Welcome to the Health Economic Assessment Tool (HEAT) for walking and cycling by WHO

>>>   November 2021: Update to HEAT v5.0 with global applicability, including global list of countries, expanded background data, and different user interface options (see news for details).   <<<

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The HEAT is designed to enable users without expertise in impact assessment to conduct economic assessments of the health impacts of walking or cycling.

What is HEAT?

The HEAT estimates the value of reduced mortality that results from specified amounts of walking or cycling, answering the following question:

If x people regularly walk or cycle an amount of y, what are the health impacts on premature mortality and their economic value?

Next to the health benefits from physical activity, HEAT also allows taking into account the mortality effects of exposure to air pollution and traffic crashes while walking or cycling. HEAT can further assess the effects on carbon emissions from shifting travel by motorized modes to walking or cycling.

The tool is based on the best available evidence and transparent assumptions. It is usable for a wide variety of professionals at both national and local levels. These include primarily transport planners, traffic engineers and special interest groups working on transport, walking, cycling or the environment.

What can I use HEAT for?

HEAT can be used for different assessments, for example:

·       assessment of current (or past) levels of cycling or walking, e.g. showing what cycling or walking are worth in your city or country.

·       assessment of changes over time, e.g. comparisons of “before and after” situations, or “scenarios A (with measures taken) vs. scenario B” (without measures taken).

·       evaluation of new or existing projects, including benefit-cost ratio calculations.

HEAT can be used as a stand-alone tool or to provide input into more comprehensive economic appraisal exercises, or prospective health impact assessments.

See examples of results you can produce with our local data or scenario here.

How does HEAT work?

More information on how HEAT works can be found here. A detailed description of the development process, evidence used and main project steps as well as a step-by-step-guide can be found in the Methodology and user guide booklet.

More information and contacts

More information and materials are also available at http://www.euro.who.int/HEAT

For questions or comments on HEAT please email to heatwalkingcycling@who.int.

 

News & Announcements

November 2021

Update to HEAT version 5.0 with global applicability

A completely updated version of HEAT is now available. HEAT was previously limited in scope to the WHO European Region. HEATv5 is now globally applicable. It includes a global list of countries and cities, expanded background data, and several new features to support its use worldwide.

HEATv5 provides three different user interface options, including a “basic”, a “flexible” and a “full” user experience with more or less options to refine the assessments, depending on the availability of local data and experience of users with the HEAT.

The website with additional information was also updated accordingly. An updated methodology and user guide booklet will be published in spring 2022.

May 2019

Update to HEAT v4.2

As part of this update we have revised large parts of the code, which will enable us to address future development needs and bug fixes more efficiently in the future.

You will also find several minor changes to the user interface, and a completely revised data input page, which now more explicitly accommodates count data.

Several bugs were also resolved with this new version.

We would like to express our thanks to all users who have been waiting patiently for this milestone! We appreciate receiving your feedback or questions at heatwalkingcycling@who.int

Your HEAT team

April 2019

Please note: currently the HEAT data input options “mode share” does not work properly. A bug-fixed and updated version is forthcoming. Please use other input options for the time being.

12 November 2018

Further HEAT Webinars are coming up in December and January, organized by the European Cyclist Federation (ECF) in collaboration with the HEAT core group. Dates will be announced as soon as available and can also be found in the ECF calendar.

 

Introduction HEAT Webinar 5/12

Link to event on ecf.com: https://ecf.com/users/ida-tange/trusted-content/introduction-heat-4-webinar

Registration link: https://ecf.com/civicrm/event/register?id=106&reset=1

 

Advanced HEAT Webinar 15/1

Link to event on ecf.com: https://ecf.com/users/ida-tange/trusted-content/advanced-heat-4-webinar

Registration link: https://ecf.com/civicrm/event/register?id=107&reset=1

 

12 November 2018

The next HEAT Webinar takes place on Monday November 12th 12:00-13:00 (CET time), organized by the European Cyclist Federation (ECF). This webinar is for users who are new to the tool, or have little experience with using HEAT. Here’s the link to the event:

https://ecf.com/users/lucy-slade/trusted-content/heat-4-webinar

10 October 2018

The next HEAT Advanced User Webinar takes place on Monday, 22 October, 17:00-18:00 CET, organized by the European Cyclist Federation (ECF). This webinar is intended for those who want to learn about the new HEAT modules for Air Pollution, Carbon and Crashes and for those who already have a basic knowledge or some practical experience with the HEAT. To find out more and register, go to https://ecf.com/civicrm/event/info?reset=1&id=102.

27 June 2018

HEAT now comes with a feedback mode which can be activated on the first page and which allows users to share their feedback throughout the tool. It also contains a brief user survey. We are confident that this will help further improve the tool in the future.

21 June 2018

HEAT 4.1 update:

With this update we introduce a number of bug fixes and features to improve user experience, like warning messages for invalid entries and the possibility to export data.

7 November 2017

The early release version of the new HEAT user guide booklet is now available for download here. A version with slightly revised references will be posted soon.

31 October 2017

Today, the new version of HEAT (4.0) has been launched. The main features and changes include:

·       combined assessments of walking and cycling;

·       calculation of impacts of exposure to air pollution, crash risk and carbon emissions, in addition the benefits from physical activity;

·       updated Values of Statistical Life (VSL) with averages and country-specific values (based on a methodology developed by the OECD);

·       a new HEAT methodology booklet;

·       updated section with HEAT examples;

·       revised workflow;

·       new user interface;

Please note: The previous 2014 version of HEAT is no longer available under the main URL. It has been moved to http://old.heatwalkingcycling.org. If you need access to assessments saved with the 2011 or 2014 version, please contact us at heat@who.int.

20 September 2017

Today, a pre-version of the 2017 update of the HEAT has been presented at the International Cycling Conference (ICC) in Mannheim, Germany. The main new features and changes include:

·       new user interface, including new HEAT modules for air pollution, road crashes and carbon effects;

·       updated Values of Statistical Life (VSL) with averages and country-specific values (based on a methodology developed by the OECD);

·       updated section of frequently asked questions (FAQs).

29 October 2014

New dates for free online trainings in English and German

Thanks to support from the Swiss Federal Office for Public Health and the collaboration with the European Cyclists’ Federation we are pleased to announce the continuation of the free live online trainings in English and German on how to use HEAT. Please see here for dates and registration:

http://www.heatwalkingcycling.org/training/

19 August 2014

2014 update: new version of HEAT for walking and cycling!

Today, the 2014 update of HEAT has been launched. Keeping the tool updated with the latest scientific evidence, the main new features and changes include:

·       updated relative risk functions for walking and cycling;

·       new Values of Statistical Life (VSL) with averages and country-specific values (based on a methodology developed by the OECD);

·       updated and more detailed mortality rates for European countries;

·       new section of frequently asked questions (FAQ); and

·       several bug fixes.

For details on the updated risk functions and new VSL values, please consult:

·       the updated Methodology and User Guide (2014);

·       the available Hints&Tips; and

·       the Report of the Expert Consensus Workshop from 1-2 October 2013.

Please note: The previous 2011 version of HEAT is no longer available. If you need access to assessments saved with the 2011 version, please contact us at heat@euro.who.int.

 

How HEAT works

The HEAT aims to promote the integration of the societal economic value of reduced premature mortality from cycling and walking into economic appraisals of transport and urban planning interventions. Users can calculate the mortality benefits only, or choose to take into account the effects of air pollution and crashes and/or to estimate the carbon emission effects from replacing motorized trips by walking or cycling.  

Who is the tool for?

HEAT is predominantly meant for:

·       Transport and urban planners

·       Traffic engineers

·       Special interest groups working on transport, walking, cycling or the environment.

HEAT core principles

The HEAT is based on the following core principles:

·       Scientific robustness and reference to the best available evidence  

·       Usability

o   minimal data input requirements

o   availability of default values

o   clarity of prompts and questions

o   design and flow of the tool

·       Transparency on assumptions and approach taken

·       In general based on a conservative approach

·       Adaptability to local contexts

·       Modularity

How does the model work?

The HEAT model applies a comparative risk assessment approach to assess impacts on health and carbon emissions. Learn more about this methodology here.

Impacts are then monetized. Learn more about the economic valuation here.

The key elements and workflow of the model are described here.

To learn more about the results HEAT produces, see here.

 

Assumptions used in HEAT
Introduction to health impact and comparative risk assessment
Data requirements
Key parts of a HEAT assessment

Scope for the use of HEAT

Please read these explanations carefully to make sure HEAT is applicable to your case.

·      HEAT is to be applied for assessments on a population level, i.e. in groups of people, not in individuals.

·      HEAT is designed for habitual behaviour, such as cycling or walking for commuting, running errands or related to child care, as well as regular leisure time activities.

HEAT is based on studies that assessed health effects of cycling or walking of a certain regularity. In the HEAT assessment, an annual average is derived. Thus, users should not use the HEAT for the evaluation of one-off events or competitions (such as single walking or cycling days etc.), since they are unlikely to reflect repeated, ideally habitual behaviour.

·      HEAT is designed for adult populations.

HEAT calculations are based on mortality rates for the age ranges of 20–74 years for walking and 20–64 years for cycling. HEAT should not be applied to populations of children or adolescents, since the scientific evidence used by HEAT does not include these age groups. The upper age boundaries have been set by consensus to avoid inflating health benefits from misrepresenting active travel behaviour among older age groups that have higher mortality risks. If the assessed population is considerably younger or older than average, the user can specify a lower or higher age range.

·      The tool is not suited for populations with very high average levels of walking or cycling.

HEAT applies evidence from studies in the general population and not in subpopulations with very high average levels of physical activity, such as bicycle couriers or mail personnel. Although the exact shape of the dose–response curve is uncertain, benefits from physical activity seem to start to slow above levels equivalent to perhaps 1.5 hours of cycling and 2 hours of brisk walking per day. The tool is therefore not suited for populations with average levels of cycling of about 1.5 hours per day or more or of walking of about 2 hours per day or more, which exceed the activity levels common in an average adult population.

·      The HEAT air pollution module should not be used for environments with very high levels of air pollution.

Most of the studies on health effects of cycling and walking and of air pollution used for HEAT have been carried out in environments with low or medium levels of air pollution (i.e. concentrations of fine particulate matter up to about 50ug/m3, see more information here. While it seems that negative effects from air pollution start to level off at higher levels, effects on cyclists and pedestrians have not yet been well studied at such levels of exposure. Thus, caution has to be used in interpreting results at higher levels of air pollution, and impacts are capped as of 100ug/m3 to avoid inflated results (see here).  

·      HEAT results involve uncertainty.

Knowledge of the health effects of walking and cycling is constantly evolving. The HEAT project is an ongoing consensus-based effort of translating basic research into a harmonized methodology. Despite relying on the best available scientific evidence, on several occasions the tool methodology required the advisory groups (see acknowledgements) to make expert judgements. The most important assumptions underlying the HEAT impact assessment approach are described here. Therefore, the accuracy of results of the HEAT calculations should be understood as estimates of the order of magnitude, much like many other economic assessments of health effects. HEAT is regularly being updated as new knowledge becomes available.

A HEAT assessment is composed of the following main steps

·       defining the assessment,

·       providing travel data

·       optionally adjust travel data

·       optionally provide additional data

·       review of calculation parameters

·       results.

Depending on the characteristics of an assessment, a varying number of questions will apply.

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For questions or comments on HEAT please email to heatwalkingcycling@who.int.

 

More information about how HEAT works
Data requirements
How to navigate the tool

HEAT methodology and user guide

A detailed description of the development process, evidence used and main project steps, the HEAT methodology as well as a step-by-step-guide can be found in the Methodology and User Guide Booklet on the HEAT website of the WHO Regional Office for Europe.

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HEAT Methods and user guide (2017)

Most of its contents are also available on this website through the “how HEAT works” link below.

Examples of end results you can produce with the WHO/Europe Health Economic Assessment Tool (HEAT)

HEAT can be used to evaluate interventions that have led to an increase in walking or cycling. The evaluations can assess the current situation, hypothetical scenarios or projected changes. Below we provide examples of different questions that can be answered with HEAT, with an explanation of the type of calculation and the data you need for each one.   

What would be the value if we doubled cycling in my city?

This is a projection; for this you need only data on the current levels of cycling in your city, or from a comparable setting if no local data is available. You then do a before-and-after analysis (using the “two-case assessment” option of HEAT) with a 100% percentage change of your input data.  You will need to decide in what manner you want to project the change in cycling e.g. a doubling in the number of people cycling (i.e. doubling the number of cycling trips); a doubling of distance cycled; or a doubling of days per week cycled.

What would be the value if we increased modal share for walking and cycling by x%?

This is also a projection case; for this you need only data on the current modal share of walking and cycling. Modal share refers to the percentage share of people using a particular mode of transport for completing trips. You then do a two-case assessment by applying x% percentage change of your input data.

What would be the value if we cycled as much as – say - the Dutch?

This is also a projection case; for this you need data on the current level of cycling, and a comparable figure for the level of cycling in the hypothetical situation (in this case, cycling in the Netherlands). Hypothetical scenarios can be useful to illustrate the benefits of different options before they have been implemented. 

What would be the value if every adult in our city walked for 10 minutes more per day?

Although this appears to be two-case assessment, it can be done most easily by valuing 10 minutes walking among the population of the city. So for this you only need to know the number of adults in the city.

What is the value of current levels of cycling/walking in my city?

This is a single-case analysis; for this you need only data on the current levels of walking and cycling in your city.

What would be the value of building this new bike path?

This is a two-case assessment (before and after), but the ‘after’ is not known at this stage. You therefore need some way to project the level of cycling on the new path, and then do a ‘before and after’ analysis using data on current levels of cycling. In this case, it can be helpful to try different outcomes and see how this affects the results.  For example ‘if people use the path once a week we find x, whereas if they use it three times a week we find y. 

What is the value of the increase in walking/cycling we have measured across our city?

This is also a two-case assessment (before and after), using data on the levels of walking/cycling before and the levels of walking/cycling after.

What would be the value of a decrease in walking due to policy changes?

HEAT can also be used to value negative changes, e.g. if walking or cycling decreased after a change in policy. This is also a two-case assessment (before and after), using data on the levels of walking before and after the change in policy.

 

 

General acknowledgements

The health economic assessment tool (HEAT) has been developed from an original idea of Harry Rutter, London School of Hygiene and Tropical Medicine, United Kingdom. It is based on the principles of HEAT for cycling first published in 2007.

This multi-phase, open-ended project is coordinated by WHO, steered by a core group of multidisciplinary experts and supported by ad hoc invited international experts from various fields who kindly give input for developing and updating of the tool (see also the acknowledgement sections for the various project phases on the right). The affiliations of some of the participants have changed during this project, and they are listed here as they were at the time.

Project coordinating team (2021)

·       Thiago Herick de Sa, WHO Headquarters

·       Francesca Racioppi, WHO Regional Office for Europe

·       Sonja Kahlmeier, Swiss Distance University of Applied Science (FFHS), Switzerland

·       Thomas Götschi, University of Oregon, United States of America

Project advisory and expert group

Karim Abu-Omar, Heba Adel Moh’d Safi, Yousaf Ali, Lars Bo Andersen, Hugh Ross Anderson, Nelzair Araujo Vianna, Olivier Bode, Tegan Boehmer, Nils-Axel  Braathen, Christian Brand, Hana Bruhova-Foltynova, Fiona Bull, Daniel Buss, Juan Castillo, Nick Cavill, Dushy Clarke, Andy Cope, Baas de Geus, Audrey de Nazelle, Ardine de Wit, Hywell Dinsdale, Damien Dussaux, Rune Elvik, Santiago Enriquez, Mark Fenton, Jonas Finger, Francesco Forastiere, Richard Fordham, Charlie Foster, Virginia Fuse, Eszter Füzeki, Rogerio Gama, Frank George, George Georgiadis, Regine Gerike, Eva Gleissenberger, Rahul Goel, Shifalika Goenka, Anna Goodman, Thomas Götschi, Maria Hagströmer, Mark Hamer, Yahaya Hassan Said, Holger Haubold, Eva Heinen, Thiago Herick de Sa, Marie-Eve Heroux, Max Herry, Gerard Hoek, Stefanie Holzwarth, Luc Int Panis, Nicole Iroz-Elardo, Luis Jorge Hernandez, Sonja Kahlmeier, Paul Kelly, Meleckidhesi Khayesi, Caryl Koinange, Michal Krzyzanowski, I-Min Lee, Christoph Lieb, Jose Lobo, Mazen Malkawi, Brian Martin, Marco Martuzzi, Markus Maybach, Adriannah Mbandi, Guy Mbayo, Gabriel Michel, Irina Mincheva Kovacheva, Hanns Mooshammer, Pierpaolo Mudu, Carlos Muñoz Piña, Marie Murphy, Nanette Mutrie, Bhash Naidoo, Keiko Nakamura, Daisy Narayanan, Maria Neira, Amanda Ngabirano, Mark Nieuwenhuijsen, Åse Nossum, Pekka Oja, Edith Patouillard, Uttam Paudel, Genandrialine Peralta, Laura Perez, Julie Powel, Francesca Racioppi, Hussain Rasheed, Lisa Robinson, David Rojas Rueda, Gabe Rousseau, Candace Rutt, Harry Rutter, Randy Rzewnicki, Kjartan Saelensminde, Elin Sandberg, Alexander Santacreu, Andreia Santos, Parth Sarathi Mahapatra, Lucinda Saunders, Daniel Sauter, Shari Schaftlein, Peter Schantz, Tom Schmid, Peter Schnohr, Christoph Schreyer, Christian Schweizer, Nino Sharashidze, Heini Sommer, Jan Sørensen, Joe Spadaro, Gregor Starc, Dave Stone, Marko Tainio, Robert Thaler, Vo Thi Hue Man, Meelan Thondoo, Miles Tight, Sylvia Titze, Lan Wang, Miriam Weber, Wanda Wendel Vos, Paul Wilkinson, James Woodcock, Tian Xiangyang, Hou Xiaohui, Mulugeta Yilma, Masud Yunesian.

Development team

Tomasz Szreniawski (lead), Thomas Götschi, Alberto Castro Fernandez, Ali Abbas, Vicki Copley, Duy Dao, Hywell Dinsdale.

A complete list of acknowledgements for all phases of the health economic assessment tool (HEAT) development is available on the subsequent websites (see links on the right).

 

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© World Health Organization, 2021

 

Acknowledgements 2021
Acknowledgements 2017
Acknowledgements 2015
Acknowledgements 2014
Acknowledgements 2011
Acknowledgements 2010

Information on previous versions

The first version of HEAT for cycling was presented in 2007, and officially launched in 2009 as a Microsoft Excel document. The first version of HEAT for walking was launched in 2011 as a website together with an updated version of HEAT for cycling.

In 2014, updated versions of the HEAT for walking and cycling were published (http://heat3.heatwalkingcycling.org/tool/).

In 2017, HEATv4 was published, which added additional impact pathways for air pollution, crashes and carbon emissions (https://heat4.heatwalkingcycling.org/tool/).

Please note that these by now deprecated versions of HEAT are no longer supported.  

If you require additional information on previous versions of HEAT, please contact us at mailto:heat@euro.who.int.

