Mobility Patterns of Scholar Communities in Southwestern European Countries

Citation: Lage, J.; d’Espiney, A.; Canha, N.; Manteigas, V.; Alexandre, J.L.; Gonçalves, K.; Chacartegui, R.; Lizana, J.; Lechón, Y.; Gamarra, A.R.; et al. Mobility Patterns of Scholar Communities in Southwestern European Countries. Sustainability 2022,14, 16704. https://doi.org/10.3390/ su142416704 Academic Editors: Renata ˙ Zochowska and Marianna Jacyna Received: 26 October 2022 Accepted: 6 December 2022 Published: 13 December 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 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/). sustainability Article Mobility Patterns of Scholar Communities in Southwestern European Countries Joana Lage 1,2,*, Ana d’Espiney 1, Nuno Canha 1,3 , Vítor Manteigas 1,4 , JoséLuís Alexandre 5, Karla Gonçalves 5, Ricardo Chacartegui 6, Jesus Lizana 7, Yolanda Lechón8, Ana Rosa Gamarra 8, Amaia Fernandez 9, Patrice Blondeau 10, Margarida Gomes 11 and Susana Marta Almeida 1 1Centro de Ciências e Tecnologias Nucleares (C2TN), Instituto Superior Técnico, Universidade de Lisboa, 2695-066 Bobadela, Portugal 2Faculdade de Engenharia, Universidade Lusófona de Humanidades e Tecnologias de Lisboa, 1749-024 Lisboa, Portugal 3CESAM—Centre for Environmental and Marine Studies, Department of Environment and Planning, University of Aveiro, 3810-193 Aveiro, Portugal 4H&TRC—Centro de Investigação em Saúde e Tecnologia, ESTeSL—Escola Superior de Tecnologia da Saúde, Instituto Politécnico de Lisboa, 1990-096 Lisboa, Portugal 5Parque de Ciência E Tecnologia da Universidade do Porto, 4200-135 Porto, Portugal 6Energy Engineering Department, Universidad de Sevilla, 41004 Sevilla, Spain 7Engineering Science Department, University of Oxford, Oxford OX1 3PJ, UK 8Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), 28040 Madrid, Spain 9Research and Development Officer, University of Gibraltar, Gibraltar GX11 1AA, Gibraltar 10 LaSIE, University of La Rochelle, 17000 La Rochelle, France 11 Associação Bandeira Azul da Europa (ABAE), 1350-355 Lisboa, Portugal *Correspondence: [email protected] Abstract: The present study aimed to provide an in-depth assessment of the commuting patterns of scholar communities of southwestern European countries and to identify measures to improve their sustainable performance regarding mobility. The adopted methodology characterised the mobility pattern of students as a sustainability indicator and the availability of related infrastructures and local public transport network. Data were gathered by qualitative (behavioural questionnaires) and quantitative (technical audits) approaches, based on measurable indicators (key performance indicators and scores (ranging between 0–5)). Overall, French schools showed the best sustainable performance regarding mobility (2.0) and Gibraltar had the lowest (1.2). The existence of bike parking and electric car charging points were the main weaknesses founds (with their related mean scores being 0.6 and 0.2, respectively). The score associated with annual CO 2 emissions due to students’ mobility had the best performance, where all countries managed to obtain an average of 3.1. The global score, which assessed the sustainable performance of scholar communities regarding mobility, had a mean value of 1.5 for all studied countries, which highlights the potential for improvement of the studied schools, mainly targeting the public transport network optimisation and the enhancement of scholar infrastructures concerning bicycle parking and electric cars. Keywords: mobility pattern; key performance indicators; schools; environmental performance; behavioural and sustainable indicators 1. Introduction Commuting plays an important role in our daily lives and it may impact several aspects of it, not only personally (from wellbeing to travel times and costs, safety, and health) [ 1 ] but also globally (from energy consumption to greenhouse gas emissions and environmental degradation) [2]. Moreover, urban air pollution is known to be an emergent environmental problem with an impact on our health [ 3 ], and scientific evidence has shown that the increase in Sustainability 2022,14, 16704. https://doi.org/10.3390/su142416704 https://www.mdpi.com/journal/sustainability Sustainability 2022,14, 16704 2 of 18 urban traffic is the main contributor to the degradation of local air quality, where cars play a primary role as a source of pollutants in on-road and roadside environments [4]. Different modes can be used for commuting, such as by using soft mobility strategies (like walking and cycling), public transport, and by private car. Several factors can condition an individual’s personal choice for commuting. For instance, the topography and the level of physical effort required can influence the choice of a specific activity mode [5]. The time spent commuting is also known to contribute significantly to the total daily dose of pollutants inhaled by inhabitants in urban areas [ 6 , 7 ]. For instance, it was found that commuting travel contributed to 17% of the average daily dose of carbon monoxide (CO) [ 8 ] and to 12% of black carbon (BC) [ 9 ]. Some studies have shown that citizens that choose soft mobility solutions are less exposed than those that travel in motorised vehicles [ 10 , 11 ] but, depending on the location of the bicycle pathways, the bicycle mode can promote higher inhaled doses of pollutants than other modes due to the combination of longer travel periods with enhanced physical activity (which promotes higher inhalation rates) [7]. Among the general population, the scholar community is of particular interest since students are more susceptible to adverse effects of air pollution due to their short stature, higher rate of respiration, and developing lungs [12]. Considering the technological advancements in the automobile industry, the increase in the access of the population to motorised transportation, along with socioeconomic variability and urban barriers (such as the distance from home to school or family decisions), a change in mobility behaviours in commuting to school has been observed [ 13 ]. It is known that the Active School Transport (AST), by walking or cycling, has been decreasing among the school community and, nowadays, students typically resort to motorised vehicles as a mode of transportation [ 14 ]. Several factors have been highlighted as being crucial to the complex process of decision-making regarding which transport mode to travel to school with, namely economic, environmental, geographical, and social factors [ 15 ]. For instance, the improvement of neighbourhood facilities (such as walking/cycling facilities and aesthetics) and its social environment (such as pedestrian/traffic safety along with crime rate), which varies accordingly with the neighbourhood income, has been found to be supportive