The Potential of Electric and Non-Electric Bicycles to Reduce Energy Consumption and Emissions in Private Transport

The potential of electric and non-electric bicycles to reduce energy consumption and emissions in private transport

Simon Árpád Funke & Patrick Plötz Fraunhofer Institute for Systems and Innovation Research ISI Breslauer Strasse 48 76139 Karlsruhe Germany [email protected] [email protected]

Keywords Introduction electric vehicles, bicycle-lane, transport Electric mobility is widely acknowledged to be a necessary instrument for supporting environmental protection and thus an Abstract instrument that can help to slow down climate change (IEA 2012). To meet emission reduction targets such as the EU Electric vehicles are widely considered as an important future climate and energy targets for 2020 a reduction of transport option to reduce green house gas emissions from car traffic related emissions appears inevitable. For that reason several due to their efficient propulsion technology and the possibil- regulatory measures have been installed to reduce these emis- ity to be charged from renewable energy sources. However, sions (e.g. EU regulation No 443/2009). This regulation limits replacing car trips by bicycle trips in principle allows a strong the emissions of European cars in the future and it therefore reduction of fossil fuel demand in private transport even with could be an instrument to force the market entrance of electric currently available technologies. Two factors of current devel- vehicles, since these are regarded as zero emission vehicles (see opment increase the likelihood of this option: the introduc- Funke, Plötz 2012). tion of electric bicycles and the construction of bicycle fast In contrast to political efforts, the share of electric vehicles lanes. The former have already reached significant sales shares is still negligible today. In Germany, electric vehicles make up in different European countries and the latter are currently less than 1 % of all vehicle registrations (KBA 2011). This is on being introduced as special bicycle “motor ways” in differ- one hand due to their higher price and on the other hand due ent regions of Europe. Both increase the likelihood of slightly to the fact that this vehicle type seems not yet fully accepted longer trips to be performed by bicycle. However, the real in the population due range limits and the long charging time potential for CO2 mitigation and possible substitution rates (Dütschke et al., 2012). (substitute car trips by bicycle trips) are not properly under- In contrast to electric vehicles, bicycles are a well-established stood yet. Here, we combine different statistical sources to es- means of zero-emission transportation. They are easy to use timate the potential of increased bicycle driving and the pos- and disposable for almost everyone. In Germany ca. 80 % of the sible reduction of energy use in private transport and green households own at least one bicycle (Sinus 2011), the number house gas emissions. Using a large data set of German vehi- of bicycles in Germany sums up to an estimated number of cle usage we analyse their driving behaviour in terms of the 70 million (Bundesregierung 2012) clearly outnumbering the distribution of trip lengths, working trips and their weather 42 million cars. Bicycles have the potential to reduce trans- dependence. Our analysis shows that electric and non-electric portation related emissions, when bicycles are used instead two-wheelers have the potential to mitigate traffic and emis- of internal combustion engine vehicles. An existing potential sions in densely populated regions. for a higher bicycle usage has been suggested earlier (Follmer, undated). The potential for higher bicycle usage could be increased by current developments for a higher comfort of bi-

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16%

14% Netherlands Switzerland 12% Germany 10%

4% Sales share Sales bicycles electric 2%

0% 2003 2004 2005 2006 2007 2008 2009 2010 2011

Figure 1. Sales shares of electric bicycles (pedelecs) in different European countries. cycle riding. We take electric bicycles and the installation of Data and Assumptions bicycle fast lanes to be two central drivers of a change in mobil- To understand the substitution potential of car trips due to a ity behaviour to a higher bicycle use. higher bicycle share, we study German mobility patterns. We Electric bicycles do not have limitations in comparison to extract bicycle trips in a range up to 25 kilometres and deter- non-electric bicycles. The electric driving range poses in fact mine the modal split for these trip lengths. We focus on work- no limitation, the cost of the battery is due to their limited size ing trips which should show high substitution potential. We relatively low and an electric bicycle for most of the people af- analyse different weather conditions and seasonal circumstanc- fordable. Sales figures in Germany for electric bicycles substan- es to estimate the variability in user behaviour under changing tiate the importance of electric bicycles. In 2007, 70,000 elec- weather conditions. The main goal of the paper is not to analyse tric bicycles were sold, while this number has risen to 200,000 seasonal variations in bicycle use itself, we only study seasonal in 2010, reaching a market share of approximately 5 % in the variations as a measure for quantification of the variability of year 2010 (ZIV 2012). Even higher sales shares of electric bicy- bicycle usage. The varying share of bicycle use is than assumed cles were reached in other European countries (see velosuisse to be of the same order of magnitude as the substitution of car (2010), bike-EU (2010) and Figure 1). trips by bicycles. The CO2 savings based on this assumption will A second driver could be the installation of bicycle fast then be calculated. lanes. They are usually between three and 15 kilometres long We use the dataset “Mobilität in Deutschland 2008” (Mobil- and are meant to shorten bicycle travel time for the respective ity in Germany 2008, see MiD 2008). The dataset stems from distances. In the Netherlands the potential of bicycle fast lanes a representative nation-wide survey on private mobility, which was recognised many years ago. Many bicycle fast lanes exist questioned 25,922 households with 60,713 individuals and already and the installation of further lanes is planned. But also 34,601 cars for one qualifying day (MiD 2008). The households in Germany their construction is planned, e.g. in the region kept record of all their daily trips with the corresponding dis- Hannover-Braunschweig-Göttingen-Wolfsburg or in the Ruhr tances, durations, the modal choice and much more. In total, district (Forschung Radverkehr 2010). They could lead to a over 190,000 trips were recorded. For scientific purposes the higher bicycle use as a consequence of a shorter travel time and dataset is freely available, for commercial use the dataset has to the higher security of bicycle driving. be purchased. However, the main results are publicly available The focus of the present paper is to analyse the potential of (see MiD 2008). In the dataset, a total of ca. 2.1 million kilome- a higher bicycle modal split in commuter trips and to quantify tres of trips are reported. Table 1 summarises some statistics of the resulting emission reduction potential of substituted car the data set. As the focus of this work is to evaluate the poten- trips. The paper is organised as follows. The following section tial of replacing car trips by bicycle trips, the statistics focus on introduces the data set used for our analysis. In the substitution these aspects. potentials section a way to determine a change in commuting Electric bicycles are considered to be one possible driver for behaviour is shown, as well as our main results, namely the a higher bicycle share in the modal split, as they make bicycle higher share of bicycles in the modal split of commuter trips. riding less exhausting and therefore should allow for longer As a next step, in the reduction potentials section, we show the distance trips (Paetz et al., 2012). In the ongoing analysis of the resulting emission reduction as a consequence of a change in substitution potential of car trips by bicycle trips, only pedal travelling behaviour. We conclude by summarizing the results electric bicycles, in the following shortly pedelecs, are consid- presented. ered to be a substitute for the conventional car. With pedelecs, driving without pedalling is not possible since the electric motor only serves as a support for the cyclist. In contrast, other

