1. Problem identification: how to improve thermal comfort, knowing in advance the constructive elements that influence the thermal energy flow of a certain volume of social coexistence;

Further on, the conclusion of the stock and flow diagram considers the addition of auxiliary variables, which will be further explained in this paper. These values remain the same in all scenarios when only the solar factor of the glass, wall thermal transmittance, and room window opening percentage parameters are changed to simulate the behavior of the system over time. In the computational model, the constructive parameters of an individual room in Baltimore Residential are also transcribed. After this data collection, the elements of the system are interrelated through mathematical equations. Ultimately, changing values of the constructive elements of the system results in a scenario variety with different indoor thermal sensitivity of the room.

### *2.1. Selection Criteria for Building Materials*

A myriad of building materials is currently available to provide a desirable level of thermal comfort. Nevertheless, it comes with a broad array of costs, with top-tier technologies requiring higher expenditure [46]. Considering the MCMV program, the use of poor-quality materials has contributed to general dissatisfaction [25,35]. Although dealing with a restricted budget, the federal housing program may rely on a better material selection that does not necessarily reflect higher costs. In this sense, the following items were chosen from the literature reviewed in the introduction section [24,33–35], especially concerning cost/benefit [11], thermal efficiency [37,38,40], and national availability [10] aspects:

green tempered glass presents much higher protection for a reasonable cost, and is considered an optimized solution among distinct possibilities, especially regarding budget restraints in the national program. The simulation with green tempered glass is only carried out when the window is half open, as in this situation the glass is an element that participates in the heat flow. However, when the window is completely open, the glass is out of the picture and does not affect the system.

#### *2.2. Calculation of Natural Ventilation*

According to Lamberts, Dutra, and Pereira [3], natural ventilation through unilateral openings is an energy-efficient method to adjust the thermal comfort of buildings. Thus, it is necessary to understand the climatic conditions of the region, such as wind speed and average temperature. The wind speed, provided by meteorological stations, must be corrected for the height of interest, also as a function of the distance between houses [3]. In addition, the wind pressure coefficients of the region must be observed. From this information, the average corrected wind speed must then be calculated [3], as given by Equation (1):

$$\text{Vcorrected} = \text{Vaverage} \times \text{F}\_{\text{t}} \times \left(\text{H}^{\text{Fr}}\right) \tag{1}$$

Vcorrected: average corrected wind speed (m/s);

Vaverage: average annual wind speed measured by the weather station (m/s);

H: building ridge height (m);

Ft: topographic factor;

Fr: terrain roughness factor.

The average annual wind speed gauged by the weather station in the Uberlândia region is close to 1.67 m/s [56]. According to Lamberts, Dutra, and Pereira [3], the topographic and terrain roughness factors are, respectively, 0.35 and 0.25. Moreover, as reported by Villa, Saramago, and Garcia, the building ridge height is equivalent to 15 m [25].

Window opening is pivotal to estimating the corresponding airflow in the room [3]. Depending on the window opening ratio, its useful area may vary and, in this case, a 50% or 100% opening is considered. Regarding Baltimore Residential's rooms, only singlesided ventilation is available, which directly contributes to the air flux amount carried into the room.

$$\mathbf{Q} = 0.025 \times \mathbf{a} \times \text{Vcorrected} \tag{2}$$

Q: airflow with natural ventilation in the room (m3/s);

a: opening window useful area (m2);

Vcorrected: average corrected wind speed (m/s).

The living room window has a total area of 1.78 m2 [25]. To ensure the air quality of a room, a minimum number of air changes per hour is defined. As airflow is given in cubic meters per second, the result is multiplied by 3600 to establish the number of air changes per hour, according to Equation (3) [3]:

$$\mathbf{n} = \left(\frac{\mathbf{Q} \times \mathbf{3600}}{\mathbf{v}}\right) \tag{3}$$

n: number of air changes per hour;

Q: airflow with natural ventilation in the room (m3/s);

v: ventilated room volume (m3).

According to the floor plan of the Baltimore Residential apartment evaluation (Figure 3), the room has an area equivalent to 10.95 m2 and a ceiling height equal to 2 m, thus the volume of the room is equal to 21.9 m3 [25]. The average annual temperature of the Uberlândia region is approximately 22.1 ◦C [57]. Moreover, the internal room temperature becomes the desired variable, in which values vary when modifying the constructive parameters of the environment.

After determining the number of air exchanges for the room, which depends on the tightness of the air openings, it is important to understand that this exchange will translate into a heat removal in the room. To calculate the thermal load, the sensible heat must be determined, which is related to the temperature difference between the interior and exterior, as shown in Equation (4) [3]:

$$\mathbf{Q\_{SE}} = \mathbf{m\_{air}} \times \mathbf{c\_{air}} \times \left(\frac{\mathbf{n} \times \mathbf{v}}{3600}\right) \times \Delta \mathbf{T} \tag{4}$$

QSE: sensible heat (W); mair: air density (1.2 kg/m3); cair: specific heat of the air (1000 J/kg· ◦C); n: number of air changes per hour; v: ventilated room volume (m3); ΔT: internal and external temperature difference (◦C).

Heat flux represents the heat transfer rate through a unit area section of the window and it is uniform (invariant) across the entire area of the window opening as well as the glass. The heat loss through the window area is given by Equation (5) [58]:

$$
\Phi = \frac{\mathbf{Q}\_{\text{SE}}}{\mathbf{a}} \tag{5}
$$

φ: heat flux removed from the room (W/m2); QSE: sensible heat (W); a: opening window useful area (m2).

#### *2.3. Calculation of the Internal Thermal Energy of the Room*

To understand which material has better thermal performance, it is necessary to know its thermal transmittance index, which means the amount of heat in watts that passes from one surface of the wall to another, per square meter at the degree of variation of temperature between the surfaces [47].

In this project, two types of envelopes are simulated: a 12.5 cm 8-hole brick wall and an EPS (expandable polystyrene) monolithic panel. The thermal transmittance of the first material is 2.94 W/m2· ◦C [3], while the second is 0.42 W/m2· ◦C [59]. From these indexes, it is possible to calculate the heat flux of Baltimore Residential's rooms, as given by Equation (6) [3]:

$$\mathbf{q} = \mathbf{U} \times \Delta \mathbf{T} \tag{6}$$

q: added heat flux in the room (W/m2);

U: thermal transmittance (W/m2 ◦C);

ΔT: internal and external temperature difference (◦C).

It is understood that Equation (6) calculates the heat flux added in the room, due to the thermal transmittance of the wall, while Equation (5) calculates the heat flux removed from the room, determined by the number of air changes through the window. The difference between both added and removed heat flux from the room results in the internal thermal energy of the room. This calculation is better understood by noting that the room is considered to be a stock where energy accumulates. From the initial internal energy value at a given time "t", it is known that what is added to the stock between time t and the next point in time, denoted by "t + 1", is also added to obtain the stock value at the, "t + 1" point, given by Equation (7) [60,61]:

$$\mathbf{E}\left(\mathbf{t}\right) = \mathbf{E}\left(\mathbf{t}\_0\right) + \int\_{\mathbf{t}\_0}^{\mathbf{t}} \left[ \left(\mathbf{q} \times \mathbf{A}\right) - \left(\boldsymbol{\Phi} \times \mathbf{a}\right) \right] \tag{7}$$

E: internal energy of the room (J);

q: added heat flux in the room (W/m2);

A: area of the wall where solar radiation falls on (m2);

φ: heat flux removed from the room (W/m2);

a: ventilation area of the living room window (m2).

Thus, it is concluded that the room ambiance works as heat storage, where the subtraction of the inlet and outlet flows results in the internal temperature of the room, given by Equation (8) [60]:

$$\text{Tinternal} = \frac{\text{E}}{\text{c}\_{\text{air}} \times \text{m}\_{\text{air}} \times \text{v}} \tag{8}$$

Tinternal: internal room temperature (◦C); cair: specific heat of air at constant pressure (1000 J/kg· ◦C); mair: air density (1.2 kg/m3); v: ventilated room volume (m3).

#### *2.4. Model Construction*

A causal diagram depicts a reduced representation of causal links and mathematical equations to facilitate the understanding of the variables' evolution and their interconnected rationale. As can be seen in Figure 4 (made with the Vensim software PLE 9.0.1x64 version), there are two negative balancing loops (B1/B2), whose functions are to smooth the internal thermal energy inside the room, which is directly related to the internal room temperature. Loop B1 is related to the heat flux that passes through the wall and window glass, while Loop B2 considers the heat flux removed from the room by the number of air changes through the window opening.

**Figure 4.** Causal diagram for Baltimore Residential's rooms (source: authors' own elaboration using the Vensim software).

To develop a stock and flow diagram (SFD), four building blocks are used, according to Ahmad et al. [62]: stock, flow, and auxiliary variables, and also a connector. A stock shows an accumulation of any variable; in the case under study, energy is the variable accumulated in the room. The flow is attached to a stock and responsible for increasing or depleting the stock level, with the variable "added heat flux in the room" as the input and the variable "heat flux removed from the room" as the output. Auxiliary variables can be parameters or values calculated from other system variables. All auxiliary variables and their values can be seen in Table 1. Finally, a connector or an arrow denotes the connection and control between the system variables. Ultimately, the four building blocks are displayed within the stock and flow diagram, as seen in Figure 5.

**Table 1.** List of auxiliary variables and their respective values (source: authors' own elaboration).

**Figure 5.** Stock and flow diagram for Baltimore Residential's rooms (source: authors' own elaboration using the Vensim software).

#### **3. Results and Discussion**

The thermal comfort evaluation in indoor environments is commonly performed according to the American Society of Heating, Refrigerating and Air Conditioning Engineers (ASHRAE) standard ASHRAE 55 [64], which has been largely explored in the literature [65–71] and defines indoor operating temperatures ranging from 23.0 ◦C to 26.0 ◦C (for 35% relative humidity) and from 22.5 ◦C to 25.5 ◦C (for 65% relative humidity). In the Uberlândia region where this simulation was carried out, the relative humidity is above 65% [63]. For analysis purposes, a temperature of 25.5 ◦C was adopted as the thermally comfortable value, being the maximum value according to its operating range.

For the model purposes, a 5-month period between October 2021 and February 2022 taken from the National Institute of Meteorology (INMET) database [63] contained significant data on the temperature of Uberlândia during the hottest months of 2021/2022 time frame. Bringing a harsh-condition sample to an extensive but not exhaustive simulation would reflect how far thermal discomfort has long been affecting Baltimore Residential's occupants, which is critical to inflict health disorders, especially in the considered period. Moreover, the potential benefits of the building materials combination would also be stressed to the fullest.

From 3000 temperature samples registered at least twice per day (also at night), it was noticed that 33% remained above ASHRAE's thermally comfortable threshold value of 25.5 ◦C. In this sense, it was reasonable to extract a random day from this selection for the simulation.

The model also considers the average solar radiation in the region and the outdoor air temperature, which were obtained from the National Institute of Meteorology (INMET), specifically from the Uberlândia station [63]; in Figure 6, the solar radiation presents a low level at the beginning of the day, eventually increasing at times close to noon, and decreasing at the end of the afternoon. On the other hand, on this particular day, between 10h00 and 15h00, the outdoor temperature kept rising even with less solar radiation, also seen in Figure 6. These data are displayed over time, between 06h00 and 18h00, considering the solar radiation occurrence.

**Figure 6.** Regional solar radiation and outdoor air temperature (source: authors' own elaboration using the Vensim software).

After incorporating the data into the model, the next step requires establishing values for the input variables such as useful ventilation area, glass material, and wall material, which make up each scenario, as shown in Table 2.

**Table 2.** Technical specifications of simulated scenarios (source: authors' own elaboration).

\* Once the window is completely open, the window glass material does not affect the system.

As an outcome obtained from the simulation, once initially set with the average annual temperature of the region (22.1 ◦C) at 06h00, the internal room temperature variation over time is represented in Figure 7, in which is possible to identify three local maxima similar to Figure 6, since it is directly linked to solar radiation.

With a 50% window opening ratio combined with a 3 mm regular glass window and a 12.5 cm rectangular 8-hole brick wall, this scenario presents the highest internal room temperature value held during the entire period, which illustrates the thermal discomfort peak pointed out by Villa, Saramago, and Garcia [25]. Even with the substitution of window glass, wall materials, or both for allegedly more sustainable and efficient ones, while remaining with a 50% window opening ratio, there was only a small drop in perception regarding the internal room temperature.

