Impacts of technology evolution and consumer choice on the direct CO2 emissions from new passenger cars in Switzerland J. Hofer, E. Wilhelm, W. Schenler Paul Scherrer Institut, CH-5232 Villigen PSI, Switzerland
Abstract Many advanced technology options are currently being pursued to reduce vehicle fuel consumption, while maintaining performance, cost and other consumer criteria. This is strongly intertwined with debate on vehicle CO2 emissions, and whether future vehicles will be dominated by advanced ICE, battery, hybrid or fuel cell drivetrains. The di culty is that incremental e ciency gains are gradual and advanced powertrains not yet ready to su ciently penetrate the vehicle eet in time to achieve near term CO2 emissions goals phased in between 2012 and 2015. This paper instead asks how far it would be necessary to reverse the historical trend to larger and higher performance vehicles in order to meet these carbon emissions targets. Using a novel methodology to estimate historic e ciency improvement rates, a macro-analysis of new vehicle sales nds that short-term CO2 reductions critically depend on the development of average weight, power, and e ciency of conventional diesel and gasoline vehicles. If the trend to heavier and higher performance vehicles continues the 2015 emission target of 130 g CO2/km will not be met. However it can be reached with the current drivetrain sales share if the high level e ciency improvement of -3 %/year that has been observed throughout the last three years can be continued and annual reductions of -1.1 %/year in vehicle weight at constant performance occur. A multinominal logit model of new vehicle choice is used to estimate what average vehicle characteristics and eet composition may be expected if consumers keep their current preferences for size and performance, but are faced with an increased purchase price based on the regulatory penalty to be imposed on vehicles exceeding the limit value of allowed CO2 emissions. Model results indicate that if all consumers of vehicles with a CO2 penalty higher than 5% relative to the purchase price select new vehicles subjected to a lower relative penalty the target can be met. This implies a decrease of -1.7% in average size and -6.5% in performance and is accompanied by a continuation of the historical trend from gasoline toward diesel and hybrid electric vehicle sales, as these technologies oer similar performance and size but at reduced CO2 emissions and a lower CO2 penalty. In this scenario CO2 reductions are achieved in equal shares by incremental e ciency gains and by a shift of sales to lighter, less powerful, and more e cient vehicles.
1. Introduction 1.1. Context Since 1950 the world's passenger vehicle eet has grown annually by about 5% corresponding to a doubling every 15 years and reached approximately 620 million vehicles in 2008 [1, 2]. The strong increase in motorization in India, China, and other parts of the developing world is expected to continue this rapid growth over the coming decades [3]. The increase in transportation demand and its dependence on fossil oil as the primary transport energy source will continue to cause rising greenhouse gas (GHG) and other exhaust emissions leading to global warming and health damages. Furthermore, the political instability of oil-producing regions and the declining production from present reserves make oil's security of supply and price stability uncertain. Email address:
[email protected] (J. Hofer)
There are a number of ways to reduce fuel consumption and direct CO2 emissions from new vehicles. The energy e ciency of conventional ICE vehicles can be improved in many ways, including cleaner turbocharged engines with direct fuel injection, improved transmissions, lightweighting through materials substitution, reducing aerodynamic drag, rolling resistance, and engine friction. Emissions can also be reduced by switching to fuels with lower carbon contents, and electri cation via hybrid or all-electric drivetrains. The extent to which these gains are re ected in future average energy use and emissions for the entire vehicle eet depends upon which technologies are implemented by manufacturers and chosen by car buyers.
1.2. Questions and issues How best can these technologies be used to meet aggregate energy and emissions targets? In the past, grad-
ual but signi cant incremental e ciency gains have to large extent been used in Western markets to increase vehicle size and performance, while keeping average fuel use reductions moderate. Future, incremental e ciency improvements will come gradually, and, even if they are promptly adopted, will not have rapid eet impacts. Major drivetrain changes (EV and hybrids) are not yet ready for large scale adoption, and will take a long time to penetrate the market against the current dominance of gasoline and diesel powertrains (97% of Swiss vehicle sales in 2010). The only way to use both past and future e ciency gains to reduce energy use and emissions would be if customers reversed their historical preferences for bigger and faster cars. This seems unlikely unless forced by regulation or fuel prices, but the question is none the less interesting: How much smaller and slower cars would the public need to accept, and at what cost, to meet near and moderate term emissions and energy targets?
a macro-analysis of aggregate new vehicle sales. Second, it develops and applies a utility function for new vehicle choice to analyze what average vehicle characteristics and eet composition may be expected if customers keep their current preferences for size and performance, but are faced with an increased purchase price based on the expected CO2 penalty.
2. Macro-analysis of new vehicle sales 2.1. Background on the new regulation The EU regulation limiting the CO2 emissions from new passenger cars [5] was recently adopted in the same form for Switzerland by the Swiss parliament and Federal Council. It limits the CO2 emissions from new passenger cars to an average of 130 g CO2/km for 65% of vehicles sold by 2012 and 100% by 2015, with a further probable long-term target of 95 g CO2/km by 2020. The regulation speci es penalties for Swiss vehicle importers or manufacturers on vehicles exceeding this target: the rst g/km overshoot is charged EUR 5 (CHF 7.50) per vehicle; with EUR 15 for the second and EUR 25 for the third g/km. Above a 3 g/km overshoot the full penalty of EUR 95 (CHF 142.50) per gram will then apply. The penalty will presumably be added to the purchase price of the vehicle by the importer or manufacturer and thereby passed on to the consumer. The emission limit will phase in gradually, meaning that in 2012 65% of new vehicle sales must meet the 130 g/km limit, 75% in 2013, 80% in 2014 and nally 100% in 2015.
