Would personal carbon trading reduce travel emissions more effectively than a carbon tax?

Transportation Research Part D 35 (2015) 72–83

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Transportation Research Part D journal homepage: www.elsevier.com/locate/trd

Would personal carbon trading reduce travel emissions more effectively than a carbon tax? Charles Raux a,⇑, Yves Croissant a,b, Damien Pons a a b

Laboratoire d’Economie des Transports, CNRS, Université de Lyon, France Université de la Réunion, France

a r t i c l e

i n f o

Keywords: Personal carbon trading Carbon tax Car use Mobility Stated preferences

a b s t r a c t The application of personal carbon trading (PCT) to transport choices has recently been considered in the literature as a means of reducing CO2 emissions. Its potential effectiveness in changing car travel behavior is compared to the conventional carbon tax (CT) by means of a stated preferences survey conducted among French drivers (N  300). We show evidence that PCT could effectively change travel behavior and hence reduce transport emissions from personal travel. There is however a definite reluctance to reduce car travel. We were unable to demonstrate any significant difference between the effectiveness of PCT and the CT with regard to changing travel behavior. However, in the experiment, the PCT scheme provided consistent results while this was not the case for the CT scheme. Further research is needed into the ‘‘social norm’’ conveyed by a personal emissions allowance. Ó 2014 Elsevier Ltd. All rights reserved.

Introduction Transport generated 22 per cent of global anthropogenic CO2 emissions in 2011 and the road was responsible for threequarters of this figure. Moreover, worldwide, emissions from road transport increased by 52 per cent between 1990 and 2011 (IEA, 2013). As shown by Stern (2006) and the IPCC (2014), there is a need for a sharp reduction in greenhouse gas emissions in the next few decades. Some industrialized countries have set their own ambitious targets, for example cutting emissions by a factor of four by 2050 (as France has done in a recent energy law). In the sphere of transport there is a broad consensus that improvements in vehicle efficiency will not be sufficient and that behavioral changes are required, such as shifting from private to public transportation or modes with lower emissions per passenger-km, or even reducing kilometers travelled (IPCC, 2014). Carbon taxes and vehicle taxes are recognized by several economists as cost-effective instruments for fostering these shifts in transport technology and travel behavior (Parry et al., 2007). A carbon tax system has already been adopted in a number of countries (Finland, Sweden, Italy, Germany and Switzerland) and was considered fairly recently in France – in 2007–2008 – before the plan was withdrawn. Indeed public opinion has been shown to be very sensitive if not resistant to fuel tax increases (Lyons and Chatterjee, 2002). Instead of conventional taxation, another instrument, which combines economic incentives and quantity control, namely marketable or Tradable Permits (TPs) has been proposed in the economic literature (Baumol and Oates, 1988). In its so-called ‘‘cap-and-trade’’ form, this consists of setting a cap, allocating generally free CO2 emission permits (sometimes known as

⇑ Corresponding author at: LET, ISH, 14 av. Berthelot, 69363 Lyon Cedex 07, France. Tel.: +33 472726454; fax: +33 472726448. E-mail address: [email protected] (C. Raux). http://dx.doi.org/10.1016/j.trd.2014.11.008 1361-9209/Ó 2014 Elsevier Ltd. All rights reserved.

