DECrEasing CO by inCrEasing biCyClE UsE · Baltic Environmental Forum Deutschland e.V. Osterstraße 58 D-20259 Hamburg Dipl.-Geogr. Matthias Grätz The sole responsibility for the

More biking in small and medium sized towns of Central and Eastern Europe

by 2020

www.mobile2020.eu

DECrEasing CO2 by

inCrEasing biCyClE UsEcollect data - estimate - decide

Co-funded by the Intelligent Energy Europe

Programme of the European Union

iMPrinT

Prepared by (editor and author):

Hamburg University of Technology (TUHH) Institute for Transport Planning and Logistics Schwarzenbergstr. 95 E D-21073 Hamburg

Dipl.-Ing. Christian Rudolph

www.vsl.tu-harburg.de

General Editor and Distribution:

Mobile 2020 project consortium www.mobile2020.eu

This brochure presents edited material from various other sources on bicycle and transportation planning and own results from ongoing research work.

Any duplication or use of objects of this brochure such as photos, illustrations, diagramms or texts is not permitted without written agreement.

The project is managed by

Baltic Environmental Forum Deutschland e.V. Osterstraße 58 D-20259 Hamburg

Dipl.-Geogr. Matthias Grätz

www.bef-de.org

The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the European Union. Neither the EACI nor the European Commission are responsible for any use that may be made of the information contained therein.

Hamburg, April 2014

Estimation of CO2-Emission Savings

3

COnTEnTs

list of abbreviations ............................................................................................. 4

1. introduction .................................................................................................. 7

2. gHg-Emissions ............................................................................................... 8

3. Evaluation of Existing bicycle Projects ................................................................. 8

4. Evaluation Criteria .......................................................................................... 9

5. available Data ............................................................................................... 9

6. assumptions ................................................................................................. 10

7. CO2-Emission Calculation Toolkit ........................................................................ 11

8. Example: Tartu, Estonia .................................................................................. 12

9. Final Conclusions and lesson learnt ................................................................... 17

references ........................................................................................................ 18

appendix ........................................................................................................... 20

TablEs

Table 1: Criteria for monitoring and evaluation of bicycle projects ................................... 9

Table 2: Modal split data from EPOMM website ........................................................... 10

Table 3: summary of scenarios ................................................................................ 13

Table 4: results (spreadsheet no. 2) ........................................................................ 15

FigUrEs

Figure 1: separate bicycle path ................................................................................ 5

Figure 2: Workshop during mobile2020 seminar ........................................................... 6

Figure 3: separate bicycle and pedestrian infrastructure ................................................ 7

Figure 4: speed reduction at pedestrican precinct ........................................................ 9

Figure 5: input mask (spreadsheet no. 1) .................................................................. 13

Figure 6: scenario input and modal split tables (spreadsheet no. 2) .................................14

Figure 7: Pie charts Ms and VMT for each scenario (spreadsheet no. 3) .............................16

Figure 8: bike racks ............................................................................................. 17

4

list of abbreviations

BEF Baltic Environmental Forum (www.befgroup.net)

BEV battery electric vehicle

CFCs chlorofluorocarbons

CH4 methane

CNP compressed natural gas

CO2 carbon dioxide

e.g. exempli gratia (for example)

eq equivalent

EPOMM European Platform for Mobility Management

EU European Union

FLEV fuel cell electric vehicle

g gram

GHG greenhouse gas

GHGs greenhouse gas emissions

HEV hybrid electric vehicle

HFCs hydrofluorocarbons

ICV internal combustion engine vehicle

inh. inhabitants

kg kilogram

km kilometer

LPG liquefied petroleum gas

MS Modal Split

MS© Microsoft

NF3 nitrogen trifluoride

NGO none governmental organisation

no. number

N2O nitrous oxide

NOx nitroxide

ODS ozone-depleting substances

PFCs perfluorocarbons

PM particular matter

PT public transport

REC Regional Environmental Center (www.rec.org)

SEAP sustainable energy action plan

SF6 sulphur hexafluoride

SOx sulphuroxide

SUMP sustainable urban mobility plan

TEMS The EPOMM Modal Split Tool

TTW tank-to-wheel

VMT vehicle miles travelled

WTT well-to-tank

WTW well-to-wheel


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Task Definition and Objectives

This study was elaborated within the mobile2020 proj-ect. The mobile2020 project addresses in particular the priority of encouraging a modal shift from the car to bi-cycle, which does not consume primary energy. Effect-ing such shifts will improve the overall energy efficien-cy of the entire transport system in the participating countries. The project goal is to mobilise, educate and enable relevant stakeholders in small and medium sized cities in eleven Central and Eastern European countries to promote and support cycling in their cities.

The outcomes of the mobile2020 project are manifold. Networks amongst urban planners are set up to improve cycling planning policies. Citizens are motivated in changing their mobility habits and routines towards bi-cycle use as a means of daily transport (utility cycling). Greenhouse gas emissions (GHGs) are reduced due to a modal shift from passenger car use to non motorized means of transport especially the bicycle.

This study shall provide an easy to use toolkit (carbon calculator) to visualize and present an estimation about the CO2-reduction potential by measures that aim on increasing bicycle use. Different carbon calculators are accessible via internet that allow to estimate individu-al CO2-emissions for different sectors like fuel, energy, air travel, food, waste, etc. (e.g. carbonneutral.com.au1, nature.org2 or atmosfair.de3). All carbon calcula-tors lack in the ability to estimate the reduction poten-tial by cycling respectively the ability to consider modal shifts from passenger car use to bicycle use and the ecological effect on greenhouse gas emissions.

