This chapter explores the impacts of Catalonia’s current passenger transport system on emissions, travel patterns and well-being. It also compares the attractiveness of the various transport modes, finding that private motorised vehicles (cars and motorcycles) are more attractive than active and shared modes in many areas. It describes how the underlying structure of the system influences people’s travel choices, identifying three dynamics that foster emissions-intensive travel patterns: induced car demand, urban sprawl and the inequality in space allocated to sustainable modes.
Transforming Catalonia’s Mobility System for Net Zero
Abstract
Total greenhouse gas (GHG) emissions in Catalonia grew steadily between 1990 and 2005. They plummeted between 2005 and 2013 because of the 2008 economic crisis but started growing again in 2013 (except during the COVID-19 pandemic). According to the latest data, emission levels were 4% higher in 2022 than in 1990 (Figure 3.1).
Transport is the highest-emitting sector in the region (Generalitat de Catalunya, 2024[1]). Transport-related emissions display a similar trend to total GHG emissions but are higher relative to 1990. In 2022, transport-related emissions (including freight transport) were 22% higher than in 1990 (Figure 3.1). No data are available on passenger transport emissions in Catalonia – the focus of this report.
Note: Transport-related GHG emissions include passenger and freight transport. GHG-emissions include CO2, CH4, N2O, HFCs, PFCs, SF6 and are expressed in CO2 equivalents based on global warming potential. Total GHG-emissions do not include land use, land-use change and forestry.
Source: Authors, based on data from (Generalitat de Catalunya, 2024[1]), “Catalonia GHG emissions”, https://canviclimatic.gencat.cat/en/canvi/inventaris/emissions_de_geh_a_catalunya/#:~:text=GHG%20emissions%20in%20Catalonia%20in,tonnes%20of%20CO2%20equivalent%20more, accessed 27 May 2024.
The distance travelled by light-duty vehicles (i.e. passenger cars and vans) and transport-related GHG emissions (Figure 3.2) show similar trends, both in Catalonia (Generalitat de Catalunya, 2024[1]; Observatorio del Transporte y la Logistica, 2023[2]) and globally (Federal Reserve Bank of Saint Louis, 2024[3]; IPCC, 2000[4]). In Spain, emissions from cars and motorcycles account for 67% of total emissions from road transport (UNFCCC, 2024[5]). Across Europe, private car use currently accounts for 61% of road transport emissions (Krause et al., 2020[6]; European Environment Agency, 2022[7]).
Note: Transport-related GHG-emissions include carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) and are expressed in CO2 equivalents based on their global warming potentials.
Source: Authors, based on data from (Observatorio del Transporte y la Logistica, 2023[2]), “Passenger and freight transport traffic volumes by road and by region since 2003”, https://apps.fomento.gob.es/bdotle/visorBDpop.aspx?i=321, accessed 3 June 2024; and (Generalitat de Catalunya, 2024[1]), “Catalonia GHG emissions”, https://canviclimatic.gencat.cat/en/canvi/inventaris/emissions_de_geh_a_catalunya/#:~:text=GHG%20emissions%20in%20Catalonia%20in,tonnes%20of%20CO2%20equivalent%20more, accessed 27 May 2024.
Almost half of Catalonia’s population owns a car. Between 1997 and 2001, the motorisation rate1 for private cars increased from 420 to 470 registrations per 1 000 inhabitants, and between 2001 and 2021 the number fluctuated between 470 and 445. For motorcycles, the rate increased from 61 to 116 registrations per 1 000 inhabitants between 1997 and 2021 (IDESCAT, 2023[8]; IDESCAT, 2024[9]). The provinces of Girona, Tarragona, and Lleida have higher motorisation rates (542, 535 and 519 respectively) than Barcelona Metropolitan Area (424) (IDESCAT, 2023[8]; IDESCAT, 2024[9]).
While recent aggregate data are not available, at the regional level, the total number of commuting trips (for study or work-related purposes) grew between 1981 and 2001 (Generalitat de Catalunya, 2006[10]). Interurban trips accounted for most of such increase (Figure 3.3). The share of motorised private vehicles in interurban trips also increased over the same period, from 43 % in 1981 to 65 % in 2001 (data on modal split for intraurban trips not available). The region’s Mobility Guidelines describes this upward trend as “individual motorised vehicles hav[ing] taken – in these 20 years – the hegemony that public transport had in 1981 (29 % in 2001 for a usage rate of 46 % in 1981)” (Generalitat de Catalunya, 2006[10]).2
Intraurban and interurban commuting (study- or work-related) trips (1981-2001)
Source: Adapted from (Generalitat de Catalunya, 2006[10]), Catalonia Mobility Guidelines (DNM), https://participa.gencat.cat/uploads/decidim/attachment/file/628/directrius_nacionals_mobilitat_tcm32-36328.pdf (accessed on 6 June 2023).
Travel patterns have been stable on average across the region since the year 2000 (Generalitat de Catalunya, Forthcoming[11]) but have evolved differently across territories. On average at the regional level, private vehicles account for 43% of trips, followed by walking (38%), public transport (17%) and cycling (2%) (2017, latest data available) (Figure 3.4) (Generalitat de Catalunya, Forthcoming[11]). Over time, data suggest a tendency towards active modes in the Barcelona Metropolitan Area, while the opposite trend is apparent in Camp de Tarragona and Terres de l’Ebre (Figure 3.5).
