Environmental Impact of Urban Surface Transportation: Influence of Driving Mode and Drivers’ Attitudes

Abstract

This paper focuses on the environmental impact of urban surface transportation and the influence that driving mode and drivers’ attitudes have on it. This article emphasizes the importance of a respectful attitude toward the environment and adopting moderate or conservative driving modes. This study covers driving GHG emissions in urban and peripheral areas for variable driving conditions, evaluating reductions or increases in CO₂ emissions depending on the way of driving. The analysis of the different cases shows that pollutant emissions are significantly lower if a car driver reduces the acceleration rate and slows down by 10 to 20 km/h, or 6 to 12 mph, depending on the urban zone, downtown or peripheral, and traffic conditions. The reduction in GHG emissions can be as high as 0.083 kg of CO₂ per day, on average, representing a global yearly reduction of 30 kg of GHG emissions per vehicle. This paper analyzes how inadequate driving speed and the above traffic regulation limits have caused a surplus in GHG emissions and a severe impact on urban areas, which are sensitive to pollution, increasing the GHG emission rate by between 28% and 40% depending on driving mode and driver attitude. This study shows that vehicle speed reduction did not significantly increase traveling time, with an average time extension of 0.2 min per km, representing a global extended daily traveled time of 6.4 min for the average daily journey distance in many countries. GHG increases due to inadequate driving increase the early human mortality rate by 0.4%, representing nearly 35 million early deaths per year.

1. Introduction

Increasing pollution levels due to human activities represent a worry for people because of pernicious consequences like climate change, global warming, meteorological disasters, and health problems. Human concentrations in big cities increase the problem due to the many activities in a confined space. Among them, traffic is one of the biggest contributing factors to the high pollution levels in urban areas.

Urban surface transportation, private and public, uses internal combustion engines (ICEs) for vehicle propulsion, either in atmospheric or hybrid cars, representing 93% and 96.7% of the vehicles in China [1] and Western Europe [2]. The situation is worse in the USA, where 99% of the cars run on internal combustion engines [3]. Despite the growing trend of replacing ICE cars with electric vehicles (EVs) [4], the current situation continues to be worrying.

Regional, national, and supranational authorities have promoted the implementation of electric vehicles to replace ICE cars, either subsidizing EV purchases or penalizing the use of fossil-fuel-engine cars [5,6,7,8,9,10]. In past decades, many countries have dictated and adopted laws and regulations focusing on ICE car replacement by EVs [11,12,13,14,15]. Nevertheless, people continue driving conventional vehicles because of the lower price, easy access to refueling stations, longer driving range, and ignorance of potential damage to health [16,17,18,19,20,21,22].

The awareness campaigns against using vehicles with internal combustion engines due to their harmful effects on human health have not yet penetrated people’s minds because there is no clear relationship between the use of conventional vehicles and the damage caused by polluting emissions [23,24,25,26,27,28,29].

Due to this disconnect between cause and effect, it is necessary to take action focused on managing the situation, even against people’s will. To this goal, we propose implementing an interactive procedure between drivers and vehicle management through a manual or automatic control system to reduce vehicle GHG emissions.

Many factors influence vehicle fuel consumption and GHG emissions, like traffic congestion, road characteristics (slope variation, pavement type, etc.), and climatic conditions. The influence of these factors on vehicle performance was studied and analyzed in previous works developed by the author and coworkers [30,31,32,33,34,35]. Although some studies were conducted on electric vehicles and not atmospheric engines, many of the above-mentioned effects apply to internal combustion engines (ICEs).

This paper focuses on the light-duty vehicle sector because it is a regular type used in many modern cities. Of course, this analysis can be expanded to other popular types, like SUVs with higher mass; however, the results will only change by a factor and will not provide additional information about the effects of driving modes and drivers’ attitudes but instead about increasing fuel consumption and the proportional rise of GHG emissions.

The present study deals with GHG pollution’s effects on the environment and people’s health. Other pollutant components, like fine particles, are not the topic of this study.

2. Fundamentals

Gasoline and diesel combustion engines emit carbon dioxide into the atmosphere while working. The CO₂ emission rate depends on the driving conditions and engine type, but on average, we can establish a value between 0.091 and 0.144 kg/km [36]. Considering an average daily journey distance of 33.2 km (20.74 miles) [37], the daily average carbon emission rate per vehicle is

$$m_{C{O}_2}=33.2\left({\displaystyle \frac{0.091+0.144}{2}}\right)=3.9 \mathrm{kg}$$

(1)

This value corresponds to standard driving conditions, which do not represent the average driving mode. Indeed, the reference value should be modified if the vehicle speed and acceleration change.

