Evaluating uncertainty in the

Italian GHG Inventory

Workshop on Uncertainties in GHG inventories Helsinki, 5-6 September 2005

Agency for the Protection of the Environment and for Technical Services

Daniela Romano

APAT

The national Agency for the Protection of the Environment and for Technical Services (APAT) is responsible for the compilation of the national air emission inventory through the collection, elaboration and diffusion of data. The Agency is also responsible for evaluating and quantifying uncertainty in emission figures

Institutions involved in the compilation of national emission inventory

• The Tier 1 is applied to the whole national emission inventory at different level of details

• The Tier 2, by Monte Carlo and Bootstrap, applied only to some sources in order to make comparison and to evaluate the added value

• Alternative approaches are also studied

Activity data• Gaps in time series • Use of surrogate or proxy variables• Lack of references (calculation or estimation methods,

representativeness at local or national level)

Emission Factors• Usually high uncertainty• Scarcity of quantitative information (measurements,

sample representativeness) as compared to qualitative information (experts judgement)

Sources of uncertainty

Main problems

• Lack of measurements

• Individuation of the shape and parameters of distributions (classical distributions vs mixture or twin peaks distributions)

• How to use qualitative information/ knowledge

• Information provided in the IPCC Good Practice Guidance as well as expert judgement has been used

• Standard deviations have also been considered when measurements available

• Emission figures are disaggregated into 60 sources, according to the categories listed in the Good Practice

• General approach:set values within a range low, medium and high according to the confidence the expert has on the value

Uncertainty analysis – Tier 1

• low uncertainty (e.g. 3-5%) to activity data derived from the energy balance and statistical yearbooks

• medium-high uncertainty (20-50%) to the data not directly or only partially derived from census or sample surveys or estimated data

Activity data

• Uncertainties set for emission factors are higher than those for activity data

• IPCC uncertainty values are used when the emission factor is a default value

• low values are used for measured data

• otherwise uncertainty values are high

Emission factors

IPCC Sorce category

Base year 2003 Activity data uncertainty

Emission factor uncertainty

Combined uncertainty

Combined uncertainty as % of total emissions 2003

Uncertainty in trend in national emissions introduced by emission factor uncertainty

Uncertainty in trend in national emissions introduced by activity data uncertainty

Uncertainty introduced into the trend in total national emissions

CO2 stationary combustion liquid fuels 153.097 124.462 3% 3% 0,042 0,009 -0,003 0,010 0,011 CO2 stationary combustion solid fuels 58.021 61.629 3% 3% 0,042 0,005 0,000 0,005 0,005 CO2 stationary combustion gaseous fuels 85.065 143.988 3% 3% 0,042 0,011 0,003 0,012 0,012 CH4 stationary combustion 770 1.096 3% 50% 0,501 0,001 0,000 0,000 0,000 N2O stationary combustion 6.740 7.025 3% 50% 0,501 0,006 0,000 0,001 0,001 CO2 Mobile combustion: Road Vehicles 93.995 116.346 3% 3% 0,042 0,009 0,001 0,010 0,010 CH4 Mobile combustion: Road Vehicles 743 579 3% 10% 0,104 0,000 0,000 0,000 0,000 N2O Mobile combustion: Road Vehicles 1.612 3.670 3% 50% 0,501 0,003 0,002 0,000 0,002 CO2 Mobile combustion: Waterborne Navigation 5.419 6.148 3% 3% 0,042 0,000 0,000 0,001 0,001 CH4 Mobile combustion: Waterborne Navigation 29 33 3% 50% 0,501 0,000 0,000 0,000 0,000 N2O Mobile combustion: Waterborne Navigation 39 45 3% 100% 1,000 0,000 0,000 0,000 0,000 CO2 Mobile combustion: Aircraft 1.596 2.771 3% 3% 0,042 0,000 0,000 0,000 0,000 CH4 Mobile combustion: Aircraft 1 2 3% 50% 0,501 0,000 0,000 0,000 0,000 N2O Mobile combustion: Aircraft 12 20 3% 100% 1,000 0,000 0,000 0,000 0,000 CO2 Mobile combustion: Other 1.888 1.410 3% 5% 0,058 0,000 0,000 0,000 0,000 CH4 Mobile combustion: Other 5 3 3% 50% 0,501 0,000 0,000 0,000 0,000 N2O Mobile combustion: Other 131 73 3% 100% 1,000 0,000 0,000 0,000 0,000 CH4 Fugitive emissions from Coal Mining and Handling 122 95 3% 300% 3,000 0,000 0,000 0,000 0,000 CO2 Fugitive emissions from Oil and Gas Operations 3.048 2.499 3% 25% 0,252 0,001 0,000 0,000 0,000 CH4 Fugitive emissions from Oil and Gas Operations 6.631 4.993 3% 25% 0,252 0,002 -0,001 0,000 0,001

