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Considering Climate Change in Latin American and Caribbean Urban
Transportation: Concepts, Applications, and Cases
Final Report
June 5, 2009
Lee Schipper
Elizabeth Deakin
Carolyn McAndrews
Lynn Scholl
Karen Trapenberg Frick
Center for Global Metropolitan Studies
University of California, Berkeley
Disclaimer
This report was prepared with funding from the World Bank and the University of California.
The statements, findings, and recommendations are those of the authors and do not represent
the views of the World Bank. The authors are responsible for any errors or omissions in this
report.
Acknowledgements
The authors wish to thank José Luis Irigoyen and Ramón Muñoz-Raskin, both formerly of the
World Bank‘s Latin American and Caribbean Region Transport Unit (LCSTR), for their interest
and support for this project. María Catalina Ochoa, Lies Goller, and Emmanuel A. James
(LCSTR) provided many helpful comments during the preparation of this work. Many other
members of the World Bank‘s Latin American and Caribbean Region and the Sustainable
Development Network provided helpful information in the initial phase of this work as
anonymous interviewees or as peer reviewers of the report. Thanks also to the participants in
the Berkeley workshop. A special acknowledgement is due John A. Rogers of the World Bank
for providing his Mexico City Metrobús calculations, as well as to Juan Carlos Goicoechea
(SECTRA, Santiago Chile), Mauricio Osses (formerly Universidad of Chile, now University Of
Sussex), Prof. Eduardo Berentz, (Universidad de los Andes, Bogotá), Eduardo Vasconcellos
(ANTP, Sao Paulo), Victor Hugo Páramo (SMA, Mexico City), and Centro de Transporte
Sustentable, Mexico City for providing data and reports on their respective cities/countries and
photographs as well.
Executive Summary
About This Report
The World Bank‘s Latin American and Caribbean Region Transport Unit (LCSTR) asked
researchers at the Global Metropolitan Studies Center at the University of California, Berkeley
(UC Berkeley) to help develop a framework and advise on methods and approaches for
integrating CO2 concerns into urban transportation in Latin America and the Caribbean (LAC1).
The UC Berkeley team conducted an analysis of the current portfolio of World Bank transport
projects in the region and carried out interviews with Bank staff working in the region. The
review of the World Bank‘s work was followed by a four-day workshop, held at UC Berkeley and
attended by 17 members of the World Bank‘s staff. At the workshop, UC faculty presented
information on greenhouse gas (GHG) science, issues, and impacts, other regions‘ responses
to the GHG challenge, LAC urban transport problems and their GHG consequences, and
opportunities for intervention to reduce GHG emissions in the LAC context. Presentations were
interspersed with group discussions and commentary. Then, drawing upon the literature on
GHG reduction strategies, LAC transport issues, the review of the World Bank portfolio and staff
interviews, and the workshop discussions, the UC Berkeley team developed a framework for
consideration of GHG emissions reduction strategies in the Latin America and Caribbean
region. The UC Berkeley team also prepared guidance on methods and procedures that could
be used to analyze GHG emissions, and developed recommendations on ways to enhance or
strengthen the interventions proposed to the World Bank from the client countries.
This report presents the framework, discusses methods and approaches that can be used for
transport project CO2 evaluation, and presents two case examples. A companion paper
presents the findings from the portfolio review and interviews.
The introductory chapter presents background and context, describing the current and projected
transport and greenhouse gas emissions situation in LAC. This chapter shows that the LAC
region is a low carbon emitter on a per capita basis but its road transport emissions are high
relative to its GDP. Furthermore, unless interventions moderate trends, the LAC region is
expected to triple its CO2 emissions from road transport in the next twenty years, due to
population growth and economic development.
Chapter two presents a conceptual framework for integrating CO2 considerations into urban
transport with the aim of moderating CO2 emissions in Latin America. Strategies that can
reduce transport emissions include improved vehicles and fuels, high quality transit, bike and
pedestrian facilities, coordinated land use and transport, and improved traffic operations and
management. Curitiba is an example of a Latin American city where integrated transport and
land use planning has structured the city‘s growth and helped keep automobile use low. Bogotá
is an example where the deployment of a bus rapid transit system has greatly improved urban
transportation in a carbon-efficient way. Many more LAC cities could use strategies such as
those pioneered by Curitiba and Bogotá to manage urban transport‘s carbon impacts.
Many of the transport measures that cities are pursuing and the World Bank is supporting in
LAC urban areas are on track for reducing emissions compared to business-as-usual growth
1
In this document LAC will refer to the Latin American and Caribbean region. Where data for the region are taken
from the Organisation for Economic Co-operation and Development (OECD) or the International Energy Agency
(IEA), Mexico, a member of the OECD, is included with the LAC region and excluded from the OECD unless
otherwise noted.
1
Executive Summary
projections. In some cases, emissions could be reduced even more by packaging measures
together and improving organizational and technical capacity to coordinate across modes,
manage traffic, and use pricing and regulation as a demand management tool.
Chapter three addresses ways to estimate the impacts of CO2 from urban transport projects.
Formal models offer the most comprehensive analysis approach, but simpler analysis tools also
can be valuable. Methods already in use in Latin America range from integrated land usetransport-emissions modeling producing detailed quantitative outputs, to surveys, counts, and
analyses conducted specifically for a project, to quick-response approaches using spreadsheet
calculations, elasticities, estimated data, and evidence from experience in other cities. Two case
studies, presented in appendices to the report, illustrate ways in which CO2 impacts can be
estimated.
Chapter four provides a summary of key points from the previous chapters and presents the
authors‘ conclusions.
Summary of Key Findings
1. Latin America in the Global CO2 Context
Chapter one examines Latin America in the global CO2 context. While Latin America‘s global
share of CO2 emissions is small, emissions from road transportation are high relative to income.
The relatively high emissions are almost entirely due to the region‘s relatively high levels of auto
use. Automobiles and light trucks typically account for around two thirds of the CO2 emissions
in LAC metropolitan areas, despite their accounting for only a small share of total urban travel.
Emissions from road transport in LAC are expected to rise sharply in the coming decades if
current trends continue. Using projections of passenger and freight activity, vehicle use, and
CO2 emissions, a trends-extended rise in car use would push up overall CO2 emissions by a
factor of three by 2030, even with fuel economy improvements. The increase in car use is in
part a result of growing incomes and economic activity, but it also reflects the poor quality of
transit and non-motorized travel options in many LAC cities.
Low carbon fuels and new vehicle technologies can help lower CO2 emissions and often have
important health and livability benefits as well. However, the lower emissions per kilometer that
these measures produce can be overwhelmed by rapidly growing kilometers of travel. This is
expected to be the case in LAC. Further emissions reductions can be obtained by implementing
good urban transport, including high quality transit, pedestrian and bicycle facilities, traffic
management, and appropriate pricing of transport facilities and services. Such policies will
improve services for large portions of the population, and are also likely to moderate the growth
in car ownership and use and thus help hold back the increase in CO2 emissions.
LAC cities have the opportunity to contribute to the worldwide effort to reduce greenhouse gas
emissions and can build upon notable LAC accomplishments in urban development and
transportation to provide leadership on this pressing issue.
2. A Framework for Integrating CO2 Concerns into Transport
A framework is a conceptual structure intended to serve as a support or guide for action. Taking
into consideration background and context, a framework outlines the broad set of ideas and
principles that will guide future activities, identifying systems and subsystems and showing how
2
Executive Summary
they interrelate. Chapter two provides the proposed framework for integrating CO2
considerations into transport plans, programs, and projects in Latin America.
The basic concept proposed for the LAC region is to analyze and take credit for reducing
transport‘s CO2 emissions relative to what would happen in the absence of interventions. Such
analyses also can be used to identify ways to enhance CO2 performance of transport
investments. A systematic set of inquiries can be made that will integrate CO2 considerations
into the broader process of transport and urban development in the LAC region. The steps are:
A) Determine the scope and scale of the proposed intervention, and the time frame of
implementation. Will the intervention‘s effects be felt as soon as it is implemented, or will effects
build up over time (e.g., as a vehicle fleet turns over) or perhaps decline over time (e.g., as
traffic patterns change)? Will the intervention have a national, regional, corridor, or localized
impact? Is the intervention designed to alter fuel composition, and if so, for how many vehicles?
Will it affect vehicle types and the fuels they use? Will it alter vehicle ownership and operating
costs? Will it affect the number of trips made, travel patterns, and mode choice? In the medium
to longer run, is it likely to affect auto ownership, land use, and location choice? Are there ways
that the intervention could be circumvented? Are additional steps necessary to maintain the
effectiveness of the measure, e.g., enforcement of fuel standards, maintenance of vehicles,
periodic signal re-timing for bus rapid transit? Are there other complementary or conflicting
measures planned or proposed that need to be considered in evaluating the impact of the
intervention, e.g., a parallel road widening, a major new development project?
B) Estimate the impact of the proposed project. Using formal models, special studies, quick
response techniques or comparative case examples, analyze the project and its likely effects
over the short term (e.g., the next two to five years) and where applicable, over the longer term
(e.g., a 10-20 year period). For transport projects affecting travel behavior, analyze the resulting
mode choices, travel patterns, traffic levels, fuel use and CO2 emissions. For interventions that
are expected to have a regional or corridor-level impact, also consider likely changes over the
longer term in patterns of location choices and land use and their effects on emissions.
C) Monetize the benefits and costs of the intervention. To estimate the CO2 saving co-benefits
of a transport strategy, consider lifecycle costs including production or construction, operation,
maintenance, and decommissioning. Consider travel benefits and costs as well as community
and environmental benefits and costs.
D) Develop a business-as-usual case (―no project‖ alternative) for the target years of analysis in
the absence of the intervention. Compare the results to those with the proposed project to
gauge the amount by which the project will provide net benefits, including lower emissions,
compared to no intervention.
E) Establish performance measures for ongoing monitoring and evaluation.
3. Methods for Evaluating the CO2 Impacts of Transport Projects
Estimating the CO2 impacts of transportation projects can be a complex matter because the
estimates must account for system effects, now and in the future. Changes in the metropolitan
region – population, economic activity, land use, the legal and regulatory environment – will
affect the performance of transport projects and should be taken into account in preparing
estimates of project performance. Likewise changes in transportation technology (fuels,
vehicles, operations) and in the lifecycle costs of infrastructure should be accounted for.
3
Executive Summary
Travel and its CO2 impacts can be estimated in a variety of ways, from formal modeling to
sketch planning analyses and case study comparisons. In most analyses, several methods are
used. For example, population, employment, and income forecasts may be taken from national
or international sources and adjusted or updated for the local case. Mode choice may be
estimated using a regional model or derived from a local survey. Traffic flow may be measured
before and after or calculated and predicted with regional transportation models, traffic
operations micro-simulation software, or simple spreadsheet models. The choice of methods
will usually depend on the nature and size of the project to be analyzed, the quality of the data
available, and personnel resources available for the analysis.
Estimating the CO2 impacts of interventions requires the analyst to combine information from
travel and traffic models with information on the vehicles used, their occupancy, and their fueluse characteristics. Standard coefficients published by the Intergovernmental Panel on Climate
Change (IPCC) then can be used to convert fuel use estimates into CO2 estimates.
The most advanced method for carrying out these tasks today is an integrated transportationland use model system. These models analyze and predict location choices and activities in
which individuals, households, and businesses engage over different time frames, and estimate
travel and its impacts from these broader analyses. The models sometimes include traffic microsimulation, or they may be linked to separate traffic models. Integrated transportation-land use
models have been developed in Europe, Japan, the US, and Latin America and have been
applied in a number of cities, but they are not yet in common use, in part because of the high
levels of data and advanced technical skills they require.
Simpler travel models estimate trip generation, trip origin-destination patterns, mode choices,
and network flows including travel times and costs. Considerations such as the effects of a
transport investment on land uses and location choices, vehicle ownership levels and vehicle
type choices, and time of day of travel are either modeled separately or are provided as expertdeveloped scenarios.
Modeling has a number of well recognized limitations. Relatively few model systems include
non-motorized modes, although development of walk and bike models has accelerated in recent
years. Most models are carried out on data aggregated for sub-areas or corridors or a region,
and if these aggregations are large, interventions that affect a small part of the city or a single
corridor may not ―show up‖ in model results. The impacts of transport changes on location and
land use are ignored in some modeling systems, a practice that is increasingly viewed as
problematic. Data requirements are heavy, and when data are not available analysts may rely
on ―default‖ values for model coefficients, the equivalent of assuming travel behavior in the
application area is similar to that in the area where the model was estimated. Because of these
difficulties, less sophisticated but more transparent approaches are often used instead.
Widely used ―quick-response‖ approaches include using only one model from a model system,
e.g., applying only the mode choice model to forecast changes in ridership due to improved
transit travel times. Another simplified approach is to derive elasticities from models (e.g.,
changes in VKT with respect to changes in travel times) and use these elasticities to estimate
the impact of proposed interventions. Retrospective or prospective surveys also can be used to
estimate travel changes. These methods have their own limitations, e.g., modeling the effects of
a traffic flow improvement only for the affected corridor ignores potential effects on other
corridors; respondent recall may be poor; behavioral intentions often differ from actual behavior.
Methods that rely on before-after evaluations of aggregate data, such as regional fuel sales or
4
Executive Summary
measured emissions can be particularly problematic, since other changes in the region (e.g.,
population growth, economic upturns or downturns) can cloud the attribution of observed
changes. What is needed is not just a before-after analysis but also without-project-with-project
comparisons. This requires an approach combining models, estimates and observations.
For infrastructure investments, an important emerging method is Life Cycle Analysis (LCA),
which accounts for total costs, energy use, emissions, and other key attributes of products and
services from ―cradle to grave‖ resource extraction and production or manufacturing through use
and decommissioning or disposal. For transport project CO2 analyses, LCA would account for
the emissions associated with producing fuels, constructing vehicles, stations and guideways,
operating and maintaining the system, and retiring or disposing of worn out system
components.
4. Conclusions and Recommendations
The final chapter of the report presents the study team‘s conclusions and recommendations.
The principal conclusion is that while the LAC region is already a good performer with regard to
CO2, its activities to improve transportation also reduce CO2 and packages of projects could
achieve even more. Systematically incorporating CO2 concerns into transport planning, project
development, and analysis would provide a sound basis for such further accomplishments.
The World Bank could encourage CO2 minimization in the following ways:
First, the World Bank could provide more technical assistance for travel surveys, traffic counts,
emissions measurements, and fuel-use measurements. Good data are a critical building block
for any evaluation and such data are needed to build better models for analysis and forecasting,
not just for CO2 purposes but for a broad array of urban planning and economic development
tasks.
Second, the World Bank could encourage and assist local authorities to develop modeling
capabilities for travel demand, traffic operations, and life cycle analysis. Such modeling
capabilities would enable more sophisticated analyses of growth, development, and travel, and
also would enable more explicit and formal consideration of the longer-term impacts of World
Bank-supported projects. This latter capability is important because road and transit projects
might increase the ability of people to make longer trips and lead to, or reinforce, development
in more distance parts of urban regions. Better analysis of system-wide changes due to projects
could result in changes in project design to avoid undesirable side-effects.
Third, the World Bank could provide assistance to both local and national governments to
evaluate strategies such as stronger fuel economy standards, stronger emission standards,
congestion pricing, parking pricing, and tolls. Some of these measures may be politically
difficult, yet they have been effective where used, with proven impacts on vehicle use, mode
choice, and vehicle fuel intensity. Information on the benefits and costs of such measures could
increase the comfort level with which such measures are regarded and over time could expand
the kinds of choices local and national authorities make.
Treating CO2 considerations as a regular, required element of Bank plans, evaluations, and
projects would signal the importance of action on the topic, and recognizing that many
transportation projects reduce greenhouse gases from what would otherwise occur lays the
groundwork for more vigorous action in the future.
5
Executive Summary
5. Case Studies Applying the Framework
Two case studies applying the framework to LAC projects and showing how available methods
and data can be used to estimate CO2 emissions are included as appendices to the report.
Mexico City: CO2 Reductions from Metrobús
Photo courtesy of CTS, Mexico City.
The Metrobús BRT project in Mexico City was conceived of as a way to simultaneously reduce
traffic congestion (caused by high volumes of paratransit ―colectivos‖ and growing private auto
use) and improve transit service in a major corridor. The project aimed to improve bus speed,
convenience, and reliability and thereby improve transport for the poor, attract riders away from
cars and colectivos, and reduce air pollution and CO2 emissions from colectivos and
automobiles. The project was implemented at a cost of approximately USD $80 million in 2005.
It involved construction of the BRT stations and exclusive lanes and retirement of the colectivos
formerly plying the route, replaced by new articulated buses using conventional diesel fuel.
The Metrobús project did not explicitly aim to reduce CO2 emissions, but it nevertheless cut
CO2 emissions associated with the Insurgentes corridor by about 10%, or 50,000 tonnes/year.
This amounts to about 0.25% of total transport emissions in the Mexico City region, a
substantial achievement for a project that serves only one corridor in a very large metro area.
About one-third of the emissions reduction comes changing vehicles; the rest comes from
changes in traffic flow and from mode shifts. If Metrobús had acquired hybrid articulated buses
like those currently in use in Seattle and elsewhere, an additional 3,000 tonnes of CO2 would
have been saved, but at high extra cost.
A cost-benefit study done by Mexico‘s Instituto Nacional de Ecología found total transport
benefits of at least USD $15 million/year from Metrobús. Using values of CO2 that bracket
current literature and practice, USD $250,000 could be added to the benefits if CO2 were
valued at USD $5/tonne, or as much as USD $4.2 million at a CO2 value of USD $85/tonne.
The benefits included fewer local pollutant emissions, lower travel times on Insurgentes, and
less wear on the roads because of the reduced number of vehicles (buses). CO2 is not critical
for the project‘s benefits at the lower value and not decisive at the higher value, but especially at
the higher level, the CO2 benefits are significant in comparison to the total benefits.
These analyses were possible because the region had a good data base on vehicles, fuel
economy, and emissions, a product of World Bank technical assistance provided in the early
1990s. New field observations of traffic and on-board surveys of passengers had to be carried
out to estimate most travel changes, however.
6
Executive Summary
Santiago de Chile: CO2 Reductions from Cycle Paths
Photo courtesy JC Goicoechea, SECTRA, Santiago.
As part of a long-term vision of urban development, in 2004 Santiago de Chile built a number of
cycle paths connecting various municipal districts. Since then, it has developed into a bikeway
network with close to 200 km of facilities. The World Bank-administered project, funded by the
Global Environmental Facility (GEF), financed the construction of about 10 km of bikeways and
the lighting for another 10 km of new bikeways in three municipal districts. Advocates hope for
a network of close to 700 km of bikeways by 2012.
Consultants implemented an intercept survey to evaluate the bike paths‘ impacts. From the
survey, they determined that slightly under one-third of all bike trips would not have been made
without the cycle paths, and that half of the bike trips were for recreation. The number of cyclists
who reported previously using cars for the trips now on bike was used to estimate the
automobile kilometers of travel removed. With a fuel use simulation program, the fuel saved
and emissions of CO2 (and other pollutants) were then estimated. Close to 1,000 tonnes of
CO2/year were eliminated by the bike path investment.
An analysis of CO2 saved, fuel saved, travel time reductions, and the net reduction in accidents
showed a total annual project value of about USD $628,000. CO2 valued at USD $10/tonne
accounted for less than 2% of this total. Even at USD $85/tonne, the CO2 benefits would have
reached around 10% of the total project benefits. At either level, however, the analysis showed
the added value of CO2 co-benefits of a sound cycle project.
The analysis demonstrates the value of field observations and project surveys in transportation
project evaluation. However, Santiago also had a relatively recent travel survey against which
to calibrate these observations and surveys, as well as a good data base of vehicles, their fuel
use and emissions, and a model for fuel use calibrated for local conditions. The project-specific
survey used together with these available tools resulted in a sophisticated evaluation.
7
Executive Summary
Table of Contents
Executive Summary ................................................................................................................... 1
1.
Latin America and the Caribbean in the Global CO2 Context ............................................. 1
A.
B.
C.
2.
Global GHG and CO2 trends – Where is Latin America and the Caribbean?.................. 1
Road Transport in Context in LAC: Motorization and Emissions in Urban Regions ......... 4
Summary: The Transport - CO2 Challenge ...................................................................13
A Conceptual Framework for Integrating CO2 into Urban Transport ..................................14
A. Determining the Scope and Scale of the Intervention and Time Frame for
Implementation .....................................................................................................................15
B. Estimating the Impact of the Proposed Project ..............................................................26
C. Monetizing Benefits and Costs ......................................................................................26
D. Comparing Project Impacts to the ―No Project‖ or Business-As-Usual Alternative .........30
E. Establishing Performance Measures for Ongoing Monitoring and Evaluation ................32
F. Summary .......................................................................................................................33
3.
Methods for Assessing the CO2 Impacts of Transport Projects .........................................34
A.
B.
C.
D.
E.
F.
4.
An Overview of Transportation Analysis and CO2 Estimation Methods .........................36
A Note of Caution: Limitations of Modeling ....................................................................44
Selecting an Analysis Approach ....................................................................................46
What to Do When Data and Models are Unavailable or Limited.....................................48
Flags – What to Watch for Before, During, and After a Project is Completed .................51
Summary .......................................................................................................................54
Conclusions and Recommendations .................................................................................56
A. A Framework for Incorporating Restraint of CO2 Emissions in Transport Planning and
Policy ....................................................................................................................................56
B. Estimating the Impacts of Interventions on CO2 Emissions in Urban Transport: ―You
Can‘t Master What You Can‘t Measure‖ ................................................................................57
C. Recommendations.........................................................................................................57
Appendix One: Mexico City‘s Metrobús – A Case Study in Estimating CO2 Impacts ........59
5.
A.
B.
C.
D.
E.
6.
Introduction ...................................................................................................................59
Detailed Analysis of Major CO2 Impacts from Metrobús as a Transportation Project.....64
Technological and Policy Options for the Long Run.......................................................70
Longer Term Impacts ....................................................................................................73
Institutions for Better Monitoring ....................................................................................75
Appendix Two: CO2 Emissions Reductions from a Bikeway Project in Santiago de Chile 78
A.
B.
C.
D.
Description of the Bikeways Project ...............................................................................79
Analysis of the Bikeways Project ...................................................................................80
Costs and Benefits of the Project...................................................................................87
Conclusions for Informing Project Design and Evaluation with the Framework ..............88
7.
References ........................................................................................................................89
8.
Glossary ............................................................................................................................96
i
Preface
Today, Latin America is a small contributor to the world's emissions of greenhouse gases. Latin
American and Caribbean (LAC) countries' emissions are a fraction of those of the US, China,
and India. However, the region's car ownership, use, and emissions are higher than would be
predicted on the basis of population or GDP, and car traffic clogs the streets and pollutes the air
of many LAC cities. Furthermore, LAC carbon emissions from transport - mostly cars - are
predicted to grow three-fold by 2030 as both auto ownership and vehicle-kilometers traveled
expand. The total emissions will still be small compared to those of OECD countries, but they
will not be trivial.
LAC cities are developing new transport facilities and services in order to boost economic
development and increase opportunities for the poor and the growing middle classes. How can
the region moderate carbon emissions while expanding transportation?
Low-carbon fuels and cleaner vehicle technologies are part of the answer. Compared to today's
vehicles and fuels, such strategies could achieve carbon savings of up to 30% per vehicle
kilometer by 2030. However, because the projected growth in the number of motor vehicles and
their use is so large, LAC CO2 emissions are expected to increase substantially, even after
accounting for improved vehicles and fuels, if current trends continue.
Left: Cyclist in heavy traffic on the Circuito Interior Mexico City.
Right: Smoking bus in Porto Alegre, Brazil.
Photographs courtesy of Lee Schipper.
Additional CO2 reduction can be attained through well-planned urban transport investments.
Many LAC cities are already steering transport growth in more carbon-efficient directions by
investing in high quality public transportation and new facilities for bikes and pedestrians. These
travel choices improve accessibility for a large portion of the population while managing traffic,
cutting pollution, and moderating CO2 emissions.
LAC leadership in implementing new travel options is creating models from which others can
learn. Cities such as Curitiba and Bogotá are already widely emulated for their creative
investments in urban planning and bus rapid transit. These activities provide good transport
while reducing carbon emissions, and their success puts pressure for change on countries that
have been slow to adopt carbon reduction policies.
Additional investments in transportation facilities and services that increase access and quality
of life while also cutting carbon would benefit cities in Latin America and around the world.
Transit, pedestrian and bicycle facilities, improved traffic management, and coordinated
ii
transport and land use are important low-carbon access and mobility strategies. Most cities
could also gain by strategically coordinating transport investments, creating networks of transit
operating on traffic-managed streets and arterials conveniently reached by bikeways and
pedestrian ways and serving mixed-use neighborhood and commercial district centers. In
addition, most cities could benefit from pricing policies for fuels, parking, and other transport
services that better reflects marginal social and economic costs. Such pricing is not only
efficient but can generate revenue that can be used for further transport improvements.
Left: Buses unloading in Curitiba.
Right: Transmilenio bus scoots by a traffic jam in Bogotá.
Photos courtesy of Lee Schipper.
Reducing the CO2 emissions from LAC urban transport as population and incomes in urban
areas grow is a challenging goal, but it is one that many LAC cities are already pursuing.
Substantial additional gains seem achievable. This report reviews the challenges and
opportunities and offers a framework for evaluating the CO2 consequences of transportation
choices in the years to come.
iii
1. Latin America and the Caribbean in the Global CO2 Context
A. Global GHG and CO2 trends – Where is Latin America and the Caribbean?
There is broad consensus that greenhouse gases (GHG) are warming the planet.2 Many
human activities produce GHG emissions, but roughly two thirds of the total anthropogenic
emissions comes from fossil fuel combustion for transportation, buildings, and industry (2005
data). Anthropogenic greenhouse gases (including methane and small quantities of other
potent GHG, as well as CO2) also come from agriculture, mining, natural gas production,
landfills, and industrial processes. Land use changes that remove CO2-absorbing plants
contribute to the problem.
Figure 1.1 shows the origin of CO2 emissions from all fossil fuel combustion by region of the
world. About half of the total CO2 emissions comes from OECD countries (excluding Mexico),
about 20% from China, and only 7% from Latin America3. On a per capita basis, the world
average was 4.3 metric tonnes of CO2/capita while that from LAC was only 2.5 tonnes/capita.4
Figure 1.1 CO2 Emissions from All Fossil Fuel Combustion by Country or Region in 2006
12,500
Million metric tonnes
10,000
7,500
5,000
2,500
0
Africa
Middle
East
NonOECD
Europe
Former
USSR
Asia excl
China
China
Av.
Latin OECD ex
Sea
Bunkers Bunkers America Mexico
incl
Mexico
Source: International Energy Agency (IEA, 2008).
Figure 1.2 shows global CO2 emissions another way, by main energy consuming sector (as
shares) in 2006. Figure 1.3 shows the pattern for Latin America only (including Mexico) in the
2
See the Fourth Assessment report of the Intergovernmental Panel on Climate change (IPCC, 2007).
The IEA and OECD count Mexico with ―OECD North America‖ in their statistics. Unless otherwise stated, this study
moves Mexico into LAC. All data are for direct combustion only.
4
In this work carbon or carbon dioxide is always given in metric tonnes of CO2. One tonne of CO2 weighs 44/12 as
much as one tonne of pure carbon. (A carbon atom has a weight of 12, and each oxygen atom has a weight of 16,
giving each molecule of CO2 an atomic weight of 44.) ―Combustion‖ includes both stationary and mobile sources.
Conversion from quantities of fuel (in liters, tones, or energy units) is made with coefficients supplied by the IPCC.
3
1
same year. Interestingly, as Figure 1.3 shows, road transport represents a full one third of the
total CO2 emissions in LAC, higher than the world average share.5
Figure 1.2 CO2 Emissions for Entire World by Sector in 2006 (total 4.3 tonnes/capita)
Residential.
Commercial,
Agriculture **
34%
Other energy
industries*
7%
Manufacturing
industries and
construction
35%
Other
Transport
Road
7%
Transport
17%
Figure 1.3 CO2 Emissions for LAC by sector in 2006 (total 2.5 tonnes/capita)
Other energy
industries*
9%
Residential.
Commercial,
Agriculture **
22%
Other Transport
3%
Manufacturing
industries and
construction
34%
Road Transport
32%
In explaining differences in CO2 emissions among regions or countries, the most obvious
factors are population and level of development, as measured by per capita income. But a host
of additional factors share in explaining differences – geography and local climate, degree of
urbanization, land uses, fuel mix, and the efficiency of energy use. (IEA, 1997) Differences in
policies, available technologies, and fuel prices shape the latter factors.
5
In these two figures, emissions for electric power production are allocated to the sectors where electricity is
consumed, which has very little impact on transportation (because of low electricity use) but nearly doubles the
emissions counted for residential, commercial and agriculture. Note that transportation‘s emissions are almost solely
CO2.
2
The transport share of global emissions has risen slowly but steadily since 1971. (IEA, 2008)
The LAC region also reflects this upward trend. Figures 1.2 and 1.3 show that in comparison
with the world as a whole, LAC CO2 emissions are more heavily from transport, which produces
35% of the LAC‘s total emissions compared to a 24% transport share of the world total. Further,
the LAC transport emissions are heavily due to road transport, which accounts for over 90% of
LAC‘s transport emissions.
Figure 1.4 shows that for the world as a whole, the transport emissions/GDP ratio has declined
by about 20%. (IEA, 2008) However, regional differences are large, with some regions showing
increases in the ratio while others have achieved substantial decreases. For LAC, the ratio of
road transport CO2 emissions to GDP has declined slightly, by less by 0.5%/year. In other
words, LAC transport emissions have increased at almost the same rate as GDP has grown.
