Impact of the car purchase subsidy policy in Andorra: an
ex-post analysis of CO
, Marc Pons
, Marti Rosas-Casals
Observatori de la Sostenibilitat d’Andorra (OBSA), Sant Julià de Lòria, Andorra
Sustainability Measurement and Modelling Lab (SUMMLab), Universitat Politècnica de
Catalunya (UPC), EET-Campus Terrassa, Barcelona, Spain
Research Institute in Science and Technology for Sustainability - IS.UPC, Universitat
Politècnica de Catalunya (UPC), Campus Nord - TG Building, Barcelona, Spain
This paper analyses the impact of two car purchase subsidies schemes applied in the
Principality of Andorra during 2014 and 2015 in terms of energy and CO
emissions. A model
representing car fleet energy consumption in 2013 is used to conduct the analysis. It is
developed through a bottom-up methodology using vehicle registration and technical
inspection data. The model estimates energy consumption and CO
emissions using fleet
structure, mileage profile and fuel economy as explanatory variables. Private car fleet in
2013 is disaggregated into different categories according to fuel type, engine size and
vintage (car age). New registered and scrappage vehicles associated to the 2014 and 2015
subsidy programs have been introduced to the base model in order to quantify their impact
in the car fleet energy consumption and CO
emissions. These effects are compared
between the two programs as they had different characteristics. In contrast to the previous
year, the 2015 scheme did not link the economic incentives to the purchased car fuel
economy. Finally, the effectiveness of the subsidy policies are assessed in terms of CO
abatement costs (€/TCO
) and compared to the effectiveness of other energy efficiency
Key Words: Subsidies, private car fleet, CO
emissions reduction, Andorra
In 2012, global transport energy consumption accounted for 105 EJ (28% of overall
final energy consumption). It has grown 132% since 1973 and current projections suggest
a continuous increase until 2035 . The associated CO
emissions rose broadly in line with
this increased energy consumption due to the reliance of main transport modes on oil-based
fuels . Within the European Union framework, transport is currently the most energy-
consuming sector (32% of final energy consumption) and the one with the highest growth
since 1990. Road transport is by far the largest energy consumer and accounted for 72% of
total transport energy consumption . Strong efforts are required to drastically reduce the
emissions in the transportation sector to achieve the goals put forward in the 2011
* Corresponding author. (O. Travesset-Baro)
E-mail address: email@example.com
Plaça de la Germandat 7, AD600, Sant Julià de Lòria, Andorra
White Paper on Transport , i.e. a 20% reduction in the CO
emissions by 2030 relative to
2008 levels and a 60% reduction by 2050 relative to 1990 levels.
The first main measure of the EU strategy to reduce transport CO
emissions was the
implementation of the regulations on passenger cars (Regulation (EC) No 443/2009).
standards for new passenger cars introduced in 2009 set a target of 130
/km for the fleet average of all new cars in 2015 and a further indicative target of 95
/km in 2020. Later on, Regulation (EU) No 333/2014 confirmed the 95 gCO
by 2021, phased in from 2020. In addition, the new regulation introduced super-credits as a
measure to give manufacturers additional incentives to produce vehicles with extremely low
emissions (below 50 gCO
/km). At country scale, recent road transport policies have focused
on three main areas: (1) vehicle efficiency improvements such as promoting cars with lower
emissions, vehicle scrappage programs and promotion of more efficient transport modes
(i.e. support for public transport or non-motorised modes), (2) behavioural measures
focused on demand reduction such as promotion of eco-driving, teleworking or travel
planning and (3) switching to alternative fuels such as EVs, CNGVs, biofuels, etc. . The
most common specific measures implemented to reduce GHG emissions are subsidies (i.e.
car purchase subsidies, subsidy of public transport), car scrappage schemes, fee
adjustments (i.e. fuel taxes, car purchase and ownership taxes, infrastructure charges, VAT
reductions/exemptions) or mixing some of them (i.e. feebate which combines taxes and
subsidies for most and less polluting cars respectively). Although governments invest a
significant amount of money in policy instruments to address CO
emissions through car
sales and car retirements, only few studies examine their effectiveness. Notable exceptions
are found in the fields of car taxation policy –, subsidy policy of low emission vehicles
, , vehicle scrappage programs , , feebates on the purchase of new cars 
and government incentives to promote the adoption of hybrid-electric vehicles .