1062 ECEEE 2013 SUMMER STUDY – RETHINK, RENEW, RESTART 4. Transport and mobility: How to deliver energy efficiency 4-342-13 Funke, Plötz electric bicycles, like S-Pedelecs or e-Bicycles, which allow for Considering the fact that the central 50 % of bicycle commuter a driving without the need for pedalling and thus have more trips range between 1 and 3.4 kilometres (Table 1) and the rela- the characteristics of (small) motorcycles, are not considered as tively low driving distances of cars (25 % of car trips to work substitute and are therefore not part of the our analysis. are shorter than 3.8 kilometres, see Table 1), this suggests a high Table 1 shows the distribution of distances driven by car and substitution potential, which has to be investigated in more de- by bicycle. It is not surprising that the car is used for longer tail. Especially for commuter trips in urban areas, the bicycle distances which can be seen at high maximum distances and a is a suitable vehicle. According to (UBA 2009) the bicycle is higher average distance for car trips in comparison to bicycle the most time efficient vehicle in cities when driving a distance trips. Although this last fact being intuitive, the data also re- below 6 kilometres. This is underlined by the fact that the share veals that higher distance car trips are more of an exceptional of bicycle drivers for intra-municipal commuter trips is signifi- kind. Firstly, the upper quartile indicates that e.g. a 75 % of car cantly higher than for commuter trips in general (Statistisches trips to work are shorter than 20.8 kilometres (see Table 1) and Bundesamt 2005). are thus in a range that could theoretically be feasible with an electric or conventional bicycle. Secondly, the same fact is in- Data preparation and processing methodology dicated by the difference between the median and the average The available data was analyzed by using different classes of value. While the average value considers all data points pro- distances in the way that clusters of distances were created portionally to the data value and is therefore more sensitive for with a class-width of 1 kilometre up to a maximum distance of outliers, the median indicates the value that divides the sorted 25 kilometres. The underlying assumption for the choice of this dataset into two parts in a way that 50 % of the data is below the maximum distance is that the majority of commuter routes by median and the other 50 % above. The median is therefore less bicycles and pedelecs is shorter than 25 kilometres per route. sensitive to outliers. The remarkably higher average value for Over 80 % of commuter routes by bicycle are supposed to be car trips in relation to the respective median thus underlines shorter than 25 kilometres and for pedelecs this share is ~75 % the fact that longer distance trips form a minor part of all trips (Paetz et al. 2012). In addition, with an electric driving range made by car. of 40 to 50 kilometres for pedelecs, see e.g. (NOW 2012), they Another point that has to be made is that the mean distance allow for a one way distance of 20 to 25 kilometres without of bicycle trips ranges clearly below a distance of 5 kilometres. recharging, corresponding to the assumed maximum distance. Assuming an average speed of 10–15 kilometres per hour for For further discussion the analysis focuses on these distances bicycle riding (see e.g. Emberger 2009), a distance of 5 kilome- shorter than 25 kilometres as it is assumed that substitution ef- tres is equivalent to a trip duration of 20 to 30 minutes, which is fects of bicycle riding concentrate on this driving range. in accordance to the fact that ~70 % of the German commuters For each class, the share of the transport volume was de- get to work within 30 minutes (Statistisches Bundesamt 2009). termined as a proportion of the total transport volume of the

Table 1. Descriptive statistics of trip lengths (all distances in km).

