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Article

The State-of-the-Art of Smart Cities in the European Union

1
Research Unit on Governance, Competitiveness and Public Policies (GOVCOPP), Department of Economics, Management, Industrial Engineering and Tourism (DEGEIT), School of Design, Management and Production Technologies—North Aveiro School (ESAN), University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
2
Research Unit on Governance, Competitiveness and Public Policies (GOVCOPP), Department of Social, Political and Territorial Sciences (DCSJP), University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
3
Intelligent Systems Associate Laboratory (LASI), Department of Economics, Management, Industrial Engineering and Tourism (DEGEIT), Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro, 3010-193 Aveiro, Portugal
*
Author to whom correspondence should be addressed.
Smart Cities 2022, 5(4), 1776-1810; https://doi.org/10.3390/smartcities5040089
Submission received: 29 October 2022 / Revised: 2 December 2022 / Accepted: 5 December 2022 / Published: 8 December 2022

Abstract

:
Today, policymakers struggle to obtain information from specific smart city case studies. The literature lacks a unified view of current initiatives. This paper performs an empirical study with the aim of collecting evidence from the literature about existing smart city initiatives in the European Union (EU). The contribution of each paper and its geography are analyzed using content analysis to identify the number and type of initiatives in each country. A cluster analysis is performed to find relationships between countries and their development phase as well as the categories (areas) they are focused on. The results suggest that there are different levels of smart city development between the member states despite the initial year of their first result in the literature. Furthermore, 22 smart city categories clustered in four different groups were found. When compared to countries’ socio-economic characteristics, the results suggest the development of smart cities is significantly related to the public budget balance, gross domestic product and EU structural funds. In summary, this paper portrays the state-of-the-art of smart city initiatives in the member states of the European Union. Moreover, it represents a valuable contribution to decision makers to discuss ways to standardize smart city approaches in the European scope. Furthermore, the method used in this paper can inspire the development of collaborative dashboards for the exchange of best practices and data accessibility about case studies’ details.

1. Introduction

Smart cities emerged in the late 1990s, motivated by the vision of decision makers who have the capacity to simulate the urban environment in real time [1,2,3]. This vision was initially biased by the interests of technological companies to attach cities’ strategies to their interests [4,5,6]. It has led cities to test solutions and implement pilots without a strategic direction. This challenged the existence of case studies in the literature and the exchange of best practices between cities. Although the concept has evolved to consider cities’ sustainability, the quality of life of inhabitants and their involvement in the co-creation of strategies [7,8,9,10], policymakers have been struggling to access information on specific smart city implementations. This information exists but is spread across the literature. Thus, the literature lacks a unified view of the state-of-the-art in terms of current initiatives.
The creation of a successful smart city strategy depends on existing data and correct benchmarking [11,12]. Since the increase in attention to the topic in 2010 [13], there has been an exponential growth of publications with a strong multidisciplinary nature in their subjects, dominated mainly by China, Italy, USA, Spain, and England [14]. However, no attention has been given to smaller countries which tend to have lower bibliometric indicators, less publications and a lower number of citations in the literature [15]. Correia, Teixeira and Marques [16] reviewed the state-of-the-art of smart cities in Portugal through content analysis of a Portuguese smart cities magazine (the leading journalistic source dedicated to the topic, distributed to all Portuguese cities) and found that the literature had only a small subset of existing initiatives. The authors concluded that cities with an integrative strategy usually have national or European funds, which explains the fact that cities are very dependent on public calls to support smart city approaches. In addition, it also explains the existing heterogeneity between cities of the same country and of different countries. Furthermore, through this magazine, Portuguese policymakers had only information about few flagship cities and case studies, namely Amsterdam, Barcelona, Bristol, Budapest, Columbus, Copenhagen, Coruña, Curitiba, New Deli, Dublin, Hamburg, London, Madrid, Medellin, Moscow, New York, Ontario, Riyadh, San Francisco, Santander, California, and Songdo. Thus, even from non-scientific sources, the available information for decision makers is scarce.
In this line of thinking, Ruohomaa et al. [17] point out that research on smart cities usually focuses on the transformation of big cities, with the topic being widely neglected for medium and small cities. Several examples can be found in the literature. Ojo, Curry and Janowski [18] present the findings and lessons from a study of ten smart city programs (Amsterdam, Malmö, Malta, Masdar, PlanIT Valley, Singapore, Curitiba, Songdo, Tianjin, and Yokohama). Chang and Kuri [19] analyze four case studies (Barcelona, Curitiba, Singapore, Hammarby Sjöstad) to merge features that are commonly fragmented, and created a broader perspective for designing strategies for urban renovations and sustainable development in Latin American cities. Angelidou [20] identifies 10 distinctive characteristics of smart cities and analyzes their presence in the smart city plans of 15 cities based on studies published in the literature (Amsterdam, Barcelona, London, PlanIT Valley, Stockholm, Cyberjaya, Singapore, King Abdullah Economic City, Masdar, Skolkovo, Songdo, Chicago, New York, Rio de Janeiro, and Konza). Giest [21] studied five cities (Copenhagen, London, Malmö, Oxford, and Vienna) to conclude that the complexity of big data integration limits the capacity of local government to set up data management structures, which is why current solutions focus on local pilot sites and outsourcing data analytics to private companies. Based on evidence of European projects, Camboim et al. [22] identified the elements and features to develop a smart city strategy and stressed that the challenge lies in defining how to coordinate these driving elements in each dimension to develop the urban innovation ecosystem (Amsterdam, Barcelona, Vienna, and Lisbon). Noori, Hoppe and De Jong [23] classified the pathways for smart city development according to design, governance, and implementation (Amsterdam, Barcelona, Dubai, and Masdar). Csukás and Szabó [24] positioned nine cities into four clusters (Environmental Efficiency, Quality of Life Applications, Citizen Engagement and Social Inclusion) to ultimately allocate them to four different types of smart cities depending on their focus (Amsterdam, Barcelona, London, Helsinki, New York, Vienna, Berlin, Budapest, and Moscow).
Furthermore, the characterization of current initiatives would give policymakers and researchers the knowledge about existing case studies based on the area/category and location, which would enhance the sharing of best practices and benchmarking within the sector. Thus, the aim of this paper is to portray the state-of-the-art in the European Union by identifying existing smart city initiatives throughout its member countries in the scientific literature.
To answer the research question “What are the smart city initiatives in the European Union?” a review of the literature was performed with a search in Scopus about evidence of smart city implementations in each territory to reach conclusions about the concept development phase of each country and the typology of existing projects.