**Figure 7.** Internal room temperature variation over time—scenario performance (source: authors' own elaboration using the Vensim software).

On the other hand, the lowest temperatures over time were achieved by applying a fully open window (100% opening ratio), regardless of its material, with an additional reduction when used alongside an EPS wall. In this context of a wide-open window, it is worth mentioning that the window glass material did not impact the simulation since heat flow and solar radiation had no obstacles whatsoever. It is noticed that, even with the maximum outdoor temperature shown at 15h00, the room remained at a lower temperature.

The generated results in Figure 8 show the amount of time (as a percentage of the full period) during which each scenario exhibited temperature values above 25.5 ◦C.

**Figure 8.** Percentage of time in which the internal room temperature was above 25.5 ◦C (source: authors' own elaboration).

Regarding the worst-case scenario, it is verified in the corresponding situation that the internal room temperature remains above 25.5 ◦C for the longest time, equivalent to 80% of the considered interval, peaking at 33.7 ◦C close to 15h00. The other three scenarios that also include a 50% window opening have lower percentages, with a temperature drop at the decimal order, including:

(+) EPS wall: 0.6 ◦C temperature peak reduction and 79% of the time with an internal room temperature above 25.5 ◦C;

(+) Green tempered glass: 0.7 ◦C temperature peak reduction and 79% of the time with an internal room temperature above 25.5 ◦C;

(+) Green tempered glass and EPS wall: 1.6 ◦C temperature peak reduction and 78% of the time with an internal room temperature above 25.5 ◦C

Indeed, the 100% window opening ratio contributes to the greatest sensitivity to the system, exerting a strong influence on the heat flux removal as heat change frequency increases. Other elements such as the solar factor of the glass and thermal transmittance of the wall influence the amount of energy added to the environment. As heat struggles to enter the room, it also struggles to leave it, meaning that the impact of both does not significantly alter the internal temperature of the room.

The best scenario encompasses a fully opened window and an EPS wall, with the temperature staying above 25.5 ◦C for 70% of the time, peaking at 28.2 ◦C, which is still above the operative range for thermal comfort. However, it is considered that by applying this scenario to the entire residence, it would be possible to offer a thermally comfortable environment.

In addition to these results, the predicted mean vote index (PMV) method from standard 55-2004 of the ASHRAE [64] can be applied to determine the thermal comfort of the room. The PMV index is used to measure the level of thermal comfort, as shown in Table 3.

**Table 3.** PMV index scales with thermal perception (source: authors' own elaboration).

The results of the scenarios taken from the SD model were analyzed according to the ASHRAE 55 thermal comfort PMV method using the CBE Thermal Comfort Tool [72,73], developed at the University of California at Berkeley. Table 4 shows the summary of the input values of the factors used in the method.

**Table 4.** Parameters used to calculate the thermal comfort indices (source: authors' own elaboration).

The inputs in Table 4 remain the same for all scenarios, only the internal temperature of the room varies according to each scenario, and the temperature value used was the peak temperature in order to analyze the most extreme situation presented by the SD model. For the PMV analysis purpose using the CBE Thermal Comfort Tool, the operative temperature used is equal to the peak temperature without variation over time.

As the window opening factor leads to a compelling contribution to the temperature drop, modifying the window size may also bring an additional benefit when combined with the previous construction elements [48,49], following the same rationale presented in Equation (2), in which the airflow volume is directly related to the useful area of the window. According to Uberlândia's Municipal Construction Code [50], each window area should be at least equivalent to 50% of the required illuminated area, which is 1/6 of the total room area. In this case, a minimum window area of 0.9125 m2 complies with the local regulation; it is worth mentioning that the previous results were obtained from a window area equal to 1.78 m2.

A new simulation for the internal room temperature regarding different sizes for the window was performed, ranging from 1.00 to 3.00 m2 (the minimum window area of 0.9125 m2 was rounded up for better comparison). Figure 9 shows the internal room peak temperature encompassing the previous six scenarios for different window areas.

**Figure 9.** Internal room peak temperature behavior according to the window area variation (source: authors' own elaboration).

Compared to the MCMV standard window size (1.78 m2), it is observed that the smallest window area (1.00 m2) provides the highest peak temperature in the room for all scenarios, as a direct result of less airflow volume and air exchange. In this case, a smaller window can raise the internal room peak temperature close to 11 ◦C ("50% Window Opening", "50% Window Opening + EPS", and "50% Window Opening + Tempered Glass + EPS" scenarios). With a 2.00 m2 window area, a temperature decrease of up to 3 ◦C is detected in the "50% Window Opening + EPS" scenario, while others remain lower than a 1 ◦C drop. When a 3.00 m2 window area is applied, almost 5 ◦C is withdrawn from the room temperature, shown in the "50% Window Opening" and "50% Window Opening + EPS" scenarios.

Both the "100% Window Opening" and "100% Window Opening + EPS" scenarios demonstrated a slight temperature drop for 2.00 m2 and 3.00 m2 window areas in comparison to the standard MCMV window measure (1.78 m2), whereas the smallest window area provided an addition of up to 3 ◦C to the peak temperature. Moreover, it was noticed that as the window area gets closer to 3.00 m2, the internal room peak temperature difference between the simulated scenarios reaches a maximum value of 1.37 ◦C. In conclusion, there is a temperature threshold that limits the benefits from the window area increase, which decision-making should take into account when determining building material costs and building structure safety. Table 5 contains the PMV results from the CBE Thermal Comfort Tool [73].

Since the smallest window area (1.00 m2) brings the highest internal room temperature for all the simulated scenarios, it is also expected to show a PMV index that translates to thermal discomfort. In this case, a PMV index above 3 (hot) is shown for all four scenarios that include "50% Window Opening", whereas the "100% Window Opening" scenarios stay within the neutral scale (close to 0).

**Table 5.** PMV results for each scenario and window area (source: authors' own elaboration).

\* Lowest PMV values among all scenarios.

For the MCMV standard window area (1.78 m2), the analysis results show a PMV index close to Scale 2 (warm), while the most favorable scenario (100% Window Opening + EPS) places between Scale 0 (neutral) and Scale -1 (slightly cool). As can be seen, a fully opened window has a slight difference either combined with an EPS wall or a brick wall alternative, nevertheless, both scenarios have a larger distinction when it comes to compared to halfopened-window scenarios, which is an indication that wide-open windows are relevant to reaching the thermal comfort goal. With a larger window area (2.00 m2 and 3.00 m2), a greater airflow volume and more frequent air exchanges are key to improving thermal comfort: the previous shows PMV values between 1 (slightly hot) and 0 (neutral), whereas the latter resides mostly between 0 (neutral) and -1 (slightly cold). Interesting to notice that the "50% Window Opening" scenario benefits the most from the adoption of a larger window, which demonstrates the potential gains from the window area variation on the internal room temperature.

#### **4. Conclusions**

This research demonstrates that natural ventilation and the use of constructive elements of low complexity and wide availability in the market (tilting window with up to 100% opening, green tempered glass, and EPS wall) can contribute to the thermal comfort of a residential room. From the worst to the best-case scenario, a substantial reduction in the peak temperature was obtained from window size variation, without any use of electrical equipment, such as a ventilator or air conditioning. In addition, this paper shed light on solutions such as natural ventilation and more sustainable and energy-efficient building materials that would not incur power consumption, considering that the majority of electric appliances are not affordable for most of the low-income population, which would also increase the household electricity costs, but mainly, elevate anthropogenic greenhouse gas emissions.

Moreover, the fact that obtaining a significant decrease in temperature just by fully opening the window or modifying its size makes the solution more tangible, given the ease of its implementation, it is something that can be done even in existing homes through a well-planned renovation. Serving as a baseline to encourage continuous field development in new buildings and renovations [11], it will also avoid any increase in electricity consumption during the operational phase for buildings, considering their entire life cycle [10].

The EPS wall has other advantages in addition to thermal comfort, as it is a light material and easy to install, and also makes construction more agile; therefore, it is recommended and its effect on thermal comfort remains positive. The green tempered glass alternative should be analyzed in terms of costs and benefits compared to the tilting window with 100% opening; if its cost is considerably higher, it is worth installing the full opening window or a larger one.

The effect caused by green tempered glass and EPS wall in hampering the heat entrance is important, but it should be noted that the heat flow exit also depends on the same window opening, thus the prioritized constructive element should be the 100% opening tilting window, which also assures the privacy of residents.

In conclusion, the advancement of constructive technologies and materials used in civil construction significantly contributes to expanding access to more efficient buildings in the thermal field without increasing electrical consumption. The system dynamics method may be further adapted and replicated to other circumstances, considering various locations and different realities among low-income housing in Brazil. Ultimately, managing thermal comfort for a national-size social housing program means adding significant value to the quality of life for millions at a reasonable cost. For policymakers, social housing should not remain only a matter of using low-cost materials but elevating their benefits to the fullest on a sustainable path.

**Author Contributions:** Conceptualization, C.L., V.E.d.S. and R.M.; methodology, V.E.d.S. and J.C.d.S.I.G.; software, V.E.d.S. and J.C.d.S.I.G.; validation, D.M. and J.C.d.S.I.G.; formal analysis, C.L., V.E.d.S. and R.M.; investigation, C.L., V.E.d.S., R.M. and M.M.; resources, C.L., V.E.d.S., R.M. and M.M.; data curation, C.L. and V.E.d.S.; writing—original draft preparation, C.L., V.E.d.S. and R.M.; writing—review and editing, C.L., V.E.d.S. and J.C.d.S.I.G.; visualization, C.L. and V.E.d.S.; supervision, D.M. and J.C.d.S.I.G.; project administration, D.M.; funding acquisition, E.M.d.S. and J.C.d.S.I.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** We gratefully acknowledge the support of the RCGI—Research Centre for Greenhouse Gas Innovation, hosted by the University of São Paulo (USP) and sponsored by FAPESP—São Paulo Research Foundation (2014/50279-4 and 2020/15230-5) and Shell Brasil, and the strategic importance of the support given by ANP (Brazil's National Oil, Natural Gas and Biofuels Agency) through the R&D levy regulation. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001. This work is based upon financial support from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq - 407631/2021-6).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** Special recognition to Samantha Maduro, whose valuable architectural experience within the Minha Casa Minha Vida program shed light on sustainable materials and state-ofthe-art practices.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

#### **References**

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### *Article* **The Influence of Cultural Factors on Choosing Low-Emission Passenger Cars**

**Ioana Ancuta Iancu 1, Patrick Hendrick 1, Dan Doru Micu 2, Denisa Stet 2, Levente Czumbil 2 and Stefan Dragos Cirstea 3,\***

**Abstract:** The decrease in greenhouse gas emissions by passenger cars is one of the key factors for climate protection measures. Besides EU strategies for low-emission mobility, policy makers must consider the behavioural factors of buyers. This study aims to cover this gap by investigating the relation between the national cultural dimensions (Hofstede model) and car adoption by fuel type in EU countries. This could help car sellers to find better solutions for advertising cars with medium and low greenhouse gas emissions. To find better ways to increase the usage of mediumand low-emission cars using targeted advertising, correlations and a multiple regression analysis were used. The results show that the consumer preference for one type of fuel is correlated with at least one of Hofstede's six cultural dimensions: the power distance index; individualism versus collectivism; masculinity versus femininity; the uncertainty avoidance index; long-term orientation versus short-term normative orientation; indulgence versus restraint. The major conclusion of the study underlines that, with increases in the individualism versus collectivism and indulgence versus restraint scores, the usage of low- and medium-emission cars also increases, and with the increase in the power distance and uncertainty avoidance index, the usage of low- and medium emission cars decreases. At the same time, the driving preference for low- and medium-emission vehicles decreases with the tendency towards collectivism and restraint of EU countries.

**Keywords:** battery electric vehicles; plug-in hybrid electric vehicles; hybrid electric vehicles; CO2 emissions; Hofstede; advertising

#### **1. Introduction**

The climate and environment changes has become one of the most important topics in recent years. The release of carbon dioxide (CO2) and other greenhouse gas (GHG) emissions due to human activities has led to global warming [1] and, therefore, to climate change. Factors such as the increases in urbanization, population, wealth, energy consumption, and agriculture activities have resulted in environmental change [2]. As a result, the European Green Deal for the European Union (EU) emerged. One of the aims of this commitment is to transform the EU into a region where there are no net GHG emissions by 2050 [3].