This paper analyses the car subsidy policy introduced in the Principality of Andorra
during 2014 and 2015 focused on improving the energy efficiency and also the security of
the national vehicle fleet. Andorra is a small country located between France and Spain,
with a population of approximately 70,000 inhabitants in 2014. The transport sector is the
main energy consumer of the country (50% of the final energy consumed in 2013) and the
leading source of greenhouse gas (GHG) emissions, representing 69% of energy-related
emissions . Its location, in the middle of the Pyrenees, means that transport energy
demand is entirely focused on road transport. According to an estimate developed in 
private cars are responsible for 45.4% of total fuel imports related with transport. The
remaining are distributed among motorbikes, buses, freight transport and mostly fuel
tourism, promoted by Andorra’s fiscal policy. The ratio of private cars per population in
Andorra has continuously risen since 1995 and nowadays is among the highest in the world
(690 cars per 1,000 inhabitants in 2014). In addition, after a decline in car sales in the period
2006-2012, new registrations started rising in 2013. This context, combined with the national
climatic agreements, represent a significant challenge. In April 2015, Andorra submitted its
intended nationally determined contribution (INDC) to UN where targets to reduce GHG
emissions by 37% as compared to a business-as-usual (BAU) scenario by 2030. Transport
and particularly private cars, as the main source of GHG emissions, are called to play a
central role in addressing this challenge.
The impacts of two recent car purchase subsidy programs in Andorra have been
quantified and compared in terms of energy consumption and CO
emissions savings. The
analysis is conducted through a car fleet bottom-up model which uses fleet structure,
mileage profile and fuel economy as explanatory variables. The evaluation of the
implemented policy is particularly relevant for Andorra but also for any countries where there
is a need to achieve energy savings in transport sector. The paper is organised as follows:
Andorra’s car purchase subsidy policy, data sources used in the analysis and methodology
are presented in Section 2. Section 3 and 4 provide results and discussion, respectively.
Finally, conclusions, policy implications and some prospects for future work are presented
in Section 5.
2. MATERIALS AND METHODS
2.1 Car purchase subsidy scheme (Programa Engega)
Engega is a two years program developed during 2014 and 2015, which encourages
Andorrans to buy new cars and replace their older ones. Its official objectives were improving
the security and energy efficiency of the national vehicle fleet, and as a consequence
reducing its CO
emissions. The program differs in some characteristics between both years
of implementation. Engega-2014 offered a subsidy of 2,000 € (shared between car
distributors and Government) to purchase a new vehicle and the option to replace an older
one without age criterion (1,000 € per retired car provided by the Government). Limits on
/km) were established for eligible vehicles (see Table 1). Engega-2015
was specifically a scrapping program more focused on improving vehicle fleet security than
energy efficiency. It offered a subsidy of 2,000 € (shared between car distributors and
Government) to buy a new vehicle with a retirement obligation of ten years or older. In this
case, no CO
limits was established in the acquisition of the new vehicle. Table 1
summarizes the main characteristics of both Engega schemes.
Table 1. Main characteristics of 2014 and 2015 programs (asterisks refers to amounts only provided
in case of vehicle retirement)
M1 category (cars)
N1 category (vans)
L3 category (motorcycles)
M1 category (cars)
N1 category (vans)
M1: <= 120 gCO
N1: <=160 gCO
L3: <= 80 gCO
Car retirement obligation
Retired vehicle age criterion
>= 10 years
Subsidy amount per vehicle
M1, N1: 2000 € + 1000 €*
L3: 300 € + 150 €*
2.2 Data preview
Two main data sources have been used to develop this study: data needed to
implement the model and information data on vehicle fleet variability associated with
Programa Engega. The car fleet model used to conduct the analysis is built with data
compiled from the National Vehicle Registration bureau and vehicle technical inspections
up to the end of 2013 . Engega program data  provides information on brand, model,
fuel type and year of manufacture of new and retired vehicles due to the subsidy program in
both years. 2014 Program contributed with 455 new registrations and scrapped 240
vehicles. Among all subsidized vehicles only two were motorcycles. For this reason and in
order to homogenize data with 2015 Program we have not considered this category in our
analysis. 2015 Program registered and scrapped the same number of vehicles (246).
Figure 1. Number of new and scrapped vehicles (Programa Engega 2014-2015)
Table 2 shows the variability in the vehicle fleet due to the combination of subsidy and
scrappage programs in 2014 and 2015. The number of vehicles is grouped according its
fuel type and engine size as determinant variables on energy consumption and CO
Table 2. Variability (new registers-retirements) impact in the vehicle fleet
New registers - retirements
Vehicle age is also an influential factor in the fleet energy consumption and CO
emissions. With average new-car energy intensity decreasing over time and with ageing
cars’ intensity increasing over their lifespan, a younger fleet with a higher turnover rate will
be more efficient . Figure 2 shows scrapped vehicles due to Engega scheme according
to its year of manufacture.