C. Raux et al. / Transportation Research Part D 35 (2015) 72–83

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rights or credits) to agents, making agents liable to return permits in proportion to their emissions, authorizing agents to trade, i.e. to sell unused permits or to buy additional permits in excess of their free allowance in order to make higher emissions. Of course since the objective is to reduce emissions, the level of the cap would be reduced over the years. While TPs have been implemented in the European Union since 2005 in the form of the Emission Trading Scheme that applies to large fixed sources of emissions, although the issues associated with extending a scheme of this type to dispersed sources whether buildings or vehicles have been discussed in the literature, no implementation has taken place so far. Household energy use and personal travel account on average for 45% of domestic emissions in major developed countries (Fawcett and Parag, 2010). If applied to the transport sector, CO2 emissions trading would amount to trading rights to consume fossil fuels since CO2 emissions from fossil fuels are almost proportional to their carbon content, as between 95% and 99.5% of the carbon is converted into CO2. In recent years, the idea of involving ‘‘individuals’’ (or households) as energy end-users has emerged in a series of studies conducted in particular in the UK. In its domestic form, with emission rights allocated and surrendered by eligible ‘‘individuals’’, the scheme is known as personal carbon trading (PCT). This covers the two most widely discussed schemes: Tradable Energy Quotas, as proposed originally by Fleming, which would apply to the whole domestic economy (but not air travel) with carbon units being allocated to and surrendered by individuals and organizations, and Personal Carbon Allowances (PCA), as proposed originally by Hillman and developed further by Fawcett, which would apply to household energy use and personal travel including air transport (for a detailed review of these and other schemes see Fawcett, 2010; Starkey, 2012). A theoretical discussion of the pros and cons of TP schemes in transportation and the issue of transaction costs can be found in Raux (2004, 2011). Raux and Marlot (2005) and Raux (2010) have proposed a PCT scheme which is specific to car users, and which therefore applies only to the fuel consumed for private car travel. Regarding potential implementation, trails have been conducted of a PCA scheme in the UK with 100 volunteers in the framework of the ‘‘RSA Carbon Limited’’ program (Prescott, 2008) which successfully showed the technical feasibility of the scheme. Most research in this area has focused on specifying how this kind of scheme could be put in place in different sectors (Fleming, 2007; Raux and Marlot, 2005; Watters and Tight, 2007). Research that sets out to evaluate the impact of such measures on households is scarcer and has mainly considered redistributive effects (Combet et al., 2009; Wadud, 2007). Bristow et al. (2010) have explored in detail the influence of design attributes on the acceptability of a PCT scheme using a stated preference technique. The specific nature of tradable credits for personal fuel consumption means we may expect additional impacts, of a psychological rather than an economic nature, compared to a carbon tax (Fawcett, 2010). One possible effect might arise from the fact that end users have a carbon account that provides frequent feedback on their travel behavior. The social norm associated with a personal allowance fixed within a collective shared goal of carbon reduction is another example of a psychosocial incentive to change ones’ behavior (see also Parag and Strickland, 2011). Given the novelty of the instrument, empirical knowledge regarding its potential effectiveness in changing behavior is limited. Some empirical studies indicate that PCT may generate greater emissions reductions than carbon taxation (e.g. Harwatt et al., 2011). Capstick and Lewis (2009) show evidence of carbon budgeting behavior on a sample of 65 individuals subjected to a computer-based simulation of a personal carbon allowance. However, when they presented a sample of 124 individuals with a hypothetical context with either a personal carbon allowance scheme, a carbon tax scheme or a tax on energy scheme, no difference in willingness to reduce emission-related behavior was apparent between the different schemes. So far, these studies have been largely exploratory due to the size of the samples. One exception is the survey by Parag et al. (2011) on a national sample of individuals in the UK (N = 1096). With a between-subjects experimental questionnaire method they showed that carbon policy instruments (PCT or a CT) have a greater impact on stated willingness to reduce energy consumption than a ‘‘pure’’ energy tax. However, the evidence regarding the comparative effectiveness of a CT and PCT is mixed. The second exception is the exploratory survey by Zanni et al. (2013) which compared the stated behavioral effectiveness (either no change and pay or engage in carbon-savings actions) of PCT and the CT on a sample (N = 189). They also concluded that the evidence concerning the respective effectiveness of PCT and a CT is mixed. This paper aims to explore whether a PCT scheme may generate different behavioral changes from a CT. We have already mentioned the novel nature of PCT, and this means a revealed preferences study would not be appropriate for this case. For this reason, the project we shall describe was in two stages. The first involved a qualitative study by means of in-depth interviews with French households (N  20) regarding their yearly travel habits and their attitudes and behavioral reactions to various scenarios including a large rise in oil prices and a PCT scheme. The second stage involved a stated preferences survey of a sample of French individuals (N  300) which provides the empirical basis of this paper. The remainder of the paper is structured as follows: Section 2 describes the methodology of data production for the survey, Section 3 presents the results and Section 4 offers a discussion with conclusions and outlook.

Methodology of the stated preferences survey Stated preferences (SP) methods involve a family of techniques which use individuals’ statements about their preferences when presented with a set of options in a hypothetical context in order to estimate utility functions (Kroes and Sheldon, 1988). They are part of more general ‘‘stated choice’’ methods (Louviere et al., 2000). The design of an SP survey includes several steps: sample selection, setting up the context of the experiment and experimental design.

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Sample pre-selection The first stage of the stated preferences study consisted of setting up a ‘‘fact base’’, consisting of the trips carried out by the individual, upon which the hypothetical scenarios (or trade-offs) in the SP survey could be applied. Since the focus is on the effectiveness of the various schemes with regard to fuel consumption resulting from car use, the sample selection criterion was essentially being a car user. In order to select a sample of this type effectively, interviewers were posted on the street in Lyon city center and at stands in suburban shopping malls (June–July 2009). The interviewers approached members of the public claiming to be conducting a travel survey, and those individuals who were car users were given a brief questionnaire about their travel habits for all trip purposes (for daily travel, long distance travel for holidays for instance, but not for business trips) and their car ownership. They were promised a small gift (a shopping card) if they were willing to be called back by phone for a quick complementary survey. At this stage no indication was given about the aim of the survey, i.e. the testing of rationing schemes. We obtained 788 exploitable questionnaires from this pre-selection. The design of the experiment and the customization of the trade-offs for each interviewee took around 3 months, including the pilot survey. Finally the SP survey itself was carried out in January and February 2010. A paper copy of the reported trips along with the set of SP trade-offs had already been sent to the individuals’ home in order to facilitate conduct of the survey which was undertaken by phone. The list of trade-offs was placed in an envelope which respondents were asked not to open before the phone call. About half of the preselected sample could not be contacted by phone or refused to submit to the survey. For those willing to answer the survey (N = 310) the call lasted between 10 and 30 min. Finally, the telephone interviewers checked that the respondents had properly understood and were genuinely committed to the SP survey. The responses of 268 individuals were selected for the statistical analysis. Table 1 shows some socio-demographic attributes of the full original sample and the SP survey samples. With regard to home location, ‘‘urban center’’ stands for the central municipality within an urban area (here mainly the cities of Lyon and Villeurbanne, which together have a population of 616,000, and are well served by metro and tramways), ‘‘outskirts’’ for the municipalities around the central one where public transport mainly consists of buses and ‘‘peri-urban’’ for the more distant low density or rural municipalities. This table illustrates the variety of the sample and shows, with Table 2, that there is no noteworthy selection bias between the original survey sample and the final SP survey samples for either socio-demographic data or travel behavior, except that individuals living in rural areas are more highly represented in the latter and average annual mileage is higher (20,045 km). The SP sample is also compared with the overall profile of the French population (see last column). This shows that, as expected, our sample is younger, more engaged in working activity, more urban, with more presence of minor children in the household, but with roughly the same distribution of income (49% of the sample under 2500 € per month).