Worldwide, the transport sector was responsible for 16.2% of the GHG emissions [4], and it was the main pollutant in Europe [5], responsible for 22.3% of the total GHG emissions in 2020. Most of the GHGs come from passenger cars (44%) [6]. Therefore, the reduction in GHGs for passenger cars is one of the key factors for climate protection measures [7]. Besides EU strategies for low-emission mobility, policy makers must consider the behavioural factors of buyers. Buying behaviour is influenced by cultural, personal, social, and psychological factors [8]. A large body of literature focuses on three of them

**Citation:** Iancu, I.A.; Hendrick, P.; Micu, D.D.; Stet, D.; Czumbil, L.; Cirstea, S.D. The Influence of Cultural Factors on Choosing Low-Emission Passenger Cars. *Sustainability* **2023**, *15*, 6848. https://doi.org/10.3390/su15086848

Academic Editors: Oz Sahin and Russell Richards

Received: 8 March 2023 Revised: 7 April 2023 Accepted: 12 April 2023 Published: 19 April 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

(personal, social, and psychological factors), and only a few studies focus on the cultural characteristics of car buyers by fuel type. Furthermore, the studied literature shows that considering only the incentives when promoting a certain type of fuel will influence buyer behaviour only for a short period of time. We do not argue the relevance of price, incentives, taxes, and other already studied factors that affect the decision to buy a car with a certain engine.

The objective of our study is to find out how specific traits of national culture, as described by Hofstede's culture dimensions, influence the usage of low-emission passenger cars. To complete the objective, after reviewing the literature, the first step was to find out whether there is a correlation between the cultural dimensions and fuel choice when driving a passenger car. Using multiple regression analysis, we found which cultural traits can best predict the choice of a passenger car.

To the best of our knowledge, there is no similar study that shows how to improve the marketing strategies of low-emission passenger cars using culture as a determinant. This study aims to complete the literature by analysing the relations between the national cultural dimensions and car adoption by fuel type in EU countries. By taking into consideration cultural factors, this study could help policy makers, scientists, and marketing specialists find better solutions for promoting the sales of cars with low CO2 emissions by properly addressing them to each country.

To accomplish the research objective, the paper begins with a concise overview of the research topic, and it then reveals an extensive literature review in Section 2, which explores carbon dioxide emissions from passenger cars and the cultural factors that impact the purchasing decisions for these vehicles. Section 3 provides details on the research methodology, while Section 4 presents the findings, results, and a discussion of the study. Finally, the paper concludes with a summary of the key insights from the research in the last section.

#### **2. Literature Review**

#### *2.1. Carbon Dioxide Emissions of Passenger Cars by Fuel Type*

Inside the EU, in the last years, the total GHG emissions have dropped, but the transport sector has not followed the same trend. Moreover, in 2019, GHG emissions increased by 0.8% (shipping was not included) [9]. The emissions of GHGs in the transport industry consist mainly of NOx, CO, and NMVOCs, the bigger share being CO2 [10].

In the transport sector, passenger cars and light-duty vehicles are the main pollutants, and together they are responsible for 70% of the total GHG emissions in the EU [11]. Due to this matter, the EU is obliged to find new ways to address and encourage the acquisition of less polluting passenger cars.

On EU roads, in 2021, the passenger cars by fuel type were as follows: 52.9% petrol; 42.3% diesel; 3.4% alternative fuels; 0.8% hybrid; 0.4% electric cars (BEVs) [12]. The most used passenger cars are those with internal combustion engines, which are the ones that emit the most GHGs [13]. Figure 1, which represents the average CO2 emissions from different fuel types, shows that BEVs have zero emissions, and that the cars that use E85, LPG, diesel, and petrol as fuel pollute the most.

The literature shows that cars that use bioethanol (E85) have the highest CO2 emissions (Figure 1), but they have the lowest total greenhouse effect when sugar cane-based fuel is used. The reason behind this contradiction dwells in the uptake of CO2 from the air during the growth of sugar cane [14]. In the EU, the countries with the highest rates of CO2 emissions from new cars that come from E85 are Germany (292 gCO2/km) and France (185.5 gCO2/km) [15].

**Figure 1.** Average NECD emissions (gCO2/km) from new passenger cars in EU countries (2020). Source: [14].

Due to the high rate of GHG emissions from diesel cars, many cities in the EU are considering banning them in the central areas [16]. Diesel cars are mostly used in Lithuania and Latvia (69.2% and 63.2%, respectively, from the total fleets of passenger cars), and they are used the least in Greece (8.1%) [17]. Petrol is the most used fuel in the EU (52.9%), ranging between 91.1% of the cars in Greece and 26.1% of the cars in Lithuania [18].The popularity of petrol and diesel cars resides in the range of vehicles being designed for long-distance trips [18]. However, a 2019 study made in Portugal shows a decrease by up to −27% in emissions when cleaner vehicles are used that comply with the post-Euro 6 standards [19].

LPG is a colourless gas that is derived from petroleum, and it is most often used when converting the existing technologies of passenger cars to run on cheaper and lower GHG emissions [20]. From the 9,787,916 passenger cars registered in the EU in 2020, 151,999 used LPG as fuel, which emits, on average, 110.9 NECD (gCO2/km). In this case, the differences between the CO2 emissions in EU countries are small, ranging between 121.42 /km in Belgium and 102.3 gCO2/km in Romania [14]. Most of the registered cars that use LPG are in Italy (93,339 (6.3% of the cars)), with fewer in Luxemburg (only five cars) [14,17]. Compressed natural gas (CNG) is used by 0.5% of the passenger car fleet in the EU, being most common in Italy (2.4%) [17]. In 2020, the average emissions of gCO22/km from new registered cars ranged between 127.4 (Poland) and 81 (Croatia) [9]. CNG is described as one of the promising low-emission alternatives for the short- and mid-term decarbonization of road transport in the EU [21], but studies show that there are no benefits in terms of GNG emissions [22].

The below figure (Figure 2) shows the distribution of high-emission passenger vehicles in EU-27. The most passenger cars that use fuels with high polluting rates (petrol, diesel, LPG, GNG) are in Latvia, Croatia, Slovenia, and Estonia, while Sweden, the Netherlands, and Lithuania have less.

**Figure 2.** Percentages of passenger cars in EU-27 (except Bulgaria) with high CO2 emissions in 2019. Source: all the data from the ACEA [14], except for the following: Austria—all passenger cars [23]; Croatia—BEVs [24]; Cyprus, Estonia, France, Latvia, and Malta—all passenger cars [24]; Denmark—LPG [25].

The differences between the average emissions from new petrol–electric and diesel– electric cars are small (41.1 and 40.2 gCO2/km, respectively), being mostly preferred by Germans in the EU [14].  These cars are mostly used in Sweden, the Netherlands, and Ireland (2.4% of the total passenger car fleet), and they are less used in Croatia and Romania (0.2% of the total passenger car fleet) [17]. Another solution is plug-in electric cars (PHEVs), which are cars can be directly charged from the power grid and can be driven at 20–50 km using only electricity [26]. It is safe to say that, on small trips, the CO2 emissions of PHEVs are zero.

The available data (Figure 3) show higher rates of registered HEV and PHEV passenger cars in Sweden, the Netherlands, Finland, Ireland, and Belgium, and fewer in Poland, Romania, Croatia, and Latvia.

**Figure 3.** Percentages of passenger cars in EU-27 (except Bulgaria) with medium CO2 emissions in 2019. Source: all the data from ACEA [14], except for the following: Austria—all passenger cars [23]; Croatia—BEVs [24]; Cyprus, Estonia, France, Latvia, and Malta—all passenger cars [24]; Denmark—LPG [25].

A 2021 study shows that only electric and hydrogen fuel can help in achieving the goals of the Paris Agreement [27]. As the above figure illustrates (Figure 4), hydrogen used as a fuel for passenger cars emits only 1.2 g CO22/km, and BEVs have 0 gCO22/km emissions. Hydrogen cars use H2 to generate electricity, the main advantage being that they are easily refuelled at filling stations in 3–5 m and they have a good driving range [26]. Many studies show that using electricity to power BEVs has the lowest climate impact. The Netherlands has the highest rate of BEVs (1.2%), followed by Austria, Denmark, and Sweden (0.6%) [17]. A study from 2018 indicates that GHG emissions are reduced by 50–60%, on average, when using BEVs compared with internal combustion engines [28]. The collected data show that cars that use hydrogen and electricity as fuel are more common in the Netherlands, Sweden, Denmark, and Luxembourg.

**Figure 4.** Percentages of passenger cars in EU-27 with low CO2 emissions in 2019. Source: all the data from ACEA [14], except for the following: Austria—all passenger cars [23]; Croatia—BEVs [24]; Cyprus, Estonia, France, Latvia, and Malta—all passenger cars [24]; Denmark—LPG [25].

To decrease the CO2 emissions, which are the main component of GHGs, the policy makers in the EU use almost 700 measures to address road transport emissions [11], and to decrease the demand for polluting cars and promote the use and production of more energy-efficient vehicles [16,29,30]. They use "push" or "pull" strategies, [31,32]. The first type of strategy addresses the car manufacturers and fuel suppliers, and the second (pull strategies) applies to the demand side [11]. EU and national policies related to taxes (on fuel, vehicles, emissions) can decrease the demand for polluting cars and aim to promote the production of more fuel-efficient vehicles. Besides EU and national strategies for low-emission mobility, policy makers must consider the behavioural factors of buyers. Through economic incentives, buyers are motivated only for the period during which they have benefits; afterwards, they will return to their old buying habits [33–35]. Another study demonstrates that besides financial help, it is necessary to have a certain level of self-sustainability [36].

#### *2.2. Cultural Factors Influencing Passenger-Car-Buying Behaviour*

Buying behaviour is influenced by cultural, social, personal, and psychological factors [37]. A large body of literature focuses on the personal, social, and psychological factors that influence passenger-car-buying behaviour when choosing the fuel type of the car, but only a few focus on the cultural characteristics of buyers.

Reviewing the literature, we find many diverse definitions of culture. In 1952, Kroeber and Kluckhohn listed 160 definitions [38]. Some authors explain culture through empirical studies, while others use more generic formulations [39]. Kotler and Armstrong explain culture as a cumulus of fundamental values, perceptions, wishes, and learned behaviours, which are different from one society to another [8]. Moreover, they consider culture as the most profound cause that influences consumer behaviour [37].

One of the most cited authors, Schwartz, sees culture as a "complex of meanings, beliefs, practices, symbols, norms, and values prevalent among people in a society" [40]. The model developed by the author introduces seven dimensions that can predict consumer behaviour: intellectual autonomy; affective autonomy; embedded cultures; cultural egalitarianism; cultural hierarchy; harmony; mastery [40].

Hofstede defines culture as a collective "programming of the mind" phenomenon [41,42]. He explains that culture represents a set of elements, such as beliefs, attitudes, collective activities, role models, and the language common to a particular group [43]. By repeated

empirical research, Hofstede created one of the most comprehensive models with which we can characterize national cultures. His model provides a scale from 0 to 100 for all the dimensions, by country [42].

Hofstede's six cultural dimensions are as follows: the power distance index (PDI); individualism versus collectivism (IDV); masculinity versus femininity (MAS); the uncertainty avoidance index (UAI); long-term orientation versus short-term normative orientation (LTO); indulgence versus restraint (IND).

The model has been used in various sectors and for various perspectives; for example, for the prediction of proenvironmental behaviour in hospitality and tourism [44], changing the organisational culture to increase innovation and productivity [45], understanding the perceived risks related to self-driving cars [46], etc.

The PDI reflects the acceptance of the power distribution in a society. When a country has a score close to 100, the less powerful members will more easily accept the hierarchy and inequalities [47,48]. In countries with high PDIs (Figure 5), luxury articles, fashion items [49], and expensive cars [50] are used to make one's status clear.

**Figure 5.** EU-27 countries' power distance indexes (PDIs) according to Hofstede's culture dimension model [42].

In individualist cultures (Figure 6), people look after themselves and immediate family, and they are striving for identity [47]. Furthermore, they use explicit communication, contrary to collectivist cultures. The countries that score low in this dimension consider that to sell something, first you must create trust [49]. In collective cultures, people are more inclined to develop a pro-environmental attitude, being ready to pay more for the wellbeing of all society [51]. A study conducted in Germany, Mexico, and Spain shows that a higher level of collectivism develops stronger eco-friendly behaviours, and stronger intentions to adopt renewable energy technologies [52]. The preferred advertisements in collectivist countries are focussed on the idea of team, collaboration, and the victory of the community [53].