 

Acknowledgements

Health Economic Assessment Tool for cycling (2007-2010)

This tool has been developed by:

·       Harry Rutter, South East Public Health Observatory, United Kingdom

·       Nick Cavill, Cavill Associates, United Kingdom

·       Hywell Dinsdale, South East Public Health Observatory, United Kingdom

·       Sonja Kahlmeier, WHO European Centre for Environment and Health, Rome, WHO Regional Office for Europe

·       Francesca Racioppi, WHO European Centre for Environment and Health, Rome, WHO Regional Office for Europe

·       Pekka Oja, Karolinska Institute, Sweden

Web programming and design:

·       Duy Dao, Switzerland

Text editing:

·       Nicoletta di Tanno

·       Sonja Kahlmeier

·       Nick Cavill

An international advisory group contributed to the development of this tool:

Lars Bo Andersen*, School of Sports Science, Norway

 

Bhash Naidoo, National Institute for Health and Clinical Excellence (NICE), United Kingdom

Finn Berggren, Gerlev Physical Education and Sports Academy, Denmark

 

Åse Nossum/Knut Veisten, Institute for Transport Economics, Norway

Hana Bruhova-Foltynova, Charles University Environment Centre, Czech Republic

 

Kjartan Saelensminde, Norwegian Directorate for Health and Social Affairs

Fiona Bull, Loughborough University, United Kingdom

 

Peter Schantz*, Research Unit for Movement, Health and Environment, Åstrand Laboratory, School of Sport and Health Sciences, Sweden

Andy Cope*, Sustrans, United Kingdom

 

Thomas Schmid, Centers for Disease Control and Prevention, USA

Maria Hagströmer/Michael Sjöström, Karolinska Institute, Sweden

 

Heini Sommer*, Ecoplan, Switzerland

Eva Gleissenberger/Robert Thaler, Lebensministerium, Austria

 

Jan Sørensen*, Centre for Applied Health Services Research and Technology Assessment, University of Southern Denmark

Brian Martin, Federal Office of Sport, Switzerland

 

Sylvia Titze, University of Graz, Austria

Irina Mincheva Kovacheva, Ministry of Health, Bulgaria

 

Ardine de Wit/Wanda Wendel Vos, National Institute for Health and Environment (RIVM), Netherlands

Hanns Moshammer, International Society of Doctors for the Environment

 

Mulugeta Yilma, Road Administration, Sweden

 * members of the extended core group

Pilot testing:

·       Hana Bruhova-Foltynova, Charles University Environment Centre, Czech Republic

·       Sean Co, Metropolitan Transportation Commission, Oakland, California, United States of America

·       Werner Hagens, Liesbeth Mathijssen, Yonne Mulder, National Institute for Public Health and the Environment (RIVM), the Netherlands

·       Ruth Hunter, Centre for Public Health, Queen's University Belfast, United Kingdom

·       Sam Margolis, LBTH and NHS Tower Hamlets, United Kingdom

·       Angela Wilson, Research and Monitoring Unit, Sustrans, United Kingdom

The project was supported by the Austrian Federal Ministry of Agriculture, Forestry, Environment and Water Management, Division V/5 – Transport, Mobility, Human Settlement and Noise and by the Swedish Expertise Fund, and facilitated by the Karolinska Institute, Sweden. The project benefited greatly from systematic reviews being undertaken for the National Institute for Health and Clinical Excellence (NICE) in the United Kingdom. The consensus workshop (Graz, Austria, 15–16 May 2007) was facilitated by the University of Graz.

The update of HEAT Cycling in 2011 was also financially supported by the European Union in the framework of the Health Programme 2008-2013 (Grant agreement 2009 52 02). The views expressed herein can in no way be taken to reflect the official opinion of the European Union.

 

             

 

 

Acknowledgements

Health Economic Assessment Tool for walking (2010-2011)

This tool has been developed from an original idea of Harry Rutter, National Obesity Observatory England, United Kingdom and it is based on the principles of the Health Economic Assessment Tool for Cycling first published in 2007.

Lead authors

·       Hywell Dinsdale, National Obesity Observatory England, United Kingdom (Tool programming and technical development)

·       Nick Cavill, Cavill Associates, United Kingdom (Project management and technical development)

·       Sonja Kahlmeier, University of Zurich, Switzerland (Project management and technical development)

Project core group

·       Sonja Kahlmeier, University of Zurich, Switzerland

·       Nick Cavill, Cavill Associates, United Kingdom

·       Hywell Dinsdale, National Obesity Observatory England, United Kingdom

·       Harry Rutter, National Obesity Observatory England, United Kingdom

·       Thomas Götschi, University of Zurich, Switzerland

·       Charlie Foster, University of Oxford, United Kingdom

·       Paul Kelly, University of Oxford, United Kingdom

·       Dushy Clarke, University of Oxford, United Kingdom

·       Pekka Oja, UKK Institute for Health Promotion Research, Finland

·       Richard Fordham, University of East Anglia, United Kingdom

·       Dave Stone, Natural England, United Kingdom

·       Francesca Racioppi, WHO European Centre for Environment and Health, Rome, WHO Regional Office for Europe

Web programming and design:

·       Duy Dao, Switzerland

Text editing:

·       Nicoletta di Tanno

·       Sonja Kahlmeier

·       Nick Cavill

International advisory group

Lars Bo Andersen, School of Sports Science, Norway

 

Elin Sandberg / Mulugeta Yilma, Road Administration, Sweden

Andy Cope, Sustrans, United Kingdom

 

Daniel Sauter, Urban Mobility Research, Switzerland

Mark Fenton, Tufts University, United States of America

 

Peter Schantz, Mid Sweden University  and Swedish School of Sport and Health Sciences

Mark Hamer, University College London, United Kingdom

 

Peter Schnohr, The Copenhagen City Heart Study, Denmark

Max Herry, Herry Consult, Austria

 

Christian Schweizer, WHO Regional Office for Europe

I-Min Lee, Harvard School of Public Health, United States of America

 

Heini Sommer, Ecoplan, Switzerland

Brian Martin, University of Zurich, Switzerland

 

Jan Sørensen, Centre for Applied Health Services Research and Technology Assessment, University of Southern Denmark

Markus Maybach / Christoph Schreyer, Infras, Switzerland

 

Gregor Starc, University of Ljubljana, Slovenia

Marie Murphy, University of Ulster, United Kingdom

 

Wanda Wendel Vos, National Institute for Health and Environment (RIVM), Netherlands

Gabe Rousseau, Federal Highway Administration, United States of America

 

Paul Wilkinson, London School of Hygiene and Tropical Medicine, United Kingdom

Candace Rutt / Tom Schmid, Centers for Disease Control and Prevention, United States of America

Pilot testing:

·       Hana Bruhova-Foltynova, Charles University Environment Centre, Czech Republic

·       Sean Co, Metropolitan Transportation Commission, Oakland, California, United States of America

·       Werner Hagens, Liesbeth Mathijssen, Yonne Mulder, National Institute for Public Health and the Environment (RIVM), the Netherlands

·       Ruth Hunter, Centre for Public Health, Queen's University Belfast, United Kingdom

·       Sam Margolis, LBTH and NHS Tower Hamlets, United Kingdom

·       Angela Wilson, Research and Monitoring Unit, Sustrans, United Kingdom

The project was supported by a consortium of donors from the United Kingdom under the leadership of Natural England. The consortium included the Department of Health England, Environment Agency, Countryside Council for Wales, Public Health Wales, Physical Activity & Nutrition Networks for Wales, Forestry Commission and the Scottish Government, Public Health Directorate. It was also supported by the Swiss Federal Office of Public Health and by the WHO Regional Office of Europe.

It was carried out in close collaboration with HEPA Europe, the European network for the promotion of health-enhancing physical activity, and the Transport, Health and Environment Pan-European Programme (THE PEP). The consensus workshop (Oxford, United Kingdom, 1-2 July 2010) was facilitated by the University of Oxford.

The development of HEAT Walking was also financially supported by the European Union in the framework of the Health Programme 2008-2013 (Grant agreement 2009 52 02). The views expressed herein can in no way be taken to reflect the official opinion of the European Union.

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Acknowledgements

HEAT for cycling and walking 2014 update

These tools have been developed from an original idea of Harry Rutter, National Obesity Observatory England, United Kingdom and they are based on the principles of the Health Economic Assessment Tool for Cycling first published in 2007.

Project core group

·       Sonja Kahlmeier, University of Zurich, Switzerland

·       Paul Kelly, University of Oxford, United Kingdom

·       Charlie Foster, University of Oxford, United Kingdom

·       Thomas Götschi, University of Zurich, Switzerland

·       Nick Cavill, Cavill Associates, United Kingdom

·       Hywell Dinsdale, Public Health Analyst, Hyde, United Kingdom

·       James Woodcock, University of Cambridge, United Kingdom

·       Christian Schweizer, WHO Regional Office for Europe

·       Harry Rutter, London School of Hygiene and Tropical Medicine, United Kingdom

·       Christoph Lieb, Ecoplan, Switzerland

·       Pekka Oja, UKK Institute for Health Promotion Research, Finland

·       Francesca Racioppi, WHO Regional Office for Europe

Web programming and design:

·       Duy Dao, Switzerland

Text editing:

·       Nicoletta di Tanno,

·       Sonja Kahlmeier,

·       Nick Cavill,

·       Christian Schweizer

International advisory group

·       Karim Abu-Omar, University Erlangen, Germany

·       Lars Bo Andersen, School of Sports Science, Norway

·       Finn Berggren, Gerlev Physical Education and Sports Academy, Denmark

·       Tegan Boehmer, Centers for Disease Control and Prevention, USA

·       Nils-Axel Braathen, Organization for Economic Cooperation and Development (OECD), France

·       Audrey de Nazelle, University College London, United Kingdom

·       Jonas Finger, Robert Koch Institute, Germany

·       I-Min Lee, Harvard School of Public Health, USA

·       Eszter Füzeki, Johann Wolfgang Goethe-Universität, Germany

·       Frank George, Health Economics , WHO Regional Office for Europe

·       Regine Gerike, University of Natural Resources and Life Sciences Vienna, Austria

·       Marie-Eve Heroux, Air Quality, WHO Regional Office for Europe

·       Michal Krzyzanowski, King's College London, United Kingdom

·       Nanette Mutrie, University of Edinburgh, United Kingdom

·       Peter Schantz,The Swedish School of Sport and Health Sciences, Sweden

·       Luc Int Panis, VITO, Belgium

·       Laura Perez, Swiss Tropical and Public Health Institute, Switzerland

·       Heini Sommer, Ecoplan, Switzerland

·       David Rojas Rueda, Centre for Research in Environmental Epidemiology (CREAL), Spain

Acknowledgements

This 2014 update of HEAT for cycling and walking was supported by the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety. The third consensus workshop (Bonn, Germany, 1-2 October 2013) was chaired by Michal Krzyzanowski, King’s College London, facilitated by the University of Zurich, Switzerland, and carried out in collaboration with the Universitätsclub Bonn e.V.

We wish to thank Nia Roberts, Peter Scarborough, Justin Richards, Andrew Wright, Aiden Doherty and Anja Mizdrak, Oxford University, for their contributions to the systematic reviews on cycling and walking and all-cause mortality and Dareskedar Workie, University of Alberta, Canada, for input to the economic valuation approach.

 

            

 

 

 

 

 

 

Acknowledgements

Development of the HEAT air pollution module (2014-2015)

Lead authors

·       David Rojas Rueda, Centre  for Research in Environmental Epidemiology, Spain

·       Audrey Nazelle, University College London, United Kingdom

·       Sonja Kahlmeier, University of Zurich, Switzerland

·       Christian Schweizer, WHO Regional Office for Europe

Project core group

·       Harry Rutter, London School of Hygiene and Tropical Medicine, United Kingdom

·       Francesca Racioppi, WHO Regional Office for Europe

·       Sonja Kahlmeier, University of Zurich, Switzerland

·       Christian Schweizer, WHO Regional Office for Europe

·       Nick Cavill, Cavill Associates, United Kingdom

·       Hywell Dinsdale, consultant, United Kingdom

·       Thomas Götschi, University of Zurich, Switzerland

·       James Woodcock, Institute of Public Health, Cambridge, United Kingdom

·       Paul Kelly, Oxford University/University of Edinburgh, United Kingdom

·       Christoph Lieb/Heini Sommer, Ecoplan

·       Pekka Oja, UKK Institute for Health Promotion Research, Finland

·       Charlie Foster, Oxford  University,  United Kingdom

International advisory group

·       Karim Abu-Omar,  University of  Erlangen, Germany

·       Hugh Ross Anderson, St George’s University of London, United Kingdom

·       Olivier Bode, University College London, United Kingdom

·       Tegan Boehmer, Centers for Disease Control and Prevention, United States of America

·       Francesco Forastiere, Azienda Sanitaria Locale RME, Rome, Italy

·       Eszter  Füzeki, Johann  Wolfgang  Goethe-Universität, Germany

·       Gerard Hoek, University of Utrecht, the Netherlands

·       Frank George, WHO Regional Office for Europe

·       Marie-Eve Heroux, WHO Regional Office for Europe

·       Michal Krzyzanowski, King’s College London, United Kingdom

·       Mark Nieuwenhuijsen, Centre  for Research in Environmental Epidemiology, Spain

·       Luc Int Panis, VITO, Belgium

·       Laura Perez, Swiss Tropical and Public Health Institute, Switzerland

·       Marko Tainio, University of Cambridge, United Kingdom

Acknowledgements

The development of the air pollution module of HEAT and the 2015 version of this publication was supported by the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety. The fourth consensus workshop (Bonn, Germany, 11-12 December 2014) was chaired by Michal Krzyzanowski, King’s College London and facilitated by the University of Zurich,  Switzerland.

 

 

     

 

Acknowledgements

HEAT crash and carbon modules and updated 2017 version (HEAT 4.0) (2016-2017)

Lead authors

·       Thomas Götschi, University of Zurich, Switzerland

·       Alberto Castro Fernandez, University of Zurich, Switzerland

·       Christian Brand, University of Oxford, United Kingdom

·       James Woodcock, University of Cambridge, United Kingdom

·       Sonja Kahlmeier, University of Zurich, Switzerland

Software development and design

·       Tomasz Szreniawski,

·       Alberto Castro Fernandez,

·       Ali Abbas,

·       Vicki Copley

·       Thomas Götschi

Text editing

·       David Breuer

·       Sonja Kahlmeier

·       Nick Cavill

Project core group

·       Harry Rutter, London School of Hygiene and Tropical Medicine, United Kingdom

·       Francesca Racioppi, WHO Regional Office for Europe

·       Sonja Kahlmeier, University of Zurich, Switzerland

·       Thomas Götschi, University of Zurich, Switzerland

·       Nick Cavill, Cavill Associates, United Kingdom

·       James Woodcock, University of Cambridge, United Kingdom

·       Paul Kelly, University of Edinburgh, United Kingdom

·       Christian Brand, University of Oxford, United Kingdom

·       David Rojas Rueda, Barcelona Institute for Global Health (ISGlobal), Spain

·       Alberto Castro Fernandez, University of Zurich, Switzerland

·       Christoph Lieb/Heini Sommer, Ecoplan, Switzerland

·       Christian Schweizer, WHO Regional Office for Europe

·       Pekka Oja, UKK Institute for Health Promotion Research, Finland

·       Charlie Foster, University of Bristol, United Kingdom

International advisory group

·       Andrew Cope, Sustrans, United Kingdom

·       Bas De Geus, Free University Brussels, Belgium

·       Audrey de Nazelle, Imperial College London, United Kingdom

·       Rune Elvik, Institute of Transport Economics, Norway

·       Frank George, WHO Regional Office for Europe

·       Anna Goodman, London School of Hygiene & Tropical Medicine, United Kingdom

·       Nicole Iroz-Elardo, Urban Design 4 Health, Rochester, United States

·       Thiago Herick de Sa / Meleckidzedeck Khayesi / Pierpaolo Mudu, WHO Headquarters

·       Eva Heinen, University of Leeds, United Kingdom

·       Michal Krzyzanowski, consultant, Poland

·       Pekka Oja, UKK Institute for Health Promotion Research, Finland

·       Randy Rzewnicki, European Cyclists’ Federation, Belgium

·       Alexandre Santacreu, International Transport Forum, France

·       Lucinda Saunders, Greater London Authority/Transport for London, United Kingdom

·       Jan Sorensen, Royal College of Surgeons, Ireland

·       Joe Spadaro, WHO Regional Office for Europe

·       Marko Tainio, University of Cambridge, United Kingdom

·       Miles Tight, University of Birmingham, United Kingdom

·       George Georgiadis / Virginia Fuse, United Nations Economic Commission for Europe

Acknowledgements

The 2017 update of HEAT for cycling and walking was supported in part by the project “Physical Activity through Sustainable Transport Approaches” (PASTA) (http://pastaproject.eu), which is funded by the European Union’s Seventh Framework Program under EC-GA No. 602624-2 (FP7-HEALTH-2013-INNOVATION-1).

The 5th consensus workshop (28-29 March 2017, Copenhagen, Denmark) was chaired by Michal Krzyzanowski, Poland, and facilitated by the University of Zurich, Switzerland.

 

   

Acknowledgements

HEAT global version (HEAT 5.0) (2019 - 2021)

Lead authors

·       Thomas Götschi, University of Zurich, Switzerland and University of Oregon, United States of America

·       Thiago Herick de Sa, World Health Organization (WHO) Headquarters, Switzerland

·       Sonja Kahlmeier, Swiss Distance University of Applied Science FFHS, Switzerland

Software development and design

·       Tomasz Szreniawski, lead programmer, Poland

·       Thomas Götschi, University of Zurich, Switzerland and University of Oregon, United States of America

·       Alberto Castro, University of Zurich / Swiss Tropical and Public Health Institute, Switzerland

Project coordinating team

·       Thiago Herick de Sa, World Health Organization (WHO) Headquarters, Switzerland

·       Francesca Racioppi, European Centre for Environment and Health, WHO Regional Office for Europe, Germany

·       Sonja Kahlmeier, Swiss Distance University of Applied Science (FFHS), Switzerland

·       Thomas Götschi, University of Oregon, United States of America

Project advisory and experts group

·       Yousaf Ali, GIK Institute of Engineering Sciences and Technology

·       Elisabeth Belpaire, International Society of City and Regional Planners (ISOCARP), The Netherlands

·       Christian Brand, University of Oxford, United Kingdom

·       Ralph Buehler, Virginia Tech, United States of America

·       Fiona Bull, WHO Headquarters, Switzerland

·       Daniel Buss, WHO Regional Office for the Americas

·       Alberto Castro, University of Zurich / Swiss Tropical and Public Health Institute, Switzerland

·       Juan Castillo, WHO Regional Office for the Americas

·       Nick Cavill, Cavill Associates, United Kingdom

·       Oscar Chamat, Metropolis, Spain

·       Tom Cole-Hunter, WHO Regional Office for the Western Pacific, Philippines

·       Massimo Cozzone, European Centre for Environment and Health, WHO Regional Office for Europe, Germany

·       Audrey de Nazelle, Imperial College London, United Kingdom

·       Damien Dussaux, United Nations Economic Commission for Europe

·       Carly Gilbert-Patrick, United Nations Environment Programme UNEP, Kenya

·       Rahul Goel, Indian Institute of Technology, India

·       Shifalika Goenka, Public Health Foundation of India Dionisio González, International Union of Public Transport (UITP), Belgium

·       Rogerio Gutierrez Gama, Prefeitura de Niteroi, Brazil

·       Holger Haubold, European Cyclists' Federation, Belgium Stefanie Holzwarth, UN-HABITAT, Kenya

·       Alex Johnson, Accra Metropolitan Assembly, Ghana

·       Meleckidzedeck Khayesi, WHO Headquarters, Switzerland

·       Paul Kelly, University of Edinburgh, United Kingdom

·       Michal Krzyzanowski, consultant, Poland

·       Stefan Lambert, Environment Agency Austria

·       Christoph Lieb and Heini Sommer, Ecoplan, Switzerland

·       Ze Lobo, World Cycling Alliance, Belgium

·       Lucy Mahoney, C40 Network, United States of America

·       Mazen Malkawi, WHO Regional Office for the Eastern Mediterranean, Jordan

·       Victoria Martinez, Federal Highway Administration, United States of America

·       Marco Martuzzi, WHO Regional Office for the Western Pacific, Philippines

·       Guy Mbayo, WHO Regional Office for Africa, Congo

·       Leslie Meehan, Tennessee Department of Health, United States of America

·       Gabriel Michel, Red de Ciclovías Recreativas de las Américas, University de los Andes

·       Pier Mudu, WHO Headquarters, Switzerland

·       Maria Agnes Muriel, WHO Headquarters, Switzerland

·       Maria Neira, WHO Headquarters, Switzerland

·       Amanda Ngabirano, Makerere University/National Physical Planning Board, Uganda

·       Gary O'Donovan, University de los Andes, Colombia

·       David Rojas Rueda, Colorado State University, United States of America

·       Harry Rutter, University of Bath, United Kingdom

·       Heba Adel Moh’d Safi, WHO Regional Office for the Eastern Mediterranean, Jordan

·       Yahaya Said, Ministry of Works and Transport, Tanzania

·       Andreia Santos, London School of Hygiene and Tropical Medicine, United Kingdom

·       Olga Lucia Sarmiento, University of Andes, Colombia

·       Genandrialine Peralta, WHO Regional Office for the Western Pacific, Philippines

·       Julie Powell, United Nations Department of Economic and Social Affairs, United States of America

·       Hussain Rasheed, WHO Regional Office for South-East Asia, India

·       Nathalie Röbbel, WHO Headquarters, Switzerland

·       Alexandre Santacreu, International Transport Forum, France

·       Joseph Spadaro, Basque Centre for Climate Change, Spain

·       Lucinda Saunders, Healthy Streets, United Kingdom

·       Shari Schaftlein, US Department of Transport, Federal Highway Administration, United States of America

·       Nino Sharashidze, European Centre for Environment and Health, WHO Regional Office for Europe, Germany

·       Robert Smith, University of Sheffield, United Kingdom

·       Bronwen Thornton, Walk21, United Kingdom

·       Geetam Tiwari, Indian Institute of Technology, India Dániel Tordai, KTI Institute for Transport Sciences, Hungary

·       Cristina Vert Roca, WHO Headquarters, Switzerland

·       Shelley Wallace, WHO Regional Office for the Western Pacific, Philippines

·       Lan Wang, Tongji University, China Miriam Weber, City of Utrecht, The Netherlands

·       Johannah Wegerdt, WHO Regional Office for the Western Pacific, Philippines

·       Meghan Winters, Simon Fraser University, United States of America

·       James Woodcock, University of Cambridge, United Kingdom

·       Masud Yunesian, Tehran University of Medical Sciences, Iran

Expert groups for updating the economic valuation methodology in the HEAT global context

·       Andreia Santos, London School of Hygiene and Tropical Medicine, United Kingdom

·       Thomas Götschi, University of Oregon, United States of America

·       Thiago Herick de Sá, World Health Organization, Switzerland

·       Lisa Robinson, Harvard University, United States of America

·       Joseph Spadaro, Basque Centre for Climate Change, Spain,

·       Edith Patouillard, World Health Organization, Switzerland

·       Christoph Lieb, Ecoplan, Switzerland

·       Damien Dussaux, Organisation for Economic Cooperation and Development (OECD), France

·       Carlos Muñoz Piña, World Resources Institute, Mexico

·       Santiago Enriquez, Abt Associates Int., Mexico

Acknowledgements

The 2021 update of HEAT for cycling and walking was supported in part by the Urban Health Initiative, the WHO Regional Office of the Americas, and the Norwegian Ministry of Foreign Affairs.

