, A. M., Arent, D., Wofsy, S.,
Brown, N. J., Bradley, R., Stucky, G. D., Eardley, D., and Harriss, R.:
Methane Leaks from North American Natural Gas Systems, Science, 343,
733–735, 2014.
Bun, R., Hamal, K. H., Gusti, M., and Bun, A.: Spatial GHG inventory on
regional level: Accounting for uncertainty, Climatic Change, 103, 227–244,
2010.Carlson, D. and Oda, T.: Editorial: Data publication – ESSD goals,
practices and recommendations, Earth Syst. Sci. Data, 10, 2275–2278,
10.5194/essd-10-2275-2018, 2018.CIA: Central Intelligence Agency, The World Fact Book, Washington DC, available at: http://www.cia.gov/library/publications/the-world-factbook (last access: 30 April 2017), 2016.Crippa, M., Janssens-Maenhout, G., Dentener, F., Guizzardi, D., Sindelarova,
K., Muntean, M., Van Dingenen, R., and Granier, C.: Forty years of
improvements in European air quality: regional policy-industry interactions
with global impacts, Atmos. Chem. Phys., 16, 3825–3841,
10.5194/acp-16-3825-2016, 2016a.Crippa, M., Janssens-Maenhout, G., Guizzardi, D., and Galmarini, S: EU
effect: Exporting emission standards for vehicles through the global market
economy, J. Environ. Manage., 183, 959–971,
10.1016/j.jenvman.2016.09.068, 2016bCrippa, M., Guizzardi, D., Muntean, M., Schaaf, E., Dentener, F., van
Aardenne, J. A., Monni, S., Doering, U., Olivier, J. G. J., Pagliari, V., and
Janssens-Maenhout, G.: Gridded emissions of air pollutants for the period
1970–2012 within EDGAR v4.3.2, Earth Syst. Sci. Data, 10, 1987–2013,
10.5194/essd-10-1987-2018, 2018.
Crippa, M., Solazzo, E., Huang, G., Guizzardi, D., Koffi, E. Muntean, M.,
Schieberle, C., Friedrich, R., and Janssens-Maenhout, G.: Towards time
varying emissions: development of high resolution temporal profiles in the
Emissions Database for Global Atmospheric Research, Sci. Total Environ.,
STOTEN-D-19-06014, submitted, 2019.Denier van der Gon, H. A. C., Bergström, R., Fountoukis, C., Johansson,
C., Pandis, S. N., Simpson, D., and Visschedijk, A. J. H.: Particulate
emissions from residential wood combustion in Europe – revised estimates and
an evaluation, Atmos. Chem. Phys., 15, 6503–6519,
10.5194/acp-15-6503-2015, 2015.Dentener, F., Drevet, J., Lamarque, J., Bey, I., Eickhout, B., Fiore, A. M.,
Hauglustaine, D., Horowitz, L., Krol, M., and Kulshrestha, U.: Nitrogen and
sulfur deposition on regional and global scales: a multimodel evaluation,
Global Biogeochem. Cy., 20, GB4003, 10.1029/2005GB002672, 2006.Ding, J., Miyazaki, K., van der A, R. J., Mijling, B., Kurokawa, J.-I., Cho,
S., Janssens-Maenhout, G., Zhang, Q., Liu, F., and Levelt, P. F.:
Intercomparison of NOx emission inventories over East Asia,
Atmos. Chem. Phys., 17, 10125–10141, 10.5194/acp-17-10125-2017, 2017.
Doorn, M. J. and Liles, D. S.: Quantification of methane emissions and discussion of nitrous oxide, and ammonia emissions from septic tanks, latrines, and stagnant open sewers in the world, EPA, Washington, EPA report EPA-600/R-99-089, October 1999.
Doorn, M. R. J., Strait, R. P., Barnard, W. R., and Eklund, B.: Estimates of global greenhouse-gas emissions from industrial and domestic waste water treatment, Report no. NRMRL-RTP-086. R 8/18/97, Pechan & Ass., Durham, 1997.EC-JRC/PBL, European Commission, Joint Research Centre (JRC)/Netherlands
Environmental Assessment Agency (PBL): Emission Database for Global
Atmospheric Research (EDGAR), release EDGAR version 4.2, available at:
http://edgar.jrc.ec.europa.eu/overview.php?v=42 (last access:
31 December 2017), 2011.EEA: EMEP-EEA emission inventory guidebook, European Environment Agency, available at:
https://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009
(last access: 8 June 2019), 2009.EEA: EMEP-EEA emission inventory guidebook, European Environment Agency,
available at:
https://www.eea.europa.eu/publications/emep-eea-guidebook-2013 (last
access: 8 June 2019), 2013.EIA: International Energy Statistics, U.S. Energy Information Administration,
Washington DC, USA, available at:
http://www.eia.doe.gov/emeu/international/contents.html, last access:
30 October 2014.EIA: Shale gas and tight oil are commercially produced in just four
countries, Today in Energy, 13 February 2015, available at:
http://www.eia.gov/todayinenergy/detail.cfm?id=19991, last access:
30 October 2015.Elvidge, C. D., Zhizhin, M., Baugh, B., Hsu, T.-C., and Ghosh, T.: Methods
for Global Survey of Natural Gas Flaring from Visible Infrared Imaging
Radiometer Suite Data, Energies, 9, 14, 10.3390/en9010014, 2016.EPRTR: European Pollutant Transfer Register, database version v4.2, available
at: http://prtr.ec.europa.eu/ (last access: 30 October 2017), 2012.FAO Geonetwork: Digital Soil Map of the world and Digital Climate Map of the world, Food and Agriculture Organisation of the UN, available at: http://www.fao.org/geonetwork/srv/en/main.home, (last access: 30 April 2017), 2011.FAOSTAT: Statistics Division of the Food and Agricultural Organisation of the
UN, Live animal numbers, crop production, total nitrogen fertiliser
consumption statistics till 2012, available at:
http://www.fao.org/faostat/en/#home, last access: 30 October 2014.Feenstra, R. C., Inklaar, R., and Timmer, M.: The Next Generation of the Penn
World Table, NBER Working Paper no. 19255, available at:
http://cid.econ.ucdavis.edu/pwt.html (last access: 8 June 2019), 2013.Ganesan, A. L., Manning, A. J., Grant, A., Young, D., Oram, D. E., Sturges,
W. T., Moncrieff, J. B., and O'Doherty, S.: Quantifying methane and nitrous
oxide emissions from the UK and Ireland using a national-scale monitoring
network, Atmos. Chem. Phys., 15, 6393–6406, 10.5194/acp-15-6393-2015,
2015.Gately, C. K. and Hutyra, L. R.: Large uncertainties in Urban-Scale Carbon
Emissions, J. Geophys. Res.-Atmos., 122, 11242–11260,
10.1002/2017JD027359, 2017.Goldewijk, K., van Drecht, G., and Bouwman, A: Mapping contemporary global cropland and grassland distribution on a 5×5 minute resolution, Journal of Land Use Science, 2, 167–190, 2007.Grassi, G., House, J., Kurz, W., Cescatti, A., Houghton, R. A., Peters, G.