The carbon dioxide emitted by a car engine results from fossil fuel combustion, gasoline, diesel, liquefied petrol gas (LPG), or compressed natural gas (CNG). In any of the mentioned fuels, hydrocarbon reacts with oxygen to produce carbon dioxide and water, releasing energy to propel the vehicle.

Considering the most commonly used fossil fuels for vehicle propulsion, we obtained the following results (

Table 1. Fossil fuel heat power [38].

	Heat Power
Fuel	kWh/kg	kWh/L
Gasoline	12.19	9.02
Diesel	11.8	9.82
LPG	12.75	0.02
CNG	12.53	2.38

).

The required energy to propel the vehicle derives from the classic equation of dynamics:

$$\xi =P_t{t}_{op}=F\stackrel{\_}{v}{t}_{op}=Fd$$

(2)

P_t is the power supply, t_op is the running time, F is the global dynamic force, $\stackrel{\_}{v}$ is the average vehicle speed, and d is the travel distance.

The global dynamic force comprises four contributions, inertial, drag, rolling, and weight force, according to the expression

$$F=ma+\kappa v^2+\mu mg+mg\sin \alpha$$

(3)

m, v, and a are the vehicle mass, speed, and acceleration; κ and μ are the drag and rolling force coefficients; and α is the road slope.

Combining Equations (2) and (3):

$$\xi =\left(ma+\kappa v^2+\mu mg+mg\sin \alpha \right)d$$

(4)

The fossil fuel consumption rate, in kg, is, therefore,

$$r_o={\displaystyle \frac{\xi }{Q}}={\displaystyle \frac{\left(ma+\kappa v^2+\mu mg+mg\sin \alpha \right)d}{Q}}$$

(5)

Since we could consider the drag and rolling force coefficients, road slope, vehicle mass, and heat power unchanged, we realized that the vehicle consumption rate depends on driving dynamic conditions, speed, and acceleration.

We developed Equation (5):

$$\begin{array}{l}{r}_o={\displaystyle \frac{mad}{Q}}+{\displaystyle \frac{\kappa v^{2}d}{Q}}+{\displaystyle \frac{\mu mgd}{Q}}+{\displaystyle \frac{mgd\sin \alpha }{Q}}=C_{1}a+C_2{v}^2+C_3\\ C_1=md/Q ; C_2=\kappa d/Q ; C_3=mgd\left(\mu +\sin \alpha \right)/Q\end{array}$$

(6)

When we modified the vehicle speed and acceleration, the rate coefficient changed according to

$$R={\displaystyle \frac{r}{r_o}}={\displaystyle \frac{C_{1}a+C_2{v}^2+C_3}{C_1{a}_o+C_2{v}_o^2+C_3}}$$

(7)

Sub-index o accounts for standard driving conditions.

Now, representing the new vehicle speed and acceleration as a variation of the standard values,

$$R={\displaystyle \frac{r}{r_o}}={\displaystyle \frac{C_1{f}_a{a}_o+C_2{f}_v^2{v}_o+C_3}{C_1{a}_o+C_2{v}_o^2+C_3}}$$

(8)

f_v and f_a are the speed and acceleration modifying factors, with values between 0 and 1.

Since the acceleration factor is restricted to values corresponding to the conservative, moderate, and aggressive driving modes—ECO, normal, and sport mode in the automobile industry terminology—we reduced the Equation (8) analysis to two options, conservative and aggressive, since moderate corresponds to the standard driving conditions, therefore:

$$\begin{array}{l}{R}_{sp}={\displaystyle \frac{C_1{f}_{sp}{a}_o+C_2{f}_v^2+C_3}{C_1{a}_o+C_2{v}_o^2+C_3}}\\ R_{eco}={\displaystyle \frac{C_1{f}_{eco}{a}_o+C_2{f}_v^2+C_3}{C_1{a}_o+C_2{v}_o^2+C_3}}\end{array}$$

(9)

Sub-indexes sp and eco account for the sport (aggressive) and ECO (conservative) driving modes.