New Scrapped New Scrapped
Figure 2. Scrapped vehicles (Engega 2014-2015)
A bottom-up technological stock model which represents Andorra’s 2013 car fleet is
used to conduct the analysis. A detailed description of the implementation and validation of
the model is explained in . It uses fleet structure, mileage profile (km/year) and specific
energy consumption (MJ/km) as explanatory variables. The private car fleet in 2013 is
disaggregated into different categories according to fuel type (petrol or diesel), engine size
(<1400 cc, 1400-2000 cc, >2000 cc) and vintage (0-29 years), factors that have a major
impact on energy consumption. Figure 3 shows Andorra’s 2013 private car fleet.
Subsequently, a mileage profile for different car categories has been defined, based on data
from vehicle technical inspections. As observed in similar studies for other countries ,
, diesel and larger sized engine cars tend to drive longer distances and a similar mileage
decay over time is observed in diesel and petrol cars. On the other hand, for both fuel types,
the mileage decay during its lifetime is greater than for medium and small sized cars .
Each category has a specific energy consumption (SEC) value, which takes into account
official test consumption test data, on-road factor (to reflect the gap between real and test
vehicle efficiency), and ageing factor (to include vehicle vintage in fuel consumption).
Year of manufacture
2014 Program 2015 Program
Figure 3. Andorra’s 2013 private car fleet by fuel type, engine size and vintage
Combining all factors explained above, the fleet’s annual final energy consumption is
calculated according to the Eq. (1).
· 𝑂𝑅𝐹 · 𝐴
where FE is the car fleet’s annual final energy consumption, f is the fuel type, cc is the
engine size, a is the car age, S the car stock, M the mileage, SEC the specific energy
consumption, ORF the on-road factor, and A the ageing factor. In this way, the main drivers
of energy consumption and the existing different car categories in the fleet can be assessed
Andorra’s fleet CO
emissions are calculated using an emission factor for each fuel
type. The emission factor have been estimated with data for specific emissions against SEC
data provided by the UK’s Vehicle Certification Agency (VCA) database
. The VCA data is
based on the New European Driving Cycle (NEDC), the legislative cycle in the EU countries
for certifying vehicle fuel consumption and emissions levels. Figure 4 shows the correlation
of SEC and CO
emissions for the 56,440 analysed vehicles. An emission factor of 68.6 and
/km per MJ/km is estimated for diesel and petrol vehicles respectively.
Year of manufacture
Figure 4. Emission factor using VCA test data 2000-2015
Car sales and scrappage impact on energy and CO
emissions due to 2014 Program
is estimated by the model following six steps:
(1) 2013 fleet structure is considered as the base year. Then, all vehicles are aged one
year to represent 2014 fleet. This is considered as the reference year to quantify
energy and CO
savings due to Programa Engega 2014-2015. The same reference
is considered in both years to obtain comparable results.
(2) Scrapped vehicles associated to the scheme are deduced from each stock category.
(3) New sales due to Programa Engega are introduced in each stock category. SEC of
new registers in each car category is updated according to newly introduced
(4) Mileage of each car category is updated using total fleet activity and the new stock
structure. First, the average mileage for diesel and petrol cars is calculated using a
fuel weighting factor for each considered car category. Then, the average mileage
according to fuel type and engine size is calculated by applying engine-size
weighting factors, and finally, vintage weighting factors are applied to obtain the
disaggregated mileage for a given year. Weighting factors are deduced from 2013
mileage profiles (see , Section 2.1.2). Total fleet activity is considered constant
in order to isolate the effects of stock changes.
(5) Final energy consumption and CO
emissions of the fleet are calculated using Eq.
(1) and emission factors.
(6) Results are compared with the Reference Year to obtain energy and CO
caused by Programa Engega.
The impact of 2015 Program is evaluated in the same way. In this case, 2013 fleet
structure is also taken as the base year model in order to obtain comparable results between
the effects of both plans.
y = 68,011x
R² = 0,9952
y = 68,603x
R² = 0,9945
0 2 4 6 8 10
Petrol Diesel Linear (Petrol) Linear (Diesel)
3.1 Base and reference year
2013 is defined as the base year in our model. Andorra’s car fleet CO
calculated using Eq. (1) and corresponding emission factors for diesel and petrol vehicles.