1.3. Swiss and European conditions In 2010 44% of Swiss CO2 emissions came from transportation fuels, 98% of which came from gasoline and diesel fuel. According to the Swiss CO2 law that was rati ed in the context of meeting the Swiss Kyoto treaty targets, CO2 emissions from transport fuels should decrease to 8% below 1990 levels by 2012, even though by 2010 they were 13% above the 1990 level [4]. This growth trend is due not just to the increase in transport demand, but also to the continuous increase in size and performance of passenger vehicles. Since 2000 the average mass and power of newly registered Swiss vehicles increased by 7% and 10%, respectively, while average CO2 emissions have decreased 17%, showing that for this period e ciency gains outpaced increases in mass and performance. A voluntary agreement between the European Commission (EC) and vehicle manufacturers failed to reduce average CO2 emissions from new passenger cars. In response, the EC has adopted a regulation [5] limiting CO2 emissions from new passenger cars to a eet average of 130 g/km by 2012, with a long-term target of 95 g/km by 2020. The sharp decline of CO2 emissions for vehicles sold in the EU and the strong shift to diesel technology (now 50% of new vehicle sales in the EU) in the last years highlights the impact that mandatory reduction targets can have. Average CO2 emissions from new cars sold in Switzerland are considerably higher than the EU average (161 g/km in 2010 in Switzerland v. 146 g/km in 2009 in the EU). Thus, with Switzerland adopting the EU regulations for new passenger cars, the question becomes whether and how this target can be met. This paper addresses this question in two ways. First, it assesses the most important factors in uencing the short-term development of speci c CO2 emissions with
2.2. Historic and projected trends in speci c CO2 emissions from new passenger cars In 2010 the average speci c CO2 emission from new passenger cars registered in Switzerland was 161 g CO2/km, with 159 and 164 g CO2/km from gasoline and diesel vehicles, respectively [6]. From 2000 to 2010 the average annual fuel consumption, and hence CO2 emissions, declined 1.9 %/a for all newly registered vehicles, but this was much stronger for gasoline (2.3 %/a) than for diesel vehicles (0.8 %/a), partially due to a stronger weight increase for diesel vehicles. Throughout this period gasoline vehicle sales declined from 90% to 68%, and diesel sales increased from 10% to 30.2%. Alternative powertrain technologies gained a total market share of sales of 1.8% in 2010, including 1.5% hybrid electric vehicles (HEVs), 0.2% compressed natural gas vehicle (CNGs), 0.1% ex-fuel vehicles (FFVs), and battery electric vehicles (BEVs). To estimate short-term CO2 reduction potentials of dierent drivetrain technologies three bounding case scenarios are developed. In the no change (NC) scenario the vehicle sales shares of the year 2010 are kept constant until 2020. In a second scenario (Diesel) an increased replacement of gasoline with diesel vehicles 2
a) 90% 80% 70%
b)
No Change No Change
Gasoline
CO2 emissions (g/km)
100%
Diesel
Diesel Hybrid Electric
Sales share
Other 60% 50% 40%
New Technologies
c)
200 180 160 140 120 100
CH (historic) EU (historic) NC (BAU) Diesel (BAU) NTE (BAU) EC 2020 EC 2015
80 2000 120%
2005
2010
2015
2020
110%
30%
100%
20%
90%
10% 80%
0%
70% 1995
CO2 emissions Mass Acceleration performance
1997
1999
2001
2003
2005
2007
2009
2011
Figure 2.1: a) Drivetrain sales shares in the three considered scenarios from 1996 to 2010. b) Historic and projected speci c CO2 emission from new passenger cars for the no change (NC), increased dieselization (Diesel) and new technology expansion (NTE) scenario. Drivetrain speci c CO2 emissions are assumed to follow annual reductions which have been observed during the last ten years (BAU). c) Development of historic average acceleration performance, weight and fuel consumption from 1996 to 2010 relative to 1996 values.
