Measuring and visualising 15-min-areas for fair CO₂ budget distribution

National undertakings throughout Europe aim at reducing the amount of CO₂ emissions caused by traffic in order to fight the climate crisis [1]. Usually, they concentrate on efficiency (e.g. improving vehicle technology or traffic flow) and consistency (e.g. changing from conventional to electric cars). However, these mostly technological changes will not suffice to reach climate neutrality across Europe in 2050, see [2] for the Austrian example or the German transport ministry stating that behaviour change will be necessary [3]. These observations show that efficiency and consistency strategies are significantly limited in their effectiveness, as they trigger rebound effects in the form of behavioural responses that work against the intended impact [4]. Savings in fuel consumption due to higher efficiency and in travel time due to faster connections for example have shown to be “reinvested” in more travelling or larger vehicles and longer average distances, compensating the intended effect of reducing transport emissions [5‐7]. The call for “behaviour change” is therefore driven, in part, by the aim of avoiding unintended or undesirable behavioural reactions and, in other part, by the realisation that measures to merely reduce emissions from car traffic will not be sufficient and that a substantial shift to public and active forms of transport as well as a trend reversal regarding travel distances must be achieved. This approach represents a “sufficiency” strategy, which focusses on the responsible use of resources within a given framework in accordance with the principle of “as much as necessary, as little as possible”. In this perspective, “enough” is regarded as the guiding principle, whereas “more” (than necessary) is considered wasteful or even harmful [8].

Measures for achieving the intended behaviour change are extensively researched and can be basically divided into three categories based on the Social Practices Theory (SPT) [9, 10] stating that executing a behaviour (change) needs three mutually complementing elements: material, competences, and meaning. In the mobility context, this can be applied to barriers which may be hindering behaviour change [11]: (1) Measures to overcome barriers related to the “access” to transport options (e.g., due to reduced financial means, local unavailability of specific transport options or information, or health restrictions preventing a person from using a form of transport), (2) measures reducing complexity of services or increasing competences of persons to overcome limitations in the “ability” to use a form of transport (e.g., usability improvements, trainings), and (3) measures aiming at increasing the “ambition” to change mobility habits (e.g., by positively or negatively incentivising behaviour change by lowering or increasing costs, or by increasing motivation through awareness-raising, nudging or appeals to certain values of life). There is a vast amount of work existing on the topic of behaviour change in the mobility context exploring the effectiveness of different measures as well as potential pitfalls related to specific approaches. Financial incentives (positive and negative), for example, can have significant impact on behaviour patterns, but also bear the risk of aggravating or creating social injustice for specific groups.

The European joint undertaking “MyFairShare” therefore aims to apply the sufficiency principle in the form of fair personal mobility carbon budgets which make national carbon reduction targets comprehensible and relatable in the everyday context of individuals by scaling them down to the individual space of action. This would enable people to make responsible, informed decisions, while at the same time allowing the identification of concrete problems in the fulfilment of basic needs due to mobility deficiencies. As research indicates [12, 13] that people are rather willing to accept restrictions and disadvantages if these can be perceived as being fair, the individual CO₂ mobility budgets developed in the project are distributed in a fair way considering socio-regional circumstances. To achieve this task, so-called minimum mobility budgets [14] are introduced to ensure a basic right to mobility: A fair minimum CO₂ budget shall allow an individual to perform all necessary activities, ideally at locations in the vicinity of the individual’s residence, and if no nearby locations exist by providing an appropriate supply of preferably sustainable mobility options [15]. The advantage of the minimum mobility budgets approach is that it is multi-modal and has minimal CO₂ emissions at its core making it a tool that is directly applicable for planning interventions to decarbonize the mobility system. The integrated view at the distribution of population and of visited places has a strong correlation to the so-called 15-min cities [16], a promising approach to reduce the amount of CO₂ emissions in traffic. The concept of minimum mobility budgets also extends the set of applications for accessibility measures, which so far mostly have in view the travel times to locations of single activities with different modes of transport [17, 18].