Figure 5 shows the total car fleet emissions for each car category. Its profile is similar to that
given in Figure 3, but with a proportionally lower contribution of old vehicles. Despite new
vehicles have a lower SEC than old cars, this fact can be explained because of its higher
Figure 5. CO
emissions of private car fleet in 2013
The total emissions of the car fleet in 2013 is 142 kTCO
, derived from diesel (101
) and petrol (41 kTCO
). According to the model results and taking into account the
most recent national inventory of CO
emissions , Andorra’s car fleet is responsible for
28% and 41% of energy and transport related emissions, respectively. These high values
show the importance of climate policies focused on the transportation sector and specifically
in the private car fleet.
2013’s car fleet is aged one year to represent 2014 fleet, which have been used as
the reference year to quantify energy and CO
savings. The ageing of all cars is responsible
for an energy and emissions increase of 1.64%. Table 3 summarizes the change in CO
emissions due to ageing the car fleet for one year. It can be observed that all car categories
increase their emissions with the exception of low size diesel vehicles. In this category, the
effect of mileage decrease overcomes the increase in SEC due to ageing.
Year of manufacture
Table 3. CO
emissions in the model’s base and reference year segregated by fuel type and engine
Fuel type and
2013 - Base year (kTCO
2014 - Reference year (kTCO
3.2 Engega 2014-2015 CO
As can be observed in Figure 6, before the subsidy and scrappage program took
place, the share of low emission car sales (i.e. <120 gCO
/km) was near 30%. In 2014
program, more than 95% of subsidized vehicles were on label band A and no one emitted
more than 155 gCO
/km. In 2015 program, in which no CO
emission limits were established
for subsidized cars, the share of label band A was reduced to 62% and near 10% of new
sales was in the most pollutant bands (i.e. >155 gCO
Figure 6. Shares of new cars by emissions label class 2013 and 2014-2015 Program
emissions of new car sales and scrapped vehicles due to Engega 2014-
2015 are shown in Figure 7. CO
emission limits established in 2014 for subsidized cars
seem to be the reason why the average of new car’s emissions is lower than in 2015 (108
and 122 gCO
/km respectively). No considerable differences are observed in the average
emissions of scrapped vehicles between 2014 and 2015 program. In this sense, the impact
of age criterion introduced in 2015 for retired cars seems to be negligible.
Share of new car sales
G- >225 gCO₂/km
F- (190-225 gCO₂/km]
E- (170-190 gCO₂/km]
D- (155-170 gCO₂/km]
C- (140-155 gCO₂/km]
B- (120-140 gCO₂/km]
A- <120 gCO₂/km
Figure 7. Average CO
emissions of new and scrapped vehicles (Engega 2014-2015)
The introduction of new vehicles in the car fleet and the replacement of older ones
contribute to energy and emission savings in relation to reference year. Table 4 compares
energy and CO
savings introduced by both 2014 and 2015 programs. Yearly emission
savings in 2014 were near two times those in 2015 but with almost three times the 2015’s
abatement cost was 955 and 753 €/TCO₂ for 2014 and 2015 program
respectively. These values are referred to savings achieved during the first year after the
introduction of the program. Total CO
savings and consequently real cost effectiveness of
the policy will depend on new introduced vehicle’s lifetime. A deeper discussion in this point
is presented in Section 4.
Table 4. Energy and emission savings and CO
Program cost (€)
Diesel (% of diesel)
Petrol (% of petrol)
Emissions (% of emissions)
CO₂ abatement cost (€/TCO₂)
As explained in Section 2.1, Engega-2014 offered a subsidy to purchase new cars
without the obligation to replace an older one. Simultaneously it offered an additional amount
to replace an old car. In order to estimate the impact of Engega-2014 if grants would have
been conditioned to the scrappage of an old car, we simulated a new scenario where only
new cars with an associated retirement are considered. In this scenario we considered that
purchase subsidies are equivalent as in Engega-2015 to obtain comparable results. In this
case, the program cost is reduced to 239,000 €, emission savings decay to 374 TCO
the emissions reduction are achieved at an abatement cost of 638 €/TCO
Engega 2014-2015 caused a relatively small variability in the structure of the total car
fleet through the introduction of 701 new vehicles and the scrapping of 486. As can be
observed in Table 2, the stock of diesel vehicles and low engine sizes have increased due
to the implemented scheme. On the other hand high engine size vehicles have decreased
as very few new vehicles have been introduced in this bands.
