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

Uncertainty analysis – Tier 1

per hectare Growing stock year Drain and Grazing Mortality Fire Harvest per hectare Growing Stock year-1 Growth function Current Increment year - + 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 Factors aboveground biomass / growing stock Wood Basic Density [m3] dry weight ton / fresh volume Wood Basic Density [m 3 ] dry weight ton / fresh volume Root/shoot Ratio belowground biomass / growing stock mass Growing stock [ m 3 ]Dead mass expansion factor Dead mass [t d.m.] Aboveground biomass [t d.m.]Belowground biomass [t d.m.] Conversion Factor carbon content / dry matter Dead carbon [t] Conversion Factor carbon content / dry matter Aboveground carbon [t] Conversion Factor carbon content / dry matter Belowground carbon [t] Linear regression carbon per ha / carbon per ha Litter carbon [t] Linear regression carbon per ha / carbon per ha Soil carbon [t] Growing stock [ m 3 ha -1 ] Area [m 3 ] 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 E NFI uncertainty associated to the growing stock data (I National Forest Inventory) E BEF1 uncertainty related to biomass expansion factors for aboveground biomass E BD basic density uncertainty E CF conversion factor uncertainty E BEF2 uncertainty related to biomass expansion factors for belowground biomass E DEF uncertainty of dead mass expansion factor E LS uncertainty related to litter carbon stock data (State Forestry Corps) E SS uncertainty related to soil carbon stock data (State Forestry Corps) E LR uncertainty related to linear regressions used to assess litter carbon stock E SR uncertainty related to linear regressions used to assess soil carbon stock

The uncertainty linked to the year 1985 has been computed (the first National Forest Inventory was carried out in 1985) with the relation: Carbon stocks t CO 2 eq. ha -1 Aboveground biomass V AG Belowground biomass V BG 29.6 Dead mass V D 19.4 Litter V L 14.5 Soil V S Uncertainty Growing stock E NFI 3.2% BEF 1 E BEF1 30% BEF 2 E BEF2 30% DEF E DEF 30% Litter (stock + regression) E L 45% Soil (stock + regression) E S 152% Basic Density E BD 30% C Conversion Factor E CF 2% where V AB, V BG, V D, V L, V S 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 overall uncertainty are similar to the 1985 equation, except for the terms linked to aboveground biomass aboveground biomass uncertainty Forest Land: uncertainty calculation Tier 1

Carbon pools Aboveground 93.74% Belowground 93.74% Dead mass 98.42% Litter 42.09% Soil % Overall uncertainty 88.29% 2003 Uncertainties 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 Gas Base year emissions 1990 Year t emissions 2003 Combined uncertainty Uncertainty introduced into trend in total national emissions Gg CO 2 eq % A. Forest LandCO 2 -58,286-80,04463%56% B. CroplandCO 2 -20,236-19,724106%36% C. GrasslandCO 2 16,17316,395106%29% D. WetlandsCO E. SettlementsCO 2 1,4651,473106%3% F. Other LandsCO G. OtherCO TOTAL -60,884-81,90071%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 % uncertainty in the trend between 1990 and 2003 LULUCF sector: 71% level uncertainty in % 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 (CO 2 ): measurements available for EF factors/low uncertainty Agriculture (N 2 O agricultural soils): no information available/high uncertainty Tier 2 - examples

Road Transport CO 2

Road Transport CO 2 : assumptions Activity data: normal distribution st dev derived by expert judgement (U=3%) Emission factors Data Bootstrap 2  /  *100 U

Agriculture N 2 O 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 N 2 O: assumptions

Agriculture N 2 O: other tests

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 N 2 O: comments

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

Descriptive statistics (coal plant mg/Nm 3 )

Descriptive statistics (fuel oil plant mg/Nm 3 )

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

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

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

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?

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