In this study we develop a carbon calculator based on the facts and figures of a German nationwide surveys on transportation4 that enables multipliers of the mo-bile2020 project, urban planners and decision makers to give an impression about the amount of possibly re-duced input of CO2 into the atmosphere. The calculator can be easily used with any spreadsheet software like MS Excel© or other open-source software (e.g. libre of-fice©). With the tool effects on GHGs savings are pre-sented instantly which make it handy in any discussion about setting up targets and their ecological effects. By entering 1) the numbers of inhabitants, 2) the modal split (MS)5 and 3) the targeted bike share first estima

1 http://www.carbonneutral.com.au/carbon-calculator.html2 http://stage.nature.org/greenliving/carboncalculator/index.htm3 http://www.atmosfair.de/4 infas (2010)5 The modal split describes the share of each means of transport

in [%]. Most common classifications are by car, by public transport, by bicycle and by foot considering only passenger tranport.

tions can be made. The more information about mobil-ity patterns are entered, the better the results of the calculation can become.

The toolkit will be available throughout the whole mo-bile2020 consortium and will be distributed by local multipliers. Stakeholders who might profit from the tool can be

• National multipliers (Network of the BEF, REC and Atgaja community),

• Urban and transport planners in municipal admin-istrations and their

• Political principals - the local decision makers like city councillors,

• Energy agencies, unions of cities, ministries,

• Interest groups and NGOs.

We see mainly five important advantages and opera-tional areas to apply the use and results of the tool:

1. Estimating CO2-reduction potentials The generic idea of this study was to develop a carbon calculator that is capable to estimate the CO2-reduction potential that exists by realizing a certain share of vehicle miles travelled (VMT6) by bike instead of motorized cars. Each litre of fuel which is burned inside an internal combus-tion engine (ICV) produces around 2,5 kg CO2. The calculator shall not only translate an average fuel consumption by car into zero consumption by bike, but it shall also take into account the mod-al shifts between all transport modes as a whole.

6 Common expression in UK and USA; within the mobile2020 consortium we use the metric system: vehicle kilometers travelled; nevertheless since VMT is the common expression we stick to this term in this study.

Figure 1: Separate bicycle pathSource: Tomas Hefter, Bolzano, Italy

6

The ulterior motive is that whenever people tend to cycle more often they also tend to cycle longer distances. This also affects the average distance performed by other means of transport (public transport, passenger vehicles, foot). The carbon calculator based on German mobility patterns takes those facts into account.

2. Instant Information One of the big advantages of the calculator is that information and results come instantly. Once the required data is typed into the spreadsheet, re-sults and pie-charts are presented immediately on the next spreadsheet. Time consuming simula-tions are not required.

3. Cost effective The programme was developed within the mo-bile2020 project. It can be used by any multipli-er working for the mobile2020 consortium or any other person who is involved in any action related to mobile2020. Since the programme is based on MS© Excel© it does not produce any further costs.

4. Defining Objectives and Targets Discussions amongst multipliers, urban planners and decision makers can be very vague when it

comes to the factual effect of measures in respect to CO2-reduction. The tool can be consulted even within a discussion for instance by using a tab-let to show some scenarios or play around with some data to show their ecological effects. The tool can become very handy especially during the process of developing a Sustainable Urban Mobility Plan (SUMP) or any strategic document, where objectives and targets have to be defined. Against the background of EU GHGs reduction tar-gets the bicycle will play one important role in achieving the targets within the transportation sector. The tool is ideal to visualize effects on VMT and the joint CO2-emissions of a defined tar-get. E.g.: Differences between an aspired modal shift of 5 %, 10 % or 20 % can be compared easily.

5. Transportation Data The tool requires actual transportation data. The more data is available the better. The output of the programme will be more realistic. With the programme in hand it is easy to realize if there is a lack of transportation data. The programme can be used to justify budgets for surveys on trans-port to base any calculation on real data to ob-tain realistic estimates based on actual data. We realized that for urban planning departments of towns and cities in Central and Eastern European Countries with a size not bigger than 300.000 in-habitants (within the mobile2020 project we ap-proached approx. 350 cities), transportation data is scarce. Capital cities have more transport data and strategic documents available but are outside the scope of mobile2020. The mobile2020 project provides knowledge about survey methods and re-quired criteria to assess transportation data. Mon-itoring and evaluating measures are key elements in the SUMP. Monitoring and reporting transporta-tion data is generally important for any justifica-tion for using tax money for new infrastructure or other measures to foster bicycle use (e.g. cam-paigns, increasing parking facilities, development of strategic planning documents, etc.).

Figure 2: Workshop during mobile2020 seminarSource: Tomáš Kažmierski


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1. introduction

“With the goal of limiting climate change below 2°C [...] the EU needs to reduce emissions by 80-95 % below 1990 levels by 2050 [...]”7. “[A] reduction of at least 60 % of GHGs by 2050 with respect to 1990 [...] is re-quired from the transport sector, which is a significant and still growing source of GHGs”8. The decision of the European Commission is based on the Report about The Economics of Climate Change by the British economist Nicholas Stern9 and the Synthesis Report by the Inter-governmental Panel on Climate Change IPCC10.

The reduction of GHGs is a major challenge for trans-portation policy. The transportation sector contributes approximately a quarter to CO2-emissions11. In order to reduce atmospheric CO2-emissions there are three main options12:

• Reduction of kilometres travelled by car.

• Mode shift from car use to alternatives (public transport, bicycle, foot).

• Reduction of emissions by the use of cleaner pro-pulsion technologies.

The first two options require policy measures that are closely related to the topic of the mobile2020 project. Necessary is a land use development to support short distances, local supply and measures to enhance non motorized modes. Huge investments are made by the EU, its member states and world-wide to foster re-search and development of more efficient passenger cars and alternative propulsion technologies like bat-tery electric vehicles (BEV), plug-in hybrid electric vehicles (PHEV) and fuel-cell electric vehicles (FCEV) using hydrogen as fuel. Trends that show a rethinking of transportation planning away from a car orient-ed transport system towards a multimodal transport system can be observed in many cities within the Eu-ropean Union and world-wide. Measures to enable multi-modal and intra-modal transport by different push and pull factors (e.g. extending public transport13, improving cycling infrastructure14, displacement of

7 European Commission (2011)8 European Commission (2011)9 Stern (2006)10 IPCC (2008)11 Achtnicht (2009, p. 1)12 ECF (2011)13 City of Tallinn, Estonia: free public transport (Fearnley 2013)14 City of London, UK: cycle super highways (Transport for London

2011)

passenger cars by imposing city entry fees15 or park-ing fees16, fostering car-sharing17 and bike-sharing18).