2. Theoretical Background

This section provides an overview about the evolution of smart city initiatives in Europe and reviews the research studies that aim to define smart city categories. This literature review supports the importance of the study and discussion of the paper because it gives readers an overview about the classification of smart city categories and their associated projects.

2.1. Smart City Initiatives

Until 2010, the number of smart city studies reported in the literature was low. Only after the emergence of the smart city projects supported by the European Commission was a proliferation of writings and academic publications on the topic noted [25]. Moreover, the European Commission has been supporting and investing in smart city initiatives since its early days. In 2016 there were 34 dedicated projects in the European Union (EU) [26].
One of the first smart city references was the city of Barcelona. In 2011, the city was focused on experimentation and technological transformation via the introduction of new, innovative technologies, with a view to improving the operation and management of the city in general, promoting economic growth and strengthening the well-being of citizens [27]. The top-down approach benefited technology solution providers [28]. Among the initiatives implemented, there were new models of service management and relationship with citizens inspired by e-government principles as well as sustainable growth projects in the categories of smart lighting, mobility and energy, the installation of the municipal Wi-Fi network, and the creation of a living lab district “22@” [27,29].
Furthermore, in 2012, Lee and Hancock [30] mentioned the existence of 143 ongoing smart city projects. Of these, 47 were located in Europe and 30 in the US [31].
In China, according to the Chinese Smart Cities Forum, six provinces and 51 cities have included smart cities in their government plans [32].
Many projects and applications can be found in the literature about flagship cities such as Santander, Manchester, and London [33,34]. The references are primarily to the applications of sensors and network infrastructure for different categories. Among them are parking solutions, waste management, traffic control, air quality monitoring and Wi-Fi or IoT networks.
The capital of Finland, Helsinki, has a smart city development area, Smart Kalasatama, facilitated by Forum Virium Helsinki (FVH), which allows the implementation of agile smart city pilots with multi-stakeholder collaboration. However, it does not have a specific smart city strategy [35].
Gohari et al. [36] literature review stresses that although the Norwegian region has come as far as its European counterparts in terms of smart city applications and projects, strategies remain in the planning stages and are still very fragmented.
These two examples of developed northern European countries raise the concern of whether cities are still committing the past errors of neglecting strategic and social aspects and focusing on technological applications.
Jonek-Kowalska and Wolniak [37] studied 287 Polish cities and concluded that the implementation of smart cities in most cities has not been possible due to the unsatisfactory level of prosperity of the residents, the difficult financial condition of cities, and unfavorable demographic trends. Therefore, the priority areas are social infrastructure and human capital [38]. It may also reveal the discrepancy and heterogeneity between those cities with financial support and those without, which in this case is directly associated with their size. Thus, specific city interventions are related to national and European funding opportunities, with a lack of strategic planning for becoming a smart city in the official city documents [39]. Cities have embedded various forms of smart city-related projects in their digital strategies; however, only partial implementation of relevant initiatives in their daily operations is noticed [40]. The associated projects are typically supported by municipalities, funded by subsidies, and implemented in partnerships which fade out after the pilot stage. Thus, scaling is widely perceived as a major concern [41].
Smékalová and Kučera [42] studied the implementation of the smart city concept in the Czech Republic and concluded that the larger the city, the more intensive investment activity, confirming the relationship between the size and absorption capacity of European funds. Thus, cities have been focused on specific applications and themes to leverage their smart city approaches, rather than promoting holistic strategies. Examples can be found in Finland [43], Romania [44], Slovakia [45,46], Poland and Ukraine [47]. In the broader picture, municipalities have been prioritizing actions, mainly regarding the development of ICT infrastructures and e-government [48]. In addition, there are countries such as Hungary where the topic has still not brought meaningful change nor impacted urban policy practices [49]. A contrast between the national and the local levels is also noted, even for Sweden, which chose to invest in a national digitization council to decide whether local experimentations can move forward [50].