2.4). The fact that average speci c CO2 emissions from new vehicles in the NTE scenario are only 2 g/km below the no change scenario highlights the fact that despite the high long-term reduction potential that alternative powertrain technologies oer, they cannot play a significant role in meeting short-term CO2 reduction targets. Even in the case of a very strong introduction of alternative powertrain technologies as considered in the NTE scenario, ICE vehicles represent 93% of vehicle sales in 2015 and 77% in 2020. Therefore short-term CO2 reductions are particularly sensitive to the future development of the fuel consumption of conventional vehicles. The approach used so far only projects historic fuel economy trends, but does not reveal information about eet composition and consumer preferences for certain vehicle attributes. As illustrated in Fig. 2.1c, from 1996 to 2010 the average vehicle fuel consumption decreased by more than 22% while during the same period average curb weight and acceleration performance (de ned by the power to weight ratio) increased by 11% and 8%, respectively. This trend suggests that not all of the technological development was directed towards reducing fuel consumption but in fact signi cant development contributed to increasing average vehicle performance and mass. It can be concluded that despite the long-term potential that new alternative drivetrains oer, compliance with short-term targets is most sensitive to the reduced fuel consumption of conventional gasoline and diesel vehicles due to their short-term (2015 to 2020) dominance. The goal of the next section is to identify this reduction potential independent of the development in weight and power, i.e. to determine the CO2 reduc-
is assumed. The diusion of diesel vehicles is modeled with a similar approach as in [7] by a Gompertz function that is calibrated with historical data and adjusted to reach 50% of sales in 2020, corresponding to a similar future growth of diesel in Switzerland as has occurred in the EU during the last years. Finally in a new technologies expansion (NTE) scenario high annual growth rates of 25% for HEVs, 20% for CNG, FFVs, and 40% for BEVs are assumed. Kromer et al. [8] pointed out that such high growth rates are possible for technologies that have not yet gained signi cant market share but is di cult to maintain once a higher level of market penetration is reached. In the NTE scenario the relative share of gasoline to diesel are kept constant at the 2010 level. The sales shares in the dierent scenarios are depicted in Fig. 2.1a. Average e ciency improvements of conventional internal combustion engine (ICE) vehicles are assumed to follow the average 10 year reduction observed from 2000 to 2010 (BAU), i.e. -2.3 %/a for gasoline and -0.8 %/a for diesel vehicles. For alternative drivetrain technologies an annual CO2 reduction of -2%/a is assumed. As can be seen from Fig. 2.1b the average CO2 emissions from new passenger cars lie signi cantly above the intended target of 130 g/km in 2015 for all three scenarios (by 14g for the NTE scenario, 16g for NC, and 18g for diesel, which would lead on average to a penalty range of 1600 to 2200 CHF per vehicle). Higher CO2 emissions for the diesel relative to the no change scenario are due to the low reference CO2 reduction observed for diesel vehicles from 2000 to 2010 and the assumption that consumers shifting from gasoline to diesel purchase buy a car with average diesel characteristics (this assumption will be further discussed in 3
tion potential if mass and power were to stay constant.
and power, a regression analysis of vehicles with similar power and weight sold at the same time was performed. Several regression functions were tested for suitability. A physically intuitive model was chosen, based on driving cycle parameters and Eq. (2.1) with e ciency parametrized as a function of powertrain type and performance. Polynomial regression models of multiple degree in power, weight and mixed terms as in [13, 14] were also investigated. Higher-order polynomials did not lead to signi cantly better results and for simplicity we continue with a linear regression model (the following analysis can be equally applied with a more complex regression function). Since the aim is to determine the in uence of power and weight separately, mixed terms terms combining power and mass were excluded. The fuel consumption FCi of the ith vehicle is parametrized separately for gasoline and diesel vehicles as a function of mass mi and power Pi according to
2.3. Technical e ciency improvement independent of shifts in vehicle weight and power Regardless of trends in vehicle mass and power driven by the manufacturers' oerings and consumers' choices of size and performance, the fuel economy of new vehicles is continuously improving due to e ciency technologies implemented by manufacturers. The goal here is to determine these e ciency improvements separately for gasoline and diesel vehicles sold in Switzerland in the period from 1996 to 2010. Several studies have tried to assess technical e ciency gains [9, 10, 11, 7]. For example, An and DeCicco [9] analyzed a combined performance, size, and fuel economy index (PSFI) and observed a linear trend for this value in the US. Here we quantitatively determine the dependence of fuel consumption on power and mass by a regression analysis of a large dataset comprised of most vehicles newly registered in Switzerland from 2000 to 2010 and apply it to the development of sales average fuel consumption, mass, and power during the same period. Vehicle mechanical energy demand and fuel supply can be linked by combining the equation of motion with the thermodynamic e ciency of the primary energy converter. In [12] it is shown how the mechanical energy demand for a certain driving cycle and vehicle can be calculated by distinguishing the phases of traction, coasting, and braking. Using a single operating point approach with an average engine e ciency η the vehicle energy use per unit of distance x traveled can be approximated by the formula E Af · cd · A + m · cr · B + m · C = x η
FCi = D + E · mi + F · Pi
(2.2)
where the parameters D, E, and F were estimated using a least squares regression. The dataset is based on the MOFIS database from the federal roads o ce ASTRA and includes most vehicles registered in Switzerland from the years 2000 to 2010. It contains among other technical characteristics each model's curb weight, power, fuel type, fuel consumption and CO2 emission as reported by the vehicle manufacturer and measured in a dynamometer test using the New European Driving Cycle (NEDC). In order to cover vehicles representative of average models sold and not distort the regression with vehicles having extreme values of performance and/or weight, the regression was limited to vehicles sold more than 50 times, and having power of 60-250 kW and a curb weight of 1000-2000 kg. Since the number of vehicles sold was only available for 2010 the regression was limited to vehicles sold in 2010 (parameters E and F vary only very little from 2000 to 2010).