IEA data indicate that the LAC emissions increases were driven in large part by the rising
importance of fossil fuels for transport, especially in populous Brazil. Emissions from other
sectors in LAC grew less rapidly than those from road transport. Thus the importance of road
transport in the LAC emissions story has increased over time.
Figure 1.4 Ratio of Road Transport CO2 Emissions to GDP for Regions, 1990 and 2006
0.18
Kilograms CO2/$US of GDP (2000 PPP)
0.16
1990
2006
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
US, Canada
OECD
Europe
OECD
Pacific
E Europe
LAC w
Mexico
Source: IEA 2008.
3
China
India
B. Road Transport in Context in LAC: Motorization and Emissions in Urban Regions
An understanding of CO2 emissions from road transport in the LAC requires a clear picture of
the vehicle fleet and vehicle use (in vehicle-km). Data on vehicle ownership and yearly usage
have been developed by International Energy Agency and the World Business Council for
Sustainable Development (WBCSD, 2004) and are used here, with some modifications.
i. Vehicle Ownership
Figure 1.5 shows light duty vehicle (LDV) ownership in different regions, relative to both
population and GDP, in 2005. Among the developing regions shown, Latin America had a per
capita ownership of light duty vehicles of 86 vehicles per 1,000 people – mostly private cars,
SUVs, and light trucks6. The high level of motorization in Eastern Europe is explained in large
part by a rapid increase in cars bought used after 1990 and stronger presence of Western
European automobile manufacturing in Eastern Europe after that time.7 Even though China and
India have much larger populations, the per capita auto ownership is very low and even the
absolute numbers of LDVs in those two giants are still well below the number in LAC.
Figure 1.5 Light Duty Vehicle Ownership vs. Income and Population, 2005, Selected Regions
Light Duty Vehicles/Capita
36.26
700
LDV/1000 people
40
Light Duty Vehicles/$1mn of GDP
35
GDP/Capita, US$ 2000 PPP
600
30
25.64
500
25
22.68
400
20
300
15
10.12
200
7.63
10
6.15
3.07
100
di
a
In
M
ex
w
C
hi
na
ic
o
e
Eu
ro
p
C
LA
EC
D
E
Pa
ci
fi
c
pe
ro
O
O
EC
D
Eu
C
an
U
S,
5
0
ad
a
0
LDV/$Mn GDP (2000 PPP)
800
Source: IEA MoMo Database (IEA, personal communication, 2009).
Notes: 10-20% of these light duty vehicles are commercial vans or pickups. GDP/Capita in USD $1,000 (2000 PPP)
shown above each region.
6
LAC has high auto ownership in comparison to the Middle East and Africa, not shown in the table, although some oil
rich nations in both regions have higher car ownership.
7
Defined by WBCSD to include Albania, Bulgaria, Poland, Romania, Slovakia, and all of the former Yugoslavia, with
a per capita GDP in 2006 of USD $10,500, or roughly 30% higher than LAC according to OECD national data.
4
ii. Vehicle Use and Emissions in LAC
Data estimated by the WBCSD‘s Sustainable Mobility Project (WBCSD, 2004) and more
recently refined by the International Energy Agency (IEA, personal communication, 2009)
provide information on vehicle types, their energy intensities, and the average km driven each
year for LAC countries.8 CO2 emissions by vehicle type can be calculated from these data.
Table 1.1 presents the results.9
Table 1.1 Road Transport Emissions in LAC 2000 by Vehicle Type: The Role of Light Duty Vehicles
Vehicle
type
Vehicles
(100,000)
Km /
year
Energy,
EJ
Emissions
Mtonnes
CO2
Share of
total CO2
emissions
40,127
6,948
930
511
4,459
5,385
2,314
13,000
7,500
40,000
40,000
13,000
22,000
50,000
2.11
0.05
0.21
0.20
0.23
1.15
1.38
5.33
155.4
3.0
14.1
14.5
16.2
77.6
92.2
372.9
41.7%
0.8%
3.8%
3.9%
4.4%
20.8%
24.7%
LDV Pass.
Motorcycles
Minibuses
Busses
LDV freight
Med Truck
Heavy Truck
Total
Source: WBCSD Sustainable Mobility Project and IEA.
Note: 1 EJ (exajoule=10^18 joules) = 24 MTOE (million tonnes of oil). Data adjusted to include Mexico. Emissions for
rail were included in the original Sustainable Mobility Project spreadsheets but are omitted here.
Table 1.2 Estimated Urban Share of Traffic and Emissions by Vehicle Type, LAC 2000
Vehicle
type
LDV and
motorcycles
Mini Buses
Buses
Light Truck
Medium Truck
Heavy Truck
Total
Urban
Share of
VKT
Urban
VKT,
Billion
Vehicle
Occupancy
People
Passenger
km,
Billion
Emissions
MTonnes
CO2
Share of
urban
CO2
80%
453
2
907
127
61.5%
80%
50%
80%
50%
10%
30
10
46
59
12
510
20
50
595
511
11
7
13
39
9
208
5.5%
3.5%
6.3%
18.8%
4.5%
100*
2013
Source: Original calculations.
For the LAC region as a whole, about half of road transport emissions are for passenger traffic,
the other half for freight travel. The dominant vehicle type is light duty vehicles, most of which
8
The IEA used their ―MoMo‖ model (Fulton and Cazzola, 2009) for the Sustainable Mobility Project work and is
currently developing it further. This includes a major effort to develop a set of data on vehicles in use by fuel type,
fuel use per vehicle per kilometer, and total fuel use totals that match figures reported by each country to the IEA.
9 The total fuel use for each particular fuel and vehicle type is calculated using the estimated numbers of vehicles,
distance/vehicle, and fuel/distance, with national road fuel use as tabulated by the IEA used as the control total.
5
are passenger cars.10 For this study, we further estimated the urban share of traffic (VKT) and
emissions, as well as passenger kilometers traveled. Results are shown in Table 1.2.11
Table 1.2 shows that about 60% of all road transport emissions in LAC appear to be associated
with urban areas, with light duty vehicles responsible for well over half of the urban emissions.12
Further assuming that LDVs in urban regions have average occupancy of two people,
motorcycles one person, minibuses 20 people, and large buses 50 people, we estimate that in
2000, two trillion passenger km were produced in these motorized modes in LAC urban areas.
Data from major metropolitan regions of LAC are consistent with the estimates of urban traffic
and emissions generated from national and regional data for specific cases. Table 1.3 and
Figure 1.6 show the results for Mexico City in 2006. The data come from the region‘s emissions
inventory, which is updated every other year.
Table 1.3 CO2 Emissions, Vehicles, and Traffic, Mexico City, 2006
Vehicle
type
Mtonnes CO2,
all fuels
Vehicles
Billion VKT,
(100,000)
all fuels
all fuels
Cars
10.49
3,395.8
46.31
Taxis
2.60
155.1
10.38
VW Bus Colectivos
0.70
39.7
2.64
Other Colectivos
0.74
36.1
2.54
Pick Up
0.83
133.4
3.48
Other veh < 3 t
0.63
81.6
1.80
Truck Tractors
1.63
60.9
1.38
Autobuses
1.87
43.1
1.79
Other Veh < 3 t
0.54
100.8
2.20
Motorcycles
0.37
180.7
4.47
Totals
20.40
4,227.3
76.98
Source: Mexico City Emissions Inventory (SMA, 2006).
10 LDV, or light duty vehicles, include all cars, vans, pickups and SUVs, of which an estimated 10% are for strictly
commercial purposes and counted under LDV freight.
11
Data for Table 1.2 are based on recent International Energy Agency refinements of country-level data from the
Sustainable Mobility Project (WBCSD, 2004), provided to us for this work by the (Private communication with IEA,
2009). To develop urban area estimates from the country-level data, we assume that 80% of car, motorcycle and
minibus fuel is consumed in or around urban areas, largely because the incomes to support car ownership as well as
mini-bus use are 80% in urban areas. We estimate that 50% of large bus traffic is in cities, but that 90% of the truck
activity, the other half of the bus activity, and 10% of car traffic is intercity. The term ―urban area‖ is thus used loosely
here to exclude emissions arising from long-distance intercity road traffic as well as traffic confined to rural areas.
Since congestion tends to be much worse in urban areas than elsewhere, and congestion tends to boost fuel use per
km, our assumptions for apportioning fuel use probably underestimate the urban share. To estimate passenger
kilometers, we assume the vehicle occupancies shown in the table. Finally, to estimate emissions, we assume that
the urban fleet characteristics and fuel types are the same as those for the national reports. Since urban vehicles may
be somewhat cleaner and better maintained than those in rural areas, this may overestimate the urban portion of
emissions.
12
Rail is excluded from the table, but urban rail, mostly electric-powered, contributes very little emissions from
electricity generated to run it in even countries and cities with the most urban rail, e.g., European countries (or cities
like Paris and London). See Schipper and Marie (1999).
6
Figure 1.6 CO2 Emissions from the Main Classes of Transport Emitters of CO2, Mexico City Metropolitan
Area, 2006
16
CO2 Emissions. Million Metric Tonnes
14
12
Compressed Natural Gas
10
LPG
8
Diesel
6
Gasoline
4
2
-
Cars, Pickups and Taxis
Buses, colectivos, VW Buses
All Trucks
13
Source: Mexico City SMA Emissions Inventory estimated by vehicle, distance, and fuel intensity.
The results show that in Mexico City CO2 from transport arises overwhelmingly (68%) in
individual vehicles, i.e., cars, pickups, taxis and motorcycles. (SMA, 2006) Traffic is also
dominated by the same small individual vehicles, which account for almost 83% of VKT.
Interestingly, Mexico City car ownership is lower than that in many other large Mexican cities, so
the share of emissions in light duty vehicles may be even higher in other Mexican urban areas
where there are more cars per capita. This also implies that the light duty personal vehicle fleet
in other Mexican cities is an even greater contributor to CO2 emissions than it is in Mexico City.
Patterns for Santiago de Chile (Escobar, 2007), Bogotá (Giralto, 2005), and Sao Paulo
(Vasconcellos personal communication, 2008; Melor de Alvares, personal communication,
2008) are similar. Light duty vehicles account for less than 25% of travel, but more than 60% of
VKT and CO2 emissions in these urban areas.
Light duty vehicles are also at the heart of congestion in LAC cities (as in most of the world). An
extreme but not unusual example is shown in Figure 1.7, where cars illegally crowding into the
contra-flow lane in Mexico City are shown getting out of the way of an oncoming bus (from
which the photo was taken). High car use and high levels of congestion are key reasons why
surface transport by bus or trolley sharing the same roadways is slow, and in this case the cars
even slow the contra-flow bus lane. And the heavy flow of traffic along the wide boulevards of
LAC cities makes pedestrian crossing difficult and cycling almost impossible, despite the
attempt by the pedestrian in Figure 1.8 below.
13
Combis are Volkswagen buses, while Microbuses are colectivos, mostly 29-36 passenger compartments fitted to 23 tonne trucks.
7
Figure 1.7 Cars In Contra-flow Lane of Eje Central, Mexico City, Hurrying to Exit the Lane as a Bus Moves
Against Their Flow
Photo: Lee Schipper.
Figure 1.8 Pedestrian Stranded Trying to Cross the Periférico in Guadalajara
Photo: Carolyn McAndrews.
iii. Projections of Vehicles and Emissions to 2030 and Beyond
Present trends in the LAC region point to increasing auto ownership and use. LAC will probably
approach Europe‘s level of motorization of the1960s by 2030, but with far more urban regions of
over 5 million than Europe has even now.14 Traffic in these largest cities tends to be the most
congested. Thus the prospects for future traffic problems in the face of growing motorization in
all these large LAC cities are daunting.
Figure 1.9 shows WBCSD (2004) forecasts light duty vehicle ownership for five year intervals,
2000 to 2050.15 Per capita GDP is on the horizontal axis. The points for 2030 for Latin America,
14
In 2004-6, LAC had four urban agglomerations with over 10 million (Mexico City, Sao Paulo, Buenos Aires, and Rio
were all about 10 million). Europe had just one, Paris (just below 10 million). Between 5 and 10 million, Between 5
and 10 million LAC had Lima, Bogotá, Santiago and Bel Horizonte, while Europe had London and Madrid, with
Barcelona at 4.9 million. LAC had eight more cities among the world‘s 100 largest urban areas, Europe three more.
(United Nations, 2007)
15
Mexico is not included in LAC in this projection, but its car ownership is higher than that of Brazil and growth in
recent years high, so including Mexico in LAC would raise per capita GDP and car ownership in 2000, the starting
year for projections.
8
China, the OECD, the Former Soviet Union and Eastern Europe, have been enlarged to stand
out.
Figure 1.9 - Sustainable Mobility Project Projections of Future Light Duty Vehicle Ownership by Region
Cars, Light Trucks, SUVs per 1000 People
700
OECD
600
All OECD
500
Eastern Europe
E Europe
400
Former Soviet Union
L. America
Latin America
300
Middle East
200
Other Asia
Africa
China
100
India
China
0
$0
$10
$20
$30
$40
$50
$60
GDP per Capita, Thousand US Dollars base 2000 using Purchasing Power
Source: WBCSD, 2004.
According to this projection, by 2030, Latin America‘s per capita income will almost double, with
per capita light duty vehicle ownership – predominately cars – rising to 200 per 1000 when
Mexico is included, the level of ―Eastern Europe‖ as defined by WBCSD. Further, the
Sustainable Mobility Project projects that most of the growth will be in cars and light duty trucks,
not two wheelers. This seems likely because: 1) there are so few two wheelers in Latin America,
2) automobile manufacture (or at least assembly) has been important in Brazil and Mexico, and
to some extent Chile, for many decades. Mexico and Brazil also export to other LAC countries.
This means that relative to GDP growth emissions could continue to rise faster in LAC than in
other developing countries, where fuel-efficient motor scooters and e-bikes are a major portion
of motorization.
The Sustainable Mobility Project foresees a more than tripling of total LDV VKT in Latin America
by 2030 and a six-fold increase by 2050. The VKT growth is pushed up by growth in population,
and LDV ownership increases are supported by rising affluence. The estimates are consistent
with historical evidence from Europe and North America (Schipper and Marie, 1999; US BTS,
2009). However, the Sustainable Mobility Project did not foresee any major changes to
transportation policy that could slow the rise in LDV use, including the kinds of measures
discussed in this report. Thus the WBCSD projections should not be seen as inevitable, but as
illustrative of where present trends lead.
Table 1.4 shows the WBCSD data for 2000 and projections for 2030 for light duty vehicle
ownership over 1000 population, VKT per vehicle, and per capita VKT. Note that VKT per
9
vehicle is treated as constant, which is approximately the OECD experience from the 1970s and
1980s (outside of times with very high oil prices). Although other developing regions close the
gap, LAC remains high.
Table 1.4 Global Projections of Light Duty Vehicles (LDV) and Use
Region
OECD North America
OECD Europe
OECD Pacific
FSU
LDV/1000
2000
2030
779.7
825
390.2
511.0
438.0
546.1
100.0
308.4
Eastern Europe
China
Other Asia
India
Middle East
Latin America
Africa
201.0
13.0
21.0
10.0
42.0
95.2
20.0
VKT/LDV
2000
2030
17,600
17,600
12,500
12,500
10,000
10,000
13,000
13,000
442.6
11,000
86.0
10,000
56.1
10,000
39.8
8,000
68.9
13,000
181.5
12,000
41.9
10,000
Source: WBCSD, 2004.
11,000
10,000
10,000
8,000
13,000
12,000
10,000
VKT/Capita 2030
2000
2030
13,723 14,080
4,877
6,388
4,380
5,461
1,300
4,009
2,211
130
210
80
546
1,142
200
4,869
860
561
318
896
2,178
419
On-road fuel economy in LAC is projected to improve from an estimated 11.8 l/100 km in 2000
to about 9.4 liters/100 km by 2030 and to 8.3 liters/100 km over 50 years. The improvement is a
drop of some 20% in fuel use per km. For comparison, the EU hopes that by 2030 its fleet will
use less than 6.5 liters/100 km on the road, below the present value of 7.8 l/100 km, also a 20%
improvement (Schipper, 2009). Since cars in LAC are smaller and less powerful than those in
the EU, the high fuel intensity for light duty vehicles in LAC may seem odd. The explanation
appears to be poor traffic conditions, as suggested by the relatively high in-use fuel intensities of
small cars in the Mexico City, Sao Paulo, Bogotá, and Santiago emissions inventories. Models
used to simulate fuel use in traffic in LAC, like MODEC (Goicoechea, 2007; Osses et al., 2000)
or Mobile 6 Mexico and COPERT (COPERT, 2009; Rogers, 2006) show rising fuel use/km with
greater congestion. If congestion continues to worsen in LAC cities, this gap between vehicles‘
potential fuel economy and real-world performance will increase, erasing some of the benefits of
improved vehicles. Conversely, measures that reduce congestion lead to improvements in inuse fuel economy. (Skabardonis, 2004)
In fact, when the Sustainable Mobility Project projections for vehicles, VKT, and fuel economy
for each mode are combined, but no other mitigation is included, emissions from passenger
vehicles in LAC are forecasted to more than double by 2030 despite improvements in vehicle
fuel economy (Figure 1.10). By 2050, emissions are expected increase to four times their
current value (not shown). Emissions from trucks, not shown, grow less rapidly than those for
cars, while emissions from buses are not seen as growing much at all. Indeed, while
opportunities to reduce emissions per vehicle-km or passenger-km in buses should not be
ignored, those reductions would be minor compared to the growth in emissions from light duty
vehicles.
10
Figure 1.10 Sustainable Mobility Project Estimates of CO2 Emissions from LAC Road Transport. 2000 Actual
and 2030 projected
500
Emissions in 2000
450
CO2 Emissions. Mn Tonnes
400
Emissions in 2030, no fuel
economy improvements
350
300
Emissions in 2030, w fuel
economy improvements
250
200
150
100
50
0
Cars, Mcycles
All Buses
Source: Sustainable Mobility Project, 2004.
How do these projections compare to those of other regions? Table 1.5, based on a business as
usual forecast prepared for the Sustainable Mobility Project, shows that emissions growth in
LAC is expected to be substantial, but will still be outpaced by that of other regions or countries.
Some of the other countries start with lower individual motorization and are catching up over the
forecast period. Others have higher overall incomes or rates of economic growth.16 While on
this basis the projections foresee LAC remaining a relatively modest contributor to total world
CO2 emissions, it would still be a relatively high emitter from road transport compared to
population and GDP.
Projected GHG emissions could change substantially if the basic factors driving them –
incomes, vehicle fuel economy – are different from those assumed in Table 1.5. For example, a
number of analysts believe that the vehicle fuel economies could be much higher. To illustrate
how this might change emissions, Table 1.6 shows the effect of a global achievement of 6.4
liters per 100 km by 2030. Such fuel economy, consistent with current projections for the EU in
2030, would mean that Canada and the US would see a decline in CO2 production from LDVs
rather than the WBCSD-estimated increase. LAC would still see an increase in emissions, but
smaller one.
16
Note that in Figure 1.8, China grows more in income (along the logarithmic income axis) than most other regions.
11
Table 1.5 CO2 Emissions from Light Duty Vehicles 2030 over 2000
Region
OECD North America
w/o Mexico
OECD Europe
OECD Pacific
Former Soviet Union
Eastern Europe
China
Other Asia
India
Middle East
Latin America w. Mexico
Africa
Total World
Developing World
L/100 km
2000
L/100 km
2030
Total Emissions
Change 2030/2000
10.9
L/100 km
Change
2030/2000
94.8%
11.5
8.0
10.6
10.6
9.2
11.4
11.9
11.2
12.0
11.8
13.9
10.5
6.4
8.2
9.6
8.4
9.8
9.6
9.4
9.5
9.5
11.1
9.4
80.8%
77.7%
90.8%
91.3%
86.1%
80.8%
83.8%
78.9%
80.9%
79.5%
89.8%
109.6%
99.7%
272.4%
166.3%
664.1%
322.6%
459.1%
253.6%
250.7%
313.3%
159.6%
355.1%
132.4%
Source: WBCSD Projections.
Table 1.6 Effects of a Global Fuel Standard of 6.4 Liters/100 Km Achieved in Actual Traffic
I
II
III
IV
Base Case:
2030 6.4 l/100 km
Emissions
2030 Emissions w/
Emissions Change 2000emissions w/
Global 6.4
as % of
2030 w. Base
WCSD Fuel l/100 km Fuel
Base Case
Case Fuel
Region
Economies
Economy
Emissions
Economies
OECD North America
1623
952
58.7%
132.4%
OECD Europe
535
532
99.5%
109.6%
OECD Pacific
219
171
77.7%
99.7%
Former Soviet Union
229
153
66.7%
272.4%
Eastern Europe
82
63
76.5%
166.3%
China
303
198
65.2%
664.1%
Other Asia
174
116
66.6%
322.6%
India
103
70
68.0%
459.1%
Middle East
67
45
67.5%
253.6%
Latin America
29
198
67.2%
266.8%
Africa
168
97
57.9%
313.3%
Source: Columns I and IV WBCSD 2004. Columns, II, III and VI, this study.
12
V
Emissions
Change 20002030 Using 6.4
l/100 km Fuel
Economy
77.6%
109.1%
77.5%
181.8%
127.2%
433.0%
214.9%
312.3%
171.2%
179.2%
181.3%
C. Summary: The Transport - CO2 Challenge
Present levels of CO2 emissions from road transport in LAC are high by developing world
standards. Not coincidentally, per capita ownership and use of light duty vehicles in LAC are
also high. In urban regions, around 70% of CO2 emissions from road transport arise from the
use of light duty vehicles, which are by far the most common vehicle on the streets and in
general the greatest contributors to both congestion and pollution as well. The high CO2
emissions from road transport in LAC can be seen as a symptom of transport problems caused
by high car ownership and use. Addressing these transport problems likely would reduce car
use and fuel consumption somewhat, which would reduce CO2 emissions as well.
The data and trends-extended forecasts for vehicle ownership and use, fuel economy
improvements, and predicted emissions present serious challenges for transport policy-makers
in LAC and elsewhere. Without additional interventions, emissions will grow substantially during
a period where combating global warming would necessitate their substantial reduction. The
large forecasts of increased VKT in LAC also would increase traffic in urban regions, which in
turn implies worsening congestion and other transport problems (unless increases in road
capacity keep pace with or exceed traffic growth).
Strategies that improve the fuel economy of LDVs and bus fleets are likely to reduce emissions
per kilometer by 20% by the year 2030, according to current projections. Yet this important step
still leaves emissions from road transport in LAC more than doubling over the same period.
Even a major increase in fuel efficiency over and above the projected levels would still result in
significantly increased emissions in LAC. This means that there is reason to consider additional
interventions.
If reductions in transport emissions are to be achieved, many analysts now conclude that the
growth in individual vehicle use must be moderated and transit vehicle use and non-motorized
travel increased in relative importance. Further reductions in CO2 emissions can be
accomplished through changes in urban development and transport paths, not just in LAC but
around the world. Such changes could reduce growth in vehicle ownership, vehicle use, or
both.
13
2. A Conceptual Framework for Integrating CO2 into Urban Transport
A high percentage of CO2 emissions in LAC cities are from light duty vehicles, principally cars.
Absent strong intervention, travel (VKT) in light duty vehicles is likely to triple by 2030, and CO2
emissions will also increase. Can urban transport interventions moderate this increase?
Good transportation is a critical ingredient for growth, and most developing countries need to
expand their transport systems in order to support economic development, address social equity
concerns, and reduce environmental impacts. As transportation activities expand, decisionmakers who wish to minimize CO2 emissions will invest in collective and non-motorized travel
modes and manage and moderate car use. Such investments, coupled with strategies that
improve vehicles and fuels, will produce a multimodal transport system that uses carbon far
more efficiently than one that is dominated by motor vehicles.
High quality public transit, safe and convenient facilities for pedestrian and bicycles, responsive
multimodal traffic operations and management, and well-coordinated transportation and land
use are key elements of a balanced multimodal transport system. Effective pricing, regulation
and enforcement policies also can contribute to improved transport system performance. Such
interventions are attractive because they deliver significant benefits of mobility, accessibility,
environmental quality, social inclusion, and economic opportunity. In most cases they also
deliver CO2 emissions reductions.
CO2 emissions are a global concern, but their local consequences are not always apparent.
Interventions that deliver local benefits are more likely to win political support. Further, the
monetary value of CO2 emissions reductions tends to be low in comparison with other transport
benefits such as time savings. For these reasons, CO2 is not likely to be the main reason, or
even one of the explicit reasons, for pursuing transport measures that produce CO2 co-benefits.
However, measures that are good investments based on their transportation benefits will often
produce CO2 benefits as well. In some cases the value of the CO2 reductions are a significant
share of the overall benefits of a project.
To assure that the CO2 effects of transport measures are fully considered, a framework is
required that explicitly considers them at each step of transport decision-making, from policy
formulation to system planning to project development and implementation. In this chapter we
present such a framework.
A framework is a conceptual structure intended to serve as a support or guide for action. Taking
into consideration background and context, a framework outlines the broad set of ideas and
principles that will guide future activities, identifying systems and subsystems and showing how
they interrelate. It is not a best practices manual or a toolbox of evaluation techniques, although
a framework may advise the identification and dissemination of best practices and the
development of better evaluation tools.
The framework presented here is designed to lead transport agency staff and their consultants
and advisers through a process of assessment that will both evaluate the CO2 consequences of
proposed transportation actions and uncover opportunities for enhanced outcomes. The
process helps analysts examine the scope, scale and time frame of interventions, the relative
role CO2 savings play as co-benefits to other widely recognized transport project outcomes, and
the impact on CO2 emissions of a transport intervention against a background of overall
transport activity and emissions, which usually increase over time.
14
A. Determining the Scope and Scale of the Intervention and Time Frame for
Implementation
A framework for integrating CO2 into transport requires at the outset an examination of the
scope and scale of the intervention as well as the time frame over which it will be implemented
and will operate. Is the project designed to change a wide range of development characteristics
of the metropolitan region, or to improve conditions in a particular corridor or district? Will the
intervention directly or indirectly change land uses and population densities or land and property
values? Will the intervention affect the entire system (e.g., a tariff reform) and therefore the
entire metro region as well, or is it aimed at particular vehicles and fuels without changing the
transport level of service (e.g., using hybrid buses for center city circulator services)? Will it be
implemented in the short term or will it require a phased implementation over many years?
i.
Urban Development: Moderating Auto Use and Avoiding CO2 Emissions through
Excellent Urban Planning
Urban development is an outcome of many forces and processes. Population and
demographics, the regional economy and employment base, income per capita and its
distribution, the density of development and the mix of land uses, the types and levels of urban
infrastructure and services provided, and architecture and urban design all contribute to shaping
urban form. Transportation is one of the key elements in the urban development process, and
transport in turn is shaped by the levels and patterns of urban development. Urban development
strategies can have a strong influence on the amount of transport that is needed, the distances
that are traveled, the modes that are used, and the conditions of travel. Coordinated transportland use strategies aim to harness these relationships to create healthy economic growth, high
quality natural and built environments, and greater social equity.
In Latin America, Curitiba, Brazil, a city of about 2 million in a region of about twice that size, is a
prime example of a city that has successfully guided growth through a broad urban development
strategy, of which transportation is an integral part. (Lerner, 2009) Curitiba is one of Brazil‘s
wealthiest cities with high car ownership but low car use, showing that a relatively affluent
community need not be car dependent or carbon intensive if the region develops with a good
transport system.
Curitiba‘s master plan, outlined by Mayor Jaime Lerner in 1965, integrates transportation, urban
development, social welfare planning and community and economic development rather than
planning separately sector by sector. The plan aimed to focus growth along major arteries
connecting the city center to new industrial and commercial zones, and set about to integrate
transportation and land use planning to provide jobs, housing, and commercial services, and
good connections among them. Figure 1 shows the configuration of main arteries (left) in a
recent year and the densest and most built up regions (right, in the red and dark brown) in the
early 2000s. Figure 2 shows how the BRT network and its feeder lines evolved over time. The
transport network was explicitly used to shape urban development, and vice versa.
15
Figure 2.1 Map of Curitiba Transport Network and Intensity of Land Uses
Source: Jaime Lerner.
Figure 2.2 Evolution of the Curitiba Transport Network
Source: Jaime Lerner
The plan also aimed to provide all districts of the city with good schools, parks and recreation
facilities, and clinics. The resulting land use pattern is amenable to walking for many trips (since
schools and services are within walking distance in the communities) and is also amenable to
transit use for longer trips (since retail, office and industrial districts are well connected to
residential areas by bus, and the bus network offers nearly seamless transfers and predictable
travel times). Because the planned multi-nodal development avoided over-congesting the
center, the city was able to create a pedestrian network, now expanded to nearly fifty blocks, in
the downtown area. Figure 2.3 shows a famous pedestrian zone. Local merchants were
opposed to the idea initially but the mayor created arts in the street for children and this turned
public opinion, including merchant views, in support of the pedestrianization (Figure 2.4).
16
Figure 2.3 Modern Street Scene in Curitiba
Photo courtesy of Jaime Lerner.
Figure 2.4 Children Panting on the Street, 1972
Photo courtesy of Professor Allan Jacobs, UC Berkeley
Street design has dedicated space to buses and given them priority treatment. Major arteries
contain exclusive lanes dedicated to buses, as well as mixed traffic lanes. Over time, stations
have been developed along the exclusive bus lanes with operational and design features that
increase efficiency and speed service, including raised boarding platforms or tubes for level
boarding and alighting and prepayment of fares. In addition to the express buses operating on
the dedicated lanes, rapid buses operate on a variety of routes and local buses and inter-district
and feeder services operate between the arterials and for shorter trips to stations.