The 6th consensus workshop (29-30 June 2021, online) was chaired by Michal Krzyzanowski, Poland, and facilitated by experts from the Swiss Distance University of Applied Science FFHS, Switzerland, the University of Oregon, United States and the London School of Hygiene and Tropical Medicine, United Kingdom.

 

        

User interface options and data adjustments for the HEAT calculations

Input data on active modes of transport provided by the user may not be adequate or sufficient for all calculations of impact. HEAT therefore offers several options to adjust the data or provide additional information to inform the calculation, depending on the characteristics of the assessment and the selected user interface option. Users can choose between 3 different "user experiences":

·       basic user interface , with only the most important adjustment options, which is relying mostly on default values and assumptions (good for initial exploration of the tool and simple assessments).

·       flexible user interface , where users can select "additional amendment options" as they see fit (good for users who are somewhat familiar with HEAT, have additional local data available and know how they intend to refine their assessment).

·       full user interface, which offers all amendment features available in the tool (see below), as long as they are of relevance for the selected assessment (good for users who have additional local data available and would like to take advantage of all options to refine their assessment).

Data adjustment options in HEAT may include the following (depending on the type of assessment and only available in the “flex” and “full” user interface options):

·       proportion excluded

·       temporal and spatial adjustment

·       uptake time for active travel demand

·       proportion of new trips

·       proportion of reassigned trips

·       proportion of shifted trips

·       proportion in traffic

·       proportion for transport

·       traffic conditions

·       change in crash risk

·       substitution of physical activity.

General adjustments of active travel data

Proportion excluded due to unrelated factors (“two case assessments” only)

When the impact of an intervention is assessed, not all the cycling or walking observed may be directly attributable to the intervention. For example, cycling may have become more fashionable over time, or gasoline or public transport prices may have changed and affected active transport behaviour. Walking or cycling arising from such external effects should not be included in the assessment of the infrastructure or project.

The precise effects of an intervention and unrelated factors can rarely be disentangled. Estimate the proportion you would exclude from the assessment (such as –30%) to the best of your knowledge. For more guidance on this, see also here.

The default setting is 0%.

Temporal and spatial adjustment

HEAT requires long-term average input on active travel (such as annual means). Active travel is highly affected by such factors as season, weather and time of day. Short-term counting, for example, is typically carried out in summer or fall and often during rush hour. If active travel data is from a short-term survey or count, it likely under- or overestimates the long-term average. This can be adjusted here (such as + 20% or – 30%). Data from continuous counters can be helpful in assessing the potential need for adjusting for time.

Similarly, the location where count data or intercept surveys are collected may not represent average volumes for the complete facility of interest (such as a bike path, trail, or network). This slider can be used to apply a spatial adjustment (such as + 20% or – 30%). Data from multiple locations are usually needed to inform spatial adjustment, but crude guesses may be adequate in some cases. Accurate spatial adjustment would require a spatial modelling approach.

The default setting is 0%.

Uptake time for active travel demand (“two case assessments” only)

Here users can specify a take-up time (in years) until the maximum volume of active travel is reached. This allows adjusting for the estimated time to reach the full level of walking or cycling entered, such as after an intervention has been implemented. For example, if a new footpath is built, and an estimated 5 years will elapse for usage to reach a steady state, this figure should be changed to 5. For steady-state situations, with no build-up time considered, this should be set to zero.

The default setting is 1 year.

Investment costs (“two case assessments” only)

This input field allows the user to provide an estimated cost for the investment that led to the assessed active travel. HEAT will compare this to the monetized value of the effects and calculate a benefit–cost ratio.

Information to characterize the assessed active travel

To improve the calculations for certain types of assessments, HEAT allows the comparison to be informed using some additional questions. HEAT automatically only presents the questions needed for assessment.

A first set of questions asks “if, where and how the trips in the reference case would occur in the comparison case”. The three questions specifically request the proportion of new trips, reassigned trips and shifted trips.

Proportion of new trips (“two case assessments, carbon” only)

New trips are trips that did not take place in the comparison case: they were neither shifted from another mode nor reassigned from another route. This information is captured through other entry options for physical activity, air pollution and road crash assessment. For carbon emission assessment for which no motorized input data are available, this additional information is needed to adjust the cold-start emissions, which are calculated based on the number of trips by active mode per year (see operational emissions).

The default setting is 0%.

Proportion of reassigned trips (“two case assessments, sub-city level” only)

Reassigned trips are trips that merely follow a different route to now take place using new infrastructure (such as a new footpath or a cycling network). These reassigned trips will not be considered in the assessment because they do not reflect a net increase in active travel. The percentage of reassigned trips has to be estimated, unless a specific survey has been carried out before the new infrastructure has been put into place (asking for example: “Prior to this facility being built, did you use a different route for this trip?" or "Prior to this infrastructure being built, did you also use your bike for this particular kind of trip (e.g. to work, or for recreation)?”). It is likely that the percentage is higher on very attractive new facilities (e.g. a nice bike path) or which play a key role in the network (e.g. a bridge), and lower on less attractive facilities (e.g. a stretch of sidewalk).

This adjustment will only be applied to sub-city-level assessments, since trips cannot be reassigned for countrywide or citywide assessment.

The default setting is 0%.

Proportion of trips shifted from another mode (“single case assessments, carbon” only)

Shifted trips are active mode trips that replace a trip by another mode in the comparison case. Users are first asked to provide the total proportion shifted (such as 80%).

The default setting is 0%.

Thereafter users can specify the other mode of active travel from which was shifted. The sum of the modal shift percentages cannot add up to more than 100%  (see more information on carbon emissions assessment).

These sliders are set to default values, which will apply if no adjustments are made.

 

Some additional questions further characterize the assessed active travel.

Motorized traffic influences both carbon emissions and exposure to air pollution. Three questions capture the relevant information:

Proportion of active travel done “in traffic” (“air pollution assessments” only)

This question asks what proportion of active travel (in the reference case) takes place in traffic (versus away from major roads, in parks etc.) and adjusts accordingly the air pollution levels to which the cyclists or pedestrians being assessed are exposed (see more information here.

The default setting is 50%.

Proportion of travel done “for transport” (“air pollution and carbon assessments” only)

This information is used to correctly assign air pollution concentrations in the comparison case. Trips for transport are assumed to replace modes of transport (time in traffic environments with higher air pollution concentrations), whereas recreational trips replace time at home (at background air pollution concentrations). Transport-related means to get to and from places, to pursue a specific purpose at the destination (such as work, shop, visit friends or play tennis). Recreation means that the main purpose of the trip is exercise or recreation. Please specify the proportion of the travel entered that is for transport purposes (versus for recreation).

For more information on air pollution assessments, see here.

For carbon assessment, only active travel for the purpose of transport is considered, presuming that it replaces other modes of transport. Recreational trips are presumed not to replace other modes of transport.

For more information on carbon emission assessments, see here.

The default setting is 50%.

Traffic conditions (“carbon assessments” only)

For carbon emission assessment, users are also asked to specify the local traffic conditions, referring to the times when people walk or cycle. Traffic conditions affect carbon emission rates. Users can select between European average (urban and rural), free flow (little or no congestion, 45 km/h mean traffic speed), some peak-time congestion (morning commute, school run, afternoon commute, 35 km/h mean traffic speed) or heavy congestion on most days (20 km/h mean traffic speed).

The default setting is European urban average.

Change in crash risk (“two case assessments, crashes” only)

The road crash risk for active modes of transport depends, among many other factors, on the volume of walking or cycling (also called safety in numbers). To consider a change in road crash risk between the two comparison cases, specify it here as a percentage change relative to the reference case. Leaving this blank will apply the same road crash risk to both cases. The changes in road crash risk may result from an increase in active modes, improved infrastructure or any other reason.

The default setting is 0%.

Substitution effect (“two case assessments, physical activity” only)

In some cases, some of the observed cycling or walking may substitute for other physical activity, such as sport previously done in leisure time. This proportion does not contribute to a net gain in physical activity and should be excluded from the assessment.

The default setting is 0%.

 

Information on proportion of walking or cycling excluded

Assumptions underlying assessments done with HEAT

Knowledge on the health effects of walking and cycling is constantly evolving. The HEAT project is an ongoing consensus-based effort to translate relevant research into harmonized methods. Although HEAT relies on the best available scientific evidence, on several occasions the methods required the advisory groups (see acknowledgements) to make expert judgements. The most important assumptions underlying the HEAT impact assessment approach are described below (for more detailed information on how the HEAT assessments work, see here).

General remarks

The variables HEAT uses are estimates, and the results are therefore liable to some degree of error. HEAT applies several “default values”, but allows the users to overwrite these if they prefer to use other values, such as from their specific local context. Values considered to represent the best possible scientific consensus (such as estimates based on numerous epidemiological studies) are referred to as “background values” and cannot be changed by the user.

To get a better sense of the possible range of the results, users are strongly advised to rerun their assessment, entering higher and lower values for variables for which estimates have been provided.

Remember that HEAT approximates the health effects of walking and/or cycling on the population level. The results cannot be applied to predict health effects among individuals, since individual health depends on many additional factors (genes, lifestyle, etc.).

Key assumptions include the following:

Physical activity

·       The relative risk data from the meta-analysis, which includes studies from China, Europe, Japan and the United States (see also here), can be applied to populations in other settings.

·       The tool applies a linear relationship between walking or cycling duration (assuming a constant average speed) and the mortality rate. Thus, each dose of walking or cycling leads to the same risk reduction, up to a maximum of about 60 minutes of cycling or walking per day (447 minutes of cycling and 460 minutes of walking per week).

·       The populations assessed do not disproportionately comprise sedentary or very active individuals. This could lead to a certain overestimation of benefits in highly active populations or a certain underestimation of benefits in less active ones.

·       Any walking assessed is of at least moderate pace: about 4.8 km/hour (3 miles/hour), which is the minimum walking pace necessary to require a level of energy expenditure considered beneficial for health; for cycling, this level is usually achieved even at low speeds.

·       No thresholds of active travel duration have to be reached to achieve health benefits.

·       The relative risks of reduction in all-cause mortality from walking and cycling are the same in men and women.

·       The relative risks of reduction in all-cause mortality from walking and cycling are the same across adult age groups (20–74 and 20–64 years, respectively).

·       A five-year build-up time is needed for health benefits from regular physical activity to manifest in full, based on expert consensus. In single-case assessment, a steady-state situation is assumed (active travel, and physical activity therefore took place in previous years already) and no build-up time for the health effects is applied.

Air pollution

·       The mortality rate and air pollution exposure are related linearly. Thus, each dose of air pollution (expressed as concentrations of particulate matter) leads to the same risk reduction, up to a maximum of 100 µg/m3 to avoid inflated results.

·       The relative risk from the meta-analysis on the health effects of PM2.5  (see also here), including 107 studies from Canada, China, France, Germany, Israel, Italy, Japan, New Zealand, Norway, the Netherlands, South Korea, Sweden, Taiwan, the United Kingdom and other European countries as well as the United States, can be applied to other countries with comparable levels and compositions of air pollution.

·       No minimum air pollution thresholds have to be reached for health effects.

·       Men and women have approximately the same increase in relative risk.

·       A five-year build-up time is needed for health effects from chronic air pollution exposure to manifest in full, based on expert consensus. In single-case assessment, a steady-state situation is assumed (active travel, and exposure to air pollution therefore took place in previous years already) and no build-up time for the health effects is applied.

Road crashes

·       Generic background road crash rates of sufficient quality and reliability for national assessment can be derived by combining data from national (and in some cases international) databases, dividing the number of traffic fatalities (by mode of travel) by the exposure (volume of active travel) within the administrative boundaries (see also here).

·       National road crash rates (total number of pedestrian or cyclist fatalities divided by the total km walked or cycled, respectively) can be used as proxies for road crash risks in city-level assessments if no city-specific road crash rates are available.

Carbon emissions

·       There is a linear relationship between changes in travel activity by motorised modes (passenger-km by mode), changes in carbon emissions (mass of CO2e) and the underlying carbon emissions factors (mass of CO2e per passenger-km per mode).

·       To derive emissions factors for cars, the European Environment Agency’s COPERT method is the most appropriate approach, computing energy consumption (MJ per vehicle-km) using non-linear speed-emission curves, multiplied by the carbon content of that energy (mass of CO2e per MJ), taking into account the share of biofuels in the transport fuel mix and the carbon content of electricity (for electric vehicles). Emission factors per passenger-km are best derived using a linear relationship of emissions per vehicle-km and average vehicle occupancy rates by mode of travel (varying by country and year of assessment). Typical average occupancy rates are 1.6 passengers per vehicle for cars, 12.2 for local buses, 40 for urban rail and 1.05 for motorbikes.

·       The effect of “real world driving” can be sufficiently approximated by adding 21.6% to “official” lab-based carbon emissions factors, taking account of cold start emissions, which add to hot emissions during the initial cold phase for each trip (about the first 3.4 km depending on country).

·       Future vehicle fuel type shares and average occupancy rates have been approximated based on international databases, including the IIASA’s GAINS model reference projection for 2014.

·       For cars, five generic traffic conditions can be derived that reflect most European contexts:

o   European average, urban (32 km/h);

o   Little or no congestion, urban (“free flow”) (45 km/h);

o   Some peak time congestion (commute, school run), urban (35 km/h);

o   Heavy congestion most days (am, pm and inter peak), urban (20 km/h);

o   European average, rural (60 km/h).

·       There is a linear relationship between well-to-tank (WTT) carbon emissions and the fuel/energy used for energy and vehicle production, including upstream electricity generation and fossil fuel production.

·       There is a linear relationship between changes in emissions (mass of CO2e) and the social cost of carbon (USD/tonne of CO2e).  The social cost of carbon values for countries or contexts not covered in existing evidence or policy guidance can be allocated the values recommended by the European Commission (US$ 44 in 2015, rising to US$ 66 by 2030).

 

Health impact and comparative risk assessment introduction
Default and background values used in HEAT

More information on the crash risk assessment approach in HEAT

Estimates of fatality risk are calculated by dividing the national number of cyclists or pedestrians killed in road crashes per year by national estimates of total cycling or walking in km per year. Fatality and exposure data are typically derived from different sources and may vary in data quality. Below describes the methodology developed by Castro et al. 2018, which derived fatality rates for European countries in HEAT. Data for other countries is being added to HEAT on a continuous basis.

 

Fatality data from the international data set of the International Transport Forum (82) were given priority over data from the World Health Organisation (WHO) (8) (Figure 1), since this dataset comprises observations for time series over multiple years. A five-year average (2011–2015) was calculated for HEAT to reduce the effect of the usual variation of fatality data from one year to another. However, the International Transport Forum data set does not include information for all countries considered in HEAT. For these countries, fatality data from WHO (8) were used. This data set contains data from many countries but only for one year (mostly 2013) and can include observations as well as model estimates when observations are not available. The number of fatalities for the transport mode was calculated by multiplying all-mode fatalities by the share of fatalities for each mode.

Because of the scarcity of international databases for exposure data (km travelled by bicycle or by foot per year), data were compiled from several national sources. If data were available for different years, more recent data (from 2015) were given priority. If exposure data were available for more than one year, averages (optimally from 2011–2015) were calculated. In some cases, national exposure data were incomplete and required additional calculations using assumptions. For countries with no available exposure data from national sources, cycling or walking exposure was estimated by multiplying the population (83) by the number of all-mode trips per person and day (assumption), the distance per cycle or walking trip (assumption) and modal share by world region extrapolated from city data (9) (Figure 2).

The following assumptions were applied:

·       3 daily trips per person and day. According to (10), all-mode mobility demand ranges from 3.0 to 4.6 in low- and middle-income countries in sub-Saharan Africa, which is similar to those found in higher-income countries such as France. The lowest value of the range is used in HEAT to obtain conservative estimates.

·       4.1 kilometers per bicycle trip, according to travel surveys in the Netherlands and England (11, 12).

Figure 1 Sources used for exposure data

[1] E = Population * MD * BMS * BTL     

E=Exposure, i.e. km travelled by bicycle per year

Population: WHO data

MD= Mobility demand (3 all-mode trips per day, assumption)

BMS=Bicycle modal share, i.e. bicycle trips per total trips (ITDP-ITS extrapolations by world region)

BTL= Bicycle trip length (3 km per bicycle trip, assumption)

ITDP-ITS = Institute for Transportation and Development Policy and Institute of Transportation Studies

WHO-GHO = WHO Global Health Observatory

The above-mentioned sources are of different quality, and combining them implies different levels of reliability of the resulting fatality risk estimates. Five levels of reliability were considered based on the data quality of the datasets used for fatalities (numerator of the fatality risk estimate) and exposure (denominator).

·       very high: numerator from International Transport Forum fatality data and denominator from national sources;

·       high: numerator from International Transport Forum fatality data and denominator from national sources that imply some calculations or assumptions;

·       medium: numerator from International Transport Forum fatality data and denominator estimated based on modal share extrapolation by the Institute for Transportation and Development Policy;

·       low: numerator from observed WHO Global Health Observatory fatality data and denominator estimated based on modal share extrapolation by the Institute for Transportation and Development Policy; and

·       very low: numerator from modelled WHO Global Health Observatory fatality data and denominator estimated based on modal share extrapolation by the Institute for Transportation and Development Policy.

·        

Sources

1.      Development of the Health economic assessment tools (HEAT) for walking and cycling. Consensus meeting. Copenhagen, Denmark, 28-29 March 2017. Meeting background document. Copenhagen: WHO Regional Office for Europe, 2017.

2.      Teschke K, Harris MA, Reynolds CCO, Winters M, Babul S, Chipman M, et al. Route infrastructure and the risk of injuries to bicyclists: a case-crossover study. Am J Public Health. 2012 Dec;102(12):2336–43

3.      Jacobsen PL. Safety in numbers: more walkers and bicyclists, safer walking and bicycling. Inj Prev. 2003 Sep 1;9(3):205–9

4.      Elvik R. The non-linearity of risk and the promotion of environmentally sustainable transport. Accid Anal Prev. 2009 Jul;41(4):849–55

5.      Schepers JP, Heinen E. How does a modal shift from short car trips to cycling affect road safety? Accid Anal Prev. 2013 Jan;50:1118–27

6.      Development of the Health economic assessment tools (HEAT) for walking and cycling (PASTA – WP4). Core group meeting. Copenhagen, Denmark, 2-3 November 2016. Meeting report. Copenhagen: WHO Regional Office for Europe, 2017.

7.      ITF-IRTAD International Transport Forum – International Traffic Safety Data and Analysis Group. Database Casualties by age and road use. 2017. Online database. http://stats.oecd.org/Index.aspx?DataSetCode=IRTAD_CASUAL_BY_AGE

8.      WHO-GHO World Health Organisation - Global Health Observatory data repository. 2016. Online data base. http://apps.who.int/gho/data/node.main.A997?lang=en

9.      ITDP-ITS. Institute for Transportation & Development Policy and the University of California. A Global High Shift Cycling Scenario: The Potential for Dramatically Increasing Bicycle and E-bike Use in Cities Around the World, with Estimated Energy, CO2, and Cost Impacts. 2015. https://www.itdp.org/wp-content/uploads/2015/11/A-Global-High-Shift-Cycling-Scenario_Nov-2015.pdf

10.    Diaz Olvera L, Plat D, Pochet P. The puzzle of mobility and access to the city in Sub-Saharan Africa. J Transp Geogr. 2013 Oct;32:56–64

11.    Department for Transport. National Travel Survey [website] (national travel survey of England and Wales 2010-2014). https://www.gov.uk/government/collections/national-travel-survey-statistics. Published 2013, last updated 2017. 