(2.1)
where Af is the vehicle frontal area, cd is the aerodynamic drag coe cient, cr is the rolling resistance coef cient, and m is vehicle mass. These terms summarize aerodynamic drag, tire rolling resistance, and kinetic losses, respectively. The coe cients A, B, C depend mainly on the driving cycle and the recuperation e ciency of the vehicle. As can be seen from Eq. (2.1), besides cd and cr , fuel consumption depends mainly on vehicle weight, frontal area, and powertrain e ciency. Vehicle weight and frontal area are both strongly related to vehicle size, and powertrain e ciency depends on both the type of the primary energy converter (gasoline, diesel, degree of electri cation, etc.) and further engine characteristics, such as the engine displacement, turbocharging, or variable valve timing. A high power to vehicle weight ratio generally aects fuel consumption negatively as the engine is oversized and more often used at partial load operating points with lower e ciency. In order to calculate the sensitivity of the average fuel consumption of vehicles sold to changes in mass
Table 1: Resulting parameters E and F of the linear regression determining the dependency on mass and power in Eq. (2.2)
Gasoline
E 0.0029
F 0.0136
Diesel
0.0042
0.0055
The normal distribution of residuals indicates suitability of the model function. The standard deviation (SD) of the normal t is 0.6 liter/100km gasoline equivalent (GE). For 90% of cars the deviation of modeled to observed fuel consumption is less than 1 liter/100km GE. The coe cient of determination R2 is 0.67 for this subset, however increases to 0.84 for the whole set of gasoline cars. Knowing the dependence of average fuel consumption on mass and power, the fuel consumption 4
a)
b) SD = 0.6 l/100km
Figure 2.2: a) Result of a multiple linear regression for a set of vehicles representative of average new vehicle models sold in Switzerland in 2010. b) Deviation of fuel consumption as modeled and observed for gasoline vehicles and normal t.
that could have been reached if mass and power had stayed constant can be calculated. First the observed annual fuel consumption reduction Rx+1 from year x to x+1 is expressed as Rx+1 =
FCx+1 − FCx FCx
While from 1997 to 2004 the e ciency improvement is constant at a level of about -2 %/a, there is a clear increase for both gasoline and diesel vehicles since 2005 to an average of about -3 %/a in the last three years. This trend indicates that since 2005 a higher degree of e ciency measures has been implemented in new gasoline and diesel vehicles sold in Switzerland. The same methodology as described above applied to the development of average sales in the EU shows a similar increase of the reduction potential from 2005 to 2010 as discussed here for Switzerland. The results for Switzerland seem plausible if one compares them to de Haan [15] who estimates a technological potential (corresponding to RP) of -2.4%/a for the period of 1996 to 2007. Applying the annual reduction potential as calculated above to the average CO2 emissions from gasoline cars in 1996 we nd that if mass and power would have stayed constant at the 1996 level, CO2 emissions in 2010 would have been 4.2% below the actual level for 2010 (cf. Fig. 2.3a). If both the average mass and power had decreased by 10% relative to 1996 (meaning that the acceleration given by the power to weight ratio had remained constant), average CO2 emissions of gasoline cars in 2010 would be 10.4% below the actual level for 2010.
(2.3)
Based on the values for E and F found in the regression analysis, the potential fuel consumption in year x+1 using the average mass and power of vehicles in year x is calculated according to FCpot, x+1
= FCx · (1 + Rx+1 ) − (mx+1 − mx ) · E =
−(Px+1 − Px ) · F
(2.4)
The fuel consumption reduction potential RPx+1 follows then as RPx+1 =
FCpot, x+1 − FCx FCx
(2.5)
Resulting values calculated for the average annual fuel consumption reduction (observed and potential) of the last 15, 10, 5, and 3 years are tabulated separately for gasoline and diesel vehicles in Table 2. Figure 2.3 illustrates the application of the described methodology to Swiss gasoline sales in the period from 1996 to 2010.
2.4. Sensitivity of scenarios to the development of e ciency improvement, weight and power
Table 2: Annual fuel consumption reduction (%/a) real (R)
Average CO2 emissions from new passenger cars in 2015 for the three scenarios compared in 2.2 above signi cantly exceed the target of 130 g CO2/km. In all scenarios an annual CO2 reduction equal to the average fuel consumption reduction of the last ten years was assumed (indicated by R in Table 2) without consideration of the development of average mass and power. In the following analysis e ciency improvements similar to the reduction potential shown in Table 2 are applied
and potential (RP) of vehicles newly registered in Switzerland on average over the last 3, 5, 10, and 15 years. 2010 to... Rgasoline
2007 - 4.2
2005 - 3.4
2000 - 2.3
1995 - 2.0
RPgasoline
- 3.1
- 2.9
- 2.5
- 2.3
Rdiesel
- 3.2
- 1.3
- 0.8
RPdiesel
- 2.8
- 1.9
- 1.7 5
a) 130%
b)
Gasoline vehicles
1.0%
120%
0.0%
Power 100% Fuel consumption 90% FC potential (m and P constant at 1996 level) FC potential (m and P 10% below 1996 level)
80% 70%
Annual reduction
Mass 110%
1997
2000
2003
2006
2009
‐1.0%
Reduction due to change in mass Reduction due to change in power
‐2.0%
Fuel consumption reduction (real) ‐3.0%
Reduction potential (m and P constant)
‐4.0% 60% 1996 1998 2000 2002 2004 2006 2008 2010
‐5.0%
Figure 2.3: a) Development of gasoline sales average mass, power, and fuel consumption in Switzerland from 1996 to 2010, as well as potential fuel consumption (according to model) if mass and power would have stayed constant or would have decreased by 10 % relative to 1996. b) Average fuel consumption reduction (real) that has been observed for gasoline vehicles from 1997 to 2010 and reduction potential independent of contributions from weight and power.
to the no change and diesel scenario. A moderate (-2 %/a) and high (-3 %/a) level of e ciency improvement are used, re ecting two levels of future e ciency technology implementation. For comparison: simulated ef ciency improvements due to expected engine improvements, reduction of vehicle resistances, etc. at constant mass and performance from [16] translated to the Swiss situation in 2010 are -2.4 %/a for gasoline, -2.2 %/a for diesel, and -3.0 %/a for hybrid electric vehicles. This shows that the e ciency improvement assumed is technically feasible. The sensitivity of the scenarios to four speci c modi cations regarding future development of e ciency, weight, and performance is evaluated.
