Transport. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change

Full text

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Coordinating Lead Authors:
Ralph Sims (New Zealand), Roberto Schaeffer (Brazil)
Lead Authors:
Felix Creutzig (Germany), Xochitl Cruz-Núñez (Mexico), Marcio D’Agosto (Brazil), Delia Dimitriu
(Romania / UK), Maria Josefina Figueroa Meza (Venezuela / Denmark), Lew Fulton (USA), Shigeki
Kobayashi (Japan), Oliver Lah (Germany), Alan McKinnon (UK / Germany), Peter Newman
(Australia), Minggao Ouyang (China), James Jay Schauer (USA), Daniel Sperling (USA), Geetam
Tiwari (India)
Contributing Authors:
Adjo A. Amekudzi (USA), Bruno Soares Moreira Cesar Borba (Brazil), Helena Chum (Brazil / USA),
Philippe Crist (France / USA), Han Hao (China), Jennifer Helfrich (USA), Thomas Longden
(Australia / Italy), André Frossard Pereira de Lucena (Brazil), Paul Peeters (Netherlands), Richard
Plevin (USA), Steve Plotkin (USA), Robert Sausen (Germany)
Review Editors:
Elizabeth Deakin (USA), Suzana Kahn Ribeiro (Brazil)
Chapter Science Assistant:
Bruno Soares Moreira Cesar Borba (Brazil)
This chapter should be cited as:
Sims R., R. Schaeffer, F. Creutzig, X. Cruz-Núñez, M. D’Agosto, D. Dimitriu, M. J. Figueroa Meza, L. Fulton, S. Kobayashi, O.
Lah, A. McKinnon, P. Newman, M. Ouyang, J. J. Schauer, D. Sperling, and G. Tiwari, 2014: Transport. In: Climate Change
2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovern-
mental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler,
I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)].
Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

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Contents
Executive Summary � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 603
8�1 Freight and passenger transport (land, air, sea and water) � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 605
8�1�1 The context for transport of passengers and freight � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 606
8�1�2 Energy demands and direct / indirect emissions � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 608
8�2 New developments in emission trends and drivers � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 610
8�2�1 Trends � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 611
8.2.1.1 Non-CO2 greenhouse gas emissions, black carbon, and aerosols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 611
8�2�2 Drivers
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 612
8�3 Mitigation technology options, practices and behavioural aspects � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 613
8�3�1 Energy intensity reduction — incremental vehicle technologies � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 613
8.3.1.1 Light duty vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613
8.3.1.2 Heavy-duty vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613
8.3.1.3 Rail, waterborne craft, and aircraft . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 614
8�3�2 Energy intensity reduction — advanced propulsion systems � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 614
8.3.2.1 Road vehicles — battery and fuel cell electric-drives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 614
8.3.2.2 Rail, waterborne craft, and aircraft . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615
8�3�3 Fuel carbon intensity reduction � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 615
8�3�4 Comparative analysis � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 616
8�3�5 Behavioural aspects � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 616
8�4 Infrastructure and systemic perspectives� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 618
8�4�1 Path dependencies of infrastructure and GHG emission impacts � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 618
8�4�2 Path dependencies of urban form and mobility � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 619
8.4.2.1 Modal shift opportunities for passengers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 620
8.4.2.2 Modal shift opportunities for freight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 621

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8�5 Climate change feedback and interaction with adaptation � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 622
8�5�1 Accessibility and feasibility of transport routes � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 622
8�5�2 Relocation of production and reconfiguration of global supply chains � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 622
8�5�3 Fuel combustion and technologies � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 622
8�5�4 Transport infrastructure � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 623
8�6 Costs and potentials � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 630
8�7 Co-benefits, risks and spillovers � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 630
8�7�1 Socio-economic, environmental, and health effects � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 633
8�7�2 Technical risks and uncertainties � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 633
8�7�3 Technological spillovers � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 633
8�8 Barriers and opportunities � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 633
8�8�1 Barriers and opportunities to reduce GHGs by technologies and practices � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 633
8�8�2 Financing low-carbon transport � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 636
8�8�3 Institutional, cultural, and legal barriers and opportunities � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 636
8�9 Sectoral implications of transformation pathways and sustainable development � � � � � � � � � � � � � � 637
8�9�1 Long term stabilization goals — integrated and sectoral perspectives � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 637
8�9�2 Sustainable development � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 641
8�10 Sectoral policies � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 642
8�10�1 Road transport � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 642
8�10�2 Rail transport � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 645
8�10�3 Waterborne transport � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 645
8�10�4 Aviation � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 646
8�10�5 Infrastructure and urban planning � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 647

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8�11 Gaps in knowledge and data � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 647
8�12 Frequently Asked Questions � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 647
References � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 650
Dedication to Lee Schipper
This Transport chapter is dedicated to the memory of Leon Jay
(Lee) Schipper. A leading scientist in the field of energy research
with emphasis on transport, Lee died on 16 August 2011 at the
age of 64. He was a friend and colleague of many of the Chapter
authors who were looking forward to working with him in his
appointed role as Review Editor. Lee’s passing is a great loss to
the research field of transport, energy, and the environment and
his expertise and guidance in the course of writing this chapter
was sorely missed by the author team, as were his musical tal-
ents.

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Executive Summary
Reducing global transport greenhouse gas (GHG) emissions
will be challenging since the continuing growth in passenger
and freight activity could outweigh all mitigation measures
unless transport emissions can be strongly decoupled from GDP
growth (high confidence).
The transport sector produced 7.0 GtCO2eq of direct GHG emissions
(including non-CO2 gases) in 2010 and hence was responsible for
approximately 23 % of total energy-related CO2 emissions (6.7 GtCO2)
[8.1]. Growth in GHG emissions has continued since the Fourth Assess-
ment Report (AR4) in spite of more efficient vehicles (road, rail, water
craft, and aircraft) and policies being adopted. (robust evidence, high
agreement) [Section 8.1, 8.3]
Without aggressive and sustained mitigation policies being imple-
mented, transport emissions could increase at a faster rate than emis-
sions from the other energy end-use sectors and reach around 12 Gt
CO2eq / yr by 2050. Transport demand per capita in developing and
emerging economies is far lower than in Organisation for Economic
Co-operation and Development (OECD) countries but is expected
to increase at a much faster rate in the next decades due to rising
incomes and development of infrastructure. Analyses of both sectoral
and integrated model scenarios suggest a higher emission reduction
potential in the transport sector than the levels found possible in AR4
and at lower costs. Since many integrated models do not contain a
detailed representation of infrastructural and behavioural changes,
their results for transport can possibly be interpreted as conserva-
tive. If pricing and other stringent policy options are implemented in
all regions, substantial decoupling of transport GHG emissions from
gross domestic product (GDP) growth seems possible. A strong slow-
ing of light-duty vehicle (LDV) travel growth per capita has already
been observed in several OECD cities suggesting possible saturation.
(medium evidence, medium agreement) [8.6, 8.9, 8.10]
Avoided journeys and modal shifts due to behavioural change,
uptake of improved vehicle and engine performance technolo-
gies, low-carbon fuels, investments in related infrastructure,
and changes in the built environment, together offer high miti-
gation potential (high confidence).
Direct (tank-to-wheel) GHG emissions from passenger and freight
transport can be reduced by:
• avoiding journeys where possible — by, for example, densifying
urban landscapes, sourcing localized products, internet shopping,
restructuring freight logistics systems, and utilizing advanced infor-
mation and communication technologies (ICT);
• modal shift to lower-carbon transport systems — encouraged by
increasing investment in public transport, walking and cycling
infrastructure, and modifying roads, airports, ports, and railways
to become more attractive for users and minimize travel time and
distance;
• lowering energy intensity (MJ / passenger km or MJ / tonne km) — by
enhancing vehicle and engine performance, using lightweight
materials, increasing freight load factors and passenger occupancy
rates, deploying new technologies such as electric 3-wheelers;
• reducing carbon intensity of fuels (CO2eq / MJ) — by substituting oil-
based products with natural gas, bio-methane, or biofuels, electric-
ity or hydrogen produced from low GHG sources.
In addition, indirect GHG emissions arise during the construction of
infrastructure, manufacture of vehicles, and provision of fuels (well-to-
tank). (robust evidence, high agreement) [8.3, 8.4, 8.6 and Chapters
10, 11, 12]
Both short- and long-term transport mitigation strategies are
essential if deep GHG reduction ambitions are to be achieved
(high confidence).
Short-term mitigation measures could overcome barriers to low-car-
bon transport options and help avoid future lock-in effects resulting,
for example, from the slow turnover of vehicle stock and infrastructure
and expanding urban sprawl. Changing behaviour of consumers and
businesses will likely play an important role but is challenging and the
possible outcomes, including modal shift, are difficult to quantify. Busi-
ness initiatives to decarbonize freight transport have begun, but need
support from policies that encourage shifting to low-carbon modes
such as rail or waterborne options where feasible, and improving logis-
tics. The impact of projected growth in world trade on freight trans-
port emissions may be partly offset in the near term by more efficient
vehicles, operational changes, ‘slow steaming’ of ships, eco-driving and
fuel switching. Other short-term mitigation strategies include reducing
aviation contrails and emissions of particulate matter (including black
carbon), tropospheric ozone and aerosol precursors (including NOx)
that can have human health and mitigation co-benefits in the short
term. (medium evidence, medium agreement) [8.2, 8.3, 8.6, 8.10]
Methane-based fuels are already increasing their share for road
vehicles and waterborne craft. Electricity produced from low-car-
bon sources has near-term potential for electric rail and short- to
medium-term potential as electric buses, light-duty and 2-wheel
road vehicles are deployed. Hydrogen fuels from low-carbon sources
constitute longer-term options. Gaseous and liquid-biofuels can pro-
vide co-benefits. Their mitigation potential depends on technology
advances (particularly advanced ‘drop-in’ fuels for aircraft and other
vehicles) and sustainable feedstocks. (medium evidence, medium
agreement) [8.2, 8.3]
The technical potential exists to substantially reduce the current CO2eq
emissions per passenger or tonne kilometre for all modes by 2030

604604
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8
Chapter 8
and beyond. Energy efficiency and vehicle performance improvements
range from 30 – 50 % relative to 2010 depending on mode and vehicle
type. Realizing this efficiency potential will depend on large invest-
ments by vehicle manufacturers, which may require strong incentives
and regulatory policies in order to achieve GHG emissions reduction
goals. (medium evidence, medium agreement) [8.3, 8.6, 8.10]
Over the medium-term (up to 2030) to long-term (to 2050 and
beyond), urban (re)development and investments in new infrastruc-
ture, linked with integrated urban planning, transit-oriented develop-
ment and more compact urban form that supports cycling and walking
can all lead to modal shifts. Such mitigation measures could evolve
to possibly reduce GHG intensity by 20 – 50 % below 2010 baseline by
2050. Although high potential improvements for aircraft efficiency are
projected, improvement rates are expected to be slow due to long air-
craft life, and fuel switching options being limited, apart from biofu-
els. Widespread construction of high-speed rail systems could partially
reduce short-to-medium-haul air travel demand. For the transport sec-
tor, a reduction in total CO2eq emissions of 15 – 40 % could be plau-
sible compared to baseline activity growth in 2050. (medium evidence,
medium agreement) [8.3, 8.4, 8.6, 8.9, 12.3, 12.5]
Barriers to decarbonizing transport for all modes differ across
regions, but can be overcome in part by reducing the marginal
mitigation costs (medium evidence, medium agreement).
Financial, institutional, cultural, and legal barriers constrain low-car-
bon technology uptake and behavioural change. All of these barri-
ers include the high investment costs needed to build low-emissions
transport systems, the slow turnover of stock and infrastructure, and
the limited impact of a carbon price on petroleum fuels already heav-
ily taxed. Other barriers can be overcome by communities, cities, and
national governments which can implement a mix of behavioural mea-
sures, technological advances, and infrastructural changes. Infrastruc-
ture investments (USD / tCO2 avoided) may appear expensive at the
margin, but sustainable urban planning and related policies can gain
support when co-benefits, such as improved health and accessibility,
can be shown to offset some or all of the mitigation costs. (medium
evidence, medium agreement) [8.4, 8.7, 8.8]
Oil price trends, price instruments on emissions, and other measures
such as road pricing and airport charges can provide strong economic
incentives for consumers to adopt mitigation measures. Regional dif-
ferences, however, will likely occur due to cost and policy constraints.
Some near term mitigation measures are available at low marginal
costs but several longer-term options may prove more expensive. Full
societal mitigation costs (USD / tCO2eq) of deep reductions by 2030
remain uncertain but range from very low or negative (such as effi-
ciency improvements for LDVs, long-haul heavy-duty vehicles (HDVs)
and ships) to more than 100 USD / tCO2eq for some electric vehicles,
aircraft, and possibly high-speed rail. Such costs may be significantly
reduced in the future but the magnitude of mitigation cost reductions
is uncertain. (limited evidence, low agreement) [8.6, 8.9]
There are regional differences in transport mitigation pathways
with major opportunities to shape transport systems and infra-
structure around low-carbon options, particularly in developing
and emerging countries where most future urban growth will
occur (robust evidence, high agreement).
Transport can be an agent of sustained urban development that priori-
tizes goals for equity and emphasizes accessibility, traffic safety, and
time-savings for the poor while reducing emissions, with minimal det-
riment to the environment and human health. Transformative trajecto-
ries vary with region and country due to differences in the dynamics
of motorization, age and type of vehicle fleets, existing infrastructure,
and urban development processes. Prioritizing access to pedestrians
and integrating non-motorized and public transit services can result
in higher levels of economic and social prosperity in all regions. Good
opportunities exist for both structural and technological change around
low-carbon transport systems in most countries but particularly in fast
growing emerging economies where investments in mass transit and
other low-carbon transport infrastructure can help avoid future lock-
in to carbon intensive modes. Mechanisms to accelerate the transfer
and adoption of improved vehicle efficiency and low-carbon fuels to all
economies, and reducing the carbon intensity of freight particularly in
emerging markets, could offset much of the growth in non-OECD emis-
sions by 2030. It appears possible for LDV travel per capita in OECD
countries to peak around 2035, whereas in non-OECD countries it will
likely continue to increase dramatically from a very low average today.
However, growth will eventually need to be slowed in all countries.
(limited evidence, medium agreement) [8.7, 8.9]
A range of strong and mutually-supportive policies will be
needed for the transport sector to decarbonize and for the co-
benefits to be exploited (robust evidence, high agreement).
Decarbonizing the transport sector is likely to be more challenging
than for other sectors, given the continuing growth in global demand,
the rapid increase in demand for faster transport modes in developing
and emerging economies, and the lack of progress to date in slowing
growth of global transport emissions in many OECD countries. Trans-
port strategies associated with broader non-climate policies at all gov-
ernment levels can usually target several objectives simultaneously to
give lower travel costs, improved mobility, better health, greater energy
security, improved safety, and time savings. Realizing the co-benefits
depends on the regional context in terms of economic, social, and polit-
ical feasibility as well as having access to appropriate and cost-effective
advanced technologies. (medium evidence, high agreement) [8.4, 8.7]
In rapidly growing developing economies, good opportunities exist for
both structural and technological change around low-carbon trans-
port. Established infrastructure may limit the options for modal shift
and lead to a greater reliance on advanced vehicle technologies. Policy
changes can maximize the mitigation potential by overcoming the bar-
riers to achieving deep carbon reductions and optimizing the synergies.
Pricing strategies, when supported by education policies to help cre-