The resulting high quality transit service has proven to be very popular, providing fast, safe, and
reliable transport. The buses serve over a million passengers a day and include many middle
class riders, including auto owners. Curitiba has very high car ownership by Brazilian standards
but relatively low car use, according to Santoro (1999). Unfortunately there has never been a
travel or vehicle use survey undertaken for Curitiba to fully document its performance, but
comparisons with other Brazilian cities of similar population and geography suggest that a large
amount of car use and CO2 emissions has been avoided.
In large part, Curitiba‘s success is largely that it did NOT just focus on transportation, but
integrated it into an overall development program implementing a master plan that is both long
term and amenable to updates as new ideas and opportunities have emerged.
17
The value of coordinating transport and land use planning can be seen in other cases, and the
negative consequences of ignoring the interrelationship also can be observed. Bertaud (2003)
provides an extreme example, comparing Atlanta, Georgia (metro population 5.1 million), the
US city with the longest trips and highest overall level of sprawl, to the (much older and slower
growing) city of Barcelona, Spain (population nearly 4 million in the metropolitan region). In the
1990s, Atlanta‘s density was approximately 6 persons per hectare. Barcelona, squeezed by the
Mediterranean to the east and mountains on most of the north and northwest, had 171 persons
per hectare. In Barcelona nearly 80% of the population lived within 500 meters of a major bus
or metro line, and about a third of the travel was by transit. (ATM Barcelona, 1997) In Atlanta,
only a small fraction of the population was located near transit, and over 90% of trips were
made by auto. The result was almost five times more CO2 per capita emitted from light duty
vehicles in Atlanta than in Barcelona. While geography and history, fuel prices, household
incomes, and city size are surely all part of the explanation for the observed differences, urban
land use and transport policies also are surely major factors in the high carbon impact of
Atlanta.
Around the world, many cities have developed urban plans with the objective of creating
attractive residential neighborhoods with most services easily accessible by walking or biking
and steering larger-scale commercial development to transit corridors and mixed use centers.
Such urban plans are frequently linked to policies intended to preserve natural features and
important agricultural lands. Transportation investments are a key element of the plan and are
evaluated as part of the overall development objectives. Cities such as Amsterdam,
Copenhagen, Stockholm and Portland, OR, are well known for their integrated policy plans that
both shape the city and protect natural features and agriculture. (Cervero, 1998)
Cities also have taken steps to recapture urban land and rebalance transportation systems in
their city centers. In the US, San Francisco, Boston, and Portland all removed major freeways
and returned the land the freeways had consumed to other urban uses. Seoul has transformed
its urban center by tearing down an elevated highway and re-establishing an urban creek,
surrounding it with an urban park and mixed-use development supported by high quality transit.
(Cervero, 2006) The removal of the freeways in each of these cases not only created important
economic development opportunities for the cities but also greatly reduced environmental harms
including noise and exposure to emissions.
In sum, strong urban plans have been shown to provide an effective framework for economic
development, environmental improvement, social equity, and public health, with transportation
and urban development serving as instruments for accomplishing these outcomes. CO2
reduction can be an implicit or explicit part of such plans. Thus encouraging urban-plan
development and implementation can be an important way to both improve transportation
effectiveness and reduce carbon emissions.
Questions that should be posed in considering the effects of a project or other intervention from
an urban planning context are the following:
1.
Does the region have a long-term development plan linking land uses and transport?
Have the plan’s impacts been analyzed for future years? Is CO2 one of the impacts
analyzed?
2.
Do the projects or other interventions being considered fit into this plan?
3.
Are proposed collective transportation projects serving dense corridors or well planned
outlying regions?
18
4.
Is the intervention providing homes, businesses, schools close to transit corridors? Are
such projects being undertaken in parallel by private or public authorities? Are there
potential sites for such development?
5.
Are there projects to create new, outlying districts not served by transit? Have the
impacts of these projects been analyzed?
ii. Good Transport: Improve the Transport System to Achieve Better Performance
with CO2 Reduction as a Co-Benefit
Even if a region does not have a broad based urban development plan, a systematic approach
to transportation planning and implementation can produce a transport system that fosters
economic development and social equity while reducing CO2 emissions as a co-benefit. A
recent World Bank report (World Bank, 2008) presents a strategic approach to improving urban
transportation. The strategic approach aims to a lower the share of travel in motor vehicles and
increase the use of collective modes, and uses financial instruments (such as congestion
pricing) to manage vehicle use and generate revenues, In addition, the approach aims to
develop institutional capacity (e.g., the ability of authorities to enforce rules on pollution, safety,
and transit fares and level of service) as a necessary element of good transport. Strategic steps
include linkages to national authorities, who are usually those who have the power to address
fuel standards and vehicle fuel efficiency, and to land use planners, whose regulations can
promote development readily served by collective transport and non-motorized transport.
Implementation of this strategy will reduce CO2 intensity of the present system – the ratio of
CO2 emitted to total travel – and keep it from rising as rapidly as in the past. Such a transport
strategy also will alter land development opportunities and help create a more accessible and
flexible urban pattern.
Bogotá, Colombia is an example of a Latin American city that has successfully implemented a
transportation-focused strategy on a large, multi-corridor scale. The Bogotá BRT system,
Transmilenio, operates on several corridors and is accompanied by auto restraints, parking
restrictions, bikeways, pedestrian improvements. Some land use initiatives also were proposed
as part of the plan.
Transmilenio is a bus rapid transit system running on several major corridors of this city of 7
million. The first lines were implemented during Mayor Enrique Peñalosa's term in office.
Currently there are 84 km of BRT lines. Additional lines are planned to eventually provide
service throughout the metro area, although some local politicians are arguing to complement
the BRT with a metro. As of this writing the issue is undecided.
Transmilenio partially replaced a system of often dirty and unreliable private buses with high
quality, fast, frequent and reliable bus rapid transit and did so at relatively low cost in a short
period of time. The BRT system uses articulated buses on express lanes serving stations
modeled after light rail stations. On major trunk routes both a local lane and an express lane
are provided, allowing for very high capacity as well as a choice of service type. Private
operators under contract to Transmilenio are paid by the kilometer of service provided. Contract
provisions cap the service mileage for vehicles and require daily cleaning of the buses. The
resulting service carries about 1.2 million passengers a day, or about 20% of transit ridership.
(Ardila, personal communication, 2009) In 2005, the time of the last travel survey, Transmilenio
had about 10% of all trips, cars 16%. Suarez (2006) estimated about 10% of Transmilenio
19
riders formerly used cars. Further, the service has reportedly attracted users from a wide range
of social classes. Local and feeder buses operated on an open entry system by private owners
continue to carry the majority of riders, however, and in some cases compete with Transmilenio.
In addition to Transmilenio, the Peñalosa Administration implemented auto restraints, including
car free days and removal of a large numbers of parking spaces from city streets.
Complementary public works include a pedestrian /transit street in the city center and expansion
of the bikeway system to about 300 km, with connections in many locations to the BRT system.
The bikeway system reportedly has attracted both recreational trips and purposeful, destinationfocused travelers (estimated by one local planner to now carry 5% of total travel).
Suarez (2006) estimated that the combination of bus substitution and mode switch (saved about
1.5 petajoules of fuel (both diesel and gasoline), or about 80,000 tonnes of CO2 annually.
Former Mayor Peñalosa made the point repeatedly that his emphasis was on improving
transport, with these savings of CO2 a co-benefit of a transport revolution for Bogotá.
Figure 2.5 The Old (Colectivo) and the New (Transmilenio) in Bogotá
Photos: Lee Schipper.
Mexico City began with a more modest approach toward BRT, initially building it in a single
corridor. The project, on the major arterial Insurgentes, was developed primarily to relieve
congestion. The city‘s own bus company RTP and privately owned and operated colectivos
competed along much of the corridor, and the ―bus only‖ lanes along the curb were usually
congested with cars and delivery vehicles. A Metro line had been considered, but soil tests
indicated it would be impossible to build a Metro line there. BRT became the favored strategy as
a result.
The initial route, inaugurated in 2005, ran 19.5 kilometers from Indios Verdes, a major terminal
for buses serving the northern suburbs of the region and beyond, to Dr. Gálvez in the south. It
also served with the busiest Metro line in Mexico City. In 2007 the BRT was extended to the
Universidad Nacional Autónoma de México (UNAM). It currently carries over 300,000
passengers a day or about 1.5% of all trips in the region.
20
Metrobús operations in the main Insurgentes corridor have saved almost 50,000 tonnes of CO2
every year.17 More than two thirds of this CO2 savings came from modal switch and improved
traffic. The remainder came from more fuel efficient vehicles.
The success of the BRT in this corridor led to the city adopting a region-wide vision and plan for
BRT. A second line, Eje 4 Xola, opened in December 2008, and the current Mayor plans many
more.
As these examples illustrate, both system plans and individual projects improving transportation
services can both improve travel opportunities and reduce CO2 emissions. Complementary
projects such as improved feeder services and pedestrian and bicycle facilities can augment the
results. On the other hand, competing facilities or services could also reduce the effectiveness
of projects and this needs to be taken into account. Key questions that should be asked about
transport strategies include the following:
Key Questions:
1.
Does the proposed transport project meet existing needs for service, or is it designed to
meet future needs?
2.
What trip purposes will the project serve and how many of the trips would be made, by
what modes of travel in the absence of the project?
3.
What will be the socioeconomic characteristics of the travelers and what other travel
choices will they have?
4.
Have the impacts of the project on location and land use been taken into account? Does
the project support infill or does it open up new areas for development, and if the latter,
are there land use plans in place that are transit-oriented?
5.
Is the project scaled to the system level or is it focused on a particular district or
corridor? If the latter, how will it interact with the rest of the system?
6.
What additional interventions would strengthen the performance of the proposed project
(e.g., along a BRT corridor, advanced signal systems, pedestrian improvements, bike
lanes and parking at stations)? Can these interventions become part of the project?
7.
Are there other projects being proposed that could reduce the efficacy of the proposed
intervention, e.g. new highways?
8.
If new vehicles are being acquired, are they clean and fuel efficient compared to
existing public transport vehicles? Will they require new fueling and maintenance
facilities?
9.
Will new staff skills be required to maintain and operate the project?
10.
Do existing policies on transit fares, fuel price, parking, and congestion management
support shifts towards collective transport?
11.
Are the agencies responsible for critical elements of transportation system management
and enforcement, e.g., traffic signal timing, enforcement of parking regulations and traffic
laws, on board with the proposed intervention and empowered to support it through their
actions?
17
See the appendix to this report for a detailed analysis.
21
iii. Cleaner Vehicles and Fuels
CO2 emissions can be addressed directly by influencing the choice of vehicles and fuels, and to
some extent the operation of vehicles. (Sperling and Cannon, 2007) However, there is a lively
debate over the most efficient and effective policy actions to reduce carbon emissions from
vehicles and fuels (OECD, 2007), as well as over the extent to which changes in fuel prices
might induce travelers to switch modes, travel less, or acquire less fuel intensive cars (WBCSD,
2004).
Improving vehicle efficiency and encouraging low-carbon fuels are important elements of any
long-term CO2 strategy. Most of the required policy measures – fuel taxes and carbon taxes,
vehicle efficiency standards, or both, as well as fuel standards (and perhaps long-term low
carbon fuel research and development) have to be carried out at the national level. However,
urban policies can complement and reinforce national policies by creating incentives for the
purchase and use of low carbon vehicles and fuels.
The US was the first to establish national standards for vehicle fuel efficiency (Greene, 1999;
Schipper, 2009), although the standards remain well below the efficiencies obtained through
pricing and regulation in other developed countries. Canada followed with a Voluntary
Agreement with motor vehicle manufacturers on fuel efficiency (Lawson et al., 2009). More
recently voluntary agreements have been established for the EU (Fontaras and Samaras,
2007), Japan (Sano, 2008), and China (Wagner, et al., 2009).
The US, Japan and the EU have all moved to tighten their standards recently, and Mexico is
considering a similar program of standards. (Carbonell, 2008) In the EU case, the voluntary
agreement failed to reach its interim goals by 2008 and is slated to be replaced by a stronger,
mandatory target. Like the US system, the EU system will mandate sales-weighted tested fuel
economy and/or emissions averages and levy penalties if these are not met. In the US case,
recent government policy changes will lead to moderate improvements in vehicle efficiencies
but the proposed standards will be below the EU levels.
The mandatory standards in the US have proven effective, though they also have been
criticized by some. Alternative approaches including high fuel taxes can also lead to the
purchase of more efficient vehicles. (Schipper, 2009)
In most countries there is interest in low-carbon fuels, particularly biofuels. Internationally, Brazil
is the leader in biofuels production with its low carbon ethanol from sugar cane, and Brazil‘s
substitution of sugar-cane based alcohol for gasoline is considered the best national example of
a low carbon fuels strategy. (Goldemberg, 2006; Goldemberg 2008)
Because there are concerns about the broader impacts of biofuels, including effects on land
use, food supply, and poverty, their impacts have been studied widely. The impacts of biofuels,
including their potential for greenhouse gas and other emissions reductions, have been
investigated both for the Brazilian case and more generally (Dondero and Goldemberg, 2005;
Goldemberg and Guardabassi, 2009). While Goldemberg and Guardabassi maintain that
Brazil‘s approach to sugarcane-based ethanol is both sustainable and could be developed in
other regions producing cane sugar, work by Searchinger et al. (2008) warn that expansion of
any kind of land-intensive biomass production might push farmers to produce food on less
22
productive land, with more GHG emissions than otherwise, eating significantly into the CO2
gains of the biofuels production. In addition, as the US experience with ethanol made from corn
(maize) shows, not all biofuels are low carbon, especially if life cycle analysis is carried out
(Farrell et al., 2006).
At present, the ability to produce substantial quantities of biofuels at reasonable prices is
unclear. Even Brazil‘s large production of bio-ethanol has stagnated, as IEA data show,
resulting in a rise in the share of gasoline and diesel among fuel sales there, essentially a ―recarbonization‖. As of this writing, it seems possible to achieve large-scale production of low
carbon biofuels within the next 30 years, say 20% of expected road fuel demand, but a
breakthrough in costs is required. (IEA, 2004; OECD, 2007; IEA, 2009) Some are more
optimistic about the prospects for biofuels to provide most of the liquid fuel for the US by 2020
through a massive conversion of the US light duty vehicle fleet. (Spatari, et al., 2009) However,
Spatari et al note that only Brazilian ethanol and some US corn ethanol is competitive with
gasoline at USD $2.50 a gallon, the approximate 2008 wholesale prices excluding taxes and
only Brazilian ethanol at under USD $2 per gallon wholesale. Should large quantities of biofuels
appear at a reasonable cost, they could reduce, but not eliminate, CO2 emissions in transport
from its growing baseline, as IEA noted. (IEA, 2004)
Urban regions have little direct regulatory sway over fuel economy of new vehicles or the
production of less carbon intensive fuels. However, they can develop programs to purchase
buses and other fleet vehicles (e.g., service trucks) that are highly efficient and/or use low
carbon fuels, or to reward private owners who do so. Urban regions also can choose to
promulgate hybrid rather than conventional buses and vans, as Seattle has done for articulated
diesel hybrid buses. The resulting fuel/km savings are on the order of 20% (Chandler and
Walkowicz, 2006). Finally, urban regions can enforce traffic improvements that will allow all
vehicles to achieve better on-road fuel economy. Traffic signal timing can reduce fuel use by 5%
to 30% depending on initial conditions, for example. (Skabardonis, 2004)
Stockholm has developed an aggressive city-led low-CO2 emissions vehicles program that
complements national actions aimed at improving fuel economy and exploring low carbon fuels.
(Paedam, 2009) The vehicles and fuels strategies were also supported by a national CO2 tax
on gasoline and diesel of approximately USD $0.35/liter in 2009 on top of other taxes totaling
close to USD $1/liter.
Stockholm‘s low CO2 vehicles promotion has three components. Starting in 1992, the city
procured low-CO2 vehicles for its own fleet. The procurement phase was a way of testing and
demonstrating the performance and fueling of ―clean vehicles‖. In the second component, the
city‘s regional bus company, Stockholms Lokaltrafik, began to acquire diesel buses modified to
run on ethanol. The company now has over 500 such buses.
The third component of Stockholm‘s approach was to incentivize households and businesses to
acquire ―clean vehicles‖ even before this happened at the national level. Initially limited to
electric and ethanol vehicles, the definition of ―clean vehicle‖ was expanded by Stockholm
authorities to include any non-petroleum vehicle, any conventional car emitting less than 120
gm/km of CO2 in tests, and gasoline hybrid vehicles. Stockholm itself procured 500 flex fuel
vehicles from Ford and together with Malmö and Gothenburg set out definitions of ―clean
vehicle‖ ahead of those that the national government set. At the national level the most
commonly acquired vehicle ran flexibly on ethanol or gasoline, followed by biogas, followed by a
limited number of low carbon gasoline vehicles. Various national tax measures were used to
reduce the cost of the car.. Local incentives included exemption of bio-fueled-vehicles from the
23
Stockholm congestion pricing fee, as well as free on-street resident parking for biofuel vehicles,
and free parking for biofuel delivery vans. ―Clean vehicles‖ also have identifying registration
tags. Because nearly half of all new cars are purchased by companies for employees (Schipper
and Price, 1994), a taxation differential was introduced on these schemes at the national level
making ―clean vehicles‖ much more attractive to acquire than conventional gasoline cars. This
change was especially important because previously company cars National and local
authorities also devoted special attention to increasing the availability of both ethanol and
biogas in stations, even requiring that every station selling over a certain volume of fuel have
ethanol available.
Stockholm carried out very detailed monitoring and analysis of the program to assess its effects.
The evaluation tracked availability and sales of each ―clean‖ fuel, changes in the company car
market, and the effects of ancillary policies such as free parking and exemptions from
congestion pricing At the end of 2008, about 5% of the cars at the national level and 8% in
Stockholm (where the incentives were stronger) were ‖clean. However, the collapse of world oil
prices has had apparently disastrous effects on the road fuel ethanol sales Sweden, and this
has led to a decline in the effectiveness of the program, By fall 2008, the price of gasoline had
fallen almost 30% from its peak while that of ethanol had moved up slightly. Sales of ethanol in
December 2008 were barely a quarter of their peak level in the summer of 2008, The result is
that many flex fuel vehicles were are being operated on gasoline (based on the drop in sales of
ethanol) and market interest in the purchase of ―clean‖ vehicles has sharply declined. Thus the
Stockholm program cannot be declared an unqualified success.
The Stockholm case shows that a city can stimulate acquisition of low-CO2 fuels and vehicles.
However, a policy focused on fuels alone, even in an environment of heavy taxation for CO2
and other reasons, still remains subject to the movements of the international fuel market.
When fuel and vehicle strategies are being proposed, key questions that might be posed are:
1.
Are national policies in place that support the use of low carbon fuels for the vehicle fleet
as a whole or for specialized fleets such as buses, trucks, and taxis?
2.
If biofuels, compressed natural gas, or other alternatives to gasoline and diesel are
being considered, has a full fuel cycle or life cycle analysis been carried out to measure
how much CO2 these fuels embody, compared to the fuel being replace?
3.
Are national policies in place (price incentives such as taxes, technology regulations
such as vehicle standards) that support the development, purchase and use of fuelefficient vehicles?
4.
Do emissions saving new vehicles require special infrastructure investments, such as
natural gas compression or special tanks to hold very clean fuels? Do costs and benefits
estimates include full life cycle emissions and leakage of vehicles and fuels across
project boundaries? Are polluting vehicles that are replaced by clean ones scrapped or
just sold or used elsewhere?
5.
If efficient transit vehicles (such as hybrid buses or vans) are being considered, are they
“cost effective’ in the view of the transit providers, i.e., at the provider’s rate of interest
and payback time? If a carbon price is added to the cost effectiveness calculation, at
what price does the acquisition become cost effective?
6.
At what scale is a proposed vehicle or fuel strategy cost-effective? Can it work if only a
small number of vehicles or a small number of fueling stations are introduced, or does it
24
require a large scale application to be effective? Would a larger-scale application
increase cost-effectiveness?
7.
Considering all investments in advanced vehicle technology, fuels, or biofuels, what are
the expected savings in fuel and/or CO2 relative to the incremental investment costs?
Would similar investments elsewhere in the system to improve service and boost
ridership save greater amounts of CO2 per passenger-km provided?
iv. Time Frame and Longevity
In addition to determining the scope and scale of the proposed intervention, it is important to
consider its time frame for implementation and for effective impact, i.e., its longevity. One
consideration is the useful life of the project. For example, if a project will take five years to
design and construct and then will have a useful life of 30 years, the benefits and costs will need
to account for that period. Another consideration is that effectiveness of a project may change
over time. For example, vehicle fuel economy and emissions reductions tend to decline as a
vehicle ages, and can do so quite quickly if the vehicle is not well maintained. Traffic operations
projects such as traffic signal timing can lose effectiveness in a few years; periodic retiming is
necessary because traffic conditions change, often rather quickly. Pricing strategies likewise
have to be updated as costs and incomes change or they will lose their effectiveness. Major
capital projects may take many years to implement or may be implemented in phases rather
than all at once, and so their benefits may not flow for some time. In addition, some projects will
gain in acceptance and popularity over time; pedestrian districts and bike projects may fall into
this category. The reverse is also true: for example, carpooling was popular in the US several
decades ago but has substantially declined in popularity in many urban areas, including ones
where other collective modes have registered ridership gains. Finally, if a project depends for
its success on a particular subsidy or the support of a particular leader, it may fail if that subsidy
ends or the leader leaves office.
Key questions to ask about the time frame and longevity of a proposed intervention include
these:
1.
Have the proposed interventions been designed with implementation and effectiveness
time frames in mind? Can an intervention’s performance be expected to degrade or to
increase over time, and if so, how does the plan for the intervention handle this change?
2.
If the intervention is part of a broader program, how tolerant is that program of these
time frames and unforeseen changes in them?
3.
Are the implementation and operation processes iterative enough to enable new
information about important conditions to update the intervention?
4.
Are plans and institutions for future monitoring, tracking, updating, evaluating consistent
with the expected time frames for implementation and effectiveness?
5.
Is there an agreed-upon procedure to account for changes in population growth,
economic growth, or major land-use changes and how they might affect the project and
its estimated CO2 impacts?
6.
Is there capacity and funding in place to monitor the project’s impacts?
25
B. Estimating the Impact of the Proposed Project
Understanding the scope, scale and time frame of a proposed intervention helps to determine
its likely impacts. Transport projects are planned with the expectation that they will produce
benefits such as improved access (or reduction of barriers), more route or mode choices,
improved travel time, less congestion, lower travel costs, greater safety and security, and lower
pollutant emissions. Costs of the projects typically include construction, operation, and
maintenance costs as well as negative externalities such as community disruption, noise, air
and water pollution, and damage to species or habitat. (NAS, 1997) Analysts must identify these
costs and benefits, their timing and duration, and wherever possible monetize them in order to
allow a consideration of benefits vs. costs. While the valuation of each externality or the value of
time and other variables in a transport model may vary from place to place, planners must
consider many of these variables in order to estimate the value of CO2 along with other
changes arising from a transport intervention. Formal models, quick response techniques, or
comparative case examples can be used estimate the expected impacts of the intervention. For
interventions that are expected to have a regional or corridor level impact, likely changes over
the longer term it may include altered patterns of location and land use and their effects on
emissions.
Chapter three provides an overview of methods for transport project analysis and forecasting. It
also discusses how to add fuels and emissions calculation to the analysis.
C. Monetizing Benefits and Costs
Transport planners and economists monetize project outcomes or benefits and costs as a
means of valuing the various impacts transport projects have on users and others. This allows
decision-makers to weigh tradeoffs both among variables like travel time and costs, as well as
among travel variables and externalities such as air pollution, safety, noise, and CO2.
Maddison et al. (1996) reviewed externalities as a general problem, as well as how they were
addressed in the UK, Sweden and elsewhere, reviewing estimated costs of air pollution,
accidents, congestion, road damage, and CO2. MacKenzie et al. (1992) reviewed some of the
estimated values of externalities for the United States. Transport Canada (2008) estimated
various external costs of transport for all modes of intercity and urban transport. The key
conclusion is that most studies of CO2 damages yield small values compared with damages
from other transportation-related externalities. Conversely, transportation project benefits
excluding CO2 are likely to be much larger than those from CO2 alone. This is why we refer to
reductions in CO2 emissions from transport projects as ―co-benefits‖ of those projects.
CO2 emissions are complicated to monetize. Since CO2 is a collective problem, difficulties arise
because different people place different values on the damage from CO2 and their willingness
to avoid such damages. Moreover, costs and damages accumulate over time, both because of
the long slow build up and long residence time of CO2 in the atmosphere and because the
damages themselves take time to build up – in an uncertain way. Using a low discount rate,
Stern (2006) attributes a global damage value of USD $85/tonne of CO2. Nordhaus (2008)
argues that Stern‘s discount rate is far too low, Nordhaus uses a much lower damage cost of
only USD $7.40/tonne CO2 in 2005 dollars, slightly more than what Mexico City was first offered
in 2005 for CO2 reductions from Metrobús. While few authors agree over the ―right‖ carbon
26
price, all agree that the price or tax should be equal all over the globe for optimal reduction of
CO2 emissions from a baseline, which is by no means the case today.
The low carbon price Nordhaus argues for at present, USD $7.40/tonne of CO2 in 2005 dollars,
would hardly change the price of gasoline, even in the United States. It works out to about 2 US
cents/liter, compared with the current (May 2009) price of approximately 60 US cents/liter. This
low price would not have a large impact on decisions about vehicle choice or fuel use, much
less transport projects.
Even putting a high value on CO2 and fuels rarely makes them the dominant considerations in a
transport project. Table 2.1, from Harrington (2008; see also: Parry et al., 2007) illustrates this
point by compiling estimates of the costs of highway driving in the US. While the ranges are
wide and would vary from location to location, the general principle is clear – the valuation of the
climate externality is small compared to congestion, local air pollution, safety and in some cases
energy security.
Table 2.1 Range of Reported External Costs per Mile of Driving for the US, US Cents/Mile
18
Externality
Low
High
Range from
Parry, 2007
Comments on LAC situation
Air Pollution
1
14
2.3
Values are probably higher for LAC cities
because of higher levels of air pollution,
even after adjusting for quality-adjusted
value of life. See Vergara et al 2002 and
Harvard School of Public Health 2003.
Climate Change
0.3
1.1
0.3-3.5
Value widely disputed (Nordhaus 2008;
Stern 2006) and certainly dependent on
national and local situation.
Congestion
4
15
5-6.5
Does not apply to all travel. Depends on
value of time and wage rate.
Accidents
1
10
2-7
Depends on valuation of accidents and life.
See INE 2006 for MC perspective.
Energy Security
1.5
2.6
0-2.2
Values depend on local energy supply
situation.
Source: Parry, 2007, as modified for this project
From Table 2.1 it appears that CO2 by itself is unlikely to sway major transport investment
decisions. The low figure, USD $0.003/mile, works out to Nordhaus‘ value, while the higher
value is USD $25/tonne. The high value is still less than 1/3 of that advocated by Stern. Even if
the high Stern CO2 cost were imposed, e.g., as a tax on fuel, it is hard to imagine the transport
system, not to mention the urban system, taking on a different evolution because of this CO2
price. But the benefits of CO2 reduction can still be important and may be large enough in some
cases to affect the design of a project or the priority given to it.
18
These costs per mile can be converted into costs per unit of fuel and cost per unit of CO2. At an average US fuel
economy of 20 MPG (the average for household vehicles in 2002), the lower range of CO2 costs implies a cost per
tonne of CO2 of USD $6.60/tonne, while the higher range in column three implies a cost of nearly USD $85/tonne of
CO2. Nordhaus (2008) suggests values closer to USD $12/tonne CO2, while Stern (2006) favors the higher range.
27
To estimate the costs and benefits of a proposed intervention, an evaluation method or set of
methods must be used that allow the analyst to evaluate both short term and longer term
impacts. For many projects, costs are incurred before benefits start to flow. There also are
ongoing costs for operations and maintenance, which may be offset by revenues in some
cases, as well as externality costs and benefits. Since benefits and costs incurred in the future
are of less value than those incurred today, a discount rate must be applied to the future stream
of costs and benefits in order to determine the net present value of the proposed project.
While there remains debate over the appropriate discount rate, many agencies have established
guidelines or software packages for use in benefit-cost analysis, specifying or offering advice on
appropriate discount rates for different types of project. Such guidance may also include default
values for key elements of costs and benefits for various project types.
A benefit-cost approach has been applied to Mexico City‘s Metrobús on the heavily traveled
Insurgentes corridor. (INE, 2006) Table 2.2 shows the value of travel time savings, fuel savings
valued at the 2007 price, and CO2 savings quantified at USD $5/tonne as well as at USD
$85/tonne. A description of these findings and the methods used to calculate them is found in
the case study for Mexico City in Appendix 1. The total annual CO2 reduction was
approximately 50 000 metric tonnes of CO2.
Table 2.2 Annual Benefits of Metrobús Project
Nature of annual benefit or savings
Low CO2
value (USD
$5/tonne)
High CO2
value (USD
$85/tonne)
Time Savings of Bus Riders
$1.32
$1.32
VKT external costs -- reduction in traffic
$2.19
$2.19
Air Pollution Reduction /Health Benefits
$3.00
$3.00
Fuel Savings from bus switch
$3.68
$3.68
Fuel saving, mode switch car to bus
$3.66
$3.66
Fuel savings to parallel traffic
$1.56
$1.56
CO2 reduction from bus switch
$0.09
$1.75
CO2 reduction, mode shift car to bus
$0.13
$2.58
CO2 reduction in parallel traffic
$0.05
$0.87
Co2 Reduction, total value
$0.27
$5.20
Reduction in accidents/death (not
estimated)
Total first year annual value
$15.69
$20.62
US$ Million (2005)
Source: CO2 and fuel calculations made in this study,
Based on Rogers 2006; other savings taken from INE 2006.