In the current conditions, the acceleration factors for the sport and ECO driving modes adopt the values of 1.4 and 0.6; replacing these in Equation (9) yields

$$\begin{array}{l}{R}_{sp}={\displaystyle \frac{1.4C_1{a}_o+C_2{f}_v^2+C_3}{C_1{a}_o+C_2{v}_o^2+C_3}}\\ R_{eco}={\displaystyle \frac{0.6C_1{a}_o+C_2{f}_v^2+C_3}{C_1{a}_o+C_2{v}_o^2+C_3}}\end{array}$$

(10)

3. Materials and Methods

3.1. Driving Conditions

The speed factor depends on the vehicle velocity variation, which is uncertain; therefore, considering the current habits of drivers in modern cities, we can establish five different speed patterns: standard, fast, and moderate speed-up and fast and moderate slow-down. According to this configuration, we obtained the following speed values for urban and peripheral circulation (

Table 2. Vehicle speed patterns and value for urban and peripheral circulation.

Condition	Speed (km/h)
Fast speed increase	120	95	65	40
Moderate speed increase	105	80	57.5	35
Standard conditions	90	70	50	30
Moderate speed decrease	80	60	40	25
Fast speed decrease	70	50	35	20

).

Since these values are representative of the different driving environments but not totally accurate, we determined the standard deviation by applying statistical methods to the data sources. According to this procedure, the standard deviation is as follows (

Table 3. Standard deviation for vehicle speed in urban and peripheral circulation.

Condition	Standard Deviation
Fast speed increase	3.4	2.9	2.5	1.8
Moderate speed increase	3.5	2.8	2.4	1.9
Standard conditions	2.9	2.9	2.5	2.3
Moderate speed decrease	2.8	2.7	1.8	1.9
Fast speed decrease	2.9	2.5	1.9	2.1

).

Calculating the vehicle speed ratios related to the standard conditions, we obtained the following (

Table 4. Vehicle speed ratios related to standard conditions.

Condition	Speed Ratio
Fast speed increase	1.33	1.36	1.30	1.33
Moderate speed increase	1.17	1.14	1.15	1.17
Moderate speed decrease	0.89	0.86	0.80	0.83
Fast speed decrease	0.78	0.71	0.70	0.67

).

Analyzing the speed ratios for the different vehicle speeds, we noticed that all values for the same condition were in a narrow range around the average value.

Table 5. Average speed ratio and deviation.

Condition	Speed Ratio	Standard Deviation (%)
Fast speed increase	1.33	2.0
Moderate speed increase	1.16	1.0
Moderate speed decrease	0.84	3.3
Fast speed decrease	0.71	4.0

shows the average speed ratio and deviation.

Since the deviation is low in all cases, we were able to adopt the average speed ratio as the reference value for the case study.

We replaced the values in Equation (10):

$$R_{sp}=\left\{\begin{array}{c}{\displaystyle \frac{1.4C_1{a}_o+1.77C_2+1.33C_3}{C_1{a}_o+C_2{v}_o^2+C_3}}\to rough speed increase\\ {\displaystyle \frac{1.4C_1{a}_o+1.35C_2+1.16C_3}{C_1{a}_o+C_2{v}_o^2+C_3}}\to moderate speed increase\\ {\displaystyle \frac{1.4C_1{a}_o+0.71C_2+0.84C_3}{C_1{a}_o+C_2{v}_o^2+C_3}}\to rough speed decrease\\ {\displaystyle \frac{1.4C_1{a}_o+0.50C_2+0.71C_3}{C_1{a}_o+C_2{v}_o^2+C_3}}\to moderate speed decrease\end{array}\right.$$

(11)

$$R_{eco}=\left\{\begin{array}{c}{\displaystyle \frac{0.6C_1{a}_o+1.77C_2+1.33C_3}{C_1{a}_o+C_2{v}_o^2+C_3}}\to rough speed increase\\ {\displaystyle \frac{0.6C_1{a}_o+1.35C_2+1.16C_3}{C_1{a}_o+C_2{v}_o^2+C_3}}\to moderate speed increase\\ {\displaystyle \frac{0.6C_1{a}_o+0.71C_2+0.84C_3}{C_1{a}_o+C_2{v}_o^2+C_3}}\to rough speed decrease\\ {\displaystyle \frac{0.6C_1{a}_o+0.5C_2+0.71C_3}{C_1{a}_o+C_2{v}_o^2+C_3}}\to moderate speed decrease\end{array}\right.$$

(12)

We considered a standard vehicle of 1500 kg, a drag coefficient of 0.67, a rolling coefficient of 0.015, and flat terrain (α = 0) for the standard driving conditions.