605605
Transport
8
Chapter 8
ate social acceptance, can help reduce travel demand and increase the
demand for more efficient vehicles (for example, where fuel economy
standards exist) and induce a shift to low-carbon modes (where good
modal choice is available). For freight, a range of fiscal, regulatory, and
advisory policies can be used to incentivize businesses to reduce the
carbon intensity of their logistical systems. Since rebound effects can
reduce the CO2 benefits of efficiency improvements and undermine a
particular policy, a balanced package of policies, including pricing ini-
tiatives, could help to achieve stable price signals, avoid unintended
outcomes, and improve access, mobility, productivity, safety, and
health. (medium evidence, medium agreement) [8.7, 8.9, 8.10]
Knowledge gaps in the transport sector
There is a lack of comprehensive and consistent assessments of the
worldwide potential for GHG emission reduction and especially costs
of mitigation from the transport sector. Within this context, the poten-
tial reduction is much less certain for freight than for passenger modes.
For LDVs, the long-term costs and high energy density potential for
on-board energy storage is not well understood. Also requiring evalua-
tion is how best to manage the tradeoffs for electric vehicles between
performance, driving range and recharging time, and how to create
successful business models.
Another area that requires additional research is in the behavioural
economic analysis of the implications of norms, biases, and social
learning in decision making, and of the relationship between trans-
port and lifestyle. For example, how and when people will choose to
use new types of low-carbon transport and avoid making unnecessary
journeys is unknown. Consequently, the outcomes of both positive and
negative climate change impacts on transport services and scheduled
timetables have not been determined, nor have the cost-effectiveness
of carbon-reducing measures in the freight sector and their possible
rebound effects. Changes in the transport of materials as a result of
the decarbonization of other sectors and adaptation of the built envi-
ronment are unknown. [8.11]
8.1 Freight and passenger
transport (land, air,
sea and water)
Greenhouse gas (GHG) emissions from the transport sector have more
than doubled since 1970, and have increased at a faster rate than any
other energy end-use sector to reach 7.0 Gt CO2eq in 20101 (IEA, 2012a;
1 CO2eq units are used throughout this chapter for direct emissions wherever
feasible, although this is not always the case in some literature that reports CO2
emissions only. For most transport modes, non-CO2 gases are usually less than
5 % of total vehicle emissions.
JRC / PBL, 2013; see Annex II.8). Around 80 % of this increase has come
from road vehicles (see Figure 8.1). The final energy consumption for
transport reached 28 % of total end-use energy in 2010 (IEA, 2012b), of
which around 40 % was used in urban transport (IEA, 2013). The global
transport industry (including the manufacturers of vehicles, providers
of transport services, and constructors of infrastructure) undertakes
research and development (R&D) activities to become more carbon
and energy efficient. Reducing transport emissions will be a daunting
task given the inevitable increases in demand and the slow turnover
and sunk costs of stock (particularly aircraft, trains, and large ships)
and infrastructure. In spite of a lack of progress to date, the transition
required to reduce GHG emissions could arise from new technologies,
implementation of stringent policies, and behavioural change.
Key developments in the transport sector since the Intergovernmen-
tal Panel on Climate Change (IPCC) Fourth Assessment Report (AR4)
(IPCC, 2007) include:
• continued increase in annual average passenger km per capita,
but signs that LDV2 ownership and use may have peaked in some
OECD countries (8.2);
• deployment of technologies to reduce particulate matter and black
carbon, particularly in OECD countries (8.2);
• renewed interest in natural gas as a fuel, compressed for road
vehicles and liquefied for ships (8.3);
• increased number of electric vehicles (including 2-wheelers) and
bus rapid transit systems, but from a low base (8.3);
• increased use of sustainably produced biofuels including for avia-
tion (8.3, 8.10);
• greater access to mobility services in developing countries (8.3,
8.9);
• reduced carbon intensity of operations by freight logistics compa-
nies, the slow-steaming of ships, and the maritime industry impos-
ing GHG emission mandates (8.3, 8.10);
• improved comprehension that urban planning and developing
infrastructure for pedestrians, bicycles, buses and light-rail can
impact on modal choice while also addressing broader sustainabil-
ity concerns such as health, accessibility and safety (8.4, 8.7);
• better analysis of comparative passenger and freight transport
costs between modes (8.6);
• emerging policies that slow the rapid growth of LDVs especially
in Asia, including investing in non-motorized transport systems
(8.10);
• more fuel economy standards (MJ / km) and GHG emission vehicle
performance standards implemented for light and heavy duty vehi-
cles (LDVs and HDVs) (8.10); and
• widely implemented local transport management policies to
reduce air pollution and traffic congestion (8.10).
2 LDVs are motorized vehicles (passenger cars and commercial vans) below
approximately 2.5 – 3.0 t net weight with HDVs (heavy duty vehicles or “trucks”
or “lorries”) usually heavier.

606606
Transport
8
Chapter 8
Figure 8�1 | Direct GHG emissions of the transport sector (shown here by transport mode) rose 250 % from 2.8 Gt CO2eq worldwide in 1970 to 7.0 Gt CO2eq in 2010 (IEA, 2012a;
JRC / PBL, 2013; see Annex II.8).
Note: Indirect emissions from production of fuels, vehicle manufacturing, infrastructure construction etc. are not included.
2010200520001995199019851980
19751970
Total Direct and Indirect 2.9
(Total Direct 2.8)
Total Direct and Indirect 4.9
(Total Direct 4.7)
Total Direct
and Indirect 7.1
(Total Direct 7.0)
0
1
2
3
4
5
6
7
8
Indirect Emissions from Electricity Generation
Road
Rail
Pipeline etc.
HFC & Indirect N
2
0
International Aviation
Domestic Aviation
International & Coastal Shipping
Domestic Waterborne
GHG Emissions [GtCO
2
eq/yr]
100%
1.12%
5.55%
72.06%
2.38%
+2.11%
1.60%
2.16%
6.52%
4.10%
9.26%
1.91%
+2.83%
71.00%
3.34%
3.45%
5.39%
5.94%
2.09%
7.66%
3.26%
11.66%
5.71%
1.38%
2.81%
9.78%
59.85%
+2,71%
For each mode of transport, direct GHG emissions can be decomposed3
into:
• activity — total passenger-km / yr or freight tonne-km / yr having a
positive feedback loop to the state of the economy but, in part,
influenced by behavioural issues such as journey avoidance and
restructuring freight logistics systems;
• system infrastructure and modal choice (NRC, 2009);
• energy intensity — directly related to vehicle and engine design
efficiency, driver behaviour during operation (Davies, 2012), and
usage patterns; and
• fuel carbon intensity — varies for different transport fuels includ-
ing electricity and hydrogen.
Each of these components has good potential for mitigation through
technological developments, behavioural change, or interactions
3 Based on the breakdown into A (total Activity), S (modal Structure), I (modal
energy Intensity), and F (carbon content of Fuels) using the ‘ASIF approach’.
Details of how this decomposition works and the science involved can be found in
Schipper et al. (2000); Kamakaté and Schipper (2009).
between them, such as the deployment of electric vehicles impacting
on average journey distance and urban infrastructure (see Figure 8.2).
Deep long-term emission reductions also require pricing signals and
interactions between the emission factors. Regional differences exist
such as the limited modal choice available in some developing coun-
tries and the varying densities and scales of cities (Banister, 2011a).
Indirect GHG emissions that arise during the construction of transport
infrastructure, manufacture of vehicles, and provision of fuels, are cov-
ered in Chapters 12, 10, and 7 respectively.
8�1�1 The context for transport of passengers
and freight
Around 10 % of the global population account for 80 % of total
motorized passenger-kilometres (p-km) with much of the world’s
population hardly travelling at all. OECD countries dominate GHG
transport emissions (see Figure 8.3) although most recent growth
has taken place in Asia, including passenger kilometres travelled by
low GHG emitting 2- to 3-wheelers that have more than doubled
since 2000 (see Figure 8.4). The link between GDP and transport has
Figure 8�2 | Direct transport GHG emission reductions for each mode and fuel type option decomposed into activity (passenger or freight movements); energy intensity (specific
energy inputs linked with occupancy rate); fuel carbon intensity (including non-CO2 GHG emissions); and system infrastructure and modal choice. These can be summated for each
modal option into total direct GHG emissions. Notes: p-km = passenger-km; t-km = tonne-km; CNG = compressed natural gas; LPG = liquid petroleum gas (Creutzig etal., 2011;
Bongardt etal., 2013).
Physical
Units
Decomposition
Factors
Examples
Activity
Energy
Intensity
Fuel Carbon
Intensity
Fuels
∑ ∑
Total GHG Emissions =
Modal Shares
tCO2eq / MJp-kmmode / p-kmtotal
t-kmmode / t-kmtotal
MJ / p-km
MJ / t-km
p-kmtotal
t-kmtotal
Fuel Carbon IntensitySystem-Infrastructure
Modal Choice
Energy Intensity Activity
• Number of Journeys
• Journey Distance
• Journey Avoidance
(Combining Trips, Video
Conferencing, etc.)
… of:
• Diesel
• Gasoline
• CNG / LPG
• Biofuels
• Electricity
• Hydrogen
… of:
• Light Duty Vehicles
(LDVs), 2-/3-Wheelers
• Heavy Duty Vehicles
(HDVs), Buses
• Trains
• Aircraft
• Ships and Boats
• Cycling, Walking
• Occupancy / Loading Rate
• Urban Form
• Transport Infrastructure
(Roads, Rail, Airports, …)
• Behavioural Choice
between Modes
(Speed, Comfort, Cost,
Convenience)
Total GHG Emissions

607607
Transport
8
Chapter 8
been a major reason for increased GHG emissions (Schafer and Vic-
tor, 2000) though the first signs that decoupling may be happening
are now apparent (Newman and Kenworthy, 2011a; Schipper, 2011).
Slower rates of growth, or even reductions in the use of LDVs, have
been observed in some OECD cities (Metz, 2010, 2013; Meyer etal.,
2012; Goodwin and van Dender, 2013; Headicar, 2013) along with a
simultaneous increase in the use of mass transit systems (Kenwor-
thy, 2013). The multiple factors causing this decoupling, and how it
can be facilitated more widely, are not well understood (ITF, 2011;
Goodwin and Van Dender, 2013). However, ‘peak’ travel trends are
not expected to occur in most developing countries in the foreseeable
future, although transport activity levels may eventually plateau at
lower GDP levels than for OECD countries due to higher urban densi-
ties and greater infrastructure constraints (ADB, 2010; Figueroa and
Ribeiro, 2013).
As shown in Figure 8.3, the share of transport emissions tended to
increase due to structural changes as GDP per capita increased, i. e.,
countries became richer. The variance between North America and
other OECD countries (Western Europe and Pacific OECD) shows that
the development path of infrastructure and settlements taken by
developing countries and economies in transition (EITs) will have a sig-
nificant impact on the future share of transport related emissions and,
consequently, total GHG emissions (see Section 12.4).
between them, such as the deployment of electric vehicles impacting
on average journey distance and urban infrastructure (see Figure 8.2).
Deep long-term emission reductions also require pricing signals and
interactions between the emission factors. Regional differences exist
such as the limited modal choice available in some developing coun-
tries and the varying densities and scales of cities (Banister, 2011a).
Indirect GHG emissions that arise during the construction of transport
infrastructure, manufacture of vehicles, and provision of fuels, are cov-
ered in Chapters 12, 10, and 7 respectively.
8�1�1 The context for transport of passengers
and freight
Around 10 % of the global population account for 80 % of total
motorized passenger-kilometres (p-km) with much of the world’s
population hardly travelling at all. OECD countries dominate GHG
transport emissions (see Figure 8.3) although most recent growth
has taken place in Asia, including passenger kilometres travelled by
low GHG emitting 2- to 3-wheelers that have more than doubled
since 2000 (see Figure 8.4). The link between GDP and transport has
Figure 8�2 | Direct transport GHG emission reductions for each mode and fuel type option decomposed into activity (passenger or freight movements); energy intensity (specific
energy inputs linked with occupancy rate); fuel carbon intensity (including non-CO2 GHG emissions); and system infrastructure and modal choice. These can be summated for each
modal option into total direct GHG emissions. Notes: p-km = passenger-km; t-km = tonne-km; CNG = compressed natural gas; LPG = liquid petroleum gas (Creutzig etal., 2011;
Bongardt etal., 2013).
Physical
Units
Decomposition
Factors
Examples
Activity
Energy
Intensity
Fuel Carbon
Intensity
Fuels
∑ ∑
Total GHG Emissions =
Modal Shares
tCO2eq / MJp-kmmode / p-kmtotal
t-kmmode / t-kmtotal
MJ / p-km
MJ / t-km
p-kmtotal
t-kmtotal
Fuel Carbon IntensitySystem-Infrastructure
Modal Choice
Energy Intensity Activity
• Number of Journeys
• Journey Distance
• Journey Avoidance
(Combining Trips, Video
Conferencing, etc.)
… of:
• Diesel
• Gasoline
• CNG / LPG
• Biofuels
• Electricity
• Hydrogen
… of:
• Light Duty Vehicles
(LDVs), 2-/3-Wheelers
• Heavy Duty Vehicles
(HDVs), Buses
• Trains
• Aircraft
• Ships and Boats
• Cycling, Walking
• Occupancy / Loading Rate
• Urban Form
• Transport Infrastructure
(Roads, Rail, Airports, …)
• Behavioural Choice
between Modes
(Speed, Comfort, Cost,
Convenience)
Total GHG Emissions

608608
Transport
8
Chapter 8
Figure 8�3 | GHG emissions from transport sub-sectors by regions in 1970, 1990 and 2010 with international shipping and aviation shown separately (IEA, 2012a; JRC / PBL, 2013;
see Annex II.8). Inset shows the relative share of total GHG emissions for transport relative to GDP per capita from 1970 to 2010 for each region and the world. Adapted from
Schäfer etal. (2009), Bongardt etal. (2013) using data from IEA (2012a) and JRC / PBL (2013); see Annex II.8.
*1.71
*2.66
*3.14
*0.07
*0.26
*0.57
*0.14
*0.29
*0.55
*0.26
*0.51 *0.48
*0.14
*0.40
*1.15
*0.48
*0.62
*1.10
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1970 1990 2010 1970 1990 2010
OECD-1990
1970 1990 2010
ASIA
1970 1990 2010
EIT
1970 1990 2010
MAF
1970 1990 2010
LAM INT-TRA
GHG Emissions [GtCO
2
eq/yr]
Indirect Emissions
from Electricity Generation
Road
Rail
HFC and Indirect N2O
Pipelines etc.
Domestic Waterborne
International and
Coastal Shipping
International Aviation
Domestic Aviation
Total
(Without Indirect Emissions)
*
Transport Sector Share in CO2-Emissions [%]
0 5000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000
0
5
10
15
20
25
30
GDP per Capita [Int$2005]
East Asia North America Sub Saharan Africa
Economies in Transition South-East Asia and Pacific Western Europe
Latin America and Caribbean Pacific OECD World
Middle East and North Africa South Asia
8�1�2 Energy demands and direct / indirect
emissions
Over 53 % of global primary oil consumption in 2010 was used to
meet 94 % of the total transport energy demand, with biofuels supply-
ing approximately 2 %, electricity 1 %, and natural gas and other fuels
3 % (IEA, 2012b). LDVs consumed around half of total transport
energy (IEA, 2012c). Aviation accounted for 51 % of all international
passenger arrivals in 2011 (UNWTO, 2012) and 17 % of all tourist
travel in 2005 (ICAO, 2007a; UNWTO and UNEP, 2008). This gave 43 %
of all tourism transport CO2eq emissions, a share forecast to increase
to over 50 % by 2035 (Pratt et al., 2011). Buses and trains carried
about 34 % of world tourists, private cars around 48 %, and water-
borne craft only a very small portion (Peeters and Dubois, 2010).
Freight transport consumed almost 45 % of total transport energy in
2009 with HDVs using over half of that (Figure 8.5). Ships carried
around 80 % (8.7 Gt) of internationally traded goods in 2011 (UNC-
TAD, 2013) and produced about 2.7 % of global CO2 emissions
(Buhaug and et. al, 2009).
Direct vehicle CO2 emissions per kilometre vary widely for each mode
(see Figure 8.6). The particularly wide range of boat types and sizes
gives higher variance for waterborne than for other modes of trans-
port (Walsh and Bows, 2012). Typical variations for freight movement
range from ~2gCO2 / t-km for bulk shipping to ~1,700gCO2 / t-km for
short-haul aircraft, whereas passenger transport typically ranges from
~20 – 300 gCO2 / p-km. GHG emissions arising from the use of liquid
and gaseous fuels produced from unconventional reserves, such as
Figure 8�5 | Final energy consumption of fuels by transport sub-sectors in 2009 for freight and passengers, with heat losses at around two thirds of total fuel energy giving an
average conversion efficiency of fuel to kinetic energy of around 32 %. Note: Width of lines depicts total energy flows. (IEA, 2012d).
Rail
2 EJ
Air
10 EJ
Light
Road
48 EJ
Passenger
53 EJ
Freight
40 EJ
Mechanical
Energy
30 EJ
Losses
63 EJ
Heavy
Road
23 EJ
Water
9 EJ
Heavy Oil,
Biofuels,
Kerosene
20 EJ
Gasoline
39 EJ
Electricity
0.71 EJ
Diesel
32 EJ
Gaseous
0.74 EJ

