Effects of having company cars on modal choice, and substitution effects

Effects of having company cars on modal choice, and substitution effects A.W. Kloosterboer 1325434 Faculty of Technology, Policy and Management, TU Delft, Jaffalaan 5, 2628BX Delft, The Netherlands Delft, 13/12/2012 Abstract Dutch policy for company cars does not limit car usage, which hampers achieving the climate goals. In this paper multiple regression analysis is conducted to study effects of having company cars on other modalities, and substitution effects between modalities. German and Dutch data are analyzed. We found that having a company car in The Netherlands is related to more car usage and fewer by other modalities. We did not find similar effects for the German data. In both countries, car can be substituted by trains, buses, bicycles and walking. By making company cars less attractive, car usage can be reduced in The Netherlands. The same effect is not found for the German data, even though having company cars is cheaper in Germany than in The Netherlands. This could imply that costs are a more important factor in modal choice for Dutch people than for German people. The problem with Dutch policy is that the user of the company car is insensitive to the marginal costs of usage. Therefore, new policy must financially incentivize the end user to reduce company car mileage by making them partially responsible for the marginal costs. Keywords: Company car, Dutch company car policy, German company car policy, substitution effect, travel behavior, modal choice, modalities, multiple regression 1. Introduction: aims and scope The Dutch government wants to comply with European and global climate goals. One aspect of this is reducing negative effects of car use by encouraging the use of more sustainable transport modes (Rijksoverheid, 2012). The Dutch policy regarding company cars does not reduce car mileage. Having a company car leads to a higher mileage per household. In 2009, the daily average car mileage per household with one or more company cars was 100 km, compared with 65 km per household having only private cars (Cohen, 2009). We see that the company car policy does not achieve the desired results of meeting the government s environmental goals and reducing the negative effects of car usage. The fiscal policy of any country can affect the transport decisions made by individuals and organizations (Potter et al., 1999). This paper studies the effects of having company cars on other modes of transport, and substitution effects between different modalities. With this information, policy can be geared towards encouraging employees to travel to, from, and at work by more environmentally friendly modes than the car. The focus in this paper is on the policy in The Netherlands, but the paper also considers relationships in the context of Germany. Next to conducting empirical research, this paper gives a reasoned recommendation for policy changes which could stimulate use of cleaner modalities. Firstly, the paper describes what the effects of car usage in general are. Secondly, company cars and the tax benefits for them are discussed for Germany and The Netherlands. Thirdly, the approach for the empirical research is described. Fourthly, the key results from the analyses are presented. Fifthly, the most important takeaways from the results are discussed. Sixthly, conclusions are drawn from the most important findings and recommendations for policy changes are given. Finally, we reflect on the research in this paper and several recommendations for future research are given. 1

2. General effects of car usage The energy demand for transport of EU15, the configuration of the European Union as of 1995, increased from 150 Megaton oil-equivalent (Mtoe) in 1975 to 270 Mtoe in 1995. Transport accounts for 30% of the total energy demand. When no measures are taken this will increase to 33% by 2020. From 1995 to 2020 transport will account for 45% of the incremental energy demand (Vlieger et al., 2000). Furthermore, growing levels of mobility have brought adverse effects to urban areas like congestion, air pollution, noise and vibrations, and risk of accidents (Monzón de Cáceres, 1994). Congestion does not only increase journey time, but it also leads to increasing volatility in trip times and uncertainty of arrival times, making trip planning more difficult. In the US, congestion costs continue to rise from 18 billion euros in 1982 to 88 billion euros in 