Lower Upper Trips Min Median Max Avg. SD Quartil Quartil All Trips (N=191,610) 0.05 0.98 3.33 9.5 950.00 11.23 36.61 Bicycle (N=19,133) 0.1 0.98 1.86 2.94 196.00 3.22 6.51 Car (N=110,287) 0.1 2.4 5.7 14.3 950.0 14.92 40.39 All Trips to work (N=50,011) 0.1 2.0 6.3 16.2 902.50 15.91 42.03 Bicycle (N=4,965) 0.1 1.0 2.0 3.4 49.00 2.99 3.33 Car (N=29,581) 0.1 3.8 9.5 20.8 855.00 19.24 44.46 Trips to work, good weather (N=23,018) 0.1 2.56 7.6 18.05 902.5 17.14 42.27 Bicycle (N=2,487) 0.1 1.18 1.96 3.92 44.10 3.28 3.68 Car (N=14,490) 0.1 3.9 9.5 21.9 855.0 20.00 43.55 Trips to work, bad weather (N=17,833) 0.05 2.71 7.6 18.05 798.00 17.24 42.15 Bicycle (N=1,503) 0.1 0.98 1.96 3.92 49.00 3.10 3.38 Car (N=11,269) 0.2 3.8 9.5 20.9 798.0 19.60 42.77 Trips to work, spring (N=11,042) 0.1 1.96 6.65 17.1 902.5 16.32 42.56 Bicycle (N=1,124) 0.2 0.98 1.96 3.92 27.44 3.03 2.94 Car (N=6,483) 0.1 3.8 9.5 21.9 855.0 20.07 45.15 Trips to work, summer (N=9,762) 0.05 2.38 7.13 17.1 855 16.56 38.88 Bicycle (N=1,272) 0.2 1.445 2.16 3.92 44.10 3.42 3.85 Car (N=5,858) 0.1 4.3 9.5 22.2 855.0 20.45 42.72 Trips to work, autumn (N=14,400) 0.1 1.9 5.7 15.2 855 14.70 39.74 Bicycle (N=1,477) 0.1 0.98 1.96 3.43 49.00 2.85 3.57 Car (N=8,622) 0.1 2.9 8.0 19.0 855.0 17.66 42.17 Trips to work, winter (N=14,807) 0.09 1.96 6.33 16.15 812.25 16.35 45.63 Bicycle (N=1,092) 0.1 0.98 1.96 2.94 24.50 2.67 2.59 Car (N=8,618) 0.1 3.8 9.5 20.0 807.5 19.38 47.21

ECEEE SUMMER STUDY proceedings 1063 4-342-13 Funke, Plötz 4. Transport and mobility: How to deliver energy efficiency respective vehicle. The analysis was made for trips by car and bicycle driving for the different seasons and in dependence of by bicycle separately. As commuter routes are assumed to have the weather conditions to deduct a potentially higher modal the highest substitution potential due to e.g. load limitations of split for bicycle use. the bicycle for shopping purposes, see for example (Bergström, Magnusson 2003), trips with the purpose “work or education” Fluctuations in modal split are of central interest. In addition, commuter trips are made The following diagram (Figure 2) depicts the modal split (ve- regularly every day, the emission reduction potential of substi- hicle kilometres) in dependence of the distance for all report- tuting car commuter trips by bicycle commuter trips is there- ed trips and in comparison for trips to work (Figure 3). The fore promising. distribution of the modal choices is very similar for both trip The share of the investigated driving ranges (routes shorter purposes, but the share of bicycle trips to work is higher than than 25 km) sums up to a 40 % of the total transport volume the respective share for all trips. Nevertheless, the importance stated in the dataset and thus forms a remarkable proportion. of the car for work trips is comparable with the conclusions With regard to the two examined vehicles, the car and the bi- drawn in the previous section. It is the most important mode cycle, the modal split of these two driving modes form a major for all trip length although the modal split for the car is lower part of vehicles being used for the stated distances. Figure 1 for trips to work than in general. We thus assume that limit- depicts that about 80 % of the total transportation volume for ing the substitution potential to work trips reduces the risk of all trips below 25 km are made by car or by bicycle. With longer overestimating the overall potentials. distances, the bicycle loses share, with a modal split up to 20 % As stated before, the differences in seasonal mobility patterns for trips within a range of 2 kilometres falling below a modal are regarded as a good possible measure to determine the share share of 3.5 % for distances longer than 10 kilometres. Walking of car drivers that could transfer to the bike if the overall con- is a preferred mode for very short trips and part of the “other” ditions for bicycle driving were improved. Figure 4 shows the section in Figure 2. differences in mobility patterns of cycling commuter trips for Weather conditions are assumed to be essential for modal the different seasons. Depicted are trips with a distance up to choice, especially regarding due to insufficient weather protec- 15 kilometres, for longer distances the modal share of bicycles tion for bicycle riding. Table 1 shows that the median of bicycle is not relevant. Additionally, travelling time seems to be one of routes to work under bad and good weather conditions are the the most important factors for commuters who never use the same. Furthermore, the trip length statistics for autumn and bicycle, see (Bergström, Magnusson 2003). Conversely, higher winter trips is similar with trip length generally shorter than distances are not likely to be made by bike. for trips during spring time and summer with the summer time The differences in seasonal mobility patterns observed in being the most attractive for bicycle riding. While the number Figure 4 are not surprising. While bicycle commuting in sum- of bicycle trips in winter has a share of ~7.5 %, in spring and mertime is apparently more attractive than in other seasons, autumn this share totalizes to ~10 % and in summer even to notably for distances up to four kilometres, the winter time is 13 %. It is hypothesised that under better road conditions, the the less attractive for bicycle riding. Mobility patterns in spring distribution of bicycle riding patterns could be influenced in a and autumn are close to the all-year average (indicated by the way that bicycle riding could be more attractive, especially in dotted line), spring mobility patterns being above the average cold seasons. A more detailed investigation will be carried out for small distances while autumn mobility pattern being below in the next section of this paper. the average. Above a distance of ten kilometres, the mobility To summarise, the car is the most important vehicle for all patterns of all seasons except for the summer season reduce to distances, even for short trips. With regard to bicycle trips the a bicycle modal split below 5 %. summary statistics of trip lengths is similar but not identical for But how can these differences in seasonal mobility patterns good and bad weather conditions in general. This suggests that be an indicator for substitution effects? Reasons for differences there is a constant proportion of bicycle drivers who drive the in seasonal patterns are probably due to temperature differenc- bicycle the whole year (see also Bergström, Magnusson 2003) es, the higher probability for precipitation and road conditions. as no tendency towards shorter trips under bad weather con- According to (Bergström, Magnusson 2003), these factors are ditions is observable. During summer time however, bicycle the most important obstacles for bicycle drivers who only ride riding is remarkably more attractive than in other seasons, es- the bicycle during summer. They also state that about 40 % of pecially in winter. If summer mobility patterns of bicycle use the respondents claimed to cycle more in winter when road could be transferred to winter mobility patterns, this would maintenance was improved. This underlines the opinion that imply a large theoretical substitution potential through an in- under better road conditions a higher bicycle modal split is creased bicycle use, which has to be evaluated and quantified in realistic. the following parts of this work. Calculation of substitution potentials For the substitution potentials, two effects are considered. Substitution Potentials First, it is assumed that a general higher share, i.e. a higher In the following characteristics of German mobility patterns modal split, for bicycles is possible, for example due to better are shown with a special focus on bicycle use. As explained maintenance of bicycle lanes and thus a higher modal split for in the previous section, mainly trips to work are considered bicycles especially in the winter season. This effect is denomi- in the analysis for the substitution rates. For the substitution nated the “general substitution effect”. Another effect that will potential we assume that a change in seasonal bicycle driv- be examined is the effect that e.g. as a result of a higher share ing behaviour is possible. We compare the modal shares of of pedelecs, a longer distance for bicycle-commuters appears