Uncertainty analysis – Tier 1

CO2 Cement production 16.084 17.322 3% 10% 0,104 0,003 0,000 0,001 0,001 CO2 Lime production 1.711 2.092 3% 10% 0,104 0,000 0,000 0,000 0,000 CO2 Limestone and Dolomite Use 3.481 3.303 3% 10% 0,104 0,001 0,000 0,000 0,000 CO2 Iron and Steel production 1.346 1.384 3% 10% 0,104 0,000 0,000 0,000 0,000 CO2 Other industrial processes 3.643 2.435 3% 10% 0,104 0,000 0,000 0,000 0,000 N2O Adipic Acid 4.579 6.417 3% 10% 0,104 0,001 0,000 0,001 0,001 N2O Nitric Acid 2.169 644 3% 10% 0,104 0,000 0,000 0,000 0,000 CH4 Industrial Processes 108 58 3% 50% 0,501 0,000 0,000 0,000 0,000 PFC Aluminium production 143 277 5% 10% 0,112 0,000 0,000 0,000 0,000 SF6 Magnesium production 0 136 5% 5% 0,071 0,000 0,000 0,000 0,000 SF6 Electrical Equipment 482 290 5% 10% 0,112 0,000 0,000 0,000 0,000 SF6 Other sources of SF6 0 0 0,000 0,000 0,000 0,000 0,000 SF6 Production of SF6 120 0 5% 10% 0,112 0,000 0,000 0,000 0,000 PFC, HFC, SF6 Semiconductor manufacturing 59 286 30% 50% 0,583 0,000 0,000 0,000 0,000 HFC, PFC substitutes for ODS 365 4.544 30% 50% 0,583 0,005 0,004 0,004 0,006 HFC-23 from HCFC-22 Manufacture and HFCs fugitive 441 23 5% 10% 0,112 0,000 0,000 0,000 0,000 CH4 Enteric Fermentation in Domestic Livestock 12.341 10.933 20% 20% 0,283 0,005 -0,001 0,006 0,006 CH4 Manure Management 4.026 3.821 20% 100% 1,020 0,007 -0,001 0,002 0,002 N2O Manure Management 3.829 3.972 20% 100% 1,020 0,007 -0,001 0,002 0,002 CH4 Savanna Burning 0 0 0,000 0,000 0,000 0,000 0,000 N2O Savanna Burning 0 0 0,000 0,000 0,000 0,000 0,000 CH4 Agricultural Residue Burning 13 11 50% 20% 0,539 0,000 0,000 0,000 0,000 N2O Agricultural Residue Burning 4 4 50% 20% 0,539 0,000 0,000 0,000 0,000 Direct N2O Agricultural Soils 9.122 8.771 20% 100% 1,020 0,016 -0,003 0,005 0,006 Indirect N2O from Nitrogen used in agriculture 7.878 7.991 20% 100% 1,020 0,014 -0,002 0,004 0,005 CH4 from Rice production 1.539 1.562 3% 40% 0,401 0,001 0,000 0,000 0,000 CH4 from Other agriculture 0 0 0,000 0,000 0,000 0,000 0,000 N2O from animal production 1.867 1.682 20% 100% 1,020 0,003 -0,001 0,001 0,001 N2O from Other agricultural soils (wetlands, waters) 0 0 0,000 0,000 0,000 0,000 0,000 CH4 from Solid waste Disposal Sites 10.348 9.690 20% 30% 0,361 0,006 -0,001 0,005 0,005 CH4 Emissions from Wastewater Handling 1.340 1.432 100% 30% 1,044 0,003 0,000 0,004 0,004 N2O Emissions from Wastewater Handling 1.044 1.062 30% 30% 0,424 0,001 0,000 0,001 0,001 CO2 Emissions from Waste Incineration 493 168 5% 25% 0,255 0,000 0,000 0,000 0,000 CH4 Emissions from Waste Incineration 161 261 5% 20% 0,206 0,000 0,000 0,000 0,000 N2O Emissions from Waste Incineration 88 113 5% 100% 1,001 0,000 0,000 0,000 0,000 CH4 Emissions from Other Waste 0 4 10% 100% 1,005 0,000 0,000 0,000 0,000 CO2 Emissions from solvent use 1.747 1.324 30% 50% 0,583 0,001 -0,001 0,001 0,001 N2O Emissions from solvent use 796 857 50% 10% 0,510 0,001 0,000 0,001 0,001 N2O Emissions from Other Sources (forest fires) 15 7 50% 50% 0,707 0,000 0,000 0,000 0,000 CH4 Emissions from Other Sources (forest fires) 143 65 50% 50% 0,707 0,000 0,000 0,000 0,000 TOTAL 510.489 569.828 0,032 0,024