Source: Extracted from (Generalitat de Catalunya, Forthcoming[11]), Catalonia Mobility Guidelines (DNM).
Recent changes in modal share compared to 2006
Note: The geographical coverage of the figure is determined by data availability.
Source: Authors, based on data from (ATM Barcelona, 2023[12]; Generalitat de Catalunya, 2020[13]; ATM Tarragona, 2020[14]).
Data shows that less densely populated areas feature higher shares of private car use. For example, trips on working days in Tarragona and Terres de l’Ebre are predominantly carried out using private motorised vehicles (54% and 60% respectively), followed by active modes (41%3 and 37% respectively), and public transport (6% and 3% respectively) (OMC, 2021[15]; ATM Tarragona, 2020[14]). In the Barcelona Area (SIMMB4), a more densely populated territory, trips on working days are predominantly carried out by active modes (52%5) followed by private motorized vehicles (32%6), and public transport (16%) (IERMB, 2023[16]). The modal split also varies within territories. Figure 3.6 shows the modal split in the SIMMB. The use of private vehicles is the lowest within Barcelona, gradually increasing in each successive zone (see map on the right-hand side of Figure 3.6) further from the city. A similar trend is observed between the city and the wider territory of Tarragona. Among residents of the city of Tarragona - compared with the wider territory, Camp de Tarragona - in working day trips, the share of public transport is 3 percentage points higher (9% of total trips), the share of active modes is 6 percentage points higher (47% of total trips), and the share of private vehicles is 9 percentage points lower (45% of total trips) (ATM Tarragona, 2020[14]).
Note: SIMMB: integrated mobility system in the Barcelona area; RMB: Barcelona Metropolitan Area; AMB: Metropolitan Authority of Barcelona Region. Barcelona: Barcelona city.
Source: adapted from (ATM Barcelona, 2023[12]), Enquesta Mobilitat en Dia Feiner 2023 (EMEF 2023) - executive summary [Survey on Weekday Mobility 2023 (EMEF 2023)].
Modal split also varies by gender and by trip purpose. On average in the SIMMB, 36% of the trips by men are done with private vehicles, compared with 25% for women (ATM Barcelona, 2023[17]) (ATM Barcelona, 2022, p. 6[18]). Men walk less than women: 46% of men and 53% of women choose walking as their mode of transport (ATM Barcelona, 2023[17]). While most work-related trips (54% of total) are done with private vehicles, trips for study (17%) and personal motives (27%) show a lower reliance on private modes (ATM Barcelona, 2023[12]). In Camp de Tarragona and Terres de l’Ebre respectively, 60% and 65% of the trips by men are by private vehicles, compared with 49% and 55% by women. Private vehicles account for most work or study-related trips (61% and 69% of total), and approximately half (48% and 55%) of trips for other purposes (Generalitat de Catalunya, 2020[13]; ATM Tarragona, 2020[14]).
Travel patterns are correlated with various well-being dimensions, including safety, health and equity (Reardon and Abdallah, 2013[19]; ITF, 2024[20]). While road fatalities have decreased in Catalonia in recent decades, the latest available statistics indicate that every week 4 people die and 33 are seriously injured in road accidents (OMC, 2018[21]; 2022[22]). Car-centric travel patterns also affect citizens’ health via noise and air pollution, and reduced opportunities for physical activities.7 In Catalonia, noise exposure is estimated to be responsible for 91 premature deaths annually, with 47 of these occurring in the Barcelona metropolitan area (ISGlobal, 2024[23]). In 2019, 10.6% of the population was exposed to air quality levels higher than the annual limit value (Generalitat de Catalunya, 2024[24]).8 In Barcelona, air pollution is estimated to cause 1 500 premature deaths, 900 additional cases of childhood asthma, and 130 additional cases of lung cancer each year (Agència de Salut Pública, 2022[25]). The totality of Barcelonian schools are exposed to air pollution levels that exceed World Health Organization (WHO) guidelines, with 1 in 10 schools facing nitrogen dioxide (NO2) levels over legal limit values (Agència de Salut Pública, 2022[25]). The social cost of pollution-related mortality in Barcelona is estimated at EUR 1 041 million annually, with annual health costs related to childhood asthma and lung cancer estimated at EUR 3.5 million and EUR 2.1 million respectively (Agència de Salut Pública, 2022[25]).
Travel patterns can also contribute positively to well-being. For example, a study conducted in Barcelona shows that individuals commuting by bicycle four or more days per week experience lower stress levels than those who use motorised modes, both private and public transport (pedestrians were excluded from the study) (Avila-Palencia et al., 2017[26]). Better access to public transport could also increase employment opportunities for those – such as youth, women, and migrants – who do not own a private vehicle (Cebollada, 2008[27]; 2009[28]). Box 3.1 draws on international literature to compare the impact of car-centric and sustainable transport systems on well-being.
The effect of transport systems on well-being varies for car-centric and sustainable transport systems (Reardon and Abdallah, 2013[19]; ITF, 2024[20]). Car-centric systems are those in which most trips involve private cars, whereas in sustainable transport systems, active and shared modes (including public transport) account for most trips.