12.    Statistics Netherlands. OViN (Onderzoek Verplaatsingen in Nederland). 2013-14: Statistics Netherlands, The Hague/Heerlen/Bonaire.

13.    Castro, A., S. Kahlmeier and T. Gotschi (2018), “Exposure‐adjusted Road Fatality Rates for Cycling and Walking in European Countries”, Discussion Paper, International Transport Forum, Paris.

HEAT data requirements

There are two main types of assessment in HEAT:

·       Single case assessment

o   In a "single case" assessment, only data on the so called "reference case" has to be provided. This is then compared to a hypothetical “comparison case” of "no walking or cycling".

o   This option is for example used when assessing the status quo, such as valuing current levels of walking and cycling in a city or country.

·       Two case assessment

o   In this case, users have to provide input data for both cases, the so called "reference case" and the "comparison case". This option is used when assessing the impact of an actual intervention or hypothetical scenarios. Typical examples are "before and after" an intervention, or comparisons of alternative "scenarios A and B" or “with measures” vs. “without measures”. 

The tool will also ask to specify your assessment:

·       whether to assess walking, cycling, or both;

·       geographic scale and time scale of the assessment;

·       type of assessment (single or two-case assessment); and

·       which impacts to assess
(i.e. physical activity only, or also air pollution exposure during active travel and/or crash risks and/or effects on carbon emissions due to replaced motorized trips)

·       which user interface option to use (i.e. how many amendment options and features you get presented; note that this does not affect the underlying methodology used, see also here).

Before you begin, check that you have the following data available:

·       An estimate of the average amount of walking or cycling in the study population, which can come from various sources, such as surveys, counts or scenario assumptions. To learn more about data sources for travel data, see here. The amounts can be specified in various units, including:

o   duration (e.g. 30 minutes walked on average per day);

o   distance (e.g. 8 km cycled on average per day);

o   trips (e.g. 2 bike trips per day);

o   frequency (e.g. proportion that cycles on average 1-3 times per week)

o   mode share (e.g. on average 24% of the total traffic volume measured as distance are done by walking; here also the total traffic volume has to be entered)

o   for walking also as steps (average number of steps taken per person, e.g. 9,000 steps per day)

·       An estimate of the size of the assessed population. This can be either pedestrians or cyclists only, or the general population, possibly including people who do not walk or bike.  It is important that the population size reflects the same “population type” as the average amounts of walking or cycling assessed. The population size also needs to reflect the age range assessed (e.g. excluding children and young people below 20, which are not considered in HEAT assessments (see here.) In most cases, HEAT will provide an estimate of the assessed population size, based on the user selection made of the geographic scale (more information see here); in some cases no default value is available and data has to be entered by the user (particularly for sub-national assessments)..). Where available, HEAT provides population data and suggests which proportion thereof to include in the assessment, based on the selected age range.

·       For carbon assessments, users can also enter data on the volume of motorized modes (driving and public transport, or by more refined categories, including car (driver/passenger), motorcycle, local bus, lightrail, train). If you no data is available, default values are provided (see also below).

 

The figure below may be of help in deciding what data to use for a HEAT assessment.

Graphical user interface

Description automatically generated

Typical decision tree for HEAT data inputs on (active) mode data for a two-case assessment

 

After having specified the assessment type and entered travel data, HEAT will offers certain options to adjust the data for the selected impact calculations, depending on the type of user interface selected (i.e. basic, flex or full). Here some assumptions might need to be made for which no data are available, e.g. on the supposed impact of an intervention on newly induced levels of walking or cycling. Default values are provided for such assumptions. For more information see here.

In addition, users can provide details of the cost of promoting cycling or walking, if they wish to calculate a benefit-cost ratio.

Wherever possible, HEAT provides default values. Users can use these or overwrite them with their own values. The most influential variables are:

·       mortality rate (national average all-cause mortality rates are provided by default, but local rates may vary considerably);

·       value of a statistical life (a default estimate is provided, and the flexible and full user interface option offer an alternative estimate. Estimates are approximate and local circumstances may dictate the most adequate value to use).

A complete list of the default values for the different modules is provided here.

 

Guidance on travel data
Guidance on population data
Travel data adjustment in HEAT
Unit conversions in HEAT

Carbon module: guidance on “mode shift” and “diversion rates” (step 1)

What are diversion rates? How is mode shift handled in the carbon module?

The carbon tool excludes new trips that do not displace trips done previously by motorized modes from an assessment of carbon emissions from motorized travel. This has been implemented by parameterizing so-called “diversion rates” [1] for walking and cycling for transport, with values between 0 and 1 for the shares of new cycling (or walking) that is estimated to be shifted from motorised travel and walking (or cycling). The motorised modes considered include car (as driver or passenger), local bus, urban rail (incl. tram, underground) and motorcycle (not shown separately in Figure 1 for space reasons). For intervention assessments, the user will be asked to consider excluding any induced (entirely new trips not replacing motorized travel) or reassigned (route shift rather than mode shift) travel activity. The diversion rates are applied to the volume of walking and/or cycling entered by the user.

Figure 1: Assessment of mode shift using travel activity and ‘diversion rates’

Note: the dashed boxes are only really relevant for assessments of ‘before’ vs. ‘after’ comparisons.

An example

Assume an infrastructure intervention has led to an increase in connectivity, making it easier/safer to travel via the new route. ‘New’ cycling trips are being recorded along that route (e.g. using trip counters or user intercept surveys). Some of the new trips were done previously using different modes (mode shift), while some were on a parallel route that perhaps wasn’t as convenient or safe to use (route shift, or route reassignment). Some even did not exist at all before the intervention (newly generated or induced demand that was suppressed before). The carbon module will focus on the former (mode shift) and not take into account the latter (route shift, newly induced trips).

Changeable default values

For the diversion rates of the remaining volume of walking or cycling, HEAT provides recommended values based on a conservative assessment of the evidence found in the appraisal guidance [1], project evaluation [2-5] and impact scenario [6-8] literatures.

For example, UK transport appraisal guidance (WebTAG) [1] and Mulley et al [9] reported car substitution rates of 25% to 30%. ECF [10] suggested to use: car 32%, bus 42% and walking 26%, based on mode substitution rates observed for bicycle trips of bicycle share schemes.

The HEAT recommended default values for diversion rates from other transport modes to cycling or walking are:

Table 1: changeable default values for mode shift / diversion rates

From:

To: cycling

To: walking

Car or van (as driver or passenger)

30%

20%

Local bus

40%

50%

Urban rail (light rail, trams, metro) (if relevant)

10%

10%

Walking

20%

--

Cycling

--

20%

 

Sources

1.      DfT. Transport analysis guidance: WebTAG, https://www.gov.uk/guidance/transport-analysis-guidance-webtag. 2014.

2.      Dons, E., et al., Physical Activity through Sustainable Transport Approaches (PASTA): protocol for a multi-centre, longitudinal study. BMC Public Health, 2015. 15: p. 1126.

3.      Brand, C., A. Goodman, and D. Ogilvie, Evaluating the impacts of new walking and cycling infrastructure on carbon dioxide emissions from motorized travel: A controlled longitudinal study. Applied Energy, 2014. 128(0): p. 284-295.

4.      Goodman, A., S. Sahlqvist, and D. Ogilvie, Do new walking and cycling routes increase physical activity? One- and two-year findings from the UK iConnect study. Am J Public Health, 2014. 104.

5.      European Commission, OBIS (Optimising Bike Sharing in European Cities), Final Report. 2011, Brussels: European Commission.

6.      Woodcock, J., et al., Public health benefits of strategies to reduce greenhouse-gas emissions: urban land transport. The Lancet, 2009. 374.

7.      Keogh-Brown, M., et al., A whole-economy model of the health co-benefits of strategies to reduce greenhouse gas emissions in the UK. The Lancet, 2012. 380, Supplement 3(0): p. S52.

8.      Buekers, J., et al., Health impact model for modal shift from car use to cycling or walking in Flanders: application to two bicycle highways. Journal of Transport & Health, 2015. 2(4): p. 549-562.

9.      Mulley, C., et al., Valuing active travel: Including the health benefits of sustainable transport in transportation appraisal frameworks. Research in Transportation Business & Management, 2013. 7(0): p. 27-34.

10.    ECF, Cycle more Often 2 cool down the planet! - Quantifying CO2 savings of Cycling. 2011, European Cyclists' Federation (ECF): Brussels.

 

Operational emissions

Monetization of health impacts and carbon emissions

HEAT also allows calculating an economic value of the mortality and carbon effects. To monetize the effects on mortality, the “Value of statistical Life” (VSL) is used (for more information see here).

For effects on carbon emissions, a methodology based on the “Social Costs of Carbon” (SCC) is used (for more information see here).

The results can also be discounted to consider that economic benefits occurring in the future are generally considered less “valuable” than benefits occurring in the present. The lower the discount rate the higher the user weighs future economic benefits in the overall assessment.

As default value, a discount rate of 5% has been set.

If the results of economic appraisal with HEAT will be included as one component into a more comprehensive cost-benefit analysis of transport interventions or infrastructure projects, the final result of the comprehensive assessment would then be discounted to allow the calculation of the present value. In this case, enter "0" here.

Value of statistical life
Social cost of carbon

Carbon emissions (step 2)

Energy Supply Emissions

Carbon emissions from energy supply include upstream emissions from the extraction, production, generation and distribution of energy supply, including fossil and electric fuels to power cars, buses and trains. The adopted approach uses ‘well-to-tank’ (WTT) emissions factors for different energy supply pathways for transport fuels (gasoline, diesel, electricity, etc.) [1], taken from published and well respected sources including the JEC Well-to-Wheels (WTW) study [2] and its use in developing national recommended values [3]. For e-bikes, cars, buses, urban rail, this is based on different WTT values for gasoline (0.654 kgCO2e per kg of fuel), diesel (0.688 kgCO2e per kg of fuel) and delivered electricity[1] (e.g. for India this is a relatively high 1.64 kgCO2e in 2019, due to the high share of coal generation). As with operational emissions, an uplift of 21.6% was applied to account for ‘real world’ driving conditions. Note the electricity factors vary significantly between countries (up to 3 orders of magnitude, reflecting the use of high shares of renewable sources vs. high shares of fossil fuels); therefore, the tool is using country specific factors that were based on the widest possible country comparison from an authoritative source [4]. An example for India in 2019 is given in Table 1.

Table 1: Derived average energy supply emissions factors (well-to-tank) per passenger-km, showing values derived for India in 2019 (NB: HEAT uses country and year specific factors)

Example: India, year 2019

Average traffic conditions

Average emissions factors, energy supply (gCO2e/passenger-km)

‘Global’ average, urban

Little or no congestion (‘free flow’)

Some peak time congestion

Heavy congestion most days

Global average, rural

Car or van 1,2

28

25

27

35

23

Local bus 1,2

21

--

--

--

--

Urban rail, trams, metro (100% electric) 2

63

--

--

--

--

Motorcycle 1,2

17

--

--

--

--

E-bike / bicycle

16 / 0

--

--

--

--

Notes: (1) Takes into account weighted fuel/engine type shares for each mode, e.g. for cars in India in 2019 (44% petrol, 55% diesel, 1% electric), bus (98% diesel), motorcycle (99.5% gasoline). (2) Car occupancy rate of 1.5 (all trip purposes), local bus 14.3, average urban rail 120, motorcycle 1.05. ‘--' not applicable.

Main sources: well-to-tank emissions factors [2, 3]; electricity emissions factors [4]; vehicle fuel shares [5].

 

Sources

1.      Odeh, N., N. Hill, and D. Forster, Current and Future Lifecycle Emissions of Key „Low Carbon‟ Technologies and Alternatives, Final Report. 2013, Harwell, UK: Ricardo AEA for the Committee on Climate Change.

2.      JEC, JEC Well-To-Wheels Analysis, Report EUR 26237 EN - 2014. Last accessed at http://iet.jrc.ec.europa.eu/about-jec/sites/iet.jrc.ec.europa.eu.about-jec/files/documents/report_2014/wtt_report_v4a.pdf on 10/03/2017. 2014, Brussels: JEC - Joint Research Centre-EUCAR-CONCAWE collaboration.

3.      DEFRA/DECC, UK Government conversion factors for Company Reporting, full 2016 dataset. 2016, Department for the Environment, Food and Rural Affairs and Department for Energy and Climate Change: London.

4.      Ecometrica, Electricity-specific emission factors for grid electricity. 2011: Ecometrica.

5.      IIASA, IIASA GAINS model, scenario '6DS' for IEA World Energy Outlook, available for Europe (for 48 countries), Asia, with separate implementations for China (31 provinces and India (15 States), and for Annex I countries of the UNFCCC Convention. Accessed at https://gains.iiasa.ac.at/models/gains_models3.html in July 2019. 2016, IIASA: Laxenburg, Austria.

 



[1] This includes emissions from electricity generation and its transport and distribution (T&D) and from the generation well-to-tank and T&D well-to-tank stages.

Vehicle lifecycle

Default and background values for the HEAT calculations

Wherever possible, the HEAT provides generic data which are based on best available evidence or expert judgement. There are two types of generic values in HEAT:

1.      default values that are provided for a HEAT assessment but that can be overwritten by the users if they prefer to use other values, e.g. from their specific local context.

2.      background values considered to represent the best possible scientific consensus (e.g. estimates based on numerous epidemiological studies) that cannot be changed by the user.

Below, an overview of the main default and background values is provided, with their source, used either for general calculations (e.g. to derive volume data for the calculations) or in the different HEAT modules.

Default values

General values

Description

value

unit

source

Average number of trips per day using all likely modes

3

trips (all-modes)/person*day

1, 2

Average walking speed

5.3

km/h

3

Average cycling speed

14.0

km/h

3


Average distance per walking trip

1.3

km/trip

4,5

Average distance per bicycle trip

4.1

km/trip

4,5

Time frame for calculating mean annual benefit

10

years

HEAT advisory group decision*

Average length of walking steps

72

cm

6

Discounting rate

5

%

HEAT advisory group decision

* This means a decision taken by the HEAT advisory group based on expert judgement

In addition, country-specific default values are provided (as available) for population data, mortality rates, air pollution concentrations, values of statistical life and social costs of carbon (9,10). 

Population data

Description: Population data by country, sex and age

Source: United Nations, Statistics Division

URL: https://population.un.org/dataportal/about/dataapi

Description: Population data for cities

Source: United Nations, Statistics Division
URL: http://data.un.org/Data.aspx?d=POP&f=tableCode%3A240

Source: Global Burden of Disease Collaborative Network (IHME-GBD)
Date: 2015-2017 (original data 1950-2017)
URL: http://ghdx.healthdata.org/record/ihme-data/gbd-2017-population-estimates-1950-2017

Geonames.org
URL: www.geonames.org

 

Mortality data

Description: Mortality rates by country, sex, agegroup

Sources:

WHO Global Health Observatory
URL: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death

Global Burden of Disease Collaborative Network (IHME-GBD)
Global Burden of Disease Study 2017 (GBD 2017) Results.
Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018.
URL: http://ghdx.healthdata.org/gbd-results-tool.

 

Physical activity

The HEAT physical activity module uses only non-changeable background values, see next section.

Air pollution data

The HEAT air pollution module provides default PM2.5 concentrations by country and cities as available from the WHO Global Health Observatory Repository (11).

Crashes

Description

value

unit

source

Average number of trips per day using all likely modes

3

trips (all-modes)/person*day

1

Reduction in crash rate over time (non-linear adjustment)

0

%

HEAT advisory group decision

Besides that, the HEAT crashes module uses only non-changeable background values, see next section.

Carbon

Description

value

unit

source

Average distance per walking trip

1.3

km/trip

4,5

Average distance per bicycle trip

4.1

km/trip

4,5

Average distance per car

15.6

km/trip

4,5

Average public transport speed

22.7

km/h

4,5

Average speed car

42.0

km/h

4,5

Average motorbike speed

29.8

km/h

4,5

Average bus speed

15.4

km/h

4,5

Average light rail speed

16.1

km/h

4,5

Average train speed

37.4

km/h

4,5

Share of walk trips shifted from bike

20

%

12,13,14

Share of walk trips shifted from car

20

%

12,13,14

Share of walk trips shifted from public transport
(50% bus + 10% rail)

60

%

12,13,14

Share of walk trips shifted from bike

20

%

12,13,14

Share of bike trips shifted from car

30

%

12,13,14

Share of bike trips shifted from public transport
(40% bus + 10% rail)

50

%

12,13,14

Background values

General value

Description

value

unit

source

Time needed to obtain full health impacts in single case assessment

0

years

HEAT consensus and core group

Time needed to obtain full health impacts in two cases assessment

5

years

HEAT consensus and core group

Physical activity

Description

value

unit

source

Capped risk reduction for walking

30

%

15

Capped risk reduction for cycling

45

%

15

Relative risk for cycling

0.903

ratio

3

Relative risk for walking

0.886

ratio

3

Reference duration of cycling

100

minutes/person*week

3

Reference duration of walking

168

minutes/person*week

3

Relative risk for cycling without air pollution effect

0.899

ratio

16

Relative risk for walking without air pollution effect

0.883

ratio

16

Air pollution

Description

value

unit

source

Relative risk for PM 2.5

1.08

ratio

17

Reference concentration for PM2.5

10

u/m3

18

Conversion rate PM-exposure for walking

1.6

ratio

19

Conversion rate PM-exposure for cycling

2

ratio

19

Conversion rate PM-exposure for using a car

2.5

ratio

19

Conversion rate PM-exposure for using public transport

1.9

ratio

19

Minute ventilation for walking

1.37

m3/hr

20,21

Minute ventilation for cycling

2.55

m3/hr

20,21

Minute ventilation for car

0.61

m3/hr

20,21

Minute ventilation for public transport

0.61

m3/hr

20,21

Minute ventilation for sleep

0.27

m3/hr

20,21

Minute ventilation for rest

0.61

m3/hr

20,21

Activity duration for sleeping

480

minutes/person*day

20,21

Cap for PM2.5 effects

100

ug/m3

22

Crashes

For the HEAT crash assessment, country-specific crash rates per 100 M km (as available) are being used. For more information, see here (22).

Carbon

Description

value

unit

source

Average road traffic speed for European average standards in urban areas

32

km/h

24,25,26

Average road traffic speed for nearly free flow at all times in urban areas

45

km/h

24,25,26

Average road traffic speed for minor congestion mainly at peak times in urban areas

35

km/h

24,25,26

Average road traffic speed for heavy congestion throughout the day, urban areas

20

km/h

24,25,26

Average road traffic speed for European average standards in rural areas

60

km/h

24,25,26

Share of bus trips compared to rail trips

50

%

27

Average CO2e emissions per vehicle-km for bike

4.93

gCO2e/vkm

28,29

Average CO2e emissions per vehicle-km for e-bike

9.31

gCO2e/vkm

28,29

Average CO2e emissions per vehicle-km for car by country

31.01

gCO2e/vkm

13,30

Average CO2e emissions per vehicle-km for bus by country

39.51

gCO2e/vkm

13

Average CO2e emissions per vehicle-km for rail

158.03

gCO2e/vkm

13

Average CO2e emissions per vehicle-km for motorcycle

10.78

gCO2e/vkm

30

Number of walking trips per year

372

trip/year

31

Number of cycling trips per year

248

trip/year

30

 

Sources:

1. Diaz Olvera et al. The puzzle of mobility and access to the city in Sub-Saharan Africa. Journal of Transport Geography Volume 32, October 2013, Pages 56-64 (http://www.sciencedirect.com/science/article/pii/S0966692313001634).

2. WALCYNG. How to enhance WALking and CYcliNG instead of shorter car trips and to make these modes safer (Deliverable D6) [Internet]. 1997. Available from: https://safety.fhwa.dot.gov/ped_bike/docs/walcyng.pdf

3. Kelly P et al. Systematic review and meta-analysis of reduction in all-cause mortality from walking and cycling and shape of dose response relationship. International Journal of Behavioral Nutrition and Physical Activity. 2014, 11:132  doi:10.1186/s12966-014-0132-x.)

4. Department for Transport. National Travel Survey [website] (national travel survey of England and Wales 2010-2014). https://www.gov.uk/government/collections/national-travel-survey-statistics. Published 2013, last updated 2017.  

5. Statistics Netherlands. OViN (Onderzoek Verplaatsingen in Nederland). 2013-14: Statistics Netherlands, The Hague/Heerlen/Bonaire.