Uncertainty analysis – Tier 1

per hectare Growing stockyearDrain and

Grazing

Mortality

Fire

Harvest

per hectareGrowing Stockyear-

1

Growth function

Current Incrementyear

-+

Forest Land emission-removals: For-ests model flowchart

Growing stock estimates:

starting from growing stock volume reported in the INFI, for each year, the current increment per hectare is computed with the derivative Richards function, for every specific forest typology

growing stock per hectare is computed from the previous year growing stock volume adding the calculated current increment and subtracting losses due to harvest, mortality and fire occurred in the current year

Biomass Expansion Factorsaboveground biomass / growing

stock

Wood Basic Density [m3]dry weight ton / fresh volume

Wood Basic Density [m3]dry weight ton / fresh volume

Root/shoot Ratio belowground biomass/ growing

stock mass

Growing stock [m3 ]

Dead mass expansion factor

Dead mass [t d.m.]

Aboveground biomass [t d.m.]

Belowground biomass [t d.m.]

Conversion Factorcarbon content / dry

matter

Dead carbon [t]

Conversion Factorcarbon content / dry

matter

Aboveground carbon [t]

Conversion Factorcarbon content / dry

matter

Belowground carbon [t]

Linear regressioncarbon per ha / carbon

per ha

Litter carbon [t]

Linear regressioncarbon per ha / carbon

per ha

Soil carbon [t]

Growing stock [m3 ha-1]

Area [m3]

x

Forest Land emission-removals: For-ests model flowchart

Forest Land: uncertainty calculation Tier 1

Uncertainty linked to the five carbon

pools has been computed, for each year

1990–2003, in order to assess the

overall uncertainty for Forest Land

ENFI uncertainty associated to the growing stock data (I National Forest Inventory)

EBEF1 uncertainty related to biomass expansion factors for aboveground biomass

EBD basic density uncertainty

ECF conversion factor uncertainty

EBEF2 uncertainty related to biomass expansion factors for belowground biomass

EDEF uncertainty of dead mass expansion factor

ELS uncertainty related to litter carbon stock data (State Forestry Corps)

ESS uncertainty related to soil carbon stock data (State Forestry Corps)

ELR uncertainty related to linear regressions used to assess litter carbon stock

ESR uncertainty related to linear regressions used to assess soil carbon stock

Carbon pool Relation for uncertainty assessing

Aboveground 2221

21985 CFEBDEBEFENFIEAGE

Belowground 2222

1985 2 CFEBDEBEFENFIEBGE

Dead mass 2

1985

2

19851985 DEFEAGEDE

Litter 2

5

2

19851985 LRELSELE

Soil 2

5

2

19851985 SRESSESE

The uncertainty linked to the year 1985 has been computed (the first

National Forest Inventory was carried out in 1985) with the relation:

