1. Introduction
A global increase in the population living in cities has taken place, leading to projections such as 70% living in major urban centers by 2050 [
1]. This is a process linked to infrastructures and lifestyles, and that entails externalities for the environment and society with implications for the health of its citizens [
2,
3,
4,
5,
6]. The urban environment is linked to sustainable development, health and well-being, being considered one of the greatest challenges for environmental health [
7,
8,
9]. Mobility, a complex urban system with multiple interactions, is gaining relevance within the urbanization process and plays a critical role in the daily life of cities [
10,
11,
12]. The modernization of urban mobility plays a key role in the healthiness and sustainability of cities [
11,
13,
14], prompting cities of all sizes to have sustainable mobility goals on their agenda [
15]. Any reference to sustainability must bear in mind the Sustainable Development Goals (SDG), through which the United Nations established 17 global goals designed as a plan to achieve a better and more sustainable future for all [
16]. Sustainability is one of the three key elements—together with safety and intelligence—of a modern mobility system [
12], meaning that sustainable mobility is linked to different SDGs (see
Table 1). This is clearly reflected in SDG11, which aims to achieve the development of sustainable, inclusive, safe and resilient smart cities, ensuring socio-economic growth and high living standards, and paying attention to the benefits generated for society, the environment and mobility [
17,
18].
The greater the concern for the SDGs, the greater the attention should be paid to the events that take place in cities [
8]. Such is the relevance of urban transformation for sustainability that many of the SDGs find in the city the necessary scale for its achievement [
19], with cities being considered the drivers of sustainable development [
17]. For this reason, cities around the world demonstrate the need to transform their urban mobility systems into more sustainable ones [
20]. While the traditional approach to the smart city focuses on the provision of services through technologies, a later approach aligned with the SDGs is not only committed to developing a smart city that helps to define and solve social challenges, but also to increasing the well-being of citizens and meet their needs through a flexible and efficient conception that incorporates the human capital variable [
5,
6,
21,
22]. In fact, the smart city offers citizens a good environment to live in and increases their quality of life. From the SDG perspective, the smart city emphasizes the importance of providing citizens with health and well-being, as cities cannot be truly smart without being sustainable [
6,
21].
To explain the processes and dynamics manifested in cities, as well as the interactions between cities and their context, urban transformation recognizes radical innovations as determinants in achieving social and environmental improvements [
2,
19,
23,
24]. In this sense, the innovations in mobility become a key point, helping to solve many of the obstacles presented by urbanization, and leading to an improvement in the quality of life of inhabitants, favoring a sustainable future [
2,
21,
25,
26]. Since the relevance of the implementation of mobility innovations is evident for cities to face mobility challenges, this paper answers the following research question: are there combinations of conditions that explain the readiness of cities for the implementation of innovations in mobility? Therefore, the purpose of this paper is to identify how the conditions that determine a city’s readiness to implement urban mobility innovations could be combined.
A city’s smartness is measured based on the factors that determine its level of readiness for transformation and change [
2,
27,
28]. Therefore, the components that contribute to the creation of smart cities should be carefully analyzed to identify the areas in which work should be undertaken [
2]. Little attention has been paid to assessing the necessary level of readiness for smart city transformation [
27,
28], and few studies have explained the factors that determine its implementation [
2,
28,
29]. Thus, considering the readiness of smart city is an area of study that has rarely been addressed [
28]. We therefore identify as the first research gap of this paper the possibility of identifying the combinations of factors that determine the level of readiness of cities to implement sustainable mobility innovations. In this field, several frameworks have been used to determine the readiness of smart cities [
30].