We used a_o = 2.5 m/s² for the standard acceleration.

3.2. Fuel Consumption and GHG Emissions

Applying the data from Table 5 to Equation (7), we obtained the fuel consumption rates for the different driving conditions. Since the GHG emissions are proportional to the fuel consumption rate, the factors shown in

Table 6. Rate coefficients for the driving conditions.

Condition	vo (km/h)	Rsp	Reco
Fast speed increase	90	1.285	0.881
Moderate speed increase	70	1.225	0.869
Moderate speed decrease	50	1.099	0.849
Fast speed decrease	30	1.053	0.826

indicate increases or decreases in GHG emissions.

Because fuel consumption depends on car type, driving conditions, and driver attitude, we ran a simulation for a wide fuel consumption range to cover all the possible configurations. Based on the fuel consumption for the selected vehicles (m = 1500 kg) under standard driving conditions, 4.4 L per km (54 MPG), we obtained the following results (

Table 7. Fuel consumption rates (L/100 km) for the different driving conditions.

		Driving Mode
Condition	vo (km/h)	Sport	ECO
Fast speed increase	90	5.655	3.878
Moderate speed increase	70	5.390	3.823
Moderate speed decrease	50	4.834	3.736
Fast speed decrease	30	4.635	3.634

).

We retrieved from the literature the average fuel consumption per engine type under standard driving conditions (

Table 8. Average fuel consumption per engine type.

Engine Type	Gasoline [39]	Diesel [40]	LPG [41]	CNG [41]
Consumption	4.8 L/100 km 49.5 MPG	4.1 L/100 km 57.9 MPG	7 L/100 km 33.9 MPG	4.5 L/100 km 52.8 MPG

).

On the other hand, the carbon dioxide emissions for the different fuel types are as follows (

Table 9. GHG emissions (kg/L) for different fuel types.

Engine Type	Gasoline [42]	Diesel [43]	LPG [44]	CNG [45]
Consumption	2.3	3.1	1.5	2.2

) [33,34,35,36].

Applying the data from Table 7, Table 8 and Table 9 to the yearly vehicle GHG emissions for variable driving patterns and driver attitudes, we obtained the following (

Table 10. Vehicle yearly GHG emissions for variable driving patterns.

Gasoline Yearly Standard Emissions: 1337.8 kg	GHG Emissions
	Year (kg)		Year Difference (kg)
	Driving Mode
Condition	Sport	ECO	Sport	ECO
Fast speed increase	1576.1	1080.9	238.3	−257.0
Moderate speed increase	1502.3	1065.5	164.4	−272.3
Moderate speed decrease	1347.3	1041.3	9.5	−296.6
Fast speed decrease	1291.8	1012.8	−46.0	−325.0
Diesel Yearly standard emissions: 1540.2 kg	GHG emissions
	Year (kg)		Year difference (kg)
	Driving mode
Condition	Sport	ECO	Sport	ECO
Fast speed increase	2124.3	1456.8	584.1	−83.4
Moderate speed increase	2024.8	1436.1	484.6	−104.1
Moderate speed decrease	1815.9	1403.5	157.5	−136.7
Fast speed decrease	1741.2	1365.1	201.0	−175.1
LPG Yearly standard emissions: 1272.4 kg	GHG emissions
	Year (kg)		Year difference (kg)
	Driving mode
Condition	Sport	ECO	Sport	ECO
Fast speed increase	1610.9	1104.7	338.5	−167.7
Moderate speed increase	1535.4	1089.0	263.0	−183.4
Moderate speed decrease	1377.0	1064.2	104.6	−208.1
Fast speed decrease	1320.3	1035.2	48.0	−237.2
CNG Yearly standard emissions: 1199.7 kg	GHG emissions
	Year (kg)		Year difference (kg)
	Driving mode
Condition	Sport	ECO	Sport	ECO
Fast speed increase	1507.6	1033.9	307.9	−165.8
Moderate speed increase	1437.0	1019.2	237.3	−180.5
Moderate speed decrease	1288.7	996.0	89.0	−203.7
Fast speed decrease	1235.7	968.8	36.0	−230.9

).