2.2. Smart City Categories

The smart city categories have expanded over the years in terms of scope and number. Several authors have reflected on and identified the main dimensions of smart cities in the literature [51].
Mahizhnan [52] defined four dimensions: IT education, IT infrastructure, IT economy and quality of life. Giffinger [53,54] considered six dimensions: smart economy, smart people, smart governance, smart mobility, smart environment and smart living, Albino, Berardi and Dangelico [55] considered the same dimensions and linked them with aspects of urban context such as industry, education, e-democracy, logistics and infrastructures, efficiency and sustainability, security, and quality. Eger [56] referred to technology, economic development, job growth, increased quality of life. Thuzar [57] considered the quality of life, sustainable economic development, management of natural resources through participatory policies, and the convergence of economic, social, and environmental goals. Nam and Pardo [58] mentioned economic, sociopolitical issues of the city, economic-technical-social issues of the environment, interconnection, instrumentation, integration, applications and innovations. Other studies have proposed: smart health, smart security systems, smart building, smart government, smart tourism, smart grid, smart transportation, smart environment, smart home and smart lifestyle [59,60,61]. Barrionuevo et al. [62] considered economic (GDP, sector strength, international transactions, foreign investment), human (talent, innovation, creativity, education), social (traditions, habits, religions, families), environmental (energy policies, waste and water management, landscape), and institutional aspects (civic engagement, administrative authority, elections). Kourtit and Nijkamp [63] stated human capital (e.g., skilled labor force), infrastructural capital (e.g., high-tech communication facilities), social capital (e.g., intense and open network linkages), and entrepreneurial capital (e.g., creative and risk-taking business activities). Chourabi et al. [64] considered management and organizations, technology, governance, policy context, people and communities, economy, built infrastructure, and natural environment. Neirotti et al. [65] presented twelve domains for urban development. Mohanty, Choppali and Kougianos [66] categorized smart cities into ten components: smart infrastructure, smart building, smart transportation, smart energy, smart healthcare, smart technology, smart governance, smart education, and smart citizens. Ahvenniemi et al. [67] considered ten sector categories: natural environment; built environment; water and waste management; transport; energy; economy; education, culture, science and innovation; well-being, health and safety; governance and citizen engagement; and ICT. Wolf et al. [68] suggested an unified framework combining some of the previous approaches, which include six different dimensions, structured on two levels (macro and micro). Level one: living (quality of life), economy (competitiveness) and environment (natural resources); and level two: institutions (governance/participation), digital systems (transport and ICT) and people (social and human capital).
After a systematic literature review, Camboim, Zawislak and Pufal [22] identified the most frequent words and categorized the main dimensions associated with the smart city concept. However, they organize this taxonomy into high-level dimensions (environ-urban, techno-economic and social-institutional) and do not match the elements with existing project categories. Furthermore, it remains unclear what the existing categories and projects by country are.

3. Materials and Methods

As stated before, this study focused on the analysis of the current state of the European Union. For each of the 27 countries of the European Union, evidence was sought that could portrait the scope of the existing smart city initiatives. The methodology used for the analysis of the literature is demonstrated in Figure 1. Moreover, quantitative, and qualitative analyses were performed in order to find the smart city categories and existing country clusters.
Furthermore, a search on Scopus using the search keywords “Smart Cit*” + [“Name of the Country”]. In total, 27 queries were performed (one for each European Union member country). From the obtained results, the titles, abstracts and keywords were analyzed to allocate a first code connected to the identification of the city, and a second code concerning the contribution of the paper. This information was updated in a dedicated database (built in a spreadsheet) where the fields “City Location” and “Category Keywords” were filled, respectively. Furthermore, in terms of eligibility (inclusion and exclusion criteria) two exclusion filters were applied.
First, repeated and non-relatable papers (that do not fit the purpose of this research) were excluded from the sample, as were non-English manuscripts. Furthermore, only journal and conference papers were considered.
The second filter aimed to remove the articles that did not mention any specific city or case study. Furthermore, if there was no location in the title, abstract and keywords, the paper’s contribution was considered as being on the country, since the aim of this paper was to study the current situation of the country by the existing specific projects in the cities. The same happened if it was a generic study of numerous cities in the country. Nevertheless, these results were excluded from the final detailed analysis. Furthermore, if the abstract mentioned a neighborhood or an urban district of the city, the initiative was given to that city. Double counting was only allowed when the paper referred to specific case studies in different cities, regardless of the country. In addition, each paper was only allocated to a single category.
The number of search results is illustrated in Figure 2. From an initial sample of 1665 papers, 945 results were considered in the present study (56.76%).
Of these results, on the one hand, Italy, Spain and Germany were the countries with the most results. On the other hand, countries such as Malta, Lithuania, Luxembourg, Bulgaria, Cyprus, Slovenia and Croatia showed that this subject is still in its early days.
After that, the cities were organized according to their origin countries, and an inductive thematic analysis was performed to find the smart city categories of the initiatives. Finally, quantitative analysis was performed to reflect the number of results per country and the years of publication on the one hand, and on the other hand a hierarchical cluster analysis based on Ward’s method was performed to find country clusters to study their relationship between the categories of the initiatives (from the qualitative analysis) and socio-economic characteristics (e.g., population and area of the territory). Figure 3 summarizes both directions of the methodology.

4. Results

This section presents the results of the literature analysis. Thus, it mirrors the countries’ representation according to the number of smart city initiatives. Several country clusters emerged by looking at the existing relationships between their associated results. Furthermore, the clusters were obtained by aggregating them according to (1) the number of literature results, and (2) the category groups (from factorial analysis) of the projects involved. Ultimately these results are compared with the socio-economic aspect and discussed according to the existing literature.

4.1. Countries’ Representation According to the Number of Smart City Initiatives

4.1.1. Emergence of the Smart City Initiatives by EU Country

Figure 4 shows this is a recent topic. As stated in the literature, until 2010, the number of smart city studies was low. Only after the emergence of the smart city projects supported by the European Commission was a proliferation of writings and academic publications on the topic noted (Jucevičius, Patašienė, & Patašius, 2014). Furthermore, the first countries to have initiatives under this theme were Germany (2010), the Netherlands (2011), France (2011) and Spain (2011). Nevertheless, every member of the European Union is represented. The last countries to present results were Bulgaria (2017), Luxembourg (2017), Slovakia (2016), Latvia (2016), Hungary (2016), Cyprus (2016), and Croatia (2016).