In the following, an approach for determining minimum mobility budgets is presented. While an ideal city of 15 min allows access to all facilities by walking, current cities are far from meeting this target. Hence, the approach looks at the accessibility of different types of facilities using the respectively least CO₂ intensive mode. It is a first step in developing fair mobility budgets that shall reveal how much CO₂ would be emitted by individuals, even if they try to use the most sustainable mode of transport when trying to get to their destinations within a time span of 15 min. This approach also benchmarks areas regarding their applicability for being called areas of 15 min. The term “15 min areas” is chosen herein, as the benchmark disaggregates a city into a grid on the spatial level. In subsequent steps of the MyFairShare project, depicted in Fig. 1, this information is used to derive fair mobility budgets, which are afterwards evaluated in the project’s Living Labs. Finally, recommendations and guidelines for introducing mobility budgets will be derived.

Other approaches to map 15 min cities are usually based on walkability and accessibility of facilities by foot [19, 20], including scientific on-line examples like the “CityAccessMap”¹ [21]. Another web-based solution is the “PTV Access”² index developed by the German company PTV which extends the regarded modes to the common ones – walking, using a bicycle, public transport, and motorised individual transport. Going beyond determining the conformity of areas with the concept of cities of 15 min, the approach presented herein computes the amount of CO₂ emissions inevitable for accessing the activity places and additionally weights these emissions by the respective frequencies of visiting them. For classifying activity places and determining their visit frequencies, the activity types from the major Austrian mobility survey “Österreich unterwegs” [22], which distinguishes between trips to work, education, leisure, shopping, and errands, are used. [22] considers further activity types, namely business-related trips, accompanying other persons, visiting persons, and other purposes. They were neglected as the respective destinations can be hardly determined—they may include all existing destinations. This activity classification matches the one used in the largest German mobility survey “Mobilität in Deutschland” [23].

The approach relies completely on open data and open source applications and includes the computation of the needed metrics as well as their web-based visualization. Within the MyFairShare project itself, this approach has been applied to the areas of the project’s Living Labs, namely the cities of Berlin (Germany), Jelgava (Latvia), London (UK), Sarpsborg (Norway), and Vienna (Austria). The cities as well as the regions they are located within are very different in their nature regarding population density and distribution as well as land use. Thereby, the computation of the described metrics for each of them delivers heterogeneous results which broaden the view on the necessary budgets.

The remainder of the paper is structured as follows. First, the methods for computing minimum budgets and the data needed for this purpose are given in Sect. 2. Second, the results obtained from applying the method to the Living Labs of the MyFairShare project are presented in Sect. 3. A discussion of the results is given Sect. 4. The paper ends with conclusions presented in Sect. 5.

The presented results do not resemble the current behaviour of the population, but rather the attempt to quantify a minimal CO₂ budget needed if everyone would use the most sustainable mode of transport, yet assuming a reasonable travel time of 15 min. Besides forcing a certain mode choice behaviour, the model also takes harsh influence on the location choice, see Table 1. These choice constraints may be realisable for work place locations—e.g. by reinforcing work from home or co-working spaces—and partially already match the real world as, e.g., in Germany children visiting an elementary school are assigned to one of the three nearest to their home. Yet, one should expect that limiting the access to a certain number of shopping or leisure activities would not be accepted by a large part of the population. However, in the debate regarding transport justice in low carbon energy transitions, critics of the political philosophy of liberal egalitarianism and its distrust of constraining individualism argue that “liberal choice” assumes that people can decide as if in a vacuum and independent of context, which is considered an implausible idea of how people act, as they exist in relation to the world, the society, and the rules and social norms within this context. The context in which people make their choices is therefore never unrestricted and prone to many influences, for example a societal goal to reduce inequalities [31]. In this context, Pereira et al. [32] advocate that transport policies should be evaluated by setting minimum standards of accessibility to key destinations in order to analyse the distributional impact of transport policies and to assess the extent to which these policies respect individual rights and favour disadvantaged groups, reduce inequality of opportunity and mitigate the externalities of transport. The approach developed in MyFairShare sets an important first step towards the definition of such a minimum standard.