Table 1 Some socio-demographic attributes of the sample. Attribute

Category

Full sample (%)

SP participation (%)

SP selection (%)

French populationa (%)

Age

18–24 25–34 35–49 50–65 Over 65

17 27 29 21 6

14 25 31 24 7

15 25 32 23 6

8 16 28 25 22

Gender

Male Female

55 45

53 47

54 46

48 52

Occupation

Work full time Work part time Students Others (retired ...)

59 10 10 22

61 12 6 21

62 12 7 19

56 8 36

Home location

Urban center Outskirts Periurban and rural

45 35 20

40 35 25

41 34 25

28 33 40

Minor children in the household

No Yes

67 33

65 35

63 37

72 28

Monthly household income (interval in €)

Less than 1500 Between 1500 and 2500 Between 2500 and 3500 Between 3500 and 4500 More than 4500

22 29 24 12 14

22 28 24 12 14

23 26 24 13 14

Annual mileage

Average (km)

N

17,379

19,692

20,045

788

310

268

Median income 2483 €

–

a In 2009. Sources: Laganier and Vienne (2009); Insee, RP1982 sondage au 1/20 – RP1990 sondage au 1/4 – RP1999 à RP2011 exploitations complémentaires; Les revenus et le patrimoine des ménages, Edition 2014, INSEE, 2014.

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C. Raux et al. / Transportation Research Part D 35 (2015) 72–83 Table 2 Distribution of trips and mileage according to distance category. Distance category

]0–10] km ]10–20] km ]20–60] km ]60–300] km ]300–. . .] km Total

Code

vs s m l vl

Percentage of all trips

Percentage of total mileage

Full sample (%)

SP survey (%)

Full sample (%)

SP survey (%)

45 31 17 6 1

41 33 19 6 1

7 17 20 28 28

6 18 23 28 25

100

100

100

100

Setting up the context In this SP survey, the focus is on respondents’ stated behavioral adaptation to various scenarios (or trade-offs) in which an economic instrument is used (either a CT or PCT), with various levels of taxation (in the case of a CT) or credit prices (in the case of PCT) and various free allowances. An allowance threshold was included in order to make the CT and PCT fully comparable. The individual would be liable to pay the CT above a specific threshold, comparable to the free allowance in the case of PCT. For instance, in the case of the CT, with a threshold of 500 l of gasoline per year, the individual would have to pay the CT (over and above the current excise tax) on any fuel consumed over 500 l. Similarly, in the case of PCT, with a free allowance of 500 l of gasoline per year, the individual would have to buy additional credits for any fuel consumed over 500 l. Note that if an individual consumes less than the free allowance on current travel one possible stated adaptation is for them to increase their fuel consumption by travelling more in order to use up the entire free allowance. As shown by the examples given in the questionnaire in the Appendix some additional explanations were given in order to acquaint the respondent with the PCT scheme. Individuals can reduce their travel emissions in a number of ways: by changing their driving style, by reducing their vehicle-kilometers (for example by increasing the number of passengers in vehicles, reorganizing trips or changing the destinations); by changing their vehicle or changing transport mode in favor of one which consumes less energy. Some of these actions may be implemented in the short term, while others such as changing one’s vehicle or changing one’s place of work or residence may take much longer. This has been analyzed by a qualitative survey within the framework of this research project by Lejoux and Raux (2011). Since in an SP study respondents must be presented with a closed list of choices, the potential behavioral adaptations have been restricted here to reducing their car mobility in order to reduce or eliminate their need to pay carbon tax or purchase additional carbon credits, or alternatively changing nothing and paying the necessary amount. Since people generally do not have an accurate representation of the fuel consumption associated with each of their trips or even of the distance covered, an indirect way of describing potential behavioral adaptations has been devised. This is based on reducing the number of trips, which are grouped together according to their distance range. Based on records of the actual trips performed by the sample, five distance categories have been created: very short trips (up to 10 km), short trips (from 10 km to 20 km), medium trips (from 20 km to 60 km), long trips (from 60 km to 300 km) and very long trips (over 300 km). Table 2 shows the percentage of individuals making trips of each length and the percentage of total mileage accounted for by each distance category. Experimental design Table 3 shows the complete set of eight distance categories and their levels that were used in experimental design of the SP trade-offs. In each of the trade-offs proposed to the respondent, it is possible to reduce the number of trips in each category by either 0% (no change), 15%, 30% or 45%; the instrument was either a CT or PCT; the allowance of liters of gasoline free from taxation (CT) or the free allowance (PCT) were either minus 20%, minus 30% or minus 40% below current consumption; and the amount of the tax (CT) or the credit price (PCT) was either 10, 40, 70 or 100 euro cents per liter of gasoline. The allowance was customized for each individual, depending on their current consumption, in order to maximize the variability of situations in which individuals may find themselves with an inadequate allowance (when their current consumption is above the allowance) or an excessive allowance (current consumption below the allowance). Each respondent was presented with four trade-offs (or choice situations), two with each type of instrument (a CT and PCT) (these four trade-offs are referred to as ‘‘scenarios’’ in the example given in the Appendix). The order in which the two instruments were presented to the different individuals was varied to avoid a systematic bias which could be caused by the order of presentation. Besides the type of instrument, the trade-offs proposed a free allowance level and a tax or credit price level. Note that at the time of the survey the average pump prices for diesel and gasoline were respectively €1.0 and €1.2 per liter. For each trade-off, the individuals were presented with three adaptation options, which were personalized according to their travel behavior and the percentage reduction in the number of trips as shown in Table 3. The individual had to choose one of these options. The first two combined different levels of car trip reduction according to the five distance categories.