Analyzing the results from Table 9, we realized that driving aggressively in sport mode increased the GHG emissions in all cases independently of the driver’s attitude while the ECO mode decreased them.

The increase or decrease in GHG emissions is not uniform because it depends on the driver’s attitude. The GHG emission increase in sport mode progressively decreases as the driver’s attitude becomes more conservative, from a fast speed increase to a fast speed decrease. A similar situation occurs in the ECO mode, where the GHG emissions decrease progressively as the driver’s attitude becomes more conservative. This situation was reproduced for all fuel types.

Concerning the engine type, we observed that the LPG engine is the most environmentally respectful, showing the lowest GHG emission values among the tested ICE types. The CNG and gasoline engines showed similar GHG emission values, nearly 50% higher than LPG. Finally, the diesel engine was the most pollutant ICE, with an emission value twice the value of the LPG engine.

This analysis shows that the GHG emission trend is based on the GHG emission rate of every engine type, with LPG as the lowest and diesel as the highest, especially as fine particles; therefore, despite the lower consumption rate of the diesel engines, the combined effect produces higher GHG emission values. The CNG and gasoline engines show intermediate fuel consumption and GHG emission rates, generating GHG emission values between those of LPG and diesel.

We should split every driving pattern into three categories—all cases, speed increases, and speed decreases—if we want to develop a deeper analysis to evaluate the influence of driving patterns and drivers’ attitudes on GHG emissions. By doing so, we can obtain how driving mode and the driver’s attitude determine the GHG emission trend and the rising or lowering percentage regarding the average value.

Table 11. Influence of driving patterns and driver attitudes on GHG emissions.

Gasoline	Driving Mode		Diesel	Driving Mode
Driver Attitude	Sport	ECO	Driver Attitude	Sport	ECO
All cases	1.068	0.785	All cases	1.251	0.919
Speed increase	1.151	0.802	Speed increase	1.347	0.939
Speed decrease	0.986	0.768	Speed decrease	1.155	0.899
LPG	Driving mode		CNG	Driving mode
Driver attitude	Sport	ECO	Driver attitude	Sport	ECO
All cases	1.148	0.844	All cases	1.140	0.837
Speed increase	1.236	0.862	Speed increase	1.227	0.856
Speed decrease	1.060	0.825	Speed decrease	1.052	0.819

shows the trending factors for the increases or decreases in GHG emissions based on the average values for standard driving conditions.

We observed that the speed increase cases showed higher values than the global configurations for all the cases; however, the speed decreases showed lower values, indicating how an aggressive driver attitude generates an increase in GHG emission values and a moderate attitude lowers the value. On the other hand, the sport mode, corresponding to an aggressive way of driving, shows factors of above one in all situations, except that the speed decreases with this driver attitude in the gasoline-engine car; this trend confirms that the aggressive way of driving tends to increase GHG emissions. The ECO mode, corresponding to a conservative way of driving, shows an opposite trend, with factor values of below one for all cases, reinforcing that a conservative attitude toward driving favors GHG emission reduction.

The ECO mode is the less pollutant because of the lower fuel consumption, thanks to a reduced average driving speed, resulting in a lower power demand: thus, reduced fuel consumption and GHG emission values. The opposite is the sport mode, characterized by a faster driving speed. On the other hand, the conservative driver attitude represents a lower acceleration rate. The aggressive one is characterized by high acceleration values, resulting in higher fuel consumption and GHG emissions. Therefore, we may conclude that the higher the acceleration rate, the higher the fuel consumption and GHG emissions. A similar conclusion applies to vehicle speed: the faster the vehicle, the higher the GHG emission value.

4. Results from Experimental Tests

We ran a series of tests to validate the developed study and analysis. To this goal, we used four light vehicles of approximately equal mass, around 1500 kg, as stated in the simulation analysis, powered by gasoline, diesel, LPG, and CNG.

Table 12. Characteristics of the tested vehicles.

			Fuel Rate (L/100 km)
Vehicle	Mass (kg)	Engine Type	Reference	Tested
Toyota Yaris	1110	Gasoline	4.8	4.75
Peugeot 208	1177	Diesel	4.1	4.12
Fiat Panda	1055	LPG	6.7	6.82
Renault Clio	1080	CNG	4.0	4.11

lists the tested vehicles.