Table 2.2 shows that for the lower CO2 price, the ―value‖ of CO2 saved by the Metrobús project
is almost nil compared to other benefits of the project. For the higher CO2 price, the CO2
represents almost 25% of the other benefits included.
The proportions of the three terms of CO2 savings bear comment. The largest savings come
from modal shifts, almost entirely cars to Metrobús. The substitution of large articulated buses
for smaller buses and colectivos (i.e., paratransit) was the second largest source of savings,
while the smoothing of traffic along the Insurgentes corridor contributed the third main source of
28
CO2 savings.19 Each of these measures was implemented to improve transport in the corridor.
CO2 was reduced, but that reduction was not a necessary or sufficient condition for Metrobús to
be considered ―successful‖. Instead, the CO2 reduction was a co-benefit to a transport strategy.
Indeed, if the Metrobús project were evaluated only against CO2 savings, the cost per ton of
CO2 saved would be excessive relative to its capital costs. But clearly the project is NOT
simply a CO2 project and the benefits are far greater than CO2 alone, as the partial evaluation
presented in Table 2.2 shows.
Note that the measures leading to overall transportation project savings of CO2 include none
addressing CO2 emissions directly, through fuels and vehicles. Mexico City authorities tested
two diesel hybrid buses that used roughly 20% less fuel per seat-km than conventional diesel
buses. Even an optimistic outcome from using hybrid buses would have yielded very small
savings (approximately 3,000 additional tonnes of CO2/year) compared with the main project
components shown in Figure Table 2.2: shifts from cars to buses, improvements to parallel
traffic, and the actual substitution of fewer, larger buses for many smaller ones. At the extra cost
of these buses over conventional diesel articulated buses (USD $100,000-200,000), the savings
in fuel (at USD $ 0.48/liter) and CO2 (at USD $85/tonne of CO2) the monetary benefits would
be small.20
An important point that the Metrobús example illustrates is that significant CO2 benefits can flow
from transport projects that are cost effective overall but would not be cost effective if evaluated
only on the basis of their CO2 reduction benefits. Furthermore, the strategies that produce the
largest direct CO2 reductions (usually fuel and vehicle strategies) are not necessarily cost
effective on their own.
One concern that some observers have raised is that transportation improvements often lead to
greater transport activity, which will increase overall transport emissions. Yet more transport
activity is needed in many areas, especially in developing countries such as those of the LAC. A
broader perspective is required in such cases.
First, the appropriate comparison is not project emissions vs. current conditions, but project
conditions vs. conditions that will prevail if the project is not implemented. This is usually the
―trends extended‖ scenario. Further, Kopp (personal communication, 2009) has suggested that
an appropriate metric would be maximizing the welfare from every kg of CO2 emitted rather
than minimizing the CO2 emissions from every unit of transport. Such a metric would credit
transport projects for all their benefits, not simply focus on CO2 savings.
Evaluation of transport interventions requires good data, models, and staff expertise. Key
questions that might be asked about these issues include:
1.
Do urban authorities have data and models that permit measurement or estimation of
changes in travel times and costs, exposure to pollutants, accidents, wear and tear on
roads, CO2 emissions, etc?
19
The details of derivations are given in the Mexico City case study included as an appendix to this report.
Metrobús, the largest operator, obtained a loan for buses at 10.5% nominal interest and a five-year payback rate
(Schipper et al., 2007). With these parameters, the capital recovery factors was close to 25% while the payback of
fuel and CO2 was only half that rate.
20
29
2.
Do urban authorities have data and models that permit monetization of the costs and
benefits of the project? What are the interest rates and payback times used in
evaluations?
3.
If data and models are not available, do urban authorities have the staff skills or other
resources to carry out before-after studies and trends extended evaluations, including
data collection as needed for such studies?
4.
What are the major benefits for the projects being considered? How robust are the
results? Do the benefits accrue throughout the population or only to particular groups?
Are there particular benefits (or new costs) that accrue/fall on lower income groups?
5.
Could CO2 impacts be improved by making modifications to the project, e.g., by using
low carbon vehicles and fuels, adding pedestrian and bike facilities along the right of
way?
6.
For a range of assumptions about health costs, time values, carbon values, fuel prices,
etc., how does the importance of projected CO2 savings vary?
7.
What are the interest rates and payback times used by operators to evaluate benefits?
Are these consistent with the values from the economic and professional literature?
D. Comparing Project Impacts to the “No Project” or Business-As-Usual Alternative
Just as project benefits and costs change over time, the ―no project‖ alternative also changes
over time. These changes must be taken into account and are especially important for projects
with a useful life greater than a few years.
In a fast-growing urban region, changes in a decade or less can be considerable. (See, e.g.,
Lefevre (2007) for Bogotá and Santoro (1999) for Curitiba.) Large transport projects can have
almost immediate impacts, as both Transmilenio (Lefevre, 2007) and Metrobús (Rogers, 2006)
demonstrate, but the impacts change as new network segments are completed (and as urban
development responds to new contours of accessibility (see Muñoz-Raskin, 2007 for Bogotá).
At the same time, population and economic growth change the metropolis, sometimes
outpacing the effects of new policies and investments.
In this context, ―savings‖ from a project will usually lead to lower fuel use or CO2 emissions than
would have occurred in a business-as-usual situation, i.e., with exogenous growth and change
but without the project. Figure 2.5 illustrates the kind of comparison to make. The diagram could
symbolize a specific corridor, a part of a city, or an entire metro region.
30
Figure 2.5 A Moving Baseline (Business as Usual) and a Project Outcome
Before & after
project
Emissions
Original
Baseline
Difference between
with & with-out
project
Revised Actual:
Second Project?
Project line:
First Phase
Time
Source: Rogers and Schipper, 2005; Schipper and Cordeiro, 2007.
The solid blue line in Figure 2.5 represents emissions as projected under a business-as-usual
baseline, i.e., no new projects or policies. In the example shown, emissions rise over time
because population and incomes are rising, more individuals use cars, traffic worsens, and the
urban region expands (increasing trip lengths). The spike where the project is initiated
illustrates what often happens when projects themselves cause temporary disruptions during
construction or during their initial phase.
A project or intervention can change both the absolute level of emissions – the ―project line‖ in
green in Figure 2.5 -- quickly and the slope of the change in emission over time. What is
illustrated in Figure 2.5 has been exaggerated to differentiate the baseline from the actual
development because of a project or policy. Note, however, that the slope of the project line has
been drawn to be less than the slope of BAU. This illustrates another important outcome,
namely that projects and policies can slow the rate of growth of CO2 emissions relative to that
rate of growth in the BAU case. Some projects might lower the absolute level of emissions
briefly, only to see growth return at the same rate as before.
Analysis of the business as usual case is normal in many regions. In the US, a comparison is
required for a project vs. no project, or ―build/no build‖, as part of environmental impact studies.
In a key study leading to the Stockholm congestion pricing system, Transek (Transek AB, 1997)
modeled traffic in Stockholm in 2010 with no change in trends, with a cordon pricing scheme
(similar to what was implemented in 2006) and with a road pricing scheme with variable prices
per kilometer depending on location and time.
31
Key Questions:
1.
Do urban authorities have a baseline forecast of urban growth and activity that can be
used as the “without project” comparison? How far into the future have forecasts been
prepared, e.g., 5, 10, 20, 30, 50 years?
2.
Are the traffic and travel data recent enough to give a robust portrayal of traffic and
travel before implementation of the interventions is being considered? Are there onboard surveys of riders of collective transport?
3.
Is there a network of cameras or sensors to record traffic levels? Is there a network of
ambient air quality monitoring stations and are monitors well located and sufficient in
number?
4.
For projects being considered, would ongoing monitoring and data collection provide the
information needed to evaluate the project’s short and long term impacts, such as
ridership and modal shift, traffic flow changes, etc? If not, are there plans to conduct
special studies for project evaluation?
5.
Are models available that are capable of simulating the impacts of changes in economic
activity like a boom or slow-down, faster or slower population growth, acquisition of new
kinds of vehicles (low cost mini-cars, motor cycles, etc)?
6.
Are there institutions with budgets to support long-term measurement, modeling, and
monitoring of the development of the region, its land uses and its transport system,
vehicle activity, fuel use and emissions of criteria pollutants?
7.
Establish measures of performance for ongoing monitoring and evaluation of the major
expected benefits and costs of the intervention, as well as changes in CO2 emissions.
E. Establishing Performance Measures for Ongoing Monitoring and Evaluation
The previous steps estimate the performance of a proposed intervention using modeling and/or
evidence from analogous projects. It is advisable, in addition, to monitor projects during and
after implementation to track costs and benefits and see if the projects perform as predicted.
Since performance also may be affected by exogenous factors (e.g., actual population and
economic growth, technology changes, etc.), some of which may differ from forecasts, this step
requires investment in transport and fuel use data gathering, travel and emissions modeling
capability, and methods for observing traffic flow and speeds.
Monitoring can be complex and costly, and it usually is necessary to select a set of key
indicators or performance measures that will be used to evaluate the project. Typical
performance measures are travel times and travel costs by mode for key origin-destination (OD) pairs, ridership or traffic volume by mode and O-D, pollutant concentrations at key locations,
emissions by vehicle type, and speed by mode for various times of days and locations or
corridors. Reduction in CO2 can be one of the measures of performance that is monitored as
long as travel and fuel use data are available, since it can be calculated from such data.
Questions that should be asked about performance monitoring include the following:
32
1.
For projects being considered, are there plans to monitor short and long term impacts,
such as ridership and modal shift, traffic flow changes, etc.?
2.
Do local authorities have the tools to monitor performance of the various elements of the
transport system – e.g., regional travel surveys, transit ridership counts, on-board
surveys of transit riders, traffic sensors monitoring traffic volumes, speeds, and queue
lengths, air pollution monitoring stations vehicle fleet inventories, travel surveys? Are
these tools up to date and deployed at sufficient times and locations to produce useful
data?
3.
Do local authorities have the budgets and staffing to support long-term measurement
and monitoring of the transport system and its impacts?
F. Summary
This chapter presents a framework, that is, an outline of the broad set of ideas and principles
that we recommend as a guide to activities aiming to integrate CO2 considerations into
transport planning for the LAC. A series of questions that can be asked to help guide the
process are presented.
Key ideas in the framework include the following:
CO2 reductions must be measured against a business-as-usual or trends -extended base case.
This is especially important for projects that have a long useful life.
CO2 reductions will rarely be large enough to justify, by themselves, the costs of a
transportation intervention. However, CO2 reductions can be a valuable co-benefit of transport
projects implemented to improve accessibility and mobility, reduce pollution, etc.
A robust evaluation approach would consider the scope, scale and time frame of the proposed
project, its likely impacts, the value of those impacts, and the costs and benefits of not
implementing the project. It also would include monitoring (field evaluations) of the project over
time.
A valuable metric for CO2 projects in areas undergoing rapid economic development would be
the benefits per unit of carbon emitted, rather than carbon reductions per se. The benefits per
unit of carbon metric acknowledges the desirability of growth and improved access and mobility.
Further applications of this Framework are found in Appendices 1 (Mexico City) and 2 (Santiago
de Chile).
33
3. Methods for Assessing the CO2 Impacts of Transport Projects
Estimating the CO2 impacts of transport projects requires the analyst to combine information on
the flows of people and vehicles (number, origin-destination, and length of trips made, mode of
travel used, network conditions during the time of travel, vehicle occupancy) with information on
the types of vehicles used and their fuel-use characteristics, to produce an estimate of fuel
used. Standard coefficients published by the Intergovernmental Panel on Climate Change
(IPCC) can then be used to convert the estimate of fuel combusted into CO2 and if necessary
into other greenhouse gases.
The choice of specific methods for assessing transportation projects‘ CO2 impacts can be a
complex matter, both because of the nature of the analysis problem and because, at least at
present, data are not always available for some of the key variables and must be approximated.
The analysis problem is complex because transportation is an open system characterized by
feedback among its several elements as well as multilayered interactions with the broader
environment. Most interventions therefore have not only direct effects but secondary and tertiary
effects, which can be significant and which often depend on initial conditions. Feedback in
transportation systems occurs in multiple ways. Congestion at terminals and on networks affects
travel times and therefore can alter mode choices, destination choices, and even the number of
trips made. In the longer run congestion also can affect location choices of both households and
businesses. Changing costs of transport likewise can have effects that reverberate through the
transport system. However, the scale of transportation interventions varies widely, and some will
have a region-wide impact while others will affect only on specific locations or corridors.
In addition, most transportation interventions will have an influence over a number of years, and
their performance is likely to change over time. This sometimes is a characteristic of the
intervention itself – rail systems become noisier due to wear and tear as they age; fuel efficiency
standards for new cars are increasingly felt as the vehicle fleet turns over. However, the
influence of a transportation intervention also may be affected by exogenous changes in the
metropolitan region in which the project is located. Changes in population and demographics,
the regional economy, and household and business location choices are among the many
factors that can have an important effect on travel behavior and transportation system
performance. Three examples illustrate this point: First consider the effects of changes in
household income on travel behavior. As incomes rise, households often increase the number
of discretionary trips they make. They also may move to larger housing units with more
conveniences and purchase an automobile. Second, consider the effects of business location
decisions on travel, Businesses may locate or relocate in office complexes along peripheral
highways to avail themselves of modern facilities and services, often at lower cost than in the
central city. The relocation is likely to affect the housing location choice of their employees as
well as the employees‘ travel choices. It also may reduce the market for transit downtown.
Even nominally ―technical‖ changes can have price and performance effects that can affect
travel demand as well as system costs and performance. For example, low carbon fuels are
sometimes more costly than conventional fuels, and advanced bus designs are more expensive
than conventional buses. To the extent that such costs are reflected in consumer prices and are
significant, they could alter travel demand.
Since the CO2 impacts of transportation interventions are a function of these complex changes,
a thorough evaluation of transportation interventions would include their consideration. The
anticipated impacts of the intervention would then be compared to a forecast of what would
34
happen without the intervention, i.e., a business-as-usual or trends-extended case. The
difference that the transport intervention makes to CO2 emissions then can be determined. The
CO2 effects of individual elements of an intervention, e.g., the relative importance of increased
transit ridership vs. more fuel efficient buses – also can be examined.
Models are often used for these assessments because they can represent travel choices and
system performance under a variety of conditions and essentially ―sort out‖ the contributions
that various changes and interventions make. However, reasonable estimates of the CO2
impacts of urban transport interventions can be prepared in a variety of ways, from using an
advanced integrated land-use transportation modeling system to applying simple spreadsheet
calculations. Simplified approaches typically ignore secondary and tertiary impacts and often
focus on short term effects rather than those that evolve over time. However, they are often
faster and less expensive to apply.
The choice of evaluation method(s) should reflect both the type of intervention contemplated
and its scale and scope, as well as resource availability and the extent to which accuracy is
critical. For many projects, simplified approaches may be the most practical, because data and
resource limitations may put more comprehensive and detailed approaches out of reach. In
many cases, the most robust analysis strategy will be to use several methods, in effect checking
the work in several ways. Expert judgment and evidence from the literature can be useful in
helping to shape an evaluation plan and in assessing the reasonableness of results.
Data for transportation project assessments can be taken from periodic travel and activity
surveys and ongoing traffic monitoring reports, or can be collected in special studies done for
the project. When local data are not available and project studies are not possible, data from
national data bases or other, reasonably similar metro areas can be used, adjusted to reflect
local conditions if need be. However, it is worth considering the development of a longer term
program to systematically improve data, analysis tools, and local analysis capacities. While any
one project for an urban area might not justify a major data collection and modeling effort, it
nevertheless might be appropriate to invest in data and methods, since these assets then will be
available for future projects, and can be applied to many issues, not just global warming.
This chapter provides an overview of methods and evaluation approaches that can be used to
estimate the CO2 impacts of transport projects. It also addresses ways of dealing with a
problem that arises all too frequently: what to do when local data are not available. Finally, the
chapter identifies some issues to watch out for in preparing or reviewing CO2 analyses,
including the risk of unintended consequences and the value of analyzing institutional capacity
for implementation in parallel with the technical assessments.
Local data and models are always the best source of information for transport (or other)
analyses because consumer behavior reflects the local context and that context can differ
considerably. In part this is a function of income differences; the quality of services also can
make a big difference in choices. Evidence from cross-national comparisons has shown that
status and habit also have an influence, although income and service quality are stronger
impact. For example, owning and using a car is a strong status symbol in some countries, and is
a commonplace appliance elsewhere. For these reasons we have emphasized the need to
invest not only in projects but in data and analysis capabilities, including local capacity to
develop and utilize these resources.
35
A. An Overview of Transportation Analysis and CO2 Estimation Methods
Evaluation of the CO2 impacts of transportation interventions requires that the analyst first
estimate the transportation effects of the intervention and then calculate the resulting fuel use
and CO2 emissions. Therefore, the types of methods available for transport analysis will be
reviewed here briefly. More details can be found in a variety of widely available textbooks,
manuals, and websites. (See for example Ben-Akiva and Lerman, 1985)
Transportation models are frequently characterized as demand models or operations models,
although most ―demand‖ models also utilize information about, and indeed frequently model, the
networks and facilities as well Operations models are used to analyze network capacity and
design and to develop control strategies, i.e., lane layout and restrictions, traffic signal timing for
streets and highways, and station capacity and line scheduling for transit.
Transportation demand models utilize information about the urban area – the regional economy
(e.g., the number of jobs by type of job), the characteristics of the population (demographics,
employment, income), location and land use (e.g., number of establishments by type, square
meters of floor space by type of establishment) - together with information on the transportation
system (network links and nodes, capacity, speeds, etc. by mode, patterns). Models are built to
relate observed travel patterns and behaviors to these data. The models then can be used to
forecast changes in urban conditions as well as the effects of policy and project interventions.
Characteristics of the population and the economy are usually obtained from national or regional
statistics and forecasts. Land use and location are often taken from regional databases on
current conditions and for future years may be either modeled or specified based on city plans
and expert judgment. Transportation system characteristics are measured or, for future
conditions, specified. Vehicle fleet characteristics may be derived from on national, regional or
local data sets and studies, or may be forecasted using vehicle ownership and vehicle type
choice models. The time of day that various types of trips are made may be specified based on
survey data, or may be modeled as a decision process.
Travel surveys are used both to gather information on trip making for various activities and to
relate the reported activities and travel to household and individual socioeconomic
characteristics such as age, sex, occupation, income, vehicle ownership, and location in the
region. Information on transportation facilities and their operations (streets and highways,
transit, and sometimes non-motorized modes) is used to determine the levels of service offered
in different areas.
A set of models is usually developed to allow analysis of the multiple dimensions of travel,
estimating trips by trip purpose, location, origin-destination pattern, transport mode, and time of
day, as well as the routes used and the resulting flows and speeds on networks. The models
are specified (i.e., variables are included) to show the relationships between travel and
dependent socioeconomic factors and transport-land use conditions. The data are used to
estimate coefficients for each variable and the resulting models can be used to forecast how
travel will change if the underlying traveler characteristics or transport system characteristics
change. Such changes can be the result of a specific intervention or set of interventions, e.g., a
new transit line, added highway capacity, congestion pricing, higher transit fares, or can be the
result of changes in the region‘s population and economic activity, e.g., population growth,
higher rates of workforce participation, higher incomes, aging of the population.
36
Operations models use much more detailed information on highway and transit than is used in
the typical demand model network representations. They typically include lane by lane design
details for each link. On the other hand, they typically assume demand is fixed and exogenous.
Using the results of transportation models together with data on the vehicle fleet, the types of
fuels used, and fuel intensity by mode, CO2 impacts can be estimated. The analyst can perform
a detailed analysis, using model outputs on operating conditions (e.g., link level speeds and
vehicle occupancies by mode), or can simply use model outputs on vehicle kilometers of travel
by mode together with average fuel use per km and carbon content of the fuel.
i.
Integrated Transportation-Land Use Models
The most advanced method for carrying out travel demand analysis today is an integrated
transportation-land use model. Such models analyze activities in which individuals, households,
and businesses engage over different time frames, including household and business location
choices, and estimate the resulting travel and its impacts. The models increasingly address not
only trip generation, destination choice and mode choice, but also vehicle ownership decisions,
decisions on what time of day to travel, whether or not trips will be linked together, and more.
They are integrated in the sense that information on travel times and costs from changes in the
transportation system affect choices such as location and auto ownership. Many of the models
include pedestrian and bicycle networks as well as detailed highway and transit networks.
Outputs from the models include location choices, number of trips by trip purpose, origindestination, and time of day, and travel times and costs by network link and time of day, and
increasingly include emissions and fuel use by fuel type for each mode. Alternatively, demand
model outputs may be input into separate models to estimate network performance, fuel use
and emissions.
Integrated transportation-land use models have been developed in Europe, Japan, the US, and
Latin America and have been applied in a number of cities (See, e.g., de la Barra, 1989,
Wegener, 1998; Echenique, 2004; Hunt, et al., 2005). Recently some of these models have also
included advanced microscopic traffic operations analysis methods. (U.S. FHWA, 2009)
However, integrated h models are not yet in common use. For one thing, these models are still
under development and their performance is still being evaluated. For another, they require
large amounts of high quality data and advanced modeling skills, neither of which is readily
available in most cities (including those of the high income nations). As a result, many areas
use somewhat simpler modeling approaches that treat land use, auto ownership, time of travel,
and details of network performance as outside the model system.
v. Standard Travel Demand Models
A fairly standard travel modeling process is shown in Figure 3.1. In practice, many variants of
this process are used, some more complex and sophisticated that that shown, others less so.
Standard travel models forecast trip generation rates, trip distribution (origin-destination
patterns), and mode choices, and use this information to estimate flows on networks and the
resulting travel times and costs. Most models do this for two or more time periods (e.g., peak,
off peak) and for several trip purposes (e.g., work, shopping, social-recreational.) The standard
travel demand models do not model vehicle ownership issues nor do they forecast future land
uses and location choices. These are treated separately, either through separate modeling
processes or through the exercise of expert judgment. For example, future land use forecasts
37
are often scenarios developed by experts who assess where and how much growth will occur
based regional population and economic forecasts, local plans and policies, and an assessment
of business and household preferences.
The basic unit of analysis in these standard models and their sub-models may be aggregate,
producing travel information for subareas of a region (often called Travel Analysis Zones or
TAZs), or may be disaggregate and behavioral, where the unit of analysis is the household or
the individual. For example, an aggregate trip distribution model represents flows between an
origin and a destination as a function of the ―productions and attractions‖ in the two TAZs and
the ―friction‖ (travel time and cost) of traveling between the O-D pair. A disaggregate approach
might treat the flow between, e.g., a home zone and a shopping district as a matter of shopping
destination choice, where the probability that a traveler would choose to shop in a particular
zone would be a function of the traveler‘s income and other socioeconomic characteristics, that
zone‘s attractiveness compared to other alternative destinations, the respective travel time and
costs between zones, and in some formulations, the mode choices available.
Whether the specifications are aggregate or disaggregate, the basic outputs are much the
same: the number and length of trips made, by time of day and trip purpose, origin and
destination, mode of travel, and route; as well as speeds and flows on networks (transit,
highway).
The sophistication with which network modeling is done in this process varies widely in practice.
It is generally considered standard to run the models iteratively. That is, the travel times
resulting from loading the network with the first iteration of travel estimates (which in congested
networks will be considerably slower than design speeds) are ‗fed back‖ to the trip generation or
trip distribution and mode choice sub-models and the model system is rerun until it reaches an
―equilibrium‖, i.e., the changes from run I to run J are less than or equal to a specified
acceptable difference or tolerance level. However, some model systems estimate trip
generation in ways that are insensitive to the transport network and so consider congestion
effects only for trip distribution and mode choice.
vi. Sketch Planning Methods
Sketch planning methods are simplifications that allow analyses to be conducted more quickly
and at lower cost, though often with some loss of detail. Sketch planning methods are frequently
used to conduct demand analyses, especially for projects that affect only one corridor or
subarea. A common sketch planning method is to extract a model from a regional travel model
system and use it to conduct analyses without exercising all the other modeling steps.
Examples include the use of a mode choice model to estimate the ridership of a new transit
system or use of a destination choice model to examine the effects that a new shopping center
is likely to have on retail travel patterns. The drawback of this approach is that it ignores
potential impacts that might be revealed by other modeling steps, e.g., the shopping center
traffic may increase congestion, which in turn could affect both its attractiveness as a
destination and also could alter other travel and activity behaviors.
Another commonly used sketch planning approach is to use elasticities derived from advanced
or standard travel models (e.g., changes in mode choice with respect to travel time, changes in
VKT with respect to changes in fuel price) to perform simple calculations in spreadsheet
applications. The limitation of this approach is that the elasticities may apply only in a narrow
range around the point at which they were estimated.
38
Figure 3.1 A Typical Travel Demand Model Structure
Economic conditions
+
Population and
demographics
Fuels
Land use
+
Vehicles
Transportation Models
Trip generation (activity participation)
Trips made from an origin
Destination choice/trip distribution
Pattern of travel- origin to destination
Mode choice
Means of travel used (drive alone, shared ride,
rail, bus, bike, walk,…)
Trip assignment/route choice
Traffic flows and conditions by time of day
Effects:
Impacts and Outcomes
Travel times, travel costs, volumes by link, by mode
and time of day;
fuel used, emissions,
Including calculation of CO2 emissions
39
vii. Traffic Operations Models
Traffic operations models are designed to analyze the details of network design and operations.
The most advanced of the models can represent, e.g., the effects of lane additions and
reductions, restricted lanes (e.g., bus only), weaving /merge sections, design features such as
roundabouts, and operation strategies such as traffic signal timing, ramp or cordon metering,
and priority treatment of high occupancy vehicles. The models produce detailed analyses of
speed, flow, stops, delays, and in some cases accelerations and decelerations. They treat
demand as exogenous, i.e., they operate on traffic count data and external estimates of future
traffic counts (often, anticipated growth rates or percent increases in traffic, assuming the travel
pattern does not change). Like demand models, they can be formulated in aggregate or
disaggregate ways; disaggregate methods represent each vehicle in the traffic stream.
Traffic operations models require information on current and projected traffic conditions and
network design. Most require detailed traffic counts by time of day, including turning
movements, queue lengths, etc., as well as speeds on a link by link or lane by lane basis. They
also require details about facility design and operations including current traffic signal timing.
With these data, traffic operations models can produce more detailed estimates of traffic
performance (speeds, accelerations and decelerations, idling, etc.) than the more abstract
networks used in demand modeling. Some traffic operations models also produce estimates of
fuel and emissions directly.
Traffic signal timing models are a special purpose version of traffic operations models. Given
traffic counts and turning movements, they produce plans for signal timing that minimize delays
or achieve other specified objectives. Some signal timing models can be run to minimize fuel
consumption, for example. Specific models vary in complexity and sophistication, with some
able to optimize performance of a large number of signals in a system and others able to handle
only one corridor, or in some models only one signal, at a time.
While traffic operations models are usually used on their own, they sometimes are linked to
travel demand models, especially when network details or details of fuel use and emissions are
important. Recent advances in travel demand modeling are increasing the sophistication of the
network models they use, however, and so the distinction between the model types may be
fading.
viii.
Post Processors for Fuel Use and Emissions Estimation
Because many commonly used versions of travel demand and traffic operations models do not
directly calculate fuel use and emissions, analysts use post-processors to perform these tasks.
The post-processors take the output from travel demand models (e.g., vehicle miles of travel by
link, link speeds, etc.) and use these estimates as input into fuel and emissions calculations. A
variety of fuel and emissions models are available at different levels of detail. Among those that
model specific vehicle types or categories and driving cycles are two developed by the US EPA,
Mobile 6 and MOVES (for Motor Vehicle Emission Simulator), as well as COPERT, an
emissions model developed in Europe, and Mobile for Mexico.21 Still another detailed
emissions model is MODEC, which was developed for Chilean conditions and uses the output
21
These US emissions models are discussed on the US EPA website for the Office of Transportation and Air Quality
– see http://www.epa.gov/otaq/models/mobile6/r02035.pdf, and http://www.epa.gov/otaq/models/moves/index.htm.)
COPERT, is discussed at http://reports.eea.eu.int/Technical_report_No_49/en Mobile for Mexico
is at
http://www.sma.df.gob.mx/sma/links/download/archivos/ie06_criterio_pw23oct08.pdf
40
of a travel and traffic model ESTRAUS. (Goicoechea, personal communication, 2006; de Cea et
al., 2008)
At the other end of the spectrum are straightforward spreadsheet models that use simplified
representations of travel and emissions (e.g., total travel in km * share by each mode* fuel used
by the mode* emissions per unit of fuel burned = emissions). The ―ASIF‖ formulation (Schipper,
Marie, and Gorham, 2000) is such an approach, bringing together aggregate data on vehicle
kilometers of travel by mode with average fuel use per kilometer and the CO2 content of fuels.
(Fuel use also can be expressed per passenger kilometer by dividing fuel use per vehicle
kilometer by passengers/vehicle, i.e., average vehicle occupancy.
ix. Life Cycle Analysis
A complementary analysis approach that can provide improved information on CO2 impacts is
Life Cycle Analysis (LCA) Manufacturing and eventually disposing of vehicles, building and
maintaining transport facilities, and producing fuels all create emissions of CO2 and other
greenhouse gases. A full costing of the CO2 impact of a transportation project would consider
all of these elements.
