Environmental Engineering Reference
In-Depth Information
the targets will require a significant reduction from current emission levels. A target
of CO2 emissions levels from 1990 + 71% (2012), + 61 (2020) and + 35 (2030)
was settled for the case study.
Results and Discussion for Madrid Case Study
Madrid Region is the biggest urban conglomeration in Spain and third in Europe
after London and Paris. Mobility demand in Madrid grows continuously. According
to the last mobility survey (2004), numbers of trips in a work day have increased
40% over 1996 ratios. Apart from population growth, trips per person increased
20% on the period 1996-2004
[22, 23]
, and due to urban sprawl, a further car
dependence is generated.
Impacts on CO
2
Emissions and Mobility Behavior
Regarding base line scenarios (A0, B0, A1, B1), it is clearly showed that technol-
ogy improvements (A1/B1) generate higher potential savings on CO2 emissions in
all Fig. 3. Higher fuel prices on vehicle use in scenarios B strengthen this trend.
From the technological investment scenario point of view, (CO2) seems to be
efficient but not enough to reach CO2 emissions target. It does, on the other hand,
induce a higher mobility in terms of distance and car trips (Scenarios 1).
Once a target of CO2 is settled, an optimization process is developed in order to
determinate the fuel tax needed for achieving it (Scenarios A2/B2). Duty levels in
these scenarios need to grow on average annual ratios of 18-20% in order to fulfill
CO2 emissions requirements, which are economically unfeasible, socially unac-
ceptable and politically unpopular, but environmentally efficient. What this really
shows is firstly that a deep change is needed to really achieve the targets pro-
pounded for our cities' sustainability. Secondly, strategies for sustainable mobility
could not be based on an isolated action.
As expected, the impact on mode share can be viewed in pairs of scenarios.
Obviously the demand regulation scenarios (Scenarios 2) have the greatest impact
on car use due to the significant increase in costs. Similarly A0/A1 and B0/B1 are
grouped together and the relative changes are small within these groupings. What
clearly shows is that higher technology scenarios improve environmental impacts,
but it does not impact the car dependence of our cities.
Regarding scenarios A2/B2 and from an environmental point of view, high-price
car usage appears to be a partial substitute for car dependence and changing behav-
ior. This is needed to be carefully designed in coherence with other measures in
order to seek for an optimal operating cost level. Scenarios A3 and B3, indicate that
changes are derived by same motivations plus a better alternative in PT usage which
does not limit or change mobility demands.
