Proposed Index for Assigning an Environmental Label to Passenger Cars

Abstract

At present, the classification of vehicles by means of the environmental label aims to positively distinguish the most environmentally friendly vehicles and to be an effective instrument at the service of municipal policies, both to restrict traffic during high pollution periods and to promote new technologies through tax benefits and other incentives related to mobility and the environment. However, there is a great deal of controversy about the awarding of this environmental label, since it is basically based only on the year of manufacture of the vehicle and the type of fuel it uses, without taking into account many other factors that influence the environmental characterization of a vehicle. This paper presents a proposal for an index to characterize passenger cars from an environmental point of view, considering other additional factors, such as tire wear and particulate emissions during braking. The proposed index is applied to a series of passenger cars representative of the segments currently present in the Spanish market. The index considers seven vehicle parameters. Up to three cases with different parameter weights are analyzed. This index provides greater discrimination of the environmental performance of vehicles than the current eco-labeling system, in each of the three cases studied.

1. Introduction

As concerns about climate change and environmental sustainability grow, the automotive industry’s contribution to carbon emissions has come under increased scrutiny. Governments and regulatory bodies have responded by instituting programs to encourage the development and adoption of cleaner, greener vehicles. The issuance of environmental labels, ostensibly to help consumers make informed choices consistent with environmental goals, has emerged as a tool in this effort. However, the implementation and effectiveness of these labels have become the subject of scrutiny and controversy [1].

In the realm of environmental awareness, the awarding of environmental labels to passenger cars has emerged as a controversial and polarizing issue, sparking heated debate within the automotive industry and environmental advocacy circles alike. The concept of these labels, which are intended to guide consumers toward environmentally friendly choices, has become a focal point of discussion due to the complicated criteria involved, the potential for greenwashing, and the ever-evolving landscape of technological advancements in the automotive sector [2].

The classification of vehicles by means of the environmental sticker aims to positively discriminate the most environmentally friendly vehicles and to be an effective instrument at the service of municipal policies, both to restrict traffic during episodes of high pollution and to promote new technologies through tax benefits or those related to mobility and the environment. In some cities, this environmental sticker is already used to restrict traffic on days of high pollution, prohibiting the circulation of vehicles that do not have it [3].

In Europe, the 1999/94/EC directive [4] enacted the environmental labeling of vehicles with the aim of helping consumers to buy or lease cars which use less fuel, and thereby emit less CO₂, and encourage manufacturers to reduce the fuel consumption of new cars. A review of this directive is expected by the end of 2024 [5]. As a result of this European directive, each country has adopted its own system of e-ticketing for the characterization of its vehicles. In other words, there is no universal environmental labeling system, even within the European Union (EU). However, in this case, all existing systems refer to the Euro standard that the vehicle meets. The Euro standards refer to a set of requirements imposed by the EU regarding gas emission limits for combustion vehicles marketed in all member countries. These standards include air pollutants such as carbon monoxide (CO), hydrocarbons, non-methane volatile organic compounds, nitrogen oxides (NO_x), and particulate matter (PM). Their limits have been progressively lowered since 1993, when the Euro 1 standard was established (see

Table 1. NO_x and PM emission standards (in mg/km) for European internal combustion passenger cars (category M).

Regulation (Application Period)	Gasoline or LPG		Diesel
	NOx a	PM b	NOx a	PM b
Euro 1 (1/1993−12/1996) [7,8]	970 c	---	970 c	140
Euro 2 (1/1997−12/2000) [9,10]	500 c	---	700 c	80
Euro 3 (1/2001−12/2005) [11]	150	---	500	50
Euro 4 (1/2006−12/2010) [11,12]	80	---	250	25
Euro 5a (1/2011−12/2012) [13]	60	5	180	5
Euro 5b (1/2013−8/2015) [13]	60	4.5	180	4.5
Euro 6 (9/2015+) [6]	60	4.5	80	4.5

^a NO_x = nitrogen oxides, expressed as NO₂; ^b PM = particulate matter, expressed as PM_2.5; NO_x + hydrocarbons.

). Of all these pollutants, the limits that have been most restricted over time are those for NO_x and PM. The current regulation is known as Euro 6 [6], which sets emission limits of 80 mg/km of NO_x for diesel cars, 60 mg/km of NO_x for gasoline cars, and 4.5 mg/km of PM for both types of cars.

