3rd Annual OECD Green Investment Financing Forum

1. Decarbonising
Transport
Green Financing for
Transport
The Decarbonising Transport project
OECD Green Investment Financing Forum, Tokyo, 13-14 Oct. 2016
José Viegas, Secretary-General

2. Intergovernmental Organisation
 57 member countries
(22 non-OECD)
 Politically autonomous,
administratively
integrated at the OECD
 Council of Ministers
of Transport, rotating
annual presidency

3. DECARBONISING TRANSPORT
 Objective: A commonly acceptable roadmap to bring transport
to carbon neutrality by circa 2050
 Quantitative: A comprehensive model framework covering all
modes of transport
• Allows rigorous, coherent analysis of policies and outcomes across the world
• Considers global exogenous factors (demographics/urbanisation, economic
development, digital connectivity, etc.) and impact on transport emissions
• Simulation of technological evolution, alternative policy paths, and their
expected outcomes. Adjustments to evolving results
• Common assessment method
 Inclusive: Dialogue and engagement with all partners
• Countries, multilateral organisations, technology providers, operators and
other service providers, regulatory agencies, NGOs, financial providers, etc.

4. Global transport volumes will continue to grow
4
Billiontonne-
Passenger transport volumes
Business as usual
2015-2050
Freight transport volumes
Business as usual
2015-2050
Billionpasseng
Billiontonne-kilometres
Source: International Transport Forum

5. CO2 emissions to grow if no new action taken
5
CO2 emissions from transport
Business as usual
2015-2050
Source: International Transport Forum

6. What constitutes Sustainable Transport
Sustainable Transport must ensure
• Safe mobility across all modes of transportation; and
• Very low emissions of pollutants, particles and carbon. All
these dimensions must be addressed.
• Efficient and equitable access of all citizens to jobs,
markets, services and social interaction. This implies dealing
with congestion but also much more complex issues for those
without a car;
The Decarbonising Transport project will make impact
assessments in all these dimensions
• Additional data necessary for those assessments is quite small

8. More than 50 Project Partners

9. International
freight model
Air passenger
model
Urban mobility
simulator
Urban
passenger and
access models

10. Green Finance Investments in Transport
 No other sector besides Health will undergo such massive
changes in the next 10-20 years as Transport.
 Significant investments required (and green financing solutions
for them) in safer transport infrastructure, cleaner vehicle fleets
and organisational infrastructure for smarter, demand-responsive
transport systems
• business models and the organisation of transport production will
suffer radical overhauls
 For the Financial sector this represents not only a massive surge of
demand but also a change in the way the sector risks are perceived
and dealt with.
• inevitable evolution from taxes to user charges will make the revenue
stream much more like those associated with other network industries

11. Two quick examples of big change coming
Shared Urban Mobility solutions
as best approach to tackle
emissions, congestion and
accessibility
http://itf-oecd.org/sites/default/files/docs/shared-mobility-liveable-
cities.pdf
A policy path towards
driverless road vehicles

12. Agent-based simulation for
a real city (Lisbon)
real trips on a detailed network model
(currently only urban core)

13. • While keeping subway systems, new paradigm based on two new shared, demand-
responsive modes: Shared taxis and Taxibuses. General Specs:
High Quality of new solutions for
Public Acceptance: Two modes
Shared Urban Mobility Solutions
Shared Taxis Taxibuses
Door-to-door service Street-corner to street-corner service (max 400
m walk)
Very short waiting and detour
time (thresholds variable w/
trip length)
30 min advance notice, 10 min (notified) slide
acceptable
Travel time similar to car No transfers, no standing places
Max 6 pax, high doors, easy
entry/exit
Small buses (8 and 16 pax), quite direct routes
• Plus, in both segments
 Very easy transaction (smartphone based)
 Price not higher than today

14. Some key results (simulation for Lisbon)
(except for avg. pax on board, all cases in % relative to current = year 2010):
Aggregate
Indicators
3 modes
(Shared Taxi,
TaxiBus, Metro)
Comments
Avg. Pax on board
(Sh.taxis)
2.0
(peak 2.6)
Avg. Pax on board
(Taxibus)
4.2 (c8) / 11.4 (c16)
Peak: 5.0 (c8) / 14.6 (c16)
Fleet size
(Sh. taxis + buses)
2.8% (cars)
Bus*: 568% veh. / 79 % (pl.)
Massive release of public space
from parking (95%)
Much fewer cars, but much higher
distance per car (avg. 264 km/day)
VKM (weighted)
all-day
77%
No Congestion !
VKM (weighted)
peak-hour
63%
CO2 emissions 66% Best approach to short term reduction
Mid-and long term even better due to
much faster fleet turn-around
* - but these will be micro-buses with capacities 8 and 16, not standard urban buses, with capacity 80
Shared Urban Mobility Solutions