4.1.2. Country Clusters by the Number of Smart City Initiatives

In the analysis of the relationships between countries in terms of the literature results, four different clusters emerged. The first cluster comprises the countries that have less than 25 results and is composed of Lithuania, Malta, Croatia, Hungary, Bulgaria, Luxembourg, Cyprus, Estonia, Latvia, and Slovenia. The second cluster aggregates the countries that have up to 55 results, namely Ireland, Slovakia, Romania, Finland, Greece, Austria, Belgium and Denmark. The third and fourth clusters join the countries with the highest number of results. Moreover, the third cluster is composed by the countries that have up to 130 results, namely Czech Republic, France, Sweden, the Netherlands, Poland, Portugal and Germany. The fourth and last cluster comprises Spain and Italy with more than 130 results. Furthermore, Figure 5 details the cluster analysis and its representation in the map with different colors.

4.2. Countries’ Representation According to the Smart City Categories

4.2.1. Smart City Categories

From the content analysis of the codes related to each paper’s contribution (allocated in the first step), 22 smart city categories can be organized: water and irrigation; environment and air quality; waste; energy and lighting; strategy and governance; urban planning; culture, tourism and heritage; rural and agriculture; sport; smart city foundations; digitization and interoperability; privacy, security and safety; health and well-being; infrastructure and communication networks; buildings and housing; traffic; parking; mobility and transportation; logistics; community, participation and inclusion; education; and economy and industry. Table 1 summarizes a multiple step inductive thematic analysis that produced the smart city categories (Appendix A mirrors the step before).

4.2.2. Smart City Initiatives by Category

The total of smart city initiatives by category is represented in Figure 6. Thus, energy and lighting; community, participation, and inclusion; and mobility and transportation are the categories with the greatest number of results. On the opposite side, the categories that have less than 20 results are education; health and well-being; waste; logistics; rural and agriculture; parking; and sport.

4.2.3. Representation of the Smart City Categories by Country

The initiatives sorted by category are detailed in Appendix B. Furthermore, analysis of Appendix B provides information about the cities and countries that have or had projects within each category. This provides useful information for a greater understanding of current initiatives, allowing better organization for researchers and decision makers to know the geographies where a specific problem is being addressed. Thus, the categories that are present in a higher number of countries are mobility and transportation (22 out of 27), environment and air quality (21 out of 27), and community, participation and inclusion (21 out of 27). On the other hand, the cities that have a higher representation of categories are Italy (21 out of 22) and Spain (21 out of 22), which only have no project in the category of sport, followed by Germany (20 out of 22). Furthermore, the information about the smart city categories by country is detailed below in Figure 7. This graphical representation summarizes the information of Appendix C.
Appendix C and Figure 7 enable the connection of the main initiatives to each of the countries to be identified. It is evident that the categories “energy and lighting” and “community, participation and inclusion” are those with the greatest preponderance of all the initiatives developed; in the first, only three (out of 27) countries have no initiative in this area/category (Ireland, Malta and Lithuania), while in the case of the second there are four countries (Bulgaria, Lithuania, Malta and Luxembourg). Moreover, these are the five countries with the fewest initiatives in the various categories analyzed (with the exception of Ireland, all with less than five connections).
Figure 8 uses Social Network Visualizer (https://socnetv.org/, accessed on 14 June 2022) to demonstrate the relationships between the categories and countries, highlighting those that have a greater number of initiatives among the countries. Moreover, the categories with greater representation among the countries are energy and lighting, community, participation and inclusion, and digitization and interoperability. This information can also be consulted in Appendix C.

