The Transition of Cities towards Innovations in Mobility: Searching for a Global Perspective

Citation: Medina-Molina, C.; Rey-Tienda, M.d.l.S.; Suárez-Redondo, E.M. The Transition of Cities towards Innovations in Mobility: Searching for a Global Perspective. Int. J. Environ. Res. Public Health 2022,19, 7197. https://doi.org/10.3390/ ijerph19127197 Academic Editor: Daniel A. Griffith Received: 30 April 2022 Accepted: 10 June 2022 Published: 11 June 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/). International Journal of Environmental Research and Public Health Article The Transition of Cities towards Innovations in Mobility: Searching for a Global Perspective Cayetano Medina-Molina 1,2,* , María de la Sierra Rey-Tienda 3and Eva María Suárez-Redondo 4 1Área Departamental Ciencias Sociales y de la Salud, Centro Universitario San Isidoro, 41092 Sevilla, Spain 2Facultad de Ciencias Jurídicas y Económicas, Universidad Isabel I, 09003 Burgos, Spain 3Cátedra Metropol Parasol de Gestión Sostenible y Dinamización Comercial Innovadora de Espacios Singulares en Entornos Urbanos, Universidad de Sevilla, 41004 Sevilla, Spain; sierrar[email protected] 4Dpto. Administración de Empresas y Marketing, Facultad de Ciencias Económicas y Empresariales, Universidad de Sevilla, 41004 Sevilla, Spain; [email protected] *Correspondence: [email protected] Abstract: The growing concentration of the population in urban areas presents great challenges for sustainability. Within this process, mobility emerges as one of the main generators of externalities that hinder the achievement of the Sustainable Development Goals. The transition of cities towards innovations in sustainable mobility requires progress in different dimensions, whose interaction requires research. Likewise, it is necessary to establish whether the experiences developed between cities with different contexts can be extrapolated. Therefore, the purpose of this study was to identify how the conditions that determine a city’s readiness to implement urban mobility innovations could be combined. For this, qualitative comparative analysis was applied to a model developed using the multi-level perspective, analyzing 60 cities from different geographical areas and with a different gross domestic product per capita. The R package Set Methods was used. The explanation of the readiness of cities to implement mobility innovations is different to the explanation of the readiness negation. While readiness is explained by two solutions, in which only regime elements appear, the negation of readiness is explained by five possible solutions, showing the interaction between the landscape and regimen elements and enacting the negation of innovations as a necessary condition. The cluster analysis shows us that the results can be extrapolated between cities with different contexts. Keywords: smart mobility; multi-level perspective; qualitative comparative analysis; cluster analysis; city readiness; sustainable transitions 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 – 6 ]. The urban environment is linked to sustainable development, health and well-being, being considered one of the greatest challenges for environmental health [ 7 – 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 – 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 Int. J. Environ. Res. Public Health 2022,19, 7197. https://doi.org/10.3390/ijerph19127197 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2022,19, 7197 2 of 17 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]. Table 1. SDGs linked to sustainable mobility. SDG Description 3 Good health and well-being 7 Affordable and clean energy 8 Decent work and economic growth 9 Industry, innovation and infrastructure 11 Sustainable cities and communities 12 Responsible consumption and production 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 Int. J. Environ. Res. Public Health 2022,19, 7197 3 of 17 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. 2. Materials and Methods The purpose of this article was to identify how the conditions that determine the level of readiness among cities for implementing urban mobility innovations could be combined. To achieve this, we developed a model supported by the MLP, since it has been used to explain the implementation and adoption of innovative mobility solutions [ 13 , 35 – 37 ]. Next, we explained the justification of the propositions and the choice of data source. Fuzzy set QCA (fsQCA) was employed for data analysis because it is recommended to use QCA when trying to establish the effect of multiple and complex causality, as in the case of MLP [ 35 , 38 ]. Likewise, QCA has been used to explain the complex interconnection of conditions at different levels involved in the diffusion of mobility innovations [35,39–41]. 