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 Competitiveness, employment Impacts, monetary equivalents Probability of benefits>costs Costs, ecosystem impacts

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

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. Source of biasLikely effect on benefit : cost ratio Comment Variability in meteorology from year to year (+++/---) The CAFE analysis has been based on use of meteorological data from 1997 only. Figure 11 and Figure 12 should enable readers to assess the effect of variability in meteorology on results that are based on this year. Underestimation of suspended particle concentrations, particularly through not accounting for secondary organic aerosols. --- Overall, secondary organic aerosols contribute around 10% to total aerosol concentrations in the atmosphere over Europe (D. Simpson, personal communication). Part of this will be linked to anthropogenic emissions of VOCs and part to natural emissions. Analysis below seeks to make some estimate of the importance of this effect (see Table 25). Lack of specific account of urban concs. of:  PM 2.5  Ozone 0 (assuming CITYDELTA adjustment is correct) ++ Urban concentrations of PM are factored into the RAINS model using the results of the CITYDELTA Project. Ozone concentrations are generally depressed in urban areas as a result of high local NOx emissions. Consideration of bias in EMEP outputs (from CAFE-CBA)

25. Further consideration of meteorological year bias

26. Estimating effects of secondary organic aerosols Estimated change in SOA health damage by scenario (€million) Total reduction in SOA health damage in each scenario compared to the baseline scenario VOLY median VOLY mean VSL median VSL mean Scenario VOC emitted (t)Reduction (t) CLE5,916,000- The Strategy5,230,000686,0001,7003,3003,0005,700 MTFR4,303,0001,613,0004,1007,6007,10013,000

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