In Spain, the environmental labels are divided into four categories [3,14]:

• “0 emissions”, for electric vehicles with a range of more than 40 km. This includes battery electric vehicles (BEVs), plug-in hybrids (PHEVs) and fuel cell vehicles (FCEVs).
• “ECO” for hybrid vehicles or vehicles powered by natural gas or liquefied petroleum gas (LPG). It includes PHEVs with a range of less than 40 km and non-plug-in hybrids (HEV). They must meet the criteria of the C label.
• “C” for internal combustion vehicles (ICVs) that comply with the latest Euro emissions (Euro 4, 5, or 6 gasoline cars and Euro 6 diesel). It includes passenger cars and light vans in gasoline registered from January 2006 and diesel from September 2015. It also includes vehicles with more than eight seats, excluding the driver, and heavy-duty vehicles, both gasoline and diesel, registered since 2014.
• “B” for Euro 3 gasoline cars and vans and Euro 4 diesel. It includes gasoline cars and light vans registered since January 2001 and diesel cars and vans registered since 2006. It also includes vehicles with more than eight seats and heavy-duty vehicles, both gasoline and diesel, registered since 2006.

Other countries use different labels [14]. Thus, for example, in Austria, all vehicles are labeled according to the Euro standard they meet, with a different color and the name of the standard on the sticker. In contrast, in Germany, there is a labeling system (“Umweltplakette”) that, over time, has been reduced to two stickers: a green sticker for all cars complying with Euro 4, and a blue sticker, which is more restrictive. In France, the environmental label (“Credit’Air”) consists of six numbered classifications. The first has no number, is green and can be displayed by hydrogen and electric cars. Number 1 is purple and includes Euro 5 and 6 gasoline cars. Number 2 is yellow and includes Euro 4 gasoline and Euro 5 and 6 diesel cars. In France, therefore, diesel cars are considered more polluting than gasoline cars of the same Euro standard. The rest of the labels include more polluting cars up to label 5, which is gray, and includes Euro 2 diesel cars. Euro 1 cars are not marked.

Table 2. Equivalence between environmental labels for cars in some European countries [15,16,17].

Spain	Austria	Germany	France
“0 emissions” Blue	Euro 6	Blue/Green	Green
“ECO” Blue/Green	Euro 6	Green (Class 4)	“1” Purple
“C” Green	Euro 4–6	Green (Class 4)	“2” Yellow
“B” Yellow	Euro 3	Green (Class 4)	“3” Orange

shows the equivalence between Spanish labels and those used in other European countries. This disparity in labeling systems causes confusion and hinders the mobility of vehicles between countries.

The controversy surrounding the environmental labeling of passenger cars is multifaceted. Questions remain about the accuracy of the criteria used for evaluation, the transparency of the labeling process, and the susceptibility of the system to manipulation or exploitation by automakers. In addition, critics argue that some labels may inadvertently serve as marketing tools rather than true indicators of a vehicle’s environmental impact, contributing to a phenomenon known as “greenwashing” [18,19].

The current EU environmental labeling system is primarily based on the emissions of gases and particulates that cars emit through their tailpipe. In addition, the system assumes that these emissions depend on the Euro standard to which the vehicle is registered, i.e., it only considers two variables: the type of energy driving the engine and the age of the vehicle.

This paper presents a proposal for an index to characterize passenger cars from an environmental point of view, considering other additional factors, such as tire wear and particulate emissions during braking. The proposed index is applied to a series of passenger cars representative of the segments currently present in the Spanish market. Finally, the results are compared with the current labeling in Spain.

2. Method

An index is proposed to characterize the environmental performance of a passenger car during its service life, with a view to awarding it an environmental label to replace the current system. The complete life cycle of the vehicle is not considered, i.e., the manufacturing and the disposal phases of the vehicle are excluded. These aspects have been extensively studied in previous works [20,21,22].

The proposed index will take into account the following vehicle parameters:

• $p_1$ = CO₂ emissions per driven kilometer (g/km), according to the World Harmonized Light-duty Vehicle Test Procedure (WLTP) cycle.
• $p_2$ = NO_x emissions per driven kilometer (mg/km).
• $p_3$ = PM emissions per driven kilometer (mg/km).
• $p_4$ = Energy consumption per driven kilometer (kWh/km), according to the WLTP cycle.
• $p_5$ = Top speed (km/h).
• $p_6$ = Weight (kg).
• $p_7$ = Tire-tread width (mm).