4.2.4. Country Cluster Analysis (Dimensionality Reduction)

This section presents a cluster analysis that aggregates countries according to the typology of initiatives.
Two distinct approaches were undertaken, one assuming the percentage of projects that each country carried out in each category; and the other, (ii) considering the total number of initiatives, in absolute value, in each category. However, the results of the latter approach are presented and explored in more detail since the territorial patterns are not so clear and evident when considering the relative weight of the initiatives (see the results in Appendix D). Since there is a great variety of initiatives, the importance that each type of initiative assumes in each country also varies greatly.
Thus, in order to group the categories in large dimensions, a principal component analysis (PCA) was previously carried out. PCA is an unsupervised learning method that transforms the main features of a large set of variables (based on their similarities or correlation) into a smaller set of latent variables (see for more details) [69]. Table 2 represents the loadings (correlation of the initial variables with the new variables) that resulted from the application of the PCA analysis and shows that the twenty-two (22) categories were grouped into four (4) large areas, with a loss of only approximately 15% of the initial information.
By analyzing the high values of the loadings in Table 2, each of the factors can be characterized as follows:
  • The first factor joins initiatives related to the more tangible and material issues of the smart city, such as basic infrastructures (waste, water and air) and transport (parking, communication networks, traffic and mobility). Spain is clearly the leading country in this type of project, followed by Poland, Portugal and Italy (see Figure 9—factor 1);
  • Factor 2 involves sectoral initiatives linked to sport, health, education and culture; Italy stands out as the country that invests most in this type of initiative, followed by Ireland and Romania (see Figure 9—factor 2);
  • As for the third factor, a concern with the competitiveness of the entire territory is highlighted. Thus, it is possible on the one hand to identify projects related to rural and agriculture, and on the other initiatives in the category of logistics, digitalization, economy and industry. In these categories, four countries can be highlighted, firstly Germany, prominently, and then France, Italy and Poland (see Figure 9—factor 3);
  • Finally, and contrary to factor 1, in this factor we have four initiatives of a more intangible nature linked to community participation and issues of security and privacy, in which the Netherlands appears in a leading position, followed by Ireland and Spain (see Figure 9—factor 4).
Using the scores of the four new factors as inputs, a hierarchical cluster analysis (Ward method for aggregation criteria) was applied, allowing the construction of seven different groups of countries, as shown in Figure 10. The significance levels were calculated from the application of the non-parametric Kruskal–Wallis test.
Each cluster emerged by looking at the number of projects involved in the four major categories (factor 1, 2, 3, and 4). Furthermore, by looking at Figure 10, on the left, red and green arrows can be seen that mirror the behavior of that factor for a particular cluster. In addition, on the right there is the color-coded geographic location of each country-cluster.
  • Cluster 1 includes four countries (Hungary, France, Austria, and Poland), which are countries that have many initiatives related to agriculture, logistic, housing, digitalization (factor 3) and a few in factor 2; that is, those associated with sports, health, education and culture.
  • The eight countries (Ireland, Greece, Finland, Belgium, Bulgaria, Latvia, Cyprus, and Croatia) included in cluster 2 have tended to invest little in factors 1 and 3 (basic infrastructures, transports, logistics, digitalization, economy) when compared with infrastructure-related initiatives that have greater importance.
  • Cluster 3 (Estonia, Lithuania, Slovenia, Luxembourg, Malta, Slovakia, Sweden, Denmark, Romania, Czech Republic, and Portugal) is distinguished by low investment in projects related to community participation (factor 4) and rural and agriculture activities (factor 3).
  • Clusters 4, 5, 6 and 7 are singular clusters, i.e., formed by a single member state because the values are so distinct that they appear as outliers and thus form a single cluster. In this case, the single-country clusters are Germany, the Netherlands, Spain and Italy. Those are the countries with the greatest number of initiatives in each of the major categories (factor 3, factor 2, factor 4 and factor 1, correspondingly).
Additionally, as stated before, instead of the typology of the initiatives, a cluster analysis was also performed considering the relative percentage of the categories in each country, represented in Appendix D (spatial patterns according to the percentage of the initiatives—dimensionality reduction and cluster analysis). However, as demonstrated in Figure A1, the results are homogeneous for a vast majority of the territory, which may not add meaningful insights to this research. The particularity of these results is that they justify the argument that there is no clear pattern of typology of initiatives when the analysis is purged of the fact of the size of the country. Even so, there is the possibility of identifying which latent dimensions underlie each of the categories. Nevertheless, they deserve a mention since they justified the decision to move forward on the analysis using the clusters by the type of initiatives, hence the option of placing these results in the appendix.

4.3. Socio-Economic Relationship

From a Eurostat list of 27 indicators [70] in Appendix E, this section seeks to analyze each of the cluster typologies obtained in the previous section with some socio-economic characteristics. Thus, after applying a principal component analysis, the most relevant variables (with the highest loading value) can be selected in each component. Furthermore, they are organized according to nine carefully selected, independent indicators (see Figure 11).
These independent dimensions are organized into four major categories: (i) the first, structural revenues (A, B and C), represents the economic context of each country, evaluated by the wealth produced (total and per capita) and the public revenues generated as a percentage of the GDP; (ii) the second, conjunctural revenues (D), is reflected by the “European structural and investments funds, in this case between 2014 and 2020”; (iii) the third dimension refers to public investment in general as a percentage of the GDP (F); and finally, (iv) the fourth category corresponds to expenditure at the sectorial level, according to three levels (G: general services, H: social protection and I: education). It can be said that dimensions A, B, C and D are, to some extent, the income generated by the production of the whole country’s economy and external structural supports (inputs); while dimensions F, G, H and I are each member state’s expenditures or investments (outputs). In a balance between the inputs and outputs, an additional dimension is presented that reflects the public budget balance measured in billions of euros (E).
Each of the graphs in Figure 11 describe the performance of the group of countries belonging to each cluster in the nine dimensions described, and it can also be identified which ones have significant differences, in this case dimensions A, D and E (the line of the graph of these dimensions has a greater color intensity).
Previously, each of the seven clusters shown in Figure 10 aggregates countries that have the same profile of project typologies: cluster 1—Hungary, France, Austria, and Poland; cluster 2—Ireland, Greece, Finland, Belgium, Bulgaria, Latvia, Cyprus, and Croatia; cluster 3—Estonia, Lithuania, Slovenia, Luxembourg, Malta, Slovakia, Sweden, Denmark, Romania, Czech Republic, and Portugal; cluster 4; Germany; cluster 5—the Netherlands; cluster 6—Spain; cluster 7—Italy.
Therefore, as an example of interpretation of Figure 11, in terms of structural revenue, Germany (cluster 4) has a high GDP, high GDP per capita and public revenue; regarding the conjunctural scope, it has a significant contribution of EU structural funds and a low public investment when compared to the percentage of GDP; related to sectoral investments, it has a high value in general services and social protection, and medium in education. Detailed results of the socio-economic characteristics in each of the seven clusters are presented in Appendix E.