2.1. Model The study of environmental transformations acting across different levels commonly employs the MLP of sustainable transitions in socio-technical systems [ 37 , 42 ]. A sociotechnical system is a network of actors, institutions and norms linked to technologies [ 27 ]. Based on these, a transition is defined as a social process of change in culture, structure and social practices specific to the contexts in which they occur across different socio-technical systems [ 27 , 43 , 44 ]. The MLP of socio-technical systems enables understanding the coevolutionary and multi-level interactions of infrastructure, technology and knowledge to create specific regimes. The basic principle of the MLP is that transitions to sustainable systems are non-linear processes resulting from processes taking place at three different analytical levels—landscape, regime and niche—so that the success of an innovation is not based on its intrinsic robustness, but on the interaction of its three analytical levels [ 33 , 42 ]. The landscape—the macro level—consists of deep structural trends and social or physical conditions—exogenous environment—that shape systems [ 24 , 33 , 42 , 43 ]. This represents the broadest socio-technical context [ 45 , 46 ]. This is the level that is most structured, stable over time and that evolves slowly through prolonged stages that help to destabilize the regime [ 33 ]. The regime—the meso level—is a semi-coherent set of dominant socio-technical rules, norms and values. It incorporates the socio-technical structure [ 45 , 46 ]. This level provides guidance for the activities of different stakeholders and ensures the coordination Int. J. Environ. Res. Public Health 2022,19, 7197 4 of 17 and dynamic stability of the socio-technical configuration [ 24 , 47 ]. It is composed of a set of actors and consolidates the domain of technologies in use and limits the range that can be diffused, representing the stabilization of technological trajectories while preserving the status quo [ 33 , 42 , 43 ]. The niche—the micro level—is a small, emerging network of actors generating radical innovations that diverge from and destabilize the regime [ 33 , 42 , 43 ]. It is the level that is least structured and can be considered an innovation space embedded in the local context [ 24 ]. At this level, experimentation and radical innovations can be developed [45,46]. Innovations developed in niches compete with those technologies that benefit from the socio-technical systems existing around them [ 33 , 42 ]. This is the level at which sustainable solutions linked to urban developments are tested [44]. This paper proposed a model based on the MLP to study the readiness of cities to undertake transitions towards mobility innovations, using components at three levels: landscape, regimen and niche (Table 2). The first two—landscape and regimen—functioned as conditions and the third—niche—as the outcome. In this way, we explained the different conditions that determine the level of readiness of a city to implement innovations in mobility. For the consideration of the landscape or regime conditions, their definitions in the index used for their measurement were taken as a reference. In this way, innovation was taken as a landscape condition, since in its definition it is not linked to mobility and can be considered exogenous. Table 2. Level and conditions applied. Level Condition Description Landscape Innovation (INN) How well does the city leverage local talent and resources to drive technological advances? Regimen Infrastructure (INF) Has the city developed robust infrastructure and expanded connectivity to support future mobility? Market Attractiveness (MAT) How well does the city engage the private sector and secure diverse investments to build out mobility? System Efficiency (SEF) How well does the municipal government coordinate and enhance the city’s mobility network through things like traffic management systems? Social Impact (SIM) Does the city maximize societal benefits like mobility-related employment or airport arrivals while minimizing harmful qualities like poor air quality? Niche Readiness (OVE) Readiness as an indication of its future mobility capacity. Among the landscape conditions is the city’s level of innovation. An urban transition must recognize the relevance of innovation, experimentation and initiatives developed, as well as the processes that are specific to them [ 48 ]. Cities try to alter urban mobility systems through innovations [ 20 ]. Urban innovation is linked to the adoption of technologies that enable the efficient and effective interconnection of its infrastructures [ 4 ]. Furthermore, strengthening innovation is among the factors that determine the readiness level of smart city transformation [27,49]. Regime conditions include infrastructure, market attractiveness, system efficiency and social impact. Infrastructure comprises durable assets that define the physical