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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?!

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the emep eea emissions inventory guidebook

The EMEP/EEA Emissions Inventory Guidebook

Dr Chris Dore

Chair of the TFEIP

contents
Contents
  • Accuracy In Emission Inventories
  • Principles of Uncertainty
  • Uncertainty Tools
  • Conclusions
  • Discussion Points
1 accuracy
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 accuracy1
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
2 principles of uncertainty
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
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
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
3. Uncertainty Tools (3)

Trend Uncertainties

  • Standard tool used for assessing the uncertainty in the trend included in the Guidance.
3 uncertainty tools
3. Uncertainty Tools

Strengths

  • Methodologies common with GHGs (UNFCCC)
  • Standard mathematical approaches for assessing uncertainty
  • Simple methods available.
3 uncertainty tools1
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 tools2
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 tools3
3. Uncertainty Tools

Development of New Tools

  • Uncertainty of mapped emissions
  • Combination of emissions uncertainty with mapping uncertainty
3 uncertainty tools4
3. Uncertainty Tools

Learning from the Past

4 conclusions
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
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?
slide17

THANK-YOU FOR

YOUR ATTENTION

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