609609
Transport
8
Chapter 8
from oil sands and shale deposits, vary with the feedstock source and
refining process. Although some uncertainty remains, GHG emissions
from unconventional reserves are generally higher per vehicle kilome-
tre compared with using conventional petroleum products (Brandt,
2009, 2011, 2012; Charpentier etal., 2009; ETSAP, 2010; IEA, 2010a;
Howarth etal., 2011, 2012; Cathles etal., 2012).
‘Sustainable transport’, arising from the concept of sustainable devel-
opment, aims to provide accessibility for all to help meet the basic
daily mobility needs consistent with human and ecosystem health, but
to constrain GHG emissions by, for example, decoupling mobility from
oil dependence and LDV use. Annual transport emissions per capita
correlate strongly with annual income, both within and between coun-
tries (Chapter 5) but can differ widely even for regions with similar
income per capita. For example, the United States has around 2.8 times
the transport emissions per capita than those of Japan (IEA, 2012a).
In least developed countries (LDCs), increased motorized mobility will
produce large increases in GHG emissions but give significant social
benefits such as better access to markets and opportunities to improve
education and health (Africa Union, 2009; Pendakur, 2011; Sietchiping
etal., 2012). Systemic goals for mobility, climate, and energy security
can help develop the more general sustainable transport principles.
Affordable, safe, equitable, and efficient travel services can be pro-
vided with fairness of mobility access across and within generations
(CST, 2002; ECMT, 2004; Bongardt etal., 2011; E C Environment, 2011;
Zegras, 2011; Figueroa and Kahn Ribeiro, 2013).
The following sections of this chapter outline how changes to the
transport sector could reduce direct GHG emissions over the next
decades to help offset the significant global increase in demand pro-
jected for movement of both passengers and freight.
Freight transport consumed almost 45 % of total transport energy in
2009 with HDVs using over half of that (Figure 8.5). Ships carried
around 80 % (8.7 Gt) of internationally traded goods in 2011 (UNC-
TAD, 2013) and produced about 2.7 % of global CO2 emissions
(Buhaug and et. al, 2009).
Direct vehicle CO2 emissions per kilometre vary widely for each mode
(see Figure 8.6). The particularly wide range of boat types and sizes
gives higher variance for waterborne than for other modes of trans-
port (Walsh and Bows, 2012). Typical variations for freight movement
range from ~2gCO2 / t-km for bulk shipping to ~1,700gCO2 / t-km for
short-haul aircraft, whereas passenger transport typically ranges from
~20 – 300 gCO2 / p-km. GHG emissions arising from the use of liquid
and gaseous fuels produced from unconventional reserves, such as
Figure 8�5 | Final energy consumption of fuels by transport sub-sectors in 2009 for freight and passengers, with heat losses at around two thirds of total fuel energy giving an
average conversion efficiency of fuel to kinetic energy of around 32 %. Note: Width of lines depicts total energy flows. (IEA, 2012d).
Rail
2 EJ
Air
10 EJ
Light
Road
48 EJ
Passenger
53 EJ
Freight
40 EJ
Mechanical
Energy
30 EJ
Losses
63 EJ
Heavy
Road
23 EJ
Water
9 EJ
Heavy Oil,
Biofuels,
Kerosene
20 EJ
Gasoline
39 EJ
Electricity
0.71 EJ
Diesel
32 EJ
Gaseous
0.74 EJ
Figure 8�4 | Total passenger distance travelled by mode and region in 2000 and 2010
(IEA, 2012c)
Note: Non-motorized modal shares are not included, but can be relatively high in Asia
and Africa. For RC5 region definitions see Annex II.2.
Total Passenger Distance Travelled [Trillion p-km]
0
5
2000
OECD-1990
2010
ASIA
2000 2010 2000
EIT
2010 2000
LAM
2010
MAF
2000 2010
10
15
20
Air
Rail
Buses
Light Duty Vehicles
2-3 Wheelers

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8.2 New developments
in emission trends
and drivers
Assessments of transport GHG emissions require a comprehensive
and differential understanding of trends and drivers that impact on
the movement of goods and people. Transport’s share of total national
GHG emissions range from up to 30 % in high income economies to
less than 3 % in LDCs, mirroring the status of their industry and ser-
vice sectors (Schäfer etal., 2009; Bongardt etal., 2011) (IEA, 2012a;
JRC / PBL, 2013; see Annex II.8) (see inset Figure 8.3). Travel patterns
vary with regional locations and the modes available, and guide the
development of specific emission reduction pathways.
Indicators such as travel activity, vehicle occupancy rates, and fuel
consumption per capita can be used to assess trends towards reduc-
ing emissions and reaching sustainability goals (WBCSD, 2004; Dalk-
mann and Brannigan, 2007; Joumard and Gudmundsson, 2010; Kane,
2010; Litman, 2007; Ramani etal., 2011). For example, petroleum prod-
uct consumption to meet all transport demands in 2009 ranged from
52 GJ / capita in North America to less than 4 GJ / capita in Africa and
India where mobility for many people is limited to walking and cycling.
Likewise, residents and businesses of several cities in the United States
consume over 100GJ / capita each year on transport whereas those in
Figure 8�6 | Typical ranges of direct CO2 emissions per passenger kilometre and per tonne-kilometre for freight, for the main transport modes when fuelled by fossil fuels including
thermal electricity generation for rail. (ADEME, 2007; US DoT, 2010; Der Boer etal., 2011; NTM, 2012; WBCSD, 2012).
0 25050 150 200100
0 500 1000 1500 2000 2500 3000
Long-haul cargo aircraft
Short-haul cargo aircraft
Long-haul bellyhold in passenger
Short-haul bellyhold in passenger
Passenger aircraft
Bulk tanker - ocean
Bulk carrier - ocean
Container ship - ocean
Container ship - coastal
Roll-on, roll-off ferry
Barge
Passenger ferry
Electric freight train
Diesel freight train
Passenger rail, metro, tram
HDV large
HDV medium
HDV small
LDV commercial (van)
2- and 3-wheel motorbike
Coach, bus, rapid transit
LDV Taxi gasoline, diesel, hybrid
LDV gasoline, diesel, hybrid
Road
Rail
Waterborne
Air
Freight [g CO2/t-km]
Passenger [g CO2/p-km]
Direct* CO2 Emissions per Distance [gCO2/km] Direct* CO2 Emissions per Distance [gCO2/km]
*The ranges only give an indication of direct vehicle fuel emissions. They exclude indirect emissions arising from
vehicle manufacture, infrastructure, etc. included in life-cycle analyses except from electricity used for rail.

611611
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many Indian and Chinese cities use less than 2 GJ / capita (Newman and
Kenworthy, 2011a). For freight, companies are starting to adopt green
initiatives as a means of cost savings and sustainability initiatives (Fürst
and Oberhofer, 2012). Such programmes are also likely to reduce GHG
emissions, although the long-term impact is difficult to assess.
8�2�1 Trends
As economies have shifted from agriculture to industry to service, the
absolute GHG emissions from transport (Figure 8.1) and the share
of total GHG emissions by the transport sector (Chapter 5.2.1) have
risen considerably. Total LDV ownership is expected to double in the
next few decades (IEA, 2009) from the current level of around 1 bil-
lion vehicles (Sousanis, 2011). Two-thirds of this growth is expected
in non-OECD countries where increased demand for mobility is also
being met by motorized two-wheelers and expansion of bus and rail
public transport systems. However, passenger kilometres travelled and
per capita ownership of LDVs will likely remain much lower than in
OECD countries (Cuenot etal., 2012; Figueroa etal., 2013).
Air transport demand is projected to continue to increase in most
OECD countries (see Section 8.9). Investments in high-speed rail sys-
tems could moderate growth rates over short- to medium-haul dis-
tances in Europe, Japan, China, and elsewhere (Park and Ha, 2006;
Gilbert and Perl, 2010; Åkerman, 2011; Salter etal., 2011).
There is limited evidence that reductions to date in carbon intensity,
energy intensity, and activity, as demonstrated in China, Japan, and
Europe, have adequately constrained transport GHG emissions growth in
the context of mitigation targets. Recent trends suggest that economic,
lifestyle, and cultural changes will be insufficient to mitigate global
increases in transport emissions without stringent policy instruments,
incentives, or other interventions being needed (see Section 8.10).
8�2�1�1 Non-CO2 greenhouse gas emissions, black
carbon, and aerosols
The transport sector emits non-CO2 pollutants that are also climate
forcers. These include methane, volatile organic compounds (VOCs),
nitrogen oxides (NOx), sulphur dioxide (SO2), carbon monoxide (CO),
F-gases, black carbon, and non-absorbing aerosols (Ubbels etal., 2002;
Sections 5.2.2 and 6.6.2.1). Methane emissions are largely associ-
ated with leakage from the production of natural gas and the filling
of compressed natural gas vehicles; VOCs, NOx and CO are emitted by
internal combustion engines; and F-gas emissions generally from air
conditioners (including those in vehicles) and refrigerators. Contrails
from aircraft and emissions from ships also impact on the troposphere
and the marine boundary layer, respectively (Fuglestvedt etal., 2009;
Lee etal., 2010). Aviation emissions can also impact on cloud forma-
tion and therefore have an indirect effect on climate forcing (Burkhardt
and Kärcher, 2011).
Black carbon and non-absorbing aerosols, emitted mainly during diesel
engine operation, have short lifetimes in the atmosphere of only days
to weeks, but can have significant direct and indirect radiative forc-
ing effects and large regional impacts (Boucher etal., 2013). In North
and South America and Europe, over half the black carbon emissions
result from combusting diesel and other heavy distillate fuels (includ-
ing marine oil), in vehicle engines (Bond etal., 2013). Black carbon
emissions are also significant in parts of Asia, Africa, and elsewhere
from biomass and coal combustion, but the relative contribution from
transport is expected to grow in the future. There is strong evidence
that reducing black carbon emissions from HDVs, off-road vehicles, and
ships could provide an important short term strategy to mitigate atmo-
spheric concentrations of positive radiative forcing pollutants (USEPA,
2012; Shindell etal., 2013; Chapter 6.6; WG I Chapter 7).
Conversely, transport is also a significant emitter of primary aerosols
that scatter light and gases that undergo chemical reactions to pro-
duce secondary aerosols. Primary and secondary organic aerosols, sec-
ondary sulphate aerosols formed from sulphur dioxide emissions, and
secondary nitrate aerosols from nitrogen oxide emissions from ships,
aircraft, and road vehicles, can have strong, local, and regional cooling
impacts (Boucher etal., 2013).
The relative contributions of different short-term pollutants to radiative
forcing in 2020 have been equated by Unger etal. (2010) to having
continuous constant GHG emissions since 2000. Although this study
did not provide a projection for future emissions scenarios, it did offer
a qualitative comparison of short- and long-term impacts of different
pollutants. Relative to CO2, major short-term impacts stem from black
carbon, indirect effects of aerosols and ozone from land vehicles, and
aerosols and methane emissions associated with ships and aircraft.
Their relative impacts due to the longer atmospheric lifetime of CO2
will be greatly reduced when integrated from the present time to 2100.
Although emissions of non-CO2 GHGs and aerosols can be mitigated
by reducing carbon intensity, improving energy intensity, changing to
lower-carbon modes, and reducing transport activity, they can also
be significantly reduced by technologies that prevent their formation
or lead to their destruction using after-treatments. Emission control
devices such as diesel particulate filters and selective catalytic reduc-
tion have fuel efficiency penalties that can lead to an increase in trans-
port CO2 emissions.
Non-CO2 emissions from road transport and aviation and shipping
activities in ports have historically been constrained by local air qual-
ity regulations that are directed at near-surface pollution and seek to
protect human health and welfare by reducing ozone, particulate mat-
ter, sulphur dioxide, and toxic components or aerosols, including vana-
dium, nickel, and polycyclic aromatic hydrocarbons (Verma etal. 2011).
The importance of regional climate change in the context of mitiga-
tion has prompted a growing awareness of the climate impact of these
emissions. Policies are already in place for reducing emissions of
F-gases, which are expected to continue to decrease with time (Prinn

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etal., 2000). More efforts are being directed at potential programmes
to accelerate control measures to reduce emissions of black carbon,
ozone precursors, aerosols, and aerosol precursors (Lin and Lin, 2006).
Emissions from road vehicles continue to decrease per unit of travel
in many regions due to efforts made to protect human health from
air pollution. The implementation of these controls could potentially
be accelerated as a driver to mitigate climate change (Oxley et al.,
2012). Short-term mitigation strategies that focus on black carbon and
contrails from aircraft, together with national and international pro-
grammes to reduce aerosol and sulphate emissions from shipping, are
being implemented (Buhaug and et. al, 2009; Lack, 2012). However,
the human health benefits from GHG emissions reductions and the co-
benefits of climate change mitigation through black carbon reductions
need to be better assessed (Woodcock etal., 2009).
8�2�2 Drivers
The major drivers that affect transport trends are travel time budgets,
costs and prices, increased personal income, and social and cultural
factors (Schäfer, 2011). For a detailed discussion of effects of urban
form and structure on elasticities of vehicle kilometres travelled see
Section 12.4.2.
Travel time budget� Transport helps determine the economy of a city
or region based on the time taken to move people and goods around.
Travel time budgets are usually fixed and tied to both travel costs and
time costs (Noland, 2001; Cervero, 2001; Noland and Lem, 2002).
Because cities vary in the proportion of people using different trans-
port modes, urban planners tend to try to adapt land use planning to
fit these modes in order to enable speeds of around 5 km / hr for walk-
ing, 20 – 30 km / hr for mass transit, and 40 – 50 km / hr for LDVs, though
subject to great variability. Infrastructure and urban areas are usually
planned for walking, mass transit, or LDVs so that destinations can be
reached in half an hour on average (Newman and Kenworthy, 1999).
Urban travel time budgets for a typical commute between work and
home average around 1.1 – 1.3 hours per traveller per day in both
developed and developing economies (Zahavi and Talvitie, 1980; van
Wee etal., 2006). Higher residential density can save fuel for LDVs, but
leads to more congested commutes (Small and Verhoef, 2007; Downs,
2004). While new road construction can reduce LDV travel time in the
short run, it also encourages increased LDV demand, which typically
leads to increases in travel time to a similar level as before (Maat and
Arentze, 2012). Moreover, land uses quickly adapt to any new road
transport infrastructure so that a similar travel time eventually resumes
(Mokhtarian and Chen, 2004).
Regional freight movements do not have the same fixed time demands,
but rather are based more on the need to remain competitive by limit-
ing transport costs to a small proportion of the total costs of the goods
(Schiller etal. 2010). See also Section 12.4.2.4 on accessibility aspects
of urban form.
Costs and prices� The relative decline of transport costs as a share of
increasing personal expenditure has been the major driver of increased
transport demand in OECD countries throughout the last century and
more recently in non-OECD countries (Mulalic etal., 2013). The price of
fuel, together with the development of mass transit systems and non-
motorized transport infrastructure, are major factors in determining the
level of LDV use versus choosing public transport, cycling, or walking
(Hughes etal., 2006). Transport fuel prices, heavily influenced by taxes,
also impact on the competition between road and rail freight. The costs
of operating HDVs, aircraft, and boats increase dramatically when fuel
costs go up given that fuel costs are a relatively high share of total costs
(Dinwoodie, 2006). This has promulgated the designs of more fuel effi-
cient engines and vehicle designs (Section 8.3) (IEA, 2009). Although the
average life of aircraft and marine engines is two to three decades and
fleet turnover is slower than for road vehicles and small boats, improv-
ing their fuel efficiency still makes good economic sense (IEA, 2009).
The high cost of developing new infrastructure requires significant cap-
ital investment that, together with urban planning, can be managed
and used as a tool to reduce transport demand and also encourage
modal shift (Waddell etal., 2007). Changing urban form through plan-
ning and development can therefore play a significant role in the miti-
gation of transport GHG emissions (see Section 8.4) (Kennedy etal.,
2009). See also Section 12.5.2 on urban policy instruments.
Social and cultural factors. Population growth and changes in
demographics are major drivers for increased transport demand. Eco-
nomic structural change, particularly in non-OECD countries, can lead
to increased specialization of jobs and a more gender-diversified work-
force, which can result in more and longer commutes (McQuaid and
Chen, 2012). At the household level, once a motorized vehicle becomes
affordable, even in relatively poor households, then it becomes a major
item of expenditure; however, ownership has still proven to be increas-
ingly popular with each new generation (Giuliano and Dargay, 2006;
Lescaroux, 2010; Zhu etal., 2012). Thus, there is a high growth rate
in ownership of motorized two-wheel vehicles and LDVs evident in
developing countries, resulting in increasing safety risks for pedestrians
and non-motorized modes (Nantulya and Reich 2002; Pendakur, 2011).
The development of large shopping centres and malls usually located
outside the city centre allows many products to be purchased by a con-
sumer following a single journey but the travel distance to these large
shopping complexes has tended to increase (Weltevreden, 2007). For
freight transport, economic globalization has increased the volume and
distance of movement of goods and materials (Henstra etal., 2007).
Modal choice can be driven by social factors that are above and
beyond the usual time, cost, and price drivers. For example, some
urban dwellers avoid using mass transit or walking due to safety and
security issues. However, there is evidence that over the past decade
younger people in some OECD cities are choosing walking, cycling, and
mass transit over LDVs (Parkany etal., 2004; Newman and Kenworthy,
2011b; Delbosc and Currie, 2013; Kuhnimhof etal., 2013) although this
trend could change as people age (Goodwin and van Dender, 2013).