The results can be used by policy makers to estimate the amount of inevitable CO₂ emissions, given the current land-use, population distribution, and mobility offers. At this level, the differences between the supply with activity places between urban and rural areas can be clearly seen. Disaggregated by activity types, the results reveal which kinds of facilities should be implemented to improve local accessibility and thereby to reduce necessary trips.

As to methodology, one should note some shortcomings of our approach that could be improved in the future. The first to name is the choice of the 1 km² grid as a starting point. This coarse resolution aggregates areas that may be heterogeneous internally, e.g. because the area is divided by water or because the population is not distributed uniformly. In addition, the grid size of one kilometre matches a walking time for 1 km, assuming a speed of 3.6 km/h, which in fact yields in looking into the origin cell’s facilities when computing accessibilities for walking, which may overestimate their accessibility by the cell’s population.

Currently, no distributions of socio-demographics on the spatial level were considered. One should assume that to some degree persons are choosing their home location considering their mobility needs and options. Yet, information about socio-demographics is usually available on an even higher spatial aggregation level only, and breaking it down would require models what we wanted to avoid. Accordingly, one could assume different speeds for walking and using a bicycle for different person groups. Again, this was not included in our approach due to the lack of according information about, e.g., age distribution at the spatial level we have chosen. For specific questions, other person groups than the ones used herein could be investigated. One though has to remind that the information about the groups’ individual’s behaviour obtained from the Austrian national survey may lose its statistical relevance when increasingly disaggregating the population due to a too low number of surveyed persons that belong to a population’s subgroup. One possibility to obtain a higher sample would be to derive a synthetic population and compute its behaviour using e.g. agent-based demand model. Yet, again, one would have to involve additional models and rely on their outputs in this case, instead of relying on data only.

Up to now, only the Austria mobility patterns as derived from [22] were used. Where available—e.g. “Mobilität in Deutschland” for Germany—other national surveys could be used for computing the frequencies of visiting facilities of different kinds in accordance to the behaviour of the population of the investigated country. In addition, emission factors for the different modes of transport were also taken for Austria¹⁵. Due to differences in fleets and electricity production, the emission factors should also be chosen per country.

The use of European open data and open source applications allows for applying the benchmark to almost all areas in Europe, as long GTFS data about public transport is given. Yet, when looking at the results, one could argue that public transport does not compete with using a bicycle on short trips, see Fig. 4. Of course, the named metrics can be as well computed for other parts of the world as long as the needed data is available.

It has to be mentioned that the used data has different shortcomings. The population grid is based on Census data from the year 2011, so it can be assumed that the population distribution is different today. Even more critical is the lack of information about work places so that the presented computation has to rely on proxies. As well, better information on leisure activities and their spatial distribution would be needed.

Despite all these shortcomings and simplifications, we think that the attempt presented herein is a valid approach for budgeting CO₂ at the level of single individuals and for benchmarking how well areas comply with the city of 15 min idea.

A solution for determining minimum CO₂ budgets needed to access all weekly activity places using most sustainable transport modes while assuring reasonable travel times was presented. In addition, the results can be used for benchmarking areas for being compliant with the concept of a city of 15 min. For this purpose, the locations within the respective areas were determined from the freely available OpenStreetMap database, first. This data was then merged with the information about the population within these areas into a grid with cells of a size of 1 km², conforming to the INSPIRE standard. Finally, accessibility measures between the cells’ centroids were computed for determining which parts of the respective Living Lab may be called “15 min areas”—areas where all necessary activities can be accessed using active modes within 15 min.

The described approach relies completely on open source software and freely available data. It is, to a wide degree, applicable to the complete area of the European Union. Only the needed GTFS data is not available for all European regions. The information included in the OpenStreetMap database is sufficient for many types of activity, yet a better source of information on the distribution of working places would improve the quality of the benchmark. Though, no open data sets covering this topic seem to exist for Europe in a sufficient resolution.

Fair mobility budgets will be derived within the next project steps based on the computed measures. In addition, local tests, surveys, and workshops will be performed to determine the users’ acceptance of the mobility budgets. The methodology presented herein will be refined to work on higher spatial resolutions and will be applied to benchmark areas in order to comply with the idea of the city of 15 min.