Average speed and vehicle size are correlated with both the likelihood of crashes and the severity of their consequences (Monfort and Mueller, 2020[29]; ITF, 2020[30]; VIAS Institute, 2023[31]; Tyndall, 2024[32]; WHO, 2023[33]). Sustainable transport systems reduce the incidence and severity of fatalities (ITF, 2022[34]), thanks to adapted infrastructure for active modes and a design which fosters lower speeds. Analysis of 12 cities in the USA revealed a strong correlation between road safety and the presence of protected and separated bike lanes, along with high intersection density1, often found in compact, lower-speed environments (Marshall and Ferenchak, 2019[35]). Although cycling is perceived as more dangerous than driving, empirical evidence shows that cities with a high share of trips by bike are safer for all road users (Marshall and Ferenchak, 2019[35]), suggesting a strong synergy between safe road design and bike-friendly road design. In the city of Pontevedra (Spain), a comprehensive street redesign and space reallocation programme between 1999 and 2015 (including a shift from 75-80% of public space being allocated to cars, to 75-80% of public space being allocated to pedestrians) contributed to a significant reduction in road fatalities and injuries. Since the intervention, the transformed areas have recorded zero road deaths, and the number of serious accidents has decreased from 69 people suffering permanent injuries in 1999 to only 4 cases in 2015 (Concello de Pontevedra, 2019[36]; Leániz, 2021[37]).
Traffic-related air pollution is linked to heart disease, lung cancer, and neurological conditions like dementia and depression (Boogaard et al., 2022[38]; Bakolis et al., 2020[39]; Miner et al., 2024[40]). Globally, about 246,000 deaths per year are attributed to traffic-related air pollution (caused by PM2.5 and O3)2 (Xiong et al., 2022[41]; Miner et al., 2024[40]). Motor vehicles contribute to air pollution mainly through engine emissions and material abrasion from tires, brakes, and road surfaces (Harrison et al., 2021[42]). Electric vehicles reduce engine emissions but may increase particulate matter (PM) from abrasion if they remain heavier than traditional vehicles (Soret, Guevara and Baldasano, 2014[43]; Harrison et al., 2021[42]).
Road transport also generates noise pollution, which is linked to stress, sleep disturbances, and various health issues, including heart disease (UK Department for Transport, 2019[44]). A meta-analysis of studies from 2011 to 2017 found that the risk of hypertension increased by 1.8% for every additional 10 decibels of road traffic noise exposure (Dzhambov and Dimitrova, 2018[45]). The WHO recommends that noise levels in classrooms stay below 35 A-weighted Decibel (dBA) for effective learning, and below 30 dB(A) in bedrooms for good sleep quality (WHO, 2010[46]). However, in the EU, 40% of the population is exposed to road traffic noise above 55 dBA, with 20% facing levels over 65 dBA during the day and more than 30% above 55 dBA at night (WHO, 2010[46]).
Car-centric systems also affect health via reduced opportunities for physical activity and social connections3. Insufficient physical activity contributes to 9,000 deaths a day according to the WHO (2024[47]; Douglas et al., 2011[48]). Although levels of physical activity are influenced by a person’s lifestyle and habits, how people commute can influence those levels. The design of transport systems also affects how people connect with their communities and each other. Infrastructure such as highways and bridges can contribute to social isolation by dividing communities (Mindell and Karlsen, 2012[49]), limiting access to services and opportunities, and restricting interaction, especially for those with limited mobility, such as children, the elderly, and people with disabilities (Alparone and Pacilli, 2012[50]; Huttenmoser, 1995[51]).
Car-centric systems can also exacerbate social inequality by limiting access to public transport (Kjellstrom and Hinde, 2006[52]; Rodgers and O’Neill, 2012[53]; Lutz, 2014[54]; King, Smart and Manville, 2019[55]) or by imposing a financial burden of owning and driving a private car or motorcycle to avoid dedicating several hours per day to commuting. Sustainable transport systems, which promote cycling infrastructure, public transport, and bike-sharing programmes, can enhance mobility for low-income households, reduce transport costs, and support transport equity (Henriksson, Wallsten and Ihlström, 2022[56]).
Notes: 1 Intersection density is a measure of connectivity of street networks. A high intersection density indicates distances are shorter and more conducive to taking trips by active modes. 2 PM2.5: fine particulate matter with a diameter of 2.5 micrometres or smaller, which can penetrate deep into the lungs and cause health problems. O3 (Ozone): at ground level, ozone is a major component of smog and is harmful to human health, causing respiratory issues. 3 Studies find that strong social ties are associated with better health outcomes and longevity (Holt-Lunstad, Smith and Layton, 2010[57]).
Triggering behavioural change towards active and shared travel modes can reduce emissions and simultaneously improve well-being across multiple dimensions (Box 3.1). Assuming that most people choose the mode that is most convenient to them, large-scale shifts towards active and shared modes would require making these modes the most convenient option.