In European cities typically half of all private car trips are less than five kilometres19, so that a mode shift to cycling is quite a realistic option. Cycling is considered as the most efficient mode of transport considering the GHGs relative to VMT. Against this background a mode shift from passenger car to bicycle would contribute to CO2-reduction and reduce resource consumption (crude oil) within the local transportation system. Unfortu-nately most planners and politicians are not aware to what extent cycling measures can contribute to local CO2-reduction targets (such as -20 % until 2030 com-pared to 200820). To achieve these targets an increase in bicycle usage as a daily mobility pattern is a lever that can be very cost effective for communities.

Figure 3: Separate bicycle and pedestrian infrastructureSource: Tomas Hefter, Bolzano, Italy

Bicycle use mainly depends on the perceived “cycling climate” or “bicycle culture” that means the objective perception by individuals about all aspects of cycling e.g. safety, comfort, cohesion, service, parking facili-ties and communications21. The cycling climate can dif-fer very much between countries, regions, cities and even between districts within the same city. In some Western European Cities the bike share (share of real-ised single trips) of the transport system has reached a

15 City of London, UK: congestion charge (Prud’homme and Bocarejo 2005)

16 City of Munich, Germany: parking management (LH München 2006)

17 City of Berlin, Germany: 12 different car-sharing providers available (www.carsharing-berlin.de)

18 City of Paris, France: biggest bike-sharing scheme in the world: vélib’ (DeMaio 2009)

19 e.g. MiD (2010)20 European Commission (2011)21 Cycling climate does not refer to meteorological climate.

8

considerable number, e.g. Muenster, Germany (38 %), Copenhagen, Denmark (31 %), Groningen, Netherlands (31 %), Zwolle, Netherlands (30 %), Bolzano, Italy (29 %), Freiburg, Germany & Odense, Denmark (28 %), Ferrara, Italy (27 %)22. While Western and Southern Eu-ropean countries could gain the share of bicycle use, most of the Central and Eastern European countries suffer from very small bicycle usage. Shares of 1 % and smaller are reality in many cities, e.g. Vilnius, Lithua-nia (1 %), Warszawa, Poland (1%), Bucharest, Romania (1 %), Sofia, Bulgaria (1 %)20. A lack of appropriate cy-cling infrastructure, structured and strategic programs and sufficient budget makes cycling in these countries an unsafe and partly unpleasant adventure.

2. gHg-Emissions

The chemical reaction of carbon based fossil fuel com-bustion produces carbon-dioxide (CO2). CO2 is one gas from the greenhouse gas family which highly likely con-tributes to global warming in case their atmospheric concentration rises. From the chemical viewpoint one molecule of CO2 has the smallest impact on the green-house effect in comparison to other greenhouse gases. The greenhouse gas family includes methane (CH4), ni-trous oxide (N2O), hydrofluorocarbons (HFCs), sulphur hexafluoride (SF6) and perfluorocarbons (PFCs) and ozone-depleting substances (ODS) largely from sources other than fossil fuels including CFCs, carbon tetrachlo-ride, methyl chloroform, methyl bromide and halons. All those gases may contribute to global warming. Some have a shorter lifetime than CO2, others have a higher warming effect than CO2

23. Nevertheless CO2 exhibits by far the largest share of anthropogenic GHGs into the atmosphere24. The combustion of different fossil fuel produces CO2 in the following amounts:

• Diesel: 2.640 gCO2/ litre,

• petrol: 2.392 gCO2/ litre,

• liquefied petroleum gas (LPG): 1.665 gCO2/ litre,

• compressed natural gas (CNG): 2.252 gCO2/ kg (Low calorific gas); 2.666 gCO2/ kg (High calorific gas)25.

22 Figures taken from EPOMM website (EPOMM 2011)23 E.g.: CH4 = 25 CO2-equivalent (eq); N2O = 298 CO2-eq; ODS and

HFC = 133 – 14.400 CO2-eq; PFC, NF3 and SF6 = 7.000 – 23.000 CO2-eq (Montzka et al. 2011, p. 44)

24 Montzka et al. (2011)25 http://www.ecoscore.be/en/how-calculate-co2-emission-level-

fuel-consumption

CO2-estimations in transportation can be a difficult task due to boundary conditions and assumptions that have to be made. Different approaches exist to estimate emissions caused by transportation activities. Calcula-tion approaches for passenger cars differ mostly in tank-to-wheel (TTW), well-to-tank (WTT) and well-to-wheel (WTW) considerations. The TTW-approach considers only direct local emissions caused by the combustion of fuel to move the car. The WTT-approach considers emissions that emerge between exploration of crude oil, and charging the tank of the vehicle26. For gaso-line, these indirect emissions account for up to 17 % of the TTW-emissions27. The WTW-approach considers all emissions of WTT and TTW. Some approaches even try to consider the emissions produced during the produc-tion process of the vehicle itself or the construction, maintenance and operation of roads, etc.

Estimations become even more complicated, when we want to consider the fuel of a bicycle. The required en-ergy to move a bicycle by the cyclist emits no direct or local GHGs (no TTW-emissions). Emissions that emerge by producing, transporting and storing food (similar to WTT-emissions), can hardly be estimated. Food has dif-ferent calorific values and needs different amounts of energy for production. GHGs between vegetarians and non-vegetarians can also differ about one ton of CO2-eq per year28.

The purpose of this short summary is to raise awareness of how difficult it is to make reasonable and reproduc-ible assumptions in this area.