Table 3: Average new vehicle sales CO2 emissions for adjusted scenarios by 2015.
NC (BAU)
CO2 (g/km) 146
Diesel (BAU)
148
NTE (BAU)
144
NC I
144
Diesel Ia
145
Diesel Ib
142
NC II
138
NC III
130
NC I: No change scenario vehicle sales shares, with
moderate e ciency improvement (-2 %/a), and mass and power constant at 2010 level.
Diesel Ia/Ib: Diesel scenario vehicle sales shares,
otherwise the same as NC I for Ia. In Ib it is additionally assumed that consumers shifting from gasoline to diesel cars buy diesel vehicles of the same performance and size as current gasoline cars and that these vehicles are on average only 5% heavier than the corresponding gasoline models (see box 1 for background information on this assumption and current trends).
The methodology presented in this section can be applied to data from individual countries or manufacturers to infer reduction potential and hence assist in setting fuel price and/or emissions standards (including weight classes and associated performance/mass scaling factors).
NC II: Moderate e ciency improvement (-2 %/a), mass and power annually reduced by -1% (constant performance).
NC III: High e ciency improvement (-3 %/a),
with a -1.1 %/a reduction of mass and power to ful ll the target of 130 g CO2/km by 2015.
Results of scenario modi cations are shown in Fig. 2.4 and in Table 3. 6
Key ndings:
Box 1: Trends in gasoline to diesel weight
1. Reaching short-term CO2 reductions is particularly sensitive to the e ciency improvement of conventional diesel and gasoline vehicles brought on the market and to changes of their mass and performance. 2. If the trend to heavier and higher performance vehicles continues these reduction targets will not be met, even if very e cient new powertrain technologies such as hybrid electric or battery electric vehicles penetrate the market at very optimistic rates. If a shift from gasoline to diesel and/or hybrid electric vehicles should occur, the higher e ciency that these vehicles oer should not be used to increase weight and/or performance. 3. The analysis shows that the 2015 target of 130 g CO2/km can be reached with the current drivetrain sales share if the high level e ciency improvement of -3 %/a that has been observed for conventional vehicle technologies throughout the last three years can be continued and annual reductions of -1.1%/a in vehicle weight at constant performance occur.
Since 1996 the average weight of vehicles sold in Switzerland has increased by 11% (Fig. 2.1). A similar trend occurred in the US and some European countries and there has been discussion whether increased safety standards lead to this development [7]. However, [17] and other studies indicate that enhanced safety of modern cars has a small eect on vehicle mass and we expect that an increase in size and relative power is more relevant in explaining this trend. In fact weight increases linearly with size up to a certain point, but it also correlates with performance, as large, powerful engines are generally heavier. Traditionally diesel cars are designed to be larger whereas gasoline cars have higher performance (Fig. 3.1c). This leads to a higher average weight to power ratio for diesel cars and a higher weight to size ratio for gasoline cars, which makes it di cult to determine the average weight dierence. Comparing about 3000 current gasoline and diesel models with sizes of 10-14 m3 and performance of 50-90 kW/tonne, representing more than 50% of sales in 2010, we nd that the average weight per volume and power is not more than 5% higher for diesel cars. This means that for a given car with corresponding size and performance, the average weight increase when purchasing a diesel instead of a gasoline car should not be more than 5%. The analysis in Sec. 3.4 also nds that a redistribution of gasoline to diesel sales at constant average size and performance leads to a small increase in weight. During the period 2001 to 2007 gasoline sales share decreased and diesel sales share increased continuously (see gure below). At the same time the average diesel to gasoline mass ratio increased from 116% to 121% and the average CO2 emissions ratio from 88% to 99%. If one assumes that the 14% diesel share of sales in 2001 increased on average in mass at the same trend as gasoline cars for the period up to 2007, then this suggests that consumers shifting from gasoline to diesel selected on average vehicles that were above the average diesel weight and caused the higher weight increase for diesel cars. The increasing weight dierence is the main reason for the converging CO2 emissions of gasoline and diesel cars and leads to the situation that in 2010 the average CO2 emissions of diesel cars were 2% above gasoline CO2 emissions (at a 26% higher weight of diesel cars).