P., Sanz, M. J., Viñas, R. A., Alkama, R., Arneth, A., Bondeau, A.,
Dentener, F., Fader, M., Federici, S., Friedlingstein, P., Jain, A. K., Kato,
E., Koven, C. D., Lee, D., Nabel, J. E. M. S., Nassikas, A. A., Perugini, L.,
Rossi, S., Sitch, S., Viovy, N., Wiltshire, A., and Zaehle, S.: Reconciling
global-model estimates and country reporting of anthropogenic forest
CO2 sinks, Nat. Clim. Change, 8, 914–920,
10.1038/s41558-018-0283-x, 2018.Guan, D., Liu, A., Geng, Y., Lindner, S., Hubacek, K.: The gigatonne gap in
China's carbon dioxide inventories, Nat. Clim. Change, 2, 672–675,
10.1038/NCLIMATE1560, 2012
Gupta, S., Mohan, K., Prasad, R. K., Gupta, S., and Kansal, A.: Solid waste management in India: options and opportunities, Resour. Conserv. Recy., 24, 137–154, 1998.Gütschow, J., Jeffery, M. L., Gieseke, R., Gebel, R., Stevens, D., Krapp,
M., and Rocha, M.: The PRIMAP-hist national historical emissions time series,
Earth Syst. Sci. Data, 8, 571–603, 10.5194/essd-8-571-2016, 2016.Henne, S., Brunner, D., Oney, B., Leuenberger, M., Eugster, W., Bamberger,
I., Meinhardt, F., Steinbacher, M., and Emmenegger, L.: Validation of the
Swiss methane emission inventory by atmospheric observations and inverse
modelling, Atmos. Chem. Phys., 16, 3683–3710,
10.5194/acp-16-3683-2016, 2016.Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G.,
Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T.
C., Dawidowski, L., Kholod, N., Kurokawa, J.-I., Li, M., Liu, L., Lu, Z.,
Moura, M. C. P., O'Rourke, P. R., and Zhang, Q.: Historical (1750–2014)
anthropogenic emissions of reactive gases and aerosols from the Community
Emissions Data System (CEDS), Geosci. Model Dev., 11, 369–408,
10.5194/gmd-11-369-2018, 2018.Höglund-Isaksson, L.: Global anthropogenic methane emissions 2005–2030:
technical mitigation potentials and costs, Atmos. Chem. Phys., 12,
9079–9096, 10.5194/acp-12-9079-2012, 2012.Höglund-Isaksson, L.: Bottom-up simulations of methane and ethane
emissions from global oil and gas systems 1980 to 2012, Environ. Res. Lett.,
12, 024007, 10.1088/1748-9326/aa583e, 2017.Höglund-Isaksson, L., Winiwarter, W., Wagner, F., Klimont, Z., and Amann,
M.: Potentials and costs for mitigation of non-CO2 greenhouse gas emissions
in the European Union until 2030: Results, Report to the European Commission,
DG Climate Action, Contract No. 07.030700/2009/545854/SER/C5, available at:
http://pure.iiasa.ac.at/id/eprint/9396/, last access: May 2010.
Höglund-Isaksson, L., Thomson, A., Kupiainen, K., Rao, S., and Janssens-Maenhout, G.:
Chapter 5: Anthropogenic methane sources, emissions and future projections, in: AMAP Assessment
2015: Methane as an Arctic climate forcer, Arctic Monitoring and Assessment Programme (AMAP), Oslo, 2015.Hogue, S., Marland, E., Andres, R. J., Marland, G., and Woodard, D.:
Uncertainty in gridded CO2 emissions estimates, Earth's Future, 4, 225–239,
10.1002/2015EF000343, 2016.Hooghiemstra, P. B., Krol, M. C., Meirink, J. F., Bergamaschi, P., van der
Werf, G. R., Novelli, P. C., Aben, I., and Röckmann, T.: Optimizing
global CO emission estimates using a four-dimensional variational data
assimilation system and surface network observations, Atmos. Chem. Phys., 11,
4705–4723, 10.5194/acp-11-4705-2011, 2011.Huang, G., Brook, R., Crippa, M., Janssens-Maenhout, G., Schieberle, C.,
Dore, C., Guizzardi, D., Muntean, M., Schaaf, E., and Friedrich, R.:
Speciation of anthropogenic emissions of non-methane volatile organic
compounds: a global gridded data set for 1970–2012, Atmos. Chem. Phys., 17,
7683–7701, 10.5194/acp-17-7683-2017, 2017.
Huang, G., Schieberle, C., and Friedrich, R.: Mapping and integration of
temporal profiles in the EDGAR system, JRC specific contract No. 2
implementing FC 389299, Final report, University Stuttgart, Stuttgart,
Germany, 2018.Husain, T.: Kuwaiti oil fires -– Source estimate and plume characterization, Atmos. Environ., 28, 2149–2158, 10.1016/1352-2310(94)90357-3, 1994.IAI: International Aluminium Institute, Report on Aluminum industry's global gas emissions reduction programme. Results of the 2004/2006 anode effect survey, London, UK,, available at: http://www.world-aluminium.org/statistics/ (last access: 30 April 2017), 2008.IEA: Energy Balances of OECD and non-OECD countries, International Energy
Agency, Paris, Beyond 2020 Online Database, available at:
http://data.iea.org, last access: 30 October 2014.
