Uncertainty Analysis in Emission Inventories

Transcription

1 Task Force on National Greenhouse Gas Inventories Uncertainty Analysis in Emission Inventories Africa Regional Workshop on the Building of Sustainable National Greenhouse Gas Inventory Management Systems, and the Use of 2006 IPCC Guidelines for National Greenhouse Gas Inventories Maseru, Lesotho March 2016 Kiyoto Tanabe Technical Support Unit, IPCC TFI

2 Introduction Most important is producing high quality Good Practice emission and removal estimates Accurate in the sense that they are systematically neither over- nor underestimates so far as can be judged, and that uncertainties are reduced so far as possible Uncertainty in GHG inventory: a lack of knowledge of the true value of a variable that can be described as a probability density function (PDF) characterising the range and likelihood of possible values Quantitative uncertainty analysis is performed by estimating the 95 percent confidence interval of the emissions and removals estimates for individual categories and for the total inventory

3 Specifying Uncertainty Uncertainty is quoted as the 2.5 and 97.5 percentile i.e. bounds around a 95% confidence interval This can be expressed, for example: 234 ± 23% (- 50%, + 100%) 2.5 th Percentile 95% Probability Range -30% +30% Mean 97.5 th Percentile 0 1 2

4 Benefits of Uncertainty Analysis Inventories are estimates uncertainty analysis gives a clear statement on what we do and do not know Users of the inventory need to know how reliable the numbers are especially if they are input into policy or inventory improvement actions Uncertainty analysis is a requirement of all good practice inventories All scientific analysis should include an uncertainty assessment

5 Uncertainty estimation in 2006 IPCC Guidelines Gather Information Collect uncertainty information on activity data and emission factors Decide approach to use Error Propagation Monte Carlo Perform Inventory Analysis Spreadsheet Software tool

6 Sources of Uncertainty Assumptions and methods The method may not accurately reflect the emissions. Good Practice requires that biases be reduced as much as possible Input Data Measured values have errors and EFs may not be truly representative Lack of data (e.g. use of proxies, extrapolation) Calculation errors Good QA/QC to prevent these

7 Sources of Data and Information for Uncertainty There are three broad sources of data and information information contained in models empirical data associated with measurements of emissions, and activity data from surveys and censuses quantified estimates of uncertainties based upon expert judgement Models can be as simple as arithmetic multiplication of AD and EF for each category and subsequent summation over all categories, but they may also include complex process models specific to particular categories Data collection activities should consider data uncertainties. This will ensure the best data is collected and ensures good practice estimates Wherever possible, expert judgement should be elicited using an appropriate protocol (e.g. Stanford/SRI protocol)

8 Methods to Combine Uncertainties Error Propagation Simple (standard spreadsheet can be used) Guidelines give explanation and equations Difficult to deal with correlations Standard deviation/mean < 0.3 Monte Carlo Simulation More complex (specialised software is used) Needs shape of pdf Suitable where uncertainties large, non-normal distribution, complex algorithms, correlations exist and uncertainties vary with time

9 Approach 1: Error Propagation Enter Emissions Data TABLE 3.2 APPROACH 1 UNCERTAINTY CALCULATION Data Calculated using simple equations A B C D E F G H I J K L M IPCC category Gas Base year emissions or removals Year t emissions or removals Activity data uncertainty Emission factor / estimation parameter uncertainty Combined uncertainty Contribution to Variance by Category in Year t Type A sensitivity Type B sensitivity Uncertainty in trend in national emissions introduced by emission factor / estimation parameter uncertainty Uncertainty in trend in national emissions introduced by activity data uncertainty Uncertainty introduced into the trend in total national emissions Input data Input data Input data Note A Input data Note A E F G D D 2 Note B D C I F Note C J E 2 Note D 2 2 K L Gg CO 2 equivalent Gg CO 2 equivalent % % % % % % % % E.g., 1.A.1. Energy Industries Fuel 1 E.g., 1.A.1. Energy Industries Fuel 2 CO 2 CO 2 Etc... Total C D H M Percentage uncertainty in total inventory: H Trend uncertainty: M Enter Uncertainties