6.  Kahlmeier S et al.  Health economic assessment tools (HEAT) for cycling and walking. Methodology and user guide. Copenhagen: WHO Regional Office for Europe; 2011.

7. World Health Organization Regional Office for Europe. European Detailed Mortality Database. 2015 (http://data.euro.who.int/dmdb/)

8. Mortality risk valuation in environment, health, and transport policies. Paris: OECD; 2012   (http://www.oecd.org/environment/mortalityriskvaluationinenvironmenthealthandtransportpolicies.htm, accessed 26 March 2014).

9. International Transport Forum (ITF), Adapting Transport Policy to Climate Change: Carbon Valuation, Risk and Uncertainty. 2015: OECD Publishing.

10. Smith, S. and N.A. Braathen, Monetary Carbon Values in Policy Appraisal, available at: http://www.oecd-ilibrary.org/content/workingpaper/5jrs8st3ngvh-en. OECD Environment Working Papers. 2015, Paris: Organisation for Economic Co-operation and Development.

11. World Health Organization. WHO Global Health Observatory (GHO) data [website]. (http://www.who.int/gho/en/) 2017.

12. Department for Transport. Transport analysis guidance: WebTAG, https://www.gov.uk/guidance/transport-analysis-guidance-webtag. 2014.

13. ECF, Cycle more Often 2 cool down the planet! - Quantifying CO2 savings of Cycling. 2011, European Cyclists' Federation (ECF): Brussels.

14. European Commission, OBIS (Optimising Bike Sharing in European Cities), Final Report. 2011, Brussels: European Commission.

15. Kahlmeier S, Götschi T, Cavill N, Fernandez AC, Brand C, Rueda DR et al. Health economic assessment tool (HEAT) for cycling and walking. Methods and user guide on physical activity, air pollution, injuries and carbon impact assessments. Copenhagen: WHO Regional Office for Europe; 2017

16. Development of the Health Economic Assessment Tools (HEAT) for walking and cycling. 4th Consensus meeting: meeting background document. Bonn, 11-12 December 2014. Copenhagen, WHO Regional Office for Europe, 2014.

17. Chen J and Hoek G. Long-term exposure to PM and all-cause and cause-specific mortality: A systematic review and meta-analysis. Environment International 2020 (143): 105974. https://doi.org/10.1016/j.envint.2020.105974

18. WHO expert meeting: methods and tools for assessing the health risks of air pollution at local, national and international level. Meeting report, Bonn, Germany, 12–13 May 2014. Copenhagen: WHO Regional Office for Europe; 2014 (http://www.euro.who.int/en/health-topics/environment-and-health/air-quality/publications/2014/who-expert-meeting-methods-and-tools-for-assessing-the-health-risks-of-air-pollution-at-local,-national-and-international-level, accessed 26 June 2015).

19. De Nazelle et al. Comparison of air pollution exposures in active vs. passive travel modes in European cities: A quantitative review. Environment International 2017 (99): 151-160 (Appendix C). (http://www.sciencedirect.com/science/article/pii/S0160412016304585?via%3Dihub, accessed 31 August 2017).

20. Johnson T (2002): A guide to selected algorithms, distributions, and databases used in exposure models developed by the office of air quality planning and standards. US Environmental Protection Agency.

21. de Nazelle A, Rodríguez DA, Crawford-Brown D (2009): The built environment and health: impacts of pedestrian-friendly designs on air pollution exposure. The Science of the total environment 407(8):2525-35

22. World Health Organization. Ambient air pollution data. (https://www.who.int/data/gho/data/themes/air-pollution/ambient-air-pollution), accessed 7 November 2021.

23. Health economic assessment tool (HEAT) for cycling and walking. Copenhagen: WHO Regional Office for Europe; 2011; revised edition published online 2017 (www.heatwalkingcycling.org).

24. DfT. Average speed and delay on local “A” roads. 2015, Department for Transport: London.

25. Free flow vehicle speeds in Great Britain: 2015 [website]. London: Department for Transport (https://www.gov.uk/government/statistics/free-flow-vehicle-speeds-in-great-britain-2015)

26. Statista:  Average speed in Europe's 15 most congested cities in 2008 (in kilometers per hour) (available at https://www.statista.com/statistics/264703/average-speed-in-europes-15-most-congested-cities/) (no year).

27. DfT. Transport Statistics Great Britain. 2016, Department for Transport: London.

28. Leuenberger M, Frischknecht R. (2010) Life Cycle Assessment of Two Wheel Vehicles. Uster: ESU-services

29. del Duce, A. Life Cycle Assessment of conventional and electric bicycles. Paper presented at Eurobike (31 August - 3 September 2011), 2011: Friedrichshafen, Germany.

30. Ricardo AEA. Current and Future Lifecycle Emissions of Key “Low Carbon” Technologies and Alternatives, Final Report. 2013, Ricardo AEA for the Committee on Climate Change: Harwell, UK.

31. DfT. National Travel Survey: the way we travel. 2015, Department for Transport: London

Value of statistical life
Social costs of carbon

Introduction to health impact assessment and comparative risk assessment approaches in HEAT

Health impact assessment (HIA) is a combination of procedures, methods, and tools used to evaluate the potential health effects of a policy, programme, or project and the distribution of these effects within a particular population. Health impact assessment can use qualitative, quantitative or mixed methods (including participatory) techniques.  HIA outcomes produce evidence on health risks and benefits that can cause or prevent disease, injury, or deaths. Decision makers across different sectors can use HIA outcomes to communicate existing health benefits and risks related to walking and/or cycling and to support decision making towards alternatives that benefit health.

HEAT is a HIA tool: a quantitative tool to calculate the health effects of regular cycling and/or walking. In addition, the tool assesses carbon emissions related to cycling and/or walking.

Health impact calculations in HEAT quantify the benefits and risks caused by differences in exposure levels and how these vary in a specific population over a defined time period. The tool calculates the number of premature deaths occurring in a population over a given period of time by multiplying a mortality rate by the population size and the period of time.

Example 1: in Denmark, among people aged 20-74 years old, the mortality rate is 500/100’000 people per year. Over a period of 10 years, among the approx. 4 million Danes in that age range, 200’000 are expected to die (i.e. (500/100’000) × 4’000’000 × 10)

 

Example 2: in the city of Port Louis, Mauritius, among people aged 18-64 years old, the mortality rate is 1032/100'000 per year. Over a period of 10 years, among the approx. 80 thousand Mauritians in that age range, 8’256 are expected to die (i.e. (1032/100’000) x 80’000 x 10)

 

HEAT applies the comparative risk assessment approach, in which the health outcome of interest is the difference in premature mortality between two cases: a reference case (also known as the baseline case) and a comparison case (also known as the counterfactual case). For HEAT, the difference in mortality is obtained by comparing the difference in levels of active travel. Active travel refers to walking and cycling as an alternative means to motorised transport, and which combines everyday journeys with physical activity.  The difference in active travel is quantified in levels of physical activity from regular walking or cycling between the two cases (Figure 1).

 

Figure 1 Reference case and comparison case in comparative risk assessment

To calculate impacts, HEAT uses well-established exposure response functions (ERF) obtained from epidemiological research.  Exposure response functions (ERF) quantify the strength of association between an exposure (in HEAT: amount of walking or cycling) and a health outcome (in HEAT: mortality from any cause, i.e. all-cause mortality). Risk estimation is conducted by calculating the exposure difference between the reference case and the comparison case. These effects are quantified as relative risks, comparing the risk (such as the risk of dying prematurely) among people who are exposed (walk or cycle regularly) to the risk among people who are not exposed (who do not walk or cycle or walk or cycle less).

The relative risk (taken from the literature) is scaled to the local levels of walking or cycling. Because relative risk estimates refer to long-term exposure, the local data provided by the user in HEAT assessment must also represent estimates of long-term walking or cycling behaviour.

The number of expected deaths in the population walking and/or cycling is calculated using the same method as above but now multiplied by the relative risk (scaled to reflect the local levels of walking or cycling).

In a single-case assessment in HEAT, the user only specifies walking or cycling for the reference case, which is then compared to an implicit comparison case of no walking or no cycling.

In a two-case assessment, the user specifies walking and/or cycling levels for both cases.

The impact, the number of prevented premature deaths, at the population level is the difference between the calculation in the reference case and the comparison case, again reflecting population size and assessment time (see figure below.)

Figure 2 Visualization of active travel and impacts in “single case” comparative risk assessment

In a single-case assessment, the tool assumes a “steady-state situation”: the assessed level of active travel is assumed to having been constant for several years, and subjects experience the full health effects from long-term active travel.

In a two-case assessment, the tool considers an uptake time until full levels of active travel are achieved (specified by the user) and a build-up time of five years until the health effects manifest in full (see figure below).

Figure 2 Visualization of active travel and impacts in “two case” comparative risk assessment

For detailed formulas of calculations, see here.

For more details on data needed for a HEAT assessment, see here.

For more details on assumptions that apply to HEAT assessments, see here.

 

Mortality impact calculation for physical activity and air pollution pathways

How does the HEAT model work?

HEAT allows to calculate the mortality benefits of regular physical activity from cycling or walking, and optionally to take into account the effects of air pollution and crashes on mortality, or the effects carbon emissions from replacing motorized trips by walking or cycling.

At a minimum, users need to provide data on walking or cycling, and the size of the population assessed (see data requirements). HEAT also requires basic information that defines the type and setting of the assessement.

More information on each of these modules can be found through the link boxes on the right.

 

Graphical user interface, text, application, chat or text message

Description automatically generated

Workflow of the HEAT tool.

 

 

Assesssment of physical activity impacts
Assesssment of air pollution impacts
Assesssment of crash risk impacts
Assesssment of carbon emissions
Monetization of impacts
Default and background values used in HEAT

Air pollution assessment in HEAT

A method used for comparative risk assessment of air pollution and modes of transport (1,2) was agreed to serve as basis for assessing the air pollution effects on cyclists and pedestrians in HEAT (3,4,5). This method uses PM2.5 as the air pollution measure, based on background PM2.5 concentrations. Based on the selected country and/or city, HEAT will propose a PM2.5 concentration retrieved from the WHO Global Urban Ambient Air Pollution Database (city values) (see here) or the Global Health Observatory data repository (country values) (7); users can review this value. If no value is available from the databases or the user prefers to enter a local value, a PM2.5 value can be entered. In cases where no PM2.5 data is available, it possible to derive PM2.5 values from more widely available PM10 values (4) using an internationally accepted conversion factor of 0.6 (8).

Exposure levels to PM2.5 concentrations are derived from background concentrations by adapting to the micro-environments of each transport mode and ventilation rates for each transport activity. So, the equivalent change of air pollution intake resulting from cycling or walking between the reference and the comparison case is calculated using a ventilation rate (1.37 m3/hour for walking and 2.55 m3/hour for cycling) (3,4), duration of exposure and PM2.5 concentration of the particular mode of transport. The calculated intake is added to the intake during the rest of the day.

HEAT considers two aspects when calculating the difference in air pollution exposure resulting from a specific level of cycling or walking:

1.      the location of the cycling or walking to derive the appropriate level of air pollution exposure:

o   mainly on or near a road with motorized traffic. Therefore at an air pollution concentration that is considered equal to the background concentration multiplied by an agreed conversion factor for cycling or walking (see below);

o   mainly in a park or away from roads with motorized traffic. Therefore at a concentration of air pollution that could be considered equal to the background concentration (see below).

2.      the main purpose of the cycling and walking to derive the appropriate reference case:

o   mainly for leisure where the comparison scenario for HEAT is “staying at home” (with a concentration of air pollution that is considered to be equal to background concentration and a ventilation rate of 0.61 m3/hour);

o   mainly for commuting where the comparison scenario for HEAT is “using a car” (with a concentration of air pollution equal to the background concentration multiplied by an agreed conversion factor (see below) and a ventilation rate of 0.61 m3/hour (3,4)).

Mode-specific PM2.5 concentrations are derived from the background concentrations using conversion factors. The applied factors of 2.0 for cycling, 1.6 for walking and 2.5 for using a car* (versus background) were derived for HEAT from a purposive review of studies that estimated PM2.5 concentrations while cycling or walking compared with concentrations in other modes of transport (9).

According to the systematic review of studies on effects of PM2.5 on mortality conducted in support of the WHO Air Quality Guidelines 2021 update, the relative risk for all-cause mortality per 10 µg/m3 of PM2.5 is 1.08 (1.06–1.09) (4). It is important to recognize that there will be a time lag between exposure to air pollution and negative health effects. Based on expert consensus, a time lag of 5 years – similar to that used for health effects from physical activity (see also here) – will be used for air pollution effects to build up on mortality as a reasonable and most likely conservative assumption, with an increment of 20% in benefits each year.

Scope and limitations

The use of non-linear integrated dose–response functions has been suggested to reflect indications that the relationship between air pollutants and health risk seem to become flatten at higher pollution levels (4). In addition, based on the available evidence it can be concluded that the change in exposure due to exercise is small as the increased inhalation dose is low compared to inhalation of air pollution during the rest of the day (3,5,10,11). The experts therefore adopted a linear dose–response function as being appropriate within the HEAT framework (5).

To avoid inflated values at the upper end of the range, the risk increase from exposure to particulate matter within HEAT is capped. A cap of 100 µg/m3 is used for HEAT, which reflects the majority of locations according to the WHO (13), including also the evidence on the air pollution exposure in the locations in which the studies took place that HEAT is based on (see here). For locations with somewhat higher levels of air pollution, no further health effects will be applied beyond 100 µg/m3. No lower cap is used for HEAT, as evidence shows that health effects also occur at very low concentrations of air pollution (13).  

Finally, HEAT only includes air pollution effects among cyclists and pedestrians and does not consider the (often substantial (1)) effects of reducing air pollution for the whole population by replacing motorized transport by cycling and walking.

* The literature review (9) focused on studies contrasting active vs. passive travel modes; the car vs. background ratio used in HEAT is stemming from the studies included in this review and should be seen as an approximation.

 

Sources:

1.        de Hartog JJ et al. Do the health benefits of cycling outweigh the risks? Environ Health Perspect. 2010;118:1109–16.

2.        Rojas-Rueda D et al. The health risks and benefits of cycling in urban environments compared with car use: health impact assessment study. BMJ. 2011;343:d4521.

3.        Rojas-Rueda D, Nieuwenhuijsen M. Adjustment of risk estimates of physical activity and mortality by the impact of air pollution (particulate matter of less than 2.5 µm). Barcelona: Centre for Research in Environmental Epidemiology (CREAL); 2014.

4.        Chen J and Hoek G. Long-term exposure to PM and all-cause and cause-specific mortality: A systematic review and meta-analysis. Environment International 2020 (143): 105974. https://doi.org/10.1016/j.envint.2020.105974

5.        Development of the health economic assessment tools (HEAT) for walking and cycling. Consensus workshop. Bonn, Germany, 11–12 December 2014. Copenhagen: WHO Regional Office for Europe; 2015.

6.        World Health Organization. WHO Global Urban Ambient Air Pollution Database (http://www.who.int/phe/health_topics/outdoorair/databases/cities/en/), accessed 14 September 2017.

7.        World Health Organization. WHO Global Health Observatory data repository, Exposure Country Average 2014. (http://apps.who.int/gho/data/view.main.SDGPM25116v?lang=en), accessed 15 September 2017.

8.        PM2.5, an indicator for fine particles. In: Air quality guidelines. Global update 2005. Particulate matter, ozone, nitrogen dioxide and sulfur dioxide. Copenhagen: WHO Regional Office for Europe, 2006: 40–1 (http://www.who.int/phe/health_topics/outdoorair/outdoorair_aqg/en, accessed 26 June 2015).

9.        De Nazelle et al. Comparison of air pollution exposures in active vs. passive travel modes in European cities: A quantitative review. Environment International 2017 (99): 151-160 (Appendix C). (http://www.sciencedirect.com/science/article/pii/S0160412016304585?via%3Dihub, accessed 31 August 2017).

10.     Andersen ZJ et al. A Study of the Combined Effects of Physical Activity and Air Pollution on Mortality in Elderly Urban Residents: The Danish Diet, Cancer, and Health Cohort. Environ  Health  Perspect 2015 (123):557–563;   http://dx.doi.org/10.1289/ehp.1408698

11.     WHO. Personal Interventions  and Risk Communication  on Air Pollution. 2020. Summary report of a WHO Expert Consultation, 12–14 February 2019, Geneva, Switzerland. Geneva:  World Health Organization.

12.     Cohen A et al. Urban air pollution. In: Ezzati M, Lopez AD, Rodgers A, Murray CJL, editors. Comparative quantification of health risks: global and regional burden of disease attribution to selected major risk factors. Volume 1. Geneva: World Health Organization; 2004:1353–434. (http://www.who.int/healthinfo/global_burden_disease/cra/en, accessed 31 August 2017).

13.     World Health Organization. Ambient air pollution data. (https://www.who.int/data/gho/data/themes/air-pollution/ambient-air-pollution), accessed 7 November 2021.

14.     Di Q et al. Air Pollution and Mortality in the Medicare Population. N Engl J Med 2017 (376):2513-2522. DOI: 10.1056/NEJMoa1702747.  

Mortality impact calculation for air pollution
Relative risk used for air pollution impacts assessment
What is particulate matter?
Economic valuation of impacts
Guidance on interpreting the results

Carbon assessment in HEAT – overview

The assessment of carbon emissions effects in HEAT consists of three main steps1:

1.     Assessing mode shift from motorized travel to walking or cycling (or vice-versa);

2.     Assessing the carbon emissions from substituted motorized travel; and

3.     Assessing the economic value of the social impact of changes in carbon emissions.

Step 1: HEAT can assess the mode shift from motorized travel in assessments that compare two cases (for example, ‘reference’ versus ‘comparison’, ‘before’ versus ‘after’ an intervention and ‘with policy measures’ versus ‘without policy measures’). After entering a volume of walking and/ or cycling, users are asked to adjust their data to consider the shares of walking and/or cycling that:

·       have been reassigned (shifted from other routes or destinations) or are entirely new because of induced (or generated) demand, both of which are not considered for the carbon assessment as they do not substitute any motorized travel. For example, if 5% of the cycling observed on a new route was shifted from a parallel route and 5% was newly induced travel (because the new route fulfills latent demand), the cycling activity relevant to the carbon assessment would be 100% - 5% - 5% = 90% of the volume initially entered by the user.

·       are mainly for transport (versus for recreation); assuming that any walking and/or cycling for recreation will not have been shifted from or carried out by motorized travel, the carbon assessment does not consider the volume of recreational active travel. For example, if 20% of the cycling observed on a new route was for purely recreational purposes, the cycling activity relevant to the carbon assessment would be 100% - 20% = 80% of the volume initially entered by the user.

·       have been shifted from other motorized modes. For these modal shifts, changeable default diversion rates are provided in case no ‘after’ volumes are available (see diversion rates).

Single-case assessments (which assumes a steady-state situation), by definition, exclude reassigned and induced walking and/or cycling. In this case, the proportions shifted from other modes are used to derive the amount of motorized travel that, hypothetically, would have been carried out otherwise (for no walking and/or cycling). In this case the HEAT mode shift question essentially asks: “How much of the cycling could have been done by (a) car, (b) public transport or (c) walking?” The same approach applies to two-case assessment in which the user has selected the option of “no data” on motorized modes (input data section).

Step 2: The second step converts the above changes in travel activity into carbon emissions that are potentially avoided (single case assessment) or saved (two case assessments). For this calculation step, the HEAT approach includes an assessment of operational, energy supply and vehicle lifecycle emissions:

·       operational emissions  
(including country- and year-specific background values on average trip lengths, fuel splits, vehicle fleet compositions, ambient temperature, ‘cold start’ excess emissions and a changeable default value on prevailing traffic conditions in the study area);

·       energy supply emissions
(including country- and year-specific background values on “well-to-tank” emissions for different transport fuels such as gasoline, diesel, and electricity); and

·       vehicle lifecycle emissions
(using a standard lifecycle inventory approach applying embedded carbon emissions factors for materials and energy used in vehicle manufacturing).  

Step 3: In the third step, the resulting saved carbon emissions are monetized using internationally recognized ‘carbon values’. These are typically based on the ‘Social Cost of Carbon’ (SCC) approach (more information see here.) Changeable default values are provided disaggregated by country and start year of economic assessment.

 

Sources

1.      Development of the health economic assessment tools (HEAT) for walking and cycling. 5th Consensus meeting. Meeting report. Copenhagen, Denmark, 28 - 29 March 2017. Copenhagen: WHO Regional Office for Europe; 2017.