2009, associated with 3.9 billion gallons of wasted fuel (equivalent to 130 days of flow in the Alaska pipeline), and a 621 euros cost impost per average commuter in 2009 (Li & Hensher, 2012). It is estimated that traffic congestion in Australia resulted in 7.5 billion euros of avoidable social costs in 2005 (including all extra time costs, extra vehicle operating costs and extra emission costs), increasing to 16.3 billion euros by 2020. Air pollution like airborne particles are critical risk factors for adverse health conditions (Lee et al., 2007). For instance, research shows that there are statistically significant associations between both particulate matter related air pollution (PM 10 and BS) and gaseous air pollution (NO 2 and O 3 ) for all-cause mortality and for cause-specific mortality (Fischer et al., 2011). Air pollution damages calculated as euro per kilometer, for older cars (before 1997) range from 2 to 41 eurocents/km, whereas for newer cars (since 1997), the range is from 1 9 eurocents/km (Spadaro & Rabl, 2001). Furthermore, air pollution in the form of carbon dioxide (CO 2 ) continues to rise. Road transport produces over 70% of total transport CO 2 emissions, of which passenger vehicles are responsible for more than half, and these numbers are expected to grow significantly (Ryan et al., 2009). CO 2 is considered the main indicator for global warming (Nocera & Cavallaro, 2011). Specific problems with city noise and vibrations from cars are still unresolved (Belgacem et al., 2012) but are one of the main contributing factors behind noise pollution in cities. usage also directly affects the number of accidents to pedestrians and other participants in traffic. In total, since the start of motorization there have been over 30 million deaths attributable to accidents (Wells, 2007), more than all the soldiers in the two world wars combined. Now that the effects of cars in general are explored, the paper will focus on company cars. We define what is meant by a company car and what having a company car means on an environmental and an economic level. 3. Developments around company cars 3.1 Company cars in more detail A company car refers to a passenger car that an employer provides to an employee to use for commuting, work-related, as well as for private. Often, other members of the employee's family are also permitted to use this car (Ramaekers & Wets, 2010). Company cars cause more negative environmental effects than regular private cars. Quantitative research has shown that the mileage of company car users is 24% higher than that of private car drivers. Of all passenger cars in the Netherlands, 11% are classified as company cars, consuming 21% of the energy amount by all passenger vehicles (Grauss & Worrell, 2008). Company cars, on average, are newer and are more often equipped with diesel engines but they are also larger which makes the fuel efficiency worse than the efficiency of private cars (Grauss & Worrell, 2008). Company cars also drive longer commuting distances than the national average for private cars. The use of company cars has developed from being a status symbol for board members and necessary for employees who have to travel frequently for work, to a common practice in the configuration of the salary package and as an incentive to attract talented people in specialized functions. This development is mainly caused by the fiscal preferential treatment of company cars and the heavy tax burden on labor, making it more interesting for the employer to offer a company car instead of a higher salary leading to the same monetary benefit for the employee (Neale, 1997) (Ramaekers & Wets, 2010). 3.2 Tax benefits for company cars From a tax perspective, a company car is a fringe benefit. The total of this benefit, which is determined by the Dutch Ministry of Finance, is added to the employee's gross income and tax is paid on it accordingly. The sum of the benefit is often called the value of personal use. In 2