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All trips < 25 km 100%

90%

80%

70%

60%

50%

ModalSplit 40% 30% other 20% car

10% bicycle

0% 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 ------< 1 < ------1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Distance in km

Figure 2. Modal split as a function of distance. Shown are the kilometre dependent shares of different means of transportation: bicycle (blue), car (red) and others (green). Others include walking and public transportation (bus, railroad and airplane).

Trips to work < 25 km 100%

90%

80%

70%

60%

50%

ModalSplit 40%

30% other 20% car bicycle 10%

0% 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 ------< 1 < ------1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Distance in km

Figure 3. Modal split of trips to work as a function of distance. Shown are the kilometre dependent shares of different means of transportation: bicycle (blue), car (red) and others (green). Others include walking and public transportation (bus, railroad and airplane).

feasible. This effect is called the “longer distances effect”. In important reasons not to take the bicycle are safety reasons, see the following section, these two effects are assessed and quan- (Sinus 2011). tified. According to the data, see Figure 4, there is a share of com- For the general substitution effect, seasonal differences are muters who drive the bicycle the whole year. This share is considered as the determining parameter. A higher modal present in the winter mobility pattern. The distribution of the share could be regarded as a reason for an increased bicycle use other seasonal mobility patterns can be interpreted as com- in some seasons or of a general higher share in all seasons of the posed of these winter drivers and a share of cyclists who choose year. Especially winter time could be considered to have a high the commuting vehicle depending on seasonal circumstances. substitution potential. For example, Bergström and Magnusson One part of this variable share could consist of cyclists who (2003) report that 38 to 43 % of the interviewed commuters ride the bike the whole year except for the winter time. Be- would cycle more during winter, if maintenance of roads was sides a general lower bicycle modal split in winter (compared improved. Although this result counts for Swedish surround- to autumn), the bicycle is not used during winter for shorter ings, a tendency to a higher bicycle use could also be expected distances (Figure 4). The mobility pattern for spring time is in for other regions. A German survey states that one of the most the range of autumn mobility patterns with a somewhat higher

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Bicylce trips to work by season

45%

40% spring summer autumn winter yearly average 35%

30%

25%

20%

Modal Split bicycle [km] bicycle Split Modal 15%

10%

0% 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Distance in km

Figure 4. Modal split of bicycle trips to work as a function of distance and season.