19851985198519851985

219851985

219851985

219851985

219851985

219851985

1985SVLVDVBGVABV

SVSELVLEDVDEBGVBGEAgVAGEE

Carbon stockst C

O2 eq. ha-1

Aboveground biomass VAG 128.3

Belowground biomass VBG 29.6

Dead mass VD 19.4

Litter VL 14.5

Soil VS 205.6

Uncertainty

Growing stock ENFI 3.2%

BEF1 EBEF1 30%

BEF2 EBEF2 30%

DEF EDEF 30%

Litter (stock + regression) EL 45%

Soil (stock + regression) ES 152%

Basic Density EBD 30%

C Conversion Factor ECF 2%

where VAB, VBG, VD, VL, VS stand for the

carbon stocks of the five pools,

aboveground, belowground, dead

mass, litter and soil, while, with the

letter E, the related uncertainties are

indicated.

Forest Land: uncertainty calculation Tier 1

The overall uncertainty related to 1985 has been propagated through the years. Equations for the 1986-2003 overall uncertainty are similar to the 1985 equation, except for the terms linked to aboveground biomass

2221

2222222

1986 CFEBDEBEFEMORVDVFVHVIVNFIV

MVMEDVDEFVFEHVHEIVIENFIVNFIEAGE

aboveground biomass uncertainty

Growing stock uncertainty (NFI 1985) ENFI 3.2%

Current increment (Richards) ENFI 51.6%

Harvest EH 30%

Fire EF 30%

Drain and grazing ED 30%

Mortality EM 30%

BEF1 EBEF1 30%

BEF2 EBEF2 30%

DEF EDEF 30%

Litter (stock + regression) EL 45%

Soil (stock + regression) ES 152%

Basic Density EBD 30%

C Conversion Factor ECF 2%

Forest Land: uncertainty calculation Tier 1

Carbon pools

Aboveground 93.74%

Belowground 93.74%

Dead mass 98.42%

Litter 42.09%

Soil 152.05%

Overall uncertainty

88.29%

2003

Uncertainties

20032003200320032003

220032003

220032003

220032003

220032003

220032003

2003SVLVDVBGVABV

SVSELVLEDVDEBGVBGEABVABEE

Estimates of removals by Forest Land are

based on application of the above-described

model.

To assess the overall uncertainty related to

the years 1990–2003, the Tier 1 Approach

has been followed.

The uncertainty linked to the five carbon

pools has been computed in order to assess

the overall uncertainty for Forest Land.

Forest Land: uncertainty calculation Tier 1

IPCC Source category

GasBase year emissions

1990

Year t emission

s2003

Combined uncertaint

y

Uncertainty introduced

into trend in total national

emissions

Gg CO2 eqGg CO2 eq

% %

A. Forest Land

CO2 -58,286 -80,044 63% 56%

B. Cropland CO2 -20,236 -19,724 106% 36%

C. Grassland CO2 16,173 16,395 106% 29%

D. Wetlands CO2 0 0 - -

E. Settlements

CO2 1,465 1,473 106% 3%

F. Other Lands

CO2 0 0 - -

G. Other CO2 0 0 - -

TOTAL -60,884 -81,900 71% 30%Tier 1 Approach has been followed for assessing uncertainties

concerning all the categories (Forest Land, Cropland, Grassland,

Wetlands, Settlements, Other Land)

Uncertainty analysis – Tier 1 LULUCF

• Total emissions (without LULUCF):

3.2% level uncertainty in 2003

2.4% uncertainty in the trend between 1990 and 2003

• LULUCF sector:

71% level uncertainty in 2003

30% uncertainty in the trend between 1990 and 2003

Tier 1 - Results

• Uncertainty analysis was carried out at a level at which cross-sectoral correlation was mainly avoided