IEA: World Energy Balances 2016, International Energy Agency, Paris,
ISBN 978-92-64-26311-6, 2016.IFA: International Ferilizer Industry Organisation, Historical production, trade and consumption statistics, available at: http://www.fertilizer.org//En/Statistics/PIT_Excel_Files.aspx (last access: 30 April 2017), 2015.IIASA: GAINS model. Greenhouse Gas – Air Pollution Interactions and Synergies, International Institute for Applied Systems Analysis, available at: http://gains.iiasa.ac.at/models/index.html (last access: 30 April 2017), 2007.IMA: The Magnesium Diecasters Guide 1999, Vol. III, version 23, available at https://www.intlmag.org/store/default.aspx (last access: 30 April 2017), February 1999.
IPCC: Climate Change: The IPCC Scientific Assessment 1990, Report prepared
for Intergovernmental Panel on Climate Change by Working Group I – First
Assessment Report, edited by: Houghton, J. T., Jenkins, G. J., and Ephraums,
J. J., Cambridge, 1990.
IPCC: Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories
IPCC/OECD/ IEA, Paris, 1996a.
IPCC: Climate Change 1995: The Science of Climate Change – A Contribution of
Working Group I to the Second Assessment Report, edited by: Houghton, J.
T., Meira Filho, L. G., Callander, B. A., Harris, N., Kattenberg, A., and Maskell, K., Cambridge, 1996b.
IPCC: Good Practice Guidance and Uncertainty Management in National
Greenhouse Gas Inventories, IPCC-TSU NGGIP, Japan, 2000.
IPCC: 2006 IPCC Guidelines for National Greenhouse Gas Inventories, edited
by: Eggleston, S., Buendia, L., Miwa, K., Ngara, T., and Tanabe, K., (prepared by the National Greenhouse Gas Inventory Programme), published by the Institute for Global Environmental Strategies, Hayama, Japan, IPCC-TSU
NGGIP, IGES, Hayama, Japan, 2006a.
IPCC: 2006 Guidelines for National Greenhouse Gas Inventories: Volume 1:
General Guidance and Reporting, Chapter 8: Reporting Guidance and Tables by Sanz Sánchez, M. J., Bhattacharya, S., and Mareckova, K., IGES, Hayama, Japan, 2006b.IPCC: Appendix A: IPCC Source/Sink Categories and Fuel Categories, EFDB User
Manual, 39–53, available at:
http://www.ipcc-nggip.iges.or.jp/EFDB/documents/EFDB_User_Manual_A-D.pdf
(last access: 30 April 2017), 2006c.IPCC: Guidelines for National Greenhouse Gas Inventory, Volume 5: Waste,
available at: http://www.ipcc-nggip.iges.or.jp/public/2006gl/vol5.html
(last access: October 2016), 2006d.
IPCC: AR4, Climate Change 2007: The Physical Science Basis. Cotnribution of
Working Group I to the Fourth Assessment Report of the Intergovernmental
Panel on Climate Change, edited by: Solomon, S., Qin, D., Manning, M., Chen,
Z., Marquis, M., Averyt, K. B., Tignor, M., and Miller, H. L., Chapter 2:
Changes in Atmospheric Constitutents and in Radiative Forcing by Foster, P.,
Ramasamy, V., Artaxo, T., Berntsen, T., Betts, R., Fahey, D. W., Haywood, J.,
Lean, J., Lowe, D. C., Myhre, G., Nganga, J., Prinn, R., Raga, G., Schulz,
M., and Van Dorland, R., Cambridge University Press, Cambridge, United
Kingdom and New York, NY, 2007.
IPCC: AR5, Climate Change 2014: Mitigation. Contribution of Working Group III
to the Fifth Assessment Report of the Intergovernmental Panel on Climate
Change, edited by: Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Farahani,
E., Kadner, S., Seyboth, K., Adler, A., Baum, I., Brunner, S., Eickemeier,
P., Kriemann, B., Savolainen, J., Schlömer, S., von Stechow, C., Zwickel,
T., and Minx, J. C., Cambridge University Press, Cambridge, United Kingdom
and New York, NY, USA, 2014.IRRI: World Rice statistics. Distribution of rice crop area by environment, International Rice Research Institute, https://www.irri.org/resources-and-tools/publications (last access: 30 April 2017), 2007.Janssens-Maenhout, G., Pagliari, V., Guizzardi, D., and Muntean, M.: Global
emission inventories in the Emission Database for Global Atmospheric Research
(EDGAR) – Manual (I): Gridding: EDGAR emissions distribution on global
grid-maps, JRC Report, EUR 25785 EN, ISBN 978-92-79-28283-6,
10.2788/81454, 2013.Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Dentener, F., Muntean, M.,
Pouliot, G., Keating, T., Zhang, Q., Kurokawa, J., Wankmüller, R., Denier
van der Gon, H., Kuenen, J. J. P., Klimont, Z., Frost, G., Darras, S., Koffi,
B., and Li, M.: HTAP_v2.2: a mosaic of regional and global emission grid
maps for 2008 and 2010 to study hemispheric transport of air pollution,
Atmos. Chem. Phys., 15, 11411–11432, 10.5194/acp-15-11411-2015, 2015.Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Muntean, M., Schaaf, E.,
Olivier, J. G. J., Peters, J. A. H. W., and Schure, K. M.: Fossil CO2
and GHG emissions of all world countries, EUR 28766 EN, Publications Office
of the EU, Luxembourg, PDF ISBN 978-92-79-73207-2, 10.2760/709792,
2017.Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Muntean, M., and Schaaf,
E.: Emissions Database for Global Atmospheric Research, version v4.3.2 part I
Greenhouse gases (Version v4.3.2 Greenhouse gases) [Data set], Earth System
Science Data, Zenodo, 10.5281/zenodo.2658138, also accessible on the EDGAR website: https://edgar.jrc.ec.europa.eu/overview.php?v=432_GHG&SECURE=123 (last access: 31 December 2018), 2019.Kaiser, J. W., Heil, A., Andreae, M. O., Benedetti, A., Chubarova, N., Jones,
L., Morcrette, J.-J., Razinger, M., Schultz, M. G., Suttie, M., and van der
Werf, G. R.: Biomass burning emissions estimated with a global fire
assimilation system based on observed fire radiative power, Biogeosciences,
9, 527–554, 10.5194/bg-9-527-2012, 2012.Kirschke, S., Bousquet, P., Ciais, P., Saunois, M., Canadell, J. G.,
Dlugokencky, E. J., Bergamaschi, P., Bergmann, D., Blake, D. R., Bruhwiler,
L., Cameron-Smith, P., Castaldi, S., Chevallier, F., Feng, L., Fraser, A.,
Heimann, M., Hodson, E. L., Houweling, S., Josse, B., Fraser, P. J., Krummel,
P. B., Lamarque, J. F., Langenfelds, R. L., Le Quéré, C., Naik, V.,
O'Doherty, S., Palmer, P. I., Pison, I., Plummer, D., Poulter, B., Prinn, R.