The first four parameters are directly related to the exhaust emissions produced by the combustion of the fuel (gasoline, diesel, or LPG) during the operation of the vehicle. In the case of electric vehicles, there are no direct emissions; however, there are indirect emissions depending on the energy mix used to generate the electricity to charge the batteries or to generate hydrogen in the case of FCEVs.

It is interesting to consider CO₂ and NO_x emissions separately, although both depend on the combustion temperature and the air/fuel ratio and differ depending on the type of internal combustion engine. However, an engine operating at high temperature tends to emit less CO₂ and more NO_x. On the other hand, if the engine temperature is low, it will produce more CO₂ but less NO_x [23,24].

The last three parameters are related to tire wear. The greater the vehicle weight, speed, and tire width, the higher the tire wear and the subsequent formation of microplastic emissions [25]. The greater the PM emission from such wear, the greater the risk of serious respiratory problems [26,27,28].

PM emissions from tire wear are related to the frictional energy E_friction transmitted between the tire and the road surface. This work is converted into thermal energy, which increases tire temperature, and mechanical energy, which causes wear. Therefore, a proportionality is assumed between the tire-and-road-wear-particle (TRWP) emission factor EF_TRWP and E_friction [29]:

$${EF}_{TRWP }~ E_{friction}$$

(1)

In a simplified way, friction energy can be calculated from the slip velocity v_slip between the tire and the road surface and the sum of the forces acting on the vehicle: rolling resistance, air resistance, and acceleration resistance, as follows [29]:

$$E_{friction}=v_{slip}\left[\left(f_r{m}_{veh}g\right)+\left({\displaystyle \frac{v_{veh}^2}{2}}{\rho }_{air}{c}_w{A}_{veh}\right)+\left(m_{veh}\sqrt{a_x^2+a_y^2}\right)\right]$$

(2)

Furthermore, a dynamic proportionality factor f_TRPW is added to take into account other relevant parameters, such as tire width $w$, which positively affects TRWPs [29]:

$${EF}_{TRWP}=f_{TRPW}\left(w,\dots \right)\times E_{friction}$$

(3)

From Equation (3), it is clear that PM emissions will increase as either tire width, speed or weight, or any combination of the three, increases in value.

In addition, vehicle weight and speed are related to increased braking effort. Similar to tire wear, brake wear causes particles to be released into the air. Therefore, the higher vehicle weight and speed, the higher the PM emissions from the brake-pad material [30,31,32,33].

Fleisher’s energy density theory is used to parameterize brake-wear PM emissions [34]. The object of the theory is the linear relationship between kinetic energy loss, ${∆E}_k$, (in this case, during braking) and PM emissions. This relationship can be described as follows [34]:

$${∆E}_k={\displaystyle \frac{u E_b}{K\left[\epsilon u+1\right] \gamma ·\alpha ·\rho }}·m$$

(4)

where $E_b$ is the energy density required to produce particle emissions; $m$ and $\rho$ are the mass and density of produced debris or PM, respectively; $\alpha$ is a proportionality coefficient representing kinetic energy loss to potential energy stored in friction; and the constants $u$, $K$, $\epsilon$, $\gamma$ are brake pair parameters (the number of rough frictions to produce wear debris, the ratio between actual and average wear debris energy density, the efficiency of energy conversion from total absorption to wear-debris production, and the fraction of produced PM volume from total energy storage, respectively). Since all the parameters in Equation (4) are constant, it follows that there is a linear relationship between the kinetic energy and the mass of PM emitted.

The proof that it is important to consider PM emissions from tire and brake wear is that the future Euro 7 standard proposed in 2022, to come into force from 2025, will control for these emissions [35], and their limits will apply to all cars, including electric vehicles [36].

Then, the proposed environmental index EI would be calculated as follows:

$$EI={\sum }_{i=1}^n{a}_i{\displaystyle \frac{p_i-p_i^{min}}{p_i^{max}-p_i^{min}}}$$

(5)

where $a_i$= weight of the parameter $p_i$ (between 0 and 1); $p_i^{min}$ = minimum value of the parameter $p_i$; $p_i^{max}$ = maximum value of the parameter $p_i$; n = number of parameters. Equation (5) consists of a weighted sum of the n parameters considered to determine the EI, normalized to a scale of values between 0 and 1. In our case, n = 7. Since the 7 parameters are increasing functions, the higher the value of the parameter, the greater the environmental impact, i.e., EI. That is, if all 7 parameters had their maximum value, EI would be equal to 1. Thus, EI would range from 0 to 1. The lower the EI, the better the environmental performance of the vehicle.