In this study we use a simple TTW-approach, since oth-er more detailed approaches would require too much information (which is not available) and profound re-search about GHGs which is not in the scope of this study.

3. Evaluation of Existing bicycle Projects

Sustainable urban mobility plans usually consider bicy-cle measures to improve infrastructural conditions. Bi-cycle tracks or lanes, parking facilities and other infra-structural implementations contribute to improve the situation for cyclists. Unfortunately proper evaluations of the modal split situation before and after implemen-tation of a certain measure are not performed. In or-der to review cycling projects (infrastructure, promo-

26 Exploration, production/refinement, transportation27 ECF (2011, p. 32)28 ECF (2011, p. 9 – 11)


9

tion, campaigns and implementation of better services) which were evaluated properly, we carried out a pro-found literature research and talked to consultants for bicycle planning and transportation scientists. Most of the realized projects have not been evaluated. Others have failed to define boundaries or too many measures were realized at a time, which makes it impossible to correlate effect and measure.

An unambiguous assignment of implemented measure and the increase of bicycle use is impossible. Addition-ally, the effect of measure depends strongly on the be-haviour of people and their willingness to change the current situation. Changing mobility patterns is a long term development and depends on many factors. The only proper way to investigate peoples’ motives as well as before and after is to conduct qualitative interviews with people who live in the neighborhood of the con-sidered area. Without an interview it is not possible to depict which measure had which effect.

A list with some data and criteria of the evaluated cities we could find, is given in appendix A. We listed only the cities that had at least some evaluation data available.

4. Evaluation Criteria

Due to the lack of properly evaluated projects we made a list of criteria that are necessary and required to monitor and evaluate any action or project to assess their effects - ecological and sociological. In order to conduct a proper project evaluation it is necessary to perform an inventory before the implementation starts. The inventory shall at least contain the criteria given in Table 1.

Figure 4: Speed reduction at pedestrican precinctSource: Klemen Gostič, Odense, Denmark

Surveying all these transport related data enables a proper monitoring. It also helps to assess the degree of target achievement for each criteria. After imple-mentation a second survey with the same criteria has to be conducted in order to compare the two results (evaluation). Here it is important that the 2nd survey is conducted a year or two after implementation, since mobility patterns adapt slowly to new situations.

5. available Data

The initial idea of this study was to collect data from cities participating in the mobile2020 project. We real-ized that data about transportation is quasi not avail-able in these cities. There is only data from the capi-tal cities but the number of inhabitants of these cities (<350.000) exceeds the scope of mobile2020.

Criteria Unit Type of surveyModal split no. of trips (separate for all means of transport) % of no. of trips Household survey, traffic census

Modal split VMT (separate for all means of transport) % of VMT Household survey

Installed cycle tracks km of paved track Measurement, construction plansInstalled cycle lanes km of marked/ painted lanes Measurement, construction plansSafety No. of accidents involving cyclists Police recordsInformation No. of campaigns PR-departmentService No. of installed parking facilities Counting, construction plansFixed Budget for bicycle projects EUR, fixed in strategic document SUMP, strategic documents

Reduction of noise emissions km of streets affected by noise > 70 dB (day); km of streets affected by noise > 60 dB (night) Noise survey

Table 1: Criteria for monitoring and evaluation of bicycle projectsSource: own compilation

10

Cityname Country Population Car [%] bike [%] PT [%] Walk [%] year in scopeTartu Estonia 103.000 28 5 27 40 2009 yesŠiauliai Lithuania 130.000 38 1 21 40 2008 yesPanevėžys Lithuania 115.300 38 1 21 40 2008 yesKlaipeda Lithuania 183.400 32 1 63 4 2007 yesPécs Hungary 158.000 35 1 41 23 2010 yesSzeged Hungary 169.000 22 9 47 22 2009 yesGyőr Hungary 130.478 41 7 26 26 2001 yesDebrecen Hungary 205.000 39 9 32 20 2006 yesSibiu Romania 155.000 31 2 16 51 2007 yesPloiesti Romania 230.000 22 1 40 37 2007 yesKoper Slovenia 24.600 51 6 34 9 2008 yesLjubljana Slovenia 265.900 58 10 13 19 2003 yesMaribor Slovenia 110.670 71 5 13 11 2002 yesKaunas Lithuania 358.000 20 19 60 1 2007 noGdansk Poland 456.967 39 2 38 21 2009 noBrno Czech Republic 404.345 15 2 63 20 2001 noTallinn Estonia 414.752 26 4 40 30 2009 noVilnius Lithuania 554.200 38 36 25 1 2011 noPoznan Poland 551.627 53 2 37 8 2000 noWarszawa Poland 1.702.000 24 1 54 21 2005 noKraków Poland 756.183 28 1 46 25 2010 noWroclaw Poland 632.996 42 4 35 19 2011 noBucharest Romania 1.940.000 24 1 53 22 2007 noSofia Bulgaria 1.600.000 54 1 33 12 2008 noBudapest Hungary 1.700.000 20 1 47 32 2011 no

Table 2: Modal Split data from EPOMM website (http://www.epomm.eu/tems/index.phtml)Source: own compilation

6. assumptions

Only when car rides are replaced by bicycle trips, a reduction of GHGs can be achieved. A simple increase of bicycle use that might result from additional bicy-cle trips for leisure activities for instance does not lead necessarily to less CO2-emissions. Passenger car trips have to be replaced by bicycle to be taken into account for a GHGs-reduction effect. Against this background knowledge about mobility behaviour and effects of modal shifts is required. MS and VMT for each means of transport are important indicators and have to be investigated profoundly. In regards to CO2 considering VMT is even more meaningful, because CO2-emissions can be derived from VMT.

Nevertheless, as mentioned before a clear definition of the boundary conditions and the assumptions has to be made. Findings from many transportation surveys30 are:

1. The promotion of cycling increases the absolute number of trips made with bicycles and increases the average distance covered by bike.