If in a masculine society (Figure 7), the main drivers are achievement and success, in a feminine society, the values are caring for others and a good life quality [49]. The same study shows that dimension plays an important role: big in the masculine dominated societies and small in the feminine ones. The advertising in countries dominated by masculine values is focused on success (by showing luxury brands) [49], competitiveness, dreams, expectations, and nonfictional elements [54].

The UAI refers to the ways in which the individuals in a society relate to uncertainty and ambiguity [54]. In cultures with high scores for this dimension (Figure 8), the individuals are more resistant to accepting new technologies and innovation [55], the conflicts are threatening, individuals have an aggressive driving style [54], and advertisements are structured and serious with a great deal of technical information [56].

**Figure 6.** EU-27 countries' individualism indexes (IDVs) according to Hofstede's culture dimension model [42].

**Figure 7.** EU-27 countries' masculinity indexes (MAS) according to Hofstede's culture dimension model [42].

**Figure 8.** EU-27 countries' uncertainty avoidance indexes (UAIs) according to Hofstede's culture dimension model [42].

When a society is driven by elements such as perseverance [49], pragmatism, and a focus on the future [48], it scores high in the LTO dimension (Figure 9). Moreover, LTO corelates with pro-environmental behaviours [57].

**Figure 9.** EU-27 countries' long-term orientation indexes (LTO) according to Hofstede's culture dimension model [42].

The last dimension, IND, was introduced in 2010, and it describes the inclination of a society to enjoy life in opposition to the ones that suppress gratification [58]. Usually, people living in restraint societies with low IND indexes (Figure 10) tend to be more cynical and pessimist [59]. This is the least studied dimension in the literature.

**Figure 10.** EU-27 countries' indulgence indexes (INDs) according to Hofstede's culture dimension model [42].

The Hofstede model is debated by some authors because heterogenous groups can live within a state, but we must consider that sharing the same education system, healthcare system, legal system, and institutions makes them share a common goal over a long period [43], and if it is required, Hofstede's model could be applied to smaller communities as well. Many articles demonstrate the relevance of this model for cross-cultural studies in marketing, psychology, sociology, or management [60], as it is the best way to measure national cultures [61]. The arguments that support the choice of using Hofstede's cultural dimensions in the present paper are described next. Hofstede's cultural dimensions have been used for a long time (since 1970) and have an extensive empirical base from all over the world. His research was conducted in the 1970s, and it has been expanded and validated over time, with his dimensions being used in numerous studies across different countries and cultures. Hofstede's dimensions are recognized and used in the business world so that companies can tailor their marketing strategies to different cultures [62].

McLeay et al. recommend that future studies on eco-friendly consumption should consider cultural contexts [63]. Barbarossa et al. compared the EV adoption intentions among consumers in Denmark, Belgium, and Italy using Hofstede's cultural dimensions to explain cross-national differences [33]. According to the results, public policy and social marketing campaigns should give greater consideration to the impact of cultural values when promoting environmentally sustainable technologies. It is important to note that promotional efforts should vary based on the specific cultural elements and the level of innovation of the products being promoted [63].

#### **3. Research Methodology**

The purpose of this study is to establish the influence of national culture, as described by Hofstede's cultural dimensions, on the selection of fuel for automobile usage by following a series of methodological steps to arrive at a conclusive answer.

First, a literature review and a collection of data from different sources were made. Afterwards, an assessment of the passenger cars by fuel type in every country of the EU was conducted. The percentages of passenger cars by fuel type were taken into consideration, and not the number of passenger cars in the EU, in 2019. The authors consider that new car purchases during the COVID-19 pandemic years were more influenced by the availability of cars and their prices on the market (the component crisis) than the cultural dimensions of the buyers. The share of each chosen fuel type when driving a car is more important than the effective number because it can show the preferred passenger car in a country. In the study, all the data come from five official sources: the ACEA [14], except for the following: Austria—all passenger cars [23]; Croatia—BEVs [24]; Cyprus, Estonia, France, Latvia, and Malta—all passenger cars [24]; Denmark—LPG [25]. From the EU countries, Bulgaria is the only one that does not have any official data on the number of diesel, petrol, and hybrid electric passenger cars. Bulgaria was not excluded from the study, but the absent information was noted as missing data.

The second step was to collect data on the six dimensions of Hofstede's model from the homepage Geert Hofstede [63]. The Hofstede model of cultural dimensions was last updated in 2010 within [42]. This edition includes an expanded analysis of cultural differences and updates to the original cultural dimensions based on data collected from additional countries and regions. Each dimension, for all the studied countries, has a score between 1 and 100. Cyprus was the only country with no characterization of its cultural dimensions. However, at least one study assumes that due to the similarities with Greek culture, the same score can be applied [33].

Next, the data were introduced in SPSS 28 software, and a descriptive analysis of the elements was made. To observe the existence of links between the preferred fuel in passenger cars and the cultural dimensions, we used the normal distribution of the data, using Shapiro–Wilk tests, histograms, and plots. Further, Pearson's correlation for the normally distributed data (*p* > 0.05) and Spearman's rho for the non-normally distributed data (*p* < 0.05) were used.

The results show the strength of the correlations between two variables and can range between −1, which is a perfect negative correlation, and +1, which is a perfect positive correlation. When the coefficient is close to 0, the results show no relation between the variables. It is mandatory to consider the value of the significance (sig 2-tailed), which must be less than 0.05 [64].

For answering the research question, three groups of passenger cars by CO2 emissions were considered:

To explain the usage of high-, medium-, and low-emission passenger cars by the cultural dimensions, multiple regression analysis was applied. The first step was to find out whether there is a correlation between passenger vehicles by CO2 emissions and the cultural dimensions. Using Shapiro–Wilk tests, we found which correlation tests are the best fit. Pearson's correlation for normal distribution data or Spearman tests for non-normal distribution data were used.

Next, in the multiple regression analysis, the cultural dimensions are considered as independent variables, and one of three groups of vehicles by CO2 emissions with which a correlation was found is the dependent variable. For the multiple regression, the equation used was as follows:

$$\boldsymbol{Y} = \boldsymbol{\beta}\_0 + \boldsymbol{\beta}\_1 \cdot \mathbf{X}\_1 + \boldsymbol{\beta}\_2 \cdot \mathbf{X}\_2 + \dots + \boldsymbol{\beta}\_k \cdot \mathbf{X}\_k$$

where *Y* is a dependent variable (percentage of high-, medium-, or low-emission passenger cars); from *X*1 to *Xk* are the independent variables (the cultural dimensions), considering a total of *k* independent variables; from *β*1 to *βk* are the regression coefficients; *β*0 is the intercept value (regression constant).

To identify the most significant cultural dimensions that explain the usage of high-, medium-, or low-emission passenger cars, the backward elimination method was applied. This is a widely used method that starts from a multilinear regression model that contains all the independent variables that are of interest and eliminates, one by one, the independent variables that are not significant in predicting the dependent variable until the highest accuracy regression model is identified.

#### **4. Results and Discussion**

The descriptive analysis of the fuels used for passenger cars in the EU gives a picture of the substantial differences between the consumer preferences in each country in 2019 (Table 1). The differences between the lowest and highest rates of registered cars in different countries are given by the minimum and maximum values. It can be observed that there are countries with low percentages of passenger cars that use LPG as fuel (Ireland or Sweden), CNG (Malta, Cyprus, Croatia, Latvia), and HEVs (Latvia, Croatia, Romania), and other countries with high rates of these types of cars. Even if studies show the importance of H22 in the reduction in the CO2 for passenger cars, due to barriers such as price, infrastructure, and distribution [65], this fuel is less used. Petrol- and diesel-fuelled engines are the most common in the EU, with means of 54.23% and 42.07%, respectively.

**Table 1.** Descriptive statistics of variables.

Besides the economic-related factors, the cultural dimensions of the European countries may explain some of these differences.

To find out whether there is a link between the chosen fuel type and the national cultural dimensions, first, the data were tested for normal distribution (Table 2). Using Shapiro–Wilk tests, the results show that data for petrol, diesel, HEVs, low emissions, medium emissions, high emissions, the PDI, MAS, the UAI, LTO, and IND are normally distributed (sig. > 0.05). The rest of the variables (LPG, CNG, PHEVs, H2, BEVs, and IDV) are not normally distributed (sig. < 0.05)

**Table 2.** Normal distribution of variables.

#### *4.1. High-Emission Passenger Cars*

To observe the link between the variables, a Pearson correlation test was applied for the normally distributed data. The next table (Table 3) shows that the percentage of petrol passenger cars in EU-27 countries has a moderate positive correlation with the IND cultural dimension (r = 0.4190, sig. = 0.03). This indicates that the inclination to buy petrol-fuelled cars is linked to the tendency of people living inside national borders to enjoy life, not being influenced by social norms.

**Table 3.** Correlations between cultural dimensions and usage of high-emission passenger cars. \*, \*\*—Automated highlight of SPSS software to underline greatest values.

The next results show that the consumption of LPG in passenger cars is moderately positively linked with the PDI (r = 0.596, sig. = 0.001), and is strongly negatively correlated with IND (r = −0.716, sig. < 0.001). With the increase in the power distance within a society, the consumption of LPG also increases. Nations characterized by restraint will use more LPG-fuelled cars.

The number of cars fuelled by CNG is moderately positively correlated with the tendency of a nation towards long-term orientation (r = 0.517, sig. = 0.006). The results are different compared with other studies, which show that societies with high LTO scores have more pro-environmental behaviours. The results may reflect the perception of the individuals to whom it was promoted as having lower GNG emissions than traditional fuels, when the measurements of the emissions show no difference between them. It can be summarized that individuals with pro-environmental behaviours are willing to buy cars fuelled by CNG, thinking that they are low-emission vehicles.

Correlation can only tell how a variable covaries. To find what cultural dimensions predict the usage of high-emission passenger cars in general, multiple regression analysis should be applied. In order to identify the most significant cultural dimensions that influence the usage of high-emission cars in general, a backward elimination process was applied. The highest accuracy regression model was obtained for the combination of the UAI and IND independent variables. The results (Table 4) show that the UAI and IND dimensions can describe 42.6% of the variability from the usage of high-emission vehicles in EU-27 (adj. R square = 0.426). At the same time, for 60% of the analysed EU-27 countries, the evaluation error between the predicted values and actual usage percentages of high-emission vehicles is less than 1%. The results of the test were derived from the regression model: *High*\_*Emiss* = 97.44 + 0.03·*UAI* − 0.04·*IND*. A one-point increase in the uncertainty avoidance index will increase the usage of high-emission vehicles by 0.03%. Moreover, a one-point increase in the indulgence versus restrain index will decrease the usage of high-emission vehicles by 0.04%.

**Table 4.** Multiple regression analysis between rate of high-emission vehicles and UAI and IND.

Dependent variable: *High\_Emiss.*

Figure 11 shows the map of EU countries in relation to the UAI and IND scores. As already mentioned, countries with high UAIs and weak INDs tend to use more highemission vehicles.

**Figure 11.** EU map, based on uncertainty avoidance index and indulgence versus restraint index. Mean of UAI = 73.03; mean of IND = 42.74.

To increase the usage of medium- and low-emission vehicles in countries such as Bulgaria, Romania, Czechia, Italy, Poland, Portugal, Hungary, and Croatia, the advertising of passenger cars should emphasize the low price for maintenance and the low price of fuels. Moreover, the advertising should be well structured and contain technical information.

#### *4.2. Medium-Emission Passenger Cars*

Further, the data show that the usage of HEVs and PHEVs (vehicles with medium GNG emissions) is correlated with four out of six national dimensions (Table 5). HEVs are in strong positive correlation with the IND (r = 0.635 > 0.6, sig. < 0.001), in moderate positive correlation with the IDV (r = 0.3 < 0.501< 0.6, sig. = 0.011), in moderate negative correlation with the PDI (r = −0.6 < −0.593 < −0.3, sig. = 0.002), and in moderate negative correlation with the UAI (r = −0.6 < −0.540 < −0.3, sig. = 0.005). PHEVs are in strong positive correlation with the IND (r = 0.703 > 0.6, sig. < 0.001), in moderate positive correlation with the IDV (r = 0.3 < 0.511 < 0.6, sig. = 0.006), in strong negative correlation with the PDI (r = −0.608, sig. = 0.001), and in moderate negative correlation with the UAI (r = −0.6 < −0.407 < −0.3, sig. = 0.035).