of active transportation and of active recreation [16]. Studies also have highlighted the influence of the use of motorised transport for commuting on the health of citizens. A study involving 38 countries from six continents highlighted that the use of motorised transportation resulted in worse levels of overall physical activity, active transportation, and sedentary behaviours in the study population, which represented 60% of the world’s population [ 17 ] and highlighted a “physical inactivity crisis” [ 18 ]. Therefore, public health is compromised, with negative impacts on the physiological [ 19 , 20 ] and emotional [ 21 ] patterns of the citizens. On the other hand, motorised commuting travel is also known to have an effect on ecosystems, due to the increase in exhaust and greenhouse gas emissions [22]. Therefore, it is urgent to identify the best intervention strategies for promoting a change in commuting habits. Different interventions have been implemented towards a new approach to school transportation worldwide. In Kansas (USA), the Earn-A-Bike program increased the time spent on a bike by 68% [ 23 ]. In Canada, mentorship programs allowed the participants to cycle an additional 1.35 days per week to work and school [ 24 ]. In Sweden, gamification was used to integrate learning into a health promotion program, presenting it as a successful method for promoting AST and engaging teachers [ 25 ]. In California (USA), Safe Routes to School (SRTS) programs provided funding for infrastructure improvements that encouraged children to use active transportation [26]. At the European level, it is estimated that Europeans spend an average of almost 2 h per day on the move by car, on foot, on public transport, and/or on bicycle, where 65% of them use their car in travel to school or work [ 27 ]. Moreover, it was estimated that 58% of Europeans were dissatisfied with the fluidity of traffic during rush hour, and 61% considered investments in transport infrastructures insufficient [ 27 ]. Other factors Sustainability 2022,14, 16704 3 of 18 were also considered to be of fundamental relevance to the Europeans, namely comfort and speed. Additionally, cost, congestion, availability, and connectivity were considered relevant factors to promote improvement in Europeans’ behaviour [28]. The present study aimed to understand the current state of the mobility behaviour of commuting of a scholar community, targeting children and young people of European schools from Portugal (PT), Spain (SP), France (FR), and Gibraltar (GI), and to identify the best strategies to improve their reality to mobility patterns more environmentally friendly, targeting AST. For this, key performance indicators and scores (that could translate the reality of the scholar communities regarding their mobility and associated schools’ infrastructures) were designed and used to compare schools and countries and to identify opportunities for improvement. This information is crucial to understand the potential change of individual behaviours to promote a low-carbon society. The present study was developed in the framework of a European project—Interreg SUDOE ClimACT (www. climact.net, accessed on 24 October 2022)—which targeted promoting the implementation of a low-carbon economy in schools. This study is organised as follows: Section 2explains the methodological framework and describes the used methodology, including the data collection and the assessment tools (key performance indicators and scores); Section 3presents and discusses the quantitative results of the variability of transport mode choices and evaluates the performance of each school, based in above tools, which allowed performing a comparative analysis between schools and countries. Additionally, in Section 3the results are also compared with data from the European general population regarding mobility patterns. Section 4provides a set of considerations regarding the study and its results, including limitations. Finally, Section 5provides the main conclusions of the study, including a set of measures to improve the mobility of the scholar community towards a more sustainable reality. 2. Methodology 2.1. Case Study—The ClimACT Framework The implementation of a Low-Carbon Economy (LCE), by incorporation of complementary approaches such as energy efficiency, smart growth initiatives, transportation control measures, energy efficiency, product procurement, and resource conservation is conducive to important environmental, economic, and social benefits. It reduces private and external costs and contributes to the accomplishment, not only of energy-related targets but also of the 3rd priority objective defined by the 7th Environment Action Program—to safeguard the citizens of the European Union from environment-related pressures and risk to health and well-being. The Interreg Sudoe ClimACT project [ 29 , 30 ] was created to act regarding current environmental challenges and to support the transition to an LCE of 39 pilot schools (Figure 1) from the southwestern European region (namely, Portugal—PT, Spain—SP, France—FR, and Gibraltar—GI), covering all educational levels (from elementary to university). Those 39 schools were the study sites of the present study. It is crucial for schools to reduce energy and environment-related expenditures, without affecting educational operations, by applying procurement and behavioural-related measures. Overall, a total of 39 schools from the four countries participated in the present study. There were nine Portuguese schools: six were located in Lisbon’s district (five in the municipality of Loures and one in the municipality of Lisbon) and three in Oporto’s district (two in the municipality of Matosinhos and one in the municipality of Vila Nova de Gaia). Thirteen schools were located in Spain of which eight were in Seville, one in Málaga, two in Madrid, and two in Alcaláde Henares. The nine French schools were all located in La Rochelle. Similarly, eight schools in the United Kingdom were located in Gibraltar. Sustainability 2022,14, 16704 4 of 18 Sustainability 2022, 14, x FOR PEER REVIEW 4 of 18 Figure 1. Geographic location of the studied schools in the southwestern European region. Overall, a total of 39 schools from the four countries participated in the present study. There were nine Portuguese schools: six were located in Lisbon’s district (five in the municipality of Loures and one in the municipality of Lisbon) and three in Oporto’s district (two in the municipality of Matosinhos and one in the municipality of Vila Nova de Gaia). Thirteen schools were located in Spain of which eight were in Seville, one in Málaga, two in Madrid, and two in Alcalá de Henares. The nine French schools were all located in La Rochelle. Similarly, eight schools in the United Kingdom were located in Gibraltar. Table S1 (in the Supplementary Materials) provides an overview of the pilot schools’ characteristics, such as the location, number of students, total area (including the outdoor area, namely courtyards, gardens, and grass area), and education level (elementary-, basic-, middle-, high school, and university). The schooling range was from elementary school to university: 15 elementary schools, 6 basic schools, 14 middle schools, 12 high schools, and 3 universities. The total number of students per country was: (1) Portugal—5620 students, (2) Spain—7980 students, (3) France—5151 students, and (4) Gibraltar—4102 students. 