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Benefits Analysis and CBA in the EC4MACS Project

Benefits Analysis and CBA in the EC4MACS Project. Mike Holland, EMRC Gwyn Jones, AEA Energy and Environment Anil Markandya, Metroeconomica. What benefits?. Reduced damage from regional air pollution to: Health (quantified, monetised)

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Benefits Analysis and CBA in the EC4MACS Project

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  1. Benefits Analysis and CBA in the EC4MACS Project Mike Holland, EMRC Gwyn Jones, AEA Energy and Environment Anil Markandya, Metroeconomica

  2. What benefits? • Reduced damage from regional air pollution to: • Health (quantified, monetised) • Environment (quantified in GAINS but not monetised for the CBA) • Materials (quantified, partially monetised) • Crops (quantified, partially monetised) • Benefits of reducing climate change impacts? Review only.

  3. Approach Developed through ExternE and related studies since 1991

  4. Geographic scope • Can cover all countries for which EMEP provides pollution data • Valuation issues in non-EC states

  5. The place of benefits analysis and CBA in EC4MACS PRIMES TREMOVE GAINS EMEP POLES CAPRI Benefits GEM-E3 CBA + uncertainty Costs, ecosystem impacts Impacts, monetary equivalents Probability of benefits>costs Competitiveness, employment

  6. Responsibilities within the red box • Benefits analysis • CAFE-CBA model – AEA E&E • Uncertainty analysis – Mike Holland • Methodology update • Impact assessment – Mike Holland • Valuation – Metroeconomica

  7. Common data with other models • Population data • Crop data? • Pollution data • Cost estimates for any scenario • Need for any updates in any dataset to be disseminated across the team

  8. Inputs from other models • GAINS – cost data, ecosystem effects, emissions, some pollution data • EMEP – pollution data • Data transfer protocols being refined in work for the NECD revision

  9. Inputs from other groups • WHO advice on health impact assessment • Not part of EC4MACS – not sure how this would happen • CLRTAP Working Groups, Task Forces, Expert Groups, etc. particularly: • Vegetation • Materials • Forests • Freshwaters • Linkage through Jean-Paul, Vladimir Kucera, Gina Mills/Harry Harmens

  10. Outputs • To other models: • None? GEM-E3? • To policy makers: • Magnitude of impacts • Magnitude of benefits • Balance of cost and benefits according to best estimates • Probability of deriving a net benefit when uncertainties are accounted for

  11. Example output: Monetised health benefits of Thematic Strategy

  12. Example output: Benefit : cost ratio of the Thematic Strategy

  13. Uncertainty analysis • Under CAFE we seek to address uncertainty through: • Statistical error • Sensitivity to methodological assumptions • Inherent (unquantified) bias in the analysis

  14. Statistical error • Incidence rates for health impacts • Response functions • Valuation data

  15. Sensitivities • Risk factor for chronic mortality effects of particles • Valuation of mortality

  16. Combining statistical and sensitivity analysis • Following graphs combine: • Statistical errors in incidence rates, response functions and valuation data • Sensitivity to different approaches to mortality valuation • Sensitivity to error in quantification of abatement costs

  17. Illustration of uncertainty analysis output

  18. Illustration of uncertainty analysis output

  19. Illustration of uncertainty analysis output

  20. Illustration of uncertainty analysis output

  21. Uncertainty so far… • Previosu slides show how we account for statistical error and methodological sensitivities • But what about inherent and unquantified biases?

  22. Inherent bias • Examples: • Omission of secondary organic aerosols • Failure to monetise ecological impacts • Failure to quantify impacts to cultural heritage • Failure to quantify some possible health impacts because of a lack of data • Systematic upward bias in abatement costs?

  23. Biases - general approach • Identify biases • Indicate strength and direction of bias • Provide scoping analysis if appropriate • Work out which biases matter, and if there is a consistent bias to over- or under-estimation from them

  24. Consideration of bias in EMEP outputs (from CAFE-CBA)

  25. Further consideration of meteorological year bias

  26. Estimating effects of secondary organic aerosols

  27. Further biases in GAINS and benefits assessments • Similar treatment to those in EMEP • Identify biases • Indicate strength and direction of bias • Provide scoping analysis if appropriate • Work out which biases matter, and if there is a consistent bias to over- or under-estimation from them

  28. Priorities for further work • More effective integration of ecosystem impacts and other (currently) unquantified effects • Selling willingness to pay to a sceptical audience • Integration of climate benefits with regional pollution benefits • BUT…limited scope for this in EC4MACS

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