Environmental Engineering Reference
In-Depth Information
Figure 2.18
Intelligent infrastructure systems, 'Perpetual motion'
Source
: Narinder Sagoo, Foster + Partners. Department for Trade and Industry and Office of Science and Technology,
2006.
the error of falling back into the 'old pattern' of fixing one view of the future, or of forecasting
(Schoemaker, 1998; Schoemaker and Gunther, 2002). The point of scenario analysis is to explore
and understand the implications of different futures, not to forecast what is most likely to happen.
Within transport planning this can be viewed as problematic. Practitioners involved in preparing,
for example, a local transport strategy, have to model and understand the future over the next
20 years, but the immediate intention is in developing a bid for transport funding over 3-5
years. The solution perhaps is to assess which future is most likely to have the most 'beneficial'
impacts, or to be most likely to achieve against set objectives and targets, and the short-term
investment programme can then be developed to be consistent with, and flexible and resilient
to, the range of possible long-term futures, progressing towards the longer term goals.
A potential limitation with scenario analysis is that the scenarios as developed are often viewed
only as a means of discussion, rather than one of the images signifying a preferable future or
a 'blueprint' for future strategy. For policy-makers this can be difficult, as they may have an
interest in application, and are attempting to map out a preferred route for policy and investment
choices. In recent years the quantification of the likely impacts of scenarios has become more
popular, usually against key metrics such as CO2, but sometimes using a broader range. The
latter approach can be carried out by using multi-criteria analysis (MCA) in combination with
scenario analysis (Saxena, 2012; Hickman et al., 2012b). We develop this approach to an
extent, against CO2 and wider MCA impacts, in some of the case studies that follow.
Figure
2.19
illustrates the methodology used, where policy measures are clustered into policy packages
and scenarios, then the scenarios are examined against MCA impacts. The optimal scenario
is chosen with the best results against the MCA criteria.