According to Figure 6 it is noticed that 2014 Program shifted purchasing trends
towards lower emitting cars. New cars’ average CO
emissions decay to 108 gCO
value lower than the mandatory standard of 130 gCO
/km targeted by 2015 (Regulation (EC)
No 443/2009) and close to the 95 gCO
/km target by 2021. In 2015 Program, a more
moderate but important improve on CO
standards have been observed. As this plan did not
emission limits for subsidized cars, the apparent shift towards greener
vehicles could be associated to the new cars’ energy efficiency improvement trends and
also to a natural shift on customer preferences.
Regarding scrapped vehicles, despite Engega-2015 introduced an age criterion of ten
years, the estimated average CO
emissions of retired cars is virtually the same in both
years. The reason behind this counterintuitive fact can be explained observing car
retirements’ age distribution in Figure 2. Although Engega-2014 retired some non-old
vehicles manufactured in the period 2006-2007, Engega-2015 retired more cars in the
period 1998-2006 but less in older vehicles. This fact may suggest that the established age
criterion was too low to achieve the intended purpose.
Energy and emission savings and CO
abatement cost presented in Table 4 are
referred to the first year after the implementation of the policy. To estimate the total impact
of Engega 2014-2015 and its environmental efficiency is necessary to consider the entire
life of new introduced vehicles. Using data from 1993 to 2013,  calculated the median
lifetime of the car stock as 16.3 years. We have considered this value as the expected
lifetime of new introduced vehicles due to the analyzed policy. With this consideration,
savings are 11,790 TCO
and the cost per saved TCO
is close to 59 €. In
Engega-2015 savings are smaller (5,325 TCO
) but cheaper in terms of cost (46 €/TCO
To calculate this cost effectiveness value two important assumptions have been done. We
have considered that all new vehicles introduced throw the policy were new generated
demand. If this were not true, real CO
emission savings would be lower and so the
environmental efficiency. On the other hand, tax revenues due to program results have not
been taken into account. Our costs per TCO
saved are in line with the observed in a similar
plan conducted in Spain  or in the “Cash for Clunkers” program in United States .
Heavily negative results where estimated in the subsidy policy of electric cars in Norway
where the estimated cost per saved TCO
is 13,500 USD . In any case, the policy’s
reduction cost of one TCO
is very high, especially considering that the value in the market
for a ton is near 6 €.
Despite the design of Engega-2015 was more focused on improving new car sales
and vehicle fleet security than energy efficiency, it has been more efficient (in terms of cost
saved) than Engega-2014 where a specific limit of car’s CO
defined. Average CO
of new introduced cars in 2015 demonstrate that this program could
have been even more efficient if a similar CO
limit was established. The age criterion for
scrapped cars introduced in 2015 seems to be ineffective in order to scrap less fuel efficient
cars. This fact may suggest that this criterion had to be higher than ten years.
According to the cost effectiveness results, Engega 2014-2015 program did not
perform as an efficient policy in order to reduce CO
emissions. The main benefit of this type
of plan could be the reactivation of the sector activity, especially in a context of economic
crisis. However, other significant benefits not analysed in this study are the reduction of
other pollutants (i.e. NOx, PMs) and the security improvement of the car fleet.
If the main goal of administration is to fight against climate change, other policies have
been demonstrated to be more effective in reducing CO
emissions . For instance, tax
mechanisms tools (i.e. fuel taxes and vehicle taxes) showed to have a greater potential on
the achievement of CO
savings and imply a lower cost for the government , .
Feebates are also demonstrated as a good practice in opposition of subsidies alone, but an
optimal design is key in its effectiveness , .
The environmental efficiency of Engega 2014-2015 is dependent on its capacity to
create new demand. In this paper, all new introduced vehicles have been considered as
new generated demand. Further research should be oriented to analyse the sensitivity of
this factor. In addition, new government income associated to the policy (i.e. VAT tax from
new car purchases) could be included in a future analysis. However, Andorra is a country
with a low fiscal system, so variations in these assumptions could lead to even worst results
regarding environmental efficiency.
The authors would like to thank Andorra’s Department of Industry and Department of
Transports for the valuable data provided and Ministeri de Medi Ambient, Agricultura i
Sostenibilitat for their support. The first author acknowledges pre-doctoral (ATCR012-AND
2014/2015) grant from the Government of Andorra. The Observatori de la Sostenibilitat
d’Andorra acknowledges also the scholarship granted by the Government of Andorra within
the framework of research grants of the Working Community of the Pyrenees (ACTP022-
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