3. New vehicle choice under policy constraints 3.1. Previous work and objective Discrete choice models are commonly used to model automotive demand by estimating the probability of consumer choice between competing alternatives using the principle of utility maximization [18, 19, 20, 21, 22, 23]. A multinomial logit (MNL) formulation is the predominant approach to aggregate and disaggregate choice analysis that assumes that unknown components of the utility function are uncorrelated across alternatives [24]. The relative weight consumers hold for certain vehicle attributes is usually either inferred from their stated preferences in surveys or from preferences observed in aggregated vehicle sales data. Here a multiattribute MNL model of vehicle choice is developed, based on dierential weighting. Dierential weighting in multi-attribute utility measurement has been investigated theoretically in [25]. This is the rst time that a similar methodology has been applied to a comprehensive dataset of countrywide new vehicle sales. The methodology can be applied to analyze consumer purchase decisions based on historic preferences for certain vehicle attributes. It allows constraints to be set on vehicle characteristics such as the speci c CO2 emissions or fuel consumption and to investigate the corresponding changes in new vehicle sales. The following analysis investigates what new vehicle sales and characteristics can be expected in Switzerland with the
130% 120% 110% 100%
Mass diesel/gasoline
90%
Mass diesel
80% Mass gasoline
70% 60%
CO2 diesel/gasoline
50%
Gasoline sales
40%
Diesel sales
30% 20% 10% 2001
2002
2003
2004
2005
2006
2007
7
160
CO2 emissions (g/km)
150 140 130 120 110 100 90 2009
NC (BAU) NC I Diesel Ia Diesel Ib NC II NC III EC 2015 EC 2020 2010
2011
2012
2013
2014
2015
Year
2016
2017
2018
2019
2020
2021
Figure 2.4: Prospective speci c CO2 emission from Swiss new vehicle registrations for the business as usual scenario (BAU), the advanced introduction of alternative technologies (ATE), and three cases applied to the BAU scenario regarding the future development of gasoline and diesel, e ciency improvement, weight and performance. Red lines indicate the target set by the European Commission (EC).
regulation limiting CO2 emissions from new passenger cars (Sec. 2.1) if consumers keep their current preferences for size and performance, and are faced with a higher purchase price based on the expected CO2 penalty.
available. It is expected that more alternative drivetrain technologies will be oered in the future, however it remains questionable whether these vehicles can compete with conventional ICE vehicles in terms of performance, price, and other attributes important to consumers. The main restriction of the following analysis is that for the moment it is limited to vehicle technology currently available and does not take into account new alternative models that will enter the market (expected e ciency improvements are considered). However due to the short time remaining until 2015 it is assumed that the model line-up will not signi cantly alter and that the main conclusions remain valid.
3.2. Current preferences for vehicle attributes in uencing fuel consumption The analysis is based on a dataset of more than 7000 unique vehicle models representing aggregated Swiss passenger car sales of the year 2010, courtesy of Auto Schweiz. In addition to the number of vehicles sold, it gives a technical characterization for each model, including curb weight, power, and fuel type, as well as fuel consumption and CO2 emissions as reported by the vehicle manufacturer and measured in a dynamometer test for the New European Driving Cycle (NEDC). To reveal information on vehicle size data from the MOFIS database of the federal roads o ce ASTRA was complemented with additional information from the type approval database TARGA. Figure 3.1a illustrates the trade-os between accel·v0 eration time (approximated by tacc = Pmmax , which is P ), inversely proportional to the performance metric m weight, and fuel consumption. Acceleration performance and size are the two main vehicle attributes in uencing fuel consumption. Acceleration time and size are in general positively correlated, i.e. acceleration time increases with size. Fig. 3.1b depicts the actual range and distribution of vehicles oered and sold corresponding to these attributes.While available gasoline and diesel powertrains cover a relatively homogeneous range of performance and size, this is not the case for HEVs and other alternative drivetrain technologies due to the limited number of models currently
3.3. Methodology The EU regulation limiting the CO2 emissions from new passenger cars [5] speci es a so-called limit value curve (LVC), which implies that heavier cars are allowed higher CO2 emissions than lighter cars. The CO2 emissions allowed are adjusted by vehicle weight according to the equation CO2LVC = 130 + 0.0457 · (m − m)
g km
(3.1)
where m is the mass of the vehicle and m the average mass of all sold vehicles. Fig. 3.2a illustrates how CO2 emissions from new vehicles sold in 2010 deviated from the allowed CO2 limit. In the following analysis it is expected that the ne the vehicle manufacturer or importer is subjected to is directly or indirectly passed on to the consumer and that the penalty for a speci c vehicle due to excess CO2 emissions is added to the purchase price. The penalty that would apply for the level of CO2 emissions in 2010 would have averaged 10% of the purchase price of the vehicle, but could have been 8
a)
b)
c)
Figure 3.1: a) Trade-o between fuel consumption and acceleration time of vehicles sold in 2010. Vehicle weight and engine e ciency also signi cantly in uence fuel consumption. b) Range and distribution of oered and sold vehicles performance and size.
more than 50% for some models (see Fig. 3.2b and c). Note that the penalty is calculated in this case according to CO2 emissions of the vehicle in 2010 and the LVC according to the average weight of vehicles sold in 2010 (1470 kg). Both variables change as the e ciency improves and the average weight of vehicles sold varies. If one assumes that all vehicles in the dataset undergo a moderate e ciency improvement of -2 %/a until 2015 and that consumer choice remains constant, the sales average expected penalty would reduce to 5.5% of the purchase price but can be still substantially higher for a large number of vehicles.
