fp7 project catch - carbon aware travel choice
DESCRIPTION
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.TRANSCRIPT
MMM GROUP (UK)
Achieving Low Carbon Mobility:
Urban Transportation Modelling, Public Awareness and Behavioural Change
Steve Cassidy and Umberto Pernice
10 October 2013
WHO WE AREINTRODUCTION
What we do The CATCH project
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
2
Discover Define
Deliver Develop
Technical framework
Toolkit
A set of approx. 20 product concepts
Mobility Management Design Methodology
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
■ 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)
The CATCH project - Carbon-Aware Travel CHoice in the city, region and world of tomorrow
INTERNATIONAL CONSORTIUM OF PARTNERS IN RESEARCH, TRANSPORT, CONSULTANCY AND ENGINEERING
Brazil: University of Rio de Janeiro
Edinburgh: MMM Group
Madrid: SICE
China: Handan Municipality
Milan: Systematica
Brussels: POLIS, UITP, EFORUMLondon: TRL, Q- Sphere
Bristol: University of West of England
BACKGROUND - THE PROBLEM FACED
■ (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
1Towards the decarbonisation of the EU’s transport sector by 2050. Final report from project EU Transport GHG: Routes to 2050. June 2010
Image source: http://www.carbonaware.eu/about.html
■ Examined behaviour research in transport, health, psychology, and behavioural economics.
■ Examined how CO2 was being communicated.
■ Increase awareness of transport CO2
■ Motivate change to reduce transport CO2
■ Consider the wider benefits of carbon reduction (i.e. co-benefits)
GOALS, TARGETS AND OUTCOMES
How do people perceive CO2 and climate change info?
GOALS - OUTCOMES
■ CO2 is a new and abstract concept that people talk about, but cannot interpret.
■ Difficult to interpret and be motivated by CO2 mass
How to frame messages?
■ 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
How to engage and change traveller
behaviour?
■ 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
Areas for triggering CO2 emissions reduction from travel:
ROLE OF CO-BENEFITS
Cost/Budget Time and Accessibility Health
Safety Community Planning/Land use
TECHNOLOGY INFRASTRUCTURE & VISUALIZATION TOOLS
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 two-dimensional graphical representation of data to observe the relative performance of cities across years
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 co-benefits can be included
CO-BENEFIT TOOL
-Factsheet-Video Gallery-References-Best practices
Aggregates users’ choices to see what is regarded by majority of users as top issues.
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.
Two comparative dashboards
CO2
CO-BENEFITS
SCENARIO TOOL
A simulator for future scenarios enabling:
■ 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
Main graph area - bi-dimensional 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
Selected city stick out in the graph
DATA SOURCES AND INDICATORS DATA USED TO ESTIMATE CO2 EMISSIONS
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
Map showing E-PRTR 5km grid for CO2 emissions from road transport
Sample of CO2 cells associated with cities at LUZ level
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
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
DATA SOURCES AND INDICATORS INDICATORS USED FOR CO-BENEFITS
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
EUROPEAN UNION POLICIES / TOOLS ON GHG EMISSIONS
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
Contacts:
Steve Cassidy – [email protected]
Umberto Pernice – [email protected]
Annie Li – [email protected]
MMM Group 3 Hill StreetEdinburgh, EH2 3JP, UKt: +44 (0)131 226 1045