2.2. Study Methodology The mobility in the scholar population and surroundings was evaluated in two phases: (1) a technical audit at schools’ facilities to assess physical conditions for transports onsite by using a standardised checklist; (2) an online questionnaire within the scholar population (students) focusing on mobility habits regarding commuting behaviour between home and school. Both used tools (the online behavioural questionnaire and the standardised checklist of the technical audit) are available in the Supplementary Materials (Subsections B and C, respectively). The technical audit and the application of the online questionnaire were performed in the academic year 2016/2017. The technical audit was performed in each school by the project team of each country, with the collaboration of the school staff or the contact person of the project. The audits consisted of a field visit to the school to fill out a checklist about building infrastructure and facilities. The checklist included the verification of the availability of parking spaces for low-carbon transport modes—electric cars and bicycles—and to assess the public transport networks nearby the school. The technical audit was assessed for 32 schools (representing a participation rate of 82% of the schools), in which for the Gibraltar schools it was only possible to gather partial data for one school (S39). Regarding the online questionnaire, the scholar community was invited to provide their preferences regarding the transport modes used for commuting, which allowed Figure 1. Geographic location of the studied schools in the southwestern European region. Table S1 (in the Supplementary Materials) provides an overview of the pilot schools’ characteristics, such as the location, number of students, total area (including the outdoor area, namely courtyards, gardens, and grass area), and education level (elementary-, basic-, middle-, high school, and university). The schooling range was from elementary school to university: 15 elementary schools, 6 basic schools, 14 middle schools, 12 high schools, and 3 universities. The total number of students per country was: (1) Portugal—5620 students, (2) Spain—7980 students, (3) France—5151 students, and (4) Gibraltar—4102 students. 2.2. Study Methodology The mobility in the scholar population and surroundings was evaluated in two phases: (1) a technical audit at schools’ facilities to assess physical conditions for transports onsite by using a standardised checklist; (2) an online questionnaire within the scholar population (students) focusing on mobility habits regarding commuting behaviour between home and school. Both used tools (the online behavioural questionnaire and the standardised checklist of the technical audit) are available in the Supplementary Materials (Subsections B and C, respectively). The technical audit and the application of the online questionnaire were performed in the academic year 2016/2017. The technical audit was performed in each school by the project team of each country, with the collaboration of the school staff or the contact person of the project. The audits consisted of a field visit to the school to fill out a checklist about building infrastructure and facilities. The checklist included the verification of the availability of parking spaces for low-carbon transport modes—electric cars and bicycles—and to assess the public transport networks nearby the school. The technical audit was assessed for 32 schools (representing a participation rate of 82% of the schools), in which for the Gibraltar schools it was only possible to gather partial data for one school (S39). Regarding the online questionnaire, the scholar community was invited to provide their preferences regarding the transport modes used for commuting, which allowed them to assess their associated CO 2 emissions. The online questionnaire was mainly composed of multiple-choice questions. The participants had to indicate the frequency of their use of transport modes by choosing the options “never”, “sometimes”, “almost Sustainability 2022,14, 16704 5 of 18 always”, and “always”. Other questions were short open answers or “yes/no”. For primary schools, the online questionnaire was answered with the support of teachers and parents. During the data acquisition and treatment process, the anonymity of the participants was guaranteed. A total of 5024 completed questionnaires were gathered, with the following distribution per country: Portugal (40.4%), Spain (29.4%), France (22.4%) and Gibraltar (7.8%). Specific information regarding the success rate per school of the questionnaires’ implementation is displayed in Table S1. The questionnaire was applied successfully in 34 schools (participation rate of 87% of the schools), where the schools S18, S32, S35, S38, and S39 did not participate in the questionnaire. Overall, the complete methodology (both technical audit and questionnaire) was applied with success in 30 schools, which represents 77% of the participating schools. However, for Gibraltar, it was not possible to gather data from both technical audit and questionnaire but, to provide information for the country, KPIs and scores (defined below) were assessed, considering the partial data from each school. For further analysis of the results, it is important to highlight that the study sample of schools is not evenly distributed per country, regarding the number of schools, the number of students, or the educational levels of schools, which may influence the data and its interpretation. 