more e cient cars. The main assumption is that consumers buy vehicles of similar size, performance, and price as their originally intended purchase but subject to a lower relative purchase price increase. Accordingly dierent levels of consumer attitude towards an increase in purchase price and the corresponding redistribution among a reduced set of cars are simulated. The maximum accepted purchase price increase (or CO2 penalty ratio) is steadily decreased from 25% (no change, representing approximately the sales mix in 2010) to a value su cient to meet the overall CO2 reduction target. The yearly e ciency improvement that is assumed is based on ndings of the previous section. For simplicity no distinction between dierent drivetrains is made and an average CO2 reduction of -2%/a per vehicle is assumed, corresponding to a moderate e ciency improvement rate. The set of competing alternatives available to the consumer includes all passenger cars marketed in Switzerland in 2010 comprised of six dierent fuel and powertrain types (gasoline, diesel, hybrid electric, E85, CNG, and LPG) and vehicles of all segments and classes. Vehicle attributes currently considered in the new vehicle selection pro-
In 2010, 702 of the 7123 passenger car models oered in Switzerland lay below the LVC and therefore would not have been subjected to a penalty. If a moderate e ciency improvement of -2 %/a is assumed, this number increases to 1638 or 23% of vehicles oered by 2015. It is expected that vehicle buyers whose intended car has the highest relative increase in purchase price will be among those most likely to reconsider their purchase decisions. The ratio of CO2 penalty to purchase price of the vehicle is therefore used as a model variable to determine the order in which consumers decide to buy 9
cess are acceleration performance (de ned as the power to weight ratio), size (de ned by the vehicle external dimensions S = L · W · H), and purchase price.1 Total cost (consisting of purchase price and fuel cost over the vehicle lifetime; maintenance costs are neglected and future fuel savings not discounted) is calculated as an additional economic indicator. For purpose of comparison it is assumed that all drivetrains are driven 13000 km per year for 12 years using current Swiss fuel prices for the dierent fuels. The redistribution of car sales is modeled by assuming that buyers of vehicles above a certain expected purchase price increase steadily buy vehicles of similar size, performance, and price as their original purchased vehicle but from a reduced set of vehicles subjected to a lower relative penalty. The observed 2010 consumer preference for acceleration and size is used as the baseline preference maximizing the overall utility of the speci c car buyer. The reallocation for consumers above a prede ned maximum purchase price increase is as follows. The utility Ui,t for the ith consumer above the prede ned maximum price increase shifting to a new vehicle t which has a lower relative price increase is calculated by Ui,t
=
Figure 3.2: a) CO2 emissions of new passenger cars registered in Switzerland in the year 2010 and limit value curve of permitted CO2 emissions by 2015 according to vehicle mass (red line). b) Penalty ratio that would apply for a speci c vehicle model (sorted from lowest to highest) in the year 2010 (blue) and 2015 (red) with an annual CO2 reduction of -2%. c) Distribution of vehicle sales in 2010 relative to the expected purchase price increase in 2010 (blue) and 2015 (red).
α i −At β + A4 Ai
+
Si −St 4Si
+
Pi −Pt 4Pi
where
4Ai
= (Ai − At )
4Pi
= (Pi − Pt )
4Si
= (Si − St )
In the following we set α=1 and β =0.1 which results in a relatively sharp probability distribution, i.e. only ca. 1% of alternative vehicle models lying closest to the original values of size, performance, and price achieve a signi cant probability. The maximum to minimum probability is on the order of 1 · 10−4 . If β is increased to a relatively high value (e.g. 1 · 105 ) this results in a uniform distribution of consumers from set I within set T. All consumers of vehicles below the de ned maximum price increase are unaected by the redistribution and keep their vehicle choice. The maximum accepted additional purchase price is decreased in 10 steps from 25% (no change) to a value su cient to reach average CO2 emissions of 130 g/km.
(3.2)
and i∈I is the set of vehicles above the maximum price increase and t∈T the set of vehicles below. Ai , Si , and Pi are the acceleration performance, size, and purchase price of the ith car above the maximum price increase (note that Pi is the original purchase price without penalty but Pt the new price including penalty). To allow the same weight of each dierential attribute term Xi − Xt in Ui,t it is normalized by the average deviation (Xi − Xt ) with regard to the whole new set of vehicles T. The parameters α and β determine the sharpness and maximum to minimum dierence of the probability distribution which is calculated according to a standard multinominal logit function
exp(Ui,t ) t∈T exp(Ui,t )
Pri,t = P
3.4. Results The result for this scenario redistribution according to equations (3.2, 3.3) with α=1 and β =0.1 is summarized in Table 4 and Fig. 3.3. Depending on the consumer sensitivity to an increase of purchase price, simulation results show on average a shift to more e cient, slightly smaller, lighter,
(3.3)
1 Purchase price is based on a regression and alignment of a dataset received from Verkehrs-Club Schweiz (VCS) and vehicles listed in the MOFIS database. Unfortunately not every model in MOFIS can be assigned a precise purchase price, because the VCS dataset is not as comprehensive as the MOFIS. Therefore vehicle speci c purchase price is related with some uncertainty, even though overall economic trends are valid.