G., Rigby, M., Ringeval, B., Santini, M., Schmidt, M., Shindell, D. T.,
Simpson, I. J., Spahni, R., Steele, L. P., Strode, S. A., Sudo, K., Szopa,
S., van der Werf, G. R., Voulgarakis, A., van Weele, M., Weiss, R. F.,
Williams, J. E., and Zeng, G.: Three decades of global methane sources and
sinks, Nat. Geosci., 6, 813–823, 10.1038/ngeo1955, 2013.Kort, E. A., Eluszkiewicz, J., Stephens, B. B., Miller, J. B., Gerbig, C.,
Nehrkorn, T., Daube, B. C., Kaplan, J. O., Houweling, S., and Wofsy, S. C.:
Emissions of CH4 and N2O over the United States and Canada based on a
receptor-oriented modeling framework and COBRA-NA atmospheric observations,
Geophys. Res. Lett., 35, L18808, 10.1029/2008GL034031, 2008.Leip, A., Britz, W., Weiss, F., and de Vries, W.: Farm, land, and soil
nitrogen budgets for agriculture in Europe calculated with CAPRI, Environ.
Pollut., 159, 3243–3253, 10.1016/j.envpol.2011.01.040, 2011.Lelieveld, J., Lechtenbohmer, S., Assonov, S. S., Brenninkmeijer, C. A. M.,
Dienst, C., Fischedick, M., and Hanke, T.: Greenhouse gases: Low methane
leakage from gas pipelines, Nature, 434, 841–842, 10.1038/434841a,
2005.Le Quéré, C., Andrew, R. M., Canadell, J. G., Sitch, S., Korsbakken,
J. I., Peters, G. P., Manning, A. C., Boden, T. A., Tans, P. P., Houghton, R.
A., Keeling, R. F., Alin, S., Andrews, O. D., Anthoni, P., Barbero, L., Bopp,
L., Chevallier, F., Chini, L. P., Ciais, P., Currie, K., Delire, C., Doney,
S. C., Friedlingstein, P., Gkritzalis, T., Harris, I., Hauck, J., Haverd, V.,
Hoppema, M., Klein Goldewijk, K., Jain, A. K., Kato, E., Körtzinger, A.,
Landschützer, P., Lefèvre, N., Lenton, A., Lienert, S., Lombardozzi,
D., Melton, J. R., Metzl, N., Millero, F., Monteiro, P. M. S., Munro, D. R.,
Nabel, J. E. M. S., Nakaoka, S.-I., O'Brien, K., Olsen, A., Omar, A. M., Ono,
T., Pierrot, D., Poulter, B., Rödenbeck, C., Salisbury, J., Schuster, U.,
Schwinger, J., Séférian, R., Skjelvan, I., Stocker, B. D., Sutton, A.
J., Takahashi, T., Tian, H., Tilbrook, B., van der Laan-Luijkx, I. T., van
der Werf, G. R., Viovy, N., Walker, A. P., Wiltshire, A. J., and Zaehle, S.:
Global Carbon Budget 2016, Earth Syst. Sci. Data, 8, 605–649,
10.5194/essd-8-605-2016, 2016.Le Quéré, C., Andrew, R. M., Friedlingstein, P., Sitch, S., Pongratz,
J., Manning, A. C., Korsbakken, J. I., Peters, G. P., Canadell, J. G.,
Jackson, R. B., Boden, T. A., Tans, P. P., Andrews, O. D., Arora, V. K.,
Bakker, D. C. E., Barbero, L., Becker, M., Betts, R. A., Bopp, L.,
Chevallier, F., Chini, L. P., Ciais, P., Cosca, C. E., Cross, J., Currie, K.,
Gasser, T., Harris, I., Hauck, J., Haverd, V., Houghton, R. A., Hunt, C. W.,
Hurtt, G., Ilyina, T., Jain, A. K., Kato, E., Kautz, M., Keeling, R. F.,
Klein Goldewijk, K., Körtzinger, A., Landschützer, P., Lefèvre,
N., Lenton, A., Lienert, S., Lima, I., Lombardozzi, D., Metzl, N., Millero,
F., Monteiro, P. M. S., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S.-I.,
Nojiri, Y., Padin, X. A., Peregon, A., Pfeil, B., Pierrot, D., Poulter, B.,
Rehder, G., Reimer, J., Rödenbeck, C., Schwinger, J., Séférian,
R., Skjelvan, I., Stocker, B. D., Tian, H., Tilbrook, B., Tubiello, F. N.,
van der Laan-Luijkx, I. T., van der Werf, G. R., van Heuven, S., Viovy, N.,
Vuichard, N., Walker, A. P., Watson, A. J., Wiltshire, A. J., Zaehle, S., and
Zhu, D.: Global Carbon Budget 2017, Earth Syst. Sci. Data, 10, 405–448,
10.5194/essd-10-405-2018, 2018.Li, C., Qiu, J., Frolking, S., Xiao, X., Salas, W., Moore, B., Boles, S.,
Huang, Y., and Sass, R.: Reduced methane emissions from large-scale changes
in water management of China's rice paddies during 1980–2000, Geophys. Res.