This section explores the attractiveness of the various modes by analysing accessibility, travel time, access to public transport, and people’s perceptions of the reliability of the various modes. Findings suggest that private motorised vehicles (cars and motorcycles) are more attractive than active and shared modes in many areas. These insights are aligned with assessments of Catalan plans and strategies, and by stakeholders in interviews and workshop discussions.
3.2.1. Accessibility is higher by private car
A study by the International Transport Forum (ITF) (2019[58]) compared accessibility in 121 European cities based on access to other people9 and to a selection of services (hospitals, schools, recreation, food shops, restaurants, and green spaces). The study revealed that, on average, access to other people by private car is higher than via public transport and cycling within European Functional Urban Areas10 (FUAs in Figure 3.7), and that the access gap widens in the periphery (commuting zones in Figure 3.7). Although data are limited, a similar trend is observed for the city of Barcelona and its periphery (Figure 3.7) in both ITF (2019[58]) and AMB (2023[59]) data.11 ITF data also shows that access by active modes is higher than by private cars for short trips in Barcelona (not shown in Figure 3.7) (ITF, 2019[58]).
Average absolute accessibility in European Functional Urban Areas (FUAs) for car, public transport and cycling
Note: Absolute accessibility to other people (population) expresses the total number of inhabitants whose homes are accessible within 30 minutes by other inhabitants. As it shows the average for 121 cities, the top section of the figure (at a scale of 0-0.8 million inhabitants) hides variability across cities. On average, the ratio between the best and lowest performing FUAs is 1 to 12 in accessibility to population by car, 1 to 40 by public transport and 1 to 10 by bicycle (ITF, 2019[58]). The bottom section of the figure shows the outcome for Barcelona (at a scale of 0-1.6 million inhabitants). For Barcelona, outcomes for public transport data are unavailable due to lack of open data on public transport schedules. Com. zone: commuting zone.
Source: Top section of figure extracted from (ITF, 2019[58]), Benchmarking Accessibility in Cities: Measuring the Impact of Proximity and Transport Performance, https://doi.org/10.1787/4b1f722b-en. Bottom section of the Figure (Barcelona data) adapted from the same analysis.
3.2.2. Long travel times on public transport undermine its attractiveness
Travel times across modes also affect people’s transport choices. For example, if it takes two or three times longer to get from A to B by public transport than by car, public transport is deemed less attractive than cars. Based on data generated using Google Maps directions, Figure 3.8 compares travel times by car, bike, and public transport for 100 randomly selected 4 km trips in Tarragona. Such travel times can be used as a proxy for quantifying relative attractiveness in selected territories.12 The analysis reveals that the journeys by public transport are significantly slower than by car in Tarragona, suggesting an attractiveness gap in favor of the latter. Results also show that travel times by bike and car are similar. However, the low uptake of cycling – 2% of total trips on average at the regional level – (Generalitat de Catalunya, Forthcoming[11]) suggests that other considerations, such as safety, may be limiting cycling’s attractiveness.
Travel time distribution for 100 4km-trips to Tarragona by car, by bike, and by public transport
Note: The box plot compares distributions of travel times by car, bike, and public transport for a sample of 100 trips. When public transport services are unavailable, Google provides travel time by foot. Trip origins are randomly-selected buildings at 3.8–4.2km Euclidian distance from trip destinations. The Euclidian distance is the straight-line distance between two points in space. Trip destinations are randomly selected buildings within 200m of the city centre. Travel times are obtained from Google Maps Directions API and simulated for Wednesday 25 September 2024 at 8:30am. Travel times by car include expected time spent in traffic and an assumed parking search time of 10 minutes, based on an estimate made by the local administration. The plot excludes outliers and trips for which no route could be obtained (6 cases in Tarragona). The upper range of travel times by public transport is similar to walking time (not shown in Figure), which may indicate an absence of service.
Source: Authors, based on Google Maps Directions API. See Annex C for information on the methodology.
3.2.3. Reliability of public transport is perceived to be low
The attractiveness gap between private vehicles and sustainable modes is also reflected in people’s perceptions. A recent mobility survey in the Barcelona Metropolitan Area suggests that people perceive private vehicles and walking as the most reliable modes, and public transport as the least reliable (Figure 3.9). According to the survey, 89% of citizens express sufficient or high confidence in private vehicles, in contrast to 29%, 30%, and 35% stating sufficient or high confidence in tramway, rail, and bus transport respectively. The same survey indicates that satisfaction with public transport decreases progressively as distance increases from the city of Barcelona, while the level of satisfaction with car use increases (ATM Barcelona, 2022[18]). Among car users in Tarragona and Terres de l’Ebre13 (respectively), the two most cited reasons for using a private vehicle were the lack of adequate public transport (32% and 41% of car users), and the greater comfort of private cars (36% and 29% of car users) (ATM Tarragona, 2020[14]; Generalitat de Catalunya, 2020[13]). Among users of public transport (39% and 33% of respondents in Tarragona and Terres de l’Ebre respectively), reasons for choosing this mode included comfort, lack of car parking at destinations and affordability (Generalitat de Catalunya, 2020[13]; ATM Tarragona, 2020[14]).