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and less powerful vehicles. If all consumers of vehicles with a CO2 penalty higher than 5% relative to the purchase price select a new vehicle subjected to a lower relative penalty the target of 130 g CO2/km by 2015 can be met. Fig. 3.3 shows that a decrease of -1.7% in average size and -6.5% in average performance is su cient, however, the decrease in performance and/or size for consumers of high performance and/or very large vehicles is more severe. The total necessary CO2 reduction of -19.3% is achieved by a moderate yearly e ciency improvement of 2%/a (accounting for -9.1% reduction without consumer redistribution) and an additional 10.2% by a shift of sales to lighter, less powerful, and more e cient vehicles. The development is accompanied by a shift of sales from gasoline to diesel (diesel vehicles sales share rises from 30.2% to 45.6%, corresponding to +51% relative to 2010) and hybrid electric vehicles (+33% relative to 2010) as they can oer a similar performance and size at lower CO2 emissions. The higher average purchase price of diesel and HEVs relative to gasoline vehicles is reduced by the in average lower CO2 penalty. On average purchase price decreases by -10.5% due in part to a reduced penalty (-4.5%) and a shift to less powerful and smaller vehicles. The shift to more expensive diesel and hybrid technology is more than compensated for by savings related to a decrease of average performance and size. In absolute terms total costs decrease even more than purchase price due to additional fuel savings (see Fig. 3.3c). The relatively low increase of HEVs compared to diesel is related to the still low number of models and correspondingly limited range of performance and size oered, as well as the slightly higher purchase price for HEVs. However, simulation results indicate that with more stringent targets the share of HEVs will further increase as they can oer performance and size similar to current ICE vehicles at signi cantly reduced CO2 emissions. It is expected that in the coming years manufacturers will increase the number of HEVs oered and that they will gain market share by 2015 and beyond. If the weight and power reductions as well as gasoline and diesel market shares due to the shift in vehicle choice as given in Table 4 are substituted back into the analysis of Sec. 2.4, similar to scenario Diesel Ib with corresponding adjustments in mass and power, we see that the ful llment of the target of 130 g CO2/km is con rmed. Although not presented here, this serves to con rming the methodology of Sec. 2.3.
penalty higher than 5% relative to the purchase price select new vehicles subjected to a lower relative penalty the target can be met. 2. This implies a necessary decrease of -1.7% in average size and -6.5% in average performance. CO2 reductions are achieved in equal shares by incremental e ciency gains of 2%/a and by a shift of sales to lighter, less powerful, and more e cient vehicles. 3. The development is accompanied by a further shift of gasoline vehicle sales to diesel and hybrid electric vehicles as they can oer similar performance and size but at reduced CO2 emissions. The higher average purchase price of diesel and HEVs relative to gasoline vehicles is reduced by the in average lower CO2 penalty. Purchase price and total cost are further reduced by savings related to a decrease of average performance and fuel use reductions.
4. Conclusion and outlook This work has examined the implications of vehicle e ciency, weight and performance on fuel consumption and carbon emissions. It has concluded that there needs to be a reversal of the overall historic trend to larger and faster vehicles if regulatory CO2 targets are to be met, instead devoting technological improvements to lighter and more e cient gasoline and diesel cars. This has in fact been the case in Switzerland in the past three years and a further reduction of -1.1 %/a in gasoline and diesel vehicle weight would allow emissions targets to be met. A model of consumer choice indicates a continuation of the trend toward increased diesel and hybrid electric share in the Swiss new vehicle eet, as these technologies oer similar performance and size but at reduced CO2 emissions. Note that even though diesel exhaust emissions strongly decreased throughout the last years diesel cars still emit far more nitrogen oxides and particulate matter than gasoline cars do. Besides CO2 emissions considerations for emission control technologies must be taken into account to ensure local air quality improvement. Future work should also include a full life-cycle assessment of transportation fuels. The analysis presented is currently limited to vehicle technologies on the market. Although a reasonable assumption for the time frame until 2015, long-term analysis will require modeling of future powertrain technologies to estimate consumer acceptance. A more detailed examination of advanced technology options for passenger vehicle transportation is planned within the framework of the THELMA project.
Key ndings: 1. Given the regulatory penalty to be imposed consumer sensitivity towards the expected purchase price increase is critical in reaching the new car standard of 130 g CO2/km. By using a constrained utility model of consumer choice, it is found that if all consumers of vehicles with a CO2
Acknowledgment This work was supported by the THELMA project (http://www.thelma-emobility.net/) sponsors Swisse11
Table 4: Characteristics of average vehicle sales in 2010 and for the scenario reaching 130 g CO2/km by 2015.
CO2 emissions (g/km) Size (m3 ) Acc. perf (W/kg) Curb weight (kg) Power (kW) Displacement (ccm) Purchase price (CHF) Total cost (CHF) Sales share (%)
2010average 161 11.7 71.1 1469 107 1813 39,800 58,300 100
2010gasoline 161 11.1 73.4 1360 103 1699 36,200 54,500 68.0
2010diesel 164 13.2 66.2 1708 113 2055 47,200 66,600 30.2
a)
SCEN130average 130 11.5 66.5 1445 98 1720 38,000 53,000 100
SCEN130gasoline 125 10.5 66.0 1269 85 1456 30,000 44,100 51.9
SCEN130diesel 137 12.7 67.3 1634 111 2000 46,500 62,700 45.6
Sales share
Sales average in 2015 relative to 2010
b)
Maximum accepted CO2 penalty ratio (%)
Maximum accepted CO2 penalty ratio (%)
c)
Figure 3.3: Scenario development of sales average characteristics relative to 2010 (a), drivetrain sales shares (b), and absolute vehicle characteristics (c) as a function of maximum accepted purchase price increase.
12
lectric Research and the Swiss Competence Center Energy and Mobility (CCEM). We would like to thank Auto-Schweiz, Verkehrs-Club Schweiz, and the Federal Statistical O ce for valuable discussions and access to vehicle sales statistics.
[13] Noembrini, F. G. (2009) Modeling and Analysis of the Swiss Energy System Dynamics with Emphasis on the Interconnection between Transportation and Energy Conversion. PhD thesis. [14] Mellios, G., Hausberger, S., Keller, M., Samaras, C., and Ntziachristos, L. (2010) Parameterisation of fuel consumption and CO2 emissions of passenger cars and light commercial vehicles for modelling purposes. EMISIA SA, nal report.
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