most countries, the value of personal use is biased downwards considerably (Shiftan et al., 2012). This makes the company car arrangement preferential to both the employer who is eligible to deduct most of the car expenses for tax purposes, and to the employee who receives the company car benefit but pays a relatively low amount of taxes for this benefit. The employer would need to spend a lot more in direct salary to have the employee receive the same utility, so both sides are benefiting from this arrangement and this results in an increase in the share of company cars [ (Berning, 2009); (Cohen- Blankshtain, 2008); (Puigarnau-i-Gutiérrez & van Ommeren, 2011); and (Ehrlich & Tzadik, 2006)]. Company cars are not only often offered as a fringe benefit, they are, apart from the salary, the most important compensation for the employees' work (Puigarnau-i-Gutiérrez & van Ommeren, 2011). Furthermore, in most of the countries that give this benefit it also includes full financing of the use of the car: fuel, insurance, maintenance, and sometimes even parking fees and tolls. The consequence of this is that the marginal cost of a trip in a company car is zero to the employee; the cost of the car to the employee is constant regardless of how often or to what extent it is used. Although it could be debated that some company cars may enhance the productivity of the employee, it is now commonly known that the high number of company cars in Europe is a direct consequence of their preferential tax treatment on both the demand and supply sides of the labor market (Borger & Wuyts, 2011). 3.3 Company car policy in Germany and The Netherlands Since we are looking at both German and Dutch data, it is important to note differences in the policies of each country. These could help put findings from the empirical research into context. In Germany, the employer generally adds 1% of the list price of the vehicle to the taxable salary; this is referred to as the 1% rule (Potter et al., 1999). This generalized monetary value advantage is subject to tax. If the 1% rule is not used, then drivers must keep a logbook of all the kilometers they have driven and for what purpose. The running costs are often paid with a special type of credit card. These costs are included in the fixed monthly fee. In The Netherlands, employees have to pay income tax for using their company cars. This is done through fiscal addition (bijtelling). Company car owners that use their car for work purposes and drive less than 500 kilometers privately, are exempted from paying this fiscal addition. The fiscal addition is a percentage of the value of the company car, and this percentage depends on the CO 2 output of the car (ElsevierFiscaal, 2012). The higher the CO 2 output, the higher the percentage of the value of the car that is added to the taxable income. This fiscal addition is taxed according to the income tax scale. The fiscal addition percentages are 0% for cars without CO 2 emissions, 14% for hybrid cars and very economic cars, 20% for relatively low CO 2 emissions, and 25% is the highest scale. People pay for fuel and maintenance with a special type of credit card, similar to how it is done in Germany. When we compare the German and Dutch policy, we can say that the German policy is more favorable to both employers and employees. The costs of company cars are lower in Germany. We have looked at the environmental and economic effects of company cars, and the differences in the policies of Germany and The Netherlands. Next, the paper explores what the effects of having company cars are on other modalities. We also look at relationships between different modalities. This is done for both Germany and The Netherlands. 4. Empirical research 4.1 Introduction The empirical research looks at the effects of having a company car on the number of by car, by train, by bus, by bicycle, and by foot. Furthermore, we look at substitution effects between different modalities. This research involved two datasets, one from Germany in 2009, which contains data from weekly travel behavior, and one from The Netherlands in 2010, which contains daily travel behavior data. The Dutch data on the number of bus is combined with the number of tram and metro. The datasets used have undergone preparations to be able to perform the analyses. The descriptive statistics represent the prepared data, not the raw data. 4.2 Data description The outcomes of the analyses are stronger when the data are in line with a number of model assumptions. The most important ones are normality, linearity and homoscedasticity. We looked at the Kolmogorov-Smirnov test for all dependent variables in both datasets (Table 1 3