Confidence in different modes of transport in the Barcelona-wide functional area (SIMMB)
Source: Adapted from (ATM Barcelona, 2022[18]), “Mobility survey in working days 2022 (EMEF) - key results”, https://omc.cat/documents/662112/1182871/EMEF+2022_Fullet%C3%B3.pdf/cfa7a5e9-9d7a-8311-ced4-f9f30e12cccd?t=1700034839910, accessed 16 April 2024.
3.2.4. Access to public transport is much better in Barcelona city centre than the periphery
Data analysis by the city of Barcelona and the Autonomous University of Barcelona (UAB) sheds light on the attractiveness of public transport and active modes, although data do not allow for a comparison of their relative attractiveness compared to other transport modes. In the Barcelona region, access to public transport is high in the urban core, and substantially lower in the periphery (Figure 3.10). Figure 3.11 visualizes how walkability and urban vitality vary throughout the city of Barcelona, indicating that some neighbourhoods are more pedestrian-friendly than others.
Note: Map of the Barcelona Metropolitan Area showing levels of access to public transport, based on an index that reflects the proximity and number of public transport stops that can be reached by foot. Areas coloured green have high access to the public transport network (public transport is within walking distance for most people), while areas coloured red have low access (public transport is not within walking distance for most people). Areas coloured dark red have no access at all, while grey indicates there is no available data. The map shows that access to public transport is substantially lower in the periphery than in the urban core. Note the difference between “access to” and “accessibility by” public transport. The indicator used to measure access to public transport shows the availability of public transport in different parts of the territory. The indicator does not show accessibility by public transport (e.g. the number of jobs and services within reach by public transport).
Source: Extracted from (AMB, 2023[59]), Diagnosi - Pla Director Urbanístic Metropolità, [Diagnosis - Metropolitan Urban Planning Master Plan] https://smartcity.amb.cat/portal-pdu/diagnosi, accessed 4 June 2024.
Left panel: Barcelona walkability index. Right panel Barcelona urban vitality index
Note: For both indicators, red indicates a high score, and blue indicates a low score. Details of the methodology for the Walkability Index are in (Frank et al., 2009[60]); methodology for the Urban Vitality Index in (Delclòs-Alió and Miralles-Guasch, 2021[61]).
Source: Extracted from (GEMOTT and UAB, 2021[62]), “Densidades urbanas para la caminabilidad en Barcelona”, [Urban Densities for Walkability in Barcelona] https://www.movactiva.es/barcelona/barcelona-%C2%B7-caminabilidad/ (Walkability Index); and (GEMOTT and UAB, 2021[63]), “Vitalidad urbana en Barcelona”, [Urban vitality in Barcelona] https://www.movactiva.es/barcelona/barcelona-%C2%B7-vitalidad-urbana/ (Urban Vitality Index).
Travel patterns are influenced by the relative attractiveness of transport modes, and this attractiveness is intrinsically linked to the structure of the transport system. Using causal loop diagrams (Box 3.2), this section identifies three dynamics underlying – and perpetuating – car-centric travel patterns in the territory.14 These dynamics include induced car demand, urban sprawl, and the spatial inequality of sustainable modes; and are not unique to Catalonia (OECD, 2021[64]; Pokharel, Miller and Chapple, 2023[65]). The sub-sections below explore each of these dynamics in turn.
3.3.1. Policies to decrease road congestion are inducing car demand
Induced car demand refers to the increase in traffic and congestion as a result of public policies expanding roads with the aim to reduce traffic and congestion (Mattioli et al., 2020[66]). Figure 3.12 illustrates the dynamic of induced car demand, which has been well documented in the transport literature since the 1990s (Annex D). Rising congestion (1 in Figure 3.12) increases the (public) pressure to reduce congestion (2). The response to this pressure has been, in the last few decades, to increase public investment in roads (3), to increase road capacity (4) and reduce congestion levels and travel time (1). Reduced congestion, however, increases the attractiveness of driving (5), leading to an increase in traffic volume, car use, and ownership (Hansen and Huang, 1997[67]; Goodwin, 1996[68]; Noland, 2001[69]) (Næss, Mogridge and Sandberg, 2001[70]; Yang et al., 2017[71]). As car use and traffic volume increase (6), so too does congestion (1), leading to the opposite result initially intended by the policy.
Note: The coloured arrows show the relationship between variables. A pink arrow between variables means that they vary in the same direction: an increase in a variable leads to an increase in the variable it points to; a decrease in a variable leads to a decrease in the variable it points to. A blue arrow means that the variables vary in the opposite direction: an increase in a variable leads to a decrease in the variable it points to; a decrease in a variable leads to an increase in the variable it points tp. Each loop label (e.g. B1) denotes a feedback loop. A feedback loop is either reinforcing (R) or balancing (B). See Box 3.2 for more information on how to read causal loop diagrams.
Source: Authors, adapted from (Sterman, 2000[72]), Business Dynamics: System Thinking and Modeling for a Complex World, https://www.researchgate.net/publication/44827001_Business_Dynamics_System_Thinking_and_Modeling_for_a_Complex_World.
Causal loop diagrams (CLDs) help to illustrate the system structure underlying travel patterns (e.g. see Figure 3.12). A CLD presents the causal relationships in a system and includes the following:
Elements or variables in the system, represented by text. These elements can be flows (e.g. public investment in roads for cars) or stocks (e.g. road capacity for cars). Stocks change over time due to flows (inflows and outflows). They are the “system memory”.
