The EMEP/EEA Emissions Inventory Guidebook

The EMEP/EEA Emissions Inventory Guidebook Dr Chris Dore Chair of the TFEIP

Contents • Accuracy In Emission Inventories • Principles of Uncertainty • Uncertainty Tools • Conclusions • Discussion Points

1. Accuracy Does it matter?! • Actually, it is not very important for demonstrating compliance with targets • But key for trying to reflect the real world.

1. Accuracy Some starting considerations… • Point sources vs area sources • Source/fuel mix • Activity data – trends with time vs absolute • EFs – variations across time series, applicability • Completeness vs guidebook • Completeness vs real world • Mapping emissions • Projections & scenarios

1. Accuracy

2. Principles of Uncertainty • Uncertainty analysis is generally used to represent “accuracy” • Point sources- combination of random independent errors • Area sources- one EF, prone to bias.

3. Uncertainty Tools (1) Propagation of Errors • Assign uncertainty to AD and EF • from measurement, default ranges, expert judgement • “,… root the sum of the squares…” • Simple mathematical combination of elements to give an uncertainty for the total emission.

3. Uncertainty Tools (2) Monte-Carlo Analysis • Uncertainty profiles, accounts for inter-dependencies... • Much better tool, but more challenging to use.

3. Uncertainty Tools (3) Trend Uncertainties • Standard tool used for assessing the uncertainty in the trend included in the Guidance.

3. Uncertainty Tools Strengths • Methodologies common with GHGs (UNFCCC) • Standard mathematical approaches for assessing uncertainty • Simple methods available.

3. Uncertainty Tools Weaknesses • Low uncertainty does not necessarily mean good accuracy! • incomplete inventory, use of inappropriate EFs etc. • Uncertainty ranges applied to EFs are usually no better than a guess! • Not usually enough data points for a statistical analysis • Error propagation analysis is too simple • Does not account for interdependencies/biases etc. • Modellers want uncertainty on mapped emissions.

3. Uncertainty Tools Development of New Tools • Moran’s co-efficient • A mathematical metric of spatial autocorrelation (chess board = -1, random = 0, uniform = +1). • Indicates adjacent grid cell dependencies • Uncertainty of mapped emissions

3. Uncertainty Tools Development of New Tools • Uncertainty of mapped emissions • Combination of emissions uncertainty with mapping uncertainty

3. Uncertainty Tools Learning from the Past

4. Conclusions Important Considerations • EF uncertainties are not robust enough • Error propagation analysis - too simple? • Uncertainty analysis does not indicate the ability to represent the real world • Modellers want uncertainty on mapped emissions. • … we need to improve what we are delivering! • … and in particular better explain what it represents.

5. Discussion Points Some Questions • Can we improve current EF uncertainties? • Should we all be using Monte-Carlo analysis? • Can we add to/adjust uncertainty results to give an indication of real-world representation? • Can tools be developed that better provide the information that users need? • What resources do we have to support this?

THANK-YOU FOR YOUR ATTENTION

The EMEP/EEA Emissions Inventory Guidebook