Uncertainties and target setting

In addition to the consideration of uncertainties within the framework of the model itself, attempts were made to limit the effect of uncertainties on the model optimisation outcome by selecting an appropriate method of setting the optimisation targets. In practice, the potential influence of uncertainties was minimized by using ‘gap closure’ targets (relative improvements), by developing a compensation mechanism for targets, through the use of explicit model confidence intervals, and by excluding extreme situations.

Examining a range of scenario optimisations for different combinations of targets, rather than relying on one 'central' scenario alone, further enhanced the reliability of the resulting emission ceilings.

9.5.1 Use of relative ‘gap closure’ targets

The occurrence of and the reduction potential for ground-level ozone and acidification show distinct spatial differences across Europe. Furthermore, there is robust evidence that the presently available technical emission control measures will not be sufficient to meet the environmental long-term targets (the no-damage levels) everywhere in Europe within the next one or two decades without interfering with the 'business as usual' expectations on economic development and energy consumption. In such a situation the choice of an equitable environmental interim target becomes crucial for deriving a balanced emission control strategy. Two basic concepts for setting interim targets have been considered:

• Prioritising measures in highly polluted areas by imposing uniform absolute exposure limits over the entire area;

• Postulating equal relative improvements in relation to the situation in a base year (the gap closure concept). This approach, involving relative improvements, is less prone to model uncertainties because by focusing on the differences between scenario results, some of the potential biases that apply for absolute model results cancel out. Thus, such a gap closure concept provides a more reliable target-setting framework than one based on absolute limits.

However, these two different conceptual approaches imply fundamentally different spatial distributions of environmental benefits and emission abatement efforts over Europe. These differences were explored in close interactions with the negotiating bodies, and a combination of both principles eventually proved acceptable to all Parties.

9.5.2 Compensation mechanism for targets

Earlier analysis demonstrated that the optimal allocation of emission controls might be strongly influenced by the need to exactly meet specific environmental targets at a few single grid cells, while for the majority of grid cells the targets are usually over-achieved. The sensitivity of the optimisation results towards modifications of the environmental targets of these 'binding grids' was the subject of numerous discussions in the past. It was argued during the policy negotiations that the requirement to achieve stringent targets in isolated areas could possibly imply unbalanced high costs without yielding adequate benefits. This concern is even more pronounced when the targets are not related to absolute exposure levels, but to interim targets on the way towards the ultimate environmental objective.

Alternative concepts, in which the environmental targets for single ecosystems are not allowed to drive the overall optimisation system to extreme solutions, are necessary to overcome this problem.

In order to limit the potential influence of small and perhaps atypical environmental receptor areas on optimised Europe-wide emission controls, and to increase the overall cost-effectiveness of strategies, a mechanism was developed to tolerate lower improvements at a few places without discarding the overall environmental ambition levels. This 'compensation mechanism' allows a (limited) violation of environmental targets at single grid cells or in single years as long as this excess is compensated by additional improvements in other years or at other grid cells within the same country. The compensation considers differences in the stock at risk over grid cells and puts more relative emphasis on densely populated areas or regions with large natural ecosystems. A weighting mechanism requires that excess exposure (AOT60, AOT40 or accumulated excess acidity/nitrogen) must be compensated on a population- or vegetation-adjusted basis, e.g., a small excess of AOT60 in a big city by larger improvements in less populated rural areas. The country balances ensure that for each country the

exposure indices will be reduced by at least the percentage of the selected gap closure, or phrased differently, that the desired 'gap closure' is achieved for the country population/vegetation exposure indices rather than for individual grid cells.

In order to avoid a possible inequitable treatment of large and small countries implied by the compensation mechanism, a (uniform) maximum compensation potential was introduced. This means that environmental targets may only be violated up to a certain amount, which is independent of the country. Experiments showed that such a violation limit was best defined in terms of a uniform 'minimum' gap closure, compared to other relative or absolute measures.

This compensation mechanism was also examined in terms of its economic meaning (Forsund, 2000).

9.5.3 Explicit model confidence intervals

Earlier analysis also revealed that in certain situations the original definition of the 'gap' (the difference between present and absolute 'no-damage' levels) could push areas with comparatively low exposure to costly emission reductions, while less burden would be placed on more polluted regions.

This occurs typically in areas where background concentrations resulting, e.g., from natural sources, constitute a large fraction of the total exposure. At such places, a target specified as a certain relative improvement requires higher reductions in anthropogenic emissions than in highly polluted regions, where the relative contribution of natural background is negligible.

It is important to recall that model uncertainties are, for a number of reasons, largest for just these low pollution levels. In order to maximize the robustness of results obtained from the currently available models and to prevent extremely low model results from influencing the actual strategy development, a 'model confidence interval' was introduced. The 'gap to be closed' by the optimisation is now defined as the difference between the current situation and this model confidence interval. In practice, the lower model confidence range was set for the AOT60 to 0.4 ppm.hours and for acidification for each grid cell to the accumulated excess deposition resulting from natural and hemispheric background plus five aeq/hectare.

9.5.4 Excluding extreme situations

In addition to the general gap closure targets, general exposure ceilings to be achieved throughout the modelling domain –as limits to the permitted violations of the gap closure targets - were also introduced. These uniform exposure ceilings proved to be practical tools to exert additional pressure for environmental improvements in the most polluted areas.

For ozone however, model results for five different meteorological years demonstrated that actual ozone levels do not only depend on the levels of precursor emissions, but also to a significant degree on the specific meteorological conditions. Emission control strategies addressing an extreme situation might therefore look rather different from strategies tailored towards the improvement of typical situations. For the purposes of strategy development, it was decided to exclude the 'most difficult' situations from the analysis, when considering the uniform ozone limit target. In practice, the strategy should be constructed in such a way that it would meet the absolute AOT targets in four out of five years. It is important to stress that the major motivation for this 'four out of five' principle in the context of strategy development is the concern to avoid undue reliance on model performance for extreme (and perhaps rare) situations.