5. Discussion

The results show different levels of smart city development between European countries. This corroborates the Portuguese internal context analyzed by Correia, Teixeira and Marques [16]. Moreover, there is a gap not just between cities and regions of the same country, but also from different countries, even if they are under the umbrella of the European Union. Although every European Union member had literature results and is therefore represented in this study, there is a significant difference between the member states. Although this discrepancy can be partially connected with the starting year of the literature results, it does not explain everything. For example, Czech Republic, Italy, Ireland, and Slovenia had their first literature result in 2013 and the total results are very different between them. Furthermore, it can be associated with the fact that there are different levels of local government engagement and prioritization of investment. Moreover, this demonstrates that although the European Union has been making efforts to develop this topic, it lacks standardization and enforcement in its approach.
Several authors had previously studied the smart city areas/categories [22,51,63,64,65,66,67,68], [52,53,54,55,56,57,58,62]. However, they failed to obtain a common standard understanding because of their methodological approach. After several years of existence and evolution of the topic, the smart city categories should be defined based on an inductive thematic analysis of the initiatives present in the literature. This way it can be ensured that every category is represented. The tendency is for more categories to emerge in the future. As stated in the literature review section, the scope of smart city categories has been evolving and widening to enable a more detailed understanding. Furthermore, that can also be noted in the results obtained in the cluster analysis. In line with Giffinger [53], who considered six dimensions (smart economy, smart people, smart governance, smart mobility, smart environment, and smart living), the first component that combines parking, infrastructure and communication networks, smart city foundations, environment and air quality, waste, traffic, water and irrigation, mobility and transportation, and strategy and governance covers the fundamental and initial areas of smart cities.
In the second component can be found the categories of sport, health and well-being, education, culture, tourism and heritage, and energy and lighting, which can be seen as an extended vision of component 1. Thus, most of these categories were also named by other studies [59,60,61], in which smart health, smart security systems, smart building, smart government, smart tourism, smart grid, smart transportation, smart environment, smart home and smart lifestyle were proposed.
The categories represented in the third component (logistics, buildings and housing, digitization and interoperability, and economy and industry) are in line with the dissemination and relationship with Industry 4.0 [71,72]. Furthermore, the same categories were found by Albino, Berardi and Dangelico [55] when linking the former with urban living aspects.
The fourth component (categories of privacy, security and safety, and community, participation, and inclusion) is ultimately connected with Smart City 3.0, the most recent phase of the smart city concept. Here, most of the clusters, meaning all countries, have been making efforts and creating projects under this scope. Smaller countries or those that started later have already focused on citizen engagement (skipping the first stages of the concept).
Although the results at first sight suggested that energy and lighting was the category with the most results, when looking at the number of countries per category it showed that the most representative categories were mobility and transportation (22 out of 27), environment and air quality (21 out of 27), and community, participation and inclusion (21 out of 27). This may be a good sign that countries are following the development of the concept by evolving citizens in early stages of urban planning and are aware of present-day challenges regarding mobility and climate change. Furthermore, this can be found when comparing the factors to countries’ socio-economics, as in the examples of Ireland, Greece, Finland, Belgium, Bulgaria, Latvia, Cyprus, and Croatia. Nevertheless, there are countries with similar characteristics, with low GDP and a small contribution of EU structural funds such as Estonia, Lithuania, Slovenia, Luxembourg, Malta, Slovakia, Sweden, Denmark, Romania, Czech Republic, and Portugal; although they have a neutral public budget balance and high public investment, they struggle to move to later stages of the smart city concept and are focused on their infrastructure. However, countries such as Austria and Poland that also present low GDP but have a high contribution of EU structural funds and public investment are already focused on later stages of the concept. Thus, although smart city development is often associated with greater urban centers and countries with higher GDP, there are other significant factors that are crucial to cities’ success.
Furthermore, Germany and the Netherlands are the only ones with a positive public budget balance (revenues higher than spendings) and a high GDP per capita, which enables them to be focused on the competitiveness of the entire territory or pass through the various stages of the concept (with significant projects in every factor).

6. Conclusions

This study leads to the conclusion that a positive public budget balance allows countries to focus on the entire territory. On top of this, countries with significant GDP and EU structural fund contributions were able to evolve at the pace of the smart city concept, with projects in the greatest number of categories. Conversely, countries with low GDP, small contribution from EU structural funds, a neutral public budget balance and high public investment have two different approaches. Some try to close the existing gap and move their efforts to focus on community participation, and others are still investing in infrastructure (the first phase of the concept), which may mean that the existing differences through the member countries of the European Union will continue to exist.
Furthermore, this study demonstrated the heterogeneity of smart city development among European Union member states and the lack of standardized approaches. A strategic direction is needed by the regulator to allow the definition of specific guidelines upon which countries and municipalities should base their action. This strategy should also qualify, organize, and promote the relationship between companies, academia, knowledge centers and municipalities to ensure greater adequacy and sustainability of the projects and associated research. Thus, the discrepancy between territories should be combated with specific policies promoted by the European Commission to guarantee homogeneity between countries and their cities.
This research sets the beginning of a collaborative approach towards countries’ involvement in finding synergies while promoting their financial sustainability by allowing others to learn from previous mistakes. Thus, this study should be used by decision makers as reference material to benchmark existing case studies and make contact with other policymakers to exchange knowledge and best practices (considering the type and location of the initiatives). Furthermore, an open dashboard can be created to uniformize each project where relevant characteristics (e.g., problem, solution, level of investment, methodology and people involved) can be associated. In future studies, the same methodological approach can be applied to other regions and continents to study the world state-of-the-art on the topic. Countries’ level of investment in innovation, the budget for each of the categories and whether there is a political background relationship with the dispersion of the number of initiatives can also be studied.
Some limitations can also be pointed out in the study. First, the keywords defined for each paper in the qualitative analysis may not exactly reflect the contribution of the paper, since the texts were not read in full. Nevertheless, the authors know that the title and abstract of a paper should state clearly the research contribution. Second, the results obtained may not perfectly portray the reality of the country, since the geography of the initiative could not be found in the abstract. Additionally, others may have not been included. However, this limitation will only serve as proof that there is a lack of documentation of case studies and practical implementations in the literature that can serve as a study of good practices. Third, the assessment of the level of smart city development in a specific country based on the number of Scopus-indexed articles may be disputable. However, since smart cities are an innovation-related hot topic, their case studies are usually found in the literature. If not, the reasons may be the lack of results and conclusion of the project and not the lack of innovative material to be published. Furthermore, research centers and universities are usually connected to these projects.

