Methodology and applications of the RAINS air pollution integrated assessment model

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Methodology and applications of the RAINS air
pollution integrated assessment model

• Markus Amann
• International Institute for Applied Systems
  Analysis (IIASA)

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Contents

• Cost-effectiveness analysis
• The RAINS concept
• Key methodologies and results

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Cost-effectiveness needs integration

• Economic development
• Emission generating activities (energy,
  transport, agriculture, industrial production,
  etc.)
• Emission characteristics
• Emission control options
• Costs of emission controls
• Atmospheric dispersion
• Environmental impacts (health, ecosystems)
• Systematic approach to identify cost-effective
  packages of measures

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The RAINS integrated assessment model for air
pollution
Energy/agricultural projections
Driving forces
Emission control options
Emissions
Costs
Atmospheric dispersion
Health and environmental impacts
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The RAINS multi-pollutant/multi-effect framework
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System boundaries

• Driving forces of air pollution (energy use,
  transport, agriculture)
• are driven by other issues, and
• have impacts on other issues too.
• Critical boundaries
• Greenhouse gas emissions and climate change
  policies (GAINS!)
• Agricultural policies
• Other air pollution impacts on water and soil
  (nitrogen deposition over seas, nitrate in
  groundwater, etc.)
• Quantification of AP effects where scientific
  basis is not robust enough (economic evaluation
  of benefits)

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Policy analysis with the RAINS
cost-effectiveness approach
Energy/agricultural projections
Driving forces
Emission control options
Emissions
Costs
Atmospheric dispersion
Health and environmental impacts
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Per-capita costs NEC1999 Scenario H1
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The cost-effectiveness approach
Models help to separate policy and technical
issues
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RAINS policy applications

• UN ECE Convention on Long-range Transboundary Air
  Pollution
• Second Sulphur Protocol 1994
• Gothenburg Multi-pollutant Protocol 1999
• European Union
• Acidification Strategy 1997
• National Emission Ceilings 1999
• Clean Air For Europe 2005
• Revision of National Emission Ceilings 2007
• China
• National Acid Rain policy plan 2004
• Multi-pollutant/multi-effect clean air policy
  2007
• National RAINS implementations
• Netherlands, Italy, Finland

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Review of RAINS methodology and input data

• Scientific peer review of modelling methodology
  in 2004
• Bilateral consultations with experts from Member
  States and Industry on input data
• For CAFE 2004-2005 24 meeting with 107 experts
• For NEC review 2006 28 meetings with gt 100
  experts
• The RAINS model is accessible online at
• www.iiasa.ac.at/rains

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Criteria for aggregation of emission sources

• RAINS applies six criteria
• Importance of source (gt0.5 percent in a country)
• Possibility for using uniform activity rates and
  emission factors
• Possibility of establishing plausible forecasts
  of future activity levels
• Availability and applicability of similar
  control technologies
• Availability of relevant data

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Calculating emissions
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Land-based emissionsCAFE baseline with climate
measures, EU-25
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RAINS cost estimates are country- and
technology-specific

• Technology-specific factors
• Investments
• Demand for labour, energy, by-products
• Lifetime of equipment
• Removal efficiency
• Country-specific factors
• Prices for labour, energy, by-products, etc.
• Applicability
• General factors
• Interest rate

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An example cost curve for SO2
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Scope for further technical emission
reductionsCAFE baseline with climate measures,
EU-25
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Source-receptor relationships for PM2.5derived
from the EMEP Eulerian model for primary and
secondary PM

• PM2.5j Annual mean concentration of PM2.5 at
  receptor point j
• I Set of emission sources (countries)
• J Set of receptors (grid cells)
• pi Primary emissions of PM2.5 in country i
• si SO2 emissions in country i
• ni NOx emissions in country i
• ai NH3 emissions in country i
• aS,Wij, ?S,W,Aij, sW,Aij, pAij Linear
  transfer matrices for reduced and oxidized
  nitrogen, sulfur and primary PM2.5, for winter,
  summer and annual

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Estimating the loss of life expectancy in
RAINSApproach

• Endpoint
• Loss in statistical life expectancy
• Related to long-term PM2.5 exposure, based on
  cohort studies
• Life tables provide baseline mortality for each
  cohort in each country
• For a given PM scenario Mortality modified
  through Cox proportional hazard model using
  Relative Risk (RR) factors from literature
• From modified mortality, calculate life
  expectancy for each cohort and for entire
  population

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Input to life expectancy calculation

• Life tables (by country)
• Population data by cohort and country, 2000-2050
• Urban/rural population in each 5050 km grid cell
• Air quality data annual mean concentrations
• PM2.5 (sulfates, nitrates, ammonium, primary
  particles), excluding SOA, natural sources
• 5050 km over Europe, rural urban background
• for any emission scenario 1990-2020
• Relative risk factors

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Loss in life expectancy attributable to fine
particles months

• 2020
  2020
• CAFE baseline Maximum technical Current
  legislation emission reductions

Loss in average statistical life expectancy due
to identified anthropogenic PM2.5Calculations
for 1997 meteorology
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Five stages in dynamic acidification modelling

• Important time factors
• Damage delay time
• Recover delay time

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Excess acid deposition to forests

• 2020
  2020
• CAFE baseline Maximum technical Current
  legislation emission reductions

Percentage of forest area with acid deposition
above critical loads, Calculation for 1997
meteorology
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Excess nitrogen deposition threatening
biodiversity

• 2020
  2020
• CAFE baseline Maximum technical Current
  legislation emission reductions

Percentage of ecosystems area with nitrogen
deposition above critical loads Calculation for
1997 meteorology
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Vegetation-damaging ozone concentrations

• 2020
  2020
• CAFE baseline Maximum technical Current
  legislation emission reductions

AOT40 ppm.hours. Critical level for forests 5
ppm.hours Calculations for 1997 meteorology
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Optimized emission reductions for EU-25of the
CAFE policy scenarios 2000100
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Costs for reducing health impacts from fine PM
Analysis for the EU Clean Air For Europe (CAFE)
programme
16000
14000
12000
10000
Annual Cost Millions
8000
6000
4000
2000
0
0
10
20
30
40
50
60
70
80
90
100
Health improvement (Change between baseline and
maximum measures)
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Cost savings from the RAINS approachEstimates
presented by Concawe
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Emission control costs of the CAFE policy
scenarios
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The critical question on uncertainties in the
policy context

• Not What is the confidence range of the model
  results?
• But Given all the shortcomings, imperfections
  and the goals, how can we safeguard the
  robustness of the model results?
• Conventional scientific approaches for addressing
  uncertainties do either not provide
  policy-relevant answers or are too complex to
  implement. For practical reasons alternative
  approach required

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In RAINS, uncertainties addressed through

• (1) Model construction
• (2) Identification of potential biases
• (3) Target setting
• (4) Sensitivity analyses

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Uncertainties of intermediate results95
confidence intervals
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Probability for protecting ecosystems Gothenburg
Protocol 2010
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More advanced methods for treating uncertainties
could be developed

• But
• Are Parties ready to put increased effort into
  providing and, subsequently, agreeing upon the
  data needed for such an analysis?
• Would Parties be prepared to follow abatement
  strategies derived with such a method, i.e., to
  pay more for strategies that yield the same
  environmental improvements but with a higher
  probability of attainment?