30 MiD (2010); SrV (2013); MOP (2010)

So these cities are not eligible for the mobile2020 proj-ect. The European Platform for Mobility Management (EPOMM29) provides real modal split data for some Cen-tral and Eastern European cities. The EU-funded proj-ect set up a database for 269 cities with over 100.000 inhabitants, 55 cities under 100.000 inh. and 12 non-Eu-ropean cities. The EPOMM Modal Split Tool (TEMS) pro-vides downloadable data about the number of inhabi-tants of each city and their MS of the different transport modes. Out of these 336 cities only 13 are in the scope of the mobile2020 project according to the number of inhabitants (s. Table 2). These data can be used to work with real input data from national cities or neighboring countries in case transportation data is not available at current state. Unfortunately not all mobile2020 project partner countries are represented in EPOMM database.

Further on we will describe general assumptions on which the calculations are based on, the required input data and the results.

29 The database was made with the support of Intelligent Energy Europe in the project EPOMM-PLUS.


11

2. The total number of trips (considering all modes of transport) and the VMT change only marginally31.

3. Bicycle trips can substitute journeys formerly con-ducted by car, by PT or by foot.

4. The GHGs-reduction is achieved by considering substituted VMTcar and VMTPT.

As shown we can try to break down CO2-emissions very detailed. Due to uncertainties in respect to indirect emissions and scarce data amongst participating cities, a detailed break-down is neither reasonable nor achiev-able. The tool shall give comprehensible results from the core transport data. In the consequence, we made assumptions to accustom the tool according to avail-able data, comprehensive results and easy usage:

assumption 1: indirect CO2-emissions are not taken into account.

As shown above uncertainties about indirect CO2-emis-sions exist. The added value would be marginal against the arising costs (time and personnel) to collect re-quired data. Additionally, the aspired user-friendliness would be handicapped by editing amounts of data.

Assumption 2: Bicycle trips are CO2-free.

CO2-emissions for bicycle production, maintenance and food for the cyclist are not taken into account - we consider riding a bicycle as CO2-neutral.

Assumption 3: Cycling promotion increases the total number of trips conducted by bike and in-creases the average trip length of bicycle trips.

Cycling promotion convinces and enables people to cy-cle more often and also to use the bicycle for longer distances. This leads to a higher total number of trips made by bike and increases the modal share of cycling. In the city of Vienna for instance cycling infrastructure was improved and the average travel speed could be increased amongst cyclists. The results show, that after five years cyclists can cover longer distances within the same time (trip length increased from 3,5 to 4,0 km within 5 years). Travelling time is crucial to people. If we can enable people to cover longer distances by bike within the same time span, people will use the bike for more occasions. In Germany 87 % of all cycle trips are shorter than 5 km32.

31 In this study we assume that VMT after modal shift will be the same.

32 DIFU (2014)

Assumption 4: The number of trips made by all modes of transport and the kilometres travelled with all modes of transport do not change.

Cycling promotion is about convincing and enabling people to fulfil their mobility needs by bicycle. Against this background we incorporate three considerations:

1. People replace car/PT/walking-trips by bicycle trips. All trips made by bicycle replace a trip made with another mode of transport. It might be pos-sible, that by using a bicycle another route would be chosen (e.g. cycling through the inner city in-stead of driving a road around), but we do not see a way to consider this within the calculation.

2. In reality there will be some additional bicycle trips due to limited transport capacity or recrea-tional use or the combination of both recreation-al and utility purposes. The number of additional bicycle trips is small, especially where the bicy-cle share is small. The carbon calculator does not consider additional trips.

3. People can also choose alternative destinations, e.g. a closer shopping facility. This might result in a reduced VMT. To incorporate a destination change is only possible by setting up a transport model. A transport model is able to recalculate destinations which are most probably chosen ac-cording to its attraction and mode of transport. To make things easy, we do not consider a destina-tion change.

7. CO2-Emission Calculation Toolkit

The excel-sheet (download: www.mobile2020.eu) is de-signed to estimate the potential CO2-reduction based on the amount of converted vehicle trips into bicycle trips. Reliable transport data was used to analyse mo-bility patterns and the use of different means of trans-port. Data is based on the MiD2008 and SrV mobility survey including more than 60.000 German inhabitant. We developed an Excel Spreadsheet, which uses the re-sults of this survey:

• MS (no. of trips in average per person and day),

• VMT (average trip length for each means of trans-port per person and day).

CO2-emissions are calculated by multiplying the conver-sion factor from combusted gasoline to CO2 and the av-erage consumption [gCO2/passenger-car-km] of the car fleet in the considered country with the share of car trips and the average trip length. The result is extrap-

12

olated to the total number of inhabitants of the city/region which is considered. Since emissions from pub-lic transport differ between systems, it is possible to enter an average emission value [gCO2/passenger-km]. The calculation follows the same rule as to calculate car-emissions. For values which are not available, de-fault values based on SrV and MiD2008 can be used in-stead. The values consider only trips of people living in cities with 10.000 to 350.000 inhabitant and trips that are shorter than 100 km.

In order to calculate a baseline scenario (reference val-ues) several data has to be provided and entered:

• Number of inhabitants

• Average number of trips per person and day (usu-ally approx. 333)

• Current average trip length (average of all means of transport; approx. 934 km))

• Current MS

• Current average trip length (for each means of transport)

• Average consumption of car fleet (National or lo-cal factor for gasoline considering national or local mix between gasoline and diesel powered cars)

• National primary energy factor for gasoline (if in-direct emissions of gasoline are to be considered)

• Average CO2 emission of public transport system (current mix of electricity)

The tool calculates the actual emissions from motorised vehicles (passenger cars and PT) using the provided fig-ures.

According to availability of data, different levels of de-tail can be achieved. In general, apart from the no. of inhabitants and the current modal split, default values can be used to give rough estimations. The simulation will be more reliable if the given default parameters are replaced with local data.