At present, passenger cars in the Spanish market are grouped into the following segments [37,38]:

• A-segment: minicars, less than 4 m in length.
• B-segment: small cars, about 4 m long.
• C-segment: medium or compact size cars, around 4.3 m long.
• D-segment: large cars, around 4.6 m long.
• E-segment: executive cars, over 4.7 m in length.
• F-segment: luxury cars, around 5 m long.
• J-segment: sport utility vehicles (SUVs).
• S-segment: sport coupés or roadster sports.

We compiled a list of representative cars from these segments, currently on sale in the market, selecting at least 3 models among the most sold in Spain [39] and with different engines (gasoline, diesel, LPG, hybrid fuel/electric, plug-in hybrid, and fully electric). In total, the database consists of 57 cars (

Table A1. List of passenger car models considered in this study. The data were taken from the technical data sheets of the vehicles extracted from [42].

Segment	Model	Energy a	Year	Power (kW)	Consumption (L Fuel or kg LGP/100 km) b	Battery Capacity (kWh)	Electrical Range (km)	Tire	Euro Standard	Eco-Label d
A	Fiat 500	Gasoline	2018	51	4.9			175/65 R14	6	C
A	Fiat 500e	Electrical	2022	87		42	330	185/65 R15		0
A	Fiat 500 Hybrid	GH	2020	51	5.3	0.13		175/65 R14	6	ECO
A	MINI Cooper C Essential	Gasoline	2024	115	5.9			195/60 R16	6	C
A	MINI Cooper E Essential	Electrical	2023	135		40.7	305	195/60 R16		0
A	Hyundai i10 Essence	Gasoline	2023	49	5			175/65 R14	6	C
B	Dacia Sandero	Gasoline	2020	67	5.2			185/65 R15	6	C
B	Dacia Sandero ECO-G	G/LPG	2020	74	3.6			185/65 R16	6	ECO
B	Seat Ibiza Reference	Gasoline	2024	70	5.1			185/65 R15	6	C
B	Renault Clio evolution	Gasoline	2023	67	5.2			195/55 R16	6	C
B	Renault Clio evol. LPG	LPG	2023	74	3.9			195/55 R16	6	ECO
B	Renault Clio evol. dCi	Diesel	2023	74	4.1			195/55 R16	6	C
B	Renault Clio evol E-Tech	GH	2023	105	4.2	1.2		195/55 R16	6	ECO
B	Peugeot E-208 Active	Electrical	2024	100		50	363	195/55 R16		0
C	Toyota Corolla 140H Bus	GH	2022	103	4.4	0.85		205/55 R16	6	ECO
C	Audi Q3 Sportback Adv.	Gasoline	2019	110	6.4			235/55 R18	6	C
C	Audi Q3 Sportback Advanced TDI	Diesel	2019	110	5.1			235/55 R18	6	C
C	Audi Q3 Sportback S TFSIe	PGH	2020	180	2	12.8	46	235/50 R19	6	0
C	Ford Kuga ST EcoBoost	Gasoline	2024	110	6.4			225/60 R18	6	C
C	Ford Kuga ST FHEV	GH	2024	132	5.3	1.1		225/60 R18	6	ECO
C	Renault Megane E-TECH	Electrical	2021	96		60	300	195/60 R18		0
D	Tesla Model 3	Electrical	2024	208		75	513	235/45 R18		0
D	BMW 320i	Gasoline	2022	135	6.5			225/50 R17	6	C
D	BMW 320d	DH	2022	140	4.8			225/50 R17	6	ECO
D	BMW 320e	PGH	2022	150	1.3	12	62	225/50 R17	6	0
D	Volkswagen Passat Variant eTSI	GH	2023	110	5.4			215/60 R16	6	ECO
D	Volkswagen Passat Variant Business TDI	Diesel	2023	110	5			215/55 R17	6	C