• EF fully correlated across years• Further investigation is needed to better

quantify the uncertainty values for some specific source

• A conservative approach has been followed

Correlation

• Road transport (CO2):

measurements available for EF factors/low uncertainty

• Agriculture (N2O agricultural soils):

no information available/high uncertainty

Tier 2 - examples

mean uncertaintyGg CO2 117.410 2,0594

tot emissions

Road Transport CO2

GasolineDiesel OilNatural GasLPG

Method Distribution mean std dev uncertaintyMC normal kt 15,197 227.96 3.000MC normal kt 20,587 308.80 2.999MC normal Mm3 445 6.68 3.002MC normal kt 1,209 18.14 3.000

Activity data

Method Distribution mean var uncertaintyboot t/t 3.140 0.00114 2.154boot t/t 3.166 0.00001 0.143MC normal t/km3 1.950 0.02925 2.998boot t/t 3.024 0.00052 1.481

Emission factor

mean uncertaintykt 47,700 3.579kt 65,197 2.993kt 1,175 3.685kt 3,657 3.436

Tot emissions

Road Transport CO2: assumptions

• Activity data:

normal distribution

st dev derived by expert judgement (U=3%)

• Emission factors

1002

U

Data Bootstrap

2/*100 U

2.166 2.154

0.144 0.143

1.591 1.481

Agriculture N2O

N2O agricultural soils

Method Distribution mean std dev uncertaintyMC normal Gg N 1,945 194.45 19.99

Activity data

Method Distribution mean std dev perc 2.5 % perc 97.5 % MC lognorm Gg N20/ Gg N 0.0305 1.0077 0.0301 0.0310 -1.44 1.51

uncertaintyEmission factor

mean var perc 2.5 % perc 97.5 % Gg N20 59.8973 34.234 47.9074 71.9821 -20.02 20.18

uncertaintytot emissions

U=20

%

U=100

%

Combined U

Tier1=102%

• Activity data:

normal distribution

st dev derived by expert judgemnt

• Emission factors:

lognormal

geom st dev derived by expert judment

Agriculture N2O: assumptions

1002

U

2

2001lnexp

U

g

Agriculture N2O: other tests

Method Distribution mean std dev uncertaintyMC normal Gg N 1,945 194.45 19.99

Activity data

Method Distr mean std dev perc 2.5 % perc 97.5 % MC norm Mg N20/ Gg N 30.5393 15.27 1.2041 60.8144 99.942MC lognorm Mg N20/ Gg N 30.5393 7.54 0.5901 1526.9 -98.068 4900

uncertaintyEmission factor

mean var perc 2.5 % perc 97.5 % norm Gg N20 59.7156 94.0145 41.4351 78.43 32.44

lognorm Gg N20 625.48 1.30E+10 1.088 3365.30 -99.83 438.03

tot emissionsuncertainty

• The formula is affected by the unit of measure

• It is not sensitive to changes in uncertainty figures

• MC results affect the asymmetry of the distribution

• Further study may be needed

Agriculture N2O: comments

2

2001lnexp

U

g

• Two public power plants (in continuous monitoring system)

• Coal plant (two boilers) and Fuel Oil plant (four boilers)

• SOx, NOx, CO and PM measurements

• Daily and hourly average concentration values for a year supplied by the National Electrical Company

Alternative approach: case study

Boiler 3 SO2 NOX PM CO

min 518.56 371.23 4.61 0

max 1447.97 1296.30 85.30 105.96

mean 983.58 842.51 51.34 10.50

median 1011.16 827.34 53.04 5.37

skewness -0.07 0.24 -0.67 2.94

kurtosis -0.62 1.19 0.44 11.42

st. dev. 191.13 136.81 17.04 14.56

Boiler 4 SO2 NOX PM CO

min 502.30 251.04 0 0

max 1406.60 774.31 92.31 192.46

mean 928.56 475.68 40.04 32.92

median 924.78 483.54 35.82 19.69

skewness 0.16 -0.06 0.24 1.86

kurtosis -0.65 0.36 -0.84 3.88

st. dev. 181.89 85.53 24.67 34.89

Descriptive statistics (coal plant mg/Nm3)