Causal relationships between the variables, represented by arrows. Variables can vary in the same or in the opposite direction. In this chapter, a pink arrow indicates a causal relationship in which variables vary in the same direction, e.g. as the attractiveness of driving cars increases/decreases, so does the number of people that choose to drive cars. A blue arrow represents a causal relationship in which variables vary in the opposite direction: as congestion increases, the attractiveness of driving a car decreases.
Delays, represented by two lines crossing the arrows. A delay indicates that it will take time for changes in one variable to cause changes in the other.
Feedback loop labels, indicating whether a loop is reinforcing (R) or balancing (B).
Feedback loops are non-linear causal relationships. In linear causal relationships, a variable (the cause) affects a second variable (the effect), and the causal chain stops there. In non-linear causal relationships, often referred to as feedback loops, a variable affects a second variable, which in turn affects the first variable again: the variables feed into each other, leading to circular – rather than linear – causal chains (Meadows, 2008[73]).
Feedback loops can be reinforcing or balancing. In “reinforcing” feedback loops, the effect of the first variable alters the second, which feeds back into the first variable again in the same direction (e.g. more eggs = more chickens = more eggs). In “balancing” feedback loops, variables affect each other in opposite directions (e.g. more foxes = fewer rabbits = fewer foxes). Reinforcing feedback loops lead to acceleration: when observed over time, systems dominated by reinforcing feedback loops produce exponential curves (positive or negative).1 Systems dominated by balancing feedback loops seek an equilibrium – that may be above or below the current state of the system. More complex behaviours, e.g. oscillations, s-shaped growth and overshoots, are produced by the interactions between several feedback loops and time delays.
Note: 1 Systems dominated by positive feedback cannot last over long periods of time (i.e. they are unsustainable), as all systems are embedded within an environment which will, at a certain point, place limits on exponential growth (Systems Innovation, 2021[74]).
Source: Adapted (and partly extracted) from (OECD, 2022[75]), Redesigning Ireland’s Transport for Net Zero: Towards Systems that Work for People and the Planet, https://doi.org/10.1787/b798a4c1-en.
The approach of road expansion over the second half of the 20th century, in Catalonia and across the OECD, has led to a physical lock-in of infrastructure that favours private motorized vehicles. The current stock of infrastructure in the region suggests higher accumulated investments in road infrastructure than in infrastructure dedicated to sustainable modes (Figure 3.13).15 Although road infrastructure can also be used by buses, the current stock of passenger vehicles in Catalonia (Figure 3.14) suggests that the infrastructure is currently primarily used by private vehicles and average distance travelled and modal split in Catalonia show an increase in car traffic volume between 1981 and 2006 (latest data available at the regional level) (ATM Tarragona, 2006[76]; Generalitat de Catalunya, 2006[10]).
Total length of infrastructure network and of dedicated lanes
Note: Data on railways (2022) refer to both urban and interurban connections, while data on roads (2022), bike lanes (2024) and BUS-VAO lanes (2024) refer to interurban connections only, as total length of urban streets, urban bike lanes and urban dedicated bus lanes is currently unknown. BUS-VAO refer to dedicated lanes shared by buses, private vehicles of at least two occupants, motorcycles, vehicles of people of reduced mobility, and zero-emission vehicles. BUS-VAO lanes are expressed in lane length and cover a portion of the interurban road network, which is expressed in network-length.
Source: Authors, based on (Observatorio del Transporte y la Logistica, 2023[77]), “Length (kilometres of road) of the road network per territory and road type”, https://apps.fomento.gob.es/BDOTLE/visorBDpop.aspx?i=382, accessed 4 June 2024 (for roads and railways) and internal data from Department of Territory for bike and bus lanes.
Note: Total number of passenger cars, motorbikes, vans, and buses in Catalonia in 2022. The percentages above the bars reflect shares of total number of vehicles. The figure does not include freight transport vehicles.
Source: (Generalitat de Catalunya, 2022[78]), Vehicle fleet per type of vehicle, 2022, https://www.idescat.cat/pub/?id=parcc&n=291&geo=cat.
3.3.2. Car-centric policies contribute to urban sprawl
Urban sprawl refers to the phenomenon of people moving away from urban cores into low-density suburban areas characterised by low concentrations of jobs and services and “discontinuous, strongly scattered and decentralized” development (OECD, 2018[79]). When territories sprawl, this increases the catchment area, i.e. the area used by people on a daily basis to meet their needs (Cambridge Dictionary, n.d.[80]).
The increase in the catchment area is positively correlated with the increase in road capacity for cars. As road capacity for cars expands (1 in Figure 3.15), the number of places accessible by car within a reasonable time budget increases. Simultaneously, opportunities to create proximity decrease, as space is allocated to motorised vehicles at the expense of other uses (Cervero, 2003[81]; Handy, 2005[82]; Newman and Kenworthy, 1998[83]). The housing price differential between the urban core and the suburbs (not shown in the Figure to avoid visual clattering) also contributes to the enlargement of the catchment area. In the Barcelona region, for example, the commuting distance for low-income households increases as they move to the periphery looking for affordable housing (Garcia-Sierra and van den Bergh, 2014[84]).