E	Mercedes-Benz E 200	GH	2023	150	6.4			225/55 R18	6	ECO
E	Mercedes-Benz E 300e	PGH	2023	230	0.5	25.4	118	245/45 R19 c	6	0
E	Audi A5 Sportback Advanced TFSI	GH	2020	110	6.6	0.12		245/40 R18	6	ECO
E	Audi A5 Sportback Advanced TDI	Diesel GH	2020	120	4.9	0.12		245/40 R18	6	ECO
E	BMW 520i	GH	2023	153	5.8	0.96		225/55 R18	6	ECO
E	BMW 530e	PGH	2024	220	0.6	22.1	103	245/45 R19	6	0
E	BMW i5 eDrive40 Touring	Electrical	2024	250		83.9	556	245/45 R19		0
F	BMW 740d xDrive	GH	2022	220	6.1	0.96		245/50 R19	6	ECO
F	BMW 740E xDrive	PGH	2022	360	1	22.1	87	245/50 R19	6	0
F	BMW i7 eDrive50	Electrical	2023	335		105.7	611	245/50 R19		0
F	Mercedes-Benz S 350d	Diesel	2020	210	6.6			255/45 R19 c	6	C
F	Mercedes-Benz S 580e	PGH	2021	375	0.6	28.6	113	255/45 R19 c	6	0
F	Mercedes-Benz AMG S E	PGH	2023	590	4.4	13.1	33	255/45 R20 c	6	0
F	Audi A8 TDI quattro	DH	2021	210	7.2	0.84		255/45 R19	6	ECO
F	Audi S8 TFSI quattro	GH	2021	420	11.5			265/40 R20	6	ECO
F	Audi A8 TFSIe quattro	PGH	2021	340	1.8	17.9	59	255/45 R19	6	0
J	KIA Sportage 1.6 T-GDI	Gasoline	2022	110	6.7			215/65 R17	6	C
J	KIA Sportage 1.6 T-GDI MHEV	GH	2022	110	6.5	0.44		215/65 R17	6	ECO
J	KIA Sportage HEV 1.6 T-GDI	GH	2022	169	5.6	1.49		215/65 R17	6	ECO
J	KIA Sportage PHEV 1.6 T-GDI	PGH	2022	195	1.1	13.8	70	235/50 R19	6	0
J	Nissan Qashqai DIG-T mHEV 12V Acenta	GH	2021	103	6.3			215/65 R17	6	ECO
J	Nissan Qashqai e-POWER Acenta	GH	2023	140	5.2	2.1		235/55 R18	6	ECO
J	Range Rover SWB D300 MHEV SE	DH	2021	221	7.7			275/50 R21	6	ECO
J	Range Rover SWB P460e PHEV SE	PGH	2023	324	0.7	38.2	119	275/50 R21	6	0
J	Hyundai IONIQ 5 N	Electrical	2024	478		84	448	275/35 R21		0
S	Ferrari 812 Superfast	Gasoline	2021	588	16.1			275/35 R20 c	6	C
S	Porsche 911 Carrera GTS	Gasoline	2021	353	10.4			245/35 R20 c	6	C
S	Maserati Ghibli Diesel	Diesel	2020	202	7.8			235/50 R18	6	C
S	Maserati Ghibli	Gasoline	2020	257	11.2			235/50 R18	6	C
S	Maserati Ghibli Hybrid	GH	2020	243	8.3	0.5		235/50 R18	6	ECO

^a GH: gasoline hybrid; DH: diesel hybrid; PGH: plug-in gasoline hybrid; LPG: liquefied petroleum gas; G/LPG: gasoline or LPG. ^b Combined fuel consumption following the WLTP cycle. ^c Front tire. ^d Environmental label in Spain.

). A representative 100% electric model has been included in each segment, despite the fact that electric vehicles are currently far from being the majority in the Spanish market. However, the growth rate of their sales has been increasing in recent years. No FCEV model was considered, as their significance in the current market is negligible.

All these cars (except one) have a model year greater than or equal to 2020. They have an environmental label of “C” (gasoline or diesel models), “ECO” (hybrids or LPG), or “0” (plug-in hybrids or fully electric). ICVs models meet the Euro 6 standard.