and 2). The fact that all variables have a significance of 0.000 means that the variables are not normally distributed. Plots of the data points show that the dependent variables did not have linear relationships either. The lack of normality and linearity leads to an underestimation of the results. The data is also heteroscedastic, which means that the standard errors are biased. To check the consequences of the lack of conformity with the model assumptions, we performed a Poisson regression analysis. This method assumes the logarithm of the expected values instead of the indicator values and can be modeled by a linear combination of unknown parameters. We compared the results from this analysis with the results we generated with our first results, and the differences were small. Therefore, we assume that the linear models are robust to deviations of normality. Table 1: Kolmogorov-Smirnov test for German data Tests of Normality Kolmogorov-Smirnov Statistic df Sig..075 710.000 Train.505 710.000 Bus.485 710.000 Bicycle.357 710.000 Walking.233 710.000 Table 2: Kolmogorov-Smirnov test for Dutch data Tests of Normality Kolmogorov-Smirnov a Statistic df Sig..234 16887.000 Train.532 16887.000 BusTramMetro.528 16887.000 Bicycle.447 16887.000 Walking.453 16887.000 Table 3: descriptive statistics of German data (per week) Statistics Train Bus Bicycle Walking Mean 15.026.356.355 2.293 3.944 Std. Dev. 10.440 1.432 1.410 4.728 5.398 Min.000.000.000.000.000 Max 52.200 11.150 13.620 29.760 41.630 Table 4 shows that people in The Netherlands make about 1.7 car per day, about 0.1 train and also 0.1 bus/tram/metro. The bicycle is used about 0.6 times a day and people make about 0.4 walking per day. Table 4: descriptive statistics of Dutch data (per day) Train Statistics BusTram Metro Bicycle Walking Mean 1.680.110.066.596.430 Std. Dev. 1.937.825.609 1.301 1.010 Min.000.000.000.000.000 Max 22.000 16.000 16.000 14.000 14.000 4.2 Model structure To find the effect of having company cars on the number of by other modalities, we used company car as an indicator (Figure 1). The number of by other modalities were also used as indicators, with the exception of one which was used as the dependent variable to find substitution effects between modalities. In Figure 1 we see that the number of car is the dependent variable, and having a company car and the number of by other modalities are indicators. We also added indicators to control for demographics and socio-economic influences. Figure 1 is a schematic version of the model that was actually used. When we look at Table 3, we see that the average number of car in Germany is about 15 per week. About 0.4 train are made weekly, and also about 0.4 bus. The bicycle is used about 2.3 times per week and people make about 3.9 by foot per week. 4

Independent variables fewer bicycle (decrease of 12%). We see that the number of walking has a negative relationship with car of 0.08 (decrease of 1%). Finally, we found that having a company car does not affect any of the other modalities. Table 5: effects between modalities in German 2009 data Dependent variables Figure 1: example of a model for substitution effects between modalities with company car as indicator 5. Key results Company City Residence Train Bus Bicycle Walking 1.522-0.176 0.092 0.053 0.658-0.744 0.112 0.051 0.124 0.245 Hhsize 0.674 0.229 0.046 0.480-0.041 ns 2.365-0.371-0.344-1.310-1.344 Sex -0.916-0.050 0.308 0.034 0.606 Age 0.020 0.001 0.001-0.004 0.001 5.1 Introduction Relationships between indicators and dependent variables that are not significant were made red in Table 5 and Table 6. The unstandardized coefficients are given. This means that with the increase of 1 unit of the indicator, the dependent variable increase with the value of the unstandardized coefficient. 5.2 Results from the German data Table 5 shows that having a company car does not affect any of the other modalities. It would be expected that there would at least be an effect between having a company car and the number of car that is made. We see that all modes have a negative relationship with the number of car. Every train trip is related to 1.2 fewer car per week (decrease of 9%), every bus trip to 1.4 fewer car (decrease of 11%), every bicycle trip to 0.6 fewer car (decrease of 4%), and every trip by foot to 0.2 fewer car (decrease of 2%). Number of car is the only indicator for the number of train, and it is a negative relationship of 0.028 (22%). The number of bus has a negative relationship with the number of car and bicycle. Every car trip is related to 0.03 fewer bus (decrease of 4%), and every bicycle trip to 0.04 fewer bus (decrease of 6%). The number of bicycle is negatively influenced by the number of car and bus. Every car trip is related to 