Lett., 29, 1972, 10.1029/2002GL015370, 2002.Liu, F., Choi, S., Li, C., Fioletov, V. E., McLinden, C. A., Joiner, J.,
Krotkov, N. A., Bian, H., Janssens-Maenhout, G., Darmenov, A. S., and da
Silva, A. M.: A new global anthropogenic SO2 emission inventory for the last
decade: a mosaic of satellite-derived and bottom-up emissions, Atmos. Chem.
Phys., 18, 16571–16586, 10.5194/acp-18-16571-2018, 2018.Liu, Z., Guan, D., Wei, W., Davis, S. J., Ciais, P., Bai, J., Peng, S.,
Zhang, Q., Hubacek, K., Marland, G., Andres, R. J., Crawford-Brown, D., Lin,
J., Zhao, H., Hong, C., Boden, T. A., Feng, K., Peters, G. P., Xi, F., Liu,
J., Li, Y., Zhao, Y., Zeng, N., and He, K.: Reduced carbon emission estimates
from fossil fuel combustion and cement production in China, Nature, 524,
335–338, 10.1038/nature14677, 2015.Lyon, D. R., Zavala-Araiza, D., Alvarez, R. A., Harriss, R., Palacios, V.,
Lan, X., Talbot, R., Lavoie, T., Shepson, P., Yacovitch, T. I., Herndon, S.
C., Marchese, A. J., Zimmerle, D., Robinson, A. L., and Hamburg, S. P.:
Constructing a Spatially Resolved Methane Emission Inventory for the Barnett
Shale Region, Environ. Sci. Technol., 49, 8147–8157,
10.1021/es506359c, 2015.Marcogaz: Technical statistics 01-01-2013, technical sheet of Marcogaz
technical association of the European natural gas industry, available at:
https://www.marcogaz.org/app/download/7719248963/Technical_statistics_01-01-2013_revision_on_15-09-2014_-_WEB_VERSION.pdf?t=_1529588711
(last access: 30 April 2017), 2013Marland, G., Brenkert, A., and Olivier, J.: CO2 from fossil fuel
burning: A comparison of ORNL and EDGAR estimates of national emissions,
Environ. Sci. Policy, 2, 265–274, 1999.Miller, S. M., Wofsy, S. C., Michalak, A. M., Kort, E. A., Andrews, A. E.,
Biraud, S. C., Dlugokencky, E. J., Eluszkiewicz, J., Fischer, M. L.,
Janssens-Maenhout, G., Miller, B. R., Miller, J. B., Montzka, S. A.,
Nehrkorn, T., and Sweeney, C.: Anthropogenic emissions of methane in the
United States, P. Natl. Acad. Sci. USA, 110, 20018–20022,
10.1073/pnas.1314392110, 2013.Monteil, G., Houweling, S., Dlugockenky, E. J., Maenhout, G., Vaughn, B. H.,
White, J. W. C., and Rockmann, T.: Interpreting methane variations in the
past two decades using measurements of CH4 mixing ratio and isotopic
composition, Atmos. Chem. Phys., 11, 9141–9153,
10.5194/acp-11-9141-2011, 2011.
Muntean, M., Janssens-Maenhout, G., Song, S., Selin, N. E., Olivier, J. G.
J., Guizzardi, D., Maas, R., and Dentener, F.: Trend analysis from 1970 to
2008 and model evaluation of EDGARv4 global gridded anthropogenic mercury
emissions, Sci. Total Environ., 494–495, 337–350, 2014.
Muntean, M., Janssens-Maenhout, G., Song, S., Giang, A., Selin, N. E., Zhong,
H., Zhao, Y., Olivier, J. G. J., Guizzardi, D., Crippa, M., Schaaf, E., and
Dentener, F.: Evaluating EDGARv4.tox2 speciated mercury emissions ex-post
scenarios and their impacts on modelled global and regional wet deposition
patterns, Atmos. Environ., 184, 56–68, 2018.NOAA-NGDC, National Oceanic & Atmospheric Administration, National Centers
for Environmental Information, Image and Data processing by NOAA's National
Geophysical Data Center: Visible Infrared Imaging Radiometer Suite (VIIRS),
available at: https://www.ngdc.noaa.gov/eog/viirs.html (last access: 30 April 2017), 2015.Oda, T. and Maksyutov, S.: A very high-resolution (1 km × 1 km)
global fossil fuel CO2 emission inventory derived using a point
source database and satellite observations of nighttime lights, Atmos. Chem.
Phys., 11, 543–556, 10.5194/acp-11-543-2011, 2011.Oda, T., Maksyutov, S., and Andres, R. J.: The Open-source Data Inventory for
Anthropogenic CO2, version 2016 (ODIAC2016): a global monthly fossil
fuel CO2 gridded emissions data product for tracer transport
simulations and surface flux inversions, Earth Syst. Sci. Data, 10, 87–107,
10.5194/essd-10-87-2018, 2018.
Olivier, J. G. J.: On the Quality of Global Emission Inventories, Approaches,
Methodologies, Input Data and Uncertainties, PhD thesis, Utrecht University,
ISBN 90-393-3103-0, 2002.Olivier, J. G. J. and Janssens-Maenhout, G.: CO2 Emissions from Fuel
Combustion – 2016 Edition, IEA CO2 report 2016, Part III,
Greenhouse-Gas Emissions, ISBN 978-92-64-25856-3, 2016.Olivier, J. G. J., Bouwman, A. F., Van der Maas, C. W. M., Berdowski, J. J.