**Table 5.** Correlations between cultural dimensions and usage of medium-emission passenger cars. \*, \*\*—Automated highlight of SPSS software to underline greatest values.

The higher power distance index in one country, the less inclined to use hybrid cars are its citizens. The same happens when we analyse the UAI dimension. As theory notes, a high UAI indicates that the individuals in these societies do not easily accept new technologies and innovation. For these individuals, HEVs and PHEVs use a technology that is hard to accept. IDV and IND speak to the existent link between the registered hybrid cars and the national tendency towards individualism and indulgence. People striving for identity and who enjoy life are more inclined to buy hybrid cars.

To investigate the cultural dimensions that can predict the usage of medium-emission vehicles in general, the same methods as previously described were applied. Following the backward elimination process, it was concluded that IDV and IND are the most relevant cultural dimensions. The value of the adjusted R square (0.566) (Table 6) indicates that the variability for hybrid car usage in general can be predicted as 56.6% by the two cultural dimensions: IDV and IND. The multilinear regression model, *Medium*\_*Emiss* = −0.884 + 0.023·*IDV* + 0.031·*IND*, can be explained as follows: a one-point increase in the individuality index will increase the usage of medium-emission vehicles by 0.023%.

**Table 6.** Multiple regression analysis between rate of medium-emission vehicles and IDV and IND.

Dependent variable: *Med\_Emiss.*

Moreover, a one-point increase in the indulgence versus restraint index will increase the usage of medium-emission vehicles by 0.031%. In countries such as Bulgaria, Romania, Croatia, Portugal, Cyprus, Greece, and Slovenia, characterized by collectivism and restraint (Figure 12), the advertising of medium-emission vehicles should emphasize the wellbeing of the society when choosing such a vehicle, as well as the low maintenance costs and fuel price.

**Figure 12.** EU map, based on individualism versus collectivism and indulgence versus restraint indexes (source). Mean of IND = 42.74; mean of IND = 56.63.

#### *4.3. Low-Emission Passenger Cars*

The data presented in the next table (Table 7) show similar results to the HEV and PHEV cases. The use of H2 for passenger cars is in moderate negative correlation with the PDI (r = −0.498, sig. = 0.008) and UAI (r = −0.384, sig. = 0.048), but it is in moderate positive correlation with IDV (r = 0.505, sig. = 0.007), and in strong positive correlation with IND (r = 605, sig. < 0.001). The usage of BEVs is in strong negative correlation with the PDI (r = −0.635, sig. = < 0.001), in moderate negative correlation with the UAI (r = −0.425, sig. = 0.027), in moderate positive correlation with IDV (r = 0.443, sig. = 0.021), and in strong positive correlation with IND (r = 639, sig. < 0.001).

**Table 7.** Correlations between cultural dimensions and usage of low-emission passenger cars. \*, \*\*—Automated highlight of SPSS software to underline greatest values.

Both low-emission fuels are used mostly by countries dominated by low power distances and low uncertainty avoidance. With increases in the indulgence and individualism cultural dimensions, low-emission fuels are used more often.

As in the previous cases, backward elimination was applied to identify the cultural dimensions that can most accurately predict the usage of low-emission vehicles in general. It was found (Table 8) that 32.7% of the logarithmic variability in the low-emission passenger car usage can be accounted for by IND (adjusted R square = 0.327). The other cultural dimensions do not have a significant influence on the low-emission vehicle usage variability within EU-27. The regression *Log*\_*Low*\_*Emiss* = −1.607 + 0.018·*IND* can be explained by a one-point increase in the indulgence versus restrain index, which will increase the usage of low-emission vehicles by 0.018%.

**Table 8.** Linear regression analysis between rate of low-emission vehicles and IND.

Dependent variable: *Log\_Low\_Emiss.*

#### **5. Conclusions**

The European Union, a conglomerate of 27 countries with different cultural characteristics, has a common objective to pass from traditional polluting sources to green energy. In the last years, a lot of improvement has been seen, with GHGs dropping in all sectors, with one main exception—transport. Passenger cars and light-duty vehicles are the main pollutants, and together they are responsible for 70% of the total GHG emissions in the EU [11].

The data show that Latvia, Romania, Croatia, Poland, the Czech Republic, Slovakia, Slovenia, Greece, and Italy have the lowest rates of medium- and low-emission passenger vehicles in their car fleets. In order to encourage the usage of the mentioned cars, policy makers have to find more ways to address individuals.

The results of the study show that the consumer preference for one type of fuel when using a passenger car is correlated with at least one of the following four national cultural dimensions: the PDI, IDV, UAI, and IND. With increases in the IDV and IND scores, the usage of low- and medium-emission cars also increases. With increases in the PDI and UAI, the usage of low- and medium-emission cars decreases. The marketing strategies for low- and medium-emission cars should be addressed according to these four cultural factors as well.

Studying the preference for high-emission passenger cars, it can be observed that these are preferred in countries that score high for the uncertainty avoidance index and low for the indulgence versus restrain index. In countries such as Bulgaria, Romania, Czechia, Italy, Poland, Portugal, Hungary, and Croatia, the advertising of medium- and low-emission passenger cars should be well structured and contain technical information, and it should emphasize the low price for maintenance and fuels. Medium- and low-emission passenger cars should be promoted as fashionable items in countries that score high on the PDI.

The driving preference for low- and medium-emission vehicles decreases with the tendency towards collectivism and restraint of EU countries. In Bulgaria, Romania, Croatia, Portugal, Cyprus, Greece, and Slovenia, the advertising of low- and medium-emission vehicles should first create trust and highlight the pro-environmental characteristics, low maintenance costs, and low fuel price.

Our results are in contradiction with other studies that have found a correlation between collectivist cultures and green behaviour. The reasons that could be behind the contradiction will be studied in the future, but the literature suggests that pro-environmental behaviour is linked to the standard of living [66]. However, most of the EU countries characterized by collectivism and restraint are also the countries with the lowest GDPs per

capita [67]. It is understandable that the high price of BEVs, HEVs, PHEVs, and H2 cars is a strong barrier to buying low-emission cars.

The symbolism of BEVs, HEVs, and PHEVs must also be considered. For many, it is more important to be "seen green" than "to be green" [68]. Further, we can argue that wanting to be seen as pro-environmental, and different from others, shows the tendency towards individuality rather than collectivist behaviour. Our study, which uses official data on the passenger cars that are already registered (2019), does not consider the attitudes and perceptions towards low- and medium-emission cars, but the actions taken by EU citizens.

As a potential area for future research in this study area, the authors intend to conduct a similar analysis for the pandemic and the period of conflict in Ukraine, which would be highly valuable. This would enable an examination of the changes that have arisen as a result of these exceptional circumstances, and their impacts on the findings of the current article.

**Author Contributions:** Conceptualization, I.A.I., D.S. and S.D.C.; Methodology, I.A.I., P.H., D.D.M. and S.D.C.; Software, L.C.; Validation, D.D.M., L.C. and S.D.C.; Formal analysis, P.H.; Investigation, I.A.I., P.H., D.D.M., D.S. and L.C.; Resources, I.A.I.; Data curation, P.H. and L.C.; Writing—original draft, D.D.M., D.S. and S.D.C.; Writing—review & editing, D.S. and S.D.C.; Funding acquisition, I.A.I. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research and the APC were funded by European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant 801505.

**Institutional Review Board Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

#### **References**

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### *Article* **Sustainability Topics Integration in Supply Chain and Logistics Higher Education: Where Is the Middle East?**

**Maja Rosi and Matevž Obrecht \***

Faculty of Logistics, University of Maribor, 3000 Celje, Slovenia **\*** Correspondence<EMAIL_ADDRESS>
**Abstract:** The global logistics industry has grown significantly in the last decade and has become essential to global business activities. In addition, the logistics industry is vital in transportation, urbanization in the Middle East, and transshipment through the Middle East. Due to their increasing importance and size, there is an increasing demand for adequately qualified people capable of managing the logistics systems and supply chains holistically and sustainably to avoid problems caused by unsustainable practices in mobility, transport, and supply chains. However, it is unclear whether the logistic and supply chain education of future leaders, managers, and engineers will follow SDG goals, the rise of new trends, and green technologies or lag behind. This paper pioneered a systematic approach and analyzed Middle Eastern countries regarding their sustainability integration into higher education programs related to supply chain management and logistics studies. It revealed enormous differences among countries and a lack of sustainability topics in most studied countries. Some countries are also significantly more oriented toward partial logistics challenges such as transport efficiency instead of sustainable supply chains, which are becoming critical challenges for the near future and must be accompanied by formal and life-long learning on sustainability-related topics. The circular economy and corporate social responsibility are especially neglected. It was also revealed that sustainability integration in higher education does not correlate with sustainability scores and the ranking of countries within the sustainability index.

**Keywords:** sustainability integration; Middle East; supply chain management; responsible logistics; education for SDGs

#### **1. Introduction**

The global logistics industry is facing new infrastructure development, new technology adoption, high energy consumption, and political polarization recently accompanied by new challenges in environmental protection, social challenges, better collaboration, and managing supply disruptions sustainably. In the Middle East (ME), the logistics industry plays a vital role in transportation development and urbanization and will be a crucial player contributing to regional sustainability. Due to its significant size and fast development, there is an increasing demand for adequate qualified people capable of managing not just distribution and supply disruptions but holistically dealing with business, environmental, and social issues. Information on whether logistic education follows this expansion based on a sustainable development agenda or lags behind is unavailable. Therefore, this paper focused on sustainability integration into logistics and supply chain studies in selected ME countries [1–3].

The ME has undergone tremendous cultural, political, and economic growth over the past few years. The region is facing a fundamental change in the oil market, where new technologies are increasing the oil supply on the one hand and, on the other, raising concerns over the environment, forcing a move away from oil. In order to reduce their reliance on oil and become a more sustainable society, oil-exporting countries, including ME countries, are establishing and implementing new reforms to diversify their economies

**Citation:** Rosi, M.; Obrecht, M. Sustainability Topics Integration in Supply Chain and Logistics Higher Education: Where Is the Middle East? *Sustainability* **2023**, *15*, 6955. https://doi.org/10.3390/su15086955

Academic Editors: Oz Sahin, Russell Richards and GuoJun Ji

Received: 17 February 2023 Revised: 6 April 2023 Accepted: 14 April 2023 Published: 20 April 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

with sustainable directives [1,2] and initiatives [3–5]. Similarly, new goals toward a more sustainable environment and green economy are set in the logistics industry, to become a transformative society [6]. Companies are encouraged to implement an environmentally sound approach and incentivize lowering their carbon footprint [7], and are challenged with the transition to a net-zero economy [8]. Alongside addressing environmental concerns, long-term value creation based on human capital development and good corporate governance is gaining importance [8].

The strategic location of the ME—at the junction of three continents and with the world's most critical natural resources, including over half of the world's proven oil reserves—has historically been a crossroads for trade, people, and a transition zone for political and cultural interaction [9,10]. The global logistics market is projected to grow by USD 71.96 billion by 2026 [11]. In terms of global competitiveness, the United Arab Emirates (UAE) was ranked among the top 20 countries in 2020 across 13 indexes related to transport [12]. The UEA is also considered one of the largest logistics hubs in the ME [13], and a large part of the country's economy is based on the logistics industry. Issues arising from this industry may significantly impact the business community, logistics organizations, and the overall economy of this region [14]. That proves that logistics is among the highly prioritized industries in those regions. According to Knight [15], oil-reliant economies need to maximize their attractiveness, diversify their economies, and improve their logistics industry competitiveness and industry performance as well as make their supply chains more sustainable [16].

Along with the growth of the logistics sector, there is also growing importance for adequately educated and skilled human resources, emphasizing a sustainable, green society [17]. This affects education providers, especially universities and colleges, by offering logistics-related programs and essential contributions to society and the economy. Since logistics is a growing topic, and industry demand for experts capable of sustainably managing logistics is expanding [18], logistics education can significantly influence the success of sustainability in the logistics sector. Integrating sustainability and embedding sustainable development in higher education is highly topical [19–21]. However, information about it is not yet available; our research focused on analyzing logistics-related higher education programs in selected ME countries and its integration of sustainability topics. The quality and adoptability of supply chain education are identified by adapting to local and global socio-political, environmental and economic challenges [22].