bicycle share. With these two seasons representing not too ex- Substitution Potential (∆# trips) = ¼(modal split of bicycles in summer - modal split of bicycles in spring) treme weather conditions, the difference between the two mo- + ¼(modal split of bicycles in summer - modal split of bicycles in summer) bility patterns could serve as an indicator for natural variability + ¼(modal split of bicycles in summer - modal split of bicycles in autumn) in bicycle usage behaviour. In summer time, as expected, the + ¼(modal split of bicycles in summer - modal split of bicycles in winter) bicycle modal split is highest, indicating a variable share of bi- As explained in the previous sections, especially very short cycle commuters who cycle only in summer. trips have a great potential to be substituted. This is also visu- alized in Figure 5. For distances in a range from one to three Maximal scenario kilometres, the potential sums up to an additional bicycle With regard to the substitution potential of car trips, the bi- modal share for working trips of eight to twelve percent. This cycle modal share for work trips in summer is estimated to be means that for the stated distances up to twelve percent of the maximum possible bicycle share. This approach considers the trips to work could be shifted from car driving to bicy- all restrictions to bicycle substitution potential. For example, cle driving. This suggest significant emission reduction po- a person who is dependent on the car as a company car is tentials (see section ‘Reduction potentials’). But also for all not likely to commute by bicycle as this would mean to leave other ranges a potential of around one to two percent can be the car at the company and to transfer to the bicycle. The seen. For these longer distances, pedelecs seem predestined data shows that this is not likely to happen (possibly due to since they are probably perceived as more comfortable than a significant loss of comfort). But as such a person will nei- conventional bicycles and therefore could balance comfort ther commute to work by bicycle in summer such effects are losses of bicycle riding in colder seasons. Negative values in taken into account when using the summer modal split as the the potential are due to fluctuations in the used data set. As maximum potential for bicycle commuting. One scenario for the calculation of the substitution potential does not balance the substitution potential is to assume that the bicycle modal these irregularities, they also have full impact on the repre- split for the total year is equivalent to the maximum bicycle sentation of the potential analysis. modal split in summer. In other words, it is supposed that the summer mobility pattern for bicycle riding is also valid Conservative scenario for all other seasons. This scenario will be coined maximum A second, less ambitious scenario is to consider certain vari- substitution potential scenario. ability in mobility behaviour as an indicator for a potential shift Figure 5 depicts the share of car trips that could be substitut- towards a higher bicycle share. According to the explanation ed according to this maximum substitution potential scenario. above, this variability could be seen as indicated by the differ- The graph shows the difference between summer bicycle modal ence between spring and autumn mobility patterns. split and the other seasons according the formula: Figure 6 shows the result for the substitution potential underlying the moderate scenario. As mentioned earlier, fluc-

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Maximum substitution potential 12%

10%

0% Potential of addiotional bicycle share in modal split [# of trips]of split[#modal sharein bicycle addiotional of Potential -2% 2 3 4 5 6 7 8 9 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 10 ------< 1 < ------1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Distance in km

Figure 5. Maximum substitution potential as a function of distance.

Substitution potential - moderate scenario 8%

-2%

Potential of addiotional bicycle share in modal split [# of trips]of splitmodal[# sharein bicycle addiotional of Potential -4% 2 3 4 5 6 7 8 9 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 10 ------< 1 < ------1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Distance in km

Figure 6. Substitution potential in the modest scenario as a function of distance.

tuations in the dataset can lead to some negative substitution metres seem to have a significant potential to be driven by bike, potentials. The reason might be the finite amount of data used. longer distances are not probable to be made by bike. The negative potential for short distances can be therefore seen With regard to the longer distance effect, two measures are as mistakes due to the characteristics of the original dataset. possible. The first is to assume a general shift towards longer The more important here are the positive values that indicate distances corresponding to the difference in the mean values of a substitution potential of 2–4 % higher bicycle share in com- the different seasons as carried out in the ‘reduction potentials’ muting trips for short distances. In comparison to the maxi- section. Furthermore, according to (Statistisches Bundesamt mum substitution potential, a common characteristic is that 2009), the trip length of commuter trips has grown in the past both substitution potentials have the character of a positively years. If this trend progresses, this means that he importance of skewed distribution. I.e. particularly for short distances the po- longer bicycle commuting trips could also rise. Pedelecs could tential is high. Furthermore, especially distances up to 15 kilo- be a possible solution to face this trend as it allows for longer

ECEEE SUMMER STUDY proceedings 1067 4-342-13 Funke, Plötz 4. Transport and mobility: How to deliver energy efficiency trips than a conventional bicycle does and therefore the impor- nual passenger kilometres travelled together with the modal tance of electric bicycles for the bicycle substitution effect is split between different transportation modes (walking, bicycle, given and could become even higher. car, public transport and others) and their average specific CO2 emissions. Table 2 summarises the passenger kilometres trav-

