Greener Transportation Infrastructure - Green Finance and Sustainability - page 410

Table 6. Decision matrix A before application of utility and cost functions

Criteria

Utility-based Cost-based

Alternatives C 1 C 2 … … C m-1 C m

A 1 S 11 S 12 … … S 1m-1 S 1m

A 2 S 21 S 22 … … S 2m-1 S 2m

… … … … … … …

A n S n1 S n2 … … S nm-1 S nm

Box 1. Sample procedure

Global Warming and Air Quality: Aircraft and GSE VMT and Emissions

Basic analysis: Level 1 indicators
The simplest scoring procedure states the hypothesis that emission factors for aircraft and GSE do not vary between evaluated alterna-
tives. The suggested procedure is inspired by Chester (2008).
Input data: Forecasted airport schedule (Official Airline Guide), fleet mix, estimated GSE times-in-mode and master plan data for runway
and taxiway layout and locations.
Analysis steps:
Simplify fleet mix using representative aircraft. Chester uses three representative aircraft for modeling commercial aircraft operation:
Embraer 145 (short-haul), Boeing 737 (medium-haul), and Boeing 747 (long-haul). These aircraft represent the small, medium, and large
aircraft each designed for specific travel distances and passenger loads.
Estimate average GSE requirements and service time for each representative aircraft.
Estimate average aircraft taxiing and GSE service VMT for each representative aircraft and general operational guidelines. Use values as
criteria scores.
Medium analysis: Level 1 indicators
The basic analysis is enhanced by the consideration of emissions factors for representative aircraft as well as GSE equipment. A compre-
hensive list of GSE vehicles is mentioned in section 2 (U.S. EPA, 1999). This type of analysis is appropriate if other types of fuels are
considered in certain alternatives.
Complex analysis: Level 2 and 3 indicators
The most sophisticated analysis investigates aircraft emissions generated throughout the entire landing/take-off (LTO) cycle (approach,
landing, taxi-in, taxi-out, take-off and climb-out), aircraft activity using auxiliary power units (APUs) as well as GSE emissions for
equipment operating on all major roadways, parking facilities and curbsides on the airport property 1 .
Input data: SIMMOD Analysis for total operations, fleet mix obtained from gate modeling information, operating times and characteris-
tics and master plan data for gate layout and locations.
Scores are obtained using the FAA's Emissions and Dispersions Modeling System (EDMS) which calculates emissions for both aircraft
and GSE and contains emission factors for common equipment types. (FAA, 2009).

1. Utility Function of a
Crooked Linear Form

on an absolute scale, we introduce P ij , the physical
performance of criterion j measured as a change—
compared to the original situation or relative to set
targets—and not as an absolute value. All absolute
scores S ij are converted to relative performance
for the purpose of using the crooked linear utility
function. For each criterion, a utility function is
created and enables the conversion of the score
from a physical scale ( P ij relative performance)
to an artificial scale ( U ij utilities) ranging from
-1 to 1 where -1 depicts the worst scenario, 0 the

Tsamboulas and Kopsacheili (2003) developed
utility functions for deriving quantitative criteria
scores in their methodological framework for the
strategic assessment of transportation policies.
With regard to the present methodology, this
procedure applies specifically to treatment of
quantitative values that are not addressed by the
cost function procedure. For scores that are defined