included in the core part. The extra terms will only constitute a modification of the weights attached to them in the core part. Also, the indicators of liveable streets, economic growth, accessibility to those without a car and taxpayers' money might already be elements of the "CBA", in whole or in part, and this should be acknowledged when setting separate weights for the additional terms.
3.5.5 Indicators which need not be included as targets
A strategy is implemented in the model by making certain changes in the network or in the exogenous variables. In certain instances, the degree of fulfilment of some of the goals is not influenced by subsequent behavioural changes in the model, and can be ascertained directly. In such instances the most convenient thing to do is to make sure that the goal is taken care of when implementing all strategies in the model.
There will then be no need to include terms that measure goal achievement with respect to such sub-objectives in the evaluation function. However, if we must incur investment costs or operating costs to achieve the goal, just for the record, these could be included in the CBA part of the evaluation function.
An obvious candidate for such treatment is the indicator "Accessibility for the mobility impaired". This is because the mobility impaired will not be identifiable as a group of travellers in any of the models. Another candidate for such treatment is the green areas indicator, although if it is possible to infer changes in green areas from the model output, it would be better to treat it explicitly.
In conclusion, we do not need to include all indicators as parts of the objective function or as targets in the optimisation problem. Some targets will be taken care of right at the outset. Others may be expected to be reached automatically in the solution (non-binding constraints could be removed). Still other indicators may be assigned a secondary role. Their levels will be reported as part of the analysis, but unless they turn out to be unacceptably low, they will not form a part of the optimisation problem.
3.5.6 Keep the same objective function throughout appraisal
As pointed out in Section 3.1, a basic requirement of appraisal is to use the same evaluation criteria to appraise all strategies. Once a particular objective function has been chosen, it will not do to make changes to it as targets or other constraints are varied.
and relations of the system we are studying – the urban land use and transport system.
Furthermore, there will be uncertainty about values – effectively what weights should go into an MCA model or how should a CBA be parameterised – and uncertainty in related areas – whether, for example, European Commission decisions about emissions taxes may influence the appropriateness of any city’s transport choices.
The initial response to all of these inevitable uncertainties is to use sensitivity analysis and a later search for robustness. Sensitivity analysis seeks to assess the extent to which the overall attractiveness and hence ranking of strategies changes as plausible changes are made to key input assumptions, reflecting the degree of uncertainty that might surround them. To test how a strategy performs in different scenarios is just one example of sensitivity analysis. Such testing may be simple, one input at a time, or more sophisticated, for example using Monte Carlo analysis. In practice, the output from such investigations of sensitivity would be a deeper understanding of quite how vulnerable any particular package of measures might be to changes in key input assumptions. A response to high levels of sensitivity might involve search for fuller information, to diminish the uncertainty surrounding a particular input, or re-design of the alternative to seek to make it less susceptible.
Robustness is a characteristic of strategies that reflects lack of sensitivity. It is particularly appropriate to long-term strategic planning, where strategies are often implemented in stages. For an example of a practical application of robustness analysis using different scenarios, see Allport et al. (1986, 1987).
Going beyond sensitivity tests, there are a number of other approaches to uncertainty.
In a transport planning context, we will regard them as experimental, but they at least deserve to be mentioned.
Expected utility theory might be used to define an objective function over many scenarios. The degree of risk aversion of the decision maker will then be part of the information on decision makers’ preferences that need to be extracted. We would also need to assign probabilities to scenarios. Apart from the difficulty of doing this, the approach is perhaps difficult to communicate to stakeholders. This approach is left for future research.
If a strategy consists of policy instruments that can be applied at different levels over time, an approach could be used that combines explicit recognition of the fact that some policies are irreversible with recognition that there is uncertainty about the future scenario, but that information about it will gradually emerge. This is the real options approach (Dixit and Pindyck 1994), which also requires probabilities of the scenarios.
The simplest improvement on the pure sensitivity approach in a CBA setting would be to recognise that the benefits to society of a land use/transport strategy are only a part of the total return on regional or national capital. A strategy that produces high benefits in a low-income scenario will contribute to reduce the overall uncertainty of national capital, while a strategy that performs well in high growth scenarios and poorly in low-growth scenarios will increase overall uncertainty. Thus the relevant risk to society of adopting a strategy is not tied to the uncertainty of the strategy as seen in isolation, but to the overall uncertainty of the stream of returns on national capital. It might be comparatively simple to produce such estimates of relevant risk of the strategies, and if uncertainty is about growth rates or economic conditions, this concept will be more useful than the concept of robustness.
To implement the concept of relevant risk, start by testing the strategies in high- growth, medium growth and low growth scenarios like in ordinary sensitivity analysis, and compute the annual net benefits. Before computing the net present values in the
medium growth scenario by way of a discount rate, adjust the discount rate somewhat upward for strategies that are more than average sensitive to the growth rate and somewhat downward for strategies that are less than averagely sensitive. The strategy with the highest net present value in the medium growth scenario will then be the best from a social efficiency point of view, taking into account the relevant risk to society.
An adjustment of one percentage point will probably be right, but even an adjustment of 3% down might be used if the annual net benefit of a strategy is virtually constant when the annual income of this year is assumed to increase or decrease by one per cent. If on the other hand the annual net benefit of the strategy in a certain year is increased by 2% if income in that year is assumed to be 1% higher (which is rather unlikely), a 3% upward adjustment of the discount rate might be used.
An approach very similar to this is part of official guidance in Norway, and might be adopted if income growth is uncertain and strategies turn out to be ranked differently under different income assumptions. If not, stick to sensitivity analysis and subjective assessment of robustness.
4 Presentation
This chapter concerns presentation of results from the strategic planning process to professionals, decision makers and the public. The appraisal framework of Chapter 3 provides us with a solid basis for presentation of results. However, at this point we will probably have to communicate with different audiences (professionals, decision- makers and the public), each with their own requirements on the kind of information they want.
Even more importantly, presentation of results may take place at various stages of the planning process, from early results of the first exploratory tests to assist strategy formulation, to a final report, structured to comply with national rules and regulations.
In terms of the logical structure set out in Section 2.2 (Figure 2.1), presentation of results in one form or another may inform the decisions about objectives, indicators and problems; strategy formulation; appraisal and comparison of solutions; as well as ex post assessment of performance. Each presentation may have its own purposes, including of course the purpose of providing the decision-makers with the information they need to rank or choose among the strategies, but also maybe the purpose of inviting ideas for further tests or the purpose of raising awareness of the issues at stake.