Diversion rates
Operational emissions
Energy supply emissions
Vehicle lifecycle
Economic valuation of impacts
Guidance on interpreting the results

Assessing road crashes in HEAT

HEAT assesses the effects of road crash on mortality through a basic approach (1): a generic estimate of road crash risk is multiplied by the local data on walking or cycling provided by the tool user (crash risks for other modes are currently not considered). The generic estimate of road crash risk for cycling is derived based on national statistics, dividing the total number of fatal pedestrian or cyclist crashes by the total number of kilometres walked or cycled in the particular country (see Data sources below).

Note that national data on total kilometres walking or cycled is only available for a limited number of countries. Users can review and modify, or fill in missing parameters used to calculate fatality rates, such as number of road fatalities and total distance travelled, by mode. Where no data is available, users are required to provide their own estimates to be able to estimate crash impacts.

Safety improvements over time

In two-case assessments (such as before versus after or scenario A versus B), the user has the option to specify a change in road crash risk (such as a 10% decrease) (2). The tool then applies a linear interpolation of the crash risk over time (see also here.)

Scope and limitations

If local data is available, users can conduct city-level assessment by using national default values (if available) or inputing sub-national fatality rates for cycling and/or walking crashes.

HEAT does not consider differences or changes in exposure to motorized traffic. Such assessment, as proposed by Elvik et al. (3,4) and others, may be offered in a later version. Currently HEAT also does not consider injuries from road crashes. The HEAT advisory group acknowledged that not including the health effects and costs of injuries would mean that HEAT would not yet fully consider all negative health effects from road crashes (1). However, it was recognized that the currently available data sources and the lack of internationally standardized approaches to definitions and to collecting information on road injuries do not yet allow non-fatal outcomes to be included. Such assessment may be offered later.

Formula

Flocal= FRgeneric*Dlocal

Where:

Flocal = Cycling fatalities expected due to local cycling

FRgeneric = Generic national fatality risk estimate calculated by dividing national number of killed cyclists in crashes per year by national estimate of total cycling in km per year

Dlocal = Local cycling distance in km based on data provided by HEAT user

Crash risk data

Where available, the generic estimates of road crash risk were calculated using fatality and exposure data derived from various sources. Fatality data were compiled from the international data sets of the International Transport Forum (5) and the World Health Organization (6). Due to the lack of international databases for exposures, data were compiled from a number of national sources. For some countries not included in these databases, cycling exposure was estimated using assumptions in terms of mobility demand (3 trips by any mode per person and day) and trip distance (3 km per cycle trip), population data (6) and extrapolations of mode share data (7).  For available data, the used sources are of different quality and their combination implies different levels of reliability of the resulting generic fatality risk estimates. More information is provided here.

 

Sources

1.      Development of the health economic assessment tools (HEAT) for walking and cycling. 5th Consensus meeting. Meeting report. Copenhagen, Denmark, 28 - 29 March 2017. Copenhagen: WHO Regional Office for Europe; 2017.

2.      Castro A, Kahlmeier S, Gotschi T. Exposure-Adjusted Road Fatality Rates for Cycling and Walking in European Countries. 2018. International Transport Forum Discussion Papers 2018/05, OECD Publishing.

3.      Elvik R and Bjørnskaua T. Safety-in-numbers: A systematic review and meta-analysis of evidence. Safety Science,2017(92):274-282.

4.      Elvik R. The non-linearity of risk and the promotion of environmentally sustainable transport. Accid Anal Prev. 2009 Jul; 41(4): 849–55.

5.      ITF-IRTAD International Transport Forum – International Traffic Safety Data and Analysis Group. Database Casualties by age and road use. 2017. Online database. http://stats.oecd.org/Index.aspx?DataSetCode=IRTAD_CASUAL_BY_AGE

6.      WHO-GHO World Health Organisation - Global Health Observatory data repository. 2016. Online data base. http://apps.who.int/gho/data/node.main.A997?lang=en

7.      ITDP-ITS. Institute for Transportation & Development Policy and the University of California. A Global High Shift Cycling Scenario: The Potential for Dramatically Increasing Bicycle and E-bike Use in Cities Around the World, with Estimated Energy, CO2, and Cost Impacts. 2015. https://www.itdp.org/wp-content/uploads/2015/11/A-Global-High-Shift-Cycling-Scenario_Nov-2015.pdf

8.      Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2017 (GBD 2017) Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018. Available from http://ghdx.healthdata.org/gbd-results-tool

Crash rates used in HEAT
Economic valuation of impacts
Guidance on interpreting the results

Physical activity assessment in HEAT

To assess the health benefits of physical activity derived from regular walking or cycling, the tool uses estimates of the relative risk of death from any cause among regular cyclists or walkers compared with people who do not cycle or walk regularly. The value of relative risk is extracted from a meta-analysis of published studies. For more details on the relative risks used in HEAT for cycling and walking, see here.

The tool applies this relative risk to the amount of walking or cycling entered by the user, assuming a linear relationship between walking or cycling and mortality. To illustrate this, the relative risk from the meta-analysis used for the updated version of HEAT for cycling is 0.90 for regular commuters cycling for 100 minutes per week for 52 weeks of the year (equivalent to 87 hours of cycling per year). Thus, in any given year, a population of regular cyclists receives a protective benefit of 10% (1.00 minus 0.90): that is, overall they are 10% less likely to die from all causes combined than a population of non-cyclists. If the user enters a cycling volume equivalent to 29 hours per year (one third as much), the protective benefit of this amount of cycling will be about 3%. If the user enters 174 hours (twice the time cycled in the reference population), the resulting protective benefit is 20%. This is twice the protective benefit of the reference population.

The same approach is taken for walking, in which the risk reduction is 0.89 for regular walking of 168 minutes per week for 52 weeks of the year (equivalent to 146 hours of walking per year). HEAT then uses population-level mortality data to estimate the number of adults normally expected to die in any given year in the target population. Then it calculates the reduction in expected deaths among the people in this population who cycle or walk at the level specified by the user, using the adjusted relative risk.

Unless a steady-state situation is being assessed, it is important to recognize that there will be a time lag between increases in physical activity and measurable benefits to health. Based on expert consensus, it was agreed that five years was a reasonable assumption to use for such additional physical activity to reach full effect, with an increment of 20% in benefits each year.

Scope and limitations

Although literature suggests that the dose–response relationship between physical activity and mortality is most likely non-linear (2,3), the meta-analysis carried out for HEAT (1) also showed that differences between various dose–response curves (i.e. the relationship between the size of a “dose” of physical activity and the extent of the response to it) were modest (see also here). For HEAT, a linear relationship was chosen to avoid additional data requirements on baseline activity levels (which would be needed using a non-linear dose–response function) and because a linear approximation is often adequate within the foreseen range of activity for HEAT.

To avoid inflated values at the upper end of the range, the risk reduction available from HEAT is capped. Inspection of the data points of the new meta-analyses suggested that, after about 45% risk reduction for cycling and 30% for walking, the risk reduction starts to slow (and most of the evidence relates to exposure below these levels). A large cohort study found through purposive review (4) also confirmed these limits. On this basis, the advisory group recommended using these caps in the updated HEAT. Thus, HEAT will apply a maximum 45% risk reduction (corresponding to 447 minutes per week) in the risk of mortality for cycling and a maximum 30% risk reduction (corresponding to 460 minutes per week) for walking.

Table: Caps for the benefits from physical activity in HEAT

Mode

Applicable age range

Relative risk

Reference volume

Benefits capped at

Walking

20-74 years

0.89 (CI 0.83–0.96)

168 minutes/week

30% (460 minutes/week)

Cycling

20-64 years

0.90 (CI 0.87–0.94)

100 minutes/week

45% (447 minutes/week)

CI: confidence interval.

Formula

The basic functioning of the physical activity module of HEAT is shown in the figure below:

(1-RR)×((Local volume of walking/cycling)/(Reference volume walking/cycling))   Where:

RR = relative risk of death in underlying studies (walking: 0.89; cycling: 0.90)

Reference volume of cycling per person calculated based on 100 minutes per week for 52 weeks per year at an estimated speed of 14 km/hour.

Reference volume of walking based on 168 minutes per week at 5.3 km/hour

The relative risk is then used to calculate number of deaths prevented based on mortality rate, applying a population-attributable fraction formula. For details, see here.

 

Sources:

1.      Kelly P et al. Systematic review and meta-analysis of reduction in all-cause mortality from walking and cycling and shape of dose response relationship. 2014, International Journal of Behavioral Nutrition and Physical Activity (11):132 (https://doi.org/10.1186/s12966-014-0132-x, accessed 31 August 2017).

2.      Woodcock J et al. Non-vigorous physical activity and all-cause mortality: systematic review and meta-analysis of cohort studies. Int J Epidemiol. 2011;40:121–38

3.      Wen CP et al. Minimum amount of physical activity for reduced mortality and extended life expectancy: a prospective cohort study. Lancet. 2011;378:1244–53.

4.      Manson J et al. Walking compared with vigorous exercise for the prevention of cardiovascular events in women. 2002, N Engl J Med, 347(10): 716-725.

Mortality impact calculation for physical activity
Relative risk used for physical activity impact assessments
Economic valuation of impacts
Guidance on interpreting the results

How to navigate the tool

Depending on the characteristics of an assessment, a varying number of questions will apply to a specific HEAT assessment. At the beginning of the tool, the selection of three interface options provides the hide or see more or fewer questions.

On the left side of the screen, the menu of pages helps you to orient yourself on where you are in the assessment process.

Click on “next” or “back” to move between pages. You can also go back to a previous question to check or change entries by clicking on the section in the menu to which this question belongs (see also below) in the menu on the left side of the screen. Then again use the “next” button to advance through the rest of the assessment; only sections affected by the change made will require new input; otherwise the entries made beforehand are shown.

Hovering with the mouse over a “?” icon next to an entry option will show additional information, hints and tips on this item. The HEAT website also has a section with frequently asked questions and further hints and tips. The website also offers additional information on each section of HEAT.

More information on how HEAT works can be found here.

Examples for what kind of results you can produce with your local data or scenario are shown here.

A detailed description of the development process, evidence used and main project steps can be found in the Methodology and user guide.

More information and materials are also available at http://www.euro.who.int/HEAT

 

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Mortality impact calculation for physical activity and air pollution pathways

The HEAT impact calculations for physical activity and air pollution apply a population attributable fraction (PAF) formula. This formula is used to relate the mortality rate for the general population (MRpop) to the two groups compared in comparative risk assessment:  the exposed group (reference group) (e) and unexposed group (comparison group) (u). In HEAT, exposure refers to the assessed amount of cycling or walking.

The MRpop is the weighted average of the mortality rate in the exposed (MRe) and unexposed (MRu). MRpop depends of the contrast in mortality risk between the two groups, as well as the size of the two groups.

MRpop = MRu x Pu + MRe x Pe

Epidemiological studies estimate the contrast in mortality risk and express it as a relative risk (RR): for example, RRcycling = 0.9 for x minutes of cycling per day compared with 0 minutes of cycling per day).

RR = MRe/MRu

The size of the exposed and unexposed groups is typically expressed as the proportion of exposed subjects (Pe). In the HEAT context, this quantifies the size of the assessed population cycling or walking relative to the size of the total population on which the MRpop is based (all inhabitants of a country 20–64 or 20–74 years old, respectively). In most use cases, the proportion of people exposed is quite small, such as in city-level or sub-city-level assessment (in which the population assessed is much smaller than the country’s population), and in assessments in which walking or cycling levels are not very high (with little influence on the overall mortality risk). By default, the tool therefore assumes the proportion of exposed people to be close to zero (0.001), which means that the influence of the assessed walking or cycling on the country-level mortality rate (MRpop) is negligible. Users may change this for use cases in which this does not apply, such as country-level assessments in which walking and cycling levels are very high. In these cases, mode share or an equivalent figure can be used as an approximation of the proportion exposed.

The mathematical formulas used by HEAT were derived based on these considerations. To calculate effects in terms of premature deaths (avoided), MRu and MRe are estimated based on MRpop, RR, (and Pe~=0).

MRpop = MRu x Pu + MRe x Pe

RR = MRe/MRu

Pu = 1-Pe

MRu = MRpop / [1 – (Pe*(1 – RR))] ~= MRpop

MRe = MRpop x RR / [1 – (Pe x (1 – RR))] ~= MRpop x RR

MRu and MRe are then multiplied by the assessed population to derive the number of deaths in the exposed group (the population assessed in HEAT) and unexposed group (the hypothetical counterfactual of the same population not being exposed: not cycling or walking). The difference between the two groups reflects the number of deaths attributed to the exposure or the impact of the exposure. If the impact is smaller among exposed people, the exposure prevents deaths, such as physical activity.

Du = MRu x pop

De = MRe*pop

D_attributed = De – Du

In a two-case comparison, the same assessment is calculated twice for different levels of exposure. The deaths attributed then reflect the difference between the two assessments.

More information on how the different HEAT modules calculate impact assessments can be found here.

 

Interpretation of the HEAT results

The HEAT results are shown in two ways:

·       the general results summary sums up all health impacts, carbon emission effects and economic values across all assessed modes and (as selected by the user).

·       the detailed results provide the same information separately by mode and pathway.

The results summary first displays the amount of walking or cycling the user has entered and the number of people in the assessed population.

The tool produces an overall estimate of the following outputs by summing up positive and negative effects across health-related pathways and reductions in carbon emissions (as selected by the user):

·       the number of premature deaths prevented (per year and across the full assessment period);

·       tonnes of emissions of CO2 equivalents avoided (per year and across the full assessment period);

·       sum of the economic value of effects on mortality (per year and across the full assessment period as well as discounted, if so selected), using the Value of a Statistical Life method (VSL). VSL aggregates individuals’ willingness to pay to secure a marginal reduction in the risk of premature death in relation to the years this person can expect to live (more information see here). Calculations can be adapted to the local setting by reviewing and modifying the country parameters.

·       the economic value of the effects of carbon emissions (per year and across the full assessment period as well as discounted, if so selected), using the Social Costs of Carbon (SCC); and

·       the total economic value of effects, summing up the economic benefits from the three health pathways as well as carbon emission calculations, as selected by the user (per year and across the full assessment period as well as discounted, if so selected).

In an overview table, users can select the results they want displayed; per mode (walking and/or cycling) and per pathway (physical activity, air pollution, road crashes and/or carbon emissions). According to the selection made, the results are then displayed for each of the selected modes and pathways, including the same information as the result summary described above. The results are also available as overview graphs.

Limitations and sensitivity analysis

Many of the variables used within this HEAT calculation are estimates and therefore liable to some degree of error. Remember that the HEAT tools provide you with an approximation of the level of health, carbon emission and economic benefits. Several assumptions apply. To get a better sense of the possible range of the results, you are strongly advised to rerun the model, entering slightly different values for variables for which you have provided a best guess, such as entering high and low estimates for such variables.

 

Carbon assessment in HEAT: Carbon emissions (step 2)

Operational emissions

Operational carbon emissions are derived by breaking carbon emissions down into changes in travel activity or travel demand (in passenger-kilometres by mode of travel – see step 1), differences in energy efficiency of each mode of travel (in megajoules per passenger-km, by mode and fuel type), and differences in carbon intensity (CO2e/MJ, by mode and fuel type) of the energy used. This represents a typical carbon emissions ‘decomposition approach’ illustrated below:

Figure: Composition of operational carbon emissions

HEAT considers the effects of three contextual factors on carbon emission factors:

1.     distance travelled and average trip lengths;

2.     average speed, representing various traffic conditions in the study area; and

3.     mode characteristics such as vehicle type and fuel type.

For cars, HEAT considers average traffic speeds, vehicle fleet composition and the effect of ‘real-world’ driving (adding 21.6% to the carbon emissions derived from official laboratory test data; value based on the conversion factors used in the United Kingdom [1]) in the study area to calculate the amount of CO2e emitted per km when the engine is at operating temperature. This is based on published relationships between fuel consumption, average speed and conversion to carbon emissions using a standard carbon balance method. For motorcycle, bus and rail, only fuel type shares are considered, with average emission factors based on the conversion factors taken from [1]. For most countries and cities, buses are still largely powered by diesel, motorcycles are generally running almost 100% on gasoline, and urban rail is assumed to be all electric. For cars, excess emissions from cold starts (for the initial, ‘cold’ part of a journey, typically the first 3.4 km after starting) are added to this. This can be specified as follows:

Where: Et=pollutant emissions (e.g. CO2e); t=scenario (e.g. ‘before’ and ‘after’ an intervention), efhot,t(mode)=’hot’ emissions factor for mode in scenario t; pkmt(mode)=passenger-km for mode in scenario t, Ecold=cold start excess emissions (for cars only); temp=ambient temperature; trip length=average trip length.

The first term includes speed-dependency of emissions factors, a method that is based on the European Environment Agency’s COPERT model [2] but which has been applied globally:

Where: V=average speed and coefficients a to e are empirically derived for each fuel and hard coded in the HEAT module.

‘Hot’ emissions dominate total emissions, but cold-start emissions should not be neglected, since they constitute a significant share of total emissions for shorter trip lengths (typically 15–20%). Ecold is typically derived for each vehicle fuel type k as:

Where: b = fraction of mileage driven with a cold engine or the catalyst operated below the light-off temperature, pkmk = passenger-km, ecold / ehot = cold/hot emission quotient for vehicles of technology k, temp=ambient temperature. l = 1.47, m=0.009 (gasoline); l=1.34, m=0.008 (diesel) based on [2]

The b parameter depends upon ambient temperature and average trip length (see [2] for details). In HEAT, b was derived by dividing average trip length of the substituted car trip by an average cold start distance of 3.4 km, with b <= 1.

Vehicle fuel type shares and average occupancy rates for each country are based on international databases, including the GAINS (Greenhouse gas–Air pollution INteractions and Synergies) model projections (IEA World Energy Outlook 2016 ‘6DS’ scenario) of the International Institute for Applied Systems Analysis for many countries and years up to 2050 [3-8]. Future projections of carbon emissions factors are thus based on available scenario data, not forecasts, therefore any projections beyond the 10-year timeframe should be treated with caution.

For cars, the user can choose between five generic traffic conditions based on typical road speeds observed in countries and cities around the world [9], including average speeds (all values in kilometres per hour) in “little or no congestion, nearly ‘free flow’” (Canberra peak 46, Vienna off-peak 45, Memphis peak 59, Paris off-peak 53); “some heavy traffic and peak time congestion” (Vienna peak 29, Curitiba off-peak 31, Bogota off peak 25); and “heavy congestion most days” (Curitiba peak 23, Prague peak 25, Paris peak 25, Berlin peak 23, Bogota peak 17, Rio de Janeiro peak 26) [9-11]. The five traffic categories are:

1.     Global average, urban / inner city (~32 km/h) – changeable default value;

2.     Little or no congestion, urban (“free flow”) (~45 km/h);

3.     Some peak time congestion (commute, school run), urban (~35 km/h);

4.     Heavy congestion most days (am, pm and inter peak), urban (~20 km/h);

5.     Global average, rural / inter-urban (~60 km/h).

HEAT uses country and year specific emissions factors. Table 1 provides an example for India in 2019. Other country-year combinations are available on request.

Table 1: Derived average hot and cold emission factors (tailpipe), showing values derived for India in 2019 (HEAT uses country- and year-specific factors, which can be provided on request)

Example: India, year 2019

Average traffic conditions

Average emissions factors, operational (gCO2e/passenger-km)

‘Global’ average, urban

Little or no congestion (‘free flow’)

Some peak time congestion

Heavy congestion most days

Global average, rural

Car or van 1,2

126

110

121

156

103

Local bus 1,2

81

--

--

--

--

Urban rail, trams, metro (100% electric) 2

0

--

--

--

--

Motorcycle 1,2

76

--

--

--

--

E-bike / bicycle

0 / 0

--

--

--

--

Average ‘cold start emissions per trip (gCO2e/passenger-trip) 3

 

 

 

 

 

Car or van 1,2

52

45

50

64

42

Notes: (1) Takes into account weighted fuel/engine type shares for each mode, e.g. for cars in India in 2019 (44% petrol, 55% diesel, 1% electric), bus (98% diesel), motorcycle (99.5% gasoline). (2)Car occupancy rate of 1.5 (all trip purposes), local bus 14.3, average urban rail 120, motorcycle 1.05. (3) With cold/hot ratio of 1.2 and cold trip distance of 2.2 km, derived from ambient temperature of 25 degrees C and average trip length of 8 km. ‘—' not applicable.

Sources: hot and cold emissions factor coefficients [2, 12]; vehicle fleets [3, 8], speeds [9-11].

 

Sources

1.         DEFRA/DECC, UK Government conversion factors for Company Reporting, full 2016 dataset. 2016, Department for the Environment, Food and Rural Affairs and Department for Energy and Climate Change: London.

2.         EEA, COPERT 4 (COmputer Programme to calculate Emissions from Road Transport), last accessed at http://emisia.com/content/copert-documentation on 20/02/2018. 2012, European Environment Agency: Copenhagen.