configuration of mobility systems [ 20 ]. Infrastructure provides a solution to the problems arising from massive urbanization [ 21 , 31 ] and ultimately determines the health of cities [ 22 ]. The reinforcement of infrastructure is essential to determining the level of readiness for smart city transformation [ 1 , 27 , 31 , 44 , 49 , 50 ]. The attractiveness of a city is linked to its ability to attract a creative population [ 51 ], a construct that encompasses a broad spectrum of elements [ 52 ]. Thus, attractiveness is linked to economic strengths [ 51 ] or to citizens’ perception of quality of life [ 53 ]. In any case, city governments try to make their cities as attractive as possible, not only for people to live in but also to facilitate investments [ 50 , 53 ]. Int. J. Environ. Res. Public Health 2022,19, 7197 5 of 17 The growing awareness of cities regarding their resilience to environmental challenges is causing them to embark in activities to identify new forms of urban efficiency [ 21 , 54 ]. In addition, smart cities can increase economic efficiency to a greater extent than those linked to ecological factors [ 15 , 25 ]. Elements that condition the transformation of cities include social impact [ 31 ]. Sustainable mobility plays a key role in achieving a sustainable urban environment, generating benefits for the well-being and public health of cities [12,15]. At the niche level, the readiness of a city to implement mobility innovations was used. The transformative capacity of a city is a comprehensive and holistic framework for identifying the relevant contextual conditions to advance changes towards sustainability [ 48 ]. Turning cities into sustainable ones requires a transformative process, which considers the context and needs of the city, the local interest, the quality of life of citizens and the readiness of the city for change [ 3 ]. Readiness is a multidimensional construct that reflects the simultaneous presence of political, social, economic and environmental factors [27,31,49]. 2.2. Justification of Propositions Socio-technical transition (STT) recognizes that cities are complex, adaptive, dynamic and open systems in constant evolution [23,47]. Sustainable transitions present the city as a socio-technical system that needs to be reconfigured, as guided by the SDGs [ 23 , 42 , 55 ]. From the STT perspective, an urban transformation implies regime shifts as a consequence of the tension between regime rules and landscape, internal regime frictions and the pressure of niche-generated alternatives [ 47 ]. The co-evolution between landscape and regime is an important factor in the evolution of the social system. However, the selforganizing nature of the regime only enables incremental adaptations to the landscape, keeping the system in dynamic equilibrium [ 43 ]. When the dominant way of doing something is destabilized due to events at the regime or landscape level, there is an opportunity for niche innovations to spread in the regime. Urban transition involves the creation, replacement and standardization of urban regimes and practices [ 27 , 43 ]. Therefore, the smart city involves complex transformational processes stemming from profound modifications around the hard—infrastructure and resources—and soft—human and social capital, knowledge, social innovation, etc.—components of existing urban regimes [ 1 , 3 , 27 ]. The MLP shows that changing to other forms of mobility is a challenge that not only responds to individual decisions, but also to changing the structures of the socio-technical regime [13,36]. Proposition 1. Landscape and regime conditions are combined to explain the readiness of cities in order to implement mobility innovations. Each city drives its transformation processes and development levels in different directions according to its contextual conditions, needs and resources [ 50 ]. In fact, each city designs its own smart city strategy depending on its available financial resources, cultural background and intrinsic level of development [ 56 ]. Among the factors explaining disparities in mobility choices is the income level of citizens [ 57 ]. Being wealthier gives local governments an advantage because initiatives and projects that drive a city’s competitiveness, in terms of how smart and sustainable it is, are usually capital intensive [ 58 ]. Gross domestic product (GDP) per capita is an expression of wealth, an important predictor of transitions towards sustainability [ 56 ]. Higher GDP allows for greater financial resources to be raised for investment, and cities with higher GDP growth rates can attract those who want to improve their living standards and could help to increase the smartness of an urban environment [ 56 ]. GDP per capita is an expression of wealth positively associated with a city’s smartness [56,58]. Int. J. Environ. Res. Public Health 2022,19, 7197 6 of 17 Proposition 2. The combination of landscape and regime conditions that explain the readiness of cities to implement mobility innovations differs between the contexts in which the city is located as measured by GPD per capita. 