Current knowledge and possible systematic biases Linkages with greenhouse gas policy

1. Fabian Wagner M. Amann, C. Berglund, J. Cofala, L. Höglund, Z. Klimont, W. Winiwarter Current knowledge and possible systematic biasesLinkages with greenhouse gas policy

2. Overview • How GHG emissions and controls are modelled in the GAINS model • Illustrative scenarios • Conclusion and Outlook

3. The GHG-Air pollution INteractions and Synergies (GAINS) model GAINS SO2 NOx NH3 PM VOC CO2 CH4 N2O FGAS RAINS GHG module Optimization module • Calculates an optimal scenario for any given carbon price or regional/national GHG emssion cap • Optimization module allows full integration of climate policy and air pollution policy

4. Methodology For all anthropogenic sources of GHG emissions in a country: • Identification of available mitigation options • Including structural changes (fuel switch) and add-on measures • Country-specific application potentials • Baseline activity rates: national projections • Substitution potential derived from PRIMES • Quantification of societal resource costs • Excluding transfers (profits, taxes, etc.) • Data sources • GHG emission inventories consistent with UNFCCC • GHG technology cost data from reviewed literature • Activity projections: provided by national governments and EU Commission

5. Baseline development of European GHGs 42 regions [Mt CO2-eq] Based on national and PRIMES projections of activity data: * Only Y1995 available

6. Mitigation options (230) • CO2(162 options – PP, TRA, IND, DOM) • Fuel switches • Energy/electricity saving • CH4 (28 options – AGR, WASTE, GAS/COAL) • N2O (18 options – SOILS, WASTE, PP, IND) • F-Gases (22 options – AC, REFR, IND) Not included yet (annual potentials not fully assessed): • Carbon capture and storage • Carbon sinks

7. CLE 1990 - 26% - 20% GHG cost curve in 2020

8. Approach • GAINS cost curves for GHGs combined with RAINS cost curves for air pollutants • Illustrative GAINS analysis for GHG scenarios • Starting point: National/PRIMES activity projections for 2020 • Case 1: CO2-only case • 15% GHG reduction with CO2 only • Implied carbon price: 90 €/t CO2

9. Mitigation portfolio: Changes in fuel consumption, CO2-only case [% of baseline]

10. Change in emissions and AQ impactsaccompanying the CO2 reduction, compared to the baseline 2020

11. Change in emissions and health impactsaccompanying the CO2 reduction, compared to the baseline 2020

12. Costs for the 15% CO2 reductioncompared to REF [billion €/yr]

13. The multi-gas case • 15% reduction in GHGs • Achieved by CO2, CH4 and N2O • Carbon price: ~40 €/t CO2

14. Change in emissions and health impactsaccompanying the GHG reduction, compared to the baseline 2020

15. Costs for the 15% multi-gas reductioncompared to REF [billion €/yr, % GDP 2020]

16. Health benefits vs additional cost savings For given baseline, impose CP – less coal use – less need for, e.g. FGD – lower costs and lower emissions PLUS (qualitative consideration): Either: • Lower emissions, hence health benefits (see above) Or: • Same emissions as in BL, but less application of the expensive options – additional cost savings

17. Conclusions • Co-benefits of GHG reductions on air pollution are substantial • Fuel shifts for CO2 reductions can save 1000s of lives • But GHG mitigation relying on bio-fuels can deteriorate air pollution, especially in developing countries • In situations with stringent air pollution controls, CO2 reductions can avoid significant costs forair pollution controls. Cost savings occur immediately to the same sectors. • Multi-gas GHG strategies have less CO2 co-benefits, but better cost-effectiveness ratio. Co-benefits on ozone! • The GAINS model offers a tool for quantitative analysis

18. Outlook • Publish (or perish) • GAINS data on the web (all but CO2 alre already there) • Explore systematically linkage between AP and GHG • Further study of impacts of climate policy on AP • Simultaneous optimization with AP and GHG endpoints • GAINS ASIA