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Table 3 Distance categories used for the SP survey and their levels. Distance category

Levels

Very short distance (vs)

]0–10] km

% Reduction in number of trips

0% 15% 30% 45%

Short distance (s)

]10–20] km

% Reduction in number of trips

0% 15% 30% 45%

Medium distance (m)

]20–60] km

% Reduction in number of trips

0% 15% 30% 45%

Long distance (l)

]60–300] km

% Reduction in number of trips

0% 15% 30% 45%

Very long distance (vl)

]300– . . .] km

% Reduction in number of trips

0% 15% 30% 45%

Instrument

CT PCT

Free allowance

Liters of gasoline

20% 30% 40%

Amount of tax or credit price

Euros per liter of gasoline

0.1 0.4 0.7 1.0

The third option was in all cases the ‘‘change nothing and pay’’ option, in which the individual changes nothing in their travel behavior. Each of the three options included, of course, the possible payment to be made, i.e. the amount of tax to pay or additional credits to buy, or even the amount earned by selling unused credits. In addition, a kind of ‘‘reality check’’ was performed for the stated adaptations by asking the interviewee which specific trips (in the list of the recorded actual trips) they would not make in each trade-off. Results The 268 respondents were each presented with 4 choice situations (or trade-offs), which amounted to a potential total of 1072 choice situations, of which 994 received an effective response. 41% of the choices made were option 3 (‘‘change nothing and pay’’): in principle, this indicates a strong preference for the status quo regarding mobility even if this involves additional monetary costs. One issue is whether this third option has been chosen by specific categories of individuals which could reflect constraints (or preferences) regarding their mobility. Table 4 compares the breakdown according to the various socio-demographic variables for all 994 choice situations and the 409 situations where option 3 was chosen. The comparison shows that individuals aged between 18 and 24 years chose option 3 less than average (9% vs 14%) while individuals aged between 50 and 65 years chose option 3 more than average (30% vs 23%). People working full time chose option 3 slightly more than average (66% vs 62%). Individuals with lower incomes chose option 3 less than average and those with higher incomes chose option 3 more than average. Note that there are no overwhelming differences in choice according to home location. There are, of course, probably correlations and this will be dealt with later by means of an econometric analysis. The first thing to emerge from the trade-offs is that the reduction in car trips lessens according to increasing distance categories (see Table 5). We can link this to the rise in the number of individuals not changing their mobility for longer distance categories (from 30.9% for very short distances to 65% for long distances and 56.1% for very long distances). This behavior can also be linked to the trip purpose as shown in Table 6. The interviewees tended to reduce their daily trips preferentially, i.e. commuting and shopping (corresponding to fairly short distances), while ‘‘safeguarding’’ week-end and holiday trips (corresponding to fairly long distances). The choice made by the respondent between the three options has been modeled by a discrete choice conditional logit model (Mc Fadden, 1974; Train, 2009). This model is based on the assumption of utility maximizing behavior by the decision maker n choosing between the various alternatives j (here the three options). This utility, which is only known by the

77

C. Raux et al. / Transportation Research Part D 35 (2015) 72–83 Table 4 Breakdown of ‘‘all choices’’ and ‘‘change nothing and pay’’ choice (‘‘status quo’’) according to socio-demographic attributes. Attribute

Category

All choices

‘‘status quo’’

Age

18–24 25–34 35–49 50–65 over 65

14% 25% 32% 23% 5%

9% 22% 33% 30% 6%

Gender

male female

54% 46%

53% 47%

Occupation

work full time work part time students others (retired ...)

62% 13% 7% 19%

66% 12% 6% 17%

Home location

urban center outskirts periurban

41% 34% 25%

39% 37% 24%

Children in the household who are minor

no yes

63% 37%

61% 39%

Monthly household income (interval in €)

less than 1500 between 1500 and 2500 between 2500 and 3500 between 3500 and 4500 more than 4500

23% 26% 24% 13% 14%

18% 20% 28% 17% 18%

994 100

409 41

N %

Table 5 Reduction in car trips after trade-offs according to distance. Reduction in car trips

Very short (%)

Short (%)

Medium (%)

Long (%)

Very long (%)

Average reduction (% of number of trips) Share of individuals not changing their mobility Average reduction in car trips by those who make reductions

12.8 30.9 18.6

10.7 35.9 16.8

9.0 49.3 17.7

6.2 65.0 17.8

8.4 56.1 19.2

Table 6 Reduction in car trips after trade-offs according to trip purpose. Trip purpose

%

Shopping Accompanying Commuting Day off Week-end Holidays

25 16 34 22 3 <1

individual, is broken down into two parts, a ‘‘representative utility’’ V depending on the attributes of the decision maker and the alternative which are observed by the researcher, and an additional unknown term e which is treated as random. The probability that individual n chooses alternative i is

 Pni ¼ Prob U ni > U nj







8j ¼ i ¼ Prob V ni þ eni > V nj þ enj 8j ¼ i

 ¼ Prob V ni  V nj > enj  eni



8j ¼ i

Assuming that the enj values are identically and independently extreme value distributed for all j, we obtain the well-known logit model (Train, 2009).

eV ni Pni ¼ P V nj je The representative utility V is usually specified to be linear in parameters:

V nj ¼ bxnj þ kj where xnj is a vector of individual attributes or alternative j’s attributes as presented to individual n, b are coefficients of these variables and kj is a constant that is specific to alternative j.

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C. Raux et al. / Transportation Research Part D 35 (2015) 72–83

Table 7 Logit models based on number of eliminated trips (differentiated by distance category). Overall Estimate alt2 alt3 net.inc vs.elim s.elim m.elim l.elim vl.elim Log-Likelihood McFadden R^2 LR test: chisq p.value

0.155 0.400 0.464 0.000 0.003 0.002 0.008 0.070

PCT t-value

Pr(>|t|)

1.866 3.770 1.670 0.225 2.003 0.807 1.214 1.718

0.062 0.000 0.095 0.822 0.045 0.420 0.225 0.086

Estimate . ⁄⁄⁄

. ⁄

.