elled by different modes of transportation and the related CO2 Reduction Potentials emissions in Germany from (Dena 2012). The previous section was devoted to an analysis of the potential Table 2 demonstrates that the majority of traffic related car- for substituting car trips to work by bicycle trips. We consid- bon dioxide emissions in Germany stem from motorised in- ered the natural fluctuation of bicycle usage for different ambi- dividual traffic, i.e. mainly from cars. By multiplication of the ent conditions as a measure for the variability of bicycle usage specific CO2 emissions from motorised individual traffic and its for trips to work. For the following we assume that all vehicles traffic capacity, we estimate the total CO2 emissions to be ap- of motorised individual traffic are conventional fossil fuel based proximately 127 million tons which is in good agreement with vehicles. Since car trips with conventional vehicles emit carbon other estimates of 128 million tons (Dena 2012). We are thus dioxide while driving a change to bicycles implies a reduction confident that the following calculations are able to give the of traffic related CO2 emissions. In the present section we will right order of magnitude for the potential CO2 savings. roughly estimate the carbon dioxide savings potential by an We will start with a simple estimate based on the change increased use of bicycles instead of cars for trips to work or of the bicycles’ share on overall traffic capacity and will then work related. present a more complex analysis using the kilometre-depend- We will furthermore assume that all bicycles are zero emis- ent substitution potentials from the previous section. sion vehicles. For conventional bicycles this is clearly the case. However, pedelecs use electricity which does not necessarily Rough estimate stem from carbon dioxide free generation and thus additional By comparing the average trip lengths of bicycle trips to work electricity generation. But since bikes are much lighter than under different conditions we can estimate the potential change cars their specific energy consumption is much lower. Average of average kilometres driven to work by bicycles and their cor- energy consumptions for pedelecs are about 10 Wh/km (Lewis responding share in the overall passenger kilometres travelled. et al. 2011) resulting in approximate 5 gCO2/km with the Eu- Comparing the different average trip lengths for work-relat- ropean electricity mix with 500 gCO2/kWh (see Lewis et al. ed bicycle trips summarised in Table 3, we find that the aver- 2011). This has to be compared to the European car emissions age trip length increases by 0.75 km or 25 % from winter to of an average 140 gCO2/km. The specific CO2 emissions from summer (high scenario) and by 0.43 km or 14 % between the pedelecs are thus about 30 times lower than car emissions and all year average and summer (low scenario). Taking into ac- their emissions will be neglected in the following, by treating count that 24 % of all bicycle driven kilometres are work related all bicycles as zero emission vehicles. we find that the average distance of all bike trips increases to The overall carbon dioxide emissions from transport can 3.34 km for the high scenario and 3.27 km for the low scenario. be estimated using the total traffic capacity in terms of the an- We assume that all these new bicycle trips replace existing car

Table 2. CO2 emissions and traffic capacity for German passenger transport.

Mode of transport Specific emissions Traffic capacity CO2 Emissions 9 6 2010 [gCO2/pkm] 2009 [10 pkm] 2010 [10 tons] Motorised individual traffic 141 898.7 126.7 Air transport 114 58.5 6.7 Railroad 42 82.2 6.1 Public transport 74 78.9 5.8 Bicycle 0 67.4 0 Total 122 1186 145 The first and second column with average specific emissions and total traffic capacity are taken from (Dena 2012). The third column is computed based on these data.

Table 3. Average trip lengths for different German bicycle trips (data from Table 1).

Trips Sample size Average trip length [km] All bicycle trips 19,133 3.22 All work related bicycle trips 4,965 2.99 All work related bicycle trips in summer 1,272 3.42 All work related bicycle trips in winter 1,092 2.67 High scenario: Difference between summer and winter trips 0.75 Low scenario: Difference between summer and all trips 0.43

1068 ECEEE 2013 SUMMER STUDY – RETHINK, RENEW, RESTART 4. Transport and mobility: How to deliver energy efficiency 4-342-13 Funke, Plötz

Table 4. CO2 emissions and traffic capacity for German passenger transport.

Specific emissions Traffic capacity CO2 emissions Scenario Sector 9 6 [gCO2/pkm] 2009 [10 pkm] 2010 [10 tons] Base case Motorised individual traffic 141 898.7 126.7 Bicycle 0 67.4 0 others 85 219.6 18.6 Total 1185.7 145.3 High Scenario Motorised individual traffic 141 896.1 126.3 Bicycle 0 70.0 0 others 85 219.6 18.6 Total 1185.7 144.9 Saving compared to base case 0.37 Low Scenario Motorised individual traffic 141 897.7 126.6 Bicycle 0 68.4 0 others 85 219.6 18.6 Total 1185.7 145.2 Saving compared to base case 0.14

trips. Accordingly the total passenger kilometres travelled by Note the importance between the number of trips P(l) in a bike should increase by the same share as the average bicycle given interval [l, l+dl] and the distribution of trip length T(l), trip length, reducing in turn the total passenger kilometres they are connected as travelled by car. Multiplying these slightly modified traffic ca- l+d l pacities of the different modes with their specific CO emission T( l ) = sP ( s )d s. 2 ∫l factors we obtain slightly different total CO emissions form 2 passenger transport. We used a weighted average for the emis- The difference is basically the share of persons in a population sions from other modes of transportation. These calculations that drive (or walk) a certain distance compared to the share are summarised in Table 4. certain trips have on all kilometres driven. This is similar to Our analysis based on the increase in average bicycle trip the number of people with a given income compared to the length reducing the total passenger kilometres travelled by car share of these people of the total income of a population. This difference can be highlighted by a so called Lorenz plot (see leads to an estimated decrease in traffic related CO2 emission by approximately 0.4 million tons per year in the high scenario Drăgulescu and Yakovenko (2001) for a definition and Plötz and 0.14 million tons in the low scenario. We are confident and Fleiter (2012) for an application to energy related data) that these numbers give the right order of magnitude and con- with coordinates x and y defined as clude from this rough estimate that a reduction of the traffic related CO emissions by replacing car trips by bike trips by r r 2 P( s )d s sP( s )d s 0.1–0.4 million tons or 0.1–0.25 % seems realistic. ∫0 ∫0 x( r )= ∞ and y ( r ) = ∞ . P( s )d s sP( s )d s Detailed estimate ∫0 ∫0 We now turn to a more detailed estimate of the reduction potential using the kilometre specific substitution potentials de- The Lorenz plot for all trips of the data base under discussion rived in section ‘Substitution potentials’. We will sum over all here is shown as solid line in Figure 7. It clearly shows that a trips with their length in kilometres multiplied by the actual small share of trips is responsible for a large share of the total kilometres driven. For example, the data point at (0.9, 0.4) indi- kilometre dependent modal split and the mode dependent CO2 emission factors (see below). We can then slightly modify the cates that only 10 % of all trips account for approximately 60 % modal split according to the substitution potentials derived of all driven kilometres. Such a strongly skewed result is typical for heavy-tailed distributions (see Sornette (2000) for a discus- above, repeat the summation and obtain different total CO2 emissions. sion of the effects arising from heavy-tailed distributions). Let T(l) denote the distribution of trips of length l, i.e. in an As a second input, we need the modal split for each kilome- interval [l, l+dl] in total T(l) kilometres are driven. In the nu- tre for a fixed trip purpose. The purpose takes either the value merical calculations below we choose dl = 1 km. We normalise ‘work-related’ or ‘not work related’. We denote the kilometre- dependent modal split as m (l) where i denotes the mode of this distribution to yield the 1185.7 billion passenger kilome- ij tres that were driven in Germany in 2009 (dena 2012): transport (bike, car, other) and j the purpose fo the trip (work- related or not). This kilometre-dependent modal split is nor-