2.3. Definition of Key Performance Indicators and Scores for Scholar Mobility The overall ClimACT strategy was based on a multi-criteria assessment methodology divided into seven environmental areas: energy, water, wastes, green spaces, green procurement, indoor air quality, and transport (mobility). These areas were assessed by using key performance indicators (KPIs), developed by the technical project team, using data obtained through technical audits, behavioural questionnaires, and monitoring campaigns [ 29 , 30 ]. KPIs are useful tools to understand the schools’ performance and are useful for performing comparisons between schools. The present study focuses only on the mobility dimension, for which four different KPIs were assessed, and are described in Table 1. Step-by-step calculation of the different KPIs is fully described elsewhere [29]. Table 1. Key performance indicators for mobility analysis. Key Performance Indicator Equation KPIT1 Parking spaces for bicycles per student (up to 100 m radius of the school) KPIT1 =Number of parking places for bicycle Number of the total students of the school KPIT2 Parking spaces for electric cars per school (up to 100 m radius of the school) KPIT2 =Number of charging stations for eletric cars Number of the total students of the school KPIT3 Public transports passing daily per hour (up to 1000 m radius of the school) KPIT3 = Number of public transports per hour within a 1000 m radius KPIT4 Annual CO2emissions per student (KgCO2 eq/student) KPIT4 =∑iCO2iemissions from daily commute to school Number of students of the school where i= transport mode (motorbike; car; boat; tram; train; subway; bus; bicycle; on foot) and CO2iEmissions = annual emissions associated to the transport mode i , defined by CO2iEmissions =∑ i (FEi×PEi)×daily average distance ×22 ×10 where FEi= CO2equivalent emission factor per transport mode i, and PEi= number of persons equivalent per transport mode i, considering the total number of answers of the behavioural questionnaires and the total number of students. PEi =(#never ×0+#sometimes×1 3+#almost always ×2 3+#always×1)×Number of students Number of students A global score for the mobility performance of a specific school was defined based on four scores (that were calculated considering the different KPIs), which are described Sustainability 2022,14, 16704 6 of 18 in Table 2. The global score ranges from 0 (worst performance) to 5 (best performance) and it allows us to compare all the studied schools. The scores (S1, S2, S3, and S4) were defined considering the expectation of improvement of the KPIs (less and more favourable scenarios) and, similarly to the global score, ranged from 0 to 5. Table 2. Description of the mobility scores and global score and their calculation procedures. ID Score Description Score Calculation Less Favourable Scenario More Favourable Scenario Weighting (For Global Score) S1 Parking places for bicycles S1 =5 KPIT1 1.05×max (KPIT1) Without parking places Highest KPIT2 found plus 5% 1 S2 Charging stations for electric cars S2 =5 KPIT2 1.05×max (KPIT2) Without charging stations Highest KPIT1 found plus 5% 1 S3 Public transport S3 =5 KPIT3 1.05×max (KPIT3) Without public transport Highest KPIT3 found plus 5% 1 S4 Annual CO2 emissions S 4 = 5 −5 KPIT4 KPIT4 with all students by car 100% of the students go by car 100% of the students go on foot or by bicycle 2 Global Score =S1+S2+S3+2S4 5 3. Results and Discussion 3.1. Transport Mode Use 3.1.1. By Education Level Figure 2presents the transport modes used in commuting (home to school) accordingly to the different educational levels in the four studied countries. Sustainability 2022, 14, x FOR PEER REVIEW 7 of 18 Figure 2. Use of transport modes in the different studied schools with indication of the scholar level and country. Considering that the scholar level may have a great influence on the choice of transport modes, the results will be shown firstly by scholar level. The top three preferences of students from elementary schools in Portugal included commuting by car (with an average of 51% and a maximum of 72% found in S8), by foot (with an average of 31% and a maximum of 40% found in S2), and, finally, by bus (with an average of 15% with a maximum of 24% in S2). In elementary schools from Spain, the scenario was more favourable for travelling by foot in the five studied schools (S10, S11, S12, S13, and S20), with an overall average of 53%. The second preference for mobility was the use of the car (with 42%), where it stands out in S10 with 60% of car use. Other transport choices were used with a similar frequency (average of 1% of adherence). A study conducted in ten cities in Spain regarding the mode of commuting to school used by preschool students showed similar results found in our study, where the main choice of commuting was “walking with my child” (with 48%), followed by commuting by car (41%) [31]. Another study assessed a percentage of 68% of the students of primary schools that used active commuting to school in Huesca (Spain) [32], which was higher than the value of 55% found in the present study (which accounts 2% for the use of bicycle). In France, the preferences were different, with the use of bicycles for daily commuting being more frequent. However, walking still remained the most used transport mode in France (with an average of 45% and a maximum of 60% found in S26), followed by the use of cars (with an average of 39% and a maximum of 44% found in S29), and, thirdly, by the use of the bicycle (FR elementary schools presented an average of 11%, with a maximum of 17% in S29). Gibraltar was where the scholar community of elementary schools showed more adherence to walking (with an average of 55%), followed by the use of motorcycles and cars (as passengers), with the same value (namely, an average of 23%). These results may indicate the role that social parameters may have regarding the choice of transport mode of students, as already highlighted by several studies [33–35]. In elementary schools, the range of ages varies between 6 and 10 years old, which may be an influencing factor in parental decisions, due to the fear of external dangers, such as traffic safety and stranger danger [34]. Moreover, besides gender not being a point of analysis in this study, it is known that gender is a predictor for children’s independent mobility (IM), which increases when children grow older and, typically, boys have a higher IM when compared to girls [36]. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% S2 S8 S10 S11 S12 S13 S20 S26 S27 S28 S29 S30 S31 S34 S37 S1 S3 S4 S9 S33 S36 S14 S15 S16 S17 S19 S21 S22 S5 S7 S24 S25 S6 S23 PT SP FR GI GI PT GI SP PT FR PT FR Percentage of each transport mode (%) Motorcycle Car Boat Tram Train Subway Bus Bicycle Foot Level 1 Level 3 Level 5 Level 6 Level 1: Elementary schools *Level 2: Basic schools Level 3: Basic+Middle schools Level 4: Midde schools Level 5: Middle+High schools Level 6: High schools Level 7: Universities Level 7 Level 4 * Figure 2. Use of transport modes in the different studied schools with indication of the scholar level and country. Considering that the scholar level may have a great influence on the choice of transport modes, the results will be shown firstly by scholar level. The top three preferences of students from elementary schools in Portugal included commuting by car (with an average of 51% and a maximum of 72% found in S8), by foot (with an average of 31% and a maximum of 40% found in S2), and, finally, by bus (with an average of 15% with