1074.3 0.003 5.4799 0.48

0.296 0.644 0.702 0.001 0.003 0.003 0.014 0.083

CT t-value

Pr(>|t|)

2.438 4.063 1.957 0.969 1.365 1.118 1.585 1.616

0.015 0.000 0.051 0.332 0.173 0.264 0.113 0.106

519.26 0.006 6.341 0.39

Estimate ⁄ ⁄⁄⁄

.

0.036 0.147 0.033 0.002 0.003 0.001 0.002 0.072

t-value

Pr(>|t|)

0.309 0.994 0.072 1.289 1.353 0.289 0.238 0.995

0.757 0.320 0.943 0.198 0.176 0.773 0.812 0.320

549.61 0.007 7.1371 0.31

Signif. codes: 0 ‘⁄⁄⁄’ 0.001 ‘⁄⁄’ 0.01 ‘⁄’ 0.05 ‘.’ 0.1 ‘ ’ 1.

Since only differences in utility matter for choice probability, the socio-demographic attributes of the individual such as income cannot be introduced directly as it would disappear. This is why we have created a ‘‘net income’’ which deducts from the individual’s income the supplementary expenses generated by the proposed option.

NInj ¼ In  T j  qnj c where NInj is the net income of individual n for option j, In is the income of individual n, Tj the amount of tax to pay (or credit to buy) for option j, qnj individual n’s remaining mileage for option j and c the cost of gasoline per kilometer. When evaluating the difference in utility between two options j and k, the income In disappears but the tax and the cost of fuel consumption that are specific to the individual and the option remain. Table 7 shows the estimation of three separate multinomial logit models for the probability of choosing between the three options, for the scenarios overall, for PCT scenarios and for CT scenarios. alt2 and alt3 are the alternative-specific constants for options 2 and 3 respectively (as above, since only differences in utility matter, only differences in the alternativespecific constants are relevant, alt1 being arbitrarily set to zero). For the overall choice situations, alt3 (‘‘change nothing and pay’’) is highly significant and positive, which denotes a general preference for the status quo regarding mobility behavior. The coefficient of net income (net.inc) is weakly significant but positive as expected. The coefficients of eliminated trips are negative but mostly insignificant except for short trips (s.elim) and very long trips (vl.elim). When it comes to scenarios that relate to a particular instrument, the CT scenarios give coefficient values that are mostly statistically insignificant and erratic. In contrast, in the case of the PCT scenarios, the impacts of the variables are more significant. The net income coefficient is positive and highly significant (at the 95% confidence level). Are the differences in the values of coefficients between the CT and the PCT scenarios significant? A likelihood ratio test comparing the constrained model (the first model in Table 7) with an unconstrained model (the sum of the likelihood of the other two models) gives a chi-square statistic of 10.95 with 8 degrees of freedom corresponding to a p-value of 0.20. The hypothesis of equality of coefficients between the two models cannot be rejected. Furthermore, the monetary values of eliminated trips (or willingness to pay to maintain a trip) can be deduced from the first models by dividing the coefficients of the model by the net income (see Table 8). These results indicate, as one would expect, that value increases with distance category, both for the scenarios overall and for the PCT scenarios. The results for the CT scenarios are again erratic. Assuming the individual is rational and ignoring the indivisibility of trip distance, the marginal utility of the last kilometer travelled should be the same for all categories of trips. The remaining kilometers travelled are computed by subtracting the number of eliminated trips and multiplying, for each individual, the number of remaining trips in each distance category by

Table 8 Willingness to pay to maintain a trip (in Euros).

vs.elim s.elim m.elim l.elim vl.elim

Overall

PCT

CT

0.5 6.24 3.92 17.06 151.57

1.96 3.63 4.82 19.69 118.03

58.97 95.2 30.36 72.2 2161.83

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C. Raux et al. / Transportation Research Part D 35 (2015) 72–83 Table 9 Logit models based on remaining kilometers travelled (differentiated by distance category). Overall Estimate alt2 alt3 net.inc vs.km s.km m.km l.km vl.km

0.156 0.386 0.979 0.094 0.170 0.080 0.092 0.112

Log-Likelihood McFadden R^2 LR test: chisq p value

PCT t-value

Pr(>|t|)

1.879 3.740 2.706 0.817 3.002 1.875 2.418 2.616

0.060 0.000 0.007 0.414 0.003 0.061 0.016 0.009

CT

Estimate . ⁄⁄⁄ ⁄⁄

⁄⁄

. ⁄ ⁄⁄

1071.3 0.005 11.63 0.07

0.305 0.593 1.354 0.016 0.163 0.130 0.127 0.136

t-value

Pr(>|t|)

2.507 3.866 2.948 0.103 2.263 2.275 2.621 2.530

0.012 0.000 0.003 0.918 0.024 0.023 0.009 0.011

Estimate ⁄ ⁄⁄⁄ ⁄⁄

⁄ ⁄ ⁄⁄ ⁄

516.76 0.011 11.33 0.08

t-value

Pr(>|t|)

0.305 1.148 0.492 1.053 1.807 0.012 0.314 1.151

0.760 0.251 0.623 0.293 0.071 0.991 0.754 0.250

t-value

Pr(>|t|)

0.322 1.841 0.090 0.353

0.747 0.066 0.928 0.724

0.035 0.165 0.309 0.189 0.172 0.001 0.021 0.091 548.97 0.008 8.4127 0.21

Signif. codes: 0 ‘⁄⁄⁄’ 0.001 ‘⁄⁄’ 0.01 ‘⁄’ 0.05 ‘.’ 0.1 ‘ ’ 1.