15. Impacts on Accessibility - Jobs
• % of jobs accessed from each grid cell in 30 minutes (using PT)
• Much better and more equitable access: Using demand-responsive transport,
distance matters but not the direction of travel
For each cell as origin, % of total jobs in the city accessed in 30 minutes
Current public transport + walking Taxibus + Metro + walking
Inequity
Indicator
Current
PT +
Walk
Taxibus
+ Metro
+ Walk
P90/P10
17.3 1.8
Gini coeff.
0.27 0.11
Shared Urban Mobility Solutions

16.  With professional drivers and current ICE vehicles tariffs required
would be about 26% of current prices per pax.km for taxis and
public transport (but without subsidy)
 Duty cycle very compatible with current electric vehicles (1h break after
4h duty used for driver meal and battery recharge), leading to prices
almost 20% lower
 Break-even distance of shared taxi vs. private car at 50 km/day for
small car, 98 for mid-size car
 Much faster fleet renewal given great distances covered by each
vehicle (avg. 260km/day for shared taxis)  cleaner fleets
operating
 Further reduction of VKM expectable from great improvement of
walking and cycling conditions (massive release of parking space)
Costs, Lifecycles, Active Modes

17. Autonomous driving: No longer “If”,
but rather “When” and “How”
 Projected time scales, technology options and use cases
involved vary by OEM, but horizons have been getting shorter
 Large expected benefits: improved road safety levels,
decreased emissions, and increased network capacity.
› Even larger for professional mobility services (lower costs,
higher daily performance)
 Negative effects can also be envisaged
› Induced traffic, people leaving further away from urban
centre, much heavier congestion.
 Very likely associated with electrification
 Risk of hacking must be clearly shown as under control before
serious uptake is envisaged

18. Breaking the Egg Shell
Many governments investing heavily in R&D and demonstration
of near market-ready systems
Younger and emerging IT focussed companies aggressively
pushing related systems and services into the market.
Vehicle automation becoming part of the concepts of the sharing
economy.
Key role for policy makers is the active management of the
transition period, already under way.
›Key tools are legal and regulatory frameworks.

19. Finding the Threshold
Governments need to make the call as to when the machines
can take over: Deciding how and when driverless vehicles
systems demonstrate lower (real and perceived) risks of
crashes than the current situation with humans doing the
driving
›Especially that they do not fail in situations that humans
would normally handle well.
Can there be a clear-cut percentage or success rate for
allowing operation without a driver in the cabin that would
satisfy the safety concerns of regulators (or of road users)?

20. Managing the Transition
Control rooms where professional drivers are set up in a context
that closely mimics the information and tools available in the
cabin.
›These drivers would remotely monitor a number of self-
driving trucks and intervene, taking manual control when
and where the computer detects it will not be in full control
(sensor insufficiency, ambiguity of situation) and asks for
help.
›Already in practice in limited contexts (mining, urban
delivery robots)
Much better working conditions, similar to air traffic controllers

21. Driving from a Remote Cabin
Initially, the threshold for human intervention would be set
low, but with growing experience and self-learning systems it
could be raised to higher levels
And so each driver
could be in control
of a growing
number of trucks

22. Assessing Green Finance Investments
in Transport
 System complexity and scale of changes expected impose a
Systems Dynamics modelling approach
› BaU Projections useful only as reference line and call for action,
not as estimates for horizons beyond 10 years (some massive
changes will come anyway)
 Package- or Project- focused approaches possible,
considering investments required, revenues expected and
SDG-related impacts
› As independent evaluator; or
› Engaged in co-design of projects with their carriers to ensure
political acceptance side-by-side with effective impacts on
sustainability goals

24. Thank you
José Viegas
+33(0) 1 45 24 97 10
Jose.viegas@itf-oecd.org
2 rue André Pascal
75775 Paris Cedex 16