3.         ACEA/ANFAC, European Motor Vehicle Parc 2014: Vehicles in Use (2009-2014). 2014, Madrid: ANFAC/ACEA.

4.         SMMT, New Car CO2 Report 2014: The 13th report. 2014, London: The Society of Motor Manufacturers and Traders (SMMT).

5.         IEA, World energy outlook 2015. 2015, Paris: International Energy Agency (IEA).

6.         Eurostat, Transport data database, October 2015 update. Last accessed at http://ec.europa.eu/eurostat/web/transport/data/database on 10/03/2017. 2016, Brussels: Eurostat, European Commission.

7.         IIASA, IIASA GAINS model, scenario WPE_2014_CLE: the updated ‘current legislation’ (after the bilateral consultations in 2014) of the PRIMES 2013 REFERENCE activity projection. 2014, IIASA: Laxenburg, Austria.

8.         IIASA, IIASA GAINS model, scenario '6DS' for IEA World Energy Outlook, available for Europe (for 48 countries), Asia, with separate implementations for China (31 provinces and India (15 States), and for Annex I countries of the UNFCCC Convention. Accessed at https://gains.iiasa.ac.at/models/gains_models3.html in July 2019. 2016, IIASA: Laxenburg, Austria.

9.         INRIX, Global Traffic Scorecard, accessed at http://inrix.com/scorecard/ on 05/11/2019. 2018, INRIX.

10.       Olson, P. and K. Nolan, In Depth: Europe's Most Congested Cities, last accessed at https://www.forbes.com/2008/04/21/europe-commute-congestion-forbeslife-cx_po_0421congestion_slide.html on 10/02/2017. Based on INRIX data., in Forbes. 2008.

11.       Newman, P. and J. Kenworthy, Sustainability and Cities: Overcoming Automobile Dependence. 1999, Washington, DC: Island Press.

12.       EMEP/EEA, EMEP/EEA air pollutant emission inventory guidebook 2016, Technical guidance to prepare national emission inventories. 2016, European Environment Agency: Copenhagen.

 

Energy supply emissions

Air pollution in HEAT

Air pollution is a complex mixture of different components. Particle pollution (also known as particulate matter) in the air includes a mixture of solids and liquid droplets. Some particles are emitted directly; others are formed in the atmosphere when other pollutants react. Particles come in a wide range of sizes. Those less than 10 micrometres in diameter (PM10) are so small that they can get into the lungs, potentially causing serious health problems. Even stronger health effects have been shown for particles with a diameter of less than 2.5 micrometres (PM2.5). Based on a large body of evidence, mainly from cohort studies, HEAT estimates the related health effects of ambient air pollution due to exposure to PM2.5. This is also relevant for low- and middle- income countries transitioning from predominant forms of household air pollution to higher levels of outdoor air pollution due to rapid urbanisation and mass motorisation.

In the absence of local ambient PM2.5 concentration measurements, an internationally accepted conversion factor of 0.60 was agreed (1) to use for HEAT to convert more widely available PM10 measurements into estimates of PM2.5 (more information on background values.)

Since evidence shows that PM2.5 is less directly related to emissions from road traffic than other pollutants, such as elemental carbon or black smoke, a sensitivity analysis was carried out (2) to assess the influence of the choice of pollutant on the HEAT results. Using elemental carbon, the estimates were very similar to those based on PM2.5.

For more information on air pollution see here.

 

Sources:

1.      PM2.5, an indicator for fine particles. In: Air quality guidelines. Global update 2005. Particulate matter, ozone, nitrogen dioxide and sulfur dioxide. Copenhagen: WHO Regional Office for Europe, 2006: 40–1 (http://www.who.int/phe/health_topics/outdoorair/outdoorair_aqg/en, accessed 26 June 2015).

2.      Development of the health economic assessment tools (HEAT) for walking and cycling. Consensus workshop. Bonn, Germany, 11–12 December 2014. Copenhagen: WHO Regional Office for Europe; 2015.

 

Population data

HEAT assesses impacts on a population level. Therefore, next to the data on active travel, population size is the most important variable in a HEAT assessment. The larger the population, the bigger the impacts.

Which population attributes does HEAT consider?

·       Population size and age range of the assessed population affect the calculations in HEAT.

·       Population type is a parameter collected to guide data input by the user.

How do I determine the appropriate population type?

You can either assess the general population, or pedestrians or cyclists only.  Which population type to choose primarily depends on your data. Is the average figure of walking or cycling that you enter in the Data input section based on a general population (including people who do not walk or bike) or does it only apply to pedestrians or cyclists?

Survey data typically includes the general population. But be aware that average figures, i.e. “minutes of cycling per person, per day”, may have been derived from those reporting at least some cycling only. In this case, the population size needs to be adjusted accordingly.

Count data, by definition, only includes pedestrians or cyclists.

Note that the population type you choose does not actually affect the math in the tool. Rather it will determine some input options that you will get to see (or not, depending on user interface selections made). The main purpose of population type is to raise your awareness for providing the correct population size figure.

How do I determine the appropriate age range?

Age is very influential for mortality risk, as older people have a higher risk of dying than young people. Because of this, the same amount of walking or cycling will have stronger impact in older people. Thus, it is important to approximate the age range of the assessed population engaging in walking or cycling correctly. The selected age range determines which mortality rate the tool uses for the baseline risk (i.e. the risk to die without any walking or cycling.)

HEAT provides three options for Age Range:

·       Young adult population (20-44 years old)

·       Adult population (20-74 for walking, 20-64 for cycling)

·       Older adult population (45-74 for walking, 45-64 for cycling)

Note that when choosing the age range in the Data input section, this should apply to the subjects actually engaging in walking or cycling (even if you assess a general population).

HEAT excludes subjects younger than 20 years old from the analysis, because first of all, mortality in these age ranges is very small, and second, (for exactly that reason) there are no studies available for effects on mortality in these age ranges.

HEAT further excludes subjects older than 74 years (for walking) or 64 years (for cycling) because mortality risks increase strongly in older age ranges. Including these would therefore be highly influential and potentially inflate benefits of active travel.

How do I determine the appropriate population size?

Population size is entered on the Population data page, in the Data input section. It is important to note that the population size needs to correspond to:

·       The population size specified for the population assessed

·       The age range specified for the population assessed

For assessments at the Country or City level, which typically involve the general population, HEAT tries to provide a Total population figure, if available from the United Nations Data Portal (1). HEAT further derives a factor to adjust the total population figure to the age range assessed, based on national data (Percent of total population.) This is then used to calculate the Population size used for your assessment.

Note that you can overwrite any of these values, and HEAT will recalculate the Population size used based on your entries. If there are no data available, you need to provide the Population size used for your assessment on your own, before continuing the assessment.

For assessments at the Subcity level, no default values are available for the population size. Thus, users need to input the Population size used for your assessment before continuing the assessment.

Sources:

1.      United Nations. Data API. No year (https://population.un.org/dataportal/about/dataapi), accessed 27 October 2021.  United Nations: New York.   

 

Proportion of cycling or walking excluded due to unrelated factors

HEAT is useful to predict the impact of interventions (interventions can include either policy, program, or project) in affecting health before, after or during they are framed and implemented. When the impact of an intervention is assessed (two case assessments only), not all the cycling or walking observed may be directly attributable to the intervention. For example, cycling may be more commonly practiced over time, or gasoline or public transport prices may have changed and affected active travel behaviour. Walking or cycling arising from such external factors should not be included in the assessment of an intervention. Such external factors can also refer to changes that would happen anyway under the reference scenario (also referred to as the “business-as-usual”scenario).

Figure 1 Effects of measure (intervention) versus other unrelated effects (from Evaluation matters.)

https://user-images.githubusercontent.com/13148245/28371699-96fe2a96-6c95-11e7-9bca-e1be8ae7fcea.png

This unrelated proportion can be excluded using this slider (default setting 0%).

Suggested values:

Close to 0 (0-30%):
This applies to assessments of comprehensive long-term measures, system-wide (i.e. whole communities), unaffected by major other factors that could have influenced cycling or walking (e.g. without major increases of fuel or public transport prices).

Mid-range (30-80%):
This applies to assessments where there is belief that other factors than the evaluated interventions may have contributed towards the observed changes in cycling or walking. Examples are increases in cycling or walking which were accompanied by steep increases in fuel prices, or when assessing a specific intervention that was implemented during the same time as other measures affecting walking or cycling.

High range (>80%):
Excluding more than 80% of observed walking or cycling due to unrelated factors. This may probably only be applied for unusual circumstances, when you have strong evidence that a change in active travel observed within your assessment project is attributable to a more impactful outside factor. For example, if you assess the impacts of a particular piece of infrastructure, but at the same time speed limits have been reduced area-wide, which presumably has a stronger effect on active travel.

 

Relative risk estimates used in the HEAT for exposure to air pollution

HEAT uses a relative risk from a meta-analysis including 107 cohort studies from Canada, China, France, Germany, Israel, Italy, Japan, New Zealand, Norway, the Netherlands, South Korea, Sweden, Taiwan, the United Kingdom and other European countries as well as the United States (1). It quantifies the relative risk for mortality from all causes for each increment of 10ug/m3 of PM2.5 (particulate matter with a diameter of less than 2.5 micrometres (2) (see also here) as 1.08 (confidence interval: 1.04-1.09). This translates into an 8% increase in risk of dying per each additional increase in long-term exposer to 10ug/m3 PM2.5. In other words, people who are exposed to PM2.5 levels that are 10ug/m3 higher, have a 8% increased risk of dying (at any point in time) than people who are exposed to 10ug/m3 less, assuming they do not differ in age, smoking status, or any other relevant characteristic.

The meta-analysis (1) found virtually the same effect estimates for the European Region, the Region of the Americas and the Western Pacific Region. The authors also found little evidence for a stronger association among women than among men. Studies performed in predominantly elderly showed somewhat smaller relative risks but the differences were not statistically significant.

Combining physical activity and air pollution exposure

The published relative risks for mortality and physical activity from walking and cycling used by HEAT are taken from studies in settings in which participants were exposed to (different levels of) air pollution. As such, the relative risks for physical activity include a small population-average degree of influence of air pollution while walking or cycling. When HEAT users select solely assessing the effects of physical activity, this is not specifically adjusted for, implicitly assuming that air pollution levels are comparable between the assessed setting and the settings in which the studies were conducted.

However, if the user selects both the physical activity and the air pollution assessment, then HEAT adjusts the relative risk for physical activity benefits from walking and cycling to exclude the effects of air pollution, using relative risk estimates adjusted to what they would be if the physical activity studies had been conducted in non-polluted environments. The effects of the additional air pollution exposure from walking or cycling are calculated separately (and shown in the detailed HEAT results).

To derive the adjusted relative risks, the exposure to air pollution (PM2.5) in each of the cycling and walking study settings was estimated using international databases and assuming a 50% higher historical exposure to consider the general improvement of air pollution between the time the underlying studies were conducted (between 1964 and the early 2000s) and the year 2011 for which air pollution exposure was available (3). The effect of this exposure while walking or cycling on the original relative risks from the physical activity studies (4) was calculated, using a previously published exposure response function between PM 2.5 and all-cause mortality (5) (almost identical to the currently used relative risk in HEAT), default inhalation rates and the published durations of exposure (3).

Overview of the relative risks used in physical activity assessments

Cycling

·       Unadjusted: RR = 0.903 (0.866-0.943)

·       Adjusted for air pollution (new): RR = 0.899 (0.861-0.939)

Walking

·       Unadjusted: RR = 0.886 (0.806-0.973)

·       Adjusted for air pollution (new): RR = 0.883 (0.803-0.970).

Note that the vice versa argument could be made for the influence of active travel on the published relative risks of air pollution from studies in which subjects are engaged in walking or cycling. It is fair to assume that this influence would be negligible because of the small contribution of the additional inhaled dose of air pollution while walking or cycling to the total exposure of the entire study population.

 

Sources:

1.      Chen J and Hoek G. Long-term exposure to PM and all-cause and cause-specific mortality: A systematic review and meta-analysis. Environment International 2020 (143): 105974. https://doi.org/10.1016/j.envint.2020.105974

2.      PM2.5, an indicator for fine particles. In: Air quality guidelines. Global update 2005. Particulate matter, ozone, nitrogen dioxide and sulfur dioxide. Copenhagen: WHO Regional Office for Europe, 2006: 40–1 http://www.euro.who.int/__data/assets/pdf_file/0010/263629/WHO-Expert-Meeting-Methods-and-tools-for-assessing-the-health-risks-of-air-pollution-at-local,-national-and-international-level.pdf?ua=1, accessed 10 October 2017).

3.      Development of the Health economic assessment tools (HEAT) for walking and cycling: Consensus workshop. Meeting background document. Bonn, Germany, 11-12 December 2014. Copenhagen: WHO Regional Office for Europe; 2014.

4.      Kelly P et al. Systematic review and meta-analysis of reduction in all-cause mortality from walking and cycling and shape of dose response relationship. 2014, International Journal of Behavioral Nutrition and Physical Activity (11):132 (https://doi.org/10.1186/s12966-014-0132-x, accessed 31 August 2017).

5.      Hoek G et al.  Long-term air pollution exposure and cardio-respiratory mortality: a review. Environ Health 2013 (12):43. http://www.ehjournal.net/content/12/1/4, accessed 1 November 2021. 

Relative risk estimate used in the HEAT for physical activity assessments

The strongest evidence at the time of the first project on the mortality effects of cycling was the relative risk data from two combined Copenhagen cohort studies (1,2,3). This study included about 7000 20- to 60-year-old participants followed up for an average of 14.5 years. It found a relative risk of all-cause mortality among regular commuter cyclists of 0.72 (95% CI 0.57–0.91) compared with non-cycling commuters for 180 minutes of commuter cycling per week.

In 2013, a new systematic review on the reduced relative risk of all-cause mortality from regular cycling or walking was carried out (4).

To be included in this review, a study was required:

·       to be a prospective cohort study;

·       to report the level of regular walking or cycling (such as duration, distance or MET equivalent);

·       report all-cause mortality rates or risk reductions as outcome; and

·       report results independent of (that is, adjusted for) other physical activity.

A total of 8901 titles were identified, and 431 full texts were screened. Seven cycling studies (carried out in China, Denmark, Germany and the United Kingdom) and 14 walking studies (from China, Denmark, Germany, Japan, United Kingdom and the United States) met the inclusion criteria. A meta-analysis was carried out, combining the results of these studies. Since the available studies used a range of types of exposure, conducting the meta-analysis required estimated for each study the reduced risk at a common exposure level. For this purpose, the various types of cycling and walking exposure used in the studies were converted into MET-hours per week (assuming a linear dose–response relationship and an average intensity of 6.8 METs for cycling and 4.0 METs for walking, if not otherwise stated). The common exposure level was set at 11.25 MET-hours per week. This value was derived from the global physical activity recommendations as corresponding to the recommended level of at least 150 minutes of moderate-intensity physical activity per week (5) using 4.5 METs as an average for moderate-intensity physical activity. Using 6.8 METs as an average intensity for cycling, this exposure represents about 100 minutes of cycling per week and 170 minutes of walking per week, using an average intensity of 4.0 METs.

The international advisory group recommended that, for HEAT, a linear dose–response curve based on a relative risk of 0.90 for cycling and 0.89 for walking should be used, applying a constant absolute risk reduction (6). The sensitivity of the results to various possible shapes of dose–response relationships was tested. The differences between the various curves were modest, and the difference in the final risk estimate was no more than 6%. It has to be noted, however, that no studies were available from low-income countries and only one from a middle-income country. All included studies did correct for level of income and a range of other co-variates. Nevertheless, it cannot be determined to which extent the results would be influenced in places where environmental or social factors are considerably different (e.g. air pollution, or levels of physical activity from other sources). The large global body of evidence on the risk reduction in premature mortality from regular physical activity of different types (7) (including non-recreational activities carried out outdoors), particularly in urban areas where interventions are most likely to affect walking and cycling patterns, suggests it is unlikely that the results would differ considerably.

Combining physical activity and air pollution exposure

The published relative risks for mortality and physical activity from walking and cycling used by HEAT are taken from studies in settings in which participants were exposed to (different levels of) air pollution. As such, the relative risks for physical activity include a small population-average degree of influence of air pollution while walking or cycling. When HEAT users select only the assessment of physical activity effects, this is not specifically adjusted for, implicitly assuming that on average air pollution levels are comparable between the assessed setting and the settings in which the studies were conducted.

However, if the user selects both the physical activity and the air pollution assessment, then HEAT adjusts the relative risk for physical activity benefits from walking and cycling to exclude the effects of air pollution, using relative risk estimates adjusted to what they would be if the physical activity studies had been conducted in non-polluted environments. The effects of the additional air pollution exposure from walking or cycling are calculated separately (and shown in the detailed HEAT results).

To derive the adjusted relative risks, the exposure to air pollution (PM2.5) in each of the cycling and walking study settings was estimated using international databases and assuming a 50% higher historical exposure to consider the general improvement of air pollution between the time the underlying studies were conducted (between 1964 and the early 2000s) and the year 2011 for which air pollution exposure was available (8). The effect of this exposure while walking or cycling on the original relative risks from the physical activity studies (4) was calculated, using a published exposure response function between PM 2.5 and all-cause mortality (9), default inhalation rates and the published durations of exposure (4).

Overview of the relative risks used in physical activity assessments

Cycling

·       Unadjusted: RR = 0.903 (0.866-0.943)

·       Adjusted for air pollution (new): RR = 0.899 (0.861-0.939)

Walking

·       Unadjusted: RR = 0.886 (0.806-0.973)

·       Adjusted for air pollution (new): RR = 0.883 (0.803-0.970).

Note that the vice versa argument could be made for the influence of active travel on the published relative risks of air pollution from studies in which subjects engaged in walking or cycling. It is fair to assume that this influence would be negligible because of the small contribution of the additional inhaled dose of air pollution while walking or cycling to the total exposure of the entire study population. It can also be said that one of the included studies on the risk reduction on premature mortality from regular cycling and walking has been carried out in China (10), with considerably higher levels of air pollution. The risk reductions were comparable to the other studies carried out in less polluted environments (4).

 

Sources:

1.      Andersen LB et al. All-cause mortality associated with physical activity during leisure time, work, sports and cycling to work. Arch Intern Med. 2000;160:1621–8.

2.      Rutter H et al. Economic impact of reduced mortality due to increased cycling. Am J Prev Med. 2013;44:89–92.

3.      Kahlmeier S et al. Health economic assessment tools (HEAT) for cycling and walking. Methodology and user guide. Copenhagen: WHO Regional Office for Europe; 2011 (http://www.heatwalkingcycling.org/index.php?pg=archive, accessed 26 March 2014).

4.      Kelly P et al. Systematic review and meta-analysis of reduction in all-cause mortality from walking and cycling and shape of dose response relationship. 2014, International Journal of Behavioral Nutrition and Physical Activity (11):132 (https://doi.org/10.1186/s12966-014-0132-x, accessed 31 August 2017).

5.      Global recommendations on physical activity for health. Geneva: World Health Organization; 2010 (http://www.who.int/dietphysicalactivity/factsheet_recommendations/en/index.html, accessed 26 March 2014).

6.      Development of guidance and a practical tool for economic assessment of health effects from walking. Consensus workshop, 1–2 July 2010, Oxford, United Kingdom. Background document: summary of literature reviews and issues for discussion. Copenhagen: WHO Regional Office for Europe; 2010.

7.      Lear SA. et al. The effect of physical activity on mortality and cardiovascular disease in 130 000 people from 17 high-income, middle-income, and low-income countries: the PURE study. 2017, The Lancet 390(10113): 2643-2654 (DOI:https://doi.org/10.1016/S0140-6736(17)31634-3, accessed 18 October 2021).

8.      Development of the Health economic assessment tools (HEAT) for walking and cycling: Consensus workshop. Meeting background document. Bonn, Germany, 11-12 December 2014. Copenhagen: WHO Regional Office for Europe; 2014.

9.      Hoek G et al. Long-term air pollution exposure and cardio- respiratory mortality: a review. Environ Health. 2013 May 28;12(1):43.

10.    Matthews CE. et al. Influence of exercise, walking, cycling, and overall nonexercise physical activity on mortality in Chinese women. 2007, American Journal of Epidemiology 165(12):1343-50. doi: 10.1093/aje/kwm088 (accessed 18 October 2021).

Carbon emissions (step 3)

Carbon valuation and the Social Cost of Carbon

Carbon values based on the social costs of carbon method essentially put a price on CO2 emissions. The social costs of carbon can be defined as the monetized value of the global damage caused by the incremental impact of an additional tonne of carbon dioxide equivalent (CO2e) emitted at a specific point in time.

