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Evaluating uncertainty in the Italian GHG Inventory. Daniela Romano. APAT. Agency for the Protection of the Environment and for Technical Services. Workshop on Uncertainties in GHG inventories Helsinki, 5-6 September 2005.
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Evaluating uncertainty in the Italian GHG Inventory Daniela Romano APAT Agency for the Protection of the Environment and for Technical Services Workshop on Uncertainties in GHG inventories Helsinki, 5-6 September 2005
Institutions involved in the compilation of national emission inventory • 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
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
Sources of uncertainty 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)
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
Uncertainty analysis – Tier 1 • 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
Activity data • 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
Emission factors • 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
per hectare Growing Stockyear-1 Growth function Current Incrementyear Harvest + Fire - per hectare Growing stockyear Drain and Grazing Mortality 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
Growing stock[m3 ha-1] x Growing stock [m3 ] Area [m3] Wood Basic Density [m3] dry weight ton / fresh volume Biomass Expansion Factors aboveground biomass / growing stock Root/shoot Ratio belowground biomass/ growing stock mass Wood Basic Density [m3] dry weight ton / fresh volume Aboveground biomass [t d.m.] Belowground biomass [t d.m.] Conversion Factor carbon content / dry matter Conversion Factor carbon content / dry matter Dead mass expansion factor Belowground carbon [t] Dead mass [t d.m.] Aboveground carbon [t] Linear regression carbon per ha / carbon per ha Linear regression carbon per ha / carbon per ha Conversion Factor carbon content / dry matter Dead carbon [t] Litter carbon [t] Soil carbon [t] 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
Forest Land: uncertainty calculation Tier 1 The uncertainty linked to the year 1985 has been computed (the first National Forest Inventory was carried out in 1985) with the relation: where VAB, VBG, VD, VL, VSstand 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.
aboveground biomass uncertainty 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
Uncertainties 93.74% Aboveground 93.74% Belowground 98.42% Dead mass Litter 42.09% 152.05% Soil 88.29% Overalluncertainty Forest Land: uncertainty calculation Tier 1 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. Carbon pools 2003
Uncertainty analysis – Tier 1 LULUCF Tier 1 Approach has been followed for assessing uncertainties concerning all the categories (Forest Land, Cropland, Grassland, Wetlands, Settlements, Other Land)
Tier 1 - Results • 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
Correlation • 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
Tier 2 - examples • Road transport (CO2): measurements available for EF factors/low uncertainty • Agriculture (N2O agricultural soils): no information available/high uncertainty
Road Transport CO2: assumptions • Activity data: normal distribution st dev derived by expert judgement (U=3%) • Emission factors Data Bootstrap 2s/m*100 U 2.166 2.154 0.144 0.143 1.591 1.481
Agriculture N2O U=20% U=100% Combined UTier1=102%
Agriculture N2O: assumptions • Activity data: normal distribution st dev derived by expert judgemnt • Emission factors: lognormal geom st dev derived by expert judment
Agriculture N2O: comments • 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
Alternative approach: case study • 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
Choice of distributions • 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
Comments on the results 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
Fuzzy Analysis • 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
Comments on Fuzzy analysis • 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
Conclusions • 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?