When the catchment area increases (2 in Figure 3.15), so do average travel distances and traffic volume (3 in Figure 3.15). This is widely reported in the literature (Bhat and Guo, 2007[85]; Ding et al., 2018[86]; Newman, Kosonen and Kenworthy, 2016[87]; Van Acker and Witlox, 2010[88]; Hamidi and Ewing, 2014[89]; Zhao, 2010[90]; Gössling et al., 2016[91]). Figure 3.16 shows the average increase in distances travelled in Catalonia between 1981 and 2001 (latest data available at the regional level). The 2006 Mobility Guidelines (DNM) refer to a “constant increase […] in the last decade” (1996-2006), with a “very notable increase” in the use of private cars (Generalitat de Catalunya, 2006[10]).
Note: The coloured arrows show the relationship between variables. A pink arrow between variables means that they vary in the same direction: an increase in a variable leads to an increase in the variable it points to; a decrease in a variable leads to a decrease in the variable it points to. A blue arrow means that the variables vary in the opposite direction: an increase in a variable leads to a decrease in the variable it points to; a decrease in a variable leads to an increase in the variable it points to. Each loop label (e.g. B1) denotes a feedback loop. A feedback loop is either reinforcing (R) or balancing (B). See Box 3.2 for more information on how to read causal loop diagrams.
Source: Authors, adapted from (Sterman, 2000[72]), Business Dynamics: System Thinking and Modeling for a Complex World, https://www.researchgate.net/publication/44827001_Business_Dynamics_System_Thinking_and_Modeling_for_a_Complex_World.
Average distances travelled for commuting to work or study in Catalonia
Note: RMB: Barcelona Metropolitan Area.
Source: Authors, adapted from (OMC, 2006[92]), Enquestes de Mobilitat Quotidiana (EMQ) [Daily Mobility Surveys], https://omc.cat/ca/w/enquestes-de-mobilitat-quotidiana-emq-.
Regional plans and strategies identify urban sprawl as a cause of increased travel demand. For example, the Mobility Guidelines describe the phenomenon of urban sprawl as linked to changes in housing preferences (Table 3.1) and a process of decentralisation of productive activities (Generalitat de Catalunya, 2006[10]). The Catalonia Passenger Transport Plan (PTVC) states that “The increase in interurban travel is a consequence of the dispersion of the population and the segregation between the place of residence and the characteristic place of work in the last decades. This increase in the interrelationship with other municipalities inevitably leads to a significant increase in travel distances, i.e. longer distances are travelled to satisfy the needs that years ago required shorter journeys.” (Generalitat de Catalunya, 2020[93]).
Source: Authors, based on (Generalitat de Catalunya, 2006[10]), Directrius nacionals de mobilitat de Catalunya, [National Mobility Guidelines of Catalonia] https://participa.gencat.cat/uploads/decidim/attachment/file/628/directrius_nacionals_mobilitat_tcm32-36328.pdf (accessed on 6 June 2023).
As sprawl increases, municipal “self-containment” decreases. Municipal self-containment refers to the number of trips by citizens to destinations inside the municipality’s administrative boundaries, divided by the total number of trips. Municipal self-containment in Catalonia fell between 1981 and 2001 (Generalitat de Catalunya, 2006[10]). In 2006, and depending on the territory, self-containment ranged between 66-77% on working days and 55-70% on weekends and holidays, based on the latest region-wide data available (OMC, 2006[92]).
3.3.3. There is inequality in the space allocated to sustainable modes
The dynamics of induced car demand and urban sprawl are both intrinsically linked to space allocation and have resulted in a lack of space for sustainable transport modes, further increasing the attractiveness of private car use. Over the second half of the 20th century, public space has been allocated to expanding road capacity for cars (1 in Figure 3.17), to the detriment of public space allocated to space-efficient (Box 3.3) and sustainable transport modes (2), and widening the attractiveness gap between modes in favour of private motorised vehicles. Public space allocation influences the speed, comfort, and safety of each transport mode, thus affecting people’s choices (ITF, 2024[20]). The increase in the catchment area described above also widens the attractiveness gap between cars and sustainable transport for at least two reasons. First, active modes perform best over short distances, and their attractiveness decreases as distances travelled increase. Second, public transport performs best in high-density areas, and its attractiveness decreases as density decreases (e.g. lower frequencies, fewer places conveniently accessible by bus or train). As the attractiveness of sustainable modes (3 in Figure 3.17) declines, the relative attractiveness of driving (4) increases, encouraging people to choose private motorised vehicles over sustainable transport.
Spatial inequality of sustainable modes and the widening of the attractiveness gap
Note: The coloured arrows show the relationship between variables. A pink arrow between variables means that they vary in the same direction: an increase in a variable lead to an increase in the variable it points to; a decrease in a variable leads to a decrease in the variable it points to. A blue arrow means that the variables vary in the opposite direction: an increase in a variable lead to a decrease in the variable it points to; a decrease in a variable leads to an increase in the variable it points to. Each loop label (e.g. B1) denotes a feedback loop. A feedback loop is either reinforcing (R) or balancing (B). See Box 3.2 for more information on how to read causal loop diagrams.
