The presentation was illustrated at the CEEM CoP Webinar: “Achieving Low Carbon Mobility: Urban Transportation Modelling, Public Awareness and Behavioural Change" on tge 10th of October 2013 CEEM CoP stands for Community Energy and Emissions Modelling (CEEM) Community of Practice (CoP). CEEM CoP is an informal group supporting CEEM practitioners and local governments in furthering greenhouse gas modelling, target-setting and action in communities across BC – www.toolkit.bc.ca/ceem Mobility is an important part of daily life. Progressive community planning and transportation design can greatly reduce the need for automobile travel, instead providing a diverse range of active transportation alternatives. This presentation on the CATCH project looks at how transportation-related data can be used to understand a city’s travel footprint and help to inform city planning and programs to promote individual behaviour change. It reviews the findings and lessons learned from the ‘CATCH Project’ (Carbon Aware Travel Choice): a 2 million euro-funded project, involving 11 partners across 6 European Union countries, aimed to develop a knowledge platform to help urban communities move to less carbon-intensive transportation systems. This presentation touches on the important role of developing a system to compare and contrast best practices, identify the many motivators for change to low carbon mobility, and use tools for engaging the public and decision makers to support innovation and change.

1. MMM GROUP (UK)
Achieving Low Carbon Mobility:
Urban Transportation Modelling, Public
Awareness and Behavioural Change
10 October 2013
Steve Cassidy and Umberto Pernice

2. 2
WHO WE ARE
INTRODUCTION
What we do
The CATCH project
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Background
Goals, targets and outcomes
Technology Infrastructure & Visualization Tools
Data sources and indicators
European Union Policies / tools on GHG
emissions
Lessons learned – Thoughts for the future

3. WHAT WE DO
 Strategy and
Innovation in Mobility
Management
 Smart and Integrated
Ticketing
 Smart Mobility products
and services
 Incentive-based
behavioural models
 Smart Cities
measurement and
visualization tools
 Project prioritization
Mobility Management Design Methodology
Discover
Define
Deliver
Develop
Technical
framework
Toolkit
A set of approx. 20
product concepts

4. The CATCH project - Carbon-Aware Travel CHoice in the city,
region and world of tomorrow
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Funding: European Union Seventh
Framework Programme (FP7) for
Community Research and
Development Information Service
Duration: 30 months (2009 – 2012)
Budget: €2 million
11 beneficiaries in 6 countries (UK,
Italy, Spain, Belgium, Brazil, China)

5. INTERNATIONAL CONSORTIUM OF PARTNERS IN RESEARCH,
TRANSPORT, CONSULTANCY AND ENGINEERING
Edinburgh: MMM
Group
Bristol: University of West of
England
London:
TRL,
Q- Sphere
Brussels: POLIS, UITP, EFORUM
Milan:
Systematica
Madrid: SICE
Brazil: University of Rio de
Janeiro
China:
Handan
Municipality

6. BACKGROUND - THE PROBLEM FACED
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(Urban) transport sector
needs to de-carbonise
Technology is not enough
Need modal shift and
transportation demand
management (TDM)
It is essential that both
technical and non-technical
options are taken up1
Image source: http://www.carbonaware.eu/about.html
1Towards
the decarbonisation of the EU’s transport sector by 2050. Final report from project EU Transport GHG: Routes to
2050. June 2010

7. GOALS, TARGETS AND OUTCOMES
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Increase awareness of transport CO2
Motivate change to reduce transport
CO2
Consider the wider benefits of carbon
reduction (i.e. co-benefits)
Examined behaviour research in
transport, health, psychology, and
behavioural economics.
Examined how CO2 was being
communicated.

8. GOALS - OUTCOMES
How do people
perceive CO2 and
climate change
info?
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How to frame
messages?
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How to engage
and change
traveller
behaviour?
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CO2 is a new and abstract concept that people talk about,
but cannot interpret.
Difficult to interpret and be motivated by CO2 mass
Behavioural economics highlights how we frame the
information presented.
Loss framing will improve motivation for behavioural change
– eg do not use loss for behavioural change – use benefits
Research highlights that different triggers should be used to
stimulate behaviour change.
Sell co-benefits and create doubt that current situation is
best: comparison of your locale

9. ROLE OF CO-BENEFITS
Areas for triggering CO2 emissions reduction from travel:
Cost/Budget
Safety
Time and Accessibility
Community
Health
Planning/Land use