M., Veldt, C., Bloos, J. P. J., Visschedijk, A. J. H., Zandveld, P. Y. J.,
and Haverslag, J. L.: Description of EDGAR Version 2.0: A set of global
emission inventories of greenhouse gases and ozone depleting substances for
all anthropogenic and most natural sources on a per country basis and on
1∘,× 1∘ grid, RIVM Techn. Report nr. 771060002,
TNO-MEP report nr. R96/119, Nat. Inst. Of Public Health and the
Environment/Netherlands Organisation for Applied Scientific Research,
Bilthoven, the Netherlands, 1996.
Olivier, J. G. J., van Aardenne, J. A., Monni, S., Döring, U. M., Peters,
J. A. H. W., and Janssens-Maenhout, G.: Application of the IPCC uncertainty
methods to EDGAR v4.1 global greenhouse gas inventories, in: Proceedings 3rd
International Workshop on Uncertainty in Greenhouse Gas Inventories, Lviv,
September 2010, 219–226, ISBN: 978-966-8460-81-4, 2010.Olivier, J. G. J., Janssens-Maenhout, G., Muntean, M., and Peters, J. A. H.
W.: Trends in global CO2 emissions: 2014 report, European Commission – PBL Netherlands Environmental Assessment Agency, The Hague, JRC93171/PBL1490 report, ISBN 978-94-91506-87-1, 2014.Olivier, J. G. J., Janssens-Maenhout, G., Muntean, M., and Peters, J. A. H.
W.: Trends in global CO2 emissions: 2015 report, European Commission – PBL Netherlands Environmental Assessment Agency, The Hague, JRC 98184, 2015.Olivier, J. G. J., Janssens-Maenhout, G., Muntean, M., and Peters, J. A. H.
W.: Trends in global CO2 emissions: 2016 report, European Commission – PBL Netherlands Environmental Assessment Agency, The Hague, JRC 103425, 2016.Oonk, H.: Literature Review: Methane from landfills: Methods to quantify generation oxidation and emission, Report of OonKAY Innovations in Env. Techn. Co., available at: http://www.waste.ccacoalition.org/file/1854/download?token=I2f1s17k (last access: 30 October 2014), 2010.Paruolo, P., Murphy, B., and Janssens-Maenhout, G.: Do emissions and income
have a common trend? A country-specific, time-series, global analysis,
1970–2008, Stoch. Env. Res. Risk A., 29, 93–107,
10.1007/s00477-014-0929-9, 2015.Peischl, J., Ryerson, T. B., Aikin, K. C., De Gouw, J. A., Gilman, J. B.,
Holloway, J. S., Lerner, B. M., Nadkarni, R., Neuman, J. A., Nowak, J. B.,
Trainer, M., Warneke, C., and Parrish, D. D.: Quantifying atmospheric methane
emissions from the Haynesville, Fayetteville, and northeastern Marcellus
shale gas production regions, J. Geophys. Res.-Atmos., 120, 2119–2139,
10.1002/2014JD022697, 2015.Peng, S., Piao, S., Bousquet, P., Ciais, P., Li, B., Lin, X., Tao, S., Wang,
Z., Zhang, Y., and Zhou, F.: Inventory of anthropogenic methane emissions in
mainland China from 1980 to 2010, Atmos. Chem. Phys., 16, 14545–14562,
10.5194/acp-16-14545-2016, 2016.Petrescu, A. M. R., Abad-Viñas, R., Janssens-Maenhout, G., Blujdea, V. N.
B., and Grassi, G.: Global estimates of carbon stock changes in living forest
biomass: EDGARv4.3 – time series from 1990 to 2010, Biogeosciences, 9,
3437–3447, 10.5194/bg-9-3437-2012, 2012.Pozzer, A., Zimmermann, P., Doering, U. M., van Aardenne, J., Tost, H.,
Dentener, F., Janssens-Maenhout, G., and Lelieveld, J.: Effects of
business-as-usual anthropogenic emissions on air quality, Atmos. Chem. Phys.,
12, 6915–6937, 10.5194/acp-12-6915-2012, 2012.Pulles, T.: Twenty-five years of emission inventorying, Carbon Manag., 9,
1–5, 10.1080/17583004.2018.1426970, 2018.RFA Renewable Fuels Association: World fuel ethanol production, available at: http://www.ethanolrfa.org/resources/industry/statistics/#1454099271060-171d2f93-158a, last access: 31 October 2016.Saunois, M., Bousquet, P., Poulter, B., Peregon, A., Ciais, P., Canadell, J.
G., Dlugokencky, E. J., Etiope, G., Bastviken, D., Houweling, S.,
Janssens-Maenhout, G., Tubiello, F. N., Castaldi, S., Jackson, R. B., Alexe,
M., Arora, V. K., Beerling, D. J., Bergamaschi, P., Blake, D. R., Brailsford,
G., Brovkin, V., Bruhwiler, L., Crevoisier, C., Crill, P., Covey, K., Curry,
C., Frankenberg, C., Gedney, N., Höglund-Isaksson, L., Ishizawa, M., Ito,
A., Joos, F., Kim, H.-S., Kleinen, T., Krummel, P., Lamarque, J.-F.,
Langenfelds, R., Locatelli, R., Machida, T., Maksyutov, S., McDonald, K. C.,
Marshall, J., Melton, J. R., Morino, I., Naik, V., O'Doherty, S., Parmentier,
F.-J. W., Patra, P. K., Peng, C., Peng, S., Peters, G. P., Pison, I.,
Prigent, C., Prinn, R., Ramonet, M., Riley, W. J., Saito, M., Santini, M.,
Schroeder, R., Simpson, I. J., Spahni, R., Steele, P., Takizawa, A.,
Thornton, B. F., Tian, H., Tohjima, Y., Viovy, N., Voulgarakis, A., van
Weele, M., van der Werf, G. R., Weiss, R., Wiedinmyer, C., Wilton, D. J.,
Wiltshire, A., Worthy, D., Wunch, D., Xu, X., Yoshida, Y., Zhang, B., Zhang,
Z., and Zhu, Q.: The global methane budget 2000–2012, Earth Syst. Sci. Data,
8, 697–751, 10.5194/essd-8-697-2016, 2016.Saunois, M., Bousquet, P., Poulter, B., Peregon, A., Ciais, P., Canadell, J.