10 Approach 1 uncertainty calculation A B C D E F G H I J K L M IPCC category Gas Base year emissions or removals AD uncertainties based on source of data Year t emissions or removals Activity data uncertainty Emission factor / estimation parameter uncertainty Combined uncertainty Contribution to Type A Variance by sensitivity Category in Year t 2 Input data Input data Input data Input data 2 Note B F 2 G D E D 2 Gg CO 2 equivalent Type B sensitivity Uncertainty in trend in national emissions introduced by emission factor / estimation parameter uncertainty Uncertainty in trend in national emissions introduced by activity data uncertainty Uncertainty introduced into the trend in total national emissions Gg CO 2 equivalent % % % % % % % % 1.A.1. Energy Industries CH E E-06 1.A.2. Manufacturing Industries and ConstructionCH E E-06 1.A.3. Transport CH E E-06 1.A.4. Other Sectors CH A.5. Other CH E-05 1.B.1. Solid Fuels CH B.2. Oil and Natural Gas CH B. Chemical Industry. CH E E-06 4.A. Enteric Fermentation. CH B. Manure Management. CH Note 15 short 30 list of E C. Rice Cultivation. CH E F. Field Burning of Agricultural Residues. CH E E-05 source/sinks 6.A. Solid Waste Disposal on Land. CH B. Wastewater Handling. CH A.1. Energy Industries CO A.2. Manufacturing Industries and ConstructionCO A.3. Transport CO A.4. Other Sectors CO A.5. Other CO B.2. Oil and Natural Gas CO A. Mineral Products. CO B. Chemical Industry. CO C. Metal Production. CO A. Changes in Forest and Other Woody BiomaCO A. Changes in Forest and Other Woody BiomaCO B. Forest and Grassland Conversion. CO A.1. Energy Industries N2O A.2. Manufacturing Industries and ConstructionN2O E-05 1.A.3. Transport N2O E E E-06 1.A.4. Other Sectors N2O A.5. Other N2O E E E-07 4.B. Manure Management. N2O D. Agricultural Soils(2). N2O F. Field Burning of Agricultural Residues. N2O E E E-06 6.B. Wastewater Handling. N2O Keep Blank! 0 Total H 34.6 M Percentage uncertainty in total inventory: EF uncertainties based on defaults in guidelines D C I F J E 2 2 K 2 L Trend uncertainty:

11 Approach 2: Monte Carlo Method Key Requirements Not just uncertainties but also probability density function (pdf) Mean Width Shape (e.g. Normal, Log-normal, Weibul, Gamma, Uniform, Triangular, Fractile, ) Principle Select random values of input parameters from their pdf and calculate the corresponding emission. Repeat many times and the distribution of the results is the pdf of the result, from which mean and uncertainty can be estimated

12 Probability Density Function Probability Density 2.5 th Percentile 95% Probability Range -30% +30% Mean 97.5 th Percentile Example Emission Factor

13 Probability Density Function Probability Density 2.5 th 97.5 th Percentile Percentile 95% Probability Range -50% +100% Mean Example Emission Factor

14 Illustration of Monte-Carlo Method Emission Factor Activity Data Emission Factor Activity Data Probability Probability Probability Probability Value Value Value Value Select Random Value from distribution Select Random Value from distribution Select Random Value from distribution Select Random Value from distribution Repeat Emission = Emission Factor * Activity Data Emission = Emission Factor * Activity Data Total Store in database Calculate Mean and Uncertainty Mean Constant? Finish

15 Example of Monte Carlo Results Frequency Runs Runs Runs Runs Millions Emission

16 Summary Results Emission Mean 2.5 percentile 97.5 percentile Number of Runs

17 Uncertainty Analysis: IPCC Inventory Software Click Uncertainty Analysis Click to perform analysis

18 Uncertainty Analysis: IPCC Inventory Software (cont.) Click to enter AD and EF uncertainties

19 Summary Even simple uncertainty estimates give useful information - If they are performed well Assessment of uncertainty in the input parameters should be part of the data collection Careful consideration will improve estimates as well as providing input data for uncertainty analysis If resources limited: effort spent on uncertainty analysis should be small compared with total effort At its simplest a well planned uncertainty assessment should only take a few extra hours! Uncertainty in AD assessed as data collected Uncertainty in EFs from guidelines now available Aggregate categories/gases to independent groups of sources/sinks Use Approach 1

20 Thank you Diagrams IPCC unless otherwise noted