Table 10 Logit models based on overall remaining kilometers travelled. Overall Estimate alt2 alt3 net.inc km

0.156 0.412 0.907 0.094

Log-Likelihood McFadden R^2: LR test: chisq p value

PCT t-value

Pr(>|t|)

1.878 4.305 2.594 2.590

0.060 0.000 0.009 0.010

CT

Estimate . ⁄⁄⁄ ⁄⁄ ⁄⁄

1073.4 0.003 7.336 0.026

0.301 0.580 1.358 0.133

t-value

Pr(>|t|)

2.482 4.076 3.055 2.810

0.013 0.000 0.002 0.005

517.32 0.010 10.221 0.006

Estimate ⁄ ⁄⁄⁄ ⁄⁄ ⁄⁄

0.037 0.247 0.054 0.021 552.9 0.001 0.557 0.757

Signif. codes: 0 ‘⁄⁄⁄’ 0.001 ‘⁄⁄’ 0.01 ‘⁄’ 0.05 ‘.’ 0.1 ‘ ’ 1.

Table 11 Likelihood ratio tests of the equality of marginal utilities according to distance category.

chisq p-value

Overall

PCT

CT

4.297 0.367

1.109 0.893

7.855 0.097

the average length of the trips in this category made by the individual. The results are shown in Table 9, here again for the all the scenarios, the PCT scenarios and the CT scenarios. The marginal utility of kilometers travelled is significantly positive as expected for short to very long trips in the case of the scenarios overall or the PCT scenarios. The results for the CT scenarios are mostly not significant. However, are the differences in the marginal utilities according to distance category significant? Table 10 shows the estimation of the same logit models as above, but without differentiating between the distance categories. The hypothesis of the equality of the marginal utilities of kilometers travelled whatever the distance category has been tested with a likelihood ratio test which compared the models in Tables 9 and 10 respectively for the sample overall and for the two instruments, PCT or a CT. The results are set out in Table 11. They show that the hypothesis of the equality of marginal utilities cannot be rejected at the confidence level of 95%. The results set out in Table 10 provide the ‘‘best’’ models that can be estimated from the experiment. They show that the model has definite qualities with regard to the PCT scheme while its fit with regard to the CT scheme is poor. It should be noted that the low overall quality of adjustment (McFadden R^2) is not crucial here since the focus of this experimental study is on comparing the effects of PCT and a CT. Once again, we tested the hypothesis of equality of coefficients between the models for the two instruments. A likelihood ratio test comparing the constrained model (the first model in Table 10) with an unconstrained model (sum of the likelihood of the two other models) gave a chi-square statistic of 6.40 with 4 degrees of freedom corresponding to a p-value of 0.17. As previously, the equality, and hence the absence of a difference in the effectiveness, of the two instruments cannot be rejected.

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Table 12 Logit models with individuals’ socio-demographic attributes. Coefficients

Overall Estimate

alt2 alt3 net.inc km alt2.age25–34 alt3.age25–34 alt2.age35–49 alt3.age35–49 alt2.age50–65 alt3.age50–65 alt2.age>65 alt3.age>65 alt2.female alt3.female alt2.work-parttime alt3.work-parttime alt2.students alt3.students alt2.others alt3.others alt2.outskirts alt3.outskirts alt2.peri-urban alt3.peri-urban alt2.chidren in hh alt3.chidren in hh Log-Likelihood McFadden R^2 LR test: chisq p value

0.001 0.597 0.957 0.106 0.197 0.787 0.048 0.805 0.035 1.530 1.035 1.078 0.062 0.051 0.295 0.405 0.190 0.444 0.181 0.571 0.337 0.379 0.100 0.074 0.017 0.180

PCT t-value

Pr(>|t|)

0.003 1.958 2.631 2.787 0.683 2.461 0.162 2.503 0.110 4.582 2.111 2.379 0.346 0.298 1.141 1.645 0.509 1.130 0.714 2.250 1.685 1.990 0.472 0.361 0.082 0.920

0.998 0.050 0.009 0.005 0.495 0.014 0.872 0.012 0.912 0.000 0.035 0.017 0.729 0.766 0.254 0.100 0.611 0.259 0.476 0.024 0.092 0.047 0.637 0.718 0.935 0.357

1038.4 0.032 67.638 0.000

Estimate . ⁄⁄ ⁄⁄

⁄

⁄

⁄⁄⁄ ⁄ ⁄

.

⁄

. ⁄

0.690 0.065 1.390 0.144 0.129 0.565 0.351 0.614 0.714 1.149 1.290 0.913 0.062 0.185 0.387 0.091 0.080 0.539 0.008 0.781 0.154 0.023 0.184 0.088 0.357 0.076 498.44 0.042 43.510 0.009

CT t-value

Pr(>|t|)

1.741 0.143 2.995 2.917 0.300 1.183 0.793 1.289 1.513 2.331 1.781 1.331 0.238 0.743 0.977 0.253 0.139 0.914 0.023 2.133 0.533 0.082 0.585 0.289 1.206 0.270

0.082 0.886 0.003 0.004 0.764 0.237 0.428 0.198 0.130 0.020 0.075 0.183 0.812 0.457 0.329 0.801 0.890 0.361 0.982 0.033 0.594 0.935 0.558 0.773 0.228 0.787

Estimate . ⁄⁄ ⁄⁄

⁄

.