2.3. Data The development of this work was based on the indicators presented in the Urban Mobility Readiness Index 2021 for a sample of 60 cities from different regions. Geographically diverse cites, ranging from sprawling megacities to more compact cities or to fast-developing metropolises, were examined. They present distinct mobility challenges and display varied solutions that they are pursuing. To rank the cities, the Index employs five dimensions that are the result of 57 key performance indicators that identify cities that excel in meeting their future mobility needs [ 59 ]. The Oliver Wyman indices have been used as benchmarks in several academic studies [ 13 , 60 – 63 ]. The components used at the different levels are presented in Table 2. 2.4. Method Urban sustainability is a complex system affected by economic, social and environmental dimensions, with interactions between them [ 64 , 65 ]. Therefore, by focusing the analysis on the conjunctural causation—also referred to as causal complexity—that determines the presence of a given solution, QCA was chosen. This causal complexity offers a realistic description of social phenomena [ 66 ]. QCA is an appropriate technique for applying MLP to sustainable transitions [ 13 , 35 ]. Similarly relevant are the epistemological assumptions that allow it to explain in depth the phenomena under study, such as asymmetry, equifinality and the aforementioned conjunctural causation. For all these reasons, research linked to QCA is emerging in the academic sphere [ 67 ]. To this is added the fact that it is ideal for works with a small or medium sample size—close to 50 cases [ 68 , 69 ]. Applying QCA, it is possible to systematically analyze a set of cases in order to determine causal patterns in the form of necessary and sufficient conditions between a set of conditions and a certain result [69]. QCA is a technique that integrates elements of qualitative and quantitative analysis. It uses Boolean logic and set analysis to identify combinations of conditions that explain the presence of a result. Initially, analysis was focused on crisp variables—those that can only take the value of 0 or 1—hence its name csQCA (crisp set QCA). Fuzzy set QCA (fsQCA) is a variant of QCA that allows the calibrated values to oscillate between 0 and 1, so it can identify both differences in kind and differences in degree. fsQCA is an interesting complement to regression analysis as it focuses on the analysis of effects from a different perspective. While regression methods find the net effect of the variables, fsQCA offers a set of combinations—called recipes or complex causal patterns—that explain the result [ 70 , 71 ]. The advantage of QCA over regression-based techniques is that they establish relationships between subsets of conditions in order to explain the relationships [ 69 ]. In this way, it is possible to identify both the causes that explain the outcome and the impact of each of them [ 71 ]. Complementing the assessment of the explanatory and predictive power of the model offered by statistical techniques, QCA generates deeper insights into the relationship between variables, offering a way to reach better management conclusions [72,73]. 3. Results The first step in the analysis was to calibrate the ratings received by the components of the landscape, regime and niche dimensions using the 95th and 5th percentiles for complete inclusion or exclusion and the mean for the crossover point [ 13 ]. To ensure that untenable assumptions were not incorporated, enhanced standard analysis was used [ 74 , 75 ]. Additionally, the intermediate solution was chosen for the presentation of the results. The intermediate solution is considered the preferred solution in QCA as it is the most successful in discovering robust sufficiency [ 76 , 77 ], and allows a more comprehensive and meaningful interpretation [ 73 ]. With the intermediate solution, all those remainders that Int. J. Environ. Res. Public Health 2022,19, 7197 7 of 17 are in line with the directional expectations are incorporated in the minimization process, those theoretical affirmations that guide the investigation by proposing the way in which the causal conditions could affect the presence of the outcome [ 75 , 78 ]. The intermediate solution lies between the conservative and parsimonious solutions because it includes logical remainders, but only those that are considered sensible [ 75 , 79 ]. Several authors have considered the decision to filter out all those remainders that contradict the theory as sensible [78,79]. 