0.1 fewer bicycle (decrease of 4%) and every bus trip to 0.4 Education 0.795 0.185-0.061 0.790 0.355 nkids 1.914-0.140 0.122-0.137 0.513 Income -0.499 0.016 0.036-0.141-0.208 Bicycle Walking -0.554-0.023-0.040-0.241-0.014-0.006-0.034-0.048 Bus -1.449-0.036-0.425-0.082 Train -1.206-0.034-0.231-0.193-0.028-0.032-0.132-0.080 Constant 13.600 0.128 0.723 3.638 6.180 5.3 Results from the Dutch data Table 6 shows that having a company car affects all modes of transport, except for walking. Having a company car is related to 0.4 more car (increase of 21%), 0.1 fewer train (decrease of 100%), 0.04 fewer bus/tram/metro (decrease of 22%)., and 0.2 fewer bicycle (decrease of 100%). That means that people with a company car no longer use the train and the bicycle in The Netherlands. This might be an overestimation due to the type of data collection (per day). Furthermore, we see in Table 6 that all modes have a negative relationship with the number of car. Every train trip is related to 0.2 fewer car (decrease of 12%), every bus/tram/metro trip to 0.2 fewer car (decrease of 13%), every bicycle trip to 0.3 fewer car (decrease of 16%) and every trip by foot to 0.09 fewer car 5

Independent variables (decrease of 5%). and bicycle have a negative relationship with the number of train. Every car trip is related to 0.04 fewer train (decrease of 65%), and every bicycle trip also to 0.04 fewer train (decrease of 66%). Number of bus/metro/tram has a negative relationship with the number of car and bicycle. Every car trip is related to 0.03 fewer bus/tram/metro (decrease of 13%), and every bicycle trip also is related to 0.03 fewer bus/tram/metro (decrease of 13%). Moreover, we see that the number of bicycle has a negative relationship with the number of car (decrease of 100%), the number of train (100%) and the number of bus/tram/metro (100%). So, the car, the train, and the bus/tram/metro fully substitute bicycle use. Again, this could be a biased effects because the data are collected per day. If someone travels by car one day, it make sense that he does not use the bicycle. Perhaps the same respondent takes the bicycle the next day. This is a disadvantage of this type of data collection. The number of walking is only affected by the number of car. Every car trip is related to 0.03 fewer by foot (decrease of 13%). Table 6: effects between modalities in Dutch 2010 data Train Dependent variables B/T/M Bicycle Walking Company 0.383-0.095-0.043-0.227-0.051 nkids 0.233-0.071-0.102 0.254-0.108 Age -0.006-0.002-0.002 0.005-0.001 Sex -0.035-0.013 0.003 0.229 0.189 Income -0.039 0.016 0.003-0.001-0.019 Ncars 0.224-0.071-0.032-0.222-0.032 HHsize -0.166 0.066 0.094-0.152 0.132 Education 0.130 0.071 0.006 0.097 0.022 City Residence Commuting Distance -0.091 0.029 0.021 0.027 0.031 0.000 0.000 0.000 0.000 0.000 Train -0.209-0.001-0.096-0.017 Bus/Tram/ Metro Bicycle Walking -0.235-0.002-0.288-0.042-0.025-0.094-0.011 0.001-0.013-0.104 0.002-0.009-0.041-0.025-0.130-0.028 Constant 1.811 0.063 0.196 0.056 0.209 6. Discussion of results When we look at the results from the Dutch data and those from the German data, we find that company cars in The Netherlands stimulate the number of car and have a negative influence on the number of by all other modalities other than walking. In Germany we did not found the same effects of owning company cars. None of the other modalities have a significant correlation with having company cars in Germany. This is not in line with what we expected when we described the policy for both countries. In Germany, the costs of having company cars are lower than in The Netherlands. Therefore, we expected to find more significant effects of company cars in Germany than in The Netherlands. The fact that we did not find increased car usage in Germany might imply that costs are not the most important factor in modal choice in Germany. The fact that we did find more car usage in The Netherlands could mean that costs are the main factor for the Dutch. Moreover, for both countries, we see that all other modalities form a substitution for car, and the other way around. A higher number of car is related to a lower number of by all other modes. In Germany we find that the number of car and the number of train are strong substitutes for each other. However, in the Netherlands we see that the number of car is most strongly substituted by the number of bicycle. This could be explained by the fact that the Netherlands is a lot smaller country and