We ran tests for a constant refueling value of 40 L and calculated the fuel rate for every test, dividing the 40 L refueling value by the travel distance. The tested values in Table 12 correspond to a set of four tests for a variable distance depending on the tested vehicle. We ran a single test for every vehicle’s speed. The reference values in Table 11 correspond to the manufacturer data.

The driving standard conditions were developed at average speeds of 30, 50, 70, and 90 km/h, with 5% accuracy. Average vehicle speed corresponded to downtown traffic in rush hours (30 km/h), moderate dense traffic (50 km/h), peripheral routes with moderate dense traffic (70 km/h), and peripheral routes with light traffic (90 km/h).

Table 13. Travel distance and average fuel rate for every vehicle speed.

Vehicle	Toyota Yaris	Peugeot 208	Fiat Panda	Renault Clio
Distance (km)	3368	3880	2346	3890
Fuel rate (90 km/h)	4.75	4.12	6.82	4.11
Fuel rate (70 km/h)	4.51	3.92	6.48	3.91
Fuel rate (50 km/h)	4.04	3.50	5.80	3.50
Fuel rate (30 km/h)	3.95	3.43	5.67	3.42

shows the tested travel distance and the average fuel rate for every vehicle speed.

Since GHG emission measurement is impossible while a vehicle runs, we evaluated the CO₂ emissions from fuel consumption. Because the tested fuel rate values highly influenced the GHG emissions, we determined the average values for the variable driving conditions according to Table 11.

Table 14. Comparative evaluation of fuel rate factors for different driving patterns and driver attitudes.

Gasoline	Sport Mode
Driver Attitude	Reference Fuel Rate (L/100 km)	Tested Fuel Rate (L/100 km)	Reference Factor	Testing Factor	Deviation (%)
All cases	4.75	5.08	1.068	1.069	0.07
Speed increase		5.46	1.151	1.149	−0.15
Speed decrease		4.67	0.986	0.993	0.71
Gasoline	ECO mode
Driver attitude	Reference fuel rate (L/100 km)	Tested fuel rate (L/100 km)	Reference factor	Testing factor	Deviation (%)
All cases	4.75	3.78	0.785	0.796	1.43
Speed increase		3.88	0.802	0.795	−0.83
Speed decrease		3.73	0.768	0.786	2.31
Diesel	Sport mode
Driver attitude	Reference fuel rate (L/100 km)	Tested fuel rate (L/100 km)	Reference factor	Testing factor	Deviation (%)
All cases	4.12	5.19	1.251	1.259	0.61
Speed increase		5.54	1.347	1.343	−0.29
Speed decrease		4.81	1.155	1.166	0.95
Diesel	ECO mode
Driver attitude	Reference fuel rate (L/100 km)	Tested fuel rate (L/100 km)	Reference factor	Testing factor	Deviation (%)
All cases	4.12	3.86	0.919	0.937	1.95
Speed increase		3.89	0.939	0.943	0.47
Speed decrease		3.77	0.899	0.915	1.79
LPG	Sport mode
Driver attitude	Reference fuel rate (L/100 km)	Tested fuel rate (L/100 km)	Reference factor	Testing factor	Deviation (%)
All cases	6.82	7.87	1.148	1.153	0.48
Speed increase		8.42	1.236	1.235	−0.06
Speed decrease		7.35	1.060	1.078	1.69
LPG	ECO mode
Driver attitude	Reference fuel rate (L/100 km)	Tested fuel rate (L/100 km)	Reference factor	Testing factor	Deviation (%)
All cases	6.82	5.83	0.844	0.855	1.28
Speed increase		5.90	0.862	0.865	0.38
Speed decrease		5.76	0.825	0.844	2.33
CNG	Sport mode
Driver attitude	Reference fuel rate (L/100 km)	Tested fuel rate (L/100 km)	Reference factor	Testing factor	Deviation (%)
All cases	4.11	4.71	1.140	1.145	0.47
Speed increase		5.03	1.227	1.222	−0.40
Speed decrease		4.35	1.052	1.058	0.60
CNG	ECO mode
Driver attitude	Reference fuel rate (L/100 km)	Tested fuel rate (L/100 km)	Reference factor	Testing factor	Deviation (%)
All cases	4.11	3.48	0.837	0.845	0.98
Speed increase		3.51	0.856	0.853	−0.34
Speed decrease		3.42	0.819	0.830	1.39

shows the test results. The reference fuel rates correspond to standard driving conditions, while the tested fuel rates are the determined values for the current driving conditions for the driver attitudes.

