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)– 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.

Approach

Developed through ExternE and related studies since 1991

Geographic scope

• Can cover all countries for which EMEP provides pollution data

• Valuation issues in non-EC states

The place of benefits analysis and CBA in EC4MACS

PRIMES

TREMOVE GAINS EMEP

POLES

CAPRI

Benefits

GEM-E3

CBA + uncertainty

Competitiveness, employment

Impacts, monetaryequivalents

Probability of benefits>costs

Costs, ecosystemimpacts

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

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

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

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

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

Example output: Monetised health benefits of Thematic Strategy

0

2000

4000

6000

8000

10000

12000

Austri

a

Belgium

Cypru

s

Czech

Rep

.

Denmar

k

Estoni

a

Finlan

d

France

German

y

Greece

Hunga

ry

Irelan

dIta

lyLatv

ia

Lithua

nia

Luxem

bour

g

Malt

a

Nether

lands

Poland

Portug

al

Slovak

ia

Sloven

iaSpa

in

Sweden UKH

ealth

Ben

efits

of

The

mat

ic S

trat

egy

.

Mill

ion

Eur

o (L

OW

)

Example output: Benefit : cost ratio of the Thematic Strategy

0

2

4

6

8

10

12

Austri

a

Belgium

Cypru

s

Czech

Rep

.

Denmar

k

Estoni

a

Finlan

d

France

German

y

Greece

Hunga

ry

Irelan

dIta

lyLatv

ia

Lithua

nia

Luxem

bour

g

Malt

a

Nether

lands

Poland

Portug

al

Slovak

ia

Sloven

iaSpa

in

Sweden UK EU

Hea

lth

Ben

efit

: Cos

t Rat

io (L

OW

)

Uncertainty analysis

• Under CAFE we seek to address uncertainty through:– Statistical error– Sensitivity to methodological assumptions

– Inherent (unquantified) bias in the analysis

Statistical error

• Incidence rates for health impacts

• Response functions

• Valuation data

Sensitivities

• Risk factor for chronic mortality effects of particles

• Valuation of mortality

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

Illustration of uncertainty analysis output

Aggregate damage factors, VSL

0.000

0.002

0.004

0.007

0.009

0.011

0.013

0.016

0.018

0.020

0 100 200 300

€/person.µg.m-3

Illustration of uncertainty analysis output

Scenario B

0%

20%

40%

60%

80%

100%

120%

120% 110% 100% 90% 80% 70% 60% 50%

Adjusted full cost estimate (RAINS = 100%)

Pro

ba

bili

ty o

f b

en

efit

>

cost

VOLY median VOLY mean VSL median VSL mean

Illustration of uncertainty analysis output

Scenario C

0%

20%

40%

60%

80%

100%

120%

120% 110% 100% 90% 80% 70% 60% 50%

Adjusted full cost estimate (RAINS = 100%)

Pro

ba

bili

ty o

f b

en

efit

>

cost

VOLY median VOLY mean VSL median VSL mean

Illustration of uncertainty analysis output

Scenario MTFR

0%

20%

40%

60%

80%

100%

120%

120% 110% 100% 90% 80% 70% 60% 50%

Adjusted full cost estimate (RAINS = 100%)

Pro

ba

bili

ty o

f b

en

efit

>

cost

VOLY median VOLY mean VSL median VSL mean

Uncertainty so far…

• Previosu slides show how we account for statistical error and methodological sensitivities

• But what about inherent and unquantified biases?

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?

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

Source of bias Likely effect onbenefit : 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:

PM2.5

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)

Further consideration of meteorological year bias

Estimated variation in exposure to PM2.5 in 2000 using met years 1997, 1999, 2000, 2003. EU25 excl. Cyprus. 1997 highlighted.

0.60

0.80

1.00

1.20

1.40

AT

BE

CZ

DE

DK

ES

EW F

I

FR

GR H0 IE IT LT LU LV M NL

PL

PT

SE

SK SL

UK

EU

25

Var

iati

on

aro

un

d m

ean

(=

1)

Range1997

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

ScenarioVOC emitted

(t) Reduction (t)

CLE 5,916,000 -

The Strategy 5,230,000 686,000 1,700 3,300 3,000 5,700

MTFR 4,303,000 1,613,000 4,100 7,600 7,100 13,000

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

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