Decarbonising Transport

Green Financing for Transport The Decarbonising Transport project

OECD Green Investment Financing Forum, Tokyo, 13-14 Oct. 2016

José Viegas, Secretary-General

Intergovernmental Organisation

57 member countries (22 non-OECD)

Politically autonomous, administratively integrated at the OECD

Council of Ministers of Transport, rotating annual presidency

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.

Global transport volumes will continue to grow

4

Billio

n t

on

ne

-

Passenger transport volumes Business as usual

2015-2050

Freight transport volumes Business as usual

2015-2050

Billio

n p

as

se

ng

Billio

n t

on

ne

-kilo

me

tre

s

Source: International Transport Forum

CO2 emissions to grow if no new action taken

5

CO2 emissions from transport Business as usual

2015-2050

Source: International Transport Forum

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

More than 50 Project Partners

International freight model

Air passenger model

Urban mobility simulator

Urban passenger and access models

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

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

Agent-based simulation for

a real city (Lisbon) real trips on a detailed network model

(currently only urban core)

• 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

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

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

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

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

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.

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)?

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

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

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

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