94 ■ CITIES AND CLIMATE CHANGE
including motorcycles and private cars (car pools included); and transit mode
(minibuses, buses, bus rapid transit [BRT], light rail, subways, and suburban
rail). Th e types of vehicles used in the last two modes vary enormously in
emission performance. In addition, within each mode—SOV and transit—
each city has a fl eet of vehicles, which have a wide range of GHG emissions
performance. Comparisons between vehicles oft en diff er by orders of magni-
tude depending on technology, maintenance, age of vehicle, energy source,
and load (the average number of passengers per vehicle). To see more clearly
the impact of diff erent transport strategies on the reduction of GHG emis-
sions, we have built a simple model linking the various vehicle fl eet parameters
to GHG emissions per commuter. Th e model is limited to analyzing CO 2 emis-
sions from commuting trips, which are still the most common motorized trips
in low- and middle-income cities. For each mode, the inputs of the model are
the following:
- Th e percentage of commuters using the mode
- Th e average commuting distance (in kilometers)
- Th e CO 2 equivalent (CO 2 e) emission per vehicle kilometer traveled (VKmT),
calculated for full life cycle when data available - Th e load factor per type of vehicle
Numerous publications provide GHG emissions expressed in grams of CO 2
per PKmT (table 4.1). However, the data assume a passenger load to calculate
the CO 2 per PKmT. Because the load is a crucial parameter in the model, it
has been necessary to calculate the CO 2 emissions per VKmT. However, fuel
consumption may vary for the same vehicle, depending on the load; there-
fore, load and fuel consumption are not completely independent variables. We
have therefore slightly adjusted the energy consumption values by VKmT to
refl ect this. A more sophisticated model would establish more accurately the
relationship between load and fuel consumption for each type of vehicle. For
demonstration purposes of the proposed methodology, results were found to
be robust enough to allow this simplifi cation. Th e equation used in the model
showing the daily GHG emissions as a function of the number of passengers
using diff erent modes, with diff erent average commuting distances, load factor,
and engine fuel performance, is presented in the annex.
Based on the equation given in the annex, it can be shown that trying to
reduce the average commuting distance per day (variable D)—de facto reducing
labor mobility—would not provide much eff ect on Q (GHG emissions per day)
compared with a change in vehicle fl eet performance (variable E), a mode shift
(variable P), or an increase in the load factor (variable L). As seen in table 4.1,
the possible values taken by E vary by a factor of four between a hybrid diesel