We realized that the experimental factor values are in close agreement with the predicted data from the simulation analysis, within a maximum deviation of 2.33% and an average accuracy of 99.3%; therefore, we demonstrated the validity of the proposed study and the influence of driving pattern and driver attitude on GHG emissions.

Analyzing the data from Table 13, we observed that the speed increase case always showed the highest GHG emission factors and the speed decrease case the lowest, reinforcing that speeding up provokes higher pollutant emissions while slowing down reduces them.

Considering the “all cases” configuration as a reference, the gasoline, diesel, LPG, and CNG engines increased the GHG emissions by 6.9%, 25.9%, 15.3%, and 14.5%, respectively. Diesel is the most pollutant engine because of the combined emissions of CO₂ and NO_x.

Despite the all-cases configuration representing the average of the three studied configurations, all cases, speed increase, and speed decrease (see

Table 15. GHG emission factors for variable configurations and engine types.

	All Cases		Average		Deviation (%)
Engine Type	Sport	ECO	Sport	ECO	Sport	ECO
Gasoline	1.068	0.785	1.067	0.792	0.1	0.9
Diesel	1.251	0.919	1.256	0.932	0.4	1.4
LPG	1.148	0.844	1.156	0.855	0.7	1.3
CNG	1.140	0.837	1.142	0.843	0.2	0.7

), a deeper analysis of vehicle speeding up and slowing down is necessary for evaluating the driving pattern and driver attitude influence on GHG emissions.

In sport mode, speeding up the vehicle represents an average of a 7% GHG emission increase regarding the all-cases configuration, while slowing down means an average of a 7.3% reduction; in ECO mode, speeding up represents a 0.7% GHG increase, while slowing down means an average of a 1.7% reduction.

The global analysis concluded that no matter which engine type the vehicle uses, the speed increase configuration, regarding the average driving conduction, leads to a rise in GHG emissions; however, the speed decrease configuration produces the opposite situation.

5. Discussion

Health Influence of GHG Emissions

The pollution level increase seriously affects human health, causing diseases and eventually death. Although it is difficult to obtain a dependence factor of human deaths on pollutant emissions, developed studies show a close relationship between human mortality and ambient air pollution [46,47]. Some works give specific data on the ambient air pollution impact on human mortality, indicating that 32% of total anthropogenic PM2.5 mortality derives from land transportation in the USA and nearly 24% in Europe [48]; this situation represents approximately 200,000 early deaths in the U.S. and more than 130,000 in Europe [49].

Despite the critical influence of the component nitrox oxide (NOx) among the gas emissions and of fine particles, whose influence on human health is out of focus since this study analyzes the gas emissions’ influence on people’s health, research studies on the influence of carbon dioxide effects on human health show the impact CO₂ has on people’s health deterioration because of increased CO₂ emission levels [50] or benefits derived from its reduction [51]. Carbon monoxide, a GHG component generated by incomplete combustion of fossil fuels in car engines, provokes similar consequences. References from the literature support this statement [52,53,54], proving the need for CO emission reduction.

Now, applying the data from the present study to the provided data in the literature, we can deduce that land transportation generates an increase in yearly human mortality all around the world. Considering the USA, 92.1% of vehicles run on gasoline, 3.4% on diesel, 3.2% on LPG, and 1.3% on CNG [55]. The situation in Europe is different, with 50.6% gasoline engine vehicles, 40.8% diesel, 6.6% LPG, and 2% CNG [56].

Inadequate driving causes an increase in human mortality due to a higher level of GHG emissions; we computed the health impact through the following expression:

$$F={\displaystyle \sum _{i=1}^4{f}_i{x}_i}$$

(13)

F and f represent the global and individual health impact factors, and x is the fuel type fraction in the vehicle engine market.