is more densely populated than Germany, so the travel distances are smaller as well. It can also be because the Netherlands has (even) more of a bicycle culture than Germany (Martens, 2007). These factors could affect how appealing it is to take the train over the bicycle in Germany, or the other way around in the Netherlands. This is supported by the finding that bicycle and train are substitutes for each other in the Dutch data, but not in the German data. Moreover, it is interesting to see that, in both countries, walking are only substituted by car. Finally, another observation is that the number of cars in both datasets is a significant indicator on all modes of transportation. A higher number of cars in the household leads to more car, and fewer by other modalities. 6

7. Conclusions and policy recommendations We have found that company cars affect other modalities in The Netherlands. We did not find similar results for the German data. In Germany, the costs of having company cars are lower than in The Netherlands. The fact that we did not find more car usage in Germany might imply that costs are not the most important factor in modal choice in Germany. The fact that we did find more car usage in The Netherlands could mean that costs are the main factor for the Dutch. Having a company car is related to more car usage in The Netherlands. Therefore, Dutch policy regarding company cars could affect the number of car. By making company cars less attractive, car usage might be reduced. The problem with Dutch company car policy is that the user of the company car is insensitive to the marginal costs of usage. We found that costs might be the most important factor in modal choice for Dutch people. Therefore, new policy has to financially incentivize the end user to reduce his/her company car mileage. For instance, by capping the expenditure on the special payment card that is used to pay for fuel. The user would then be incentivized to stay below that cap or threshold, otherwise he/she would have to pay for the additional costs. We also see that all other modes are suitable alternatives for cars. Therefore, making car use less attractive can lead to more use of those other modalities. We found that having more cars in the household leads to more car and fewer by other modalities. Having multiple cars in a household needs to be reduced in order to reduce overall car usage. This can be done by increasing the road and/or car taxes. The consequence of this will be that each car becomes more expensive. This could lead to other modalities becoming more attractive. Another way of addressing this problem is by determining some sort of a car per household member ratio. People pay more taxes if they have a higher ratio. Another way of dealing with this is by taxing second cars more, like is done with second houses. These are all ideas that require further research. 8. Reflection and future research There was no data regarding commuting distance for the German dataset. Since commuting distance plays an important role in what type of transport is chosen, it is important to note that this indicator was missing from the data and so we could not control for it. Future research can show how adding this indicator could change the results. Also, the Dutch data had its information on the number of bus combined with the number of tram and metro. By splitting this data, we could see more detailed substitution effects of these individual modalities. The dependent variables did not have a perfect normal distribution in either of the datasets. The variables also did not have perfect linear characteristics, and none of the variables were homoscedastic. This all means, the data did not comply with the model assumptions for the research methods that were used. To check the consequences of the lack of conformity with the model assumptions, we performed a Poisson regression analysis. This method assumes the logarithm of the expected values instead of the indicator values and can be modeled by a linear combination of unknown parameters. We compared the results from this analysis with the results we generated with our first results, and the differences were small. This led to the assumption that the linear models are robust to the deviation of normality. It could, however, still be interesting to analyze the data with non-linear functions. Finally, we suggest that more time is spent researching the effects of the policy recommendations that were given