∞ malised pkm= T( l )d l = 1185.7⋅ 109 pkm tot ∫0 ∀l =1...∞ : m ( l )= 1. ∑ij ij

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Figure 7. Lorenz plot of the driven kilometres. The Lorenz plot (see definition of coordinates in the text) for all trips in the data base is shown as solid line indicating the heterogeneity in trip lengths. The dashed line shows the distribution of all trips contributed equally to the total passenger kilometres travelled.

Figure 8. Modal split of work-related trips in the base case and two scenarios. From left to right shown are the kilometre-dependent modal splits for work related trips in the three scenarios ‘base case’ (left panel), ‘conservative’ (middle panel) and ‘maximum’ (right panel).

The total passenger kilometres travelled are then obtained as senger kilometres per mode) but are based on the two scenarios from section ‘substitution potentials’. We use the substitution ∞ pkm= m( l ) T ( l )d l potentials derived above with the modification that negative tot ∑i, j ij ∫0 potentials are set to zero. Figure 8 shows the change within the modal split of both scenarios for work-related trips. and the total CO2 emissions are obtained by multiplying each mode of transport with its specific CO emissions factor (from Performing the sum over all trips with the modified modal- 2 split according to the two scenarios, yields annual CO savings Table 3) 2 of 0.2 million tons in the conservative scenario and 0.3 mil- ∞ lion tons in the maximum scenario compared to the base case. E= e m( l ) T ( l )d l . tot ∑i, j i∫0 ij Including the few negative values for substitution potentials yields 0.07 or 0.43 million tons annual savings (not shown in In the numerical calculations we use discredited data, i.e. the Figure 9). These values are close to the one obtained by the trips are collected in groups of one kilometre bin width. Fur- rough estimate from the previous section. This comparison is thermore, the data available is truncated in the original data- summarised in Figure 9. base at 950 kilometres since longer individual trips are considered as unrealistic. In accordance with the two substitution potentials derived Discussion and Summary above, we discuss two scenarios, coined ‘maximum’ and ‘con- Motivated by the increasing market shares of electric vehi- servative’. These scenarios are not identical with the high and cles and the construction of bicycle fast lanes, we analysed low scenario from the previous section (which was based on the potential CO2 emission reductions by a shift from cars to the influence of changing average trip lengths on the total pas- bikes for trips to work. As the determining parameter for this

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Figure 9. Comparison of CO2 savings estimates. Shown are the estimated annual carbon dioxide savings from the present and previous section for the two scenarios in both sections. shift seasonal differences in driving behaviour were used. As Artikel/2012/09/2012-09-05-nrvp-2020.html (as consul- a maximum share of bicycle driving we assumed the modal ted online on 20.12.2012). split in summer time as being a good reference for a realistic Danish Road Directorate, 1995. The potential of cycling in estimation of the population share which is choosing to ride urban areas, Traffic safety and environment R17, Copen- the bike in good weather conditions. The estimated savings are hagen. of the order of 0.1–0.4 million tons per year or 0.1–0.25 % of Dena (Deutsche Energie-Agentur GmbH – Energieeffiziente the annual traffic related carbon dioxide emissions. Since trips Verkehrssysteme), 2012. Verkehr. Energie. Klima. Alles to work are performed very regularly we limited our analysis Wichtige auf einen Blick. Berlin. http://www.dena.de/pu- to trips to work only. Further analysis should include other blikationen/verkehr/broschuere-verkehr-energie-klima- trips as well and might suggest much higher saving potentials. alles-wichtige-auf-einen-blick.html However, the focus of the present study was rather to obtain Drăgulescu, A. and Yakovenko, V.M., 2001. Evidence for the realistic estimates for the order of magnitude and the analysis exponential distribution of income in the USA. The Euro- was confined to work trips only in order to remain a conserva- pean Physical Journal B 20 (4), 585–589. tive estimate. Dütschke, E., Schneider, U., Sauer, A., Wietschel, M., The estimated amounts of savings seem rather small com- Hoffmann, J., Domke, S., 2012,Roadmap zur Kundenak- pared to the overall traffic related carbon dioxide emissions. zeptanz. Zentrale Ergebnisse der sozialwissenschaftlichen However, switching to bicycles can have several other advan- Begleitforschung in den Modellregionen. tages and might indicate a change of habits or mobility culture EU regulation No 443/2009, European Council and European in certain parts of society. Furthermore, the use of bicycles in Parliament (EG) No 443/2009. urban areas can also be more time efficient than the use of cars Emberger, G., 2009, Mobilitätsuntersuchung TU Univercity (UBA 2009). 2015, TU Vienna. Follmer, R., undated, Mobilität in Deutschland – Fahrradnut- zung, Impulsvortrag Expertenworkshop BMVBS. References Forschung Radverkehr 2010, Radschnellwege, http://www. Bergström, A.; Magnusson, R., 2003, Potential of transferring nationaler-radverkehrsplan.de/transferstelle/, (consulted car trips to bicycle during winter, Transportation Research online on 08.01.2013).