613613
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Another example is that in some societies, owning and driving a LDV
can provide a symbolic function of status and a basis for sociability
and networking through various sign-values such as speed, safety, suc-
cess, career achievement, freedom, masculinity, and emancipation of
women (Mokhtarian and Salomon, 2001; Steg, 2005; Bamberg etal.,
2011; Carrabine and Longhurst, 2002; Miller, 2001; Sheller, 2004; Urry,
2007). In such cases, the feeling of power and superiority associated
with owning and using a LDV may influence driver behaviour, for
example, speeding without a concern for safety, or without a concern
about fuel consumption, noise, or emissions (Brozović and Ando, 2009;
Tiwari and Jain, 2012). The possible effects on travel patterns from
declining incomes are unclear.
Lifestyle and behavioural factors are important for any assessment
of potential change to low-carbon transport options and additional
research is needed to assess the willingness of people to change
(Ashton-Graham, 2008; Ashton-Graham and Newman, 2013). Disrup-
tive technologies such as driverless cars and consumer-based manu-
facturing (e. g. 3-D printing) could impact on future transport demands
but these are difficult to predict. Likewise, the impact of new informa-
tion technology (IT) applications and telecommuting could potentially
change travel patterns, reduce trips, or facilitate interactions with the
mode of choice (ITF, 2011). Conversely, increased demand for tourism
is expected to continue to be a driver for all transport modes (Sections
8.1 and 10.4; Gössling etal., 2009).
8.3 Mitigation technology
options, practices and
behavioural aspects
Technological improvements and new technology-related practices
can make substantial contributions to climate change mitigation in
the transport sector. This section focuses on energy intensity reduction
technology options for LDVs, HDVs, ships, trains and aircraft and fuel
carbon intensity reduction options related to the use of natural gas,
electricity, hydrogen and biofuels. It also addresses some technology-
related behavioural aspects concerning the uptake and use of new
technologies, behaviour of firms, and rebound effects. Urban form and
modal shift options are discussed in Section 8.4.
8�3�1 Energy intensity reduction — incremental
vehicle technologies
Recent advances in LDVs in response to strong regulatory efforts in
Japan, Europe, and the United States have demonstrated that there is
substantial potential for improving internal combustion engines (ICEs)
with both conventional and hybrid drive-trains. Recent estimates sug-
gest substantial additional, unrealized potentials exist compared to
similar-sized, typical 2007 – 2010 vehicles, with up to 50 % improve-
ments in vehicle fuel economy (in MJ / km or litres / 100km units, or
equal to 100 % when measured as km / MJ, km / l, or miles per gal-
lon) (Bandivadekar etal., 2008; Greene and Plotkin, 2011). Similar or
slightly lower potentials exist for HDVs, waterborne craft, and aircraft.
8�3�1�1 Light duty vehicles
As of 2011, leading-edge LDVs had drive-trains with direct injection
gasoline or diesel engines (many with turbochargers), coupled with
automated manual or automatic transmissions with six or more gears
(SAE International, 2011). Drive-train redesigns of average vehicles to
bring them up to similar levels could yield reductions in fuel consump-
tion and GHG emissions of 25 % or more (NRC, 2013). In European
Union 27 (EU27), the average tested emissions of 2011 model LDVs
was 136 gCO2 / km, with some models achieving below 100 gCO2 / km
(EEA, 2012). In developing countries, vehicle technology levels are typi-
cally lower, although average fuel economy can be similar since vehicle
size, weight, and power levels are also typically lower (IEA, 2012d).
Hybrid drive-trains (ICE plus electric motor with battery storage) can
provide reductions up to 35 % compared to similar non-hybridized
vehicles (IEA, 2012e) and have become mainstream in many countries,
but with only a small share of annual sales over the last decade except
in Japan, where over two million had been sold by 2012 (IEA, 2012e).
There is substantial potential for further advances in drive-train design
and operation, and for incremental technologies (NRC, 2013). There is
often a time lag between when new technologies first appear in OECD
countries and when they reach developing countries, which import
mostly second-hand vehicles (IEA, 2009).
Lower fuel consumption can be achieved by reducing the loads that
the engine must overcome, such as aerodynamic forces, auxiliary com-
ponents (including lighting and air conditioners), and rolling resis-
tance. Changes that reduce energy loads include improved aerodynam-
ics, more efficient auxiliaries, lower rolling-resistance tyres, and weight
reduction. With vehicle performance held constant, reducing vehicle
weight by 10 % gives a fuel economy improvement of about 7 % (EEA,
2006). Together, these non-drive-train changes offer potential fuel
consumption reductions of around 25 % (ICCT, 2012a; NRC, 2013).
Combined with improved engines and drive-train systems, overall LDV
fuel consumption for new ICE-powered vehicles could be reduced by
at least half by 2035 compared to 2005 (Bandivadekar etal., 2008;
NRC, 2013). This predicted reduction is consistent with the Global Fuel
Economy Initiative target for new LDVs of a 50 % reduction in average
fuel use per kilometre in 2030 compared to 2005 (Eads, 2010).
8�3�1�2 Heavy-duty vehicles
Most modern medium and HDVs already have efficient diesel engines
(up to 45 % thermal efficiency), and long-haul trucks often have

614614
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streamlined spoilers on their cabs to reduce drag, particularly in OECD
countries. Aerodynamic drag can also be reduced using other modifica-
tions offering up to 10 % reduction in fuel consumption (TIAX, 2009;
NRC, 2010; AEA, 2011). In non-OECD countries, many older trucks with
relatively inefficient (and highly polluting) engines are common. Truck
modernization, along with better engine, tyre, and vehicle maintenance,
can significantly improve fuel economy in many cases.
Medium and HDVs in the United States can achieve a reduction in
energy intensity of 30 – 50 % by 2020 by using a range of technology
and operational improvements (NRC, 2010a). Few similar estimates
are available in non-OECD countries, but most technologies eventually
will be applicable for HDVs around the world.
Expanding the carrying capacity of HDVs in terms of both volume and
weight can yield significant net reductions in the energy intensity of
trucks so long as the additional capacity is well utilized. A comparison
of the performance of 18 longer and heavier HDVs in nine countries
(ITF / OECD, 2010) concluded that higher capacity vehicles can signifi-
cantly reduce CO2 emissions per t-km. The use of long combination
vehicles rather than single trailer vehicles has been shown to cut direct
GHG emissions by up to 32 % (Woodrooffe and Ash, 2001).
Trucks and buses that operate largely in urban areas with a lot of
stop-and-go travel can achieve substantial benefits from using electric
hybrid or hydraulic hybrid drive-trains. Typically a 20 – 30 % reduction
in fuel consumption can be achieved via hybridization (Chandler etal.,
2006; AEA, 2011).
8�3�1�3 Rail, waterborne craft, and aircraft
Rail is generally energy efficient, but improvements can be gained from
multiple drive-trains and load-reduction measures. For example, the high-
speed ‘Shinkansen’ train in Japan gained a 40 % reduction of energy
consumption by optimizing the length and shape of the lead nose, reduc-
ing weight, and by using efficient power electronics (UIC, 2011); Amtrack
in the United States employed regenerative braking systems to reduce
energy consumption by 8 % (UIC, 2011); and in China, electrification and
other measures from 1975 to 2007 contributed to a 87 % reduction in
CO
2
emission intensity of the rail system (He etal., 2010).
Shipping is a comparatively efficient mode of freight and passenger
transport, although size and load factor are important determinants
for specific motorized craft, large and small. Efficiency of new-built ves-
sels can be improved by 5 – 30 % through changes in engine and trans-
mission technologies, waste heat recovery, auxiliary power systems,
propeller and rotor systems, aerodynamics and hydrodynamics of the
hull structure, air lubrication systems, electronically controlled engine
systems to give fuel efficient speeds, and weight reduction (IMO, 2009;
Notteboom and Vernimmen, 2009; AEA, 2007; IEA, 2009; IMO, 2009;
ICCT, 2011). Retrofit and maintenance measures can provide additional
efficiency gains of 4 – 20 % (Buhaug and et. al, 2009) and operational
changes, such as anti-fouling coatings to cut water resistance, along
with operation at optimal speeds, can provide 5 – 30 % improvement
(Pianoforte, 2008; Corbett etal., 2009; WSC, 2011).
Several methods for improving waterborne craft efficiency are already
in use. For example, wind propulsion systems such as kites and para-
foils can provide lift and propulsion to reduce fuel consumption by up
to 30 %, though average savings may be much less (Kleiner, 2007).
Photovoltaics and small wind turbines can provide on-board electricity
and be part of ‘cold ironing’ electric systems in ports. For international
shipping, combined technical and operational measures have been
estimated to potentially reduce energy use and CO2 emissions by up
to 43 % per t-km between 2007 and 2020 and by up to 60 % by 2050
(Crist, 2009; IMO, 2009).
Aircraft designs have received substantial, on-going technology effi-
ciency improvements over past decades (ITF, 2009) typically offering
a 20 – 30 % reduction in energy intensity compared to older aircraft
models (IEA, 2009). Further fuel efficiency gains of 40 – 50 % in the
2030 – 2050 timeframe (compared to 2005) could come from weight
reduction, aerodynamic and engine performance improvements, and
aircraft systems design (IEA, 2009). However, the rate of introduction
of major aircraft design concepts could be slow without significant
policy incentives, regulations at the regional or global level, or fur-
ther increases in fuel prices (Lee, 2010). Retrofit opportunities, such as
engine replacement and adding ‘winglets’, can also provide significant
reductions (Gohardani etal., 2011; Marks, 2009). Improving air traffic
management can reduce CO2 emissions through more direct routings
and flying at optimum altitudes and speeds (Dell’Olmo and Lulli, 2003;
Pyrialakou et al., 2012). Efficiency improvements of ground service
equipment and electric auxiliary power units can provide some addi-
tional GHG reductions (Pyrialakou etal., 2012).
8�3�2 Energy intensity reduction — advanced
propulsion systems
At present, most vehicles and equipment across all transport modes are
powered by ICEs, with gasoline and diesel as the main fuels for LDVs;
gasoline for 2- and 3-wheelers and small water craft; diesel for HDVs;
diesel or heavy fuel oil for ships and trains (other than those using grid
electricity); and kerosene for aircraft turbine engines. New propulsion
systems include electric motors powered by batteries or fuel cells, tur-
bines (particularly for rail), and various hybridized concepts. All offer
significant potential reductions in GHG, but will require considerable
time to penetrate the vehicle fleet due to slow stock turnover rates.
8�3�2�1 Road vehicles — battery and fuel cell electric-
drives
Battery electric vehicles (BEVs) emit no tailpipe emissions and have
potentially very low fuel-production emissions (when using low-car-

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bon electricity generation) (Kromer and Heywood, 2007). BEVs oper-
ate at a drive-train efficiency of around 80 % compared with about
20 – 35 % for conventional ICE LDVs. At present, commercially avail-
able BEVs typically have a limited driving range of about 100 – 160km,
long recharge times of four hours or more (except with fast-charging
or battery switching systems), and high battery costs that lead to rel-
atively high vehicle retail prices (Greene and Plotkin, 2011). Lithium
ion (Li-ion) batteries will likely improve but new battery technologies
(e. g., Li-air, Li-metal, Li-sulphur) and ultra-capacitors may be required
to achieve much higher energy and power densities (IEA, 2009; NRC,
2013). Compressed air as an energy storage medium for LDVs is
thermo-dynamically inefficient and would require high storage volume
(Creutzig etal., 2009).
Plug-in hybrid electric vehicles (PHEVs) capable of grid recharging
typically can operate on battery electricity for 20 to 50 km, but emit
CO2 when their ICE is operating. The electric range of PHEVs is heav-
ily dependent on the size of battery, design architectures, and control
strategies for the operation of each mode (Plotkin etal., 2001).
For HDVs, the use of BEVs is most applicable to light-medium duty
urban vehicles such as delivery vans or garbage collection trucks
whose drive cycles involve frequent stops and starts and do not need a
long range (TIAX, 2009; AEA, 2011). Transit buses are also good candi-
dates for electrification either with batteries or more commonly using
overhead wire systems (IEA, 2009). Electric 2-wheelers with lower
requirements for battery and motor capacities are a mature technology
with widespread acceptance, especially in developing countries (Wein-
ert, 2008). For example, there were over 120 million electric 2-wheel-
ers in China by the end of 2010 (Wu etal., 2011).
Fuel cell vehicles (FCVs) can be configured with conventional, hybrid,
or plug-in hybrid drive-trains. The fuel cells generate electricity from
hydrogen that may be generated on-board (by reforming natural gas,
methanol, ammonia, or other hydrogen-containing fuel), or produced
externally and stored on-board after refuelling. FCVs produce no tail-
pipe emissions except water and can offer a driving range similar to
today’s gasoline / diesel LDVs, but with a high cost increment. Fuel cells
typically operate with a conversion efficiency of 54 – 61 % (significantly
better than ICEs can achieve), giving an overall fuel-cycle efficiency of
about 35 – 49 % for an LDV (JHFC, 2011).
Although a number of FCV LDVs, HDVs, and buses have been dem-
onstrated and some are expected to become commercially available
within five years, overall it could take 10 years or longer for FCVs to
achieve commercial success based on current oil and vehicle purchase
prices (IEA, 2012e).
8�3�2�2 Rail, waterborne craft, and aircraft
Diesel-hybrid locomotives demonstrated in the UK and advanced types
of hybrid drive-trains under development in the United States and
Japan, could save 10 – 20 % of diesel fuel plus around a 60 % reduc-
tion of NOx and particulate matter compared to conventional locomo-
tives (JR East, 2011). A shift to full electrification may enable many
rail systems to reach very low CO2 emissions per kilometre where elec-
tricity generation has been deeply decarbonized. Fuel cell systems for
rail may be attractive in areas lacking existing electricity infrastructure
(IEA, 2012e).
Most ocean-going ships will probably continue to use marine diesel
engines for the foreseeable future, given their high reliability and low
cost. However, new propulsion systems are in development. Full elec-
trification appears unlikely given the energy storage requirements for
long-range operations, although on-board solar power generation sys-
tems could be used to provide auxiliary power and is already used for
small craft (Crist, 2009). Fuel cell systems (commonly solid-oxide) with
electric motors could be used for propulsion, either with hydrogen fuel
directly loaded and stored on board or with on-board reforming. How-
ever, the cost of such systems appears relatively high, as are nuclear
power systems as used in some navy vessels.
For large commercial aircraft, no serious alternative to jet engines for
propulsion has been identified, though fuel-switching options are pos-
sible, including ‘drop-in’ biofuels (that are fungible with petroleum
products, can be blended from 0 to 100 %, and are compatible with all
existing engines) or hydrogen. Hydrogen aircraft are considered only
a very long run option due to hydrogen’s low energy density and the
difficulty of storing it on board, which requires completely new aircraft
designs and likely significant compromises in performance (Cryoplane,
2003). For small, light aircraft, advanced battery electric / motor sys-
tems could be deployed but would have limited range (Luongo etal.,
2009).
8�3�3 Fuel carbon intensity reduction
In principle, low-carbon fuels from natural gas, electricity, hydrogen,
and biofuels (including biomethane) could all enable transport systems
to be operated with low direct fuel-cycle CO2eq emissions, but this
would depend heavily on their feedstocks and conversion processes.
Natural gas (primarily methane) can be compressed (CNG) to replace
gasoline in Otto-cycle (spark ignition) vehicle engines after minor mod-
ifications to fuel and control systems. CNG can also be used to replace
diesel in compression ignition engines but significant modifications are
needed. Denser storage can be achieved by liquefaction of natural gas
(LNG), which is successfully being used for long-haul HDVs and ships
(Buhaug and et. al, 2009; Arteconi etal., 2010). The energy efficiency
of driving on CNG is typically similar to that for gasoline or diesel but
with a reduction of up to 25 % in tailpipe emissions (CO2 / km) because
of differences in fuel carbon intensity. Lifecycle GHG analysis suggests
lower net reductions, in the range of 10 – 15 % for natural gas fuel sys-
tems. They may also provide a bridge to lower carbon biomethane sys-
tems from biogas (IEA, 2009).