The smart city is the result of a transformation process followed by many cities around the world aiming to increase their efficiency, facilitate the participation of their citizens, and reduce the environmental impact of their activities [
1]. Although it is an ambitious concept, and many countries prioritize smart cities as a way to strengthen their sustainability strategies [
25], it is difficult to identify shared definitions and guidelines across countries with different contexts because the development of smart city projects is tailored to each city [
4,
6,
21]. This is because local governments attribute different meanings to urban innovations and formulate ad hoc place-based smart city strategies related to their contexts, seeking to solve their main problems [
6,
21]. In this sense, smart mobility has not been established in the same way for cities around the world [
26]. Therefore, the second research gap is the investigation of the conditions that determine the implementation of sustainable mobility innovations to different urban contexts [
21]. This responds to the need to link perspectives and overcome limitations by linking place-based learning, thus enabling city-to-city learning [
19]. It also responds to the need to broaden the focus of research on city transitions to sustainability by developing work beyond Europe [
23].
Mass urbanization and resource scarcity are global phenomena for which urban transformation towards a smart city is the most viable solution [
31]. Sustainable innovation is the result of the combined effect of different elements [
32]. In this sense, the building blocks of socio-technical transitions in urban mobility rest on windows of opportunity created by the convergence of factors [
33]. However, smart city research has employed methodologies unable to analyze causal relationships between factors acting at different levels [
2]. Therefore, this paper uses the multi-level perspective (MLP). Furthermore, research has found different trajectories for cities when embarking on smart city projects [
28], making it interesting to consider the option of using qualitative comparative analysis (QCA). QCA makes it possible to apply a combinatorial perspective in which elements of the external and internal environment and organizational characteristics are used [
34]. The results obtained allow us to establish the different conditions that determine the readiness of a city to implement mobility innovations, or its negation. Likewise, it is possible to extrapolate the experiences of the cities analyzed between different contexts.
4. Discussion
Urban mobility has great implications for the health and sustainability of cities and the population that resides in them. This situation deserves our attention for two main reasons [
2,
7,
54]: the growing concentration of the population in urban areas and the COVID-19 pandemic. The disruption caused by the pandemic must become an opportunity to make changes towards achieving sustainability. These crises affect the urban environment and require transformations of urban structures, not only in terms of physical spaces but also in terms of operations and behavior [
54]. The emerging strategies in urban mobility are transformative in line with the principles of smart growth and sustainable development [
54]. For this reason, it is of interest to analyze the ways in which cities are ready for the implementation of innovations in mobility—innovations that, if they occur, would put cities on the path toward achieving SDG3, SDG11, and SDG12.
We begin by analyzing the role of INN as a landscape condition. The literature has argued that increasing innovation can help cities to prepare for the transformation into a smart city by fostering cutting-edge technologies and ideas, stimulating business growth and generating employment opportunities [
27,
49]. However, in our case, it has only been ~INN’s role as a necessary condition for ~OVE, with no such role for OVE. We must remember that INN is linked to the way in which a city takes advantage of local talent and resources to drive technological advances. This definition is in line with those who indicate that the smart city must suppose an incorporation of the human capital variable with the relevance that has already been given to technological elements in the definition of the smart city [
5,
6,
21,
22]. Likewise, the advances in INN must be reflected in the achievement of SDG11. Based on the results achieved, we see how opting for a current definition of a smart city—combining human and technological components—is not necessary to prepare for the adoption of innovations in mobility (OVE), although its absence is necessary for its negation (~OVE). In this way, the readiness for the implementation of innovations in mobility is produced as a result of the operation of the conditions at the regime level. In line with what has been stated in previous studies [
13,
36], the change towards new mobility systems will be determined by changes in the socio-technical regime. Likewise, this approach can confront those who present the regime as a stabilization of trajectories causing maintenance of the status quo [
33,
42,
43].
Since cities are systems with a constant evolution which must be guided by the SDG [
23,
42,
55], it is necessary to try to generate tension between the landscape and the regime so that innovations at the niche level emerge [
47]. Therefore, if the INN does not play this role, progress must be made in activating other elements, such as actions aimed at SDG9 linked to industry, innovation and infrastructure. Let us remember that in our model, we considered infrastructure linked to mobility as a regime condition. There remains a wide range of infrastructure and industry in which cities could try to advance.