Part A 37, 649–666. Funke, S.; Plötz, P., 2012, CO2-Effizienzsteigerung bei Pkw – Bike-EU, 2010, www.bike-eu.com/facts-figures/market- Sprint oder Spaziergang, Zeitschrift für die gesamte Wert- reports/4920/the-netherlands-2010-e-bike-saves- schöpfungskette Automobilwirtschaft (ZfAW) 4/2012, dutch-bicycle-industry.html und www.bike-eu.com/ Seiten, Bamberg. facts-figures/market-reports/4267/the-netherlands-2009- IEA (International Energy Agency) 2011: Technology Road- e-bike-dictates-dutch-market.html; (as consulted online map Electric and plug-in hybrid electric vehicles. Paris on 08.01.13). 2011. http://www.iea.org/publications/freepublications/ Bundesministerium für Verkehr, Bau und Stadtentwicklung publication/EV_PHEV_Roadmap-1.pdf (BMVBS), 2008, Mobilität in Deutschland (MiD) 2008, Kraftfahrtbundesamt (kba) 2011,Emissionen, Kraftstof- Datensatz. fe – Deutschland und seine Länder am 1. Januar 2011, Bundesministerium für Verkehr, Bau und Stadtentwicklung http://www.kba.de/cln_030/nn_1157760/DE/Statistik/ (BMVBS), 2012, Elektromobil auf zwei Rädern – Erfahrun- Fahrzeuge/Bestand/EmissionenKraftstoffe/2011/2011_ gen aus den Modellregionen, Berlin. _b__emi__eckdaten__absolut.html (as consulted online Bundesregierung, 2012, Radfahren soll noch attraktiver on 08.01.2013). werden, http://www.bundesregierung.de/Content/DE/

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Lewis, T., Edegger, C., and Schrie, E., 2011. Pedelecs and Re- Sornette, D., 2000. Critical Phenomena in Natural Sciences. newable Energy. Subtask 3.2.1, ‘Integration of Renewable Springer, Berlin. Energy into the workshops of MDM’s’. available online at Statistisches Bundesamt, 2009, Pendler: Die Mehrheit nimmt http://www.gopedelec.eu/pedelecsAndRenewableEnergy. weiter das Auto, STATmagazin, Wiesbaden. pdf. Statistisches Bundesamt, 2005, Leben und Arbeiten in Deutsch- Mobilität in Deutschland (MiD), 2008, http://www.mobili- land – Mikrozensus 2004, Wiesbaden. taet-in-deutschland.de/. Statista, 2012, Warum fahren Sie nicht regelmäßig Fahrrad?, Nilsson, A., 1995, The potential for replacing cars with http://de.statista.com/statistik/daten/studie/1930/umfra- bicycles for short distance travel, Department of Traffic ge/gruende-die-gegen-regelmaessiges-radfahren-spre- Planning and Engineering, Lund Institute of Technology, chen/ (as consulted online on 20.12.2012). Lund, Sweden. Umweltbundesamt (UBA), 2009, Sprit sparen und mobil sein, Paetz, A.-G., Landzettel, L., Fichtner, W., 2012, Wer nutzt Berlin. Pedelecs und warum?, Internationales Verkehrswesen (64) velosuisse (2010): http://www.velosuisse.ch/de/statistik_aktu- 1, 34–37. ell.html; (as consulted online on 01.01.12.). Plötz, P., Fleiter, T., 2012. Energy Efficiency Policies for Zweirad-Industrie-Verband (ZIV), 2012, Mitglieder & Kenn- Different Firm Sizes: Challenging Current Policies with zahlen 2012, Bad Soden a. Ts. Empirical Data, European Council for an Energy-Efficient Economy – ECEEE, ECEEE Summer Study 2012, Stock- holm: ECEEE, 2012. Acknowledgements Sinus Markt- und Sozialforschung GmbH, 2011, Fahrrad-Mo- The research was made possible as part of the REM 2030 nitor Deutschland 2011. Ergebnisse einer repräsentativen project, which is funded by the Fraunhofer Society and the Online-Befragung, Heidelberg. federal state Baden-Württemberg.

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