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Electricity can be supplied to BEVs and PHEVS via home or public
rechargers. The varying GHG emissions intensity of power grids directly
affects lifecycle CO2eq emissions (IEA, 2012e). Since the GHG inten-
sity of a typical coal-based power plant is about 1000 gCO2eq / kWh
at the outlet (Wang, 2012a), for a BEV with efficiency of 200 Wh / km,
this would equate to about 200 gCO2eq / km, which is higher than for
an efficient ICE or hybrid LDV. Using electricity generated from nuclear
or renewable energy power plants, or from fossil fuel plants with car-
bon dioxide capture and storage (CCS), near-zero fuel-cycle emissions
could result for BEVs. The numbers of EVs in any country are unlikely to
reach levels that significantly affect national electricity demand for at
least one to two decades, during which time electricity systems could
be at least partially decarbonized and modified to accommodate many
EVs (IEA, 2012e).
Hydrogen used in FCVs, or directly in modified ICEs, can be produced
by the reforming of biomass, coal or natural gas (steam methane
reforming is well-established in commercial plants); via commercial
but relatively expensive electrolysis using electricity from a range of
sources including renewable; or from biological processes (IEA, 2009).
The mix of feedstocks largely determines the well-to-wheel GHG emis-
sions of FCVs. Advanced, high-temperature and photo-electrochemical
technologies at the R&D stage could eventually become viable path-
ways (Arvizu etal., 2011). Deployment of FCVs (8.3.2.1) needs to be
accompanied by large, geographically focused, investments into hydro-
gen production and distribution and vehicle refuelling infrastructure.
Costs can be reduced by strategic placement of stations (Ogden and
Nicholas, 2011) starting with specific locations (‘lighthouse cities’) and
a high degree of coordination between fuel suppliers, vehicle manu-
facturers and policy makers is needed to overcome ‘chicken-or-egg’
vehicle / fuel supply problems (ITS-UC Davis, 2011).
A variety of liquid and gaseous biofuels can be produced from various
biomass feedstocks using a range of conversion pathways (Chapter
11.A.3). The ability to produce and integrate large volumes of biofu-
els cost-effectively and sustainably are primary concerns of which
policy makers should be aware (Sims etal., 2011). In contrast to elec-
tricity and hydrogen, liquid biofuels are relatively energy-dense and
are, at least in certain forms and blend quantities, compatible with
the existing petroleum fuel infrastructure and with all types of ICEs
installed in LDVs, HDVs, waterborne craft, and aircraft. Ethanol and
biodiesel (fatty-acid-methyl-ester, FAME) can be blended at low levels
(10 – 15 %) with petroleum fuels for use in unmodified ICEs. New ICEs
can be cheaply modified during manufacture to accommodate much
higher blends as exemplified by ‘flex-fuel’ gasoline engines where
ethanol can reach 85 % of the fuel blend (ANFAVEA, 2012). However,
ethanol has about a 35 % lower energy density than gasoline, which
reduces vehicle range — particularly at high blend levels — that can be
a problem especially for aircraft. Synthetic ‘drop-in’ biofuels have simi-
lar properties to diesel and kerosene fuels. They can be derived from a
number of possible feedstocks and conversion processes, such as the
hydro-treatment of vegetable oils or the Fischer-Tropsch conversion of
biomass (Shah, 2013). Bio-jet fuels suitable for aircraft have been dem-
onstrated to meet the very strict fuel specifications required (Takeshita
and Yamaji, 2008; Caldecott and Tooze, 2009). Technologies to produce
ligno-cellulosic, Fisher-Tropsch, algae-based, and other advanced bio-
fuels are in development, but may need another decade or more to
achieve widespread commercial use (IEA, 2011a). Bio-methane from
suitably purified biogas or landfill gas can also be used in natural gas
vehicles (REN21, 2012).
Biofuels have direct, fuel-cycle GHG emissions that are typically
30 – 90 % lower per kilometre travelled than those for gasoline or diesel
fuels. However, since for some biofuels, indirect emissions — including
from land use change — can lead to greater total emissions than when
using petroleum products, policy support needs to be considered on a
case by case basis (see Chapter 11.13 and, for example, Lapola etal.,
2010; Plevin etal., 2010; Wang etal., 2011; Creutzig etal., 2012a).
8�3�4 Comparative analysis
The vehicle and power-train technologies described above for reducing
fuel consumption and related CO2 emissions span a wide range and
are not necessarily additive. When combined, and including different
propulsion and fuel systems, their overall mitigation potential can be
evaluated as an integrated fuel / vehicle system (see Section 8.6). How-
ever, to produce an overall mitigation evaluation of the optimal design
of a transport system, non-CO2 emissions, passenger or freight occu-
pancy factors, and indirect GHG emissions from vehicle manufacture
and infrastructure should also be integrated to gain a full comparison
of the relative GHG emissions across modes (see Section 8.4; Hawkins
etal., 2012; Borken-Kleefeld etal., 2013).
Taking LDVs as an example, a comparative assessment of current and
future fuel consumption reduction potentials per kilometre has been
made (Figure 8.7), starting from a 2010 baseline gasoline vehicle at
about 8 lge4 / 100km and 195 g / km CO2. Using a range of technologies,
average new LDV fuel economy can be doubled (in units of distance
per energy, i. e., energy intensity cut by 50 %). Further improvements
can be expected for hybrids, PHEVs, BEVs, and FCVs, but several hur-
dles must be overcome to achieve wide market penetration (see Sec-
tion 8.8). Vehicle cost increases due to new technologies could affect
customers’ willingness to pay, and thus affect market penetration,
although cost increases would be at least partly offset by fuel cost sav-
ings (see Section 8.6).
8�3�5 Behavioural aspects
The successful uptake of more efficient vehicles, advanced technolo-
gies, new fuels, and the use of these fuels and vehicles in ‘real life’
conditions, involves behavioural aspects.
4 “Litre per gasoline equivalent” allows for a comparison between fuels with differ-
ent energy contents.
Figure 8�7 | Indicative fuel consumption reduction potential ranges for a number of
LDV technology drive-train and fuel options in 2010 and 2030, compared with a base-
line gasoline internal combustion engine (ICE) vehicle consuming 8 l / 100km in 2010.
(Based on Kobayashi etal., 2009; Plotkin etal., 2009; IEA, 2012b; NRC, 2013).
0%
0 20 40
8060
100
FCEV
BEV
Gasoline PHEV
Gasoline Hybrid
Diesel ICE
Gasoline ICE
FCEV
BEV
Gasoline PHEV
Gasoline Hybrid
Diesel ICE
Gasoline ICE
Change in Energy Use per Vehicle km [%]
2010
2030

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• Purchase behaviour: Few consumers attempt to minimize the
lifecycle costs of vehicle ownership (Greene, 2010a), which leads
to a considerable imbalance of individual costs versus society-wide
benefits. There is often a lack of interest in purchasing more fuel
efficient vehicles (Wozny and Allcott, 2010) due to imperfect infor-
mation, information overload in decision making, and consumer
uncertainty about future fuel prices and vehicle life (Anderson
etal., 2011; Small, 2012). This suggests that in order to promote
the most efficient vehicles, strong policies such as fuel economy
standards, sliding-scale vehicle tax systems, or ‘feebate’ systems
with a variable tax based on fuel economy or CO2 emissions may
be needed (Section 8.10) (Gallagher and Muehlegger, 2011). Vehi-
cle characteristics are largely determined by the desires of new-car
buyers in wealthier countries, so there may be a five-year or longer
lag before new technologies reach second-hand vehicle markets in
large quantities, particularly through imports to many developing
countries (though this situation will likely change in the coming
decades as new car sales rise across non-OECD countries) (IEA,
2009).
• New technologies / fuels: Consumers’ unwillingness to purchase
new types of vehicles with significantly different attributes (such
as smaller size, shorter range, longer refuelling or recharging time,
higher cost) is a potential barrier to introducing innovative pro-
pulsion systems and fuels (Brozović and Ando, 2009). This may
relate simply to the perceived quality of various attributes or to
risk aversion from uncertainty (such as driving range anxiety for
BEVs5) (Wenzel and Ross, 2005). The extent to which policies must
compensate by providing incentives varies but may be substantial
(Gallagher and Muehlegger, 2011).
• On-road fuel economy: The fuel economy of a vehicle as quoted
from independent testing can be up to 30 % better than that actu-
ally achieved by an average driver on the road (IEA, 2009; TMO,
2010; ICCT, 2012). This gap reflects a combination of factors
including inadequacies in the test procedure, real-world driving
conditions (e. g., road surface quality, weather conditions), driver
behaviour, and vehicle age and maintenance. Also congested traf-
fic conditions in OECD cities differ from mixed-mode conditions in
some developing countries (Tiwari etal., 2008; Gowri etal., 2009).
Some countries have attempted to adjust for these differences in
their public vehicle fuel economy information. A significant reduc-
tion in the gap may be achievable by an ‘integrated approach’ that
includes better traffic management, intelligent transport systems,
and improved vehicle and road maintenance (IEA, 2012e).
• Eco-Driving: A 5 – 10 % improvement in on-road fuel economy can
be achieved for LDVs through efforts to promote ‘eco-driving’ (An
etal., 2011; IEA, 2012d). Fuel efficiency improvements from eco-
driving for HDVs are in the 5 – 20 % range (AEA, 2011).
• Driving behaviour with new types of vehicles: Taking electric
vehicles (EVs) as an example, day / night recharging patterns and
the location of public recharging systems could affect how much
these vehicles are driven, when and where they are driven, and
potentially their GHG emissions impacts (Axsen and Kurani, 2012).
• Driving rebound effects: Reactions to lowering the cost of travel
(through fuel economy measures or using budget airline opera-
tors) can encourage more travel, commonly known as the (direct)
rebound effect (Greene etal., 1999; for a general discussion of the
rebound effect see Section 5.6.1). In North America, fuel cost elas-
ticity is in the range of a – 0.05 to – 0.30 (e. g., a 50 % cut in the
fuel cost would result in a 2.5 % to 15 % increase in driving). Sev-
eral studies show it is declining (Hughes etal., 2006; Small and van
Dender, 2007; EPA, 2012). The rebound effect is larger when the
marginal cost of driving (mostly gasoline) is a high share of house-
hold income. The implication for non-OECD countries is that the
price elasticity of demand for vehicle travel will be a function of
household income. The rebound effect may be higher in countries
with more modal choice options or where price sensitivity is higher,
but research is poor for most countries and regions outside the
OECD. Minimizing the rebound can be addressed by fuel taxes or
road pricing that offset the lower travel costs created by efficiency
improvements or reduced oil prices (see Section 8.10) (Hochman
etal., 2010; Rajagopal etal., 2011; Chen and Khanna, 2012).
5 Should a BEV run out of stored energy, it is less easy to refuel than is an ICE
vehicle that runs out of gasoline. With typical ranges around 100 – 160 km, BEV
drivers can become anxious about failing to complete their journey.
onstrated to meet the very strict fuel specifications required (Takeshita
and Yamaji, 2008; Caldecott and Tooze, 2009). Technologies to produce
ligno-cellulosic, Fisher-Tropsch, algae-based, and other advanced bio-
fuels are in development, but may need another decade or more to
achieve widespread commercial use (IEA, 2011a). Bio-methane from
suitably purified biogas or landfill gas can also be used in natural gas
vehicles (REN21, 2012).
Biofuels have direct, fuel-cycle GHG emissions that are typically
30 – 90 % lower per kilometre travelled than those for gasoline or diesel
fuels. However, since for some biofuels, indirect emissions — including
from land use change — can lead to greater total emissions than when
using petroleum products, policy support needs to be considered on a
case by case basis (see Chapter 11.13 and, for example, Lapola etal.,
2010; Plevin etal., 2010; Wang etal., 2011; Creutzig etal., 2012a).
8�3�4 Comparative analysis
The vehicle and power-train technologies described above for reducing
fuel consumption and related CO2 emissions span a wide range and
are not necessarily additive. When combined, and including different
propulsion and fuel systems, their overall mitigation potential can be
evaluated as an integrated fuel / vehicle system (see Section 8.6). How-
ever, to produce an overall mitigation evaluation of the optimal design
of a transport system, non-CO2 emissions, passenger or freight occu-
pancy factors, and indirect GHG emissions from vehicle manufacture
and infrastructure should also be integrated to gain a full comparison
of the relative GHG emissions across modes (see Section 8.4; Hawkins
etal., 2012; Borken-Kleefeld etal., 2013).
Taking LDVs as an example, a comparative assessment of current and
future fuel consumption reduction potentials per kilometre has been
made (Figure 8.7), starting from a 2010 baseline gasoline vehicle at
about 8 lge4 / 100km and 195 g / km CO2. Using a range of technologies,
average new LDV fuel economy can be doubled (in units of distance
per energy, i. e., energy intensity cut by 50 %). Further improvements
can be expected for hybrids, PHEVs, BEVs, and FCVs, but several hur-
dles must be overcome to achieve wide market penetration (see Sec-
tion 8.8). Vehicle cost increases due to new technologies could affect
customers’ willingness to pay, and thus affect market penetration,
although cost increases would be at least partly offset by fuel cost sav-
ings (see Section 8.6).
8�3�5 Behavioural aspects
The successful uptake of more efficient vehicles, advanced technolo-
gies, new fuels, and the use of these fuels and vehicles in ‘real life’
conditions, involves behavioural aspects.
4 “Litre per gasoline equivalent” allows for a comparison between fuels with differ-
ent energy contents.
Figure 8�7 | Indicative fuel consumption reduction potential ranges for a number of
LDV technology drive-train and fuel options in 2010 and 2030, compared with a base-
line gasoline internal combustion engine (ICE) vehicle consuming 8 l / 100km in 2010.
(Based on Kobayashi etal., 2009; Plotkin etal., 2009; IEA, 2012b; NRC, 2013).
0%
0 20 40
8060
100
FCEV
BEV
Gasoline PHEV
Gasoline Hybrid
Diesel ICE
Gasoline ICE
FCEV
BEV
Gasoline PHEV
Gasoline Hybrid
Diesel ICE
Gasoline ICE
Change in Energy Use per Vehicle km [%]
2010
2030