⁄

0.631 1.065 0.165 0.038 0.504 0.934 0.229 0.930 0.598 1.842 0.808 1.158 0.063 0.059 0.220 0.740 0.517 0.364 0.386 0.371 0.524 0.718 0.022 0.248 0.359 0.427

t-value

Pr(>|t|)

1.699 2.489 0.265 0.616 1.241 2.101 0.545 2.070 1.341 3.934 1.185 1.863 0.254 0.248 0.634 2.114 1.020 0.662 1.079 1.033 1.865 2.662 0.073 0.875 1.251 1.544

0.089 0.013 0.791 0.538 0.215 0.036 0.586 0.038 0.180 0.000 0.236 0.062 0.799 0.804 0.526 0.035 0.308 0.508 0.281 0.302 0.062 0.008 0.942 0.381 0.211 0.123

. ⁄

⁄

⁄

⁄⁄⁄

.

⁄

. ⁄⁄

527.76 0.042 45.943 0.004

Signif. codes: 0 ‘⁄⁄⁄’ 0.001 ‘⁄⁄’ 0.01 ‘⁄’ 0.05 ‘.’ 0.1 ‘ ’ 1.

Finally, the question arises as to whether the socio-demographic attributes of the individuals may influence their stated behavior. Table 12 provides the three kinds of logit models as previously, but in this case a series of socio-demographic attributes has been included: age per category, gender, occupation, residential location and presence of children in the household. As shown by the significant positive coefficients for ‘‘alt3.age’’, the preference for the status quo is marked for all the age categories over 25 years when compared to the reference category of 18–24 year olds. This may indicate a generation effect, with middle-aged people being more reluctant to change their mobility behavior. Residential location is significant only for those living in the outskirts who also have a preference for the status quo, but not for those living in periurban/rural areas. The effect of residential location appears to be somewhat limited in zones where there may be little alternative to the car. Gender and occupation have no obvious effect, neither does the presence of children in the household. All in all, we can conclude that, apart from age, socio-demographic attributes have a weak influence on behavioral (stated) adaptations to the schemes tested here. Discussion and conclusion First, there is some evidence that an economic instrument like PCT could change travel behavior and hence transport emissions from personal travel. People state they would reduce their car travel in all of the distance categories, but tend to protect their long and very long trips. However, this behavior is not strictly related to distance, since the marginal utility of a kilometer does not differ from one distance category to another, but rather are to trip purpose. It is clear that week-end and holiday trips are the last to be changed. Second, people, however, prefer the status quo, as reflected by the significant numbers choosing the ‘‘change nothing and pay’’ option. There is a definite reluctance to reduce car travel, as shown by the negative effects of eliminating trips or the positive marginal utilities of kilometers travelled. Third, there is no significant difference in the effectiveness of a CT and PCT when it comes to changing travel behavior. This is in line with economic theory and is indicative of the consistency of the experiment. It also concurs with other findings detailed in the introduction (Capstick and Lewis, 2009; Parag et al., 2011; Zanni et al., 2013) despite these studies used different methodologies from ours. Fourth, the CT trade-offs provide erratic and mostly non-significant estimates, while the PCT trade-offs show contrasted and significant effects, which reflects consistency in the respondent’s stated behavior. One possible explanation of this dif-

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ference could be the novelty of the PCT scheme when compared with CT. However, one would have expected the experiment to run less effectively in the case of this scheme, despite some time was taken in the survey to explain it, but the opposite seems to be the case. Another explanation could lie in a difference in the acceptability of the instruments (the ‘‘tax’’ being straightaway rejected), which could have interfered with the experiment. However, our results regarding potential behavioral adaptations have some limitations due to our survey design which obliged us to propose a closed list of adaptations. This is inherent to any quantitative survey targeting a few hundreds respondents. The consistency in the responses concerning PCT and its effectiveness in changing (stated) behavior indicates that this instrument has some potential for promoting and involving people in travel behavior that is more environmentally responsible. The characteristics of PCT, and especially the kind of social norm associated with a personal allowance fixed within a common target, could help to activate pro-environmental behavior. However, caution is required because fiscal or monetary incentives may ‘‘crowd out’’ intrinsic motivation toward pro-environmental behavior (Frey, 1997; Frey and Stutzer, 2008). More research is obviously needed on the interaction between economic and psychological mechanisms in the area of travel behavior. Acknowledgements This study was part of the CarbonAuto project which was financed in the framework of PREDIT (Program of Research and Innovation for Land Transport) and has received funding from ADEME (French Agency for the Environment and Energy Conservation), Grant n° 07 66 C 0152. Appendix Examples from the questionnaire The figures given are the outcome of the individual customization of the SP questionnaire. . . . I will now show you some imaginary situations and ask you what you would do if you were actually confronted by them. The aim is for you to try as hard as possible to imagine yourself in this situation and consider what it would really mean for you. In this way, you will ‘‘play the game’’ and try to tell us what you would actually do. I want to make it clear that we are not doing this survey for the government but for scientific research. Let us start with a first series of games. Do not hesitate to interrupt me if you have any questions. First, imagine that a tax is added to the current price of fuel at the pump. This tax has been introduced to encourage car drivers to consume less fuel. I would like to ask you now to open the envelope which was inside the letter we sent you. Scenario 1: First, imagine that a tax of 40 euro cents per liter of fuel is levied on any fuel consumed above a threshold of 400 liters onwards. You have three options for adapting your travel behavior. These three options have been computed based on the trips you reported in the first survey. It’s up to you to tell me what you would actually do. In the first option, you will no longer use your car for 302 very short-distance trips per year (approx. 5 trips per week) and you will pay a tax of €4 per year. In the second option, you will no longer use your car for 21 medium-distance trips per year (approx. 2 trips per month) and you will pay a tax of €31 per year. In the last option you will behave as now and pay a tax of €109 per year. What would you do in reality? Option 1 h

Option 2 h

Option 3 h

(if you have chosen option 1 or 2) Look at the list of your recent trips, could you tell me which car trips you would eliminate? ______________ . . .. Now forget about the tax and imagine that from now on a credit system for fuel consumption has been introduced. Each year you are entitled to a limited number of fuel credits. If you use all your credits and want to buy more fuel, you will have to buy new credits. Conversely, if you don’t use all your credits you can sell them.