The analysis of ME study programs serves three purposes: first, it provides a database with novel findings. Second, it compares ME logistics-related study programs using various criteria focusing mainly on sustainability topics. Third, the results can be used as an orientation tool for the ME logistics sector, for other fast-growing economies and societies, to develop toward a more sustainable future. However, the study approach and method must first be developed to do that. Therefore, this study's innovative approach was also identified in the developed framework for analyzing study programs structured in three steps. Furthermore, the main aim of this study was to explore logistics and supply chainrelated study programs in the ME in terms of their environmental sustainability integration. Particular emphasis was on identifying specific sustainable development priorities (e.g., sustainability, environment, eco/green, circularity, CSR). We also comparatively analyzed sustainability integration on three levels of logistics—(1) transportation, (2) logistics, and (3) supply chain management. In addition, we assumed that countries with more sustainability topics and better sustainability integration in their study programs also had better sustainability scores. Therefore, potential correlations with the sustainability score/index were also investigated and examined.

#### **2. Review of Theoretical Background**

As sustainable development is recognized as one of the biggest societal challenges nowadays, HEIs should incorporate sustainability values into their mission, curriculum, and practice to align with the global sustainability agenda [23,24]. HEIs must enable

sustainability-oriented education of future managers, experts, and other social stakeholders and raise their environmental awareness [25]. They have an important role in societal transformation by educating global citizens and delivering knowledge and innovation to society [26].

This also applies to HEIs in the ME that are also experiencing significant transformation, privatization, internationalization, and industry reforms [27]. Higher education institutions must adjust to these changes by reforming their study programs or adding new ones to meet the growing market needs. Along with this, the growth of the young population is estimated to be 65 million by 2030; education and qualified professionals are crucial elements for achieving sustained development and sustainable development goals (SDGs) [21,28]. Namely, higher education influences the development of production and sustainable management systems [29].

Regarding environmental issues, universities in the region need to comply with international and environmental requirements, including policies toward reducing the carbon footprint and integrating environmental management into daily business [30]. HEIs, directly and indirectly, impact sustainable development through all their activities, influencing society, the environment, and the economy [31]. It is challenging to integrate sustainable development and sustainability into their systems [23–25] with a particular focus on sustainability topic analysis and defining priorities such as circular economy, green transition, social responsibility, and other related topics.

Reviewing the literature, we found few studies about ME higher education, and a recent lack of scientific research on the integration of sustainability-related topics in ME higher education was noted.

Most publications on sustainability in HEIs focus on the Global North; little is known about the state of sustainability in HEIs in the Global South [32]. Similarly, Hassan et al. [33] noticed that no previous publications studied sustainability challenges in HEIs in the middle eastern region.

Romani [27] explored higher education issues as a critical political problem in the Arab ME. Education was also identified as a future challenge for the ME by Akkari [34]. Miller-Idriss and Hanauer [35] researched the landscape of transnational higher education in the ME, focusing on offshore educational institutions and programs that foreign institutions have set up in the region. Similarly, Rupp [36] focused on foreign universities and colleges in the ME. Alzyoud and Bani-Hani [37] discussed how universities in the ME could achieve development, sustainability, and competitiveness by applying University social responsibility concepts. Sherif [38] investigated a similar topic, emphasizing how corporate social responsibility (CSR) is implemented in universities in the ME. Another comprehensive survey was performed by Saab and coauthors [30] regarding the environmental content in school and university curricula across Arab countries, and Daneshjo proposed a new approach for teaching sustainability in Arab schools [39]. Keser [29] investigated the effects of higher education on global competitiveness in European countries and ME countries.

By reviewing publications and research studies related to sustainability issues in ME higher education, it was revealed that practically no findings of integrating sustainability topics in logistics and supply chain-related education in those countries could be found. It was found that even studies related to sectors other than logistics or other geographical areas did not cover an analysis of priority topics. So, which sustainability topics will be taught is of particular importance for the education of future leaders and experts in the field of green transition, circular economy, and sustainable supply chains.

#### **3. Methods**

This study identified sustainability-related topics integrated into logistics and supply chain management-related studies in the Middle East. The Middle East was selected as a niche area poorly examined from the sustainability perspective. Since the definitions of the ME countries vary, this study focus was on 15 countries: Turkey, Syria, Cyprus, Lebanon, Israel, Jordan, Iraq, Iran, Kuwait, Saudi Arabia, Bahrain, Qatar, United Arab Emirates, Oman, and Yemen as proposed by the World population review [40].

To perform this research, a conceptual framework for analyzing and identifying relevant variables for exploring sustainability integration into the logistics and supply chain management-related study programs in the selected ME countries was first developed (Figure 1). Conceptual frameworks represent a way of thinking about a study, or ways of representing how complex things work the way they do [41].

**Figure 1.** A developed conceptual framework for analyzing and identifying relevant variables and integrating sustainability topics into selected ME countries' logistics-related higher education.

We developed a conceptual framework in three steps, namely: (1) web content analysis of selected study programs, (2) grouping and categorizing analyzed topics, and (3) statistical analysis and cross-sections of included topics.

First, the web-content analysis was used as a qualitative descriptive approach. Content analysis is a systematic coding and categorizing approach used to analyze and explore much textual information [42,43]. Private and public higher institutions in chosen ME countries with English web pages were searched and listed to create a data sample. A comprehensive study of publicly available online data, accessible on each institution's web pages, identified logistic and supply chain management-related study programs at all study levels (graduate and postgraduate) and cross-compared country specifics on specific sustainability topics integration. These specific topics were also seen as study program priorities.

The second step was divided into (a) identifying common keywords and (b1) grouping and (b2) categorizing obtained data, following Jabareen's [44] conceptual framework analysis. (a) Obtained data from the first step revealed nine keywords that were commonly used in logistics-related study programs: *logistics*, *supply chain*, *mobility*/*transport (air*, *road*, *sea*, *rail*, *maritime*, *ship, etc*.), *management*, *corporate social responsibility* (*CSR*), *environmental*, *eco*/*green*, *waste*/*circular*, *sustainable and their synonyms* (*b1*). We defined groups of keywords; the first group consisted of the most common logistic education-related keywords: "*logistics*", "*supply chain*", and *"transport/mobility*", as well as "*management*". According to our previous study of sustainability and environment-related higher education study pro-

grams [45], the second group of keywords referred to sustainability education: "*sustainable*", "*environmental*", "*eco/green*", "*waste/circular*", and "*corporate responsibility*". The database listed programs related to the keywords (e.g., in the program's name, in curricula, and study outcomes) and was limited by programs accessible online and in English. Further, we categorized the keywords into logistics, management, and sustainability-related topics (b2), as presented in Table 1.

**Table 1.** Grouping logistics, management, and sustainability-related topics.

The second step provided a detailed analysis of the most and least common topics in specific countries and study cycles and defined the share of programs with specific topics included in their curricula. This information is of practical importance to see whether programs are following strategic goals of each country or group of countries. It enabled defining priorities of logistic-related study programs in investigated countries and exposing the potential for improvements.

The third step was identifying the correlations between the studied keywords. The study analyzed the correlation between obtained data with the SPSS software program and performed sequence analysis for different variables to find the correlation between sustainability integration and the sustainability index. The third step also included indepth analysis by countries, study cycles, and sustainability-related topics in logistics and supply chain HEI. Last but not least, the Venn diagram method was used to understand the crucial correlations and fundamental concepts overlapping each other and identify interconnectivity among groups of sustainability-, logistics-, and management-related topics [46,47]. Examining the interconnectivity and overlap of these topics enabled us to identify logistics-, management-, and sustainability-related programs simultaneously. This combination was seen as the most prominent for managing sustainability-related challenges of the future in the logistics sector. Therefore, this framework could also be applied in other geographical areas/environments and add additional value to this research.

#### **4. Results**

This paper's dataset included 405 higher education programs (56% private, 43% public, and 1% with programs combined as public and private partnerships) in 15 ME countries. Program curricula were analyzed by nine study topics (keywords) to determine the integration of these topics into various study programs. The most significant share of study programs could be seen at the bachelor level (59%), followed by master's study programs (35%) and PhD study programs (6%). Most study programs were found in Israel.

Of all integrated topics, the highest share of programs were focused on management (50.1% of all programs included in the ME database), followed by sustainability, transport or logistics (31.6–34.8%), and finally, Eco/green and CSR (12.6–18.0%). Results indicate that only environment and mobility/transport study topics, not sustainability topics, were integrated to some degree in all researched ME countries. The share of study topics integrated into ME higher education programs can be observed in Figure 2.

**Figure 2.** Share of study topics integrated into study programs in ME countries with minimal and maximal integration.

Further, study topics were grouped into study topics per study cycle. Figure 3 indicates that management was the study topic commanding the highest share of the curriculum in 53% of all bachelor-level and 49% of all master's programs. Interestingly, mobility/transport mainly included study topics in PhD programs.

**Figure 3.** Share of study topics included in ME study programs per study level.

Detailed analysis revealed the level of integration of specific topics by country. As can be seen from Table 2, Jordan had the highest share of integrated logistics and management study topics. Turkey indicated high integration of logistics and management study topics but low integration of sustainability-related topics. No study programs were found for the UAE and Bahrain, including management study topics. Results varied depending on the country, although meager inclusion rates were observed for CSR study topics in all researched ME countries.

**Table 2.** Most and least included topics by country.

To better understand the level of topic integration (nine keywords) by country, we analyzed obtained data with a spider diagram (Figure 4). Countries such as Cyprus, Iran, Syria, Turkey, and the UAE showed more robust integration of logistics-related study topics. Iraq, Jordan, and Syria focused on management in their study programs. Nevertheless, Bahrain, Kuwait, Jordan, Cyprus, and Israel focused more on sustainability than other countries. Kuwait's focus was on environmental study topics, and Turkey focused on management and logistics study topics.

Regarding the focus on specific study topics and locations, correlations between the researched countries were not observed. Only Iraq and Iran were neighboring countries with similar trends in high integration of management and mobility/transport study topics in study program curricula. Similarly, Cyprus and Israel indicated an emphatic focus on sustainability compared with other ME countries.

**Figure 4.** Share of study topics in study programs in ME countries.

By grouping keywords into three priority topics (management, logistics, and sustainability) and using a Venn diagram, the correlations (differences and similarities) between the three priority topics were analyzed. In Figure 5, aggregated results are presented for all ME countries, revealing a stronger focus on management and logistics study topics. This result strongly correlates with the paper results that revealed 57% of all analyzed study programs, including at least one topic of management group study topics, were less oriented toward integrating sustainability-related priorities.

**Figure 5.** The number of study programs in grouped study topics.

The logistics group showed similar results. At least one logistics -related topic was included in 56% of all study programs in the ME database. The sustainability group indicated that approximately half of all study programs included one sustainability topic in their curricula. A strong connection was observed between the logistics and management groups. a 31% share of the researched study programs included management and logistics courses in their curricula. On the other hand, for logistics and sustainability, integration was significantly weaker (approx. 23%).

As spider diagrams (Figure 4) were designed logically, by setting up keywords related to a specific topic together, notable trends could be observed. By grouping the results by country in terms of the three priority topics, it could be seen (Figure 6) that Bahrain and Kuwait had the highest proportion of programs focusing on *"Sustainability*", whereas Turkey was the country with the lowest share of "*Sustainability*" topics. "Logistics" as a study topic group was evident in Turkey, Syria, and Iran. The "*Management*" study topic group showed strong integration in Yemen, Turkey, Jordan, and Qatar. Bahrain had an insignificant share of programs that included management topics in study program curricula.

**Figure 6.** Share of sustainability, logistics, and management study topic groups per country.

Since higher education institutions are engaged in institutionalizing sustainable development, community, and engagement studies into their systems or operational practices, the research aimed to identify possible correlations with SDG sustainability scores per country and include sustainability topics in their curricula. Namely, adopting the SDGs represents a significant global challenge for higher education institutions. These goals force them to assess how they engage with these goals and how they address future societal challenges [48]. As shown in Figure 7, the highest sustainability scores were seen in Israel and Cyprus. Although the inclusion of environmental and sustainability study topics in Israel was high, this was not true for Cyprus. Additionally, Turkey's results indicated a high sustainability score (around 70%), but the inclusion of environmental and sustainability study topics was the lowest. The results showed no statistically significant correlation between sustainability scores per country and analyzed sustainability study topics integration. A correlation was also proven with the SPSS Pearson correlation test, which did not show any (zero relationships) between observed variables.