LCA is extremely important for measuring the full CO2 impacts of switching fuels. Alternative
forms of gasoline and diesel produced from coal, oil shale, heavy oil or other hydrocarbon
sources can entail significant emissions during production. Biofuels similarly may be associated
with major releases of CO2 or other greenhouse gases directly in their preparation or indirectly
due to the way in which, and the land on which, the biofuels feedstocks are grown. For some
fuels, the amount of carbon emitted in their production offsets a large share (in some cases,
nearly all) of the savings they would yield per liter of fuel or per km of travel.
For example, a liter of US gasoline contains 31.7 megajoules (MJ) and emits 74 gm of CO2 for
every MJ of energy released, and 19 additional grams from the fuel cycle delivering the
gasoline. Ethanol releases 73 gm of CO2 for every MJ. If ethanol is made from cellulosic
feedstocks, such as switchgrass, the Argonne Laboratory‘s GREET model predicts only 12 gm
of net CO2 added to the atmosphere because the rest was absorbed when the ethanol
feedstock was grown. Ethanol produced from Brazilian sugar has a net addition of 28 gm/MJ,
still quite small compared with gasoline. Ethanol from corn produced in the US, however, has a
net emissions including LCA of 69 gm/MJ. Thus while US corn ethanol has lower CO2
emissions per unit of energy than gasoline, the advantage is less than 20% because most of the
―bio‖ energy is replaced by fossil fuels for processing. (ANL, 2007) If the long-term impacts of
devoting land to biofuels on other land uses are counted, some biofuels may lead to a net
increase in greenhouse gas emission compared with gasoline or diesel (Searchinger et al.,
2008). These results are very sensitive to the processes and feedstocks used, and will vary
from country to country as well.
LCA also can be used to take into account the CO2 associated with electricity for conventional
traction (Metro, commuter rail, light rail and trolley bus) as well as electric vehicles whose
batteries are charged from the power network. Similarly, LCA offers estimates of the CO2
impact of building a road or a transit system‘s guideways, stations, and vehicles, which may be
comparable to several years‘ worth of operation. MacLean and Lave (2003) present
comparisons of the energy and CO2 associated with vehicles, fuel production, and fuel
consumption. Except for some very large heavy rail systems (Chester, 2008) most of the CO2
associated with transport is that that arises in producing and burning fuel.
41
Lifecycle analysis is not likely to be crucial to deciding for or against large transport
infrastructure projects. However, it can be decisive on determining whether alternative fuels,
particularly alcohols and bio-diesels, really do reduce CO2 emissions relative to the petroleum
based fuels they replace. The net reduction can be compared to any extra costs of producing
the biofuel to see whether the cost of the CO2 reduction really merits the effort, compared to
alternative fuels or even other investments to improve transport.
x. Summary of Methods
Table 3.1 summarizes a variety of approaches for measuring changes in transport systems and
their effects on CO2 emissions. These start with travel data and modeling and then process
results through various models or estimates of fuel consumption.
42
Table 3.1. Methods for Transport Analysis and CO2 Estimation
Method
Description
Pros & Cons
Data needs
Use
1
Integrated
transportation
and land use
models
Model transportation
and land use /location
choice processes ,
activity / travel choices,
transportation system
performance and their
interactions
More theoretically sound
and complete
representation of
transportation system and
choice processes. High
costs in terms of data,
expertise, and time
resources.
2
Standard (―four
step‖)
transportation
models
Model trip generation,
trip distribution, mode
choice, and network
assignment.; many
variants including
aggregate and
disaggregate
formulations.
Vary in quality and
sophistication. Treatment
of land use as exogenous
can be a problem if major
investments that shape
growth are being
considered.
Economic and
population data and
forecasts, current &
permitted land uses
by parcel /small area;
transport networks &
prices; travel /activity
survey incl.
socioeconomic info. &
vehicles owned.
Same as for
integrated models, but
data requirements
often lower,
especially for
aggregate models
Estimates account
for a changes in land
use and location,
travel choices,
network
performance, fuel
and emissions may
be outputs or may
require posprocessing
Estimates can
capture major
choices and
elements of
transport systems.
3
Sketch
planning
methods
Less data intensive and
time consuming to run than
full travel demand models;
less comprehensive.
Elasticities are very simple
to apply, but results may
only be valid for changes
within narrow range
Traffic
operations
models (traffic
operations,
signal timing,
etc.)
Only the data needed
for the particular
submodel, e.g. travel
times and costs of
competing modes for
a mode choice model,
land uses in each
zone and travel times
& costs between
them for trip
distribution
Traffic counts and
turning movements,
speeds by time of
day, facility design
and operations
details..
Examine a particular
issue, e.g. mode
choice, destination
choice changes in
response to a
proposed
intervention. Often
used as first-cut with
additional analyses if
needed.
4
Quick response
methods that extract a
model such as mode
choice or destination
choice and apply it
separately from the
rest of the model
system, or use
elasticities derived from
models to estimate
changes.
Model network design
(e.g.., lane additions,
weaving sections,
restricted lanes), and
operation strategies,
such as .traffic signal
timing and ramp or
cordon metering
5
Post processor
models for
CO2
estimation
Use output from
demand or traffic
operations models to
calculate fuel use and
emissions.
Quality of results depends
on other models. Level of
aggregation / detail also
depends on other model
outputs.
6
Life Cycle
Analysis
Account for "cradle to
grave" emissions due
to production /
construction, operation,
maintenance and
disposal
Emerging method that can
provide more
comprehensive accounting
of CO2 impacts of a
project. Analysis can be
data hungry and drawing
boundaries can an issue.
Produce more detailed
representation of network
features & detailed
analyses of speed, and
flow, stops, delays,
accelerations, etc. Treat
demand as exogenous.
43
Data on emissions
factors can be based
on averages or can
represent effects of,
e.g., speed, idling,
acceleration and
deceleration.
Data on energy used
in each step of
process
Design of facilities,
traffic signal timing,
other traffic
operations;; can
produce more
detailed estimates of
fuel use and
emissions than the
more abstract
networks used in
demand modeling.
Can be used to
estimate CO2
emissions when
other models do not
have that capability.
Has been used to
evaluate CO2
impacts of fuel
switching, major
infrastructure
projects
B. A Note of Caution: Limitations of Modeling
Transportation forecasting models were first developed in the 1960s to help analysts estimate
the effects of major transportation investments such as limited access highways and rail transit
systems While our understanding of travel behavior and its relationships to traveler
characteristics, urban form, and the transport choices available has moved far beyond the
simple representations in these early modeling efforts, the same general methods are still being
used in many assessments.
In the US, for example, the methods used in practice lag research and in many cases even lag
best practice. In the 1990s, the US established the Travel Model Improvement Program (TMIP)
to develop new modeling approaches that incorporated state of the art knowledge and methods,
and the National Association of Regional Councils, the Environmental Protection Agency, and
the US Department of Transportation sponsored guidance on acceptable practices in modeling
and analysis. (See, U.S. FHWA, 2009) Despite these efforts, many US agencies still have not
adopted improved methods and continue to use analysis approaches that omit many important
considerations.
Several reviews of modeling practices and their strengths and limitations have been published
over the past 15 years. Many can be found at the TMIP website (e.g., Harvey and Deakin, 1993)
along with reviews of current modeling innovations. Rodier presents a review of model types in
current use in a paper done to support California CO2 reduction efforts. (Rodier, 2008)
Shortcomings in commonly used transportation modeling approaches include the following:
Treatment of land use as exogenous to the modeling system (ignores induced growth
and changes in location patterns instigated by transport investments)
Treatment of time of day of travel as fixed (ignores the ability of many travelers to adjust
travel times to avoid congestion or peak period charges)
Use of vehicle trip generation rates that are invariant and therefore do not reflect modal
opportunities, land use mix, etc (overestimates traffic/auto demand and undervalues
pedestrian, bike, transit travel and trip linking)
Failure to feed back network travel times and costs into trip generation, trip distribution,
and mode choice modeling steps (overstates performance of new investments by
ignoring congestion effects on network performance)
Failure to represent price accurately (e.g., parking price may be omitted as a variable or
estimated as a subarea average)
Overly simplified network representation (e.g., may classify facilities into only a handful
or categories and may omit collectors and local streets altogether or represent them in a
highly approximate fashion.)
Omission of non-motorized modes of travel from the analysis.
Overly large traffic analysis zones (interventions that affect a small part of the city or a
single corridor may not ―show up‖ in model results). (Harvey and Deakin, 1993)
44
Both more advanced analysis and practical experience have shown that these shortcomings
can produce misleading assessments of transportation interventions. In particular, capacity
increases (e.g., a highway expansion designed for traffic flow improvements) can shift time of
travel, alter destination choices, support an increase in trip frequency, affect land values and
change location choices. As a result, VKT may increase and the new capacity may be used up
quickly. (Hansen and Gillen, 1998) Models that do not capture the feedback effects of
transportation investments on trip making, location, and land use would simply estimate the
initial traffic flow improvement and would miss the dampening effects of other changes set in
motion.
Another limitation of standard travel models is that they are not designed to analyze some of the
strategies of greatest current interest. Many demand management interventions are hard to
represent adequately; for example, the impacts of employer incentives and other social
marketing approaches that depend as much on group dynamics as on time and cost
considerations are not easily represented in either traditional or advanced models. Urban design
strategies also may be difficult to model with existing tools. Design features that have been
found to have important impacts on mode choice, such as the quality of sidewalks and the
presence of street trees or canopies offering shade and weather protection, are not represented
in most models either specifically or as an element of a walkability variable. Further, the effects
of design are often experienced at a micro-scale – the building, the block, the street – that is far
smaller than the scale of most models (the traffic analysis zone.) In addition, land use variables
are often only roughly related to design impact, e.g. using number of establishments as a
measure of diversity of land use.
Operations models have their own limitations. Most are focused on motor vehicle operations
and treat pedestrians in a simplified manner and bicycles not at all. Many of the simpler
operations models treat intersection counts as independent and fixed and are incapable of
assessing route choices.
More advanced models are better capable of addressing the complexities of travel behavior and
its consequences. For example, several advanced model systems use detailed traffic operations
models to represent networks, integrating the operations models with advanced land use and
activity/travel models. However, as they are currently formulated, the application of these
advanced models is heavily time-consuming and data-hungry. Partly as a result, the models are
most frequently reserved for regional studies (e.g., analysis of the impacts of alternative urban
growth and transportation investment plans and programs for a 10 or 20 year forecast year) and
for major infrastructure investments (e.g., a major new transit line or a major new highway.). For
individual projects of a smaller scale, analysts are increasingly using sketch planning methods
derived from advanced models, together with data from field evaluations that are interpreted in
light of specifics of the case at hand.
While sketch planning methods are often used because a broader modeling approach would be
too expensive, another major reason for the use of sketch planning methods is their relative
transparency. This is in part a recognition that changes in transportation and land use policies
and practices succeed or fail on the support of elected officials and acceptance of the general
public, and not on the fanciness of the analysis. Good analysis can help support decisionmaking by helping planners and engineers design projects more effectively, but clarity and quick
response are important attributes as well. Hence simplified analysis methods (e.g., spreadsheet
methods using results from research) are widely accepted, and can be extremely cost effective
and valuable.
45
Regardless of the type of model used, transport models are only as good as the data used to
calibrate them. In many urban areas this is a major problem. For example, if travel surveys are
old, they may not adequately represent current behavior. If travel surveys are small, it may not
be possible to represent infrequently chosen modes or modes that are important only in some
districts. If speeds are based on posted limits rather than measured ones, both travel times and
fuel use estimates can be highly inaccurate.
Despite these limitations, modeling is usually the best way, and sometimes the only way, to
evaluate transportation projects including their CO2 impacts. Recognizing the limitations of
modeling can help the analyst exercise caution in interpreting results and also can help identify
needed modeling improvements.
C. Selecting an Analysis Approach
Whether to use an advanced model or a simpler one is partly a question of resource availability
and partly a question of what issues need to be addressed given the proposed project and the
context in which it will be implemented. Large, complex projects are likely to need
comprehensive, sophisticated methods. A small, simple project is probably suitable for sketch
planning methods. However, size alone does not necessarily determine the complexity of a
project. Because context is important, a formulaic matching of methods to project size and scale
is not advisable.
Consider a new BRT line intended to increase accessibility to jobs in the urban core from
outlying areas of a region. Evidence from other analogous projects, including the experience of
other countries, can help determine the issues that need to be considered. In many countries,
radial transit lines were built between outlying districts and the central city. These lines enabled
housing development in the outlying areas by providing them good access to employment.
Suburban housing was followed by service establishments for the growing population, and then
by employment centers, which located in the burgeoning suburbs to take advantage of the lower
costs of land and the growing labor market there. Eventually new sub-centers emerged,
spawned by the transit lines. These sub-centers increasingly compete with the central city for
economic development, and employees there do not necessarily use transit to commute to
work. (Warner, 1962)
Because such effects are known to occur, an integrated land use-transportation model would be
apt for analyzing the transit line posited in this example. While an integrated land usetransportation model is fairly costly to develop and apply, it would be able to capture the
potential land use effects of the investment.
Now suppose that a new BRT line is proposed that would connect the central business district
to a series of long-established suburbs currently served by conventional buses operating in
congested traffic. Should the effects on land development be analyzed? The answer is possibly
yes, if the new line will increase accessibility significantly. However, if area is already largely
built up, and the new transit level of service will be about the same as the current LOS, the
opportunities and inducements for new development might be quite limited. Thus the
transportation and CO2 analyses could focus on the effects of improved vehicles and the effects
of improved travel time on mode choice.
Finally, suppose the proposal for separate lanes and signal priority for BRT proves infeasible
and authorities decide to simply replace the existing buses with new, more fuel efficient buses of
the same size and configuration. What analyses are needed? If the buses are used on the
46
same routes with the same travel times and costs, a good first-cut estimate of the CO2 effects
can be calculated on the basis of the change in vehicle fuel efficiency alone. There may also be
some effect on demand, because the newer buses are likely to be more reliable, cleaner and
more comfortable, but absent changes in travel time and cost the demand changes are likely to
be minor. If, on the other hand, the cost of new buses requires an increase in fares, the mode
choice impacts should be evaluated.
This example shows that it is not only the size and type of project but also its objectives and the
context that should help determine the analysis methods chosen. In general, if a project is likely
to affect only one component of the transport system, e.g., vehicles, fuels, guideways, stations
or operations, it is often sufficient to analyze the changes to that component only. If a project is
likely to affect several or all system components, more extensive modeling is appropriate.
How can an analyst assess whether a project is likely to have limited or wide-reaching effects?
The project justification should provide a first cut indication of issues to be addressed, but the
analyst also should consider the potential for secondary impacts that could be significant.
Transportation theory offers guidance. Table 3.2 presents a brief list of key factors affecting
different location and travel choices. By considering how a project affects these factors and
choices and whether the magnitude of the impact is likely to be significant, the analyst can
determine what analysis steps are suitable. In terms of the framework presented, what matters
is whether a project or other intervention causes a big change in these factors different than
what might have happened anyway. Large changes in per capita numbers of trips, trip
distances, or mode choice will have important impacts on fuel use and CO2 emissions, both
upward and downward. Changes in route choice could affect fuel use per kilometer upward if
congestion and traffic delays change.
Table 3.2 Factors Affecting Location and Travel Choices
Choice
Key Factors Affecting Choice
Location of residence or
business
Land/building costs, availability and suitability, accessibility (time
and cost to other destinations), urban services, neighborhood
characteristics
Number of trips (trip
generation)
In the aggregate: population size and demographics, number of
households and business units, business types; for the
individual or household: income, activity participation, modal
availability, accessibility
Length of trips
(destination choice)
Travel time, travel cost, opportunities (destinations) available'
income
Mode of travel
Travel time (access, in-vehicle, wait, transfer times) , travel cost,
user age, income, physical condition; vehicle ownership,
comfort, reliability, safety
Route choice
Link speed, stops and delays, reliability, cost
Case studies from the literature and the opinions of experts also can be of help in identifying
probable impacts and in determining suitable analysis methods. Case studies can illustrate the
issues that may arise with a particular type of project, and can illustrate evaluation approaches.
Experts can review the project description and justification and synthesize their knowledge of
the literature and practice with the situation at hand.
47
D. What to Do When Data and Models are Unavailable or Limited
A major problem that many urban areas face is that they do not have in hand the data and
models needed to thoroughly evaluate transportation projects and their CO2 consequences.
Data and models may be absent altogether or may be outdated or of limited suitability. The
problem is especially acute for data on vehicle fleets and their fuel efficiency, but may also
extend to travel data.
Good data and models are a worthwhile investment for a region. They can be used for a variety
of purposes and projects. However, if time and cost considerations preclude a major
expenditure on regional or citywide data collection and model development, there still are ways
to proceed.
First, it is often possible to collect data for the specific project. The basic building blocks of
transportation analysis – data on land uses, the transport network, population and the
economy, and travel patterns – can sometimes be obtained at relatively low cost or even free
from readily available sources. For example, a considerable amount of data on land uses can
be obtained from satellite images which can be processed using available software or, for small
applications, by hand. In addition, forecasts of population and economic activity are often
available for major metro areas from national or international agencies.
If travel surveys are lacking, project-specific surveys can often be mounted. If data on vehicle
fuel efficiencies are missing, sample measurements may be possible, but more likely data will
have to be ―borrowed.‖ Taking values from literature, tests or estimates conducted in a region
not too dissimilar to the one being analyzed may provide satisfactory results.
Two examples, both from Mexico, illustrate how data and modeling limitations have been
overcome in recent projects.
i.
Example: Mexico City’s Metrobús BRT Evaluation
Mexico City lacked a recent travel survey as well as recent traffic counts suitable for use in
evaluating its Metrobús BRT project in the Insurgentes corridor. Analysts therefore undertook
several special studies to address particular questions of concern to decision-makers.
Retrospective questions in on-board surveys were used to identify how riders had traveled
before Metrobús, allowing the analysts to estimate how many cars had been removed from the
traffic stream due to the new transit service. These surveys allowed analysts to estimate CO2
reductions due to the new services.
Because development of Metrobús required removing two lanes of traffic to dedicate to the bus
lanes, there was an expectation that this change in one of Mexico City‘s most heavily traveled
corridors could have a major impact on parallel and cross traffic. To analyze the effects, traffic
counts were carried out and photography, visual observation and on-board counts were used to
determine the number of vehicles of each type and to determine vehicle occupancy for cars,
colectivos and buses. A traffic operations model was then used to estimate traffic
consequences. No modeling was done for other corridors or the region as a whole because the
project impacts were not thought to be likely to have significant effects outside the immediate
area. (Rogers, 2006)
48
The traffic models allowed a fairly detailed analysis of the traffic consequences. The vehicle
classification and occupancy data allowed the results to be interpreted in terms of passenger
movement (recognizing that a bus carries 50 or more people while a car usually carries only one
or two.) Even without the traffic models, however, the data collected for the project would have
permitted a simple evaluation of project impacts. For example, rough estimates of emissions
changes could be made based on before-after traffic counts, vehicle classification studies, bus
rider counts, etc. While such approaches typically require a number of strong assumptions, they
may nevertheless provide a reasonable first order estimate of impacts.
Translating the traffic model results into their fuel use and CO2 consequences required
additional information on the vehicle fleet composition – vehicle types, makes and models, and
consequent fuel economies. Gathering data on the vehicles used along particular routes is a
huge task, given the typically large and diverse stock of vehicles, and a commonly used simpler
approach instead ―bins‖ (classifies) vehicles by their main fuel-use characteristics (engine
technology, fuel used, vintage.) This approach was used in Mexico City. The age and type of
cars on Insurgentes estimated from photographs of traffic at different times of day. Fuel use was
then determined using this Insurgentes fleet, rather than the citywide average fleet. Data on the
kinds of vehicles on the route allowed adjustments by size and approximate fuel economy. The
International Vehicle Emissions Model, or IVEM (Lents et al., 2004)22 was then used to estimate
fuel consumption as a function of speed for each group of vehicles observed on the corridor.
(Rogers, 2006)
A much simpler approach would have been to assume the citywide average fleet and to use a
spreadsheet model to estimated fuel and CO2 impacts. A spreadsheet model of the ―ASIF‖ type
is suitable for this (Schipper, Marie and Gorham 2000; Schipper and Cordeiro 2007). A
represents total vehicle kilometers, S the shares of kilometers by mode, I the intensity (fuel
use/km) for each mode and fuel, and F the fuel type, i.e., the CO2 content of each fuel, and
putting in appropriate estimates of these values before and after the project, or with and without
the project after a given time, gives differences in CO2 emissions. This ―ASIF‖ approach does
not calculate the changes in either kilometers driven or fuel intensity of each mode, rather helps
the analyst identify the types of data needed and puts them in a simplified multiplicative
framework. The approach still requires data vehicle kilometer data for each mode, vehicle type
and fuel type, which are available for a number of cities in LAC.
ii. Example: Querétaro Bus Restructuring
A project for Querétaro evaluated how much fuel would be saved if bus services were
restructured, the oldest and least efficient colectivos were phased out, and a BRT corridor was
added. Because local data on the fuel use of the vehicle fleet were not available, Cordeiro et al.
(2008) used Mexico City data on the fuel intensity of similar models of colectivos from Mexico
City, together with observations on the kinds of colectivos and buses used in Querétaro and
estimates of the distance they traveled in Querétaro, provided by operators.
Fuel use for the base case was estimated for each vehicle type as number of vehicles x
km/vehicle/year x fuel use/km. A new service scenario then was studied which 1) cut the
22
A number of models, including ones running in LAC countries, are available that carry out the simulations of fuel
use based on traffic, vehicle and fuel characteristics, that are then summarized by the IVEM. Lents et al. (2004)
discusses the IVEM and its uses in a number of LAC cities.
49
number of bus km provided in Querétaro by two thirds, based on a consultant study that had
identified a severe oversupply, 2) culled the oldest and least fuel efficient vehicles from the fleet,
running the new service configuration with newer, more efficient buses, 3) added a BRT service
with a small number of dedicated BRT vehicles. The resulting fleet was about 600 relatively fuel
efficient and clean buses (down from over 1,200) plus 13-18 BRT vehicles.
As Figure 3.2 shows, the reduction in bus km was estimated to produce major fuel and CO2
savings even if the remaining services continued to be offered by a fleet with the same
characteristics as those of the base case. Providing services with more efficient buses would
further cut fuel consumption and CO2 emissions substantially. The BRT services and the types
of vehicles used for BRT made relatively little difference in the fuel consumption calculation
because of their small share of the total fleet.
.
Figure 3.2. Steps to Reducing CO2 from Improving Bus Utilization in Querétaro
180
160
BRT Trunk Busses
CO2 emissions, thousand tonnes
140
Main Bus System
120
100
80
60
40
20
0
BASELINE
FEWER BUS KM
More Efficient Buses and BRT
The use of ―borrowed‖ fuel efficiency data from Mexico City deserves comment. While
conditions in Querétaro are not identical to those in Mexico City, the use of these data saved
considerable work in conducting the analysis and probably provided reasonable first-cut results.
Confidence in the results was strengthened by collecting local data on the Querétaro fleet
composition.23 Mexico City traffic is arguably more demanding than that of Querétaro, and
Mexico City is about 400 m farther above sea level than Querétaro, hence use of the fuel
economy data from Mexico City probably overestimated real consumption/km in Querétaro.
23
The original study done for Querétaro did not collect any fuel use data. Despite backing of the transport authorities
for the EMBARQ study, operators declined to provide any fuel-use data or permit any surveying.
50
Still, the predominant change was in the number of vehicles and the distances driven, with a
total change far greater than any possible error in fuel economy rates.
These two examples show that creative field work, carefully borrowed data, and simple methods
can produce informative results. In neither case was a formal model exercised in full. Both
cases used a combination of available data and data collected specifically for the project, data
analysis, and limited model application to provide an assessment. In addition, both cases used
expert judgment to determine which issues needed to be addressed and which ones could be
treated as secondary or not critical to the decision.
The evaluation approaches we have discussed are general. In practice each case has to be
studied on its own, and the best approach (or approaches) chosen. For complex, large-scale
interventions, such as the Bogotá BRT network, conventional practice is to run the entire model
system in order to account for a relatively complete set of impacts across the entire region.
However, for smaller scale changes, it may be adequate to apply only a sub-model, e.g. use a
mode choice model to study the effects of fare changes on transit ridership, or use a network
model to study the effects of adding a lane to a facility on travel times (or, in the case of
Metrobús, removal of a lane.) It should be emphasized that most of the tools required are
standard for transport planning and urban growth forecasting. If a good transportation and
urban growth planning and analysis program is in place, the additional steps needed to estimate
changes in fuel use and CO2 are to 1) add information on the vehicle fleet and its use and 2)
add information on in-use fuel efficiency. These two steps can be approximated with national
data if need be. If on the other hand the basic regional modeling systems are not developed,
special studies can fill in. However, while they do require an investment, it is definitely advisable
to develop good analysis methods and datasets, which will be used multiple times over.
E. Flags – What to Watch for Before, During, and After a Project is Completed
Every transport intervention has its own risks and pitfalls and even the best projects and project
analyses may run into problems or raise questions. Here we address three important
considerations: the accuracy needed for CO2 analysis, unintended side effects that may change
the impacts from those predicted, and institutional capacity to fully implement the project as
proposed.
i.
Accuracy of the CO2 Estimates
How accurate do estimates and observations of changes in CO2 emissions need to be? The
general answer is accuracy must permit observation or modeling of the impact of the intended
measures against a relatively noisy background of transport activity and fuel sales. While
Metrobús reduced emissions in the Insurgentes corridor by some 10%, that impact was well
under 1% of the total emissions from land transport in the Mexico City region. That small
amount itself is less than the typical annual growth in fuel use region wide.
Reducing uncertainty has a cost. Both Santiago and Mexico City are well-described by their
emissions inventories. That meant that the cost of getting an accurate estimate of the CO2
savings was laid mostly to measurement and modeling of the actual transport intervention. For
the more likely case that no such emissions inventory exists, it would be difficult to make
accurate estimates. A good transport emissions inventory is valuable since it is built from many
important transport data as well as fuel and emissions data. It would be hard to justify building
such an inventory from measurements, models, and estimates only to measure CO2 savings,
51
especially for a single project, but certainly important to have such an inventory as a planning
and evaluation tool that can be applied to many projects.
ii. Unintended Side Effects
A number of unintended outcomes may affect a project‘s CO2 emissions significantly, as well as
overall project outcomes. A happy example is offered by Metrobús, where the successful
project attracted more ridership than had been predicted.
The energy required to accelerate a vehicle depends on its weight. Therefore, adding
passengers to a vehicle increases that vehicles‘ fuel use. Metrobús, an articulated vehicle
weighing some 18 metric tonnes, can carry up to 160 passengers sitting and standing for a
gross weight of 26 metric tonnes, implying 50 kg/passenger. (SMA, 2006) Thus the passengers
of a full bus would weigh around 8 metric tonnes, slightly under half the weight of the bus itself.
But Metrobús initial ridership exceeded all forecasts and so initially the buses were very
crowded and used much more energy than originally estimated. (Eventually the authorities
added 20 buses to the original set of 70 to accommodate demand.)
Another set of unintended consequences that are fairly common could be termed ―leakages‖,
essentially transfer of activity and emissions from the project area defined as a corridor or
district to other areas outside the project boundaries. For example, the colectivos displaced in
Metrobús could have found their way onto other routes, consuming fuel and continuing to emit
CO2, but not actually increasing the number of passengers hauled on those routes. Since they
were melted down, this leakage was prevented. In addition, Metrobús may have caused a large
number of cars to avoid the Insurgentes corridor completely, which may have contributed to the
observed drop in traffic on Insurgentes. Assuming these cars have been removed from the
system when they may have only moved to other corridors would overestimate emissions
reductions.
The ―rebound‖ effect is another unintended consequence. When low emission vehicles are
exempted from tolls, congestion fees, or parking charges, or permitted to use special lanes,
there is a clear risk that drivers will respond to this lower cost of travel by traveling more.
Another example of an unintended side effect resulted from Hoy no Circula, the Mexico City
campaign to restrict car use one day a week according to the last digit of the license plate. The
goal was to reduce driving and thereby emissions of local pollutants. However, the number of
used cars in Mexico City increased markedly as drivers who could afford to do so acquired
vehicles with a different final digit than their primary car. With two cars now available to the
household, driving and fuel use actually went up, and with it the very emissions that were
supposed to be mitigated. (Eskeland and Feyzioglu, 1997)
Some of these effects probably could have been identified ahead of time with better analyses,
consultations with experts, and reference to the literature. For example, the changed travel
times on Insurgentes and other routes could have been used to estimate traffic diversion to
parallel corridors (which would have required data on those corridors). Also, evidence from the
literature would have revealed that previous attempts at vehicle-based no-drive days were met
with exactly the same consumer response (buy more vehicles with different no-drive days).
That is not to say that all consequences can be anticipated, but it does point out the value of
analyses (and a review of the literature) in providing foresight.
52
iii. The Four P’s
While modeling can help elucidate project impacts, model results are not a substitute for expert
judgment in evaluating projects before, during and after implementation. In particular, evaluators
must exercise judgment to assess whether the institutional capacity exists to successfully
implement a project. Institutional capacity can be evaluated in large part by asking about four
topics that begin with ―P‖: policy alignment, practices, performance, and progress.
Are the policies in place to support the project? For example, the hybrid buses evaluated in
connection with the Metrobús project offered roughly 20% savings in fuel and CO2 compared
with conventional buses. Hybrid buses would also show savings on conventional bus routes.
But if bus operators have no incentives to adopt advanced technologies or low carbon fuels,
then analyses or even demonstration projects may be futile, as early experience in Mexico City
with ethanol and compressed natural gas buses showed. And if fuel prices are relatively low, the
fuel cost savings of more efficient vehicles may be too small to offset the costs of more efficient
vehicles.