The damage costs are estimated using integrated assessment models such as the Dynamic Integrated Climate-Economy model (DICE) (1,2), Climate Framework for Uncertainty, Negotiation and Distribution (FUND) (3) and Policy Analysis of the Greenhouse Effect (PAGE) model (4). The values of the social costs of carbon vary widely: for example, one meta-analysis of 211 estimates from 47 studies (5) found a wide distribution of carbon values, from –€1 to €451 per tonne of CO2e.

Key issues in estimating the social costs of carbon are the extent of uncertainty in methods and data, time horizon (usually spanning a century), the use of discounting (i.e. valuing the future), geographical scope (such as global versus regional), variation in climate sensitivity across settings, and equity weighting. The carbon values used in policy assessment vary by country and increase over time.

The international advisory group agreed to use country-specific carbon values based on the social costs of carbon approach in HEAT, since this is used in project appraisal independently of national emission targets and mitigation policies (6).

Monetization of the carbon emissions

Changeable default values for carbon valuation are provided by country and year (Figure 1), based on international evidence, regional averages (6,7) or country specific values (if existing). The values for the social costs of carbon for countries or contexts not covered in existing evidence or policy guidance have been allocated either European Commission recommended values (US$ 44 in 2015, rising to US$ 66 by 2030), regional averages (e.g. some Asian countries) or global averages (e.g. many African countries, weighted by population).

Given that not many jurisdictions currently value carbon effects in policy and planning, users can override these default values and use their own recommended economic appraisal values instead.

Figure 1: Default carbon values for a range of countries (ISO3 code) and years of assessment (in USD/tCO2e, 2015 values)

 

Sources

1.      Moore FC, Diaz DB. Temperature impacts on economic growth warrant stringent mitigation policy. Nature Clim. Change, 2015. 5(2): p. 127-131.

2.      Nordhaus WD. Revisiting the social cost of carbon. Proceedings of the National Academy of Sciences, 2017. 114(7): p. 1518-1523.

3.      Anthoff, D. and R. S. J. Tol (2013) The uncertainty about the social cost of carbon: A decomposition analysis using FUND, Climatic Change 117(3): 515-530.

4.      Weisbach D, Moyer E. Discounting in Integrated Assessment. 2010, Washington D.C.: Resources for the Future.

5.      Tol RSJ. The Social Cost of Carbon: Trends, Outliers and Catastrophes. Economics: The Open-Access, Open Assessment E-Journal, 2008. 2: p. 2008-2025.

6.      ITF, Adapting Transport Policy to Climate Change: Carbon Valuation, Risk and Uncertainty. 2015: OECD Publishing.

7.      Smith, S. and N.A. Braathen, Monetary Carbon Values in Policy Appraisal, available at: http://www.oecd-ilibrary.org/content/workingpaper/5jrs8st3ngvh-en. OECD Environment Working Papers. 2015, Paris: Organisation for Economic Co-operation and Development.

Sources for active travel data

Data on walking or cycling come in different formats and vary in quality. A few considerations will help you to make best use of your data and avoid mistakes. Note that HEAT can also be used for hypothetical scenarios which do not rely on empirical data.

Population survey data

The main strength of population-based surveys is that they can provide representative estimates of the population wide average of walking or cycling. These can then be combined with commonly available estimates of the size of a population (see the Population data page on how to correctly specify the population size). Note that average figures from population surveys typically include people who do not cycle (or walk) at all, so these need to be included in the population figure as well.

However, in many places, population-based surveys are not readily available, or do not include information about active travel use.  Conducting a robust travel survey is a major endeavour.

Use of short-term counts or surveys

The main concern with short-term counts is that they do not accurately capture variations in walking or cycling over time (i.e. time of the day, day of the week, season, as well as weather). If you count on a sunny day, you may see larger numbers than on a rainy day. Since HEAT assumes that the entered data reflect long-term average levels of walking or cycling, data from short-term counts will distort the results.

This issue will mainly affect single facility evaluations (e.g. a cycle path, or a pedestrian bridge) where counts are conducted on the facility itself, or community-wide evaluations that are based on surveys conducted only during a certain time of the year.

Not affected by this issue are assessments based on large surveys, which are conducted on a rolling basis (e.g. national household surveys), or automated continuous counts.

Short term counts may also be adjusted for temporal variation, to better reflect long term levels of walking or cycling. An example for how this can be done is provided by the US National Bicycle and Pedestrian Documentation Project http://bikepeddocumentation.org/

Use of data from few locations

If route user surveys or roadside counts are used, it is important to consider that they might be affected by seasonality, weekday versus weekend behaviour, spatial variation or by other factors. HEAT requires the entered data to represent long-term averages (e.g. annual averages) but allows an adjustment of the entered walking or cycling data, if necessary. Spatial variation in walking or cycling may affect evaluations that are based on counts at a single or few locations. The choice of location may strongly influence the count numbers, which may not be representative of the wider level of walking or cycling. Results need to be interpreted carefully, and should in general not be extrapolated beyond the locations where actual data were collected. Not affected by this issue are evaluations based on surveys that sample subjects randomly from a defined area (e.g. large household surveys), and to a lesser extent count-based evaluations on linear facilities, such as trails.

Use of trip or count data

In HEAT, trip or count data needs to be combined with an estimate for average trip length, to calculate the volume of walking or cycling. An example is counts conducted on a bridge, where it remains unknown how far people walk beyond the bridge. Average trip distance estimates may be derived from user surveys on a specific facility, or from travel surveys.

Use of bikeshare data

Heat offers an option to enter data from bike share schemes (flexible and full user interface options only). Note that in contrast to most other data input options, which are “per person, per day”, this data source reflects bike share use in the whole system (average number of trips per day). The number of trips is then multiplied with the average duration of trips. Both variables are typically available from bike share system data. Users may adjust assumptions for average travel speed in the parameter review section.

Use of pedometer data

If assessments are based on pedometer data, it should be ensured that the number of steps used is predominantly composed of intentional brisk walking. Some pedometers have a function that excludes steps that are not deliberate walking. Another approach could be to include only intentional walking steps at a rate of about 100 steps per minute or to make an assumption of the proportion of total steps falling into this category.

 

Conversion of input units for travel volume data

While HEAT requires volume data to be inserted as “per person and day”, it allows the user to insert their data on travel modes in various units or formats. The tool then converts these to standard units, such as minutes and kilometres per day. Default values are used to inform these conversions as necessary (e.g. average trip distance).

The following input formats (per person per day) are supported:

·       duration: average time (minutes or hours) walked or cycled per person, such as 30 minutes walked on average per day;

·       distance: average distance walked or cycled per person, such as 10 km cycled on average per day;

·       trips: average per person or total observed across a population, such as 250 bicycle trips per year;

·       steps: average number of steps taken per person, such as 9000 steps per day;

·       mode share (in trips, duration or distance): mode share is a percentage of total travel (all modes): for example, 20% of all trips are walking;

·       frequency, referring to such questions as “How often do you use your bike?” or “How often do you walk?” (such as 20% if users cycle 1–3 days per week); and

·       percentage change: for example, compared with scenario A, in scenario B 20% of the population cycles x minutes more.

The following conversions apply:

·       To convert volume data between duration and distance, average speeds by transport mode are assumed (see here)

·       To convert steps into distance, the number of steps is multiplied by an average default step length (see here).

·       To convert number of trips into distance, average trips distances by transport mode are used (see here).

·       To convert mode share, the percent share is multiplied by the total volume (trips, distance or duration) and then the conversions as described above are applied, as necessary.

·       The following frequency categories are available: daily or almost daily, 1–3 days per week, 1–3 days per month, less than once per month and never. To convert frequency categories into distances, first the number of days walked or cycled per year is derived, using the category midpoints.

Thus, “daily or almost daily” is assigned 5.5 days per week (midpoint between 7 and 4 days in a week) and multiplied by 52; “1–3 days per week” is assigned 2 days per week; “1–3 days per month” is assigned 2 days per month and multiplied by 12; “less than once per month” is 6 days per year; and “never” is assigned zero. The days per year are then divided by 365 and multiplied by an average daily distance by mode , which is estimated by multiplying a number of trips per person per day in all modes (three) by the average trip distance by mode.

·       The input option “percent change” is available for two case assessments and allows specifying the comparison case in terms of relative change from the reference case.

 

HEAT carbon assessment: carbon emissions (step 2)

Vehicle lifecycle emissions

HEAT considers lifecycle carbon emissions from the manufacture of vehicles (the clear majority of vehicle life-cycle carbon emissions, apart from operational ones), with aggregate carbon values per vehicle type (cars, motorcycles, bikes and public transport vehicles) derived assuming typical lifetime mileages, mass body weights, material decomposition and material-specific emission and energy use factors.

The key inputs, assumptions and derived carbon emissions factors per passenger-km are shown for India in Table 1. The HEAT tool uses time independent but country-specific factors, reflecting local differences in occupancy rates for the different motorized modes [1].

Table 1: Assumptions and average CO2e emissions from vehicle manufacture

Mode

Total vehicle weight (tonnes)

CO2e per vehicle (tCO2e/veh.)

Lifetime mileage (km)

CO2e per pass-km (gCO2e/pkm) (*)

Bicycle

0.017

0.10

20,000

4.9

E-bike / pedelec ($)

0.024

0.19

20,000

9.3

Motorcycles

0.15

0.54

50,000

10.3

Average car
(~1% El. vehicles)

1.295

4.7

150,000

20.7

Average bus

11

39.5

1,000,000

2.8

Urban rail

66

237.1

1,500,000

1.3

($) battery and motor add 7kg in weight; assumed 2.5 gCO2e/km for the battery based on [2]. (*) Car occupancy rate of 1.5 (all trip purposes), local bus 14.3, average urban rail 120, motorcycle 1.05.

Sources: lifecycle data [2]; electricity emissions factors [3]; vehicle fuel shares [4]; load factors [1].

 

Sources

1.      EC DG Climate Action, Transport database TRACCS (Transport data collection supporting the quantitative analysis of measures relating to transport and climate change). 2013, Brussels: Emisia, Infras and IVL for the European Commission.

2.      Odeh, N., N. Hill, and D. Forster, Current and Future Lifecycle Emissions of Key „Low Carbon‟ Technologies and Alternatives, Final Report. 2013, Harwell, UK: Ricardo AEA for the Committee on Climate Change.

3.      Ecometrica, Electricity-specific emission factors for grid electricity. 2011: Ecometrica.

4.      IIASA, IIASA GAINS model, scenario '6DS' for IEA World Energy Outlook, available for Europe (for 48 countries), Asia, with separate implementations for China (31 provinces and India (15 States), and for Annex I countries of the UNFCCC Convention. Accessed at https://gains.iiasa.ac.at/models/gains_models3.html in July 2019. 2016, IIASA: Laxenburg, Austria.

 

Social cost of carbon

Value of Statistical Life

What is the value of statistical life (VSL)?

Value of statistical life is an economic indicator to monetize impacts on health. Despite its somewhat misleading name, it does not quantify the value of preventing the loss of “an entire individual’s life” but rather the sum of extending many lives through small reductions in risk of death. It is commonly used in transport appraisals.

The methods and theory behind VSL

The value of a statistical life (VSL) is derived using a method called “willingness to pay”. It aggregates individuals’ willingness to pay to secure a marginal reduction in the risk of premature death.

According to economic theory, the willingness to pay captures perceptions of risks and potential costs borne by the individual person, including lost consumption, immaterial costs (such as pain and suffering) and the share of health costs paid directly by the victims. It represents the societal economic value of reduced premature mortality and is often used in transport appraisal.

The VSL is not the value of an identified person’s life but rather an aggregation of individual values for small changes in risk of death: for example, how much a representative sample of the population would be willing to pay (in monetary terms) for a policy that would reduce their annual risk of prematurely dying from 3 in 10 000 to 2 in 10 000.

Users should be aware of the fact that VSL estimates come with considerable uncertainty (+- 50%, according to OECD 2012 (1)). The proper value to use may therefore be dictated by local circumstances, such as for example precedents set by local governmental institutions.

Default approach to calculate VSL values in HEAT

The default values in HEAT are extrapolated from a VSL base value for OECD countries from 2005.

This base value was derived from a comprehensive review of willingness-to-pay studies by the OECD in 2012 (1). Studies were only included if they were based on a representative population sample of at least 200 subjects and provided information on the size of the risk change in question. A total of 261 values from 28 studies were selected to calculate the base VSL for adults in OECD countries of US$ 3.0 million, with a range from US$ 1.5 million to US$ 4.5 million (in 2005 US dollars). The international advisory group concluded that the OECD report represented the best currently available evidence.

To derive country-specific VSL values, a so called “benefits transfer formula” is applied. A benefit transfer formula fulfils two objectives:

·       the VSL base value is adjusted to take into account the different income level of a target country, compared to OECD (this is sometimes referred to as “spatial transfer”).

·       the VSL base value from 2005 is adjusted to a current year (in HEAT this is currently 2017, the year for which the most recent and comprehensive data is available).

As part of expanding the scope of HEAT worldwide, the benefits-transfer approach for HEAT version 5 was revised in close collaboration with leading experts in this field.

The revised methodology takes into account country income levels to accommodate assessments in places with lower income than typical for the OECD. Further, there are two approaches for VSL estimation available. For assessments with the basic user interface option, HEAT applies the default approach for VSL estimation. Users using the flexible or the full user interface option have the possibility to use the alternative approach.

The HEAT default approach derives country-specific VSL values for the year 2017 by adjusting the VSL base value using OECD average inflation and income growth over time, and accounting for income difference between OECD and the target country in 2017. This approach uses gross national income (GNI) as the indicator for country income level. It applies the following formula:

VSL COUNTRY, 2017 = VSLOECD, 2005  * (1 + % Δ P2005-2017 ) * (1 +% Δ Y2005-2017 )IEt * (YCOUNTRY, 2017 / YOECD, 2017) IEs 

Where:

VSLOECD, 2005, USD = base value for OECD of 3.013 million US$ from OECD-study (1)

YCOUNTRY, 2017 = GNI per capita at purchasing power parity in 2017 of the respective country (2)

YOECD, 2017 = average GNI per capita at purchasing power parity in 2017 of OECD-countries (2)

IEt = income elasticity of VSL for income growth over time is set to 0.8

IEs = income elasticity of VSL for spatial transfer depends on country income level (World Bank classification): Low income = 1.25, Lower middle income = 1.15, Upper middle income = 1.0, High income = 0.8

(1 + %ΔP 2005-2017 ) = inflation adjustment with OECD change in consumer price index between 2005 and 2017

(1 +% Δ Y2005-2017 ) = adjustment for income growth with change in real GDP* per capita in the OECD between 2005 and 2017. (*Note that GNI growth is not available as far back as 2005)

Alternative benefit transfer approach to calculate the VSL values for HEAT

The alternative approach derives country-specific VSL values for the year 2017 by adjusting the VSL base value accounting for income difference between OECD and the target country in 2005, and using country-specific inflation and income growth over time. This approach uses gross domestic product (GDP) as the indicator for country income level due to lack of GNI data for 2005. It applies the following formula:

VSL COUNTRY, 2017 = VSLOECD, 2005  * (YCOUNTRY, 2005 / YOECD, 2005) IEs  * (1 + % Δ P2005-2017 ) * (1 +% Δ Y2005-2017 )IEt

Where:

VSLOECD, 2005, USD = base value for OECD of 3.013 million US$ from OECD-study (1)

YCOUNTRY, 2005 = GDP per capita at purchasing power parity in 2005 of the respective country (2)

YOECD, 2017 = average GDP per capita at purchasing power parity in 2005 of OECD-countries (2)

IEs = income elasticity of VSL for spatial transfer depends on country income level (World Bank classification): Low income = 1.25, Lower middle income = 1.15, Upper middle income = 1.0, High income = 0.8

(1 + %ΔP 2005-2017 ) = inflation adjustment with country-specific change in consumer price index between 2005 and 2017

(1 +% Δ Y2005-2017 ) = adjustment for income growth with change in real GDP per capita in the specific country between 2005 and 2017

IEt = income elasticity of VSL for income growth over time is set to 0.8

How do the default and alternative approaches compare?

Both approaches follow the same economic principles, but they use somewhat different data.

The HEAT default approach relies on OECD average data to update the VSL estimate to the current year (2017). As a result, it produces VSL estimates that are very similar, as long as country incomes are similar. This approach further uses GNI as the indicator for country income level, which is a more inclusive indicator than GDP.

The alternative approach relies on country-specific data to update the VSL estimate to the current year (2017). This approach has been applied in the previous version of HEAT (4.0), which was limited to use in Europe. This approach may reflect local circumstances (i.e. inflation and growth) more accurately, but can result in quite different VSL estimates for countries with similar income levels. In contrast to the default approach, the use of GDP further explains some discrepancies.

In some the two approaches may result in considerably different estimates. The discrepancy between the default and the alternative approach can be expected to be more pronounced in countries with considerably different inflation and growth patterns than OECD average. As a basic rule of thumb, VSL estimates should not be much greater than 100 times annual per capita income, and not much smaller than 20 times that.  

Currency conversion of VSL estimates

The above formulas produce VSL estimates adjusted for purchasing power parity (PPP) (i.e. in international dollars). These are suitable for comparisons between countries, however, for most applications of transport appraisals in local contexts, estimates based on market exchange rates are more suitable. In addition to these two estimates in USD, HEAT also offers a value in local currency (LCU). For these conversions from PPP to MER and from MER to LCU exchange rates from 2017 are used.

VSL(MER)country,2017,USD  = VSL(PPP)country,2017,USD ×  PPP_to_MERcountry,2017

Where:

VSL(MER)country,2017,USD = VSL based on market exchange rate

VSL(PPP)country,2017,USD = VSL adjusted for Purchasing Power Parity

PPP_to_MERcountry,2017 = PPP to MER conversion factor (from https://data.worldbank.org/indicator/PA.NUS.PPP)

VSL(LCU)country,2017,USD = VSL(MER)country,2017,USD ×  LCU_per_USDcountry,2017

where:

VSL(MER)country,2017,USD = VSL based on market exchange rate

LCU_per_USDcountry,2017 = Official exchange rate (LCU per US$, period average) (from https://data.worldbank.org/indicator/PA.NUS.FCRF)

How do the default and alternative approach compare to the VSL approach in HEAT 4

The VSL approach applied in the previous version of HEAT (4.0), which was limited to use in Europe, is similar to the alternative approach, aside from some key differences. In HEAT 4:

·       Income elasticity value was set to a fix value of 0.8. This would yield inadequate estimates for lower income countries.

·       Conversion to local currency was based on PPP rates from 2005 and VSL estimates were then converted to Euros. The current approaches estimate VSL based on PPP, and only apply currency conversions using current (2017) rates to avoid issues related to extreme changes in currency rates over time in some countries.

Sources

1.      Mortality risk valuation in environment, health, and transport policies. Paris: OECD; 2012.

https://www.oecd.org/environment/mortalityriskvaluationinenvironmenthealthandtransportpolicies.htm

2.      Health economic assessment tool (HEAT) for walking and for cycling. Methods and user guide on physical activity, air pollution, injuries and carbon impact assessments (2017) https://www.euro.who.int/en/health-topics/environment-and-health/Transport-and-health/publications/2017/health-economic-assessment-tool-heat-for-walking-and-for-cycling.-methods-and-user-guide-on-physical-activity,-air-pollution,-injuries-and-carbon-impact-assessments-2017

3.      World Bank datat (database). Paris, World Bank Group, 2014 (http://search.worldbank.org/data)

4.      GDP per capita https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD

5.      GDP per capita growth (annual %) https://data.worldbank.org/indicator/NY.GDP.PCAP.KD.ZG

6.      GDP OECD https://data.oecd.org/gdp/gross-domestic-product-gdp.htm

7.      GNI per capita, PPP (current international $) https://data.worldbank.org/indicator/NY.GNP.PCAP.PP.CD

8.      GNI OECD https://data.oecd.org/natincome/gross-national-income.htm

9.      Consumer price index (2010=100) (Inflation) https://data.worldbank.org/indicator/FP.CPI.TOTL

10.    OECD Consumer price index (Annual growth in %) (Inflation) https://data.oecd.org/price/inflation-cpi.htm

11.    PPP conversion factor, GDP (LCU per international $) https://data.worldbank.org/indicator/PA.NUS.PPP

12.    Price level ratio of PPP conversion factor (GDP) to market exchange rate https://data.worldbank.org/indicator/PA.NUS.PPPC.RF

13.    Exchange rates (Official exchange rate (LCU per US$, period average)) https://data.worldbank.org/indicator/PA.NUS.FCRF

14.    Country income level categories https://datahelpdesk.worldbank.org/knowledgebase/articles/