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• Vehicle choice-related rebounds: Other types of rebound effect
are apparent, such as shifts to purchasing larger cars concurrent
with cheaper fuel or shifts from gasoline to diesel vehicles that give
lower driving costs (Schipper and Fulton, 2012). Shifts to larger
HDVs and otherwise less expensive systems can divert freight from
lower carbon modes, mainly rail, and can also induce additional
freight movements (Umweltbundesamt, 2007; TML, 2008; Leduc,
2009; Gillingham etal., 2013).
• Company behaviour: Behavioural change also has a business
dimension. Company decision making can exert a strong influence
on the level of transport emissions, particularly in the freight sec-
tor (Rao and Holt, 2005). Freight business operators have a strong
incentive to reduce energy intensity, since fuel typically accounts
for around one third of operating costs in the road freight sector,
40 % in shipping, and 55 % in aviation (Bretzke, 2011). The resulting
reductions in transport costs can cause a rebound effect and gener-
ate some additional freight movement (Matos and Silva, 2011). For
company managers to switch freight transport modes often requires
a tradeoff of higher logistics costs for lower carbon emissions
(Winebrake etal., 2008). Many large logistics service providers have
set targets for reducing the carbon intensity of their operations by
between 20 % and 45 % over the period from 2005 / 2007 to 2020,
(McKinnon and Piecyk, 2012) whereas many smaller freight opera-
tors have yet to act (Oberhofer and Fürst, 2012).
8.4 Infrastructure and
systemic perspectives
Transport modes, their infrastructures, and their associated urban fab-
ric form a system that has evolved into the cities and regions with
which we are most familiar. ‘Walking cities’ existed for 8000 years;
some are being reclaimed around their walkability (Gehl, 2011). ‘Tran-
sit cities’ were built and developed around trams, trolley buses, and
train systems since the mid 19th century (Cervero, 1998; Newman and
Kenworthy, 1999). ‘Automobile cities’ evolved from the advent of
cheap LDVs (Brueckner, 2000) and have become the dominant para-
digm since the 1950s, leading to automobile dependence and auto-
mobility (Urry, 2007). A region can be defined and understood in terms
of the transport links to ports and airports regardless of the number
and types of cities located there. In all cases, the inter-linkages
between transport infrastructure and the built environment establish
path dependencies, which inform long-term transport-related mitiga-
tion options. For a general discussion of urban form and infrastructure
see Chapter 12.4.
8�4�1 Path dependencies of infrastructure and
GHG emission impacts
Systemic change tends to be slow and needs to address path depen-
dencies embedded in sunk costs, high investment levels, and cultural
patterns. Technological and behavioural change can either adapt to
existing infrastructures, or develop from newly constructed infrastruc-
tures, which could provide an initial template for low carbon technolo-
gies and behaviour. Developments designed to improve infrastructure
in rapidly urbanizing developing countries will decisively determine
the future energy intensity of transport and concomitant emissions
(Lefèvre, 2009), and will require policies and actions to avoid lock-in.
The construction, operation, maintenance, and eventual disposal of
transport infrastructure (such as rail tracks, highways, ports, and air-
ports), all result in GHG emissions. These infrastructure-related emis-
sions are usually accounted for in the industry and building sectors.
However, full accounting of life cycle assessment (LCA) emissions
from a transport-perspective requires these infrastructure-related
emissions to be included along with those from vehicles and fuels
(see Section 8.3.5). GHG emissions per passenger-kilometre (p-km) or
per tonne-kilometre (t-km) depend, inter alia, on the intensity of use
of the infrastructure and the share of tunnels, bridges, runways, etc.
(Åkerman, 2011; Chang and Kendall, 2011; UIC, 2012). In the United
States, GHG emissions from infrastructure built for LDVs, buses, and
Table 8�1 | High-speed rail transport infrastructure GHG emissions based on LCA data.
Mode / component Emissions (gCO2eq / p-km) Reference Comment
Swedish high-speed rail plans for Europabanan
infrastructure 2.7 Amos etal., 2010; Åkerman,
2011 At 25 million passengers per year
Vehicle construction and maintenance emissions;
Swedish high-speed rail 1.0 Åkerman, 2011 Over full lifetime of high-speed rail vehicles
Inter-city express (ICE) system study (Germany and
surrounds) 9.7 Von Rozycki etal., 2003 About half total emissions arise from infrastructure including non-
high-speed stretches
High-speed rail infrastructure (Europe) 3.1 – 10.9 Tuchschmid, 2009 Low emission value for 90 trains per track per day, high emission
value for 25. Current EU network is at 6.3 g / p-km
US high-speed rail plans 3.2 g / p-km Chang and Kendall, 2011 This 725 km line will emit 2.4 MtCO2eq / yr
Note: Since LCA assumptions vary, the data can only be taken as indicative and not compared directly.

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air transport amount to 17 – 45 gCO2eq / p-km, 3 – 17 gCO2eq / p-km, and
5 – 9 gCO2eq / p-km respectively (Chester and Horvath, 2009) with rail
typically between 3 – 11 gCO2eq / p-km (see Table 8.1). Other than for
rail, relevant regional infrastructure-related GHG emissions research on
this topic is very preliminary.
Opportunities exist to substantially reduce these infrastructure related
emissions, for instance by up to 40 % in rail (Milford and Allwood,
2010), by the increased deployment of low-carbon materials and recy-
cling of rail track materials at their end-of-life (Network Rail, 2009; Du
and Karoumi, 2012). When rail systems achieve modal shift from road
vehicles, emissions from the rail infrastructure may be partially offset
by reduced emissions from road infrastructures (Åkerman, 2011). To be
policy-relevant, LCA calculations that include infrastructure need to be
contextualized with systemic effects such as modal shifts (see Sections
8.4.2.3 and 8.4.2.4).
Existing vehicle stock, road infrastructure, and fuel-supply infrastruc-
ture prescribe future use and can lock-in emission paths for decades
while inducing similar investment because of economies of scale (Sha-
lizi and Lecocq, 2009). The life span of these infrastructures ranges
from 50 to more than 100 years. This range makes the current develop-
ment of infrastructure critical to the mode shift opportunities of the
future. For example, the successful development of the United States
interstate highway system resulted in a lack of development of an
extensive passenger rail system, and this determined a demand-side
lock-in produced by the complementarity between infrastructure and
vehicle stock (Chapter 12.3.2). The construction of the highway sys-
tem accelerated the growth of road vehicle kilometres travelled (VKT)
around 1970, and ex-urban development away from city centres cre-
ated a second peak in road transport infrastructure investment post
1990 (Shalizi and Lecocq, 2009). Conversely, the current rapid develop-
ment of high-speed rail infrastructure in China (Amos etal., 2010) may
provide low emission alternatives to both road transport and aviation.
Substantial additional rail traffic has been generated by constructing
new lines (Chapter 12.4.2.5), although a net reduction of emissions
will only occur after achieving a minimum of between 10 and 22 mil-
lion passengers annually (Westin and Kågeson, 2012).
Aviation and shipping require less fixed infrastructures and hence tend
to have a relative low infrastructure share of total lifecycle emissions.
Rising income and partially declining airfares have led to increased
air travel (Schäfer etal., 2009), and this correlates not only with new
construction and expansion of airports, but also with shifting norms in
travel behaviour (Randles and Mander, 2009).
8�4�2 Path dependencies of urban form and
mobility
Transport demand and land use are closely inter-linked. In low-density
developments with extensive road infrastructure, LDVs will likely domi-
nate modal choice for most types of trips. Walking and cycling can be
made easier and safer where high accessibility to a variety of activi-
ties are located within relative short distances (Ewing and Cervero,
2010) and when safe cycle infrastructure and pedestrian pathways
are provided (Tiwari and Jain, 2012; Schepers etal., 2013). Conversely
the stress and physical efforts of cycling and walking can be greater
in cities that consistently prioritize suburban housing developments,
which leads to distances that accommodate the high-speed movement
and volume of LDVs (Naess, 2006). In developing countries, existing
high-density urban patterns are conducive to walking and cycling, both
with substantial shares. However, safe infrastructure for these modes is
often lacking (Thynell etal., 2010; Gwilliam, 2013). Sustainable urban
planning offers tremendous opportunities (reduced transport demand,
improved public health from non-motorized transport (NMT), less air
pollution, and less land use externalities) (Banister, 2008; Santos etal.,
2010; Bongardt etal., 2013; Creutzig etal., 2012a). As an example,
an additional 1.1 billion people will live in Asian cities in the next 20
years (ADB, 2012a) and the majority of this growth will take place in
small-medium sized cities that are at an early stage of infrastructure
development. This growth provides an opportunity to achieve the long-
term benefits outlined above (Grubler etal., 2012) (see also 8.7 and
Chapter 12.4.1).
Urban population density inversely correlates with GHG emissions
from land transport (Kennedy etal., 2009; Rickwood etal., 2011) and
enables non-motorized modes to be more viable (Newman and Ken-
worthy, 2006). Disaggregated studies that analyze individual transport
use confirm the relationship between land use and travel (Echenique
etal., 2012). Land use, employment density, street design and con-
nectivity, and high transit accessibility also contribute to reducing car
dependence and use (Handy etal., 2002; Ewing, 2008; Cervero and
Murakami, 2009; Olaru etal., 2011). The built environment has a major
impact on travel behaviour (Naess, 2006; Ewing and Cervero, 2010),
but residential choice also plays a substantial role that is not easy
to quantify (Cao etal., 2009; Ewing and Cervero, 2010). There exists
a non-linear relationship between urban density and modal choice
(Chapter 12.4.2.1). For example, suburban residents drive more and
walk less than residents living in inner city neighbourhoods (Cao etal.,
2009), but that is often true because public transit is more difficult
to deploy successfully in suburbs with low densities (Frank and Pivo,
1994). Transport options that can be used in low density areas include
para-transit6 and car-sharing, both of which can complement individu-
alized motorized transport more efficiently and with greater customer
satisfaction than can public transit (Baumgartner and Schofer, 2011).
Demand-responsive, flexible transit, and car sharing services can have
lower GHG emissions per passenger kilometre with higher quality ser-
vice than regional public transport (Diana etal., 2007; Mulley and Nel-
son, 2009; Velaga etal., 2012; Loose, 2010).
6 Para-transit, also called “community-transit”, is where flexible passenger transport
minibuses (also termed matatus and marshrutkas), shared taxis, and jitneys
operate in areas with low population density without following fixed routes or
schedules.

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The number of road intersections along the route of an urban jour-
ney, the number of destinations within walking distance, and land use
diversity issues have been identified as key variables for determining
the modal choice of walking (Ewing and Cervero, 2010). Public trans-
port use in the United States is related to the variables of street net-
work design and proximity to transit. Land use diversity is a secondary
factor.
8�4�2�1 Modal shift opportunities for passengers
Small but significant modal shifts from LDVs to bus rapid transit (BRT)
have been observed where BRT systems have been implemented.
Approximately 150 cities worldwide have implemented BRT systems,
serving around 25 million passengers daily (Deng and Nelson, 2011;
BRT Centre of Excellence, EMBARQ, IEA and SIBRT, 2012). BRT systems
can offer similar benefits and capacities as light rail and metro systems
at much lower capital costs (Deng and Nelson, 2011), but usually with
higher GHG emissions (depending on the local electricity grid GHG
emission factor) (Table 8.2). High occupancy rates are an important
requirement for the economic and environmental viability of public
transport.
Public transit, walking, and cycling are closely related. A shift from
non-motorized transport (NMT) to LDV transport occurred during the
20th century, initially in OECD countries and then globally. However,
a reversion to cycling and walking now appears to be happening in
many cities — mostly in OECD countries — though accurate data is
scarce (Bassett etal., 2008; Pucher etal., 2011). Around 90 % of all
public transit journeys in the United States are accompanied with a
walk to reach the final destination and 70 % in Germany (Pucher and
Buehler, 2010). In Germany, the Netherlands, Denmark, and elsewhere,
the cycling modal share of total trips has increased since the 1970s
and are now between 10 – 25 % (Pucher and Buehler, 2008). Some car-
bon emission reduction has resulted from cycle infrastructure deploy-
ment in some European cities (COP, 2010; Rojas-Rueda et al., 2011;
Creutzig etal., 2012a) and in some cities in South and North America
(USCMAQ, 2008; Schipper etal., 2009; Massink etal., 2011; USFHA,
2012). Walking and cycling trips vary substantially between countries,
accounting for over 50 % of daily trips in the Netherlands and in many
Asian and African cities (mostly walking); 25 – 35 % in most European
countries; and approximately 5 – 10 % in the United States and Aus-
tralia (Pucher and Buehler, 2010; Leather etal., 2011; Pendakur, 2011;
Mees and Groenhart, 2012).
The causes for high modal share of NMT differ markedly between
regions depending on their cultures and characteristics. For example,
they tend to reflect low-carbon urban policies in OECD countries such
as the Netherlands, while reflecting a lack of motorization in devel-
oping countries. Land use and transport policies can influence the
bicycle modal share considerably (Pucher and Buehler, 2006), most
notably by the provision of separate cycling facilities along heavily
traveled roads and at intersections, and traffic-calming of residential
neighbourhoods (Andrade et al., 2011; NRC, 2011b)� Many Indian
and Chinese cities with traditionally high levels of walking are now
reporting dramatic decreases in this activity (Leather et al., 2011),
with modal shifts to personal transport including motorbikes and
LDVs. Such shifts are to some degree inevitable, and are in part desir-
able as they reflect economic growth. However, the maintenance of a
healthy walking and cycling modal share could be a sign of a liveable
and attractive city for residents and businesses (Bongardt etal., 2011;
Gehl, 2011).
Deliberate policies based around urban design principles have
increased modal shares of walking and cycling in Copenhagen, Mel-
bourne, and Bogota (Gehl, 2011). Public bicycle share systems have
created a new mode for cities (Shaheen etal., 2010), with many cit-
ies now implementing extensive public cycling infrastructure, which
results in increased bicycle modal share (DeMaio, 2009). Revising elec-
tric bicycle standards to enable higher performance could increase the
feasible commuting range and encourage this low emissions personal
transport mode. Electric bicycles offer many of the benefits of LDVs in
terms of independence, flexibility of routes, and scheduling freedom,
but with much lower emissions and improved health benefits.
With rising income and urbanization, there will likely be a strong pull
toward increasing LDV ownership and use in many developing coun-
tries. However, public transit mode shares have been preserved at fairly
high levels in cities that have achieved high population densities and
that have invested heavily in high quality transit systems (Cervero,
2004). Their efficiency is increased by diverse forms of constraints
on LDVs, such as reduced number of lanes, parking restrictions, and
limited access (La Branche, 2011). Investments in mass rapid transit,
timed with income increases and population size / density increases,
Table 8�2 | Comparison of capital costs, direct CO2 emissions, and capacities for BRT, light rail, and metro urban mass transit options (IEA, 2012e).
Bus rapid transit Light rail Metro
Capital cost (million USD2010 / km) 5 – 27 13 – 40 27 – 330
Length of network that can be constructed for 1 USD2010 billion cost (km) 37 – 200 25 – 77 3 – 37
World network length in 2011 (km) 2,139 15,000 10,000
Direct CO2 intensity (gCO2 / p-km) 14 – 22 4 – 22 3 – 21
Capacity (thousand passengers per hour per direction) 10 – 35 2 – 12 12 – 45