**Figure 7.** Sustainability score per country for 2021 [49] and found programs that include sustainability topics in their curricula.

Another essential aim of the study was to prove whether there is a relationship between the integration of logistics study topics in higher education curricula in a given country and the logistics performance index (LPI) specific to that country. According to Kabak et al. [50], higher education institutions are expected to enhance logistics performance. Namely, despite the increase in automation, logistics is still a human-centric business, and more competitive global economies have a higher demand for highly qualified logistics-related labor [50]. It is vital to invest in human capital to improve logistics performance. Namely, well-educated logistics managers will have more technical competence and problem-solving capability and, thus, be more efficient [51]. The logistics performance index (LPI) for selected ME countries was retrieved from the World Bank Organization (International LPI rankings for 2018) [52] and compared to our data regarding logistic study topics with the SPSS program. The Pearson correlation test was calculated, showing that correlations between observed variables did not exist (zero relationships).

#### **5. Discussion**

The importance of sustainability education in higher education institutions is also recognized within the United Nations SDGs. SDG 4 (quality education) target 4.7 states "that all learners acquire the knowledge and skills needed to promote sustainable development, including, among others, through education for sustainable development and sustainable lifestyles, human rights, gender equality, promotion of a culture of peace and non-violence, global citizenship and appreciation of cultural diversity and culture's contribution to sustainable development" [53]. Universities in Arab countries play an increasingly important role in achieving the SDGs through their academic programs and research activities [35]; however, no statistically significant correlation with their sustainability scores was detected in this research. It could be speculated that such dependency will appear in the long term or is more related to other fields and not so much to education. Detailed and continuous monitoring should be performed to obtain datasets for a more extended time that might enable better calculations.

The SDGs should solve many challenges through "inclusive" or "sustainable" economic growth, assuming that economic growth can be conveniently decoupled from resource consumption. However, the current hegemony of the "sustainability and growth" paradigm has increased inequalities and pressure on natural resources, exacerbating biodiversity loss, climate change, and additional social tensions. Therefore, the paradoxes of sustainable development need to be defined. Integration of sustainability in curricula should focus on various examples of alternative education (e.g., indigenous learning, ecopedagogy, eco-centric education for a steady-state and circular economy, empowerment, and liberation), emphasizing planetary ethics and degrowth, and should be holistic [54,55]. According to Wals [56], some higher education institutions see a new way of organizing and profiling sustainability. However, higher educational institutions (HEIs) must first be systematically analyzed to transform the education system, and this research revealed that, e.g., CSR and the circular economy are among the least advanced topics in ME higher education. This is surprisingly distant from international climate agreements and leading countries' progress in sustainable development. Koleva's [57] study indicates that the interpretation and integration of CSR topics could be slightly different due to the main religion and its beliefs in the ME. In addition, challenging gender equality might affect systematic avoidance of such topics, e.g., in more conservative Arab countries.

HEIs can implement sustainability concepts and translate them into practice through education, curricula, research, campus operations, community outreach, and management [58]. Increasing student internationalization could also increase the availability of sustainability education, especially for students from lower-income countries [59]. Still, it also demands that they fly frequently and consequently live less sustainably, which has changed recently with online studies. Even though the impact of staff was not a part of this study, some other studies revealed that a lack of staff interest in improvements might also be challenging [60].

Since higher education considerably influences its graduates regardless of the academic field, environmental and sustainability topics should be significant in higher education curriculums. The importance of the existence of those topics in a variety of academic fields is also notable by different authors—Mulder [61] is focused on engineering students, Boarin et al. [62] on architecture, Zoller [63] on chemistry and science literacy, Walshe [64] on geography, and Springett [65] on business studies.

Arab countries started taking note of education on sustainable development (ESD) in the early 1980s, and the emergence of ESD has provided a stimulus to reform environmental education. The Arab region is promising achievements in ESD activities on both the national and regional levels. Despite the apparent gap between the Arab region and other parts of the world regarding ESD, there are promising achievements in the Arab region toward ESD. For example, Jordan, Lebanon, Egypt, Qatar, and Oman include training on integrating ESD themes into their curricula, incorporating ESD into university courses, and funding ESD-related scholarships and programs. In Qatar, for instance, ESD is only included in selected courses [30].

On the other hand, this study revealed that most sustainability-related topics were integrated into logistics-related study programs in Bahrain, Kuwait, Jordan, Cyprus, and Israel. Cyprus and Israel emphasized sustainability compared with other ME countries, which could be related to the location and culture, since the countries are closer to Europe and more culturally related to the EU and the "western" lifestyle. There are also some specifics, e.g., in Israel, where higher education represents the highest share of study programs in the ME database. This might indicate that Israel focuses on international students more than other ME countries. The ME database included only the programs that had information about the included curriculum.

The small share (23%) of study programs that include logistics and sustainability topics in researched ME study programs indicate that logistics could be more related to sustainability topics, and future logistics program graduates might lack knowledge about sustainable logistics development. "*Management*", on the other hand, is significantly better integrated into ME study program curricula, since more than half (53%) of researched bachelor programs include management in their curricula. Specifically prioritized were Iraq, Jordan, and Syria. Surprisingly "*mobility*/*transport*" is most included in PhD programs, indicating that sustainability is still lagging even in research priorities. Logistics topics are also more visible in Turkey, Cyprus, Iran, Syria, and UAE.

When checking topic overlapping, only 11% of researched study programs included all three study topic groups (management, logistics, and sustainability). It was also seen that "*management*" and "*logistics*" were more connected and integrated than "*management*" in combination with "*sustainability*". Population growth, lower income than, e.g., in the north, and significant oil and mineral reserves might lead to a focus on economic and practical engineering issues and exploit natural reserves instead of sustainability since environmentalism is costly and usually related to the pragmatism of those lacking fossil fuel supplies.

Regarding the expected correlation of countries with a higher level of integration of sustainability topics into their study programs and sustainability score/index, no clear correlation was discovered between countries and their GDP or sustainability scores. Few countries with higher sustainability scores indicated higher inclusion of sustainability study topics in their study programs, but the proportional tendency was not observed (countries with lower sustainability scores did not show lower integration of sustainability topics in study programs). This might also be seen in the particular focus of this study on logistics and supply chain-related programs. Assessing a complete set of higher education programs might show a different picture. Therefore, assessments of logistics programs and analyses of their dependence on the LPI index were also performed. Similar results regarding the correlation between LPI and a higher level of integration of logistics topics into study programs of selected ME countries show that the correlation between observed variables was not visible. Assessing a complete set of all higher education programs might show a different picture. The results of Ekici et al. [66] show that the greater the education of the logistics manager, the greater would be the manager's performance since their knowledge and skills are significantly influenced by the higher education and training offered by governments in different countries. Similarly, Yildiz [67] showed a positive correlation between logistics performance and education, but in this case, a weak correlation was identified in some countries.

The interconnection of sustainability, management, and logistics is clear. Logistics professionals need knowledge and skills from different areas. According to Tatham and Kovács [68], general management skills, functional logistic skills, problem-solving, and people management are relevant to logistics labor. The authors' previous research [45] revealed that another critical skill is sustainability-related knowledge, which is essential for managers in the modern global economy [69]. Management education prepares human capital for jobs in logistics and provides knowledge for optimizing resources and maximizing economic returns through business management knowledge and skills [70] as well as solving complex interdisciplinary problems [71]. Since logistics takes the lead in today's business administration fields, open development for professional managers is inevitable for the acquisition of logistics- and supply chain management-related higher education knowledge [72]. This knowledge will endow the professionals with more skills in subjects such as coaching, operations management, and crisis management by providing them with the opportunity to gain leadership ability and maintain it effectively [73] and to be better prepared for the future challenges of the sustainable paradigm demanded by the EU within the EU taxonomy as well as the recovery and resilience plan, which calls also for international cooperation that should be the focus of sustainability education [74].

On the other hand, these two topics interrelate with sustainability. Sustainability has received increasing attention in management education over the last few years [75]. Regarding the UN-supported initiative "Principles for Responsible Management Education" (PRME), there is an expectation that education institutions should lead thought and action on social responsibility and sustainability issues. Namely, they prepare current and future business professionals to engage in more responsible and sustainable practices [70]. Research ideas and results are especially applicable within the EU member states since the EU recovery and resilience plan demands boosting sustainability operations, formal education, lifelong learning, knowledge, and practices. In addition, further research should be promoted in detailed studies on teaching and including sustainability topics in individual processes within selected universities in individual countries. The three-step research concept could also be applied in other countries. Moreover, the EU taxonomy demands sustainable investments, which must be accompanied by experts from the field of sustainability within the EU or globally, since many investments are global and interconnected with organizations with headquarters within the EU. Further research implications could also be identified in the cross-border dissemination of progress toward sustainable education outside the EU, with particular emphasis on the Middle East as the geographical area with continuous population growth, an economic focus on fossil fuels, and the crossroads of international trade between Asian production and European consumers.

**Author Contributions:** Conceptualization, M.O.; methodology, M.O. and M.R.; validation, M.R.; formal analysis, M.R.; investigation, M.O. and M.R.; resources, M.O.; data curation, M.R.; writing original draft preparation, M.O. and M.R.; visualization, M.R.; supervision, M.O.; project administration, M.O. and M.R.; funding acquisition, M.O. All authors have read and agreed to the published version of the manuscript.

**Funding:** The European Union-Next Generation EU & The Ministry of Higher Education, Science, and Innovation funded the research. The research was carried out within the project titled "Establishing an environment for green and digital logistics and supply chain education within the Recovery and Resilience Plan scheme".

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** Special thanks go to Z. F. for cooperation within the process of data gathering and preparing graphs. The authors thank the funder for the research funding and for covering the APC.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

#### **References**

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### *Article* **Long-Range Wireless Communication for In-Line Inspection Robot: 2.4 km On-Site Test**

**Huseyin Ayhan Yavasoglu 1,2,\*, Ilhami Unal 3, Ahmet Koksoy 3, Kursad Gokce 2 and Yusuf Engin Tetik 2**

1 Mechatronics Engineering, Mechanical Engineering, Yildiz Technical University, Istanbul 34349, Türkiye

**Abstract:** This paper presents a study of the feasibility of using in-line inspection (ILI) techniques with long-range communication-capable robotic systems deployed with advanced inspection sensors in natural gas distribution pipelines, which are rare in the literature. The study involved selecting appropriate antennas and determining the appropriate communication frequency for an ILI robot operating on Istanbul 12" and 16" steel pipelines. The paper identifies the frequency windows with low losses, presents received signal strength indicator (RSSI) and signal-to-noise ratio (SNR) information for various scenarios, and evaluates the impact of T-junctions, which are known to be the worst components in terms of communication. To utilize the pipeline as a waveguide, lowattenuation-frequency windows were determined, which improved communication by a factor of 500 compared to aerial communication. The results of laboratory tests on a 50 m pipeline and realworld tests on a 2.4 km pipeline indicate that long-distance communication and video transmission are possible at frequencies of around 917 MHz with low-gain antennas. The study also assessed the impact of the early diagnosis of anomalies without incidents on the environment, achievable with ILI robots using long-range wireless communication.

**Keywords:** natural gas pipelines; nondestructive testing (NDT); in-line inspection robot; wireless communication; antenna; circular waveguide

### **1. Introduction**

The transportation of natural gas and oil, both of which are potentially hazardous materials that can have significant environmental impacts, is commonly achieved through pipelines. According to the International Energy Agency (IEA), around 90% of natural gas is transported via pipelines globally [1]. The safe operation of pipelines is threatened by various factors, including corrosion, third-party damage, natural disaster, etc. [2]. Faults in natural gas pipelines are a threat to the energy supply's continuity and can result in the catastrophic loss of life and property [3]. Therefore, it is essential to have a thorough understanding of the condition of pipelines in order to respond to emergency situations and prevent accidents and malfunctions. In summary, a maintenance and repair decision support system is necessary to ensure the safe and effective operation of these pipelines. When it comes to identifying pipeline anomalies, in-line inspection (ILI) methods are considered to be the most reliable, providing the most accurate results [4]. Pipelines are not harmed during ILI inspections. Therefore, these technologies are referred to as nondestructive testing (NDT) or non-destructive inspection (NDI) technologies [5]. Numerous ILI sensor technologies, including magnetic flux leakage (MFL) sensors [6], ultra-sonic sensors (UTs) [7], electro-magnetic acoustic transducers (EMATs) [8], and laser profilometers (LPs) [9], are used to detect faults during in-line inspections [10].

