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Review of the RAINS Integrated Assessment Model

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Title: Review of the RAINS Integrated Assessment Model


1
Review of the RAINS Integrated Assessment Model
  • Contract with CAFE
  • Dec 2003 - Sept 2004

2
Objective
  • Review of how the RAINS framework uses scientific
    and economic understanding for the development of
    European air pollution policies

3
Timing
  • The review was made during the development of the
    RAINS model
  • Consequences
  • A good possibility to influence the process
    through advice
  • Limited possibility to examine the outcome of the
    model

4
Scope
  • Review all scientific aspects of the RAINS model
    except
  • Atmospheric source-receptor relationships (EMEP
    review)
  • Scientific information on health impact (WHO)
  • Methods for mapping critical loads and levels
    (WGE)

5
Tasks- to examine
  • Model design
  • Scientific credible representation of reality
  • Limitations in the model structure
  • Uncertainties
  • How is RAINS addressing uncertainties?
  • Robustness for policy advice
  • Biases in the outcome of the model

6
Tasks (cont.)
  • Abatement technologies and costs
  • Problems arising due to limitations to only
    technical measures
  • Verification of costs.
  • Communication
  • Quality assurance in input data
  • Involvement of stakeholders
  • Transparency in model and results

7
The review team
  • Peringe Grennfelt
  • Sweden
  • Mike Woodfield
  • UK
  • Bertil Forsberg
  • Sweden
  • Jan Willem Erisman
  • The Netherlands
  • David Fowler
  • UK
  • Janina Fudala
  • Poland
  • Oystein Hov
  • Norway
  • Terry Keating
  • USA
  • Mihalis Lazarides
  • Greece
  • Tomasz Zylics
  • Poland

8
General observations
  • The model is today much more advanced compared to
    the model used for the Gothenburg Protocol and
    the NEC directive
  • Consequences
  • Reviews and experiences from earlier versions of
    limited value
  • Difficulties in the interpretation of the outcome
    of the Gothenburg Protocol and the NEC Directive

9
Assessment of model design
  • As a general approach
  • RAINS is a reliable and scientifically defendable
    tool for policy advice
  • The modular structure gives a large degree of
    flexibility
  • EU and national sector emission control
    legislation has decreased the space for
    additional national measures under the NEC
    directive (CLRTAP protocol)
  • Cost of additional measures will be relatively
    high and country sensitive

10
Specific Aspects of the model
  • Geographical scale
  • Going from 150 to 50 km grid resolution will be
    advantageous
  • Country-to-grid approach still the best
    solution
  • Increasing ozone background will demand for
    control measures outside the EMEP area
  • Marine emissions important and should be included

11
Specific Aspects of the model
  • Scope of policy options
  • Major effects are included. Some environmental
    and health effects not or only partly included.
    If these were included they would probably
    influence the strategy. In most cases the reason
    for exclusion is lack in scientific
    understanding.
  • Model Design Recommendations
  • Inclusion of marine emissions
  • Hemispheric pollution needs to be considered.
  • Urban modelling needs further development

12
Representation of reality
  • Effects are handled in responsible and defendable
    way.
  • Each of the effects modules was analysed and
    number of recommendations have been made with
    respect to each of them.

13
Two important issues that need urgent action
  • Climate change (policy and effects)
  • Inter-annual variation in Source/Receptor
    relationships

14
Uncertainties should be handled in a more
structured way
  • Lack in scientific understanding
  • Biases caused by simplifications, assumptions,
    setting of boundary conditions etc.
  • Statistical uncertainties due to incompleteness
    in data collection and difficulties in describing
    the true situation
  • Uncertainties in the socio-economic and technical
    development

15
Uncertainties - Lack in scientific understanding
  • Scientific knowledge reviewed with respect to
  • General maturity
  • Mechanism and process understanding
  • Experimental evidence
  • Field observations
  • Source - receptor understanding

16
Uncertainties in Assumptions and Simplifications
cause biases
  • Many known assumptions and simplifications in the
    calculations for the Gothenburg protocol
  • Some are taken on board in the approach for CAFE
    and CLRTAP revision (ecosystem specific dep., SO2
    - NH3 interactions in dry dep. etc.)
  • Could be analysed with respect to their influence
    on the output of the RAINS model.
  • A number of assumptions and simplifications are
    identified in the review report.

17
Influence of biases on the model output
  • Most of the identified biases underestimate the
    control needs to give an expected outcome in
    terms of health and environment protection.
  • Recommendation on further analyses of the biases
    by other relevant bodies/organisations supplying
    data (EMEP, ICPs, WHO etc.).

18
Uncertainties in socio-economic and technical
development
  • Should be handled through a suitable set of
    scenarios covering
  • an enough wide range of energy, transportation
    and agricultural scenarios
  • climate change control options
  • technological possibilities

19
Uncertainties and Robustness
  • Robustness includes a number of user confidence
    related aspects.
  • We point to the importance of ensuring
    transparency when developing policies,
    particularly with regard to target setting and
    assumptions made.

20
Abatement technologies and costs
  • Historically, costs have been overestimated in
    RAINS
  • Inclusion of non-technical measures would
    decrease costs for achieving a given target but
    may lead to greater uncertainty.
  • The dialogue with Member States is very important

21
Communication with stakeholders
  • The opportunities for stakeholder involvement in
    the development of RAINS are good.
  • Bi-lateral communication between IIASA and
    stakeholders functions well as a means of
    verifying input data quality. Data quality,
    however, is not guaranteed by data suppliers.
  • Information related the model is good and
    improving. In addition excellent material was
    prepared for the review (available on IIASAs web
    page)

22
Thanks for your attention
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