Author Contributions

Conceptualization, D.C., J.L.M. and L.T.; methodology, D.C. and J.L.M.; validation, J.L.M. and L.T.; formal analysis, D.C. and J.L.M.; writing—original draft preparation, D.C.; writing—review and editing, D.C., J.L.M. and L.T.; supervision, J.L.M. and L.T.; funding acquisition J.L.M. and L.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Fundação para a Ciência e a Tecnologia [grant numbers: UIDB/00127/2020, UIDB/04058/2020]; The present work was developed in the scope of project SOLFI—Urban logistics optimization system with integrated freight and passenger flows [grant number: POCI01-0247-FEDER-039870], co-financed by the European Regional Development Fund (FEDER) through COMPETE 2020 (Operational Programme for Competitiveness and Internationalization).

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Smart City categories and corresponding keywords.
Table A1. Smart City categories and corresponding keywords.
KeywordsCategory
Leaks, Floods, Anomaly Detection, Infrastructure, Stormwater, Rain and Melted Water, Wastewater, Water Quality, Irrigation, Smart Meters, Household Metering, Residential Consumption, Geothermal, Supply, Distribution, PollutionWater And Irrigation
Emissions, Temperature, Air Quality, Sound and Geo-sensing, Green Roofs, Noise, Climate Change, Green Spaces, Indoor and Outdoor monitoring, Ozone, Urban Parks, Crowdsourcing Monitoring, Low Carbon, Remote Sensing, Gardening, Smoke/fire Sensing, Road Transport, Bicycle MonitoringEnvironment and Air Quality
Circular Economy, Waste Management, Fill-level Sensing, Route Optimization, Illegal Dumping Recognition System, Waste Sorting, Waste Collection, Selective Waste Collection, Organic WasteWaste
Smart Districts, Energy Systems, Urban Infrastructure, Renewable Energy, Green Solar Cities, Sustainable Energy, Intelligent Streetlight Management, Thermal Storage Systems, Efficiency, Photovoltaic, Roofs’ Solar Potential, Smart Energy, Buildings Performance, Sufficiency, System Optimization, Renewable Heating and Cooling, Electric Car, Smart Grid, Residential Microgrid, Waste Energy Recovery, Heat Storage, Geothermal, Wind Power Plants, Charging Stations, Distribution Network, Smart Lighting, Green Energy, Biomass, Climate Policy, Biogas, Fast Charging Infrastructure, Luminaires LED, Energy Storage, Self-sufficient Energy & Lighting
Governance Framework, Development Strategy, Smart Municipality, Crisis Management Systems, Cross-Border, Virtual Crowds, Smart Sustainable Cities, Ubiquitous City, Public Procurement, Open Strategy, Smart City Control Rooms, Smart Urban Monitoring, Digital City, Smart City Implementation Strategy, Smart Governance, Smart Institutions, Policy Process Standardization, City Marketing, Official Statistics, Regional Strategy, City Profiling, Smart Regions, Urban Branding, Evaluation Method, European Projects, Sustainable Governance, Advanced e-Government, Green BrandingStrategy and Governance
3D City Model, Urbanism, GIS, Public Services, Urban Policies, Spatial Data Population Distribution, Land Use, Urban Policies, Sustainable Urban Planning, Spatial Planning, Modeling Tool, Sustainable Development, Urban Computing, Urban Sustainability, Digital Twin, Urban Platforms, Planning Instruments, Urban Development Management Tools, Smart Urban Spaces, Virtual Reality, Geology, Soil permeability and Cartography, Mobile Geographic Information, Territorial Management Planning Strategies, Spatial Intelligence, City’s Mapping, Urban Accessibility, Smart Urban SpacesUrban Planning
Smart Tourism Destination, Tourism and Hospitality, Living Lab, Travel Behavior, Heritage, Preservation, Cultural Impact, Sustainable Tourism, Smart Museum, Augmented Reality, Historic City Centers Reconstruction, Sustainable Winter Tourism, Slow Tourism Experience, Digital Interactive Art, Mobile Tourism, ICT for Tourism, Rural Tourism, Smart Tourism, Beach Attendance Prediction, IndicatorsCulture, Tourism and Heritage
Rural Areas, Rural Contexts, Tree Inventory System, Small Cities, Cow Sharing, Smart Town, Urban-rural Relationships, Rural Regions, Smart Village, Olive Production QualityRural and Agriculture
Smart Stadium, Sport and Smart Communities, Sport ActivitySport
Driving Elements, Smart City Proposition, Opportunities, Challenges, Priorities, Barriers, Benefits, Value Proposition, Ideology, Myths and Utopias, Evaluation System and Model, National Scale, Practices, Case Studies, Smart Region, Indicators, Characteristics, Understandings, Findings and Lessons, ISO 37120 standard, KPIs and Index, Rankings, Sustainability Assessment, Performance Measurement Systems, Trends and OpportunitiesSmart City Foundations
Information Science, Standards, Mobile City Applications, Data Interoperability, Open Data Policy, UrbanAPI, Digital Transformation Strategies, Subsystems Integration, Sensor Aggregation, Information Modeling, Big Data Analytics, Mobile Phone Data, Urban Operating System, Mobile Sensor, Urban Sensing, Digital Society, Urban Digitalization, Digital Transformation, Information Dissemination, Open City Toolkit, Crowdsensing and Crowdsourcing, Urban Dashboards, Living Labs, Data Concentrator, Big Data, Open Data, Crowdsensed Data, Fog Computing Applications, Integrated Dashboard, Open Data Assessment, Platform-as-a-Service Architecture, Interoperability and Open DataDigitization & Interoperability