After having built this baseline scenario (as is), the targeted increase in cycle trips and average cycle trip length has to be set by the user. They are based on objectives and targets for CO2 emission reduction which are aspired by the city council, planners and pol-iticians. By entering the targeted emission values he results are simulated immediately. First hints for ap-propriate values can be found for instance in existing transport development plans (e.g. SUMP) or sustainable energy action plans (SEAP).

33 According to MiD2008 the exact average in Germany is 3,08 trips per day and person.

34 According to MiD2008 the exact average in Germany is 9,11 km per day and person.

Three different targets can be processed parallel, so effects can be compared easily.

After setting targeted increase of cycling trips and av-erage cycle trip length, the total number of VMT for all means of transport is calculated. The demand for traffic (number of trips and kilometres travelled) is considered to stay the same. Keeping the sum of VMT constant ad-ditional e.g. recreational trips which do not contribute to emission savings are not considered.

Increasing VMT by bicycle with constant sum of total VMT imposes decreasing the sum of VMT of all other modes of transport. The allocation of decreased no. of trips is carried out according to the share of the current modal split. The reduction is applied on the number of trips travelled by each mode and on the VMT per mode.

The new allocation of MS and VMT results also in a re-calculation of all trip lengths for each transport mode. With these numbers the savings of GHGs for each sce-nario are calculated separately. They are clearly repre-sented giving the results

• per day,

• per year and

• until 2020.

8. Example: Tartu, Estonia

In this chapter we will show how to use the tool with the help of real data of an existing city. The City of Tartu in Estonia serves as an average city with approx. 103.000 inhabitants (2009), representing a middle size city within the scope of the mobile2020 project35. From the EPOMM-website (www.epomm.eu) we downloaded the MS-values and the number of inhabitants for the city of Tartu. The modal split of the city of Tartu is as follows:

• car: 27,5 %

• PT: 27,5 %

• bicycle: 5 %

• pedestrians: 40 %

For missing data like average trip length, average fleet fuel consumption, etc. we used the default values. In this example we want to demonstrate CO2 savings by different plans that differ in ambition with the help of three different scenarios (compare Table 3).

35 Unfortunately, not a single participating city we approached could provide transportation data. This fact shows how much improvements in transportation planning, monitoring and evaluating transport is needed.


13

scenario 1:business as usual

scenario 2: Moderate promotion of bicycle use

scenario 3: Progressive promotion of bicycle use

MS (increase of bicycle use) 1 % 5 % 10 %VMT (increase of cycled trip length) 20 % 25 % 30 %

Table 3: Summary of scenariosSource: own compilation

Procedure

The procedure for the user is very straight forward and easy in application since MS© Excel© is used for the cal-culations. For the user only two steps are required:

step 1: general information

The user has to fill in the numbers inside the bluish shaded boxes on Spreadsheet No.1. Population and modal split at least are required. If there is no further data available default values can be applied. Figure 5 shows the input mask of Spreadsheet No.1. The boxes where values have to be entered respectively default values have to be selected are also shaded in blue.

CO2 Estimation

Input country: city: Estonia Tartu city-information: population: 103000 (total) avg trips a day: 3,08 (per person) avg trip length 9,11 km/trip

details (car): CO2 per liter fuel 2.475 g CO2/l avg consumption 8 l/100km details (public transp.): avg emissions 75 g CO2/km (per person)

Please use EITHER Modal Split of number of trips OR Modal Split of travelled km. In any case enter avg trip length.

Modal Split: number of trips Tartu avg trip length km car 28,0% car 13,74 cycling 5,0% cycling 2,02 public transport 27,0% public transport 4,9 walking 40,0% walking 0,87 Sum 100,0%

For best results, please, fill in all blue boxes! You can also choose the default

values.

Figure 5: Input mask (Spreadsheet No. 1)Source: own compilation

step 2: Targets

Once the input is done, the user has to switch to Spread-sheet No.2. There the target values for each scenario have to be entered. Basically, the percental increase of expected bicycle use has to be entered as well as the expected increase of cycle trip length. It is recommend-ed to use the given values for the increase of cycle trip length. Figure 6 shows the input mask of Spreadsheet No.2. Again the boxes that have to be filled with values are shaded in blue. The tables show the allocation of the shifted trips for each scenario, the new modal split as well as VMT for each mode.

14

step 3: results

Once all input data is entered, the tables in the lower part of Spreadsheet No.2 show all results for each sce-nario (compare table 4). The user can retrace all re-sults. The allocation of new modal split, the changes in

Modal Split avg. trip length

travelled km (total)

trips (total) Modal Split travelled km

cycling 5,00% 2,02 32.041 15.862 1,8%car 28,000% 13,74 1.220.486 88.827 68,5%public transport 27,000% 4,9 419.709 85.655 23,5%walking 40,000% 0,87 110.400 126.896 6,2%

100,0% 1.782.635 317.240 100,0%

Effect of cycling promotionscenario 1 scenario 2 scenario 3

1,0% 5,0% 10,0%20,0% 25,0% 30,0%

Changescurrent status new MS changes in

no. of tripsno. of trips

new trip length

newtravelledkm new

MStravelled km

change MStravelled

change in travelled km

cycling 6,0% 3.172 19.034 2,42 46.139 2,6% 0,8% 14.098 car 27,7% 935 - 87.892 13,77 1.210.657 67,9% -0,6% 9.829 - public transport 26,7% 902 - 84.753 4,91 416.329 23,4% -0,2% 3.380 - walking 39,6% 1.336 - 125.560 0,87 109.510 6,1% 0,0% 889 -

100,0% 0 317.240 - 1.782.635 100,0% 0,0% 0 -

current status new MS changes in no. of trips

no. of tripsnew

trip lengthnew

travelledkm new

MStravelled km

change MStravelled


cycling 10,0% 15.862 31.724 2,53 80.103 4,5% 2,7% 48.062 car 26,5% 4.675 - 84.152 14,11 1.186.978 66,6% -1,9% 33.508 - public transport 25,6% 4.508 - 81.147 5,03 408.186 22,9% -0,6% 11.523 - walking 37,9% 6.679 - 120.217 0,89 107.369 6,0% -0,2% 3.031 -