The applicability of in-line inspection technology extends beyond pipelines transporting hazardous materials, such as natural gas and oil, to include those conveying safe materials in water pipelines [11]. The material composition of the pipeline is a crucial

**Citation:** Yavasoglu, H.A.; Unal, I.; Koksoy, A.; Gokce, K.; Tetik, Y.E. Long-Range Wireless Communication for In-Line Inspection Robot: 2.4 km On-Site Test. *Sustainability* **2023**, *15*, 8134. https://doi.org/10.3390/ su15108134

Academic Editors: Manuel Fernandez-Veiga, Oz Sahin and Russell Richards

Received: 14 January 2023 Revised: 10 April 2023 Accepted: 25 April 2023 Published: 17 May 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

factor in determining the appropriate sensor technology to be employed. Depending on the type of natural gas pipeline, the application of ILI sensors varies. On the other hand, the selection of the inspection tool that conveys the sensor is dependent on the characteristics of the pipeline itself. Natural gas pipelines can be broadly categorized into two distinct classifications: distribution and transmission lines. Transmission pipelines are typically larger than distribution pipelines and operate at higher pressures [12]. In contrast, the distribution system consists of smaller pipelines responsible for delivering natural gas to various end users, including commercial and residential buildings. The distinction between transmission and distribution pipelines is important as they have different design characteristics.

For transmission pipeline inspections, the high-sensitivity sensors are carried by tools called smart pigs [13] that move with the flow. One of the key advantages of these devices is their ability to operate autonomously, without the need for remote control. This eliminates the requirement for long-distance communication during their operation. Natural gas transmission lines are lengthy and uncomplicated, making them ideal for deploying selfcontained smart pigs.

However, mostly, gas pipeline systems are not designed or constructed with ILI in mind. Distribution lines are particularly more complex, and they do not have the same structural characteristics as natural gas transmission lines. Due to the numerous special transitions and frequent diameter variations, smart pigs cannot be utilized in urban natural gas pipelines. Therefore, wireless robotic systems that can move independently of the gas flow, unlike smart pigs, are required for the ILI of city pipelines.

The scope of a robotic system capable of conducting inline inspections is extensive, encompassing a wide array of mechatronic systems with varying designs and capabilities. In a literature search conducted on this topic, numerous studies on pipeline inspection robotic systems were identified. For instance, the study [14] describes a fluid-driven small-size robotic system. The robot used in this study is more like the pig structure for transmission lines, and it is incompatible with distribution lines. The study [15] presents a crawler robot with three track-driving modules. This robot can be used in distribution lines but has a one-hour operating time and a short wireless range. Thus, it is better suited for visual inspections due to its lack of sophisticated sensors. The study [16] presents an automated vision-based navigation system for ILI pre-inspection. This is another small robotic application that drives on wheels rather than tracks. SAPER II [17] is an additional robotic system that inspects 4" to 14" natural gas pipelines and relies on a wire for power and communication.

However, these robotic systems do not include advanced sensor technology-based robotic systems, which are the subject of this study. The majority of these studies have focused on robotic systems that operate at close range and lack advanced sensors such as MFLs, UTs, and EMATs. In the literature, there are a few robotic systems equipped with MFL sensors that are capable of operating in natural gas distribution lines. One of these studies was on the Pibot robotic system, which is still in development [18]. The Pibot is a wireless [19] snake-like robot that is designed to operate in 16" steel pipes. Pipetel Explorer robots, which are used commercially, are an additional example of a robotic application that employs an MFL sensor [20]. This family of robots has been adapted for different pipe diameters and is primarily employed in North American nations.

Robotic systems with sophisticated sensors and long-range wireless communication, the subject of this paper, are extremely rare in the scientific literature. This is because developing robots with advanced sensors capable of operating in natural gas distribution pipelines presents numerous challenges. Establishing a secure, long-distance connection between the robot and the control station could be regarded as one of the greatest challenges associated with the development of an ILI robot, such as the use of wireless relay [21] for small robots. Additionally, in [22], some bi-directional relay nodes were utilized for the long-distance communication of an inspection robot in a water distribution system. Thanks to the relay nodes, the robot switches its communication between transceivers during

long-distance operation. For instance, in the study [23], an innovative concept of a robot chain for pipeline inspection based on wireless communication is presented. A robot chain is a subtype of a robot swarm in this context. These strategies are challenging to implement, as they require external structures in addition to the station and robot antenna and cause a delay in switching operations. Visible light communication (VLC) is a technique that can serve as an alternative to conventional RF communication strategies. For an actual visible light communication system, it is necessary to consider the uniformity of indoor illumination [24]. It is one of the newest technologies and has a high transmission rate and strong anti-interference capability. However, as given in the experimental study [25], VLC was developed for short- and middle-range data communication. In [26], a microwave communication system was developed for oil and gas pipelines with a good signal-tonoise ratio (SNR) (15 dB) at 2.4 GHz. However, this study presents 36 m straight pipeline communication and indicates that their proposed system can provide communication up to 150 m, which is a very short distance for ILI robots.

To increase the communication distance, there are two solutions using natural gas pipelines as a communication medium: a pipe as a waveguide for modems and a pipe as a signal conductor. A pipeline can act as a waveguide to frequencies in the few GHz range, which is suitable for commercially available radio modems. A pipeline can also support the direct signal injection of a signal with a frequency of a few kHz, which requires much more power for the same distance [26,27]. For example, in [28], a gas distribution pipeline was used as a communication channel to avoid installing a dedicated data transmission system. However, this research examined the transmission of data in the low-frequency band over a distance of approximately one kilometer. It was determined that the system under consideration was not capable of transmitting video, a crucial capability for ILI robots.

Despite the numerous studies in the literature on wireless communication, as highlighted above, there exists a gap in the ability to provide both long-distance transmission and video transmission capabilities. The implementation of ILI robots is hampered by this limitation.

Therefore, this study fills an important gap in this regard by examining the datalink system for long-distance communication without requiring an external structure. A preliminary version of the presented information was previously presented as a short paper at the 3rd Latin American Conference on Sustainable Development of Energy Water and Environmental Systems held in Sao Paulo [29].

The main contributions of this manuscript are as follows: (1) The introduction of a robot designed for deployment in pipelines with diameters of 12" and 16"; (2) a detailed description of the antenna placement on the robot; (3) a comparison of the performance of the station antennas selected for use in steel natural gas pipelines, accompanied by test results of both 10 m and 50 m lines composed of pipes with 12" and 16" diameters; (4) an evaluation of the impact of T-junction pipe components on wireless communication; (5) the determination of the low-attenuation windows to select the proper communication frequency; (6) contrary to the studies in the literature that have utilized straight pipes, this study used an experimental on-site test on a real 2.4 km long pipeline with 42 bends and various special transitions; (7) an evaluation of the environmental implications of enabling ILI robot operations.

This paper is organized as follows: In Section 2, the robotic system and antenna installation are described. In Section 3, an analysis of the frequency range and antenna gain is given. In addition, the laboratory test results for 12-inch and 16-inch pipelines are discussed, and T-junction cases are evaluated. In Section 4, the conducted site test results on a real 12-inch steel gas pipeline are presented. In Section 5, the study's impact on environmental consequences and major accidents is discussed. In Section 6, the impact of this study on natural gas distribution line-related accidents and environmental consequences are assessed. Finally, Section 7 presents the conclusion.

#### **2. Robotic System**

The natural gas inspection robot is designed in a modular manner and can move in both directions independently of the flow direction. It is classified as a snake robot with articulated joints. The articulation allows for multiple degrees of freedom within a single system, making its approach to obstacles extremely versatile.

As depicted in Figure 1, the robot's mechanical structure has been completed, and functional testing is currently in progress. The robot can automatically adapt to 12" and 16" pipe diameters by opening its arms based on the data it receives from force sensors. As illustrated in Figure 2, the mechanical structure consists of four distinct types of modules: Camera Modules, Driver Modules, Orientation Modules, and an MFL module. The front and the back modules of the robot are called Camera Modules, and they are equipped with custom-designed communication antennas. The robot communicates with the control station utilizing these antennas.

**Figure 1.** The natural gas inspection robot.

**Figure 2.** Modules of the in-line inspection robot.

#### *Camera Modules and Antenna Installation*

The camera modules at both ends of the robot have a complex multi-joint structure and include several components: two arms with traction motors, a high-resolution fish-eye camera for image acquisition, a single board computer as a supervisory controller, an LED lighting unit for illumination, proximity sensors to avoid a collision during bend transitions, and a laser profiler to detect visual defects. A custom-designed communication antenna is also placed on the front of each camera module, as shown in Figure 3, to receive/transmit data between the robot and the station antenna.

**Figure 3.** Camera module and communication antenna.

#### **3. Analysis of Frequency Range and Antenna Gain**

To enable wireless communication between the robot and the station antenna over a long distance, the proper wave frequency must be chosen and the natural gas pipe used as a waveguide.

$$(f\_{\varepsilon})\_{mn} = \frac{\chi'\_{mn}}{2\pi a \sqrt{\varepsilon\mu}}\tag{1}$$

where *a* is the radius of the waveguide, and *χ mn* is the zeros of the derivative *J m*(*χ mn*) = 0 (*n* = 1, 2, 3, . . .) of the Bessel function *Jm*(*x*) [18]. The propagation of waves can be either in the transverse electric (TE) mode or the transverse magnetic (TM) mode [30]. Since the permittivity (ε) and permeability (μ) of the natural gas are approximately the same as the ones of free space [31], the first propagating TE and TM modes and their cut-off frequencies for the 12-inch diameter circular waveguide are shown in Figure 4.

TE11 is the dominant mode. In reality, it is preferable to excite the circular waveguide with a 12-inch diameter between 585.67 MHz and 764.97 MHz for the lowest attenuation along the pipeline and polarization control. As the commercial transceiver communication modules available in the market operate mainly at the 868 MHz, 915 MHz, and 955 MHz central frequencies under 1 GHz, the excitation of one more mode, TM01, would be urgent considering not only higher losses but also dispersion and attenuation distortion to the signal since each exhibits different phase velocities and attenuation. Any propagating field in the circular waveguide (pipeline) is a superposition of the propagating modes existing for the frequency under consideration.

The TE11 dominant mode cut-off frequency of the 16-inch diameter circular waveguide is 390.7 MHz. The number of higher order modes that would be excited inside 16-inch diameter circular waveguides is at least four (TM01 = 510 MHz, TE21 = 647.4 MHz, TM11 = TE01 = 812.6 MHz, TE31 = 890.9 MHz, TM21 = 1090 MHz) under 1.1 GHz, compared to the only higher-order mode TM01 inside the 12-inch diameter circular waveguide. Due to the more common usage of underground circular pipes with a diameter of 12 inches for natural gas transportation in Turkey, fewer higher-order modes are excited compared to the 16-inch ones when commercial transceiver modules are utilized for communication inside the pipelines.

In addition to the aforementioned information, additional parameters are used to determine the quality of wireless communication. In wireless communication, the signalto-noise ratio (SNR) and received signal strength indicator (RSSI) determine the quality and dependability of the communication channel [32,33]. The SNR is the ratio between the desired signal power and the noise power. It is a crucial metric for assessing the performance of communication. A high SNR value indicates a low noise level and high signal quality. The RSSI, on the other hand, is a measurement of the received signal's strength. It is a relative measure of the signal strength that can be used to determine the distance between the receiver and the transmitter. A strong signal is indicated by a high RSSI value, while a weak signal is indicated by a low RSSI value. The SNR and RSSI are important parameters in wireless communication because they provide information about the signal's quality and strength. In Sections 4 and 5, these parameters are examined for multiple data-link cases using laboratory and field testing.

#### **4. Laboratory Tests**

To investigate the effects of antenna gain on the transmission performance inside the pipeline, two different ultra-wideband double-ridged antennas with high- and low-gain characteristics were used. As illustrated in Figure 5, the tests were initially conducted in pipelines that were constructed around the research center.

**Figure 5.** Laboratory test pipeline.