Is needed policy adopted as law and does it have popular support, so that it is likely to be
longstanding, or is it dependent on the leadership of a particular mayor, governor, or staff
member whose departure might lead to a change in policy direction? Are there countervailing
policies, , e.g., tax policies that favor company cars but not employer-provided transit passes, or
a planned ring road whose impact might offset that of a collective transport improvement serving
the same area? Such questions can help gauge whether the project is likely to succeed in the
long run.
Are the practices right? Is there institutional capacity for data collection, planning,
implementation, traffic enforcement, emissions monitoring, and evaluation of projects? A good
example is Mexico City, where authorities have been collecting ambient air quality
measurements, measuring emissions from existing vehicles to enforce pollution rules, and
keeping a record of light duty vehicle use. This practice meant that estimations of Metrobús
impacts had a true ―base line‖ to use. Santiago and Sao Paulo have similar inventories but most
LAC cities do not. Good data and analysis capabilities allow cities to identify, develop, and
implement needed projects on an ongoing basis.
Is the performance on target? Did the project or policy produce the expected results? In the
short term, authorities have to be able to see whether the performance is close to expectations.
In the longer term, targets (absolute or relative), benchmarks (comparisons with other similar
situations/projects) or thresholds (minimum levels of achievement, i.e., X % modal shift or more)
are needed to gauge performance, make adjustments as needed, and aim for continuing
improvement. For example, on Metrobús, there was extensive bunching initially, with as many
as five buses arriving at nearly the same time. Better dispatch routines were used to cure that
problem.
Over the longer run, is progress being made? Is ongoing monitoring being done to make sure
that accomplishments are maintained and or improved upon? For example, Metrobús surveys
riders every year, both to gauge customer opinions about service and to understand how the
system is performing, considering on-time performance and mode shift.
53
F. Summary
There are three parts to evaluation of changes in CO2 emissions resulting from transport
interventions. First, projected changes in travel and transport activity have to be compared with
a baseline where no project or other intervention is present. Then the impacts of the project on
transport activities have to be estimated through a combination of modeling and data analysis.
The estimated changes in vehicle activity are used to forecast changes in fuel use, which can
be converted into changes in CO2 emissions. The last quantity is then compared with modeled
emissions in the zone of influence or city wide to show the overall impact of the project on CO2
emissions. Additional iterations can help determine whether there are project changes or
enhancements that would improve CO2 performance.
Models are an important tool for evaluating transport projects. Integrated transport-land use
models are the most advanced formulation for demand analysis and can address the key
factors shaping location and travel choice. Simpler demand models can be appropriately used
for many evaluations, however, and sketch planning approaches also can provide good results
in many cases. Special purpose analysis methods including traffic operations models are useful
for evaluating alternative network design and control strategies. Life cycle analysis is
increasingly being used to account for the ―full costs‖ of interventions, especially constructed
facilities and alternative fuels or biofuels proposals.
CO2 estimates require that the transportation impacts of a proposed intervention be combined
with information on the vehicles and fuel used and the fuel intensity of each vehicle type. This
can be done in several ways, from using fuel and emissions models that calculate emissions
based on link level speeds, etc. to simple spreadsheet approaches that use simple VKT by
mode and vehicle, fuel combination together with emissions factors.
Selecting the appropriate methods for analysis is in part a function of the proposed intervention
and in part a function of resource availability. Understanding the way that different interventions
affect location and travel can help identify the analyses that may be needed. Reference to the
literature as well as advice from experts also can be useful in flagging issues and identifying
important analysis methods. Many areas have used a combination of borrowed data and new
field work and analysis to overcome shortages in models and data sets.
Analysts also are paying increasing attention to the risks of unintended consequences of
projects, such as rebound effects.
While models and analysis are useful, they are only as good as the data that go into them. A
serious commitment to integrating carbon considerations into transportation entails a
commitment to improved data on travel, the vehicle fleet, and fuels.
Finally, analyses of project impacts are only part of the evaluation picture. An analysis that
considers policy, practices, performance, and progress to assess the likelihood of successful
implementation is a necessary complement to predictions of what a successful project would
accomplish.
Road transport produces a high share of CO2 emissions in Latin America and the Caribbean,
around one third of the total. These road transport emissions arise predominantly from the use
of light duty vehicles. In LAC urban areas, light duty vehicles account for only 25-35% of travel,
yet they contribute roughly 70% of CO2 emissions. As the economies of LAC cities improve,
54
light duty vehicle ownership and use in is expected to grow, and CO2 emissions from transport
are expected to increase in turn.
Since light duty vehicles are the source of most CO2 emissions from transport in LAC urban
areas, action to restrain or reduce transport emissions must deal with these vehicles –their
efficiencies, their numbers, and the conditions under which are used. Important improvements in
fuel efficiency can be fostered by fuel economy standards and higher fuel prices. Additional fuel
savings and CO2 reductions can be attained by investing in traffic management, transit, and
non-motorized modes. These latter investments can help slow growth in CO2 while at the same
time significantly improving the quality of transportation available to the majority of the LAC
population.
55
4. Conclusions and Recommendations
Road transport produces a high share of CO2 emissions in Latin America and the Caribbean,
around one third of the total. These road transport emissions arise predominantly from the use
of light duty vehicles. In LAC urban areas, light duty vehicles account for only 25-35% of travel,
yet they contribute roughly 70% of CO2 emissions. As the economies of LAC cities improve,
light duty vehicle ownership and use in is expected to grow, and CO2 emissions from transport
are expected to increase in turn.
Since light duty vehicles are the source of most CO2 emissions from transport in LAC urban
areas, action to restrain or reduce transport emissions must deal with these vehicles –their
efficiencies, their numbers, and the conditions under which are used. Important improvements in
fuel efficiency can be fostered by fuel economy standards and higher fuel prices. Additional fuel
savings and CO2 reductions can be attained by investing in traffic management, transit, and
non-motorized modes. These latter investments can help slow growth in CO2 while at the same
time significantly improving the quality of transportation available to the majority of the LAC
population.
A. A Framework for Incorporating Restraint of CO2 Emissions in Transport Planning
and Policy
This report has presented a framework for incorporating CO2 considerations into urban
transport decisions, so as to reap the co-benefits of CO2 restraint arising from good transport
and urban development. The goal is to use carbon efficiently, to get as much good transport as
possible from each unit of carbon used.
The framework is intended to help guide project selection and evaluation. The first step in this
framework is to assess the scope and scale of measures authorities wish to undertake to
improve transport. The scope can vary from a broad urban development strategy to a strategy
for switching certain vehicles to low-carbon fuels. The scale can vary from the entire region to
single corridor or small neighborhood or a small subset of all collective transport vehicles. Multifaceted, large scale projects will usually require more sophisticated data and analysis methods
than projects that focus on a single aspect of the transport system or a single area or corridor.
The next steps are to model or otherwise estimate the potential CO2 impacts of proposed
strategies and interventions, monetize their benefits and costs, and compare them to the
―business as usual‖ case. An important part of this analysis process is the identification and
assessment of ways in which a transport project and its CO2 benefits might be enhanced. Once
a final design for the project is agreed upon, measures of performance that can be monitored
over time should be selected.
In this framework, savings in CO2 are measured in comparison to what otherwise would occur,
without the intervention. For most transport interventions, CO2 will not be the decisive factor for
a project even at a high value of each tonne of CO2, but its reduction can be an important cobenefit.
56
B. Estimating the Impacts of Interventions on CO2 Emissions in Urban Transport:
“You Can’t Master What You Can’t Measure”
Many approaches are available for estimating the changes in CO2 emissions that arise from
urban transport projects. The key tasks are to determine how the intervention is expected to
change travel behavior trip making rates, trip distances, mode choice, and routes used, and to
evaluate how these choices affect the flow of vehicles. These travel estimates are then used
together with information on vehicle type and fuel type to calculate fuel use. The resulting
changes in fuel use can be readily converted into changes in CO2 emissions. Regional
transportation-land use models and detailed transportation vehicle fleet and emissions
inventories are the ―gold standard‖, but simpler methods also can be used to produce
reasonable fuel use and CO2 estimates.
.
When resources are limited, special studies can be used to estimate the changes in travel and
vehicle activity and default values can be used to estimate fuel use/km. In general the transport
data tend to vary much more according to local conditions. Nevertheless, if the urban region
lacks data and models for transport analysis, or has no data on actual yearly vehicle utilization
and fuel use/km, it is worth investing in these items, because they will be useful for many
projects.
C. Recommendations
Chapter 1 presented daunting projections of a near quadrupling of VKT from light duty vehicles
in LAC by 2030. Is the realization of this projection as ―Business as usual‖ inevitable? We do
not believe it is. However, changing course from trends-extended requires both short-term
action and a strategy for the longer term. Lenders and other international organizations can
have a strong influence on projects through advice and analysis, as well as support for local
capacity building. Urban regions should focus more on longer-term development and transport
interventions that avoid the high car use seen in business-as-usual forecasts, rather than
yielding to those forecasts and permitting car-dependent development dominate, as is implied
by the projections. Activities also could encourage and support activities that aim to ―get prices
right‖ and to manage traffic more aggressively. Such approaches are widely viewed around the
world as the elements of ―good transport‖ as laid out by the recent World Bank urban transport
strategy.
To develop more sophisticated, substantial transportation interventions and to account for their
full impacts, tools for planning and evaluation have to be strengthened. A better mobile sources
inventory (as Mexico City has built developed in part with World Bank assistance) is one tool
that allows local planners to see the transport-fuel-CO2 connection. Improved models and data
both illustrate the transport or urban development advantages of better transport and show the
direct benefits of a project as well as the co-benefits – CO2 savings.
.
Reducing the growth in CO2 emissions from transport is not the biggest challenge transport and
LAC urban planners and urban development authorities face. Inadequate transportation is still a
problem for many of the region‘s people, especially the poor. Congestion chokes many urban
areas, negatively impacting the economy, the environment, and public health. Addressing these
transportation issues – achieving the ―good transport‖ called for in the recent World Bank
Strategy Paper – is the bigger policy challenge for transport and urban authorities.
57
Fortunately, the same policy initiatives and interventions needed to develop good transport
services will yield CO2 co-benefits. Excellent transit, safe and convenient non-motorized modes
of travel, and appropriate traffic management (including pricing) have been shown to moderate
auto ownership and use in cities from all over the world - in LAC as well as in the EU, Japan,
Korea, and even some US cities. By investing in these same transport services, it should be
possible for LAC cities to slow the rise in CO2 emissions from their main source in urban
transport, individual light duty vehicles. Combined with strong efforts to reduce the CO2 intensity
of individual vehicles, the LAC region could see a considerably different evolution from the
business as usual projects presented here. Taking on the development and transport
challenges, as Curitiba, Bogotá and Mexico City have done, is clearly the first step.
Technological changes are needed and will be valuable, to be sure. But even making every
vehicle in the world CO2 free overnight will not meet the enormous challenge from urban
transport today faced by every person getting to work, school, or shopping. It is for this reason
that CO2 concerns must be integrated into larger transport and urban development trajectories.
The World Bank could encourage CO2 minimization in the following ways:
First, the World Bank could provide more technical assistance for travel surveys, traffic counts,
emissions measurements, and fuel-use measurements. Good data are a critical building block
for any evaluation and such data are needed to build better models for analysis and forecasting,
not just for CO2 purposes but for a broad array of urban planning and economic development
tasks.
Second, the World Bank could encourage and assist local authorities to develop modeling
capabilities for travel demand, traffic operations, and life cycle analysis. Such modeling
capabilities would enable more sophisticated analyses of growth, development, and travel, and
also would enable more explicit and formal consideration of the longer-term impacts of World
Bank-supported projects. This latter capability is important because road and transit projects
might increase the ability of people to make longer trips and lead to, or reinforce, development
in more distance parts of urban regions. Better analysis of system-wide changes due to projects
could result in changes in project design to avoid undesirable side-effects.
Third, the World Bank could provide assistance to both local and national governments to
evaluate strategies such as stronger fuel economy standards, stronger emission standards,
congestion pricing, parking pricing, and tolls. Some of these measures may be politically
difficult, yet they have been effective where used, with proven impacts on vehicle use, mode
choice, and vehicle fuel intensity. Information on the benefits and costs of such measures could
increase the comfort level with which such measures are regarded and over time could expand
the kinds of choices local and national authorities make.
Treating CO2 considerations as a regular, required element of Bank plans, evaluations, and
projects would signal the importance of action on the topic, and recognizing that many
transportation projects reduce greenhouse gases from what would otherwise occur lays the
groundwork for more vigorous action in the future.
58
5. Appendix One: Mexico City’s Metrobús – A Case Study in
Estimating CO2 Impacts
A. Introduction
Metrobús is a bus rapid transit line running 19.5 kilometers from Indios Verdes in the northern
region of Mexico City to Avenida Doctor Gálvez in the south. It operates on Avenida
Insurgentes, one of the most important main roads in Mexico City with three to four lanes in
each direction. The system emerged from a World Bank/GEF/EMBARQ project examining
―Climate Friendly Transportation‖ in the Mexico City region. Metrobús opened on July 19, 2005,
after three years of intensive planning and design. The initial impact was to move more than
250,000 people/day – 1.25% of the total daily trips in the region - on large new, high-floor buses
operating in exclusive lanes with attractive median stations offering level boarding.
In this case study, we evaluate the Metrobús project using the framework presented in Chapter
two. We show how the carbon reductions from Metrobús can be calculated for the main
components of change – vehicle changes related to the bus system itself, impacts on traffic
(including vehicles not part of the bus system but traveling along or across Insurgentes), and
modal shifts away from cars or other mass transit to Metrobús.
The framework presented in Chapter 2 set forth the key dimensions of a methodology for
analyzing the production of greenhouse gas emissions from the transport sector. These
dimensions guide the analysis of the Metrobús. We articulate the relationship between the
framework and the analysis in the following questions:
(1) What were the objectives of the project undertaken, particularly its scope and scale?
a. Urban development: Was the project part of a major thrust of urban development? If not,
could it have a long run impact on that development?
b. Transportation: How did Metrobús project fit in the context of a plan for improved
transportation for Mexico City? and
c. Emissions: Were the project‘s efforts focused directly on fuels or CO2 emissions from the
project? How did the Metrobús project affect emissions of greenhouse gases from transport
in Mexico City?
(2) What was the economic valuation of the various costs and benefits of the project including
impacts on travel time, street congestion, security and safety on the buses as well as the
streets, local emissions, etc.? How was the project justified? The CO2 saved is a co-benefit
of good transport projects: Is the value of carbon co-benefit and fuel saved a significant
share of the total project benefits? How does this co-benefit vary in value over a range of
CO2 prices? What were the immediate impacts of the project on emissions that were used
in this economic evaluation?
(3) What could the longer run effects of the project be compared to a business-as-usual or
―without project‖ scenario? Was there a long-term land-use travel model that could be used
to predict what would happen to trip making and travel without Metrobús and gauge the
effects? Were there side effects, particularly social impacts or other changes around the
59
corridor that should be analyzed and understood alongside the benefits provided to the
transport system?
(4) What arrangements were made (or are still being undertaken) to monitor the longer-term
impacts of this project?
The Metrobús project was conceived of as a way to simultaneously reduce traffic congestion
(caused by high volumes of colectivos and growing private auto use), improve bus speed,
convenience, and reliability and therefore attract riders away from cars and colectivos, improve
transport access for the poor, and reduce air pollution and CO2 emissions from both colectivos
and automobiles.
In this section we evaluate the impacts of Metrobús following the Framework on Integration of
CO2 Considerations in to urban planning developed in this paper.
i.
Scope and Scale
Metrobús involved a number of changes to collective transportation along the Insurgentes
corridor. Key among these included vehicle substitutions, changes in street design and
operation, and changes in bus stop design and operation. Most of the colectivos previously
operating on Insurgentes were completely scrapped (at no small social cost to former drivers),
while the buses that belonged to the city company, RTP, were deployed elsewhere, replacing
similar but older RTP buses.
The initial conception of Metrobús was not part of a new urban development plan; it was
conceived primarily for its transport benefits, which were large. Mexico City authorities had been
considering options for dealing with nearly three dozen street corridors with very large (50
000/day) flows of people. The transport analyses behind these options (SETRAVI, 2002)
addressed the existing flows, but not longer-term urban development questions. After almost 2
years of discussions of the possibility of a BRT corridor, the Insurgentes route (along with
another option, Eje 8) were the subject of intense study in 2003, and Insurgentes was selected
in 2004.
The Metrobús project undertook no specific measures to reduce CO2 related to vehicles or fuels
other than the reduced fuel use from buses running in a protected BRT corridor. An option to
acquire diesel hybrid buses for this project was evaluated. The hybrids would have saved only
slightly more than 20% of the fuel than the buses chosen used. This would have amounted to
about 6% additional fuel and CO2 than the entire Metrobús transport project saved. Because
they would have been very expensive and untried on a large scale in Mexico City, they were not
considered further. Still, major steps to reduce CO2 emissions were successfully embedded in
a transport project.
ii. Immediate CO2 Impacts and Co-benefits- Summary
Immediate demand shifts resulting from the new service included large shifts from colectivos to
Metrobús, as well as modest shifts from auto to Metrobús. Improved traffic along the
Insurgentes corridor led to slight reductions in the fuel use of parallel traffic. The estimates of
CO2 saved, given in a subsequent section, are approximately 50,000 metric tonnes of CO2, or
about 0.25 % of total road transport sector emissions in the Mexico City Metropolitan region and
010% of the emissions associated with traffic observed along the Insurgentes corridor. The
60
calculations were undertaken in Rogers (2006) and will be explained in detail in Section III.
Figure A.1.1 and Table A.1.1 summarize the results.
Figure A1.1. Metrobús Emissions Before and After
600
550
500
Thousand Tonnes CO2
450
400
A. 20 Extra Metrobus
350
B. Original 70 Metrobus on Route
300
C. Colectivos and RTP Buses Removed
250
D. Car Users Shifting to Metrobus
200
E. Delays to vehicles crossing Insurgentes
150
F. Additional Distance for Left turns
100
G. Savings from improved parallel traffic
50
H. Remaining parallel traffic
0
Before
After
Source: Rogers, 2006 and Rogers, 2009.
Notes: Legend explanations: A and B are the emission from Metrobús after; C is the emissions of the transit vehicles
removed; D is the emissions imputed before drivers switched to Metrobús; E and F are the extra emissions from
delays and circuity imposed by Metrobús. G, shown as emissions in the corridor before that were saved because
traffic on Insurgentes is smoother after Metrobús is put in place. H gives the remaining emissions from all parallel
traffic on Insurgentes. Details given in section III.
Table A1.1 Changes in CO2 Emissions from Metrobús Project
Average Annual Reductions in Emissions
Operating condition improvements and/or the substitution of the number and technology of
buses that operate on the main route or BRT corridor
17,554
METHODS: Measured fuel consumption of original vehicles, Metrobús; daily driving distance;
carbon content of each fuel.
Improving the operating conditions for other vehicles operating on the main route
17,515
METHODS: Number, type of vehicles operating on Insurgentes, average speed before and after
Metrobús; model of fuel use vs. speed.
Modal shift from cars on the route to buses
15,610
61
METHODS: Surveyed Metrobús riders who originally took cars; average distance of trip, average
load factor of car; average fuel consumption of cars on Insurgentes based on counts of types and
City database.
Total Annual Emissions Reductions all Years, tonnes CO2
Source: Rogers, 2006 and Rogers, 2009.
50,679
Table A1.2 Increases in Emissions – Averaged Annual Increases and One Time Increases at Project Outset
Extra buses required due to Modal shift from cars, Metro or other more-fuel-efficient-transport
to buses on the BRT corridor plus rebound and new trip creation on the buses
2,996
METHODS: Measured fuel consumption of bus times distance/year times carbon coefficient of fuel
Elimination of left turns on the route or BRT corridor generates increased travel time and
distance for those vehicles that now have to go-round-the-block
693
METHODS: Observed number of left turning cars times extra distance times average fuel use/km
times carbon coefficient
Longer distance required for vehicles to cross the corridor due to the elimination of crossing
points in the with-project case
Longer time required for vehicles to cross the route or BRT corridor due to traffic signal
timing altered giving priority to buses
Detours During Construction (one time)
0
543
2,685
METHODS: Changes in average speed times distance times number of vehicles; fuel consumption
modeled as function of speed.
Greenhouse gas emissions due to construction activities of the project and energy used to
produce the construction materials. Fuel used to melt discarded colectivos. (one time)
METHODS: Use of input-output tables to convert construction expenditures of main construction
components and activities (approx USD $31 million) into energy consumption by fuel and, with IPCC
coefficients CO2 emissions.
Source and details See Section III.
62
67,774
iii. Economic Impacts and Co-benefits of CO2 Reduction
Key benefits of the project were identified as savings in time, reductions in congestion and
traffic, and reductions in air pollution. The main cost-benefit study (INE, 2006) found that when
these other benefits were monetized they reach USD $6.5 million.24 If the fuel saved to both bus
operators and drivers is included in the value of the project, the total added up more than USD
$18 million/year. CO2 savings as co-benefits valued at USD $5/tonne (the initial price offered to
Mexico City for certified savings) are about USD $0.23 million, or trivial compared to the other
benefits. If CO2 is valued at USD $85/tonne, the CO2 co-benefits add almost USD $4m to the
total, and the CO2 is worth about 20% of the total project benefits. With this higher CO2 price,
the CO2 value is substantial, but is it enough to make a transport project decisive? Over twothirds of the benefits of fuel and CO2 saving accrue to other car drivers on Insurgentes or those
leaving cars for Metrobús. City authorities might be justified in a project that brought these
benefits to the region, but it is hard to believe that the CO2 benefit alone could justify a project.25
INE did not place any value on the reduction in accidents and deaths, or value of the creating of
Metrobús itself as a faster form of transportation, something that could stimulate economic
activity. Also omitted from this analysis is any economic impact to businesses or homes along
the Insurgentes Corridor. Work by Muñoz-Raskin (2006) and Lefevre (2007) suggests that there
are changes in land values and apartment rents on or near the Transmilenio Corridors. Also
omitted was an analysis of the socio-economic changes that may have befallen former colectivo
drivers whose routes disappeared. These impacts should be part of any longer-term
understanding of the total value of the Metrobús project.
iv. Long-Term Impacts
Figure 2.1 in the text noted that a project or policy should be evaluated over time, not simply in
comparison to ―before‖ but in comparison to an estimate of what would have occurred had the
project or policy not been implemented. It is probably too soon to measure these impacts, but it
is important to speculate on what they could be and then observe whether they occur.
Longer term impacts could appear if Metrobús stimulated more travel, more trips of longer
distance, or, conversely, more development on and close to the Insurgentes corridor that
reduces trip lengths and stimulates more walking between homes, jobs, shopping and services.
Metrobús may have some impacts that raise CO2 by stimulating more travel or attracting
development (and riders) from its extreme ends. However, a region-wide system integrating
Metrobús with other buses and the Metro could result in much greater shifts away from autos
than a single Metrobús line. As we shall see, it is possible that modal shifts to Metrobús result in
a larger savings of CO2 than does the actual substitution of buses in the BRT system. Finally,
the actual CO2 impacts depend on the longer term evolution of vehicles and fuels of all modes.
24
25
INE did not monetize either the fuel saving or the CO2 co-benefits.
These figures are also summarized in Table 2.2.
63
B. Detailed Analysis of Major CO2 Impacts from Metrobús as a Transportation
Project
Figure A.2.1 summarized the main components of CO2 emissions changes from the
introduction of Metrobús. For comparison, urban traffic including trucks emitted just over 20
million tones of CO2 in 2006 (Páramo Figueroa, personal communication, 2006), the first full
calendar year of Metrobús operations. The basic figures for fuel saved come from Rogers
(2006), who used data from the region, including the MCMA 2006 Emissions Inventory and
vehicle fuel efficiency tests performed by Mexico City officials, as well as data gathered
specifically for the Metrobús project. (Rogers, 2006) INE 2006; Schipper et al., 2006; SMA,
2006; and Clarke et al., 2006).
Rogers‘ approach examined a number of components of changes in CO2 emissions related to
project vehicles (buses removed, Metrobús added), cross and parallel traffic, and modal shift
from cars or other modes to Metrobús. He included a term for additional buses added after the
initial Metrobús project started (these buses were added because Metrobús loads were higher
than anticipated). He also estimated the one-time increase in emissions from traffic delays
caused by Metrobús station construction and route preparation, the emissions associated with
making materials used and the construction process itself, as well as the emissions used in
melting the colectivos replaced. These one-time emissions were small – around 125% of a
single year‘s emissions savings – and are not considered in detail. However, for heavy
construction projects involving tunneling, bridges or elevated structures the costs of the
associated CO2 emissions can be substantial.
The RTP buses previously running on Insurgentes were used to replace older RTP buses. No
detailed assessment of the fuel use of the newer (vintage 2001) buses was made. These newer
buses, which carry 10% more passengers than the older ones may have slightly higher fuel
use/km despite electronic injection and other features not present on the older buses. No
detailed study was made of their overall impacts, which are nevertheless likely to be small.26
Rogers‘ approach to estimating emission is based on distance. He estimated changes in
number of buses, and, from passengers‘ trip information, the modal shift to Metrobús from cars
and other modes. Emissions estimates for Metrobús and the colectivos Metrobús replaced were
made by Metrobús itself, and these estimates used a large database on vehicles maintained by
SMA. Changes in emissions factors that were caused by changes in traffic were estimated from
filmed flow measurements of vehicles on Insurgentes. Counts of cross traffic and left-turning
traffic were used to estimate extra waiting time to cross Insurgentes or extra distance traveled to
make left turns. Since most of the savings in fuel and CO2 do not accrue to project vehicles
(e.g., Metrobús) but instead to non–project vehicles, (i.e., cars on Insurgentes, cars crossing
Insurgentes, and cars left at home in favor of Metrobús) collecting fuel consumption data directly
is virtually impossible. While the total reduction in CO2 emissions from changes to these other
vehicles is almost 40,000 tonnes of CO2, this figure by itself represents only 0.5% of the total
fuel sold and consumed in the Mexico City region, a change far too small to be seen against fuel
sales statistics. Thus the distance-based approach using a combination of observations,
26
In the diesel retrofit project carried out by the City, EMBARQ, and CTS (Schipper et al., 2006), the 2001 buses had
an average fuel efficiency of 1.6 -1.8km/l while the next oldest vintage buses (1992) provide 2.3 km/l despite having a
higher horsepower. The newer buses satisfied EPA 98 emissions standards as well. If this difference in fuel
efficiency is applied to 80 buses running 140km/day 365 days/year the result is an increase in CO2 emissions of
1,465 tonnes.
64
Metrobús rider surveys, vehicle counts, camera and other measurements of vehicle flows and
speeds, and fuel simulations gives the results for which details are shown below.
i. Large BRT Buses in Place of Conventional Buses and Colectivos
Rogers‘s (2006) estimation of emissions changes from Metrobús centers on a comparison of
vehicles taken out of service with those put in service. Initially 70 articulated buses with capacity
of 160 persons standing and sitting were substituted for approximately 80 smaller colectivos,
240 10-meter colectivos, and conventional 12 meter buses from the city‘s RTP company. He
obtained data on average daily running distance and average fuel consumption/km for each
vehicle type and fuel. In this case, 80 small colectivos (capacity 30 passengers) running 100
km/day on gasoline and LPG, and 240 larger diesel colectivos and diesel RTP buses running
140 km/day. Vehicles ran 365 days/year.
Using measured fuel consumption data, Rogers employed emission coefficients from the
Intergovernmental Panel on Climate change (IPCC) for each fuel used to arrive at the CO2
emissions coefficients. The small colectivos emitted on average 1.4 kg CO2/km, and the larger
diesel colectivos and RTP buses emitted on average 1.7 kg/km. Thus the original vehicles
released approximately 28,500 tonnes of CO2 per year. Over a year the initial 70 Metrobús
vehicles would release 10,487tonnes/CO2. The original flees was supplemented with 20
additional articulated buses when it was a clear that the demand for travel was higher than
forecasted. With the 20 additional vehicles supplied later the total yearly emissions increased to
almost 13,500 tonnes/year.27
The reduction in CO2 from this substitution is the difference of the ―before‖ vehicles and ―after‖
vehicles, or 14,558 tonnes of CO2.
There are some key questions to ask when using this methodology to estimate CO2 emissions
changes:
1.
How reliable are the estimates of fuel use and distance traveled from vehicles that
previously operated on Insurgentes. How many of these vehicles are out of traffic
permanently?
2.
What are the present fuel use figures for Metrobús? Have they varied over time as
drivers have become better accustomed to vehicle performance?
3.
What are the real passenger loads and trip distances on Metrobús?
4.
How much has Metrobús traffic grown or fallen from the first year of operation?
5.
How much extra energy does a vehicle require if one more passenger boards?
ii. Effect of Modal Shifts from Cars to Metrobús
Rogers used two approaches to estimate modal shift. Initially he estimated that 1% of the riders
in the 60,000 cars/day using the Insurgentes would switch to Metrobús. By assuming a 5 km car
trip and using an average fuel use of 14.2 l/100 km (based on the City‘s own vehicle inspection
data and simulations, a value corresponding to 318 gm/km), and assuming 1.5 passengers per
27 Rogers debits the additional CO2 for these 20 buses against the credit of CO2 from modal shift to Metrobús.
65
car, he estimated a savings of 1,300 tonnes/year of CO2.28 This initial estimate of modal shift
proved to be too low.
From more detailed rider surveys made available after Metrobús started, Rogers noted that
closer to 7% of the more than 250,000 riders switched from cars.29 From these surveys and
traffic observations he estimated 7,000 fewer cars ran on Insurgentes to give the increase in
ridership to Metrobús. He used a distance of 8.4 km base on separate on board surveys to
determine the trip length on Metrobús of ex-car users. Observations on Insurgentes showed the
average car had 1.5 occupants. His revised estimate for savings from modal shifts to cars is
15,610 tonnes/year of CO2.