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have been successful in some Asian megacities (Acharya and Morichi,
2007). As traffic congestion grows and freeway infrastructure reaches
physical, political, and economic limits, the modal share of public tran-
sit has increased in some OECD countries (Newman and Kenworthy,
2011b).
High-speed rail can substitute for short-distance passenger air travel
(normally up to around 800 km but also for the 1500 km in the case
of Beijing to Shanghai), as well as for most road travel over those
distances, and hence can mitigate GHG emissions (McCollum etal.,
2010; IEA, 2008). With optimized operating speeds and long distances
between stops, and high passenger load factors, energy use per pas-
senger-km could be as much as 65 to 80 % less than air travel (IEA,
2008). A notable example is China, which has shown a fast develop-
ment of its high-speed rail system. When combined with strong land-
use and urban planning, a high-speed rail system has the potential
to restructure urban development patterns, and may help to alleviate
local air pollution, noise, road, and air congestion (McCollum etal.,
2010).
8�4�2�2 Modal shift opportunities for freight
Over the past few decades, air and road have increased their global
share of the freight market at the expense of rail and waterborne
transport (European Environment Agency, 2011; Eom etal., 2012). This
has been due to economic development and the related change in the
industry and commodity mix, often reinforced by differential rates of
infrastructure improvement and the deregulation of the freight sector,
which typically favours road transport. Inducing a substantial reversal
of recent freight modal split trends will be difficult, inter alia because
of ‘structural inelasticity’ which confines shorter distance freight move-
ments to the road network because of its much higher network density
(Rich etal., 2011). If growth in global truck travel between 2010 and
2050 could be cut by half from the projected 70 % and shifted to
expanded rail systems, about a 20 % reduction in fuel demand and CO2
could be achieved, with only about a fifth of this savings being offset
by increased rail energy use (IEA, 2009). The European Commission
(EC) set an ambitious target of having all freight movements using rail
or waterborne modes over distances greater than 300 km by 2030,
leading to major changes in modal shares (Figure 8.8) (Tavasszy and
Meijeren, 2011; EC, 2013).
The capacity of the European rail network would have to at least dou-
ble to handle this increase in freight traffic and the forecast growth
in rail passenger volumes, even if trains get longer and run empty
less often (den Boer etal., 2011). Longer-term transformations need
to take account of the differential rates at which low-carbon technol-
ogies could impact on the future carbon intensity of freight modes.
Applying current average energy intensity values (Section 8.3.1) may
result in over-estimates of the potential carbon benefits of the modal
shift option. Although rail freight generates far lower GHG emissions
per tonne-kilometre than road (Table 8.3), the rate of carbon-related
technical innovation, including energy efficiency improvements, has
been faster in HDV than rail freight and HDV replacement rate is typi-
cally much shorter, which ensures a more rapid uptake of innovation.
The potential for shifting freight to greener modes is difficult in urban
areas. Improvements in intra-urban rail freight movements are pos-
sible (Maes and Vanelslander, 2011), but city logistical systems are
almost totally reliant on road vehicles and are likely to remain so. The
greater the distance of land haul for freight, the more competitive
the lower carbon modes become. Within cities, the concept of modal
split between passenger and freight movement can be related to the
interaction. Currently, large amounts of freight on the so-called ‘last
mile’ to a home or business are carried by shoppers in LDVs and pub-
lic transport vehicles. With the rapid growth of on-line retailing, much
private car-borne freight, which seldom appears in freight transport
statistics, will be transferred to commercial delivery vans. Comparative
analyses of conventional and on-line retailing suggest that substitut-
ing a van delivery for a personal shopping trip by private car can yield
a significant carbon saving (Edwards etal., 2010).
At the international level, opportunities for switching freight from air
to shipping services are limited. The two markets are relatively discrete
and the products they handle have widely differing monetary values
and time-sensitivity. The deceleration of deep-sea container vessels
in recent years in accordance with the ‘slow steaming’ policies of the
shipping lines has further widened the transit time gap between sea
and air services. Future increases in the cost of fuel may, however,
encourage businesses to economize on their use of air-freight, pos-
sibly switching to sea-air services in which products are air-freighted
for only part of the way. This merger of sea and air transport offers
substantial cost and CO2 savings for companies whose global supply
chains are less time-critical (Conway, 2007; Terry, 2007).
Figure 8�8 | Projected freight modal split in the EU-25 in 2030 comparing 2011 shares
with future business-as-usual shares without target and with EU White Paper modal
split target. Source: Based on Tavasszy and Meijeren, 2011.
Shares of Freight Movements by Mode [%]
0
10
20
30
40
50
60
70
80
If Target is MetWithout TargetActual 2011
Inland Waterway
Rail
Road

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8.5 Climate change feed-
back and interaction
with adaptation
Transport is impacted by climate change both positively and negatively.
These impacts are dependent on regional variations in the nature and
degree of climate change and the nature of local transport infrastruc-
ture and systems. Adapting transport systems to the effects of climate
in some cases complement mitigations efforts while in others they
have a counteracting effect. Little research has so far been conducted
on the inter-relationship between adaptation and mitigation strategies
in the transport sector.
8�5�1 Accessibility and feasibility of transport
routes
Decreases in the spatial and temporal extent of ice cover in the Arctic
and Great Lakes region of North America regions are opening new and
shorter shipping routes over longer periods of the year (Drobot etal.,
2009; Stephenson etal., 2011). The expanded use of these routes could
reduce GHG emissions due to a reduction in the distance travelled. For
example, the Northern Sea Route (NSR) between Shanghai and Rot-
terdam is approximately 4,600 km shorter (about 40 %) than the route
via the Suez Canal. The NSR passage takes 18 – 20 days compared to
28 – 30 days via the southern route (Verny and Grigentin, 2009). Cli-
mate change will not only affect ice coverage, but may also increase
the frequency and severity of northern hemisphere blizzards and arctic
cyclones, deterring use of these shorter routes (Wassmann, 2011; Liu
etal., 2012). It is, nevertheless, estimated that the transport of oil and
gas through the NSR could increase from 5.5Mt in 2010 to 12.8 Mt
by 2020 (Ho, 2010). The passage may also become a viable option for
other bulk carriers and container shipping in the near future (Verny
& Grigentin, 2009; Schøyen & Bråthen, 2011). The economic viability
of the NSR is still uncertain without assessments of potentially prof-
itable operation (Liu and Kronbak, 2010) and other more pessimistic
prospects for the trans-Arctic corridors (Econ, 2007). One possible
negative impact would be that the increase in shipping through these
sensitive ecosystems could lead to an increase in local environmental
and climate change impacts unless additional emissions controls are
introduced along these shipping routes (Wassmann, 2011). Of spe-
cific concern are the precursors of photochemical smog in this polar
region that could lead to additional local positive regional climate forc-
ing (Corbett etal., 2010) and emissions of black carbon (see Section
8.2.2.1). Measurement methods of black carbon emissions from ships
and additional work to evaluate their impact on the Arctic are needed
before possible control measures can be investigated.
Changes in climate are also likely to affect northern inland waterways
(Millerd, 2011). In summer, these effects are likely to adversely affect
waterborne craft when reductions in water levels impair navigabil-
ity and cut capacity (Jonkeren etal., 2007; Görgen etal. 2010; Nilson
etal., 2012). On the other hand, reduced winter freezing can benefit
inland waterway services by extending the season. The net annual
effect of climate change on the potential for shifting freight to this
low-carbon mode has yet to be assessed.
8�5�2 Relocation of production and
reconfiguration of global supply chains
Climate change will induce changes to patterns of agricultural produc-
tion and distribution (Ericksen etal., 2009; Hanjra and Qureshi, 2010;
Tirado etal., 2010; Nielsen and Vigh, 2012; Teixeira etal., 2012). The
effect of these changes on freight transport at different geographi-
cal scales are uncertain (Vermeulen etal., 2012). In some scenarios,
food supply chains become longer, generating more freight movement
(Nielsen and Vigh, 2012; Teixeira etal., 2012). These and other long
supply lines created by globalization could become increasingly vulner-
able to climate change. A desire to reduce climate risk may be one of
several factors promoting a return to more localized sourcing in some
sectors (World Economic Forum and Accentura, 2009), a trend that
would support mitigation. Biofuel production may also be adversely
affected by climate change inhibiting the switch to lower carbon fuels
(de Lucena etal., 2009).
8�5�3 Fuel combustion and technologies
Increased ambient temperatures and humidity levels are likely to affect
nitrogen oxide, carbon monoxide, methane, black carbon, and other
particulate emissions from internal combustion engines and how these
gases interact with the atmosphere (Stump etal., 1989; Rakopoulos,
1991; Cooper and Ekstrom, 2005; Motallebi etal., 2008; Lin and Jeng,
1996; McCormick etal., 1997; Pidolal, 2012). Higher temperatures also
lead to higher evaporative emissions of volatile organic compound
emissions (VOCs) (Roustan etal., 2011) and could lead to higher ozone
levels (Bell etal., 2007). The overall effects are uncertain and could be
positive or negative depending on regional conditions (Ramanathan &
Carmichael, 2008).
As global average temperatures increase, the demand for on-board
cooling in both private vehicles and on public transport will increase.
The heating of vehicles could also grow as the frequency and sever-
ity of cold spells increase. Both reduce average vehicle fuel efficien-
cies. For example, in a passenger LDV, air-conditioning can increase
fuel consumption by around 3 – 10 % (Farrington and Rugh, 2000; IEA,
2009). Extremes in temperature (both high and low) negatively impact
on the driving range of electric vehicles due to greater use of on-board
heating and air conditioning, and thus will require more frequent
recharging. In the freight sector, energy consumption and emissions
in the refrigeration of freight flows will also increase as the extent and
degree of temperature-control increases across the supply chains of
food and other perishable products (James and James, 2010).

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8�5�4 Transport infrastructure
Climate proofing and adaptation will require substantial infrastruc-
ture investments (see Section 8.4 and the Working Group II (WGII)
Contribution to the IPCC Fifth Assessment Report (AR5), Chapter 15).
This will generate additional freight transport if implemented outside
of the normal infrastructure maintenance and upgrade cycle. Climate
proofing of transport infrastructure can take many forms (ADB, 2011a;
Highways Agency, 2011) varying in the amount of additional freight
movement required. Resurfacing a road with more durable materials
to withstand greater temperature extremes may require no additional
freight movement, whereas re-routing a road or rail link, or installing
flood protection, are likely to generate additional logistics demands,
which have yet to be quantified.
Adaptation efforts are likely to increase transport infrastructure costs
(Hamin & Gurran, 2009), and influence the selection of projects for
investment. In addition to inflating maintenance costs (Jollands etal.,
2007; Larsen etal., 2008), climate proofing would divert resources that
could otherwise be invested in extending networks and expanding
capacity. This is likely to affect all transport modes to varying degrees.
If, for example, climate proofing were to constrain the development of
a rail network more than road infrastructure, it might inhibit a modal
shift to less carbon-intensive rail services.
The future choice of freight and passenger traffic between modes may
also become more responsive to their relative sensitivity to extreme
weather events (Koetse and Rietveld, 2009; Taylor and Philp, 2010).
The exposure of modes to climate risks include aviation (Eurocontrol,
2008), shipping (Becker etal., 2012), and land transport (Hunt and
Watkiss, 2011). Little attempt has been made to conduct a compara-
tive analysis of their climate risk profiles, to assess the effects on the
modal choice behaviour of individual travellers and businesses, or to
take account of regional differences in the relative vulnerability of dif-
ferent transport modes to climate change (Koetse and Rietveld, 2009).
Overall, the transport sector will be highly exposed to climate change
and will require extensive adaptation of infrastructure, operations,
and service provision. It will also be indirectly affected by the adapta-
tion and decarbonization of the other sectors that it serves. Within the
transport sector there will be a complex interaction between adapta-
tion and mitigation efforts. Some forms of adaptation, such as infra-
structural climate proofing, will be likely to generate more freight and
personal movement, while others, such as the NSR, could substantially
cut transport distances and related emissions.
8.6 Costs and potentials
For transport, the potential for reducing GHG emissions, as well as the
associated costs, varies widely across countries and regions. Appropri-
ate policies and measures that can accomplish such reductions also
vary (see Section 8.10) (Kahn Ribeiro etal., 2007; Li, 2011). Mitigation
costs and potentials are a function of the stringency of climate goals and
their respective GHG concentration stabilization levels (Fischedick etal.,
2011; Rogelj etal., 2013). This section presents estimates of mitigation
potentials and associated costs from the application of new vehicle and
fuel technologies, performance efficiency gains, operational measures,
logistical improvements, electrification of modes, and low-carbon fuels
and activity reduction for different transport modes (aviation, rail, road,
waterborne and cross-modal). Potential CO
2
eq emissions reductions
from passenger-km (p-km) and tonne-km (t-km) vary widely by region,
technology, and mode according to how rapidly the measures and appli-
cations can be developed, manufactured, and sold to buyers replacing
existing ones in vehicles an fuels or adding to the total fleet, and on the
way they are used given travel behaviour choices (Kok etal., 2011). In
general, there is a larger emission reduction potential in the transport
sector, and at a lower cost, compared to the findings in AR4 (Kahn Ribeiro
etal., 2007).
The efforts undertaken to reduce activity, to influence structure and modal
shift, to lower energy intensity, and to increase the use of low-carbon
fuels, will influence future costs and potentials. Ranges of mitigation
potentials have an upper boundary based on what is currently understood
to be technically achievable, but will most likely require strong policies to
be achieved in the next few decades (see Section 8.10). Overall reductions
are sensitive to per-unit transport costs (that could drop with improved
vehicle efficiency); resulting rebound effects; and shifts in the type, level,
and modal mix of activity. For instance, the deployment of more efficient,
narrow-body jet aircraft could increase the number of commercially-
attractive, direct city-to-city connections, which may result in an overall
increase in fleet fuel use compared to hub-based operations.
This assessment follows a bottom-up approach to maintain consis-
tency in assumptions. Table 8.3 outlines indicative direct mitigation
costs using reference conditions as baselines, and illustrative examples
of existing vehicles and situations for road, aviation, waterborne, and
rail (as well as for some cross-mode options) available in the literature.
The data presented on the cost-effectiveness of different carbon reduc-
tion measures is less detailed than data on the potential CO2eq savings
due to literature gaps. The number of studies assessing potential future
GHG reductions from energy intensity gains and use of low-carbon
fuels is larger than those assessing mitigation potentials and cost from
transport activity, structural change and modal shift, since they are
highly variable by location and background conditions.
Key assumptions made in this analysis were:
• cost estimates are based on societal costs and benefits of tech-
nologies, fuels, and other measures, and take into account initial
costs as well as operating costs and fuel savings;
• existing transport options are compared to current base vehicles
and activities, whereas future options are compared to estimates
of baseline future technologies and other conditions;