a maximum of 24% in S2). In elementary schools from Spain, the scenario was more favourable for travelling by foot in the five studied schools (S10, S11, S12, S13, and S20), with an overall average Sustainability 2022,14, 16704 7 of 18 of 53%. The second preference for mobility was the use of the car (with 42%), where it stands out in S10 with 60% of car use. Other transport choices were used with a similar frequency (average of 1% of adherence). A study conducted in ten cities in Spain regarding the mode of commuting to school used by preschool students showed similar results found in our study, where the main choice of commuting was “walking with my child” (with 48%), followed by commuting by car (41%) [ 31 ]. Another study assessed a percentage of 68% of the students of primary schools that used active commuting to school in Huesca (Spain) [ 32 ], which was higher than the value of 55% found in the present study (which accounts 2% for the use of bicycle). In France, the preferences were different, with the use of bicycles for daily commuting being more frequent. However, walking still remained the most used transport mode in France (with an average of 45% and a maximum of 60% found in S26), followed by the use of cars (with an average of 39% and a maximum of 44% found in S29), and, thirdly, by the use of the bicycle (FR elementary schools presented an average of 11%, with a maximum of 17% in S29). Gibraltar was where the scholar community of elementary schools showed more adherence to walking (with an average of 55%), followed by the use of motorcycles and cars (as passengers), with the same value (namely, an average of 23%). These results may indicate the role that social parameters may have regarding the choice of transport mode of students, as already highlighted by several studies [ 33 – 35 ]. In elementary schools, the range of ages varies between 6 and 10 years old, which may be an influencing factor in parental decisions, due to the fear of external dangers, such as traffic safety and stranger danger [ 34 ]. Moreover, besides gender not being a point of analysis in this study, it is known that gender is a predictor for children’s independent mobility (IM), which increases when children grow older and, typically, boys have a higher IM when compared to girls [36]. The basic educational level is represented only by one Gibraltar school, where it is evident that the mobility of the students is by walking (56%), followed by the use of motorcycle (25%), and by car (15%). Schools that have basic and middle joint levels (identified as level 2 in Figure 2) are located only in Portugal. Comparing their results with elementary Portuguese schools, it is possible to identify an increase of around 10% regarding the choice of walking as their transport mode with the upgrade in educational level. This fact can be related to the age of the students, along with the distance from home to school. An exception is school S9, where 71% indicated travel by car as their main transport mode and only 12% by walking. When evaluating all the studied schools of all scholar levels, it is found that the transition of education level (namely from elementary to basic and middle levels) resulted in a slight decrease in the option “foot” (5%) and an increase of 12% on the use of the bus (16%). The age of the students and the access to a good transport network may be the factors for this improvement [37,38]. Only one middle school that participated in our study was from Gibraltar. This can be considered a limitation regarding the general comparison with other countries, due to the specific and unique characteristics of Gibraltar, such as its small area (approximately 6.8 km2). Students from the middle level have shown a preference for walking (58%), followed by the use of car (18%) and bus (13%). The transition from the basic and middle schools to the middle schools showed an upgrade in students going to school by walking (+16%), in contrast with the decrease in the use of cars ( − 19%) and a slight decrease in the use of bus ( − 3%). A possible factor for this variability can be less dependence of the students on their mobility, previously associated with the accompaniment of an adult, moving more independently [ 32 ]. Comparing the behaviour between students from middle schools and from middle and high schools, a 9% increase in car use was observed. However, important changes are noticed when evaluating high schools. For instance, commuting by walking decreases by 6%, which may be due to a longer distance from home to school. Sustainability 2022,14, 16704 8 of 18 Overall, the mobility of students from the studied high schools (two from Portugal and two from France) was characterised, firstly, by the use of the car (31%), then by bus (29%), and, thirdly, by walking (28%). Students of Portuguese high schools did their commuting mainly by foot (43%), followed by car (36%), and, thirdly, by bus (18%). In French schools, the students mainly used bus (39%), followed by car (26%), and, thirdly, by foot (14%). At the university level, some changes were identified, with a particular increase in the use of public transport as a main choice for the commuting (an overall value of 37%, the highest when comparing to lower educational levels). The second transport mode used was walking (29%), followed by car (24%). This fact may be due to the increase in the distance from home to university, the students being older and more independent, and a more efficient and accessible public transportation network being available. For this educational level, only two universities were evaluated, one from Portugal and the other from France. In the Portuguese university, 66% of the students used public transport for their commute to school (mainly by train—25%, followed by subway—23%, and then by bus—16%). Walking was the commuting mode elected by 21% of the Portuguese students, followed by car, with 12%. French students presented a different reality, with active commuting representing 54% (37% by walking and 18% by bicycle), followed by the use of car (with 37%), and then by the use of public transport with only 9% (mainly bus—7%). The differences between countries may be due to the location of the schools within the city and available infrastructures and public transport networks. A study conducted in a university in the north of Portugal, where the two studied campuses were not located in the city centre (around 3 km from it), found that students mainly used the car (42%), followed by walking and the bus (both with 28%) to commute to the scholar facilities [39]. 