3.1. Analysis of the Interaction between the Conditions of the MLP In order to explain how the different conditions of the MLP interact in the suitability for implementing mobility innovations, the two-step protocol was applied. This is because, between landscape and regime conditions, a distinction can be made between remote (landscape) and proximate (regime) factors. This protocol establishes a first step corresponding to a necessity analysis for the remote conditions, and a sufficiency analysis for the proximate conditions and those that have been found to be necessary [ 74 ]. RStudio’s SetMethods package was used for the analysis. Within the first step of the two-step protocol, the necessary conditions were analyzed. A condition is necessary for the outcome if whenever the outcome is present, the condition is also present [ 75 ]. As a result, there is no atomic necessary condition for the suitability of implementing sustainable mobility solutions (OVE), but one for its negation (~OVE): ~INN (inclN = 0.947; covN = 0.845; RoN = 0.862). In necessity, consistency (inclN) is a measure of the extent to which empirical evidence is in line with a set relation; coverage (covN) expresses the difference in size between the condition and the outcome sets; and relevance of necessity (RoN) is the more conservative measure of the empirical relevance [75]. Enhanced standard analysis was applied to establish sufficient conditions. A condition is sufficient if its presence explains the appearance of the result. A consistency of 0.85 and one case per conjunction were used to create the truth table. No contradictory simplifying assumptions were identified, so only implausible counterfactuals had to be eliminated. All atomic regime conditions that were necessary from the necessity analysis were taken as directional expectations. For OVE, these are INF (inclN = 0.911; covN = 0.959; RoN = 0.958 ), SIM (inclN = 0.917; covN = 0.906; RoN = 0.895), MAT (inclN = 0.928; covN = 0.890; RoN = 0.872 ) and SEF (inclN = 0.904; covN = 0.951; RoN = 0.950). For ~OVE, they were as follows: ~INF (inclN = 0.953; covN = 0.899; RoN = 0.915) and ~SEF (inclN = 0.944; covN = 0.890; RoN = 0.908). The enhanced intermediate solution for OVE results in SIM + INF*MAT*SEF -> OVE. Thus, the solution is composed of two terms, in which the different regime-level conditions considered in the model are presented. Both terms have high parameters, SIM ( inclS = 0.906 , PRI = 0.872, covS = 0.917, covU = 0.140) and INF*MAT*SEF (inclS = 0.997, PRI = 0.995, covS = 0.819 , covU = 0.140). Consistency—also called inclusion score—is the ratio of cases that, presenting the conditions of the solution, explain the result under analysis. In fsQCA, the consistency of the solution shows a similar role as the test ratios. Coverage is the ratio of cases that, presenting the results, are explained by the solution. The coverage of a solution measures the ability of the recipe to explain all of the observations and can be assimilated to R 2 [ 70 ]. Coverage is the parameter that explains the empirical relevance of the solution. The coverage of the solution is fewer than the sum of the coverages of the different conjunctions that make up the solution, due to the existence of overlaps between said conjunctions. For this reason, unique coverage, the degree of coverage attributable to a single condition, is also presented [ 75 ]. For example, in the case of OVE, the coverage of the solution is 0.959, less than the sum of the coverages of the two conjunctions that compose it: SIM = 0.917; INF*MAT*SEF = 0.819. This is due, as indicated above, to the existence of overlapping cities. The cities explained by each solution are presented in the footnote. For example, in the case of OVE Oslo it is explained by both conjunctions. As can be seen (Table 3), both solutions have low unique coverage, demonstrating the existence of numerous overlaps between them. Int. J. Environ. Res. Public Health 2022,19, 7197 8 of 17 Table 3. Enhanced intermediate solution OVE. inclS PRI covS covU Cities Covered SIM 0.906 0.872 0.917 0.140 Doha, Abu Dhabi, Dubai, Milan, Moscow, Zurich; Istanbul, Berlin, Atlanta, Dallas, Houston, SanFrancisco, Chicago, NewYork, LosAngeles, Boston, Sydney, Helsinki, Dublin, Toronto, Vancouver, Madrid, Montreal, Munich, Oslo, Amsterdam, Seoul, Stockholm, Washington.D.C., Paris, Barcelona, London, Singapore, Tokyo, HongKong INF*MAT*SEF 0.997 0.995 0.819 SIM Warsaw, Beijing, Shanghai, Berlin, Atlanta, Dallas, Houston, SanFrancisco, Chicago, New York, LosAngeles, Boston, Sydney, Helsinki, Dublin, Toronto, Vancouver, Madrid, Montreal, Munich, Oslo, Amsterdam, Seoul, Stockholm, Washington.D.C., Paris, Barcelona, London, Singapore, Tokyo, Hong Kong Solution 0.907 0.874 0.959 In the case of ~OVE, the enhanced intermediate solution is ~INN*~INF*MAT + ~INN* ~INF*~SEF + ~INN*~SIM*MAT + ~INN*MAT*~SEF + ~INF*~SIM*MAT*~SEF -> ~OVE ( Table 4 ). The solution consists of five