collaboration between the participants, because they were guided in understanding
where their choice might fall, so as to evaluate step by step the importance of their
response. The combination of different inputs and modes of joint interaction created
the right milieu for the creation of knowledge among DMs and researchers in the
fields of transport, economics, environment and spatial planning.
In the second case study, which concerns the choice of the most suitable location
for a Municipal Solid Waste Plant (MSWP) in the Province of Torino, Italy
(Abastante et al. 2012), the decision-making relevance (of the method) is high, the
DRSA offers a useful tool for reasoning about the data involved in the decision
problem at hand, and it is suitable to elicitate the DM’s preferences and to support
them by explaining and justifying the final choice basing on easily understandable
decision rules. As for the sustainability relevance (of the case study), it is high,
because the problem of the location of MSWP is an intrinsically complex problem
involving interconnected elements as social, economic and environmental. In particular, the citizens usually show phenomena as NIMBY (Not-In-My-Back-Yard),
NOTE (Not-Over-There-Either), LULU (Locally-Unacceptable-Land-Use) and
BANANA (Build-Absolutely-Nothing-Anywhere-Near-Anything). For this reason,
Table 6.1 Interpretation of the case studies
CASE STUDIES
Infrastru
ctural
projects
Waste
manage
ment
Strategy
planning
Energy
planning
Cultural
heritage
Built
environment
Environment
al systems
MCDA methodologies
ANP + Visual.
X
Extreme
Decision-making relevance
DRSA
X
High
MACBETH
X
Medium
PROMETHEE
X
Medium
CAT-SD
X
Extreme
NAROR
X
High
ELECTRE
X
Extreme
Spatial Scale
International
Local
Local
Local
Local
Local
Regional
Sustainability
relevance
High
High
Medium
High
High
Medium
Extreme
Corridor 24
strategies
Urban solid
waste
Redevelopment
of a district in
Brussels
Net Zero Energy
District (NZED)
Adaptive reuse
in Turin
Real estate
portfolio
Requalification
of an abandoned
quarry
6 Multiple Criteria Decision Analysis to Assess Urban and Territorial. . .
97
where their choice might fall, so as to evaluate step by step the importance of their
response. The combination of different inputs and modes of joint interaction created
the right milieu for the creation of knowledge among DMs and researchers in the
fields of transport, economics, environment and spatial planning.
In the second case study, which concerns the choice of the most suitable location
for a Municipal Solid Waste Plant (MSWP) in the Province of Torino, Italy
(Abastante et al. 2012), the decision-making relevance (of the method) is high, the
DRSA offers a useful tool for reasoning about the data involved in the decision
problem at hand, and it is suitable to elicitate the DM’s preferences and to support
them by explaining and justifying the final choice basing on easily understandable
decision rules. As for the sustainability relevance (of the case study), it is high,
because the problem of the location of MSWP is an intrinsically complex problem
involving interconnected elements as social, economic and environmental. In particular, the citizens usually show phenomena as NIMBY (Not-In-My-Back-Yard),
NOTE (Not-Over-There-Either), LULU (Locally-Unacceptable-Land-Use) and
BANANA (Build-Absolutely-Nothing-Anywhere-Near-Anything). For this reason,
Table 6.1 Interpretation of the case studies
CASE STUDIES
Infrastru
ctural
projects
Waste
manage
ment
Strategy
planning
Energy
planning
Cultural
heritage
Built
environment
Environment
al systems
MCDA methodologies
ANP + Visual.
X
Extreme
Decision-making relevance
DRSA
X
High
MACBETH
X
Medium
PROMETHEE
X
Medium
CAT-SD
X
Extreme
NAROR
X
High
ELECTRE
X
Extreme
Spatial Scale
International
Local
Local
Local
Local
Local
Regional
Sustainability
relevance
High
High
Medium
High
High
Medium
Extreme
Corridor 24
strategies
Urban solid
waste
Redevelopment
of a district in
Brussels
Net Zero Energy
District (NZED)
Adaptive reuse
in Turin
Real estate
portfolio
Requalification
of an abandoned
quarry
6 Multiple Criteria Decision Analysis to Assess Urban and Territorial. . .
97