624624
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• fuel price projections are based on the IEA World Energy Outlook
(IEA, 2012b) and exclude taxes and subsidies where possible;
• discount rates of 5 % are used to bring future estimates back to
present (2013) values, though the literature considered has exam-
ined these issues mostly in the developed-world context; and
• indirect responses that occur through complex relationships within
sectors in the larger socioeconomic system are not included (Stepp
etal., 2009).
Results in Table 8.3 indicate that, for LDVs, efficiency improvement
potentials of 50 % in 2030 are technically possible compared to 2010,
with some estimates in the literature even higher (NRC, 2010). Virtu-
ally all of these improvements appear to be available at very low, or
even negative, societal costs. Electric vehicles have a CO2eq reduc-
tion cost highly correlated with the carbon intensity of electricity
generation: using relatively high-carbon intensity electricity systems
(500 – 600 gCO2eq / kWh), EVs save little CO2eq compared to conven-
tional LDVs and the mitigation cost can be many hundreds of dollars
per tonne; for very low-carbon electricity (below 200 gCO2eq / kWh) the
mitigation cost drops below 200 USD2010 / tCO2eq. In the future, with
lower battery costs and low-carbon electricity, EVs could drop below
100 USD2010 / tCO2eq and even approach zero net cost.
For long-haul HDVs, up to a 50 % reduction in energy intensity by 2030
appears possible at negative societal cost per tCO2eq due to the very
large volumes of fuel they use. HDVs used in urban areas where their
duty cycle does not require as much annual travel (and fuel use), have
a wider range of potentials and costs, reaching above 100 USD2010 / t
CO2eq. Similarly, inter-city buses use more fuel annually than urban
buses, and as a result appear to have more low-cost opportunities for
CO2eq reduction (IEA, 2009; NRC, 2010; TIAX, 2011).
Recent designs of narrow and wide-body commercial aircraft are sig-
nificantly more efficient than the models they replace, and provide
CO2eq reductions at net negative societal cost when accounting for
fuel savings over 10 – 15 years of operation at 5 % discount rate. An
additional 30 – 40 % CO2eq reduction potential is expected from future
new aircraft in the 2020 – 2030 time frame, but the mitigation costs
are uncertain and some promising technologies, such as open rotor
engines, appear expensive (IEA, 2009; TOSCA, 2011).
For virtually all types of ocean-going ships including container vessels,
bulk carriers, and oil tankers, the potential reduction in CO2eq emis-
sions is estimated to be over 50 % taking into account a wide range of
technology and operational changes. Due to the large volume of fuel
used annually by these ships, the net cost of this reduction is likely to
be negative (Buhaug and et. al, 2009; Crist, 2009).
Key factors in the long term decarbonization of rail transport will be
the electrification of services and the switch to low-carbon electric-
ity generation, both of which will vary widely by country. Potential
improvements of 35 % energy efficiency for United States rail freight,
46 % for European Union rail freight and 56 % for EU passenger rail
services have been forecast for 2050 (Anderson etal., 2011; Vyas etal.,
2013). The EU improvements will yield a 10 – 12 % reduction in operat-
ing costs, though no information is available on the required capital
investment in infrastructure and equipment.
Regarding fuel substitution in all modes, some biofuels have the poten-
tial for large CO2eq reduction, although net GHG impact assessments
are complex (see Sections 8.3 and 11.13). The cost per tonne of CO2eq
avoided will be highly dependent on the net CO2eq reduction and the
relative cost of the biofuel compared to the base fuel (e. g., gasoline or
diesel), and any technology changes required to the vehicles and fuel
distribution network in order to accommodate new fuels and blends.
The mitigation cost is so sensitive that, for example, while an energy
unit of biofuel that cuts CO2eq emissions by 80 % compared to gas-
oline and costs 20 % more has a mitigation cost of about 80 USD / t
CO2eq, if the biofuel’s cost drops to parity with gasoline, the mitigation
cost drops to 0 USD / t CO2eq (IEA, 2009).
The mitigation potentials from reductions in transport activity con-
sider, for example, that “walking and cycle track networks can provide
20 % (5 – 40 % in sensitivity analyses) induced walking and cycle jour-
neys that would not have taken place without the new networks, and
around 15 % (0 – 35 % in sensitivity analyses) of current journeys less
than 5 km made by car or public transport can be replaced by walking
or cycling” (Sælensminde, 2004). Urban journeys by car longer than
5 km can be replaced by combined use of non-motorized and intermo-
dal public transport services (Tirachini and Hensher, 2012).

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Table 8�3 | Selected CO2 eq mitigation potentials and costs for various modes in the transport sector with baselines of stock average fleet compared with 2010 new vehicles and 2030 projected vehicle based on available data. (See foot-
notes at end of Table).
050100150200250
Emissions intensity (gCO2eq/p-km)
-400 0 400 800 1200
2010 Stock average SUV
2010 Stock average LDV
2010 Stock average 2 Wheeler
LCCC* [USD2010/tCO2eq]
Indicative direct mitigation cost in
relation to the baseline
(can be positive or negative)
Indicative 2010 stock average baseline
CO2eq emissions and reduction potential
BRT system, Bogota, Colombia
has emission reductions of
250,000 tCO
2
eq/yr (12).
BRT infrastructure cost: 1–27 million USD/km (13).
Benefit-cost-ratios of selected BRT systems:
Hamilton, Canada 0.37–1.34;
Canberra, Australia 1.98–4.78 (12, 36)
Average CO
2
emissions level
of new cars in the EU decreased
from 170 gCO
2
/km in 2001 to
136 gCO
2
/km in 2011 (43, 47)
New mid-size gasoline:
2012 Toyota Yaris hybrid;
79 gCO
2
/p-km (6).
New mid-size Diesel:
Volkswagen Golf Blue motion
1.6 TDI: 99 gCO
2
/p-km (6)
EVs:
2013 Nissan Leaf: 24 kWh has
175 km range on New European
Driving Cycle, ranging from
76 to 222 km depending on
driving conditions (6).
Baseline 2010 stock average vehicles
Industry average; 164 gCO
2
/p-km (6).
Drive-train redesigns may yield 25% improvement.
Additional reductions from light-weighting, aerodynamics,
more efficient accessories (6). Most current and many
future LDV efficiency improvements are at negative cost
of USD/tCO
2
(4, 47). Potential 40–60% fuel efficiency
gains by 2030 compared to similar size 2010 LDVs (5).
2030 conventional/hybrid:
- mid-size; 70–120 gCO
2
/p-km (25).
2010 EV:
- 80–125 gCO
2
/p-km using high-carbon electricity grid at
600 gCO
2
/kWh;
- 28–40 gCO
2
/p-km using low-carbon grid electricity at
200 gCO
2
/kWh.
Likely over 200 USD/tCO
2
in 2010 even with low-carbon
grid electricity.
2030 EV:
- 55–235 USD/tCO
2
with high-carbon electricity.
- 0–100 USD/tCO
2
with low-carbon electricity (5).
EV efficiency 0.2–0.25 kWh/km on road (7).
Battery cost:
- 750 USD/kWh in 2010;
- 200–300 USD /kWh in 2030 (11).
Vehicle intensity (well-to-wheel) of 144–180 gCO
2
/100km at
0.20–0.25 kWh/km.
PHEV:
15–70% well-to-wheel more efficient than baseline ICEV (7);
28–50% more efficient by 2030 (5).
Baseline: 2010 stock average scooters
Up to 200 cc typical for Asia (48).
30% savings in fuel
consumption for hybrid buses
in Montreal (14).
Baseline: 2010 stock average medium haul bus
40-passenger occupancy vehicle.
Potential efficiency improvement 0–30%.
Mitigation options in
passenger transport
Illustrative examplesReference conditions
and assumptions made
2010 Diesel
2010 Hybrid diesel
2010 Gasoline
2010 Gasoline
2010 Gasoline
2030 Gasoline
2030 Gasoline
2030 Diesel
2030 Compressed natural gas
2010 Hybrid gasoline
2030 Hybrid gasoline
2010 Hybrid gasoline
2010 Diesel
2010 Compressed natural gas
2010 Electric, 600 g CO2eq/kWh
el
2010 Electric, 200 g CO2eq/kWh
el
2030 Hybrid gasoline
2030 Hybrid gasoline/biofuel* (50/50 share)
Road
New buses, large size
New sport utility vehicles (SUV), mid-size
New light duty vehicles (LDV), mid-size
Bus rapid transit (BRT)
New 2 wheeler
(Scooter up to 200 cm³ cylinder capacity)
2030 Electric, 200 gCO2eq/kWh
el
Optimized gasoline SUV (2030)
Optimized gasoline LDV (2030)
New gasoline SUV (2010)
New gasoline LDV (2010)
Baselines for LCCC calculation
*Levelized cost of conserved carbon (LCCC), here at 5% weighted average cost of capital (WACC)

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050100150200
Emissions intensity (gCO2eq/p-km)
-600 -400 -200 0 200
2010 Electric, 600 g CO2eq/kWh
el
2010 Electric, 200 g CO2eq/kWh
el
Rail (light rail car)
2010 Stock average
New current long-haul wide
body: Boeing 787 is 30%
more fuel efficient than
Boeing 767; Boeing 747-800
is 20% more efficient than
Boeing 747-400 (1, 51).
New 2010 medium-long-haul,
narrow body:
Airbus A320 and Boeing
737 (42).
European rail operations:
Passenger: 46% reduction in
GHG/p-km by 2050 with
11% reduction in operating
costs (43).
8% improvement via
regenerative braking systems
(Amtrak, US); 40% through
design and engine
improvements
(Shinkansen, Japan) (18).
35% reduction in energy
intensity - for US rail
operations (17).
Aviation
(Commercial, medium to long haul)
Operational measures
Baseline: 2010 stock average commercial (25)
Medium haul aircraft; 150-passenger occupancy; average
trip distance.
Aircraft efficiency: Incremental changes to engines and
materials up to 20% efficiency improvement. Most efficient
present aircraft designs provide 15–30% CO
2
emissions reductions
per revenue p-km compared to previous generation aircraft, at net
negative costs since fuel savings typically greater than cost of
improved technology. (5)
2030 next generation aircraft design: Advanced engines
up to 33% improvement; radical new designs such as ‘flying wing’,
up to 50% improvement. Medium and long-haul (narrow and
wide-body) aircraft compared to today’s best aircraft design:
- 20–35% CO
2
emissions reduction potential by 2025
for conventional aircraft
- up to 50% with advanced designs (e.g., flying wing)(2)
Costs: ~20% CO
2
reduction at <0–100 USD/tCO
2
(narrow body); ~33% reduction at <0–400 USD/tCO
2
(open rotor engine) (34).
Taxiing and flight operations including direct routing,
optimum altitude and speed; circling, landing patterns.
Improved ground equipment and auxiliary power units
can yield 6–12% fuel efficiency gains (3).
Baseline: 2010 electric medium haul train
- Based on electricity grid 600 gCO
2
/kWh: 3–20 gCO
2
/p-km (25).
2010 light rail; 60 passenger occupancy car:
- CO
2
reduction at 4–22 gCO
2
/p-km;
- Infrastructure cost 14–40 million USD/km (5).
2010 metro:
- CO
2
reduction 3–21 gCO
2
/p-km;
- Infrastructure cost 27–330 million USD/km (5).
2010 long-distance rail:
- 45–50% reduction in CO
2
/p-km (augmented if switch to
low-carbon electricity).
- 14% reduction in operating costs (allowing for increase in
speed and with energy costs excluded from cost calculation (38).
- 8–40% efficiency gains (12–19 gCO
2
/p-km).
- Infrastructure cost 4–75 million USD/km (5).
Potential GHG savings from eco-driving 15%; regenerative
braking 13%; mass reduction 6% (38).
Illustrative examplesReference conditions
and assumptions made
2010 Narrow and wide body
2030 Narrow body
2030 Narrow body, open rotor engine
Average new aircraft (2010)
Baselines for LCCC calculation
Indicative direct mitigation cost in
relation to the baseline
(can be positive or negative)
Indicative 2010 stock average baseline
CO2eq emissions and reduction potential
Mitigation options in
passenger transport
LCCC* [USD2010/tCO2eq]
*Levelized cost of conserved carbon (LCCC), here at 5% weighted average cost of capital (WACC)

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Emissions intensity (gCO2eq/t-km)
New diesel example (47)
New diesel hybrid example (47)
'Green Trucks Project'
Guangzhou, China, could save
8.6 billion l/yr of fuel and
reduce CO
2
emissions by 22.3
MtCO
2
/yr if all HDVs in the
province participated (12).
UK ‘Logistics Carbon
Reduction Scheme’
comprising 78 businesses
set target for reducing the
target intensity of road
freight transport by 8%
between 2010 and 2015,
which is likely to be achieved
by the end of 2013.
Baseline stock average medium haul HDV
Diesel fuelled HDVs: 76–178 gCO
2
/t-km (25).
55% improvement in energy efficiency of tractor
trailer HDV between 2010 and 2030 and 50% for other
categories of HDV (9, 10).
30–62% improvement by 2030 compared to a similar size 2007–
2010 HDV, including increasing load factor by up to 32% (5, 11).
Urban HDVs 30–50% reductions at 0–200 USD/tCO
2
.
Long-haul HDV up to 50% potential CO
2
reduction at
negative costs per tCO
2
saved.
Road
Mitigation options in
freight transport
Illustrative examplesReference conditions
and assumptions made
0200400 -100 1000 200
2010 stock average
2010 stock average
New heavy duty, long-haul trucks
New medium duty trucks
2010 Diesel
2010 Diesel hybrid
2010 Compressed natural gas
2010 Diesel
2010 Compressed natural gas
2030 Diesel/biofuel (50/50 share)**
2030 Diesel
2030 Diesel
**Assuming 70% Less CO2
eq/MJ Biofuel than /MJ Diesel
New diesel long-haul (2010)
Baselines for LCCC calculation
Indicative direct mitigation cost in
relation to the baseline
(can be positive or negative)
Indicative 2010 stock average baseline
CO2eq emissions and reduction potential
LCCC* [USD2010/tCO2eq]
*Levelized cost of conserved carbon (LCCC), here at 5% weighted average cost of capital (WACC)

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Emissions intensity (gCO2eq/t-km)
2010 new medium vessel:(46)
Industry initiatives through the
Energy Efficiency Design Index
and Ship Energy Efficiency
Management Programme of
the International Maritime
Organisation (IMO)(22)
Global average speed reduction
of 15% would give benefits that
outweigh costs by 178–617
billion USD by 2050 (31).
'Slow steaming' at 10% slower
speed gives 15–19% CO
2
emissions reduction; 20%
slower speed gives 36–39%
(24, 31, 37).
Inland waterways potential (46)
Baseline: Stock average international ships
10–40 gCO
2
/t-km (25).
2010 water craft: 5–30% CO
2
/t-km reduction potential;
retrofit and maintenance measures 2–20%; total reduction
43% (2020) to 63% (2050) (19). Potential up to 60% CO
2
reduction by 2030 from optimized technology and operation
(19). 30% or more reduction in CO
2
/t-km by 2030 at zero
cost (30).
2030 water craft: Business-as-usual reduction in carbon
intensity of shipping of 20% between 2010 and 2030 but
could rise to 37% with industry initiatives (39).
Operations: Potential CO
2
reductions 15–39%;
Slow steaming at 3–9kts slower than 24kt baseline.
Cost savings around 200 USD/tCO
2
at bunker fuel price of
700 USD/t and combining savings for carriers and shippers (37).
CO
2
emissions reductions of 43% per t-km by 2020 (20);
- 63% CO
2
/t-km by 2050 (21);
- 25–75% GHG intensity by 2050 (22);
- 39–57 % CO
2
/t-km ‘attainable’ by 2050;
- 59–72 % CO
2
/t-km is ‘optimistic’ by 2050 (23)
See passenger “Rail
(Light Rail Car)” above
Baseline based on electricity grid 600 gCO
2
/kWh:
6–33 gCO
2
/t-km (25).
- 40–45% reduction in CO
2
/t-km (augmented if switch to
low-carbon electricity).
- 14% reduction in operating costs (allowing for increase in
speed and with energy costs excluded from cost calculation) (38).
Also see passenger “Rail (Light Rail Car)” above.
See Passenger “Aviation”
examples above
See Passenger “Aviation” assumptions above
Freight factors for wide-bodied passenger aircraft are around
15-30% whilst narrow bodied planes are typically 0-10% (52),
Mitigation options in
freight transport
Illustrative examplesReference conditions
and assumptions made
02004006008001000 -100-200 1000 300200 400
2010 Stock average
Aviation
(Commercial, medium to long haul)
Rail (freight train)
Waterborne
Water craft operations
and logistics
Slow steaming of container vessel.
Inland waterways
2010 Belly-hold
2010 Diesel, light goods
2010 Diesel, heavy goods
2010 Electric , 200 gCO2eq/kWh
el
2010 New large international container vessel
2010 Large bulk carrier/tanker
2010 LNG bulk carrier
2010 Dedicated airfreighter
2030 Improved aircraft
2030 Improved, open rotor engine
2030 Optimized container vessel
2030 Optimized bulk carrier
Average new aircraft (2010)
New bulk carrier/
container vessel (2010)
Baselines for LCCC calculation
Indicative direct mitigation cost in
relation to the baseline
(can be positive or negative)
Indicative 2010 stock average baseline
CO2eq emissions and reduction potential
LCCC* [USD2010/tCO2eq]
*Levelized cost of conserved carbon (LCCC), here at 5% weighted average cost of capital (WACC)
2010 Stock average international shipping

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Brazilian sugarcane: 80%
GHG emissions reduction
compared with gasoline
(excluding land use change
effects) (33).
UK Government best practice
programme for freight/logistics
at –12 USD/tCO
2
(28).
Low-carbon technologies for
urban and long-haul road
freight –67–110 USD/tCO
2
;
Route management : ~330
USD/tCO
2
.
Japan: 12% fuel consumption
savings through eco-driving-
schemes in freight (12).