3.1.2. By Country Figure 3presents the variability of the preferences of transport modes in the scholar communities per country. Sustainability 2022, 14, x FOR PEER REVIEW 9 of 18 3.1.2. By Country Figure 3 presents the variability of the preferences of transport modes in the scholar communities per country. Figure 3. Mobility pattern of the scholar community in the studied countries. The preferred mode of commuting is walking, with a general average of 45%, where GI and SP have major averages of 56% and 52%, respectively. The use of the car is the second preferable transport mode (average 34%), followed by the bus (11%), the bicycle (4%), the motorcycle (3%), the train (2%), and the subway (2%). In Portugal, Spain, and France, travel by foot, car, or bus are the top three preferences. In Gibraltar, a higher preference for walking and less use of cars and buses is visible. It is unique in that it has a higher preference for the use of motorcycles (16%). It is important to highlight that the use of bicycles has a higher percentage in France (11%) than the other countries. This reflects the French urban municipal plans that strengthened local infrastructures, such as bicycle paths, as can be shown by KPIT1, described in the next section, which evaluates the available parking spaces for bicycles per student. Portugal was the country where the use of bicycles was lower (with a percentage of adherence of 1%). As stated before, the use of bicycles as an active transport mode is dependent on geographic and socioeconomic patterns [13]. Tough routes in terms of topography, with different altitudes, may make this choice unfeasible for users. Socioeconomic factors can also condition a choice, where families with lower socioeconomic levels, which is a reality of some of these schools, do not have access to bicycles. In some countries, such as Portugal, learning to ride a bike was not part of the educational curriculum, though nowadays gradually being included. Furthermore, an important aspect of this issue is the availability of the local infrastructures—bicycle paths, which are unevenly distributed across the countries under study. Figure 4 shows the preference of the students of different scholar levels, per country, distributed per categories of ATS, public transportation, and private motorised vehicles. In a general scenario, besides the variability of the choices analysed previously, the studied community from the four countries mainly use Active School Transports (AST)— walking or bicycle—with an average of 49%, representing 13% more than the use of private transport—car and motorcycle (which accounted for 36%). The use of public transport (bus, subway, train, tram, boat) had the lowest percentage, with an average of 14%. A study conducted in Slovenia (targeting a specific scholar community, namely students with ages between 12 and 15 years old, and attending grades 6, 7, 8, and 9) found a slightly lower percentage of students reporting active commuting modes to and from school, namely, 43% [40]. 37% 52% 37% 56% 45% 1% 3% 11% 3% 4% 16% 8% 11% 6% 11% 3% 2% 2% 3% 1% 2% 2% 1% 37% 33% 36% 18% 34% 1% 1% 2% 16% 3% 0% 20% 40% 60% 80% 100% PT SP FR GI Average Percentage of each transport mode (%) Country Motorcycle Car Boat Tram Train Subway Bus Bicycle Foot Figure 3. Mobility pattern of the scholar community in the studied countries. The preferred mode of commuting is walking, with a general average of 45%, where GI and SP have major averages of 56% and 52%, respectively. The use of the car is the second preferable transport mode (average 34%), followed by the bus (11%), the bicycle (4%), the Sustainability 2022,14, 16704 9 of 18 motorcycle (3%), the train (2%), and the subway (2%). In Portugal, Spain, and France, travel by foot, car, or bus are the top three preferences. In Gibraltar, a higher preference for walking and less use of cars and buses is visible. It is unique in that it has a higher preference for the use of motorcycles (16%). It is important to highlight that the use of bicycles has a higher percentage in France (11%) than the other countries. This reflects the French urban municipal plans that strengthened local infrastructures, such as bicycle paths, as can be shown by KPI T1 , described in the next section, which evaluates the available parking spaces for bicycles per student. Portugal was the country where the use of bicycles was lower (with a percentage of adherence of 1%). As stated before, the use of bicycles as an active transport mode is dependent on geographic and socioeconomic patterns [ 13 ]. Tough routes in terms of topography, with different altitudes, may make this choice unfeasible for users. Socioeconomic factors can also condition a choice, where families with lower socioeconomic levels, which is a reality of some of these schools, do not have access to bicycles. In some countries, such as Portugal, learning to ride a bike was not part of the educational curriculum, though nowadays gradually being included. Furthermore, an important aspect of this issue is the availability of the local infrastructures—bicycle paths, which are unevenly distributed across the countries under study. Figure 4shows the preference of the students of different scholar levels, per country, distributed per categories of ATS, public transportation, and private motorised vehicles. In a general scenario, besides the variability of the choices analysed previously, the studied community from the four countries mainly use Active School Transports (AST)—walking or bicycle—with an average of 49%, representing 13% more than the use of private transport—car and motorcycle (which accounted for 36%). The use of public transport (bus, subway, train, tram, boat) had the lowest percentage, with an average of 14%. A study conducted in Slovenia (targeting a specific scholar community, namely students with ages between 12 and 15 years old, and attending grades 6, 7, 8, and 9) found a slightly lower percentage of students reporting active commuting modes to and from school, namely, 43% [40]. These results highlight the importance of performing studies for mobility pattern characterisation to identify possible justifying factors that contribute to the development and implementation of interventions to promote scholar community mobility by choosing, whenever possible, ATS or public transport. Concerning this matter, special attention should be given to the variables that influence the independent mobility of children, namely their socioeconomic background and their parents’ educational level, as shown by several studies [34,41]. 