In the explanation of OVE, in line with [
31], the relevant role played by SIM in the transformation of cities is verified, so that it explains the readiness of the city for implementing mobility innovations. This means that mobility not only impacts the achievement of a sustainable urban environment [
12,
15], but also maximizes the social benefits that these initiatives can bring about, which is related to the city’s readiness for the implementation of mobility innovations (OVE). Since the definition of SIM reflects the generation of employment opportunities, it is a condition that can help achieve SDG8. The authorities must bear in mind the multiplier effect of certain actions. Thus, the commitment to promote jobs linked to the field of mobility will benefit the conditions that determine the readiness of the city to implement innovations in mobility, as well as the benefits that are linked to it.
In the case of OVE, the second solution is that in which INF, MAT and SEF are presented together. The transition processes towards sustainability can be shown through different paths that overlap each other [
37]. This reaffirms how urban development is a product of the confluence of infrastructure, and alternative solutions with the potential to disrupt the regime [
44]. The SEF component is linked to the coordination that is presented in the system. For this reason, it places us in line with those who affirm that mobility solutions include advanced forms of cooperation with the environment and companies—regime—or that large-scale changes require processes involving a large number of factors that interact to disrupt the status quo [
36]. This shared governance places us, once again, in line with SDG11. The link of INF and SEF can present a situation close to those who are committed to a flexible and efficient smart city [
21], and those advocating for infrastructures that improve health and reduce emissions [
50]. Additionally, while INF is required for the adoption of mobility innovations, it does not seem to play the key role that has been outlined previously. Cities are transforming their infrastructure towards achieving a smarter and more efficient approach to sustainable development [
1]. The pressure from the dominant automobile system on infrastructure—worsening urban congestion, increasing travel times and accidents—leads to pressure for the renewal of mobility systems, although these do not rest exclusively on the said infrastructure [
20,
36]. However, we must remember the positive effects of offering infrastructure and transport systems. These are among the key actions of cities positively impacting the health of their citizens [
9]. Likewise, mobility infrastructures are also linked to the fight against inequalities in access to health (SDG3), work (SDG8), and infrastructure (SDG9) [
81]. Being able to overcome the blockage that the dominant regime of the automotive sector presents will help initiate changes that will help to achieve SDG7 and SDG12. In relation to SDG12, we recall that achieving the leap to sustainable mobility solutions not only implies offering such modes of transport. To achieve change, and for the niche linked to sustainable mobility to emerge, it is necessary to have a set of agents and technologies that make up the regime.
If we explain ~OVE, ~INN is a necessary condition. Although from the results obtained for OVE, we cannot affirm that INN is among the factors that determine readiness for OVE [
27,
49], we can indicate that for ~OVE to be present, ~INN must be present. There are multiple solutions and conjunctions that explain ~OVE. In some of them, the union of ~INN and ~INF appears, which can be aligned with those who linked innovation and the use of infrastructures in the modernization processes of cities [
4]. Furthermore, the fact that ~INF partly explains ~OVE supports those who point to the centrality of infrastructure in determining the readiness of smart cities.
It seems striking that MAT, depending on the conditions with which it is combined, can determine both OVE and ~OVE. Thus, it appears that MAT is linked, to a greater extent, with the ability to attract a creative population or one with some economic strengths [
51], rather than with the decision to choose, or not, to implement mobility-related innovations. The MAT component is linked to smart mobility activation and the availability of public funding. The mere development of protected niches and incentives that support sustainable mobility innovations will not be enough for a profound transformation in urban mobility systems. Existing dominant regimes need to be pressured [
20,
36]. The transition towards sustainable mobility requires changes in the regulatory framework. Without such a framework, sustainable mobility services will remain a niche that increases—rather than reduces—the number of vehicles in use [
36]. In this way, the implications of promoting the niche linked to the implementation of innovations in mobility must be taken into account due to its link with SDG3, SDG7 and SDG 12.