Source: Authors, adapted from (Sterman, 2000[72]), Business Dynamics: System Thinking and Modeling for a Complex World, https://www.researchgate.net/publication/44827001_Business_Dynamics_System_Thinking_and_Modeling_for_a_Complex_World.
Space consumption varies significantly across modes. Figure 3.18 compares the space1 needed for a person to travel a 6-km round trip with a 2-hour stop in an urban area by walking, cycling, driving a motorcycle, driving a private vehicle, or using a bus, and including 2 hours of parking. It shows that car drivers consume 5 to 7 times more space than cyclists, and 60 to 110 times more space than bus users (ITF, 2022[94]). From a planner’s perspective, in urban areas where modes compete for limited space, there are opportunities to narrow the attractiveness gap between modes by making a more efficient use of public space (see Chapter 5).
Static and dynamic space consumption required for a short urban trip, by transport mode
Note: The diagram expresses space consumption in space multiplied by time duration and combines static (parking) and dynamic (in traffic) space consumption. Relative outcomes vary with vehicle speeds, assumed vehicle size, trip length and parking time. Faster vehicles consume more dynamic space per hour when in traffic. While in traffic, motorcycles are assumed to consume equal space as cars.
Source: Extracted from (ITF, 2022[94]), “Streets that fit: re-allocating space for better cities”, https://doi.org/10.1787/5593d3e2-en.
Note: 1 Space consumption is expressed in surface area consumed, multiplied by the duration.
While data on space allocated per mode or function is unavailable at the regional level, data for the city of Barcelona shows that 50% of public space is allocated to sidewalks and shared space (space shared between pedestrians and other road users), 43% to lanes for traffic and parking space, 3.8% to bus and tram lanes, and 2.6% to bike lanes (Figure 3.19).16
Public space allocation in Barcelona, 2023
Source: Adapted from City of Barcelona (2024[95]), Urban Mobility Plan Barcelona - Public Consultation, https://www.barcelona.cat/mobilitat/sites/default/files/2024-06/240603_PMU2030_S10_EspaiP%C3%BAblic_Quotidiana_Seguretat.pdf.
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← 1. The motorization rate of a territory is the number of cars per 1000 inhabitants.
← 2. A travel survey from 2006 provides data on interurban trips on working days. While the total number of trips are not comparable across the data sources, the share of motorised private vehicles in these trips - 73 % in 2006 - suggests that the growing trend towards private vehicles continued between 2001 and 2006 (OMC, 2006[92]).
← 3. Walking accounts for 40% of trips (most of active trips).
← 4. SIMMB is the integrated mobility system in the Barcelona area. This is the area surrounding Barcelona city for which integrated ticketing – managed by the Barcelona ATM – exists. It is a larger area than Metropolitan Authority of Barcelona Region (AMB) and Barcelona Metropolitan Area (RMB), see map in Figure 3.6.
← 5. Walking accounts for 49% of total trips, cycling and other micro-mobility account for 3% of total trips.
← 6. Cars account for 27% of trips, motorcycles for 4%, and trucks and vans for 1% (ATM Barcelona, 2023[12]).
← 7. Health impacts are included in the evaluation of transport projects via the SAIT. The SAIT is an evaluation tool developed by the Department of Territory to assess the socio-economic and environmental value of transport projects ex-ante and ex-post. The use of the tool is mandatory for projects costing above EUR 10 million (Generalitat de Catalunya, 2021[97]).
← 8. In 2019, 4.8% of the population was exposed to NO2 levels exceeding the annual limit of 40 µg/m³. Additionally, 5.8% of the population breathed air with NO2 levels above 36 µg/m³. Road transport contributes to 73% of total NO2 emissions (Generalitat de Catalunya, 2024[24]).
← 9. “Access to people” refers to the number of inhabitants whose homes are accessible within 30 minutes by other inhabitants.
← 10. The study by ITF defines the Functional Urban Area as “the entire urban continuum that includes the city and the commuting zone, as per the EU-OECD definition.” (ITF, 2019[58]) It defines the commuting zone as “The local administrative units surrounding a city that have at least 15% of their employed residents commuting to the city.” (ITF, 2019[58]).
← 11. AMB analysis shows that, in the Barcelona region, access to public transport is high in the urban core, and substantially lower in the periphery (AMB, 2023[59]).
← 12. While safety, comfort, and affordability are also important factors contributing to the relative attractiveness of modes, these factors could not be assessed due to data limitations.
← 13. In both the survey in Terres de l’Ebre (Generalitat de Catalunya, 2020[13]) and the first survey in Camp de Tarragona (ATM Tarragona, 2020[14]), 96% of respondents were car users.
← 14. Car-centric travel patterns refer to the situation in which private cars are used for the bulk of trips.
← 15. Because stocks take time to change, long investment time series, e.g. dating back to the 1960s, would be necessary to visualise the correlation between investments (flow) and road capacity (stock). Data on public investment in roads in the region are not available (harmonized) prior to 2015.
← 16. A study by Rhoads (2021[96]) also shows that Barcelona has a high percentage of pedestrian space compared to other global cities, particularly those outside Europe, likely due to the prevalence of large historical centres with shared street space.