100,0% 0 317.240 - 1.782.635 100,0% 0,0% 0

current status new MS changes in no. of trips

no. of tripsnew

trip lengthnew

travelledkm new

MStravelled km

change MStravelled


cycling 15,0% 31.724 47.586 2,63 124.961 7,0% 5,2% 92.920 car 25,1% 9.350 - 79.477 14,54 1.155.704 64,8% -3,6% 64.782 - public transport 24,2% 9.016 - 76.639 5,19 397.431 22,3% -1,2% 22.278 - walking 35,8% 13.357 - 113.539 0,92 104.540 5,9% -0,3% 5.860 -

100,0% 0 317.240 - 1.782.635 100,0% 0,0% 0

inpu

tsc

enar

io 3

expected increase of cycle trips:expected increase of cycle trip length:

scen

ario

1sc

enar

io 2

Please, fill in target values!

Figure 6: Scenario input and modal split tables (Spreadsheet No. 2)Source: own compilation

the number of trips, the total amount of new trips and their allocation for each mode. Also the calculated new trip length for each mode, the total VMT, the changes in MS and VMT. Easy comparisons between targets and ecological effects can be made by the user. An easy as-sessment of the different targets can be conducted.


15

rEsUlTsEmissions before: 273 t CO2/day

99.694 t CO2/year

scenario 1 scenario 2 scenario 3

Emissions after:240 235 229 t CO2/day

87.608 85.894 83.631 t CO2/year

reduction of CO2: 33 38 44 t CO2/dayper year: 12.086 13.799 16.063 t CO2/yearuntil 2020 (7 years): 84.601 96.596 112.438 t CO2/ 7 years

Table 4: Results (Spreadsheet No. 2)Source: own compilation

In the lower part of Spreadsheet No.2 a summary of the total CO2 savings is given for each scenario. We see that depending on the scenario an increasing bicycle use by 1 % (scenario 1) to 10 % (scenario 3), can re-duce the CO2 emissions by approximately 33 tons - 44 tons per day (see table 4). That results into a saving of approximately 12.000 to 16.000 tons CO2 per year for a city like Tartu with approx. 100.000 citizens (table 4). The horizon of mobile2020 project is the year 2020. A very rough approximation for the 2020 give a reduction of about 84.000 to 110.000 tons36.

36 Improvements in passenger car propulsion technology are not considered.

After reviewing the results presented on Spread-sheet No.2, the user can switch to Spreadsheet No.3 (see table 4). Here all results of the modal shift for each scenario is presented with pie charts. The up-per chart shows the current modal split. The lower diagrams show the results for each scenario. The Spreadsheet No.3 is divided into two parts. The up-per part presents the modal split results, the lower part presents the VMT results.

16

5,00%

28,000%

27,000%

40,000%

Modal Split (current situation)

cycling

car

public transport

walking

6,0%

27,7%

26,7%

39,6%

Modal Split change (scenario 1)

cycling

car

publictransport

walking

10,0%

26,5%

25,6%

37,9%


cycling

car

publictransport

walking

15,0%

25,1%

24,2%

35,8%


cycling

car

publictransport

walking

1,8%

68,5%

23,5%

6,2%

Modal Split km travelled (current situation)

cycling

car

public transport

walking

2,6%

67,9%

23,4%

6,1%

Modal Split km travelled (scenario 1)

cycling

car

publictransport

walking

4,5%

66,6%

22,9%

6,0%


cycling

car

publictransport

walking

7,0%

64,8%

22,3%

5,9%


cycling

car

publictransport

walking

Figure 7: Pie charts MS and VMT for each scenario (Spreadsheet No. 3)Source: own compilation


17

9. Final Conclusions and lesson learnt

The overall reduction of GHG-emissions by shifting trips from passenger cars to bicycle contributes to CO2 sav-ings and to the achievement of the EU GHGs-reduction targets. With this study we developed an easy to use and handy Excel-based spreadsheet that visualizes the CO2-reduction-potential by entering some basic trans-port data and modal shift targets. The share of contri-bution to CO2 savings by bicycle use is not marginal. If levels of cycling in the EU-27 were equivalent to those found in Denmark, bicycle use would help achieve 12 to 26 % of the 2050 target reduction set for the transport sector, depending on which transport mode the bicycle replaces37.

Transportation data in Central and Eastern European Countries is still scarce. No participating city was able to provide the required data about modal split and VMT. What we realized during the research for proper evaluated projects is, that monitoring and evaluation is conducted very scarce and there is no general scheme and universal indicators that everybody should use.

37 ECF (2011)

So a proper comparison cannot be realized. Cities will be challenged in future to evaluate transportation data.

Those data are also an important justification for re-alizing projects from tax money. For further projects we recommend strongly to reserve a budget for mon-itoring und evaluation. This study presents criteria for monitoring and evaluating infrastructural project that a bicycle related.

Bicycles will play an important role within cities to reduce GHGs within the transport sector. But bicycle transport also benefits communities by helping:

1. to ensure mobility for lower income households,

2. to reduce noise,

3. to reduce all kinds of emission (PM, NOx, SOx, etc.),

4. to reduce the consumption of natural non-renew-able resources,

5. to increase road safety,

6. to increase quality of life and

7. to increase social interaction.

Figure 8: Bike racksSource: Andrej Klemenc, Ljubljana, Slovenia

18

references

Achtnicht, Martin (2009): German Car Buyers’ Willingness to Pay to Reduce CO2 Emissions. Discussion Paper No. 09-058. ZEW Zentrum für Europäische Wirtschaftsforschung GmbH. Mannheim. retrieved from (12/11/2012) ftp://ftp.zew.de/pub/zew-docs/dp/dp09058.pdf.