in section 7. Some of the recommendations were quite creative but their applicability in real life needs to be investigated further. Not only whether these measures will actually reach the desired outcomes, but also whether these suggestions are politically feasible. By making car usage more expensive hence less attractive, it is likely that organizations that represent car users and commuters will try to block the legislations. Since car users form a large group of the Dutch population, this is a delicate discussion. Literature Belgacem, W., Masson, P., & Berry, A. (2012). Active vibration control on a quarter-car for cancellation of road noise disturbance. Journal of Sound and Vibration, Volume 331, Issue 14. pp. 3240 3254. Berning, E. (2009). The price of going the extra mile. BSc. Degree Thesis. Rotterdam, Netherlands: Erasmus University Rotterdam. Borger, D. B., & Wuyts, B. (2011). The tax treatment of company cars, commuting and optimal 7

congestion taxes. Transportation Research Part B, Volume 45, Issue 10. pp. 1527 1544. Cohen, R. (2009). Investigation of car availability in Israel. M.Sc. thesis. Haifa, Israel: Israel Institute of Technology. Cohen-Blankshtain, G. (2008). Framing transport environmental policy: The case of company car taxation in Israel. Transportation Research Part D, Volume 13, Issue 2. pp.65-74. Ehrlich, J., & Tzadik, A. (2006). The Leasing Department of motor vehicles in Israel. New York, NY: Knesset Center for Research and Information. Fischer, P., Marra, M., Ameling, C., Janssen, N., & Cassee, F. (2011). Trends in relative risk estimates for the association between air pollution and mortality in The Netherlands. Environmental Research, Volume 111, Issue 1. pp.94-100. Grauss, W., & Worrell, E. (2008). The principal agent problem and transport energy use: Case study of company lease cars in the Netherlands. Energy Policy, Volume 36, Issue 10. pp.3745 3753. Lee, J., Son, J., & Cho, Y. (2007). The adverse effects of fine particle air pollution on respiratory function in the elderly. Science of The Total Environment, Volume 385, Issues 1 3. pp.28 36. Li, Z., & Hensher, D. (2012). Congestion charging and car use: A review of stated preference and opinion studies and market monitoring evidence. Transport Policy, Volume 20. pp.47 61. Martens, K. (2007). Promoting bike-and-ride: The Dutch experience. Transportation Research Part A: Policy and Practice, Volume 41, Issue 4. pp.326 338. Monzón de Cáceres, A. (1994). Traffic control systems to alleviate congestion and air pollution. The Science of the Total Environmen, Volumes 146 147. pp.45-50. Neale, A. (1997). Extending the scope of environmental management: the case of company assisted travel in Britain. Business Strategy and the Environment, Volume 6, Issue 1. pp.9-17. Nocera, S., & Cavallaro, F. (2011). Policy Effectiveness for containing CO2 Emissions in Transportation. Procedia - Social and Behavioral Sciences, Volume 20. pp.703 713. Potter, S., Rye, T., & Smith, M. (1999). Tax and green transport plans: a survey of UK experience. Transport Policy, Volume 6, Issue 4. pp.197 205. Puigarnau-i-Gutiérrez, E., & van Ommeren, J. (2011). Welfare Effects of Distortionary Fringe Benefits Taxation. International Economic Review, Volume 52, Issue 4. pp.1105-1122. Ramaekers, K., & Wets, G. (2010). The Impact of Company s on Travel Behavior. 12th WCTR. Lisbon, Portugal: WCTR. Rijksoverheid. (2012). Milieuvriendelijker en stiller rijden. Opgeroepen op July 2012, van www.rijksoverheid.nl: http://www.rijksoverheid.nl/onderwerpen/auto/ milieuvriendelijker-en-stiller-rijden Ryan, L., Ferreira, S., & Convery, F. (2009). The impact of fiscal and other measures on new passenger car sales and CO2 emissions intensity: Evidence from Europe. Energy Economics, Volume 31, Issue 3. pp.365 374. Shiftan, Y., Albert, G., & Keinan, T. (2012). The impact of company-car taxation policy on travel behavior. Transport Policy, Volume 19, Issue 1. pp.139-146. Spadaro, J., & Rabl, A. (2001). Damage costs due to automotive air pollution and the influence of street canyons. Atmospheric Environment, Volume 35, Issue 28. pp.4763 4775. Vlieger, D. I., Keukeleere, D. D., & Kretzschmar, J. (2000). Environmental effects of driving behaviour and congestion related to passenger cars. Atmospheric Environment, Volume 34, Issue 27. pp.4649 4655. Wells, P. (2007). Deaths and injuries from car accidents: an intractable problem? Journal of Cleaner Production, Volume 15, Issues 11-12. pp.1116 1121. 8