Emission Projections & Integrated Assessment Modelling

1. Emission Projections & Integrated Assessment Modelling Emission Projections WorkshopThessaloniki Oct 2006 Lessons learned from the EC research projectsMERLIN and ESPREME

2. Structure The key issues • Multi-pollutant, multi-effect modelling of air pollution and climate change (MERLIN) • Abatement strategies for heavy metals in Europe (ESPREME) • Where do projections fit in a ‘generic’ IAM? Requirements and use of emission projections • Establishing a base case for validation purposes • Trend development and ‘business-as-usual’ assumptions • Alternative future scenarios and their implications for IAMs Key problems and conclusions/solutions • Harmonising bottom-up and top-down approaches • Data needs vs. data availability – how to fill the gaps? • Standards and good practise for documenting scenario assumptions • Crisis? What crisis?

3. The key issues The MERLIN Project • Aims: Developing new methodologies for the integrated assessment of multi-pollutant, multi-effect problems • Approach:Application of Genetic Algorithms and a „measure-matrix“ approach to model optimal pollution control scenarios (cost-effectiveness, cost-benefit),incorporating technical and non-technical measures into the same modelling framework. • Key projections required: • Fossil fuel use in energy production and transport • Industrial production, production and use of organic solvents, ... • Agriculture (animal numbers, fertiliser use) • Implementation of emission control technology across sectors → for the year 2010 and beyond for the EU25+ http://www.merlin-project.info

4. The key issues The ESPREME Project • Aims: Estimation of willingness-to-pay to reduce risks of exposure to heavy metals and cost-benefit analysis for reducing heavy metals occurrence in Europe • Approach:Reviewing/developing HM emission inventories and applying CTMs to determine HM concentration/deposition based on scenarios of future development; applying Genetic Algorithms to identify cost-effective bundles of control measures to further reduce HM emissions • Key projections required: • Fossil fuel use in energy production and transport • Industrial production (metals, cement, ...) • Implementation of emission control technology across sectors(business-as-usual, maximum technically feasible) → for the year 2010 and beyond for the EU25+ http://espreme.ier.uni-stuttgart.de

5. The key issues Where do projections fit in a ‘generic’ IAM? • Development of activity data • General growth/decline of activities, e.g. consumption of fuels, changes in behavioural patterns, new technologies. • Breaks in development trends. • Synergies/trade-offs between technologies and non-technical measures. • Trends in Emission Factors • Due to technology changes, efficiency in processes, other processes • Due to the implementation of control measures (primary, secondary) • System changes • Changes to the model ‚environment‘, extrinsic parametres and core model assumptions

6. Requirements and use of emission projections Establishing a base case • Activitiy data & Emission Factors • Sufficient information on the status quo in all countries? • Generic (e.g. EIGB) EFs or country-specific EFs available? • Do base case figures fit external projection base year? • Emission control equipment, implementation degrees etc. • Often discrepancy between regulations and actual implementation. • Regulations give emission targets, but technologies implemented determine retrofit/replacement options.

7. Requirements and use of emission projections Trend development and BAU assumptions • Activitiy data & Emission Factors • Country projections vs. external (model) assumptions. • Catching both technology turnover and specific EF changes. • Which technologies are implied in ‚abated‘ EFs? • Emission control equipment, implementation degrees etc. • Replacement/retrofit due to efficiency deliberations or economic reasons vs. emission control investments. • Both early compliance and non-compliance need to be taken into account, but little information available.

8. Requirements and use of emission projections Alternative future scenarios • Activitiy data & Emission Factors • The future is uncertain ... by just how much? • e.g. projected energy consumption in road transport vs. projected annual mileage by technology and driving patterns. • For long term projections in particular, breaks in trends are difficult (impossible?) to foresee: • which key behavioural or perceptual influences may result in developments not projected by mechanistic models? • e.g. political decision to phase out nuclear power plants vs. model driven projections of efficient energy systems • Changes in activity levels or EFs are often distinguished onlyby the level of detail under scrutiny.

9. Requirements and use of emission projections Alternative future scenarios • Emission control equipment & implementation • Top-Down scenario assumptions and bottom-up projectionsoften clash with economic/technical feasibility: • e.g. percentage implementation vs. technical feasibility • Side-effects and synergies need to be incorporated. • Uncertain starting points for technology projections often result in unrealistic assessments of abatement potentials.

10. Key problems and conclusions/solutions • Harmonising bottom-up and top-down approaches • For IAM on country/regional scale, top-down projections of limited use, but • bottom-up projections require considerable resources, when done thoroughly; • need to focus on the key sectors/sources creates different scenarios for different pollutants/problems. • Data needs vs. data availability – how to fill the gaps? • What can be/needs to be reported? • Utopia: reporting activities and EFs with a high level of detail to a transparent, central emission calculation facility? • Independent, central inventory reviews by experts to identify key gaps and uncertainties can only trigger in-depth national reviews and improvements, not replace them.

11. Key problems and conclusions/solutions • Standards and good practise for documenting scenario assumptions • Without a detailed description and documentation of values andassumptions, comparing model results and projections is meaningless. • In particular mixed technological and societal changes may lead to significant changes of applicability, feasibility and implementation of model options. • Crisis? What crisis? • Decision making under uncertainty is not the problem, but • improving the ‘fit’ between projections and model-data structurescan be the solution. • We may need to re-consider the question now and then …