DeMaio, Paul (2009): Bike-sharing: History, impacts, models of provision, and future. In: Journal of Public Trans-portation, Vol. 12, No. 4, p. 41–56. DIFU (2014): Der NRVP 2020. Deutsches Institut für Urbanistik gGmbH. retrieved from (3/28/2014). http://www.nationaler-radverkehrsplan.de/nrvp2020/.

ECF (2011): Cycle more often 2 Cool down the planet ! Quantifying CO2 savings of Cycling. retrieved from (3/27/2014) http://www.ecf.com/wp-content/uploads/ECF_CO2_WEB.pdf.

EPOMM (2011): European Platform on Mobility Management. TEMS - The EPOMM Modal Split Tool. retrieved from (3/26/2014). http://www.epomm.eu/tems/.

European Commission (2011): White paper, Roadmap to a single European Transport Area, Towards a competitive and resource efficient transport system. SEC (2011) 359 final, SEC (2011) 358 final, SEC (2011) 391 final. Brüssel: COM (2011).

Fearnley, Nils (2013): Free Fares Policies: Impact on Public Transport Mode Share and Other Transport Policy Goals.

http://stage.nature.org/greenliving/carboncalculator/index.htm.

http://www.atmosfair.de/.

http://www.carbonneutral.com.au/carbon-calculator.html.

http://cphpost.dk/news/local/city-delays-new-bicycle-sharing-scheme.

http://www.ecoscore.be/en/how-calculate-co2-emission-level-fuel-consumption.

IPCC (2008): Climate Change 2007. Syntheses Report. Intergovernmental Panel on Climate Change IPCC (Eds.).

LH München (2006): Verkehrsentwicklungsplan. Unter Mitarbeit von Friedrich Koppen, Maria Greis und Michael Mar-tin. Landeshauptstadt München Referat für Stadtplanung und Bauordnung (Eds.). retrieved from (3/26/2014). www.muenchen.de/plan.

MiD (2010): Mobilität in Deutschland 2008. Ergebnisbericht. Struktur – Aufkommen – Emissionen – Trends. Unter Mitarbeit von R. Follmer, D. Gruschwitz und B. Jesske et al. Bau und Stadtentwicklung Bundesministerium für Verkehr (Eds.). infas Institut für angewandte Sozialforschung; Deutsches Zentrum für Luft- und Raumfahrt DLR. Bonn and Berlin. retrieved from (02/04/2014). http://www.mobilitaet-in-deutschland.de/pdf/Nutzerhandbuch/MiD2008_Er-hebungsunterlagen.pdf.

Montzka, S. A; Dlugokencky, E. J; Butler, J. H. (2011): Non-CO2 greenhouse gases and climate change. In: Nature, Vol. 476, No. 7358, p. 43–50.

MOP (2010): Das Deutsche Mobilitätspanel. Karlsruhe Institute of Technology (KIT); tns infratest. retrieved from (08/26/2011). http://mobilitaetspanel.ifv.uni-karlsruhe.de/en/studie/methode/vorteile-der-pan-el-und-laengsschnitterhebung/index.html.

Prud’homme, Rémy; Bocarejo, Juan Pablo (2005): The London congestion charge: a tentative economic appraisal. In: Transport Policy, Vol. 12, No. 3, p. 279–287.

SrV (2013): Moblität in Städten - SrV. TU Dresden, Omnitrend. retrieved from (3/28/2014). http://tu-dresden.de/die_tu_dresden/fakultaeten/vkw/ivs/srv/2013.


19

Stern, Nicholas (2006): Stern Review: The Economics of Climate Change. Executive Summary. retrieved from (12/13/2014). http://www.hm-treasury.gov.uk/d/Executive_Summary.pdf.

Transport for London (2011): Barclays Cycle Superhighways Evaluation of Pilot Routes 3 and 7.

20

appendix

Evaluated Cities:

Odense, Denmark

• Official National Cycling City from 1999 to 2002

• Additional 20 million DKK (ca. 2,7 million EUR) be-tween 1999 and 2002 /3,6 EUR per habitant and year

• Infrastructural measures and campaigns

• Increase of bicycle journeys by 24 % and fewer ac-cidents

• 50 projects; e.g.:

• Improvements of traffic lights and junctions crossings

• Website• Trip planner• Improvements of cycle tracks• More and safer parking facilities• Publicity campaigns• Evaluation

Cycling Demonstration Town, England

• Infrastructural measures and campaigns

• 6 Medium sized cities with between 100.000 and 280.000 inhabitant

• 600.000 EUR per year and city

• Average increase in number of trips by 27 % (poor evaluation method)

• Different effects in each city: increase between +2,4 % and +56,8 %

Traffic Development, Vienna

• 30 Mio. EUR for 5 years from 2003 to 2007

• 1,8 million inhabitant

• 3,3 EUR per inhabitant and year

• Network, bicycle infrastructure and bike parking

• Rise in Modal share from 2 % in 2002 to 6 % in 2011

Traffic development plan Region Hannover „Pro Klima“, Germany

• Estimation about CO2-reduction by a traffic model

• 10 % reduction of city´s CO2-emissions of regional passenger traffic

• Regional cycle plan for the next 10 years

• Change in model parameters

• No estimation of costs / efforts

Bicycle Account Copenhagen, Denmark

• Survey and Counting

• Around 20 Mio. EUR per year38

• 40 EUR per inhabitant

Cycling promotion in Leipzig

• 5 % in 1989 to 14 % in 2010

• Quadruple of cycle tracks to 378 km from 1989 to 2010

• 1.000 bicycle racks in the city centre and 500 rest of city

• Bicycle commissioner

• 3 – 4 Mio. EUR per year

• 530.000 inhabitant

• 5,6 – 7,5 EUR per resident

38 http://cphpost.dk/news/local/city-delays-new-bicycle-sharing-scheme

2014

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