3.2. Key Performance Indicators Table 3presents the mobility KPIs for each country and their average values. When comparing the different KPIs between them, KPI T1 (parking spaces for bicycles per student—up to a 100 m radius) and KPI T2 (parking spaces for electric cars per school—up to a 100 m radius) showed the lowest results. This fact highlights that schools do not have infrastructures that favour soft and sustainable mobility, namely bicycle parks (KPI T1 ) and electric car charging stations (KPIT2). Analysing each KPI individually, KPI T1 presented a mean level near zero, considering all the countries. This number reflects a common fragility of all the studied countries, namely the absence of facilities that favour the use of bicycles in commuting. A slight variability between countries is found; however, all of them present KPI values below 0.06. As expected, as shown in the previous section, France was the country with the best results, with four schools equipped with bicycle parking spaces, where S25 stands out with 248 spaces, followed by S23 and S24 with 248 and 50 spaces, respectively. Portugal is the country with second-best results, with three schools with parking spaces, where two of them have more than 30 parking spaces (S7 and S9). Spain has seven schools where the Sustainability 2022,14, 16704 16 of 18 Author Contributions: Conceptualisation, J.L. (Joana Lage), R.C., J.L. (Jesus Lizana) and S.M.A.; methodology, J.L. (Joana Lage), N.C., V.M., R.C., J.L. (Jesus Lizana), P.B., A.R.G., M.G. and S.M.A.; formal analysis, J.L. (Joana Lage), and N.C.; investigation, J.L. (Joana Lage), A.d., V.M., J.L.A., K.G., R.C., J.L. (Jesus Lizana), Y.L., A.R.G., A.F., P.B., M.G. and S.M.A.; data curation, J.L. (Joana Lage), A.d., J.L. (Jesus Lizana), P.B., A.F., K.G. and A.R.G., writing—original draft preparation, J.L. (Joana Lage); writing—review and editing, J.L. (Joana Lage), and N.C.; visualisation, J.L. (Joana Lage); supervision, J.L. (Joana Lage) and S.M.A.; project administration, J.L. (Joana Lage) and S.M.A.; funding acquisition, R.C., P.B., N.C., Y.L., A.F. and S.M.A. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Interreg SUDOE funding programme of the European Regional Development Fund, through ClimACT Project (SOE1/P3/P0429). J. Lage acknowledges the support of the FCT—Fundação para a Ciência e Tecnologia, I.P. (Portugal) for the contract CEEC-IND/02366/2020 and N. Canha also acknowledges the support of FCT, through the contract 2021.00088.CEECIND. The FCT support is also gratefully acknowledged by C2TN/IST authors (UIDB/04349/2020 + UIDP/04349/2020) and by CESAM author (UIDB/50017/2020+UIDP/50017/2020). Authors also acknowledges the support of H2020 Project ECF4CLIM (Grant agreement ID: 101036505). Institutional Review Board Statement: All representatives of schools gave their informed consent for inclusion before they participated in the study. Behavioural data of participants from the studied scholar communities were collected by using non-interventional methods (surveys, questionnaires, etc.) assuring anonymity. The study was conducted in accordance with the protocol of data collection and according to the Ethics Management Plan included in the Quality Management Plan of the (ET93.3.1) of the ClimACT Project (SOE1/P3/P0429). Informed Consent Statement: Informed consent was obtained from representatives of schools involved in data collection and audits, and also from all subjects of the scholar community involved in the online surveys. Data Availability Statement: The data that support the findings of this study are available upon request to the corresponding author. Acknowledgments: Authors acknowledge all the educational institutions that were actively involved in the present research. 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 1. Chatterjee, K.; Chng, S.; Clark, B.; Davis, A.; De Vos, J.; Ettema, D.; Handy, S.; Martin, A.; Reardon, L. Commuting and wellbeing: A critical overview of the literature with implications for policy and future research. Transp. Rev. 2020,40, 5–34. [CrossRef] 2. Bastos, J.; Marques, P.; Batterman, S.A.; Freire, F. Environmental impacts of commuting modes in Lisbon: A life-cycle assessment addressing particulate matter impacts on health. Int. J. Sustain. Transp. 2019,13, 652–663. [CrossRef] [PubMed] 3. Jin, L.; Apte, J.S.; Miller, S.L.; Tao, S.; Wang, S.; Jiang, G.; Li, X. Global endeavors to address the health effects of urban air pollution. Environ. Sci. Technol. 2022,56, 6793–6798. [CrossRef] [PubMed] 4. Reche, C.; Tobias, A.; Viana, M. Vehicular traffic in urban areas: Health burden and influence of sustainable urban planning and mobility. Atmosphere 2022,13, 598. [CrossRef] 5. Peyroux, C.E.; Bussen, L.S.; Costello, S.B.; Dirks, K.N. Traffic pollution while commuting—Does commute mode matter? Weather. Clim. 2015,35, 2. [CrossRef] 6. Martins, V.; Correia, C.; Cunha-Lopes, I.; Faria, T.; Diapouli, E.; Manousakas, M.I.; Eleftheriadis, K.; Almeida, S.M. Chemical characterisation of particulate matter in urban transport modes. J. Environ. Sci. 2021,100, 51–61. [CrossRef] 7. Correia, C.; Martins, V.; Cunha-Lopes, I.; Faria, T.; Diapouli, E.; Eleftheriadis, K.; Almeida, S.M.M. Particle exposure and inhaled dose while commuting in Lisbon. Environ. Pollut. 2020,257, 113547. [CrossRef] 8. Lim, S.; Salmond, J.A.; Dirks, K.N. Influence of differing microenvironments on personal carbon monoxide exposure in Auckland, New Zealand. Open Atmos. Sci. J. 2015,9, 1–8. [CrossRef] 9. Dons, E.; Int Panis, L.; Van Poppel, M.; Theunis, J.; Wets, G. Personal exposure to Black Carbon in transport microenvironments. Atmos. Environ. 2012,55, 392–398. [CrossRef] 10. Boogaard, H.; Borgman, F.; Kamminga, J.; Hoek, G. Exposure to ultrafine and fine particles and noise during cycling and driving in 11 Dutch cities. Atmos. Environ. 2009,43, 4234–4242. [CrossRef] Sustainability 2022,14, 16704 17 of 18 11. Grange, S.K.; Dirks, K.N.; Costello, S.B.; Salmond, J.A. Cycleways and footpaths: What separation is needed for equivalent air pollution dose between travel modes? Transp. Res. Part D Transp. Environ. 2014,32, 111–119. [CrossRef] 12. Keast, L.; Bramwell, L.; Maji, K.J.; Rankin, J.; Namdeo, A. Air quality outside schools in Newcastle upon Tyne, UK: An investigation into NO2and PM concentrations and PM respiratory deposition. Atmosphere 2022,13, 172. [CrossRef] 13. Villa-González, E.; Barranco-Ruiz, Y.; Evenson, K.R.; Chillón, P. Systematic review of interventions for promoting active school transport. Prev. Med. 2018,111, 115–134. [CrossRef] [PubMed] 14. Haug, E.; Smith, O.R.F.; Bucksch, J.; Brindley, C.; Pavelka, J.; Hamrik, Z.; Inchley, J.; Roberts, C.; Mathisen, F.K.S.; Sigmundová, D. 12-year trends in active school transport across four european countries—Findings from the health behaviour in school-aged children (HBSC) study. Int. J. Environ. Res. Public Health 2021,18, 2118. [CrossRef] [PubMed] 15. Rojas Lopez, M.C.; Wong, Y.D. Children’s active trips to school: A review and analysis. Int. J. Urban Sustain. Dev. 2017,9, 79–95. [CrossRef] 16. Sallis, J.F.; Slymen, D.J.; Conway, T.L.; Frank, L.D.; Saelens, B.E.; Cain, K.; Chapman, J.E. Income disparities in perceived neighborhood built and social environment attributes. Health Place 2011,17, 1274–1283. [CrossRef] 17. Tremblay, M.S.; Barnes, J.D.; González, S.A.; Katzmarzyk, P.T.; Onywera, V.O.; Reilly, J.J.; Tomkinson, G.R. Global