Boiler 1 SO2 NOX PM

min 940.16 150.60 0.01

max 4066.79 858.88 149.88

mean 2981.65 532.30 62.90

median 2987.71 523.29 60.55

skewness -1.44 0.16 0.43

kurtosis 3.40 -0.65 -0.20

st. dev. 440.87 114.54 29.57

Boiler 2 SO2 NOX PM min 58.93 9.73 0.08 max 4217.61 1081.74 170.09 mean 2945.89 451.61 70.30 median 2985.47 451.44 74.51 skewness -1.25 -0.08 -0.30 kurtosis 2.40 0.74 0.64 st. dev. 506.32 76.75 22.83

Boiler 3 SO2 NOX PM min 531.10 124.52 2.10 max 1749.73 752.50 323.91 mean 1638.98 484.35 26.67 median 1642.92 493.23 25.19 skewness -2.30 0.08 7.95 kurtosis 16.68 -1.34 173.12 st. dev. 80.94 133.61 11.24

Boiler 4 SO2 NOX PM min 810.30 251.48 0.01 max 1749.72 818.44 89.11 mean 1608.03 470.60 19.45 median 1604.53 476.84 16.60 skewness -0.53 0.04 1.94 kurtosis 3.16 -0.62 6.45 st. dev. 77.32 90.01 11.31

Descriptive statistics (fuel oil plant mg/Nm3)

• Most of the empirical values show irregular features (except for CO)

• Good-fitness test Kolmogorov-Smirnov and Chi quadro do not provide good results with regard to classical distributions

• Classical distributions have been chosen considering the type of fuel burnt, type of pollutant, abatement technology

Choice of distributions

SO2 NOx PM CO

boiler 3 Normal Normal Normal Exponential

boiler 4 Normal Normal Normal Exponential

SO2 NOx PM

boiler 1 Normal Weibull Lognormal

boiler 2 Normal Normal Gamma

boiler 3 Weibull Weibull Exponential

boiler 4 Weibull Gamma Lognormal

Probability density functions (best fitting)

Montecarlo Analysis

• Good results for low asymmetric distributions

• Large discrepancies for irregular or asymmetrical distributions

Bootstrap

• Very low differences between estimated and real values in case of irregular or asymmetric basic distributions

Comments on the results

• Emission data can be considered fuzzy for the way they are measured or estimated; they are vague, indefinite, ambiguous in opposition to the neatness and exactness of the crisp data

• Does not need assumptions on the underlying distribution and parameters and it is applicable even if few data or measurements are available

• It is possible to consider qualitative information on emission factors, by means of a membership function (weights between 0 and 1)

Fuzzy Analysis

• For example, given the measured value of a parameter, the membership function gives the “degree of truth” of the parameter

• Example: if an expert chooses a default value from a Guidebook but a set of values referring to different countries and technologies is available, he could weight them differently to calculate the associated fuzzy uncertainty

Fuzzy Analysis

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Fuzzy analysis - Statistical Formalization

• The application has provided results which do not significantly differ from the real standard deviations, even if a comparison is not really appropriate because the methods derive from different and, in principle, not comparable logics

Comments on Fuzzy analysis

• When measurements are not available to quantify uncertainties every approach is highly affected by expert judgement

• The more complicated the approach the higher the uncertainty introduced in the parameters

• The simple use of Montecarlo, which suits every distribution, can lead to misunderstanding results if the choice of the input distribution is far from real

• Bootstrap, even if considering the empirical data distribution, can be affected by lack of sample data or their poor representativeness

• Fuzzy logic can be simple and useful but the transformation of qualitative information into quantitative values to characterize membership functions could be difficult and subjective

Conclusions

Conclusions

• Is it necessary to make loads of assumptions in order to estimate emission uncertainty when we do not have enough statistical information on data?

• In this scenario, isn’t the Tier1 enough simple and transparent to give a value of uncertainty for the purpose of an emission inventory?

THANK YOU !