G., Dlugokencky, E. J., Etiope, G., Bastviken, D., Houweling, S.,
Janssens-Maenhout, G., Tubiello, F. N., Castaldi, S., Jackson, R. B., Alexe,
M., Arora, V. K., Beerling, D. J., Bergamaschi, P., Blake, D. R., Brailsford,
G., Bruhwiler, L., Crevoisier, C., Crill, P., Covey, K., Frankenberg, C.,
Gedney, N., Höglund-Isaksson, L., Ishizawa, M., Ito, A., Joos, F., Kim,
H.-S., Kleinen, T., Krummel, P., Lamarque, J.-F., Langenfelds, R., Locatelli,
R., Machida, T., Maksyutov, S., Melton, J. R., Morino, I., Naik, V.,
O'Doherty, S., Parmentier, F.-J. W., Patra, P. K., Peng, C., Peng, S.,
Peters, G. P., Pison, I., Prinn, R., Ramonet, M., Riley, W. J., Saito, M.,
Santini, M., Schroeder, R., Simpson, I. J., Spahni, R., Takizawa, A.,
Thornton, B. F., Tian, H., Tohjima, Y., Viovy, N., Voulgarakis, A., Weiss,
R., Wilton, D. J., Wiltshire, A., Worthy, D., Wunch, D., Xu, X., Yoshida, Y.,
Zhang, B., Zhang, Z., and Zhu, Q.: Variability and quasi-decadal changes in
the methane budget over the period 2000–2012, Atmos. Chem. Phys., 17,
11135–11161, 10.5194/acp-17-11135-2017, 2017.Schneider, L., Lazarus, M., and Kollmuss, A.: Industrial N2O Projects
under CDM: Adipic Acid – A Case of Carbon Leakage?, Report WP-US-1006,
Washington DC, Stockholm Environment Institute, 2010.
Sharholy, M., Ahmad, K., Mahmood, G., and Trivedi, R. C.: Municipal solid waste management in Indian cities – A review, Waste Manage., 28, 459–467, 2008.
Solazzo, E. and Galmarini, S.: Comparing apples with apples: Using spatially
distributed time series of monitoring data for model evaluation, Atmos.
Environ., 112, 234–245, 2015.
Theloke, J., Thiruchittampalam, B., Orlikova, S., Uzbasich, M., and Gauger,
T.: Methodology development for the spatial distribution of the diffuse
emissions in Europe, University Stuttgart IER report, under EC contract
070307/2009/548773/SER/C4, 2011.Tian, H.: Global methane and nitrous oxide emissions from terrestrial
ecosystems due to multiple environmental changes, Ecosystem Health and
Sustainability, 1, 1–20, 10.1890/EHS14-0015.1, 2015.Tian, H., Yang, J., Lu, C., Xu, R., Canadell, J.G., Jackson, R.B., Arneth,
A., Chang, J., Chen, G., Ciais, P., Gerber, S., Ito, A., Huang, Y., Joos, F.,
Lienert, S., Messina, P., Olin, S., Pan, S., Peng, C., Saikawa, E., Thompson,
R., Vuichard, N., Winiwarter, W., Zaehle, S., Zhang, B., Zhang, K., and Zhu,
Q.: The Global N2O Model Intercomparison Project, B. Am. Meteorol.
Soc., 99, 1231–1251, 10.1175/BAMS-D-17-0212.1, 2018.Tubiello, F. N., Salvatore, M., Ferraa, A. F., House, J., Federici, S.,
Rossi, S., Biancalani, R., Condor Golec, R. D., Jacobs, H., Flammini, A.,
Prosperi, P., Cardenas-Galindo, P., Schmidhuber, J., Sanz Sanchez, M. J.,
Srivastava, N., and Smith, P.: The Contribution of Agriculture, Forestry and
other Land Use activities to Global Warming, 1990–2012, Glob. Change Biol.,
21, 2655–2660, 10.1111/gcb.12865, 2015.UN Comtrade: United Nations, Department of Economic and Social Affairs, Statistics Division, International Trade Statistics Database, available at: https://comtrade.un.org/data (last access: 30 April 2017), 2016.
UN DP: World Urbanization Prospects: The 2014 Revision, United Nations, Department of Economic and Social Affairs, Population Division, CD-ROM edn., 2014.
UN DP: World Population Prospects: The 2015 Revision, United Nations, Department of Economic and Social Affairs, Population Division, DVD edn., 2015.
UNEP: The Emissions Gap Report 2012, Appendix 1, United Nations Environment Programme (UNEP), Nairobi, 2012.
UNEP: The Emissions Gap Report 2015, United Nations Environment Programme (UNEP), Nairobi, 2015.
UNEP DTU: Clean Development Mechanisms/Joint Implementation
Pipeline Analysis and Database, Copenhagen, 2011.UNEP Risø Centre: Clean Developing Mechanisms/Joint Implementation Pipeline Analysis and Database, available at: http://cdmpipeline.org/ (last access: 30 April 2017), 2011.
UNFCCC (United Nations Framework Convention on Climate Change), United
Nations, New York, 9 May 1992.
UNFCCC: The Kyoto Protocol to the United Nations Framework Convention on
Climate Change, Conference of Parties to the UNFCCC (COP 3),
Kyoto, 11 December 1997.UNFCCC: Submitted National Communications from Non-Annex I Parties, available at: https://unfccc.int/resource/docs/natc/chnnc1e.pdf (last access: 30 October 2014), 2004.UNFCCC: Submitted National Communications from Non-Annex I Parties, available at: https://unfccc.int/resource/docs/natc/chnnc2e.pdf (last access: 30 October 2014), 2012.UNFCCC: National Inventory Report, submissions of the greenhouse gas
inventories for Annex I countries, available at:
http://unfccc.int/national_reports/annex_i_ghg_inventories/national_inventories_submissions/items/7383.php (last access: 30 October 2014), 2014.