10. TECHNOLOGY INFRASTRUCTURE & VISUALIZATION TOOLS

11. THE VISUAL INTERACTIVE TOOLS
Co-benefit tool - presents information on
city-level carbon emissions from transport
alongside other “co-benefit”: Health, Safety,
Budget, Time, Planning and Community in a
comparative way between cities
Scenario tool - allows a selection across
a wide range of cities and offers a twodimensional graphical representation of
data to observe the relative performance
of cities across years

12. CO-BENEFIT TOOL
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Explain each co-benefit area and its link
with CO2 reduction;
Explore CO2 and co-benefits
performance across a wide number of
cities:
 If a city is not in the database the
most similar city is used;
Offer the users interactive functionalities
to express their views through appealing
interfaces and dynamic interactions
directly linked to the GHG database;
Scalability - more indicators and cobenefits can be included

13. Aggregates
users’ choices
to see what is
regarded by
majority of
users as top
issues.
-Factsheet
-Video Gallery
-References
-Best practices

14. City selection: to explore CO2 and co-benefit
performance across a wide number of cities. If a city is
not present in the database, a functionality can be used
to find the “most similar city”. Similarity is measured in
terms of geography, GDP, population and car usage
levels.

15. Two comparative dashboards
CO2
COBENEFITS

16. SCENARIO TOOL
A simulator for future scenarios
enabling:
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selection across a wide range
of cities dynamically from a
map
Bi-dimensional plan to
observe the relative position
of cities across years
Axes customisation, choosing
among a wide range of
indicators
Customisation of comparison

17. Selected city stick out in the graph
Main graph area - bidimensional plan to
observe the relative
position of cities.
Possibility of axes
customisation. Choice
among a wide range of
indicators
Customisation of
comparison cluster.
Can add cities in the
graph either “one by
one” or “all”
Time scale: by moving the cursor it is possible to see position of cities across years

18. DATA SOURCES AND INDICATORS
DATA USED TO ESTIMATE CO2 EMISSIONS
Map showing E-PRTR 5km grid
for CO2 emissions from road
transport
Sample of CO2 cells associated
with cities at LUZ level
Estimates of emissions of CO2 from road transport
for 2008
■ European Pollutant Release and Transfer Register
(E-PRTR) on a 5km x 5km grid covering Europe
2020 target estimations
■ An algorithm was developed to estimate city-level
2020 goals.
■ 2020 goals are based on a 20% reduction from
1990 levels.
■ National and city-level data used where data from
1990 and 2008 was available
■ Six algorithms developed to accommodate gaps
in data

19. DATA SOURCES AND INDICATORS
INDICATORS USED FOR CO-BENEFITS
Co-benefits indicators
■ Eurostat’s Urban Audit (primary source)
■ Supplemented by:
■ UITP’s Mobility in Cities Database
■ The European Commission's Urban Transport Benchmarking
Initiative
■ EMTA - European Metropolitan Transport Authorities’
Barometer

20. EUROPEAN UNION POLICIES / TOOLS ON GHG EMISSIONS
EU Directives
■ Integrated Pollution Prevention and Control (2008/1)
■ National Emissions Ceilings (2001/81).
■ Greenhouse Gas Emissions Trading Scheme (2009/29)
Tools
ICLEI Europe's Basic Climate Toolkit is comprised of:
■ GHG Inventory Manual
■ Basic GHG Inventory Tool
■ FAQ on GHG Inventories, Glossary and Abbreviations

21. LESSONS LEARNED – THOUGHTS FOR THE FUTURE
 Use of co-benefits in transport is essential for behavioural
change: a derived demand. Think lifestyle -how do we show the
best (transport) lifestyle solution?
 CATCH tools what next: “just” need data. If adopted -what is
the aim? Behavioural change?
 City dashboards to visualise cities: Big data from everywhere
(sensors, mobile, crowd), sharp data analytics and visualization
 Move to full holistic approach to a “Low carbon style of
living” and “Smart mobility”: trigger more effective behavioural
change

22. Contacts:
Steve Cassidy – CassidySt@mmm.ca
Umberto Pernice – PerniceU@mmm.ca
Annie Li – Lian@mmm.ca
MMM Group
3 Hill Street
Edinburgh, EH2 3JP, UK
t: +44 (0)131 226 1045