Rogers estimated that 20 additional buses were necessary to take up the riders shifting from
other modes to Insurgentes and provide service for extra passengers making new trips. These
generated almost 3,000 tonnes/CO2 per day and are added to the Metrobús total.
Some travelers used to take the Metro, Trolleybus (STE) or light rail in Mexico City, which
operate on electricity. The shift was not enough to trigger a reduction in Metro, STE or LR
service, so no significant reduction in electricity use can be claimed. The difference is the CO2
equivalent energy to move a passenger on the Metro or STE Trolleys vs. marginal passenger
energy on required by BRT. No accounting for possible emissions from their travel was made
by Rogers.
iii. Impacts on Other Traffic – Increases and Decreases in CO2
A third set of CO2 impacts results improved traffic flow. Rogers (2006) noted that even small
reductions in fuel use to the tens of thousands of vehicles running parallel to Metrobús on
Insurgentes or across Insurgentes could add up to substantial CO2 savings. First, the removal
of the smaller colectivos that previously plied Insurgentes and often blocked traffic let to an
improvement in traffic. On the other hand, three or four lanes of traffic in each direction were
compressed to two or three lanes in each direction. Left turns from Insurgentes were banned,
requiring drivers to turn to the right and circle back. And cross traffic was slowed somewhat.
To estimate the effect of changes in traffic operations on emissions Rogers used traffic flow
data collected for every segment of the Insurgentes route, as well as counts of cross traffic and
left turn traffic. He estimated the extra delay time for cross traffic. By observing and averaging
the number of cars and other vehicles delayed and using an average fuel use at idle of (3.16
liters/hour) and an average delay time of 30 seconds, applied to nearly 26,000 cars/day, he
estimated the CO2 expenditure for delayed vehicles at 293 tonnes/year. Based on traffic
observations, he estimated that no vehicles had to drive circuitous routes to cross Insurgentes
because some crossings were closed. Much of Insurgentes already had a median that
restricted crossings from smaller streets.
28
The emissions factor 307 gm/km is based on a large visual sample of cars using Insurgentes and then matched to
the emission data base of the Secretaría de Media Ambiente based on twice-yearly emissions inspections.
29 The first year ridership of Metrobús exceeded 250,000 riders/year. A 2007 survey showed that nearly half of
Metrobús riders did have cars at home. 6% of those riders took their cars before Metrobús was open, and 2% took
taxi. Three quarters of riders used to take other modes on the same route before Metrobús was opened. An earlier
survey (2006) conducted for the Center for Sustainable Transport indicated that about 16% of riders previously took
cars or taxis, while the survey from the first six months, carried out in December 2005, reported only 6% switchers
from cars or taxis. The 2008 level of switchers is 9%. Taking the 2007 figure as a conservative average of the four
years implies 15,000 daily car trips not taken (out of the initial 250,000 trips/day on Metrobús).
66
For cars turning left Rogers estimated the extra distance traveled by left-turners (400 m/vehicle).
Using observations to get average numbers of vehicles by type turning left and the emission per
kilometer from the City data base, he calculated the extra driving for left turn vehicles led to 693
tonnes/year of CO2.
From traffic counts Rogers found approximately 60,000 vehicles/day using Insurgentes. He
noted that the elimination of colectivos and buses stopping erratically and blocking traffic
actually improved the flow of traffic. He relied on estimates of traffic flow, speed and
acceleration, and delay on eight segments covering the entire route. To simplify his calculations
he assumed that he could represent the difference in fuel consumption by the drop in travel time
in free flow conditions and, separately, in congested conditions. This was used to derive
average speeds on each segment by vehicle type. Using a simulation program of fuel
consumption vs. speed he derived the savings per vehicle and kilometer from improved traffic.
All together, he estimated 17,515 tonnes of CO2 saved this way. The same procedure permitted
Rogers to estimate the baseline emissions from parallel traffic in the corridor before Metrobús
as a function of number of vehicles by type and speed. He estimated approximately 510,000
tonnes of CO2 for all traffic in the corridor.
Putting all the impacts Metrobús had on traffic together thus gives a net savings of
approximately 16,500 tonnes/year of CO2.
Rogers estimated the total emissions of vehicles along the Insurgentes corridor from the
observations along eight segments.
iv. CO2 from One-Time Construction Activities
Two main fixed costs from the project construction were included in a brief life cycle analysis. All
320 colectivos removed from the corridor were melted down. Rogers estimated the emissions
from the oil required to carry out this melting as 176 tonnes of CO2, based on the thermal
energy of 2.6 GJ required to melt each 3 tonne vehicle. Using factors from input-output analysis
developed at Carnegie Mellon University (See Chester, 2008), Rogers estimated that 67,000
tonnes of CO2 equivalent was released in the construction of USD $32.9 million worth of
guideways and stations. Almost 2,700 tonnes of CO2 were released because of traffic delays
and detours during construction. With savings of nearly 50,000 tonnes/year of CO2, this
investment of CO2 paid back in less than one year.
.
In sum, Metrobús operations have ―saved‖ almost 50,000 tonnes of CO2 every year. 30 More
than two thirds came from modal switch and improved traffic. Even if the latter two estimates are
off by 100%, they still represent significant savings compared with the direct savings from
switching vehicles alone. This is an important finding for any assessment of CO2 savings from
a project or policy. Changes can occur in vehicles besides those designed as part of a project.
These non-project vehicles‘ emissions changes might dominate the changes, so they must be
studied carefully.
30
This must be adjusted downward for the slightly more than 70,000 tonnes of CO2 emissions associated with the
preparation of Metrobús. If the Metrobús project is amortized over 12 years, this means the savings should be
reduced by almost 6,000 tonnes/year.
67
v. Economic Impacts and the CO2 saving as a Co-Benefit
While it may not be trivial to monetize these changes and evaluate them in a common
framework, they must have been considered in the planning process that led to the intervention.
They can be evaluated after the fact with careful observation.
Economic evaluations were made for many of the impacts of Metrobús, including impacts on
fuel consumption, time, pollution, accidents and CO2. Stevens, from the Instituto Nacional de
Ecología (INE) conducted one such evaluation in 2006. (INE, 2006) Her work covered reduced
travel time, reduced air pollutants (as benefits to health), reduction in operating costs (but not
externalities) from fewer vehicles using the roads, savings of fuel in transit vehicles, and the
overall economic benefits of Metrobús as BRT. Savings in CO2 and the increased reliability in
travel time were quantified but not monetized. Reductions in noise, reduced accidents and
death in traffic, greater access and equity of access, more reliable commercial deliveries, and
improved working conditions and productivity stemming from improved trips to work were
described, but not quantified. Rogers (2006) estimated the effects of improved traffic and
reduced idling on both lower fuel use and CO2 emissions of non-Metrobús vehicles.
Travel time. A major goal of good transport is travel time savings. If travel time is reduced
because vehicles operate in lower levels of congestion, then CO2 is saved. If dwell time is
reduced, CO2 and local emissions are reduced. If buses are given dedicated lanes, then overall
travel is smoother, also reducing CO2. Time savings were estimated from on-board surveys.
INE (2006) valued the time savings from Metrobús at USD $1.3m, based on savings of 2.6
million rider-hours a year saved and a value of time of USD $0.575/hour.
Reduced congestion and vehicle damage to Insurgentes. Investments in good public
transport can shift travelers from individual to collective vehicles, and allow authorities to
manage all vehicles on the street more efficiently. The Mexico City government surveyed riders
on board to determine that at least 5% of these passengers previously used cars to make the
same trip. With assumptions about the cost of every km a bus or car drives in Mexico City, INE
estimated 12 million fewer veh-km and 32 million fewer private car and taxi km/year as a results
of Metrobús, which they valued at USD $2.2 million. If the greater 2007 figures for car use
were substituted, then the results would be about 1/3 higher.
Fuel Savings. The fuel savings that give rise to the CO2 savings had substantial value. Rogers
calculated the reduction in gasoline (and a small amount of LPG) used by older buses and
colectivos, the reduced use of gasoline in parallel traffic (net of the small increases for left turns
and crossing delays), and the imputed fuel not consumed by those leaving cars at home to
switch to Metrobús. The savings were tabulated in liters of gasoline (with a small amount of
LPG) or diesel. At the prevailing prices of 55 US cents/liter for gasoline and 48 cents/liter for
gasoline, these savings were substantial, close to USD $3.5 million of diesel for the buses and
$3.9 million for cars left at home by those drivers switching to Metrobús, and USD $4.1 million
for the net fuel savings from the various impacts Metrobús had on traffic (Savings from
smoother corridor traffic minus extra fuel for delays crossing and circuitous left turns).
Air quality improvements. The most obvious improvements came from shifts from smaller to
larger vehicles. In general, the larger vehicles carry more people per vehicle (lower
emissions/pass-km), the larger vehicles are more modern/cleaner (lower emissions per vehkm), and the larger vehicles are less stuck in traffic less often (less idling). Lower veh-km run
also reduces pollution. This affects everyone in a region‘s air basin, and has broad benefits.
68
Using the SMA database, data from INE, and more recent measurements undertaken as part of
the World Bank Metrobús project, INE estimated emissions/km of criteria pollutants such as
particulate matter, NOx and CO. These emissions were multiplied by changes in distance
covered by each type of vehicle per year to get total reductions in pollutants of each kind.
Where traffic was improved slightly reductions in emissions/km were counted; from studies of
the health costs of each kg of pollutant, INE produced an overall value of the annual health
benefits of the BRT line itself at approximately USD $3 million. Note that if emissions/km of all
vehicles continue to fall as a result stronger fuel and emissions standards, this benefit will be
smaller, happily so.
INE did not address the likely fall in traffic accidents from both fewer vehicles (particularly
colectivos) and smoother traffic.
vi. Overall Economic Valuation and the CO2 Co-Benefit
To capture all of these results, we have added the value of the saved CO2 estimated in the
previous section. This saved CO2 is the ―co-benefit‖ realized in pursuit of the direct benefits
noted above. To monetize these CO2 savings we used two valuations of CO2. In the low
valuation of CO2 (USD $5/tonne, the first offer made for the savings from Metrobús) with the
fuel prices for diesel and gasoline in Mexico in 2005, the total value of these benefits is over
USD $18.3 million, of which the CO2 represents slightly more than 1.25%. Note that in Figure
A1.1 the benefits from CO2 are almost invisible, showing up as the thick darkened above the
estimated fuel savings from modal shift.
Valuing CO2 with a higher value of USD $85/tonne (Stern, 2006) and taking again the 2005
prices for gasoline and diesel, the CO2 savings represent 18% of the overall project savings
that now surpass USD $22 million. The CO2 savings are clearly much larger, but are they a key
determinant? With the lower carbon price, the value of the saved fuel to Metrobús itself is over
USD $3.5 million, while the CO2 savings are insignificant. At the higher carbon value, the CO2
is worth about 40% as much of the fuel.
What is significant is that of the CO2 and fuel savings, only 30% of the saved fuel accrues to
Metrobús itself, the rest predominantly to those who left cars for Metrobús and to some extent
the 60,000 cars per day that saw overall traffic improvements yielding a small net savings in fuel
to each of them.
Interestingly, a carbon tax applied to all fuel in the Mexico City region could have had a very
large impact. The USD $85/tonne suggested above works out to about a 33% increase in the
2005 price of gasoline for Mexico City. With a long-term price elasticity of fuel economy of about
–0.7, a 33% increase in fuel price will lead to 25% less fuel use per kilometer and about 4%
fewer kilometers compared with no price increases, or 28% less fuel consumed than
otherwise.31 Applied to the gasoline used for light duty vehicles (cf. Table 1.2), this reduces
CO2 by some 350,000 tonnes/year, roughly seven times of what the Metrobús project alone
achieved. While the use of carbon pricing in evaluating transport choices in a region is
31
Studies of consumer responses suggest that most of the response to higher fuel prices will appear as more fuel
efficient vehicles rather than less vehicle use. based on elasticities estimated by Basso and Oum (2007) or
Johansson and Schipper (1997). Averaged over all the cars in the Mexico City Region (cf. Chapter 1), this would
reduce CO2 emissions from the region‘s transport system by about 20% compared with no price increase.
69
informative, the impact of such a price as applied to all or most vehicles in the same region
should not be overlooked.
vii. Impacts Over Time
Impacts of Metrobús evolve over time. While a full study is yet to be carried out, the yearly
surveys undertaken for Mexico City show that the number of riders who declare ―I was taking X
before Metrobús when I traveled on Insurgentes‖ started at 4% car and 2% taxi by December
2005, rising to 10% car and 6% taxi in May of the following year, falling back to 6%/2% by May
2007 and then rising slightly to 6%/3% in May 2008. To the extent that this response is a stable
indicator, it suggests that the switch to Metrobús shot up during the first full year, then fell back,
but 9% may be a fair average. On the other hand, the 2008 survey was undertaken near the
peak of gasoline prices, which may have influenced modal shift. The following year ushered in
a recession. Clearly, these figures should be monitored closely to understand the impact many
factors over time on ridership of Metrobús.
Analysis should take account of other changes that could occur over time. One is the possibility
of addressing CO2 directly in the Metrobús vehicles or in vehicles riders used to get to/from.
The latter option is important because in the 2008 survey, 54% of travelers surveyed took a
colectivo of one type or another to transfer to Metrobús, and about a third of travelers planned to
transfer to a colectivo after alighting from Metrobús. To measure longer-run impacts, the
distances these linked trips covered should be surveyed or estimated. One possible long-run
impact is that Metrobús actually pulls in travelers from origins and to destinations increasingly
more dista0nt from the corridor itself. Finding these trends could either point the way to feeder
routes or new BRT lines themselves.
C. Technological and Policy Options for the Long Run
i. Hybrid Buses
Although the World Bank project considered a number of fuels/propulsion combinations,
conventional diesel was chosen to power Metrobús‘s articulated buses. Conventional diesel,
ultra low sulfur diesel, parallel and series diesel hybrids, and CNG buses were tested. According
to tests carried out by the City (SMA, 2006), conventional diesel articulated buses had the
lowest fuel use and emissions per seat-km of all the different buses tested, except for 1 smaller
12 m diesel bus (of ten 12-meter buses tested). However, the volume of traffic on the
Insurgentes corridor demanded large, articulated buses, which were ultimately those chosen.
Hybrid buses were tested by the Secretary of Environment as part of the World Bank project.
One parallel hybrid showed very low criteria pollutants (particulate matter, CO and NOx) but
emitted 6% more CO2/seat-km than the average of conventional diesel articulated buses
chosen and had smaller capacity (113 places). However, diesel articulated hybrid buses similar
in capacity to those used by Metrobús are run by King Country Transit in the Seattle region and
use 20-25% less fuel/km than similar articulated diesel buses with conventional drive trains
running similar King Country Transit (KCT) Routes. Since Metrobús itself emits approximately
13,000 tonnes of CO2/year, switching to hybrids would reduce emissions by 2,600 tonnes/year
for the first 70 Metrobús vehicles, taking the lower figure for carbon and fuel savings. The extra
cost of these hybrids, in excess of USD $150,000/bus (Boone, personal communication, 2007)
are difficult to justify on the basis of fuel savings and lower pollutant emissions alone, even if a
70
25% savings was realized (Schipper et al., 2007).32 However, Volvo Bus announced a lowercost parallel hybrid for 2009 (Volvo Bus, 2007) claimed to pay back with fuel savings in four
years. If this appears on the market with a much smaller marginal cost over conventional diesel
it would be cost effective.
ii. Compressed Natural Gas (CNG)
Another option tested but not taken was to use buses running on compressed natural gas
(CNG). CNG buses tend to cost more than diesel buses, in part because of their heavy fuel
tanks. They have slightly higher maintenance costs. Moreover, a facility is required for
compressing natural gas from the network to the high pressure required by the bus fuel tanks.
Combustion of natural gas releases 25% less carbon dioxide per unit of energy than diesel
(IPCC). However, CNG engines use more energy per vehicle kilometer and more fuel is lost in
idling than diesel buses. This was borne out by the emission tests of CNG and diesel buses of
similar size carried out for the World Bank GEF project in Mexico City (SMA, 2006; Clark et al.,
2006). Only one of the CNG buses, with 140 places, emitted less CO2/km than the conventional
diesel articulated buses tested. However, understanding emissions from different fuels requires
a full fuel cycle analysis that compares not only of the energy of combustion (and resulting CO2
emissions) but emissions associated with other parts of the fuel cycle, such as the natural gas
burned in compressing the CNG, typically 7% of the energy put in the tank, or the refining of
diesel and the transportation of both fuels to where they are used.33
iii. Operational Improvements
Metrobús does not have automated priority at traffic signals, which has been shown to save fuel
for transit operations by reducing idle time and permitting smoother operations. (Skea, personal
communication, 2003) This is one additional benefit that could be harvested at a later date.
32
At USD $85/tonne of CO2, the savings in CO2 at this value and fuel at the 2005 prices from exchanging the
articulated diesel Metrobús for parallel hybrid versions currently used in Seattle Washington could be justified. While
each hybrid would cost approximately USD $150,000 more than a conventional bus (Schipper et al., 2007), each bus
would save approximately 30 tonnes/CO2, worth almost USD $2,800at the higher price of CO2, in addition to about
USD $5,500 worth of fuel. The straight line rate of return is slightly over 5%, well below the interest rate Mexico City
paid for its buses (Schipper et al., 2007). The investment in hybrids might be a justified investment for Mexico City or
a third party, however. But the savings from this step, 3000 tones/year over all buses, is still a small fraction of the
savings from the transport related changes – fewer larger buses, improved traffic, and modal shift.
33
Combustion of gas to CO2 is not the only source of greenhouse gas emissions. Leaks of natural gas in
transmission, storage, and compression could add significantly to the life-cycle greenhouse burden of natural gas as
a fuel because its chemical, methane, is a far more potent greenhouse gas than CO2 on a molar, weight, or energy
content basis. For the U.S., the upstream losses add 12-16% more GHG than the combustion of methane alone,
excluding leakage in filling (Jaramillo, 2007). Similar fuel cycle burdens for diesel are closer to 8% of the losses in
combustion. Thus overall full fuel cycle analysis suggest there is little or no real greenhouse-gas savings from using
CNG buses of similar capacity and performance to diesel buses. Unfortunately, the Mexico City tests did not
measure leakage in storage or natural gas used for compression at filling, nor were there estimates of actually
pipeline leakage of methane in the Mexico City. In the end the extra cost of both natural gas buses (over conventional
diesel) as well as the significant cost of a CNG refilling infrastructure deterred Mexico City from exercising this option.
Since other LAC countries, notably Argentina but more recently Brazil and Peru embraced CNG for use by public
transport, investigation of CNG options is an important step in CO2 analysis. As noted here, this must be done with
full fuel cycle analysis.
71
iv. Hybridization of Colectivos and Taxis
Improving the performance of smaller collective transport vehicles is also worth considering, as
there are still over 25,000 in Mexico City according to the SMA inventory. Several hundred
colectivos were forced off the corridor in this project. While most vehicles were bought up and
destroyed, many drivers lost their immediate jobs.
A more equitable and humane option might have been to explore a longer range role for these
smaller vehicles as feeders, say with the use of hybrid engines for gasoline or diesel colectivos.
For example, the older gasoline colectivos with 24-30 seats each and weighing 3 tonnes might
be fitted with an inexpensive version of the powerful hybrid engine that powers the General
Motors Tahoe of similar weight. Since the performance of gasoline engines is affected by bad
congestion and idling more than that of diesels, this hybridization could offer significant savings
to the gasoline colectivo fleet. Or the most recent diesel colectivos on Insurgentes, 10 meter
Mercedes ―Boxers‖ holding 36 places, could be adapted to diesel hybrid drive trains. The
gasoline colectivos released 1.45 kg/km of CO2 while the diesels released 1.77 kg/km, slightly
more than a Metrobús itself. Using hybrids on these routes could cut emissions by roughly 2030%. They could be used to provide long-awaited feeder services to the larger BRT trunk line.
This could guarantee the colectivo drivers better and more regular routes and incomes. If the
drivers/owners could be enticed (through financing) to collectively purchase literally thousands
of hybrid vans the price could fall. Indeed, Mercedes of Mexico financed the acquisition of
hundreds of the Boxers after 2001 by accepting small regular payments from the drivers (Vieira
Lima, personal communication, 2002). Scale purchases and financing could make this option
affordable.
The same hybridization may spread to private vehicles in Mexico City. Like colectivos, taxis,
which run mostly on gasoline with a few using CNG, see the worst driving conditions, those
where hybridization has its largest impact. Taxis are becoming increasingly gasoline hybrid in
US cities as well as in Italy. Since Mexico City procures as many as 20,000 taxis at a time, the
next opportunity for such a large scale purchase offers an opportunity for the city to explore
hybrid gasoline vehicles or even CNG. Compared to gasoline, CNG offers GHG and local air
pollution benefits even on a full fuel cycle basis. (ANL, 2007)
v. Biofuels
There are still expectations that at least modest amounts of lower CO2 fuels from biomass may
be developed. Stockholm for example is running diesel engine buses on ethanol from Brazil,
which for the time being represent the lowest CO2-emitting combustion buses in service. BEST,
the coalition organized by the city of Stockholm, is testing such buses in Brazil. Mexico did
experiment with four Scania buses using gasoline-type engines to run on Mexican produced
ethanol. The project was scrapped because of poor results. (Hedberg, personal communication,
2003) The latest results with ethanol buses in Stockholm suggest this option has opened again.
(Paedam, 2009)
vi. Summary of Long Term Technological Possibilities
This list of lower emission vehicles is not exhaustive, but indicates there are many possibilities
for both collective transport and private vehicles. These advances in lower-carbon vehicles
have one important consequence for projecting CO2 impacts of projects. If automobiles, taxis,
or colectivos have lower vehicle or modal carbon intensities, then the CO2 gains from modal
shifts away from these modes to large capacity buses have to be adjusted accordingly. In Brazil,
72
for example, about ¼ of the automobile fuel is renewable ethanol from sugar cane with almost
no net CO2 emissions compared with the gasoline replaced. Moving traffic from cars running on
pure ethanol to fossil-fueled buses, while possibly desirable from a transport perspective, may
have little or no CO2 savings and even represent an increase in CO2 emissions.
In considering technological options to reduce emissions from individual vehicles, it is important
to remember that the region and its operators have limited funds. While advanced engines and
fuels might save CO2, it is worth asking what alternative investments for the same funds (such
as traffic signal synchronization or other applications of ITS, improved access to existing
stations, or other improvements to transit service might provide more and improved travel for
the same fuel use, rather than only permit the same level of travel and service on less fuel.
Given the importance of transit remaining competitive with car use, planners should evaluation
the CO2 consequences of a broad range of investments, not simply those that save carbon
through advanced technology.
In summary, when considering long-term technological options, the following questions must be
asked:
1.
What are the costs of options for vehicles and fuels
2.
What are the expected fuel utilization rates relative to the marginal costs of the vehicles
or fuels involved?
3.
What are the net carbon savings from combustion, and what are the full fuel cycle
impacts of each fuel?
4.
What are the expected differences in operating costs among the alternatives?
5.
What are the range of expected energy prices and carbon values to be used.
6.
What are the overall carbon savings?
7.
What is the value of the carbon savings relative to other savings or costs in fuel, and
how do the total savings compare with the incremental costs of the equipment or fuels.
8.
What special training for the vehicles or fuels is required?
9.
What measures are in place to monitor the actual performance of the option (s) chosen
relative to a base line?
D. Longer Term Impacts
A number of longer-term impacts of Metrobús must be considered that may not yet have
occurred or been measured. Some could increase CO2 emissions by stimulating change in land
use. Yet with Metrobús it is likely that the overall effects maximize access or mobility
opportunities for a given amount of CO2 released because Metrobús is so much less carbon
intensive than car travel, which is what was growing the fastest at the margin.
Figure A2 below, taken from Figure 2.2 in Chapter Two of the Framework, illustrates one way of
counting the various short-and long-term impacts of Metrobús. The upper blue line represents a
project-path that emissions from traffic in the Insurgentes corridor and surrounding region might
have taken with no Metrobús. Drawing this line presumes that the region has a good travel and
emissions modeling capability. The green line represents emissions after the project was
completed, with the exaggerated spike upwards illustrating traffic delays during some of the
most intrusive phases of construction. The green line then represents the path of emissions
73
after Metrobús started. The distance between the blue (hypothetical) and actual has been
exaggerated for illustration.
Figure A2.2 Impacts of A Project Over Time, Compared to a Business As Usual Baseline
Before & after
project
Baseline
Emissions
Difference between
with & with-out
project
Project line:
First Phase
Project Line:
Second Metrobús Extension or
new line on Eje 4?
Time
Two features of these curves illustrate the subtleties of measuring the impacts of projects. First,
the green line was purposely drawn to have a smaller slope than the blue line. In other words, it
is assumed that over time, that Metrobús yields not simply a one-time reduction in emissions
from a rising baseline, but that its savings, illustrated by the red hatched area, increase over
time.
The purple line represents a 2nd phase of Metrobús or, more broadly, a new the possibility of a
new project that might build on Metrobús success (the extension of the first Metrobús line or the
recent opening of a 2nd line, Eje 4). Adding feeder lines, as noted above, could be an attractive
―2nd phase‖.
In 2007 Metrobús was extended another 9 km to the Universidad Nacional Autónoma de México
(UNAM), adding about 80 000 more daily riders. (Centro de Transporte Sustentable de México,
personal communication, 2009) In December 2008 a 2nd Metrobús line was added on Eje 4,
carrying 90 000 passengers a day along 20 km that did not cross the first line. More lines are
expected that will yield a network much like the existing Metro, with more interchanges with the
Metro. It will become important to monitor transfer traffic, as well as see whether increased
access around the region leads to greater travel.
Not all the long term impacts of Metrobús (or any good transport intervention) reduce CO2
emissions from the base line as the drawing implies. The relative speed advantage of Metrobús
over other modes can increase travel in the long term, which could turn the baseline upwards
somewhat. Developers could build housing, office, shopping and other services at distant points
along the Metrobús route, increasing the number and lengths of trips taken compared to the
74
present distribution of origins and destinations. Workers from low-income parts of Estrada de
Mexico who transfer to the northern terminus of Metrobús at Indios Verdes can reach more jobs
for a given travel time. Conversely, developers may see the advantages of large developments
along Metrobús that leave even more origins and destinations close to the Metrobús corridor.
This could decrease travel distance for many, not simply for work trips but also for other trips,
whose share of total travel tends to grow with growing incomes according to US and European
travel surveys. A policy of intensifying development along the Metrobús Corridors to increase
access to the bus could also stimulate both higher ridership and more walking along the
corridor, if accompanied by improved side-walks (or bike ways) as was done in Bogotá and
earlier in Curitiba.
Other policies could intensify Metrobús use. Special off peak tariffs, for example for families on
evening or weekend leisure and shopping outings, could reduce the variable cost of using
Metrobús to below that of a car (fuel plus parking). If the Metrobús network grows and its fares
integrated with those of other modes, a much larger portion of Mexico City would be connected
to truly fast mass transit, restraining the car share significantly. Because present car use is 510x more carbon intensive than use of Metrobús or Metro, a significant increase in per capita
travel by these modes could accompany a decrease in actual and projected CO2 emissions
from much lower car use.
Travel models can simulate some of these possible results from an improved transit network.
Good travel and traffic surveys and up-to date modeling techniques should be employed
regularly to spot these trends and use them to adjust the models the region employs.
Questions to ask about the long-term evolution of a metropolitan area and its transport system
include these:
1.
Is the region’s transport model detailed enough to portray individual projects and their
impacts on travel, traffic, etc.?
2.
Is there a simulation model to estimate emissions from traffic in the region, particularly
traffic affected by a project?
3.
Can the impact of changes in land use close to the zone influenced by a project be
modeled?
4.
Conversely, can the model simulate changes in land use resulting from improved
transport service, speed, or accessibility?
5.
Did success of a project or policy lead to strengthening of the policy or implementation of
more projects? In the case of Metrobús, the initial line was extended several kilometers,
and a 2nd line was opened in December 2008, with more planned.
E. Institutions for Better Monitoring
Since Metrobús was primarily a transport project it is important that its transport consequences
be measured. Three institutions have been involved in data gathering and analysis of
transportation and emissions. Metrobús itself has the yearly on-board surveys carried out by a
private firm. Mexico City supports an origin-destination survey published by INEGI, with
previous ones carried out in 1986 and 1994 and the most recent survey 2006. The data
published by the Transport Secretary SETRAVI in the various regional transport plans (PITV)
gave total trips and modal split but not distances traveled or trip purposes. The picture of how
Mexico City residents travel has been incomplete because this lack.
75
The city‘s environmental ministry, or Secretaría de Medio Ambiente (SMA), assembles a
detailed emissions inventory. Emissions of criteria pollutants and fuel use are based both on the
twice-yearly inspections of light duty vehicles as well as estimates for heavier diesel vehicles. A
―Mexicanized‖ version of Mobile 5 is used to simulate fuel use. Because the twice-yearly
inspection gathers odometer data, the utilization of light duty vehicles in the Mexico City region
is well understood. From these data, the use of gasoline can be tabulated bottom up by
numbers of vehicles, distance/vehicle, and fuel use/distance and compared with sales. This
inventory is one of the most complete of any city in the world.