conjunctions with acceptable parameters: ~INN* ~INF*MAT (inclS = 0.909, PRI = 0.688, covS = 0.381, covU = 0.001), ~INN*~INF*~SEF ( inclS = 0.976 , PRI = 0.964, covS = 0.873, covU = 0.496), ~INN*~SIM*MAT (inclS = 0.944, PRI = 0.806, covS = 0.342, covU = 0.007), ~INN*MAT*~SEF (inclS = 0.914, PRI = 0.702, covS = 0.390 , covU = 0.005), ~INF*~SIM*MAT*~SEF (inclS = 0.961, PRI = 0.867, covS = 0.343 , covU = 0.018). It is also worth noting how the conjunction ~INN*~INF*~SEF explains a good proportion of the cases, as it has a covU of 0.496. Beyond the presence of the negation of conditions at the regime level, it is worth noting the presence of MAT among the conditions that explain ~OVE. Likewise, the necessary condition ~INN appears in many of the solutions. Table 4. Enhanced intermediate solution for ~OVE. inclS PRI covS covU Cities Covered ~INN*~INF*MAT 0.909 0.688 0.381 0.001 Dubai, Milan, Moscow ~INN*~INF*~SEF 0.976 0.964 0.873 0.496 Johannesburg, Jakarta, Bangkok, Quito, Jeddah, Riyadh, Buenos Aires, Cape Town, Nairobi, Rio de Janeiro, Sao Paulo, Lagos, Manila, Casablanca, Santiago, Mexico City, Cairo, Lima, Delhi, Bogota, Mumbai, Doha, Abu Dhabi, Dubai ~INN*~SIM*MAT 0.944 0.806 0.342 0.007 Warsaw ~INN*MAT*~SEF 0.914 0.702 0.390 0.005 Dubai, Istanbul ~INF*~SIM*MAT*~SEF 0.961 0.867 0.343 0.018 Kuala Lumpur Solution 0.921 0.881 0.915 The graphical representation (Figure 1) of the solutions shows the existence of no deviant coverage cases for the solution of OVE and ~OVE. However, some deviant cases appear consistent in kind. Int. J. Environ. Res. Public Health 2022,19, 7197 9 of 17 Int. J. Environ. Res. Public Health 2022, 19, 7197 9 of 18 Table 4. Enhanced intermediate solution for ~OVE. inclS PRI covS covU Cities Covered ~INN*~INF*MAT 0.909 0.688 0.381 0.001 Dubai, Milan, Moscow ~INN*~INF*~SEF 0.976 0.964 0.873 0.496 Johannesburg, Jakarta, Bangkok, Quito, Jeddah, Riyadh, Buenos Aires, Cape Town, Nairobi, Rio de Janeiro, Sao Paulo, Lagos, Manila, Casablanca, Santiago, Mexico City, Cairo, Lima, Delhi, Bogota, Mumbai, Doha, Abu Dhabi, Dubai ~INN*~SIM*MAT 0.944 0.806 0.342 0.007 Warsaw ~INN*MAT*~SEF 0.914 0.702 0.390 0.005 Dubai, Istanbul ~INF*~SIM*MAT*~SEF 0.961 0.867 0.343 0.018 Kuala Lumpur Solution 0.921 0.881 0.915 The graphical representation (Figure 1) of the solutions shows the existence of no deviant coverage cases for the solution of OVE and ~OVE. However, some deviant cases appear consistent in kind. (a) (b) Figure 1. Graphical representation of solutions (a) Pimplot OVE; (b) Pimplot ~OVE. To establish the robustness of the results and increase the credibility of the conclusions, existing tests were performed [34,75]. The calibration range test shows how modifying the different calibration benchmarks would have no impact on the results within the ranges shown in the table below (Table 5). There would have been an impact on the results if the number of cases required was modified above 1; however, it would not have made sense to set an “n cut” higher than 1 because of the sample size. Likewise, changing the required consistency level beyond 0.85 would also have an impact on the results. Figure 1. Graphical representation of solutions (a) Pimplot OVE; (b) Pimplot ~OVE. To establish the robustness of the results and increase the credibility of the conclusions, existing tests were performed [ 34 , 75 ]. The calibration range test shows how modifying the different calibration benchmarks would have no impact on the results within the ranges shown in the table below (Table 5). There would have been an impact on the results if the number of cases required was modified above 1; however, it would not have made sense to set an “n cut” higher than 1 because of the sample size. Likewise, changing the required consistency level beyond 0.85 would also have an impact on the results. Table 5. Robustness test. Robustness Calibration Range Lower Bound Threshold Upper Bound INF Exclusion NA 34.2 58.2 Crossover 47.4 58.4 60.4 Inclusion 59.3 81.3 NA SIM Exclusion 18.6 34.6 56.6 Crossover 46.9 56.9 56.9 Inclusion 57.7 72.7 NA MAT Exclusion NA 19.9 52.9 Crossover 52.5 53.5 54.5 Inclusion 54.4 73.4 NA SEF Exclusion NA 34.4 45.4 Crossover 49.2 53.2 56.2 Inclusion 53.6 71.6 NA INN Exclusion -8.4 5.6 38.6 Crossover 27.6 39.6 41.6 Inclusion 40.1 75.1 NA Raw Consistency Test 0.85 0.85 0.85 N.Cut range 1 1 1 Robustness parameter OVE Fit_Oriented RF_cov: 0.743 RF_cons: 0.989 RF_SC_minTS: 0.736 RF_SC_maxTS: 0.841 Case_Oriented RCR_typ:735 RCR_dev:0.25 Rank:4 Robustness parameter ~OVE Fit_Oriented RF_cov: 0.714 RF_cons: 0.975 RF_SC_minTS: 0.695 RF_SC_maxTS: 0.772 Case_Oriented RCR_typ:0.654 RCR_dev:0.071 Rank:4 In the table above (Table 5), we observe the fit-oriented robustness, comparing fit parameters for the initial solution (IS), the robust core (RC) and the minimum and maximum test set. 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