The EI was calculated for each model in the database using Equation (5), making the following assumptions:

• The NO_x and PM emission parameters (p₂, p₃) of the internal combustion engines of ICVs, HEVs, and PHEVs are defined by the limits of the Euro standard to which they are homologated. The other parameters are taken from the technical data sheet of each vehicle.
• The electricity used to charge the batteries of electric vehicles connected to the grid (PHEVs and BEVs) comes entirely from renewable sources, so the CO₂, NO_x, and PM emission parameters (p₁, p₂, p₃) for electricity generation are assumed to be zero.
• The energy consumption parameter p₄ for PHEVs is estimated by the following equation:

  $$p_4=F_C·f+{\displaystyle \frac{B}{A}} ·100$$

  (6)

  where F_C = liters of gasoline or diesel (or kg LPG) consumed per 100 km; f = fuel-heating-value conversion factor to kWh; B = battery capacity in kWh; A = electric range on a full battery charge (km). The f factor is equal to 9.66 kWh per liter of gasoline, or 10.74 kWh per liter of diesel, or 12.79 kWh per kg of LPG [40].
• Tire wear is controlled by parameters p₅, p₆, and p₇, while brake wear is controlled by p₅ and p₆. Both types of wear occur in all vehicle categories.

Finally, the influence of two possible distributions of the weights a_i of the 7 parameters on the EI was analyzed:

• Case A: Only the pollutant emissions (including CO₂) and fuel consumption parameters are considered relevant, with the same level of importance. Therefore, a₁ = a₂ = a₃ = a₄ = 1/4 = 25%; a₅ = a₆ = a₇ = 0.
• Case B: Equal distribution of weights, i.e., a_i = 1/7 = 14.3%. In this case, all parameters are considered equally important.
• Case C: The parameters of pollutant emissions (including CO₂) are more important than fuel consumption, and the latter is more important than the other three parameters. In other words, the order of importance of the parameters would be the follwing: p₁ = p₂ = p₃ > p₄ > p₅ = p₆ = p₇. If we assign the following values of importance to each parameter, such as: 3, 3, 3, 2, 1, 1, 1, then the corresponding weights can be estimated as follows:

  $$a_i={\displaystyle \frac{o_i}{\sum _{j=1}^n{o}_j}}$$

  (7)

  where o_i is the importance value of the parameter p_i. In this case, the sum of the values o_i equals 14. Then, a₁ = a₂ = a₃ = 3/14 = 21.4%; a₄ = 2/14 = 14.3%; a₅ = a₆ = a₇ = 1/14 = 7.1%.

3. Results

The seven parameters used by Equation (5) to calculate the EI of each car specified in Table A1 are listed in

Table A2. Values of the parameters used to calculate the environmental index (EI) of the vehicles in Table A1.