Likewise, although it has been proposed in previous studies that INN and INF are the determinants of urban innovation, in our case, this is not verified. What is found is that ~INN and ~INF occur simultaneously to explain ~OVE. In the present work, based on previous works [
1,
3,
27], we can establish that the negation of readiness to implement innovative solutions in mobility emerges from deep modifications in the hard and soft components.
Cluster analysis shows us the possibility of explaining OVE and ~OVE independently of the context in which it takes place. However, this does not mean that there is only one way for cities to be ready to implement mobility innovations, as there are two options for explaining OVE. A similar situation arises for the explanation of ~OVE, where the values achieved show the suitability of analyzing all cities under analysis as a whole. The fact that the most enriched urban environments are not more proactive in investing in smart city projects can be explained by the fact that, as a result of this level, they are no longer interested in developing new projects [
56].
Finally, when analyzing the isolated effect of the conditions, the impact is similar, and there are notable differences when analyzing the joint effects of the conditions. In fact, INN, which obtains the lowest weight when establishing the isolated effects, comes to act as a necessary condition. Thus, the suitability of combining QCA and regression analysis is confirmed [
71,
72]. Additionally, the factors that explain OVE are not the inverse of those that explain ~OVE. In this way, the authorities should bear in mind the disparate effect that opting for the combination of different conditions would entail. When we compare the results obtained from the multiple regression with those corresponding to QCA, elements of notable relevance emerge. The regression shows us how OVE is significantly determined by the five components that act as independent variables, with a greater weight for INF and SIM. However, we draw two great lessons from the application of QCA. In the first place, there are two paths through which the OVE can be achieved, implying different combinations of conditions. In fact, in the first explanatory solution of OVE, only one condition appears, which, moreover, does not appear in the second solution. This element links to the second lesson, which states that the effect of the conditions depends on those with which they are combined. In this way, as we have already indicated, MAT could explain OVE or ~OVE depending on those conditions with which it occurs simultaneously.
5. Conclusions
This paper has been developed with the aim of identifying how the conditions that determine the level of readiness among cities for adopting urban mobility innovations could be combined, responding to the request of the literature [
2,
28]. This objective is relevant due to the impact that the population concentration process in urban areas and the mobility solutions linked to it have on the health of citizens; it has even been claimed that cities are fundamental to the achievement of the SDGs. More specifically, this study aimed to address two research gaps.
The first gap was identifying the combinations of factors that determine the level of readiness of cities to implement sustainable mobility innovations. The results show such combinations of factors for both OVE and ~OVE. Thanks to conjunctural causation, we have seen how, except in the case of the SIM effect for OVE generation, the conditions combine at least in groups of three to explain the phenomena under study. Thus, we can state, in line with [
30], how different types of factors combine in the preparation of smart cities.
As a second research gap, we have analyzed in different contexts the conditions that determine the implementation of mobility innovations in the smart city domain. Firstly, because all cities can be grouped together in the explanation of OVE and ~OVE, it does not seem that the context plays a determining role in the way in which the conditions determining their emergence combine. However, this does not mean that there is a single formula common to all cities. As we have already indicated, there are various combinations of factors that explain the phenomena under study in this paper.
Just as mobility has not been established in the same way all over the world [
26], the readiness to implement innovations does not necessarily have to be established in a strict way either. Thus, thanks to the application of QCA, we have been able to analyze causal relationships between conditions acting at different levels [
2]. More precisely, we can observe the benefits derived from its epistemological assumptions. We observe the asymmetry, whereby OVE and ~OVE are explained by different combinations of conditions. Additionally, thanks to equifinality, the different ways in which the same phenomenon can be explained are exposed. Finally, thanks to joint causation, we observe how certain conditions (MAT) can lead to OVE and ~OVE depending on those with which they are combined. Linked to joint causation, we find how the building blocks of STTs in urban mobility rest on the convergence of factors [
33].
Among the limitations of this work, it is shown how modifying the inclusion in the truth table would have altered the results obtained. Additionally, if we look at the case-oriented robustness parameters, the presence of shaky and possible cases is shown.