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For instance: Each person gets an allowance of 500 credits corresponding to the right to consume 500 liters of fuel. A person who drives 15,000 km per year, with a car consuming an average of 8 liters per 100 km, consumes a total of 1,200 liters and therefore needs another 700 credits. If this person wants to keep the same car travel behavior, they will have to buy the credits they don’t have. If they don’t want to buy so many additional credits, they will have to reorganize their travel. Conversely, a person driving only 5,000 km per year, with a car consuming on average 8 liters per 100 km, consumes a total of 400 liters. This person can therefore sell 100 credits. If they want to sell more credits, they will have to reorganize their travel to save more credits. Have you understood this system well? Yes h

No h

If ‘‘no’’ explain again.

Scenario 3: Imagine that you get an allowance of 400 credits this year and that the price for buying additional credits is 70 cents of euro per credit. You have three options for adapting your travel behavior. These three options have been computed based on the trips you stated reported in the first survey. It’s up to you to tell me what you would actually do. In the first option, you will no longer use your car for 201 very short-distance trips per year (approx. 4 trips per week) and you will additional credits to a value of €23. In the second option, you must no longer use your car for 302 very short trips per year (approx. 5 trips per week) and 21 medium trips per year (approx. 2 trips per month) and you will be able to sell credits to a value of €130. In the last option you will continue to behave as now and buy additional credits to a value of €104 per year. What would you do in reality? Option 1 h

Option 2 h

Option 3 h

(if you have chosen choice of option 1 or 2) Considering Look at the list of your current recent trips, could you indicate tell me which car trips you would eliminate?______________ . . .. References Baumol, W., Oates, W., 1988. The Theory of Environmental Policy. Cambridge University Press, Cambridge, 299p. Bristow, A.L., Wardman, M., Zanni, A.M., Chintakayala, P.K., 2010. Public acceptability of personal carbon trading and carbon tax. Ecol. Econ. 69 (2010), 1824–1837. Capstick, S., Lewis, A., 2009. Personal Carbon Allowances: A Pilot Simulation and Questionnaire. UKERC, Oxford, 88p. Combet, E., Ghersi, F., Hourcade, J.-C., Théry, D., 2009. Need a Carbon Tax be Socially Regressive? True Challenges et Wrong Debates, CIRED, Nogent-surMarne. Fawcett, T., 2010. Personal carbon trading: a policy ahead of its time? Energy Policy. http://dx.doi.org/10.1016/j.enpol.2010.07.001. Fawcett, T., Parag, Y., 2010. An introduction to personal carbon trading. Climate Policy 10 (4), 329–338. Fleming, D., 2007. Energy and the Common Purpose: Descending the Energy Staircase with Tradable Energy Quotas (TEQs). The Lean Economy Connection, London. Frey, B., 1997. Not Just for the Money: An Economic Theory of Personal Motivation. Edward Elgar, Cheltenham. Frey, B., Stutzer, A., 2008. Environmental morale and motivation. In: Lewis, A. (Ed.), Psychology and Economic Behavior. Cambridge University Press, Cambridge, pp. 406–428. Harwatt, H., Tight, M., Bristow, A.L., Gühnemann, A., 2011. Personal carbon trading and fuel price increases in the transport sector: an exploratory study of public response in the UK. Eur. Transport. 47 (2011), 47–70. IEA, 2013. CO2 Emissions from Fuel Combustion. IEA Statistics Highlights. OECD/IEA. IPCC, 2014. Summary for Policymakers. In: Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Farahani, E., Kadner, S., Seyboth, K., Adler, A., Baum, I., Brunner, S., Eickemeier, P., Kriemann, B., Savolainen, J., Schlomer, S., von Stechow, C., Zwickel, T., Minx, J.C., (Eds.), Climate Change 2014, Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Kroes, E.P., Sheldon, R.J., 1988. Stated preference methods: an introduction. J. Transport Econ. Policy, 11–25. Laganier, J., Vienne, D., 2009. Recensement de la population de 2006. La croissance retrouvée des espaces ruraux et des grandes villes. Insee Première, n° 1218, Insee, Paris. Lejoux, P., Raux, C., 2011. Attitudes et changements de comportement de mobilité des ménages face à l’instauration de politiques de rationnement du carburant automobile: résultats d’une enquête qualitative. Cahiers Scientifiques du Transport n°59/2011, pp. 57–82. Louviere, J.J., Hensher, D.A., Swait, J.D., 2000. Stated Choice Methods: Analysis and Applications. Cambridge University Press. Lyons, A., Chatterjee, K., (Eds.), 2002. Transport Lessons from the Fuel Tax Protests of 2000. Ashgate, Aldershot. Mc Fadden, D., 1974. Conditional Logit Analysis of Qualitative Choice Behavior. In: Zarembka, P. (Ed.), Frontiers in Econometrics. Academic Press, New York, pp. 105–142. Parag, Y., Strickland, D., 2011. Personal carbon trading: a radical policy option for reducing emissions from the domestic sector. Environ.: Sci. Policy Sustain. Dev. 53 (1), 29. Parag, Y., Capstick, S., Poortinga, W., 2011. Policy attribute framing: a comparison between three policy instruments for personal emissions reduction. J. Policy Anal. Manage. 30 (4), 889–905.

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