Matrix 2.0: Report card grades on the physical activity of children and youth comparing 38 countries. J. Phys. Act. Health 2016 ,13, 343–366. [CrossRef] 18. Larouche, R.; Saunders, T.J.; John Faulkner, G.E.; Colley, R.; Tremblay, M. Associations between active school transport and physical activity, body composition, and cardiovascular fitness: A systematic review of 68 studies. J. Phys. Act. Health 2014 ,11, 206–227. [CrossRef] 19. Ross, A.; Rodríguez, A.; Searle, M. Associations between the Physical, sociocultural, and safety environments and active transportation to school. Am. J. Health Educ. 2017,48, 198–209. [CrossRef] 20. Andersen, L.B.; Wedderkopp, N.; Kristensen, P.; Moller, N.C.; Froberg, K.; Cooper, A.R. Cycling to school and cardiovascular risk factors: A longitudinal study. J. Phys. Act. Health 2011,8, 1025–1033. [CrossRef] 21. Larouche, R.; Mammen, G.; Rowe, D.A.; Faulkner, G. Effectiveness of active school transport interventions: A systematic review and update. BMC Public Health 2018,18, 206. [CrossRef] [PubMed] 22. Brand, C.; Dons, E.; Anaya-Boig, E.; Avila-Palencia, I.; Clark, A.; de Nazelle, A.; Gascon, M.; Gaupp-Berghausen, M.; Gerike, R.; Götschi, T.; et al. The climate change mitigation effects of daily active travel in cities. Transp. Res. Part D Transp. Environ. 2021 , 93, 102764. [CrossRef] 23. Carlson, J.A.A.; Steel, C.; Bejarano, C.; Grimes, A.; Shook, R.P.P.; Reddy, A.; Green, M.; Morefield, T.; Steele, L.; Campbell, K.; et al. Impacts of an earn-a-bike cycling education program on children’s time spent cycling. J. Transp. Health 2020 ,16, 100826. [CrossRef] 24. Kearns, M.; Ledsham, T.; Savan, B.; Scott, J. Increasing cycling for transportation through mentorship programs. Transp. Res. Part A Policy Pract. 2019,128, 34–45. [CrossRef] 25. Rutberg, S.; Lindqvist, A.-K. Active school transportation is an investment in school health. Health. Behav. Policy Rev. 2018 ,5, 88–97. [CrossRef] 26. Brachman, M.L.L.; Church, R.L.L. Optimizing safe routes to school. Socioecon. Plann. Sci. 2019,67, 26–33. [CrossRef] 27. European Mobilities Observatory. The Expectations of Europeans in Terms of Mobility. Available online: https://www.ipsos. com/en/european-mobilities-observatory (accessed on 23 November 2022). 28. European Commission. Special Eurobarometer 495: Mobility and Transport—Wave EB92.l-Kantar; Directorate-General for Communication: Brussels, Belgium, 2020. 29. Lizana, J.; Manteigas, V.; Chacartegui, R.; Lage, J.; Becerra, J.A.; Blondeau, P.; Rato, R.; Silva, F.; Gamarra, A.R.; Herrera, I.; et al. A methodology to empower citizens towards a low-carbon economy. The potential of schools and sustainability indicators. J. Environ. Manag. 2021,284, 112043. [CrossRef] [PubMed] 30. Gamarra, A.R.; Lago, C.; Herrera-Orozco, I.; Lechón, Y.; Almeida, S.M.; Lage, J.; Silva, F. Low-Carbon economy in schools: Environmental footprint and associated externalities of five schools in Southwestern Europe. Energies 2021 ,14, 6238. [CrossRef] 31. Herrador-Colmenero, M.; Cadenas-Sanchez, C.; Labayen, I.; Muntaner-Mas, A.; Moliner-Urdiales, D.; Lozano-Berges, G.; Benito, P.J. ; Rodríguez-Pérez, M.A.; Delgado-Alfonso, Á.; Sanchís-Moysi, J.; et al. Patterns of active commuting to school in spanish preschool children and its associations with socio-economic factors: The PREFIT project. Int. J. Environ. Res. Public Health 2021,18, 11180. [CrossRef] 32. Corral-Abós, A.; Aibar, A.; Estrada-Tenorio, S.; Julián, J.A.; Ibor, E.; Zaragoza, J. Implications of school type for active commuting to school in primary education students. Travel Behav. Soc. 2021,24, 143–151. [CrossRef] 33. Chatman, D.G.; Broaddus, A.; Spevack, A. Are movers irrational? On travel patterns, housing characteristics, social interactions, and happiness before and after a move. Travel Behav. Soc. 2019,16, 262–271. [CrossRef] 34. Ghekiere, A.; Deforche, B.; Carver, A.; Mertens, L.; de Geus, B.; Clarys, P.; Cardon, G.; De Bourdeaudhuij, I.; Van Cauwenberg, J. Insights into children’s independent mobility for transportation cycling—Which socio-ecological factors matter? J. Sci. Med. Sport 2017,20, 267–272. [CrossRef] [PubMed] 35. Camarero, L.A.; Oliva, J. Exploring the social face of urban mobility: Daily mobility as part of the social structure in Spain. Int. J. Urban Reg. Res. 2008,32, 344–362. [CrossRef] 36. Shaw, B.; Bicket, M.; Elliott, B.; Fagan-Watson, B.; Mocca, E.; Hillman, M.; Fagan-Watson, B. Children’s Independent Mobility: An International Comparison and Recommendations for Action; London Policy Studies Institute: London, UK, 2015; ISBN 9780853740148. Sustainability 2022,14, 16704 18 of 18 37. Litman, T. Evaluating Accessibility for Transport Planning—Measuring People’s Ability to Reach Desired Services and Activities; Victoria Transport Policy Institute: Victoria, BC, Canada, 2022. 38. Šinko, S.; Prah, K.; Kramberger, T. Spatial modelling of modal shift due to COVID-19. Sustainability 2021,13, 7116. [CrossRef] 39. Ribeiro, P.J.G.; Fonseca, F. Students’ home-university commuting patterns: A shift towards more sustainable modes of transport. Case Stud. Transp. Policy 2022,10, 954–964. [CrossRef] 40. Jurak, G.; Soric, M.; Sember, V.; Djuric, S.; Starc, G.; Kovac, M.; Leskosek, B. Associations of mode and distance of commuting to school with cardiorespiratory fitness in Slovenian schoolchildren: A nationwide cross-sectional study. BMC Public Health 2021 , 21, 291. [CrossRef] 41. Stone, M.R.; Faulkner, G.E.; Mitra, R.; Buliung, R.N. The freedom to explore: Examining the influence of independent mobility on weekday, weekend and after-school physical activity behaviour in children living in urban and inner-suburban neighbourhoods of varying socioeconomic status. Int. J. Behav. Nutr. Phys. Act. 2014,11, 5. [CrossRef] 42. Caulfield, B.; Furszyfer, D.; Stefaniec, A.; Foley, A. Measuring the equity impacts of government subsidies for electric vehicles. Energy 2022,248, 123588. [CrossRef] 43. Christidis, P.; Focas, C. Factors Affecting the uptake of hybrid and electric vehicles in the European Union. Energies 2019 ,12, 3414. [CrossRef] 44. Javid, R.J.; Nejat, A. A comprehensive model of regional electric vehicle adoption and penetration. Transp. Policy 2017 ,54, 30–42. [CrossRef] 45. Gkiotsalitis, K.; Stathopoulos, A. A utility-maximization model for retrieving users’ willingness to travel for participating in activities from big-data. Transp. Res. Part C Emerg. Technol. 2015,58, 265–277. [CrossRef] 46. Turo´n, K. Social barriers and transportation social exclusion issues in creating sustainable car-sharing systems. Entrep. Sustain. Issues 2021,9, 10–22. [CrossRef] [PubMed] 47. Turo´n, K. From the classic business model to open innovation and data sharing—The concept of an open car-sharing business model. J. Open Innov. Technol. Mark. Complex. 2022,8, 36. [CrossRef]