Table 1.3 summarizes the main information contained in the Mexico City mobile source
emissions inventory (SMA, 2006). This inventory was commenced in 1994 and evaluated every
other year to monitor progress towards reduced emissions of air pollutants from all vehicles in
the Mexico City Municipal Area, which includes parts of the States of Mexico and small parts of
other states that share the same air basin. The summary figures are built from readings of
odometer readings of all light duty vehicles, which are inspected twice a year and tested for
emissions. From the inspections, the yearly distance traveled and numbers of vehicles in use by
each major vehicle/vehicle technology/fuel combination available over the last 30 years are
known. Various tests and use of the Mexican version of Mobile 6 permit authorities to construct
estimates of emission factors (in grams/km) of each major pollutant, as well as fuel use (and
CO2 emissions) for each of these classes of vehicles. Such detail permitted Rogers to base his
estimates for Metrobús savings not on average vehicle emissions but on average emissions of
vehicles observed regularly in the Insurgentes corridor.
A more detailed picture of the use of Metrobús may be available when the 2006 OriginDestination survey for Mexico City is released. The 1994 study was too out of date to provide
reliable information on travel patterns in Mexico City just before Metrobús was inaugurated.
While estimates based on traffic and bus counts were made in the planning stages of Metrobús,
there was no real OD survey carried out. Hence Rogers‘ approach was to use available data
and new traffic counts and measurements to establish distances vehicles moved and changes
in vehicle speeds.
What Mexico City has lacked, however, is a good measure of trip distance by mode and
purpose. The 1994 O-D Survey yielded only number of trips by mode. With information on
distance and trip purpose, a better picture of present travel patterns in the region and their
variation over the region by income and location of home and work can yield a better picture of
future travel. Combined with the vehicle activity and emission from the SMA emissions
inventory, Mexico City could develop a much better picture of how home location (and indirectly
land uses), travel, vehicle use, and emissions are related, as has been done for many US cities
and more recently the Paris Region. (Hivert, 2007) Such information provide invaluable
background for building a business-as-usual case and then link changes in travel and vehicle
use to changes in fuel consumption and CO2 emissions.
Most of the analysis of the CO2 impacts of Metrobús was carried out by Rogers in preparing the
Metrobús case for funding from the Clean Development Mechanism. (Rogers, 2006) Mexico
City authorities probably have the capability to do this work now after Rogers‘ approach (or
other approaches). However, as this discussion implies, the various data sources are spread
around the government, with no single authority empowered to reconcile the data. SMA collects
only vehicle-based data but nothing on passenger travel. The figures used by SETRAVI in
previous transport plans are quoted in trips, not in travel by mode. Thus there has not been a
measure of mobility of Mexico City region residents. Since Metrobús is a project that changes
76
peoples‘ travel patterns, it is difficult to imagine a thorough evaluation of the impact of Metrobús
on CO2 emissions without a clear connection to both vehicles and people‘s travel in kilometers,
which is critical for understanding not only for estimating the impacts of modal shift as well as
the CO2 associated with modes taken before passengers get on Metrobús or after they alight.
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6. Appendix Two: CO2 Emissions Reductions from a Bikeway Project
in Santiago de Chile
The framework presented in Chapter 2 set forth a methodology for analyzing the production of
greenhouse gas emissions from the transportation sector. In this section, the framework guides
an analysis of the case of bicycle improvements in Santiago de Chile.
We articulate the relationship between the framework and the analysis with the following
questions:
(1) How does the intervention affect urban development, transportation and greenhouse gas
emissions in Santiago?
Urban development: How does the bikeways project fit in the context of urban development
of Santiago?
Transportation: How does the bikeways project fit in the context of good transportation for
Santiago? and
Emissions: How does the bikeway project affect emissions of greenhouse gases from
transport in Santiago?
Following the framework, we also want to know:
(2) The costs and benefits of the project. What are the values of carbon saved and other project
benefits? Is the value of carbon saved a significant share of the total project benefits or not?
(3) The effects of the project in the short run and the long run compared to the ―without project‖
scenario. And,
(4) The first and second order effects of the bikeways on emissions, as well as on other factors
such as safety, accessibility, and livability.
We use a range of data sources to carry out this analysis, including local plans and reports,
archived data for Santiago, academic articles, World Bank project documents, and project
evaluations made by a third party, Steer Davies Gleave. The Steer Davies Gleave project
evaluation presented calculations of carbon savings and other project benefits, and in the
following sections we discuss their methodology in the context of this framework. (See Box 1)
Our description of the project and analysis of the case follow.
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Box A2.1. Steer Davies Gleave Method to Calculate the Expected Greenhouse Gas Emissions Savings from
the Bikeways Project
Data:
Demand for
bicycle trips:
VKT reduction:
Emissions
savings:
2001 Origin-Destination survey for Santiago
Intercept survey of bikeway bicyclists: what is their mode shift from
auto and other modes to bicycle? It is 5%.
Intercept survey: trip length
Bicycle flow counts: what is the actual change in bicycle flows?
Fleet characteristics: catalytic converters
Base year: Actual bicycle trips within and between the three
neighborhoods in the project area in 2001.
Construct three scenarios for growth in bicycle trips for 2001-2006
(slow, medium, and high) using actual measures of the growth in
bicycle flows.
For the final year of the constructed scenarios, 2006, 5% of the
trips would have been made by auto.
Assuming that the average bicycle trip length equals the average
auto trip length, calculate the VKT saved.
Use default values and fleet data to account for emissions
differences between vehicles with and without catalytic converters
in the project area.
Use default values to account for the effects of effect of cold starts
on emissions.
Apply the GHG emissions production formula from the COPERT III
model to the estimated VKT savings in 2006 to calculate
greenhouse gas emissions saved for each of the three scenarios.
A. Description of the Bikeways Project
In 2003, the World Bank, in coordination with the Global Environmental Facility (GEF) and local
counterparts, financed the bikeways project, among others, to promote the use of the bicycle as
a mode of transport in Santiago, Chile. The financing was a grant from the GEF, which included
USD $2.59 million for the bicycle component. The project included creating bikeway
infrastructure and promotion activities in three neighborhoods in central Santiago: Santiago,
Providencia, and Ñuñoa.
The objective of the project that included the bikeways was to ―To help reduce greenhouse
gases (GHG) from ground transport in Santiago through the promotion of a long-term modal
shift to more efficient and less polluting forms of transport…‖ and this objective related directly
79
to supporting the implementation of comprehensive, regional transportation plans for Santiago.
(World Bank Group, 2003) Increasing the mode share of bicycles and reducing bicycle
accidents were among the World Bank‘s performance measures for the project. (World Bank
Group, 2003)
The project used GEF funding to construct about 10 km of new bikeways and the illumination of
about 10 km of locally financed bikeways in Santiago, Ñuñoa, and Providencia, three municipal
districts (comunas) in central Santiago that form the project area. These new bikeways
complemented about 20 km of additional bikeways funded by Santiago, Ñuñoa, and
Providencia, and the existing 11.6 km of bikeways in these same district municipalities.
The bikeway infrastructure included a range of designs including bikeways located in central
medians separated from traffic by landscaping, bikeways separated from motorized traffic with
physical barriers, and bikeways indicated with striping. The project also supported safety
education programs.
B. Analysis of the Bikeways Project
i. Urban Development
The bikeways project was only a modest pilot project, but it fit into a much larger framework for
urban development that Santiago had been planning. This planning framework included the
broader local and national efforts to restructure the urban transportation sector in Santiago.
Thus, the bikeways project benefited from the extensive planning, design, and institutional work
that was underway in Santiago at the time. Linking the project to regional planning should
create opportunities to coordinate across sectors (e.g., to facilitate education programs, land
development planning), across transportation modes (e.g., to facilitate transit policies and
infrastructure that support bicycle-transit trips), and with planning processes (e.g., community
participation, design workshops).
Because the project was initiated by the district municipalities, and was co-financed and
planned by these municipalities, there should also have been opportunities to plan and design
the bikeways in the context of a neighborhood vision for development and transportation. This
local planning could address other things such as local transit corridors, parking, local access to
schools and shopping, security, and other neighborhood issues that should be considered in a
planning process that integrates transportation and land use.
Short Run and Long Run Effects
In the short run, residents in the district municipalities with the new bikeways perceived them as
neighborhood assets. Residents responded in surveys that they favored having the bikeways
built ―in front of their houses‖ (89%) even though many of those surveyed do not bicycle. In the
long run, neighborhoods with better non-motorized access to local amenities and with calmer
vehicular traffic may attract residents who prefer to make some of their trips by bicycle or
walking, and may even induce some substitution to cleaner modes and more recreational travel
by bicycle or walking. Indeed, in the short run the bikeways attracted existing riders, new riders,
and new trips, including recreational trips.
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Stakeholder Involvement
In this section on urban development, we should also discuss stakeholder involvement. Chile
has a growing bicycle culture (possibly a counterculture) with bicycle advocacy groups such as
the Movimiento Furioso Ciclistas (see www.furiosos.cl), organized critical mass rides, bicycle
culture festivals, and Sunday rides on streets closed to motorists. This bicycle culture also has a
web presence including weblogs, and extensive commentary on regional air quality and
transportation planning websites by bicycle advocates (see www.publimetro.cl, on October 8,
2008, for example). A San Francisco Chronicle article from 2004 cites the bicycle movement as
a factor in the increase in the use of the bicycle in Santiago, and figures from this article (from
the Ministry of Transport) indicate that the bicycle mode share in Santiago could be as high as
5%. (Ross, 2004)
Engaging the bicycle advocates was a key element in the development of the bikeway project,
and this is an example of how engaging social, environmental, and business stakeholders may
make significant long-run contributions to sustainable metropolitan development in Santiago.
ii. Transportation
The bikeway network is part of a larger network of bicycle facilities for the Santiago region.
Internet sites, press releases, and government documents reference a ―Plan Maestro Regional
de Ciclorutas‖ calling for 690 km of new bikeways in the metropolitan area by 2012. The bike
planning effort is connected to regional air quality and transportation planning. In addition to
bikeways, the region has increased bicycle parking at metro stations, and neighborhoods in the
project study area have initiated a bike-share program. This signals popular and political
support for non-motorized transportation improvements.
The cross-sector and intermodal planning is key for the bike planning, and the different transport
modes should be considered together to achieve better policies and designs. For example, in
addition to infrastructure, the bicycle planning consider bicycle parking policies and local zoning
and business codes to ensure that bicycles have safe parking at trip destinations. Coordination
with transit agencies should result in operating polices that regulate how bicycles can be
accommodated on buses and trains, and how and where safe parking will be included at
stations. Indeed, some of this coordination for infrastructure and policy was carried out in this
project, and limited progress has been achieved so far (e.g. Metro has started to install safe
bike parking facilities at some of their stations, a law to promote cycling that approaches the
issues from a safety perspective is in the making). Nevertheless, coordinating policy and
infrastructure is needed to fully promote bicycle use.
Short Run and Long Run Effects
It is also important to recognize that the bikeway project has a long history, and developing
good metropolitan transportation is a long-term endeavor. Bicycle planning for Santiago was
underway as early as 1985, with bikeway pilot projects implemented and evaluated in the late
1980s. (Latina, Ltd., 1994) During the 1990s, bicycle planning has been a component in the
regional transportation plans for Santiago. In the late 1990s, transportation economists in Chile
estimated the demand for bicycle travel in the city. (Ortúzar et al., 2000)
This prior work resulted in information that can be used for current bicycle planning. Through
household surveys of a sample of Santiago residents and forecasting Ortúzar and his
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colleagues found:
About 78% of women and about 66% of men never use a bicycle to make current trips.
About 38% of women and 50% of men who use a bicycle make bicycle trips once a
week or more.
About 23% of women and 26% of men would be willing to use a bicycle for some trips.
Half of the potential bicycle users are between 17 and 30 years old.
In a future scenario with ―a dense and properly designed cycle-way network, and Metro
and suburban rail network significantly larger than at present, and much more
congestion‖ the researchers estimated that for about 87% of the current person-trips
bicycles would not be considered an option.
Given the same future scenario, the researchers estimated the number of bicycle trips
would increase from about 1.6% of all trips (from the 1991 O-D survey) to 5.81% on
average, with more than 10% mode share in some neighborhoods.
The forecasted bicycle mode shares for Santiago, Providencia, and Ñuñoa, were
between about 4 and 5%, and these neighborhoods have medium to high levels of
income, a factor associated with lower bicycle use. (Ortúzar et al., 2000)
Through focus groups with Santiago residents they found that bicycling is associated with a
social stigma, particularly to people with higher incomes. (Although this may be the case, the
bikeway project was implemented in upper class neighborhoods in Santiago, signaling a more
complex relationship between class and active transportation modes than is commonly
assumed.)
The short-run results from the bikeways project are generally consistent with Ortúzar‘s findings
on travel behavior. Most people in the neighborhoods do not use a bicycle, more men than
women ride a bicycle (although during the project period there has been an increase of women
riding a bike during weekdays from 8% to 20%), and most cyclists are younger. These results
are presented in the next section. Long-run changes in travel behavior due to bicycle
improvements are not yet known, and strategies to learn the long-run effects should be included
in good transportation planning.
First and Higher Order Effects
In addition to knowing the effects of the bikeways on travel behavior, we need to know the
effects of the bikeways on other aspects of the transportation system such as motor vehicle
circulation, public transit service, and pedestrian travel.
For example, some, but not all, of the bikeways in this project‘s network took a vehicle travel
lane to create space for bicycles. The extent to which this decrease in the vehicle capacity of
the network affected traffic flows is not known. Consultants conducted bicycle flow counts in the
project area, but they did not conduct vehicle flow counts. This information should be included
in the design and planning of the facilities, as well as the estimation of emissions discussed in
the next section.
Similarly, if the project facilitated intermodal connections with buses or trains, and if bicycles
were allowed on these vehicles, how did these new policies affect transit service in the long run
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and short run? Prior work by Ortúzar and findings from the evaluations of the bikeways suggest
that bicycle-transit trips are rare, and not very appealing. Bike-to-transit transfers were
considered part of this project in relation to efforts to increase bike parking at metro stations and
in the study area. However, bicycle-transit trips are still rare.
Furthermore, did the presence of the bicycle facilities affect pedestrian travel in the project
area? Bicyclists surveyed reported collisions with pedestrians, but pedestrians were not
surveyed as part of the evaluation study. Additional information about the transportation context
is needed to fully evaluate the bikeways project.
iii. Emissions
How does the bikeway project affect emissions of greenhouse gases from transport in
Santiago?
Steer Davies Gleave conducted the evaluation study of the bikeway project area. This firm
designed the study, collected data before and after the project‘s implementation, and analyzed
evaluation data. They collected a baseline of bicycle flows in 2003 and 2004 at sites with and
without the bikeway infrastructure. After the construction of the bikeways, they collected bicycle
user and opinion data in 2005 and 2006 through intercept surveys, and collected data about
post-project bicycle flows at the same locations used for collecting baseline flow data. This firm
also documented the settings of the bikeways with photographs, and information about safety
from secondary sources, from bicyclists in surveys, and through observation.
Recent travel survey data for Santiago reported in the evaluation showed that bicycle trips
account for 1.9% of all trips (an increase from 1.6% in the 1991 O-D survey), and almost 5% of
non-motorized trips (see Table 1).
TABLE A.2.1. Mode Shares for Non-Motorized Trips, 2001 O-D Survey for Santiago
Mode share
Mode
Bicycle
Walking
All non-motorized
Daily trips
% of all trips
% of all nonmotorized trips
303,887
1.9%
4.8%
5,978,312
36.7%
95.2%
6,282,199
Source: Steer Davies Gleave, 2007a.
Steer Davies Gleave carried out intercept surveys and flow measurements at various locations
in the project area, with and without the new bikeways. The following are some of the
characteristics of the bikeway users and their travel behavior, measured in 2006:
On winter weekdays, 90% of the bicyclists were men (87% spring), 10% were women
(13% spring); depending on the counting method, women were 14% to 18% of riders on
Sundays in winter (20% spring). Surveyors counted higher concentrations of female
riders in Ñuñoa and areas east of Santiago. The evaluation suggested that ―better
safety conditions‖ in these areas may explain the higher counts of female riders, but it is
not more specific about what these safety conditions are.
83
The average age of bicyclists is about 33 years old.
Trade workers and ―dependent workers‖—non-independent professionals who are likely
to be salaried—accounted for 57% of the bicyclists surveyed, and students accounted
for 17%.
On weekdays, about 60% of the trips were to work, and on weekends about 65% of the
trips were for recreation.
Counts at various locations with bikeways showed higher bicycle flows on the bikeways
than on other parts of the road environment: depending on the method of counting, flows
were 67-81% on bikeways (81-82% spring), 9-21% on streets (7% spring), 9-10% on
sidewalks (10-12% spring), and 0-2% on medians (0-1% spring).
Fourteen percent of the bicyclists surveyed said that they‘d been in a bike accident on
the bikeway they were using at the time of the survey, and 50% of these accidents were
with pedestrians or other bicyclists.
Bicycle flows increased by 26% in the spring and 17% in the winter at measurement locations
where bikeways were located, and by 8% and 3% in the spring and winter, respectively, at
measurement locations without the new bikeways. There was a gender difference in the use of
the bikeways: the proportion of women was higher on streets with bikeways compared to streets
without bikeways.
The bikeways did attract new riders. Surveys of bikeway users showed that about 40% of the
bicyclists surveyed would not have made the exact same trip before the bikeways were built.
The modes used prior to the construction of the bikeway were microbus (47%), walking (8%),
metro (6%), drive (5%), with 4% traveling by motorcycle, taxi, or a passenger in a car. Thirty
percent of the trips would not have been made without the new bikeways. Men made 74%
of the induced trips. Fifty-six percent of the induced trips were for recreation. Of the 60% of
bicyclists who were making the same trip before, 88% were using the same route. (Steer
Davies Gleave, 2007b)
Tables two through four present additional information from the evaluation about bicycle flows
and survey responses regarding alternatives to the bicycle.
TABLE A3.2. Measures Bicycle Flows in the Project Area, 2004-2006
Total measured flow
(veh/day)
Spring
2003
Spring
2005
Spring
2006
Growth rate,
2004-2005
Average weekday
5,212
7,158
7,048
18.70%
-1.50%
11.70%
Sunday
3,473
4,280
3,322
11.60%
-22.40%
-1.40%
Winter
2004
Winter
2005
Winter
2006
Growth rate,
2004-2005
Average weekday
3,629
4,410
5,020
22%
14%
19%
Sunday
1,962
1,181
2,065
-40%
Source: Steer Davies Gleave, 2007a.
75%
3%
Total measured flow
(veh/day)
84
Growth rate, Total growth
2005-2006
rate
Growth rate, Total growth
2005-2006
rate
Table A.2.3 Modal Alternative to the Bicycle, Winter 2005-2006, Intercept Survey
Weekday
Sunday
Alternative mode to bicycle
2005
2006
2005
2006
Micro
59%
68%
23%
33%
Metro
8%
11%
4%
6%
Motorcycle, scooter
3%
4%
2%
4%
Auto, driver
4%
4%
5%
5%
Auto, passenger
1%
1%
1%
1%
Taxi or colectivo
1%
1%
0%
1%
15%
8%
23%
20%
7%
2%
34%
29%
2%
0%
Source: Steer Davies Gleave, 2007a.
8%
0%
Walking
Would not have made the trip
Other
Table A.2.4. Modal Alternative to the Bicycle, Spring 2005-2006, Intercept Survey
Weekday
Sunday
Alternative mode to bicycle
2005
2006
2005
2006
Micro
54%
62%
23%
27%
Metro
11%
9%
4%
4%
Motorcycle, scooter
5%
3%
2%
3%
Auto, driver
5%
4%
5%
5%
Auto, passenger
1%
1%
1%
1%
2%
0%
Taxi or colectivo
Walking
13%
Would not have made the trip
Other
1%
1%
24%
6%
34%
33%
10%
1%
Source: Steer Davies Gleave, 2007a.
23%
4%
Steer Davies Gleave also conducted telephone interviews in 2006 with 800 residents in the
project area. Thirty-one percent of respondents were students, and about 27% were
―dependent workers in the private sector‖. Their most commonly made trips were to work and
school. About 26% of these trips were made by walking, about 25% by bus, about 18% by
driving, about 17% by metro, and about 5% by bicycle. The average travel time was 25
minutes, and about 70% of the trips were made within the project area. About 34% of
respondents said that they use a bicycle, and 42% said that they regularly use the bikeways
when they ride. (Steer Davies Gleave, 2007b)
85
iv. GHG Emissions Calculations for the Project
The project evaluation by Steer Davies Gleave calculated the GHG emissions and emissions of
local pollutants saved by substituting bicycle trips for auto trips. They reported that the
evaluation method they used is an adaptation of the calculation methods in the document, ―Plan
Maestro y Diseño de Físico de Obras‖ by GEF, the World Bank, Sectra, and CGTS. (Steer
Davies Gleave, 2007b)
Estimation of Demand for Bicycle Trips
The analysis used the 2001 O-D survey for the baseline year (2001), and assumptions of the
growth in bicycle trips to project hypothetical, cumulative demand for bicycling within and
between municipal districts in the project area for workdays and weekends for the period 20012006. Weekend bicycle trips were assumed to be 0.84 of weekday trips, which is the proportion
found in the 2001 O-D survey. The three growth scenarios were: slow growth (1.7% between
2001 and 2006), medium growth (1.7% between 2001-2003 and 5.2% between 2004-2006),
and optimistic growth in bicycle trips (1.7% between 2001-2003 and 15.4% between 20042006). The results of this estimation are presented in Table 5. The growth scenarios are based
on measured increases in bicycle flows at representative locations.
TABLE A.2.5. Bicycle Trips in the Project Area by Growth Scenario, by Type of Day, 2006
Bicycle trips/day
Growth scenario
Type of day
Slow
Medium
Optimistic
Weekday
Weekend
Weekday
Weekend
Weekday
Weekend
Intra-neighborhood Providencia
6,040
5,078
6,685
5,621
8,825
7,419
Intra-neighborhood Ñuñoa
5,538
4,656
6,130
5,154
8,092
6,803
24,599
20,682
27,228
22,892
35,940
30,217
Intra-neighborhood Santiago
Inter-neighborhood Providencia
1,085
912
1,201
1,010
1,586
1,333
Inter-neighborhood Ñuñoa
1,498
1,259
1,658
1,394
2,188
1,840
2,701
2,271
2,989
2,513
3,946
3,317
41,462
34,859
45,891
Source: Steer Davies Gleave, 2007a.
38,584
60,576
50,930
Inter-neighborhood Santiago
Total
Estimation of Vehicle-km Reduced
The analysts used information from the intercept survey of cyclists about how many of these
trips would have been auto trips—between four and five percent—to estimate the daily trips
within and between municipal districts that would have been made by car. The analysis then
expanded this figure to represent annual trips saved, assuming 350 travel days in the year, 100
weekend days and 250 weekdays. The analysis did not estimate saved emissions from
microbus, metro, or taxi trips.
The intercept survey showed bicycle trip lengths were 5.36-6.27 km within neighborhoods and
8.43-10.58 km between neighborhoods. The analysis used these trip lengths to estimate the
annual reduction in kilometers traveled by car for each of the scenarios.
86
Estimation of the Saved Emissions
The analysis determined the CO2-equivalent emissions by accounting for both CO2 and
methane emissions. The analysis also accounted for the proportions of the fleet with and
without catalytic converters, and the effect of cold starts. Calculations were constructed with
data about local traffic flows and median speeds.
The evaluation considered two options for calculating the CO2-equivalent emissions: the carbon
balance method and the figures outlined by GEF. It used the carbon balance equation, and
additional equations for calculating emissions of methane for vehicles with and without catalytic
converters from COPERT III. (See Table 6.)
TABLE A.2.6 Emissions of CO2eq Reduced, COPERT Formula, 2006
CO2 emissions
CH4 emissions reduced
reduced
(tons/yr)
(tons/yr)
Growth scenario: slow (S), medium (M), optimistic
(O)
Intra-neighborhood Providencia
Intra-neighborhood Ñuñoa
Intra-neighborhood Santiago
Emissions CO2
equivalent
reduced
(tons/yr)
S
M
O
S
M
O
S
M
O
102
113
150
0.04
0.04
0.05
103
114
151
80
89
117
0.03
0.03
0.04
81
90
118
357
395
521
0.13
0.14
0.19
359
398
525
Inter-neighborhood Providencia
31
34
45
0.01
0.01
0.02
31
35
46
Inter-neighborhood Ñuñoa
34
38
50
0.01
0.01
0.02
34
38
50
Inter-neighborhood Santiago
74
82
108
0.03
0.03
0.04
75
83
109
678 751 991 0.242
Source: Steer Davies Gleave, 2007a.
0.268
0.354
684
757
999
Total
The results show that CO2 emissions were reduced between 684 and 999 tons/yr, depending
on the growth scenario, accounting only for reduced car trips. Additional savings may have
occurred due to other reduced trips e.g. by taxi but are not counted.
viii.
First and Second Order Effects
As we noted in a previous section, the emissions analysis for the project did not consider the
effects of the bikeways on traffic patterns, and therefore does not account for whether the
bikeways that took lanes away from vehicle traffic might have increased congestion in those
areas (or in other areas) or resulted in VKT increases due to circuitous route choices to avoid
areas with the bikeways.
C. Costs and Benefits of the Project
The project evaluation by Steer Davies Gleave also presented estimates of reductions in
emissions of local pollutants, accidents, time savings, and fuel savings. Consistent with the
framework, according to their estimate the tons of CO2 saved is a small figure compared to the
size of the problem, and when monetized, they are small compared to the other co-benefits of
the project. (See Table 4.6.)
87
TABLE A.2.6 Summary of the Economic Evaluation of the Bikeways Project, Slow Growth Scenario, 2005
Item
Annual benefit (USD $)
Reduction in GHG emissions
8,558
Fuel savings
166,234
Travel time savings
344,627
Reduction in accidents due to infrastructure
133,903
Costs of accidents due to mode shift
-24,473
Total 2005
628,850
Source: Steer Davies Gleave, 2007a.
Note: Exchange rate $550 per US$. GHG reduction estimate based on $10/ton CO2.
The consultants‘ cost-benefit calculations assumed USD $10/ton of CO2 reduced. If this
assumption were changed to that of Stern (Stern, 2006) and instead used US $85/ton of CO2
reduced, the benefits of the CO2 reductions would still be small than the other benefits of the
project.
D. Conclusions for Informing Project Design and Evaluation with the Framework
The bikeways project did facilitate bicycle trip substitutions for auto trips in the short run, but the
analysis should also account for a long-run scenario in which the bikeways could help to
maintain bicycle mode share in the face of increasing motorization. Also, it is possible that
bikeways make the central neighborhoods safer and more attractive, and thus a more attractive
housing and business location choice than they otherwise would be.
The bikeway network in Santiago has expanded since the GEF-funded pilot study, and it would
be worthwhile to evaluate bicycle travel behavior in different neighborhoods. This is supported
by the study by Ortúzar (Ortúzar et al., 2000) that found potential bicycle mode shares as high
at 10% in other sectors of the city, whereas the potential bicycle mode shares were lowest in the
relatively high-income neighborhoods of Santiago, Providencia, and Ñuñoa.
Finally, according to the project evaluation CO2 emissions reductions were a minor co-benefit
compared to the monetized benefits of fuel savings, accident reductions, and travel time
savings. Again, this signals the need to interpret and evaluate the potential of a project in the
broader context of good transportation and metropolitan development.
88
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8. Glossary
ANL
Argonne National Laboratory
ANTP
Associaçāo Nacional de Transportes Públicos
ASIF
aggregate travel activity, share of travel by mode, (carbon) intensity of fuel, and
fuel use per kilometer, factors affecting transportation carbon dioxide emissions
BAU
business as usual
BRT
Bus Rapid Transit
CGTS
La Coordinación de Transportes de Santiago
CNG
compressed natural gas
CO2
carbon dioxide
COPERT
computer program to calculate emissions from road transport
CTS
Centro de Transporte Sustentable de México, A.C.
EJ
exajoule
ESTRAUS
Modelo de Equilibrio Oferta-Demanda para Redes Multimodales de Transporte
Urbano con Múltiples Clases de Usuarios
EU
European Union
GDP
gross domestic product
GEF
Global Environmental Facility
GHG
greenhouse gases
GJ
gigajoule
gm
gram
IEA
International Energy Agency
INE
Instituto Nacional de Ecología
INEGI
Instituto Nacioncal de Estadística y Geografía
IPCC
Intergovernmental Panel on Climate Change
IT
information technology
IVEM
International Vehicle Emissions Model
kg
kilogram
km
kilometer
L
liter
LAC
Latin America and Caribbean (World Bank region)
LCA
life cycle analysis
LCSTR
Latin American and Caribbean Transport Unit, World Bank
LDV
light duty vehicle
LOS
level of service
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LPG
liquefied petroleum gas
LR
light rail
M
million
MCMA
Mexico City Metropolitan Area
MJ
mega joule
mn
million
MOVES
Motor Vehicle Emissions Simulator
mpg
miles per gallon
MTOE
million tonnes of oil
Mtonnes
one million tonnes
NAS
National Academy of Sciences
NOx
nitrogen oxides
O-D
origin-destination
OECD
Organization for Economic Co-operation and Development
pass-km
passenger-kilometer
PITV
Plan Integral de Transporte y Vialidad
PPP
purchasing power parity
RTP
Red de Transporte de Pasajeros del Distrito Federal
SECTRA
Comisión de Planificación de Inversiones e Infrastructura de Transporte
SETRAVI
Sectretaría de Transportes y Vialidad
SMA
Secretaría de Medio Ambiente del Gobierno del Distrito Federal
STE
Servicio de Transportes Eléctricos
SUVs
sport utility vehicles
TAZs
travel analysis zones
TMIP
Travel Model Improvement Program
UC
University of California
UNAM
Universidad Autónoma de México
US
United States of America
USD
United States dollars
US EPA
United States Environmental Protection Agency
veh
vehicle
VKT
vehicle-kilometers of travel
VW
Volkswagen
WBCSD
World Business Council for Sustainable Development
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