UNFCCC: The Paris Agreement, done at: COP 21 (the 21st meeting of the
Conference of the Parties, which guides the Conference), Paris, 12 December 2015.UNFCCC: National Inventory Report, submissions of the greenhouse gas inventories for Annex I countries,
available at: http://unfccc.int/national_reports/annex_i_ghg_inventories/national_inventories_submissions/items/9492.php
(last access: 30 October 2016), 2016.
UNFCCC: Submitted Biennial Update Reports from Non-Annex I Parties, 2017.UN HABITAT: UN Human Settlements Programme, Global Urban Indicators database, Nairobi, info on population in slums (% of urban population). Available at: http://mirror.unhabitat.org/stats/Default.aspx (last access: 30 April 2017), 2016a.UN HABITAT: UN Human Settlements Programme, World Atlas of Slum Evolution 2015, Nairobi, available at: http://unhabitat.org/world-atlas-of-slum-evolution/ (last access: 30 April 2017), 2016b.UN STATS: UN Statistics Division, Industrial Commodity Production Statistics 1970–2013, available at: http://unstats.un.org/unsd/industry/publications.asp (last access: 30 April 2017), 2014.US DA: US Department of Agriculture, Biofuel Annuals. GAIN Reports for Argentina, Brasil (Sugar Annual), China, India, Indonesia, Malaysia, Peru, Philippines and Thailand, available at: https://gain.fas.usda.gov/Pages/Default.aspx (last access: 30 April 2017), 2014.US EPA: Global Anthropogenic Non-CO2 Greenhouse Gas Emissions:
1990–2030, US Environmental Protection Agency, EPA report 430-R-12-002,
190 pp., available at:
https://19january2017snapshot.epa.gov/sites/production/files/2016-08/documents/epa_global_nonco2_projections_dec2012.pdf
(last access: 30 April 2017), 2012.US EPA: 2011-2012-2013-2014 GHGRP Industrial Profiles. Petroleum and Natural
Gas Systems. US Environmental Protection Agency, available at:
http://www2.epa.gov/sites/production/files/2015-10/documents/subpart_w_2014_data_summary_10-05-2015_final.pdf
(last access: 30 April 2017), 2015.USGS: US Geological Survey Minerals Yearbook, US Geological Survey, Reston,
Virginia, available at:
https://minerals.usgs.gov/minerals/pubs/commodity/ (last access:
30 October 2016), 2014.van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Mu, M.,
Kasibhatla, P. S., Morton, D. C., DeFries, R. S., Jin, Y., and van Leeuwen,
T. T.: Global fire emissions and the contribution of deforestation, savanna,
forest, agricultural, and peat fires (1997–2009), Atmos. Chem. Phys., 10,
11707–11735, 10.5194/acp-10-11707-2010, 2010.van Dijk, P. M., Kuenzer, C., Zhang, J., Wolf, K. H. A. A., and Wang, J.:
Fossil fuel deposit fires, Occurrence Inventory, design and assessment of Instrumental Options. WAB report 500102021. PBL Netherlands Environmental Assessment Agency,
The Hague/Bilthoven, available at: https://www.pbl.nl/en/publications/2009/Fossil-Fuel-Deposit-Fires-Occurrence-Inventory-design-and-assessment-of-Instrumental-Options (last access: 30 April 2017), 2009.Van Drecht, G., Bouwman, A. F., Harrison, J., and Knoop, J. M.: Global nitrogen and phosphate in urban wastewater for the period 1970 to 2050, Global Biogeochem. Cy., 23, GB0A03, 10.1029/2009GB003458, 2009.Wang, R., Tao, S., Ciais, P., Shen, H. Z., Huang, Y., Chen, H., Shen, G. F.,
Wang, B., Li, W., Zhang, Y. Y., Lu, Y., Zhu, D., Chen, Y. C., Liu, X. P.,
Wang, W. T., Wang, X. L., Liu, W. X., Li, B. G., and Piao, S. L.:
High-resolution mapping of combustion processes and implications for
CO2 emissions, Atmos. Chem. Phys., 13, 5189–5203,
10.5194/acp-13-5189-2013, 2013.WBCSD-CSI: World Business Council for Sustainable Development-Cement, http://www.wbcsdcement.org/GNR 2012/index.html (last access: 30 April 2017), 2015.Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J.
A., Orlando, J. J., and Soja, A. J.: The Fire INventory from NCAR (FINN): a
high resolution global model to estimate the emissions from open burning,
Geosci. Model Dev., 4, 625–641, 10.5194/gmd-4-625-2011, 2011.Winiwarter, W., Höglund-Isaksson, L., Klimont, Z., Schöpp, W., and
Amann, M.: Technical opportunities to reduce global anthropogenic emissions
of nitrous oxide, Environ. Res. Lett., 13, 014011,
10.1088/1748-9326/aa9ec9, 2018.World Bank: Population living in slums, info on urban population in slums, available at: http://data.worldbank.org/indicator/EN.POP.SLUM.UR.ZS, last access: 31 October 2016.WSA: World Steel Association, Steel statistics, available at: https://www.worldsteel.org/steel-by-topic/statistics.html (last access: 30 April 2017), 2015.Xi, F., Davis, S. J., Ciais, P., Crawford-Brown, D., Guan, D., Pade, C., Shi,
T., Syddall, M., Lv, J., Ji, L., Bing, L., Wang, J., Wei, W., Keun-Hyeok, Y.,
Lagerblad, B., Galan, I., Andrade, C., Zhang, Y., and Liu, Z.: Substantial
global carbon uptake by cement carbonation, Nat. Geosci., 9, 880–883,
10.1038/NGEO2840, 2016.Yevich, R. and Logan, J.: An assessment of biofuel use and burning of agricultural waste
in the developing world, Global Biogeochem. Cy., 17, 1095, 10.1029/2002GB001952, 2003.Yu, W., Ma, M. M. Li, Z., Tan, J., and Wy, A.: New Scheme for Validating
Remote-Sensing Land Surface Temperature Products with Station Observations,
Remote Sensing, 9, 1210–2017, 10.3390/rs9121210, 2017.