Environmental Engineering Reference
In-Depth Information
cohesion) and environmental impacts (CO2 and resource consumption, local air pollution,
accidents) are very difficult to quantify or monetise (Nijkamp et al., 2003; Bristow and
Nellthorp, 2000). Also, the dominance of particular criteria in the final decision is often heavily
criticised, either due to queries over the importance given or the validity of the metric. The
dominance of time savings in many CBA and MCA calculations is a good example, where the
time savings are often not realised or valued as expected at the individual level (Metz, 2008).
In terms of considering the likely target achievement of packages and scenarios, there are
additional issues. Although some policies can be modelled through elasticities, in other cases
there is a need for careful empirical analysis over the scale and immediacy of policy impacts.
There is good empirical information available in some cities on the effectiveness of policies
individually and in combination, but much less in others. Care must be exercised over their
transferability between cities, and indeed for some policy areas the evidence base is very weak.
Hence, many of the requirements of transport policy, particularly when developing scenarios
over the long term, cannot be effectively measured in a quantitative manner. The 'value' of
an attractive landscape, even life and death, or other such issues, should clearly be beyond
quantification and monetisation, yet this is still the conventional approach used in appraisal,
using techniques such as 'willingness to pay'. But often these simply discredit the process -
impacts are important, the MCA and CBA give only a partial view of the costs and benefits
of a scheme or strategy, and consequentially become less useful. There some possibilities to
reduce these problems, for example by developing approaches to the weighting of impacts by
different criteria, hence allowing composite scoring (Dodgson et al., 2009; Sayers et al., 2003).
There is also an emerging preference for approaches that make greater use of 'multi-actor'
participation in the design of appraisal frameworks, reflecting concerns over the lack of partici-
patory input prior to decisions been made (Macharis et al., 2010).
A further difficulty in MCA is the lack of clarity concerning what to do with the large
amount of data produced during the process. The choice of preferred option is still difficult
despite the extensive background evidence gathering - with a difficult and often unsystematic
analysis of results still remaining (
Figure 2.20
)
. In France, a move away from MCA has
recently occurred, largely due to the lack of procedures for aggregating the evaluations of
multiple criteria. It was concluded that the process became unworkable with a lack of credibility,
and certainly transparency, in results. Evaluations and non-transparent weightings have been
Decision taker(s) apply
value judgements
(sometimes made
transparent, most often
not)
Choose criteria
with which to
assess options
Define
objectives
Identify
problem
FINAL DECISION
(often overwhelmed by
data, and remains
unsystematic,
untransparent)
Assess
performance of
options with respect
to criteria
Identify potential
options to solve the
problem
Decision as to
preferred option
Figure 2.20
Decision-making with MCA
Source
: Developing Sayers et al., 2003.