Id.	Model	p1	p2	p3	p4	p5	p6	p7
		CO2 (g/km)	NOx (mg/km)	PM (mg/km)	Consumption (kWh/100 km)	Top Speed (km/h)	Weight (kg)	Tire Width (mm)
A1	Fiat 500	115	60	4.5	47.34	160	940	175
A2	Fiat 500e	0	0	0	13.60	150	1440	185
A3	Fiat 500 Hybrid	119	60	4.5	51.20	167	980	175
A4	MINI Cooper C Essential	133	60	4.5	57.00	225	1335	195
A5	MINI Cooper E Essential	0	0	0	13.80	160	1615	195
A6	Hyundai i10 Essence	114	60	4.5	48.31	156	996	175
B1	Dacia Sandero	118	60	4.5	50.24	178	1127	185
B2	Dacia Sandero ECO-G	105	60	4.5	46.04	183	1182	185
B3	Seat Ibiza Reference	118	60	4.5	49.27	186	1144	185
B4	Renault Clio evolution	118	60	4.5	50.24	180	1157	195
B5	Renault Clio evol. LPG	108	60	4.5	49.88	188	1219	195
B6	Renault Clio evol. dCi	108	80	4.5	44.02	188	1248	195
B7	Renault Clio evol E-Tech	95	60	4.5	40.58	180	1313	195
B8	Peugeot E-208 Active	0	0	0	15.40	150	1530	195
C1	Toyota Corolla 140H Bus	100	60	4.5	42.51	180	1420	205
C2	Audi Q3 Sportback Adv.	144	60	4.5	61.83	208	1525	235
C3	Audi Q3 Sportback Advanced TDI	133	80	4.5	54.75	202	1655	235
C4	Audi Q3 Sportback S TFSIe	43	60	4.5	47.15	210	1815	235
C5	Ford Kuga ST EcoBoost	146	60	4.5	61.83	195.0	1526	225
C6	Ford Kuga ST FHEV	122	60	4.5	51.20	196	1689	225
C7	Renault Megane E-TECH	0	0	0	15.80	150	1588	195
D1	Tesla Model 3	0	0	0	13.20	201	1840	235
D2	BMW 320i	147	60	4.5	62.80	235	1590	225
D3	BMW 320d	127	80	4.5	51.53	235	1645	225
D4	BMW 320e	29	60	4.5	31.91	225	1845	225
D5	Volkswagen Passat Variant eTSI	122	60	4.5	52.17	222	1572	215
D6	Volkswagen Passat Variant TDI	131	80	4.5	53.68	223	1678	215
E1	Mercedes-Benz E 200	144	60	4.5	61.83	240	1825	225
E2	Mercedes-Benz E 300e	12	60	4.5	26.36	236	2210	245
E3	Audi A5 Sportback Advanced TFSI	150	60	4.5	63.76	223	1585	245
E4	Audi A5 Sportback Advanced TDI	128	80	4.5	52.61	210	1595	245
E5	BMW 520i	130	60	4.5	56.03	230	1800	225
E6	BMW 530e	13	60	4.5	27.25	230	2080	245
E7	BMW i5 eDrive40 Touring	0	0	0	16.70	193	2255	245
F1	BMW 740d xDrive	160	60	4.5	58.93	250	2255	245
F2	BMW 740E xDrive	22	60	4.5	35.06	250	2455	245
F3	BMW i7 eDrive50	0	0	0	19.10	205	2595	245
F4	Mercedes-Benz S 350d	172	80	4.5	70.86	250	2020	255
F5	Mercedes-Benz S 580e	14	60	4.5	31.11	250	2380	255
F6	Mercedes-Benz AMG S E	100	60	4.5	82.21	290	2595	255
F7	Audi A8 TDI quattro	189	80	4.5	77.30	250	2095	255
F8	Audi S8 TFSI quattro	260	60	4.5	111.10	250	2295	265
F9	Audi A8 TFSIe quattro	42	60	4.5	47.73	250	2385	255
J1	KIA Sportage 1.6 T-GDI	152	60	4.5	64.73	189	1526	215
J2	KIA Sportage 1.6 T-GDI MHEV	148	60	4.5	62.80	189	1561	215
J3	KIA Sportage HEV 1.6 T-GDI	126	60	4.5	54.10	193	1649	215
J4	KIA Sportage PHEV 1.6 T-GDI	25	60	4.5	30.34	191	1905	235
J5	Nissan Qashqai DIG-T mHEV 12V Acenta	143	60	4.5	60.87	196	1405	215
J6	Nissan Qashqai e-POWER Acenta	117	60	4.5	50.24	170	1687	235
J7	Range Rover SWB D300 MHEV SE	202	80	4.5	82.67	218	2505	275
J8	Range Rover SWB P460e PHEV SE	16	60	4.5	38.86	225	2770	275
J9	Hyundai IONIQ 5 N	0	0	0	21.20	260	2275	275
S1	Ferrari 812 Superfast	366	60	4.5	155.54	340	1705	275
S2	Porsche 911 Carrera GTS	236	60	4.5	100.48	311	1585	245
S3	Maserati Ghibli Diesel	203	80	4.5	83.74	250	1950	235
S4	Maserati Ghibli	255	60	4.5	108.20	267	1925	235
S5	Maserati Ghibli Hybrid	187	60	4.5	80.19	255	1953	235
	Minimum	0	0	0	13.20	150	940	175
	Maximum	366	80	4.5	155.54	340	2770	275

. The first column contains a vehicle model identifier consisting of the segment letter and a number corresponding to the order number in the list in Table A1. The last two rows of Table A2 show the minimum and maximum values for each parameter.

The resulting EIs for cases A, B, and C are shown in increasing order in Figure 1, Figure 2 and Figure 3, respectively. Obviously, the results of cases B and C are more similar to each other than to case A. The relative differences between the EIs obtained in case C with respect to case B are shown in Figure 4. Overall, the average relative difference is 8.88%.

Finally,

Table 3. Minimum and maximum EI values obtained in the three cases for each group of vehicles categorized by the current Spanish eco-label system.