Digital Rights, Surveillance, Biometric Border Management, Cybersecurity Education Management, General Data Protection Regulation, Reinforcement, Camera Systems, Blockchain, Security Monitoring, Intrusion and Abnormal Behavior Detection System, Autonomous Vehicles’ Privacy and Cybersecurity, Risk Management, Emergency Evacuation Planning, Crime Prevention, Coordinated Management, Emergency Response, Cameras and LiDAR Sensors, Digital Rights, Urban Safety, Smart Security, Public Safety System, Transparency and ControlPrivacy, Security and Safety
Senior Care System, Smart Quarantine, Urban Health, Wellbeing, Urban Living, Disease Control, Predicting Diabetes Diseases, Satisfaction Estimation, Ambient Assisted Living (AAL), Elderly People Monitoring, Hospital Information Systems (HIS), Teledermatology Platform, eHealth, E-Prescription System, Quality of Life, Emotion, Stress Mapping, Covid-19, Predicting Patients’ Urgency Demand, Active Mobile Phone Intervention, Health Monitoring SystemHealth and Wellbeing
Wireless Sensors, Mobile Sensors, LoRaWAN, Internet of Things, 5G networks, Communication Technologies, LoRa Network, Wi-Fi and Interactive Displays, Digital Infrastructures, LPWAN Applications, Li-Fi Installation, Wi-Fi Network, Gateway City, Wireless Sensor Network, Cyber Physical Systems, Digital Infrastructures, ZigBee Wireless Communication, Narrowband IoT (NB-IoT), Industrial Internet of Things (IIoT), Architecture and Infrastructure, Wi-Fi hotspot, Bluetooth Sensors Network, Smart Urban Infrastructure, Power Line Communication (PLC)Infrastructure and Communication Networks
Sustainable and Resilient Buildings, Sustainable Urban Regeneration, Utilities, Smart Building, Buildings Renovation, Green Building, Living Lab, Thermal Comfort, Thermal Modelling, Energy Efficient, Sound Foundations, 3D Buildings, Smart Home, Room Temperature Control Systems, Indoor Conditions Assessment, Occupancy Rate, Municipal Building Regulation, Housing Regeneration, Building Information Modelling (BIM)Buildings and Housing
Responsive Traffic Signaling System, Traffic Forecasting and Route Guidance, Traffic Management System, Traffic Prediction, Bicycle Traffic, Road Marking, Road Damage Detection, Road Safety, Spatiotemporal Traffic Data, Road Accidents, Crowdsensing Urban Transit, Dynamic Obstacle Detecting, Smart Route Planning, Emergency Vehicle Routing, Road Assessment Conditions, Intelligent Traffic Light, Automatic Road Sign Inventory, Road Safety, Intelligent Crosswalks,Traffic
Smart Parking, Off-Street Parking, Geospatial Factors, Parking Management, Pay-As-You-Go, Controlling System, Applications, Mobile Payment, Public Parking Spots, Social Parking, Real-Time Parking Prediction System, Park-and-ride FacilitiesParking
Population Distribution, Mobility Dynamics, Walking, Intelligent Transportation System, Electric Bike Sharing, Carpooling and Electro-mobility, Urban Transport, Smart Mobility, Light Rail, Public Transport Demand, Railroad Network, Pedestrian Movement, Transportation Modes, Green Transportation, Automated Driving, Mobility Politics, Integrated Urban Mobility, Electric Carsharing, Multimodal Sustainable Mobility, Shared Micromobility, Multi-modal Mobility, Inclusive Mobility, Carpooling Gamification, Soft Mobility, Autonomous Vehicles, Mobility-as-a-Service (MaaS), Crowd Mobility, Connected CarsMobility and Transportation
Sustainable Urban Freight Transport, Logistics, Last-mile, Future Urban Fleets, Intelligent Offloading Distribution, Logistics Freight Traffic, Short-distance Freight, Autonomous Vehicle Fleet, Local Food Delivery, City Logistics, Grocery Distribution and Delivery, Surplus Food Delivery, Logistics Routing Problem, Short Term Load ForecastingLogistics
Participatory Governance, Collaborative Data Platforms, Citizen Participation, Human Centric Approach, Disabled Citizens, Participatory Sensing, Citizens Engagement, Participatory Innovation Platforms, Open Data, Co-creation, Smart Community, Bottom-up Participation, E-governance, E-participation, Democratization, Urban Experimentation, Social Policy, Virtual Community, Inclusion, Social Sustainability, Crowdsourced Governance System, Citizen Science model, Collaborative Decision-making Processes, Participatory Budget, Socio-technical experimentation, Stakeholder Involvement, Inclusive Smart Society, Local LeadershipCommunity, Participation & Inclusion
Education, Smart Campus, Hybrid Learning, Mobile Education, Public Libraries, School as a Service, Information Science, Human Capital, Knowledge and Intellectual Capital, Universities Role, Gamification Educational Strategy, Quality Management Skills, Learning and Teaching Environment, E-learning, University Curricula, Training, Employment Future SkillsEducation
High-skilled Employment, Public Private Partnerships, Industry 4.0, