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FP7 project CATCH - Carbon aware travel choice

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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.

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FP7 project CATCH - Carbon aware travel choice

  1. 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. 2 WHO WE ARE INTRODUCTION 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
  3. 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. 4. The CATCH project - Carbon-Aware Travel CHoice in the city, region and world of tomorrow ■ ■ ■ ■ 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. 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. 6. 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 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. 7. GOALS, TARGETS AND OUTCOMES ■ ■ ■ ■ ■ 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. 8. GOALS - OUTCOMES How do people perceive CO2 and climate change info? ■ How to frame messages? ■ ■ ■ How to engage and change traveller behaviour? ■ ■ 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. 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. 10. TECHNOLOGY INFRASTRUCTURE & VISUALIZATION TOOLS
  11. 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. 12. CO-BENEFIT TOOL     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. 13. Aggregates users’ choices to see what is regarded by majority of users as top issues. -Factsheet -Video Gallery -References -Best practices
  14. 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. 15. Two comparative dashboards CO2 COBENEFITS
  16. 16. 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
  17. 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. 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. 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. 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. 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. 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

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