Eco-Label	Case A		Case B		Case C
	Minimum	Maximum	Minimum	Maximum	Minimum	Maximum
“0 emissions”	0.000	0.627	0.054	0.707	0.027	0.677
“ECO”	0.550	0.787	0.350	0.759	0.491	0.780
“C”	0.576	0.938	0.337	0.881	0.480	0.905

compares the minimum and maximum values of the EIs calculated in the three cases corresponding to the vehicles identified with the current environmental labeling system in Spain, as shown in Table A1.

4. Discussion

The above results show that the proposed EI provides greater discrimination of the environmental performance of vehicles than the current eco-labeling system, in each of the three cases studied.

Furthermore, Table 3 shows that the ranges of EI values in each case overlap in all three eco-label categories, even in case A. Case A represents the closest case to the Spanish eco-label system, as it only considers pollutant emissions and fuel consumption. In this case, all BEVs (“0 emissions” label) have an EI close to 0. This is logical because their CO₂, NO_x, and PM emissions are 0, and their energy consumption is relatively low. However, some PHEVs (which also have the “0 emissions” label), such as the BMW 740d xDrive, have an EI as high as some HEVs with the “ECO” label (e.g., Renault Clio evolution E-Tech) or even ICVs with the “C” label (e.g., Fiat 500).

The reverse is also true. The KIA Sportage 1.6 T-GDI, with the label “C” and an EI within the last quartile for case A, achieves an emission level in the second quartile for both cases B and C, thus achieving a lower overall index than other cars with the “0 emissions” label, such as the Audi Q3 Sportback S TFSIe. This is an example of an over-discriminated car in the industry.

The maximum value reached by EI in all three cases corresponds to the Ferrari 812 Superfast, since it is the one with the highest values in all parameters except NO_x emissions and weight.

For the “0 emissions” category, the EI interval (the difference between the maximum EI and the minimum EI) is similar in the three cases. On the other hand, for the “ECO” and “C” categories, the EI interval is narrower in case A than in the other two cases. In other words, case A does not allow for as much discrimination among vehicles as in cases B and C.

Comparing the results of cases B and C, Figure 4 shows that case C increases the EI values for all vehicles except BEVs. This is due to the higher weight given to parameters p₁, p₂, and p₃. Secondly, it can be seen in Table 3 that the maximum EIs offered by cases A and C are higher than that offered by case B for combustion vehicles. However, the range of EIs in case C is wider compared to case A. Therefore, it seems reasonable to recommend the use of a heterogeneous distribution of weights for the parameters to ensure a better discrimination of the environmental performance of the vehicles.

The results suggest that the category with the highest difference between the actual and the proposed labeling systems are the vehicles in segment B, small cars about 4 m in length, which have “ECO” or “C” labels. Their EI values decrease significantly from case A to cases B and C. The average relative difference for the mentioned models for cases B and C is over 31% and 11%, respectively.

5. Conclusions

The current environmental labeling system does not seem to fully recognize the actual environmental performance of vehicles.

The proposed index takes into account seven characteristic parameters of the vehicle related to pollutant emissions, energy consumption, tire and brake wear. The selected parameters are available or can be derived from the technical data sheet of the vehicle. Parameters such as power were not chosen because other parameters, such as top speed and energy consumption, are more directly related to tire and brake wear on the one hand and fuel consumption and the generation of CO₂, NO_x, and PM emissions on the other.

With regard to NO_x and PM emissions, a car that has been homologated with a certain environmental label may emit higher levels of NO_x and/or PM than the standard limit if it is not properly maintained. The proposed index would allow for this fact to be considered, as it could be updated after the periodic inspections that cars must undergo at the Vehicle Technical Inspection Workshops, which could check NO_x and PM emissions.

To the best of our knowledge, we have not found any other environmental index that has been applied to the car eco-label. Perhaps the most similar model to the one proposed here is the one used by the U.S. EPA to measure air quality through the Air Quality Index (AQI) [41]. The AQI takes into account five parameters (ozone, particulate matter, CO, SO₂, and NO₂ levels) on a scale from 0 to 500. However, the procedure for its calculation is more complex than our model, which uses linear interpolations for each parameter.

All in all, the new index could serve as a powerful tool to evaluate the purchase of a vehicle and its environmental impact through a reliable metric with high accuracy that reflects reality in more ways than just the type of energy driving the engine and the age of the vehicle. This study clearly illustrates the need for a more complex and advanced environmental label for existing cars.