According to the previously mentioned values and data from Table 10, we have

$$F=\left\{\begin{array}{c}(0.921)(1.068)+(0.034)(1.251)+(0.032)(1.148)+(0.013)(1.140)=1.078 (USA)\\ (0.506)(1.068)+(0.408)(1.251)+(0.066)(1.148)+(0.020)(1.140)=1.149 (Europe)\end{array}\right.$$

(14)

Averaging over the two zones, we obtained the following global impact factor:

$$F={\displaystyle \frac{(335135000)(1.078)+(449200000)(1.149)}{335135000+449200000}}=1.119$$

(15)

representing a human death increase of 11.9% over 10% of world population.

We noticed that the situation is worse in Europe due to the higher percentage of diesel engines, a reason why the European Union authorities have enacted laws to abolish diesel engine use [56,57,58].

Converting the health impact factor in yearly human deaths, N, due to inadequate driving modes yields

$$N_1=\left\{\begin{array}{c}(1.078)(200000)=215600 (USA)\\ (1.149)(130000)=149370 (Europe)\end{array}\right.$$

(16)

Based on the estimated population in the USA [59] and the European Union [60], the increase in the percentage of human deaths, f_d, due to inadequate driving is

$$f_d=\left\{\begin{array}{c}100{\displaystyle \frac{15600}{335135000}}=4.7\times {10}^{-3} (USA)\\ 100{\displaystyle \frac{19370}{449200000}}=4.3\times {10}^{-3} (Europe)\end{array}\right.$$

(17)

The impact on human health from inadequate driving modes is similar in the USA and the European Union.

The situation in China, the most populated country in the world, differs because of an aggressive authority policy in favor of vehicle fleet electrification, reducing fossil fuel-engine-cars from 94% in 2020 to 59% in 2024 [61]. Despite this drastic reduction, the number of human deaths due to inadequate driving in China is tremendous, as reflected by the following expression:

$$N_2=(4.5x{10}^{-3})(1419321278)=6386946$$

(18)

We used the average percentage values of the human deaths from the USA and Europe in Equation (17). We observed that the yearly early deaths due to inadequate driving in China mean more than 6 million people, a significant value [62].

Applying the developed analysis to other countries with lower traffic densities, assuming that 40% of the rest of the world population operates with an impact factor of half of that previously determined for the USA and Europe and the other 60% with a one-fourth impact factor, we obtained

$$N_3=\left[(0.4)(8.2\times {10}^9-2.2\times {10}^9)(1.06)+(0.6)(8.2\times {10}^9-2.2\times {10}^9)(1.03)\right](4.5\times {10}^{-3})=28134000$$

(19)

Adding the values from Equations (16) and (18), we have

$$N_T=N_{1(USA)}+N_{1(EU)}+N_2+N_3=215600+149370+6386946+28134000=34885916$$

(20)

Nearly 35 million people die early because of extra GHG emissions from inadequate driving, representing 0.4% of the world population.

6. Conclusions

We developed a study on environmental impact caused by extra GHG emissions due to inadequate driving, evaluating the GHG emission increases because of speed increases and faster acceleration. This study also evaluates the influence on human health.

The developed analysis shows that by adopting a moderate driving mode with vehicle speed reduction from 6 to 12 km/h on downtown journeys and 10 to 20 km/h on peripheral routes, the extra GHG emissions vanish, avoiding health problems like coronary and lung diseases that may cause premature death. This study proved that moving to a conservative way of driving with drastic acceleration reduction and a speed decrease in driving mode, 10 to 20 km/h in downtown and 20 to 30 km/h in peripheral routes, will lower the environmental impact caused by GHG emissions by 7.4%, saving nearly 21.7 early human deaths per year.

This analysis shows that the GHG emission impact factor is 1.119 on average, generating more than 15 thousand extra early deaths per year in the USA and nearly 20 thousand in the European Union because of the poor air quality. The situation is dramatic in China, with more than 6 million people dying yearly, although that vehicle fleet has drastically reduced its dependence on fossil fuel in the past four years. Assuming a reduced impact factor of half the value for the USA and the EU for 40% of the rest of the world population and one-fourth for the other 60%, the global number of yearly early human deaths is significant: nearly 35 million people. These data should make people aware that applying a moderate driving mode, or even conservative, will reduce the environmental impact generated by extra GHG emissions and lower the human deaths caused by these emissions.

The developed analysis on human health deterioration and rising death rate corresponds to poor air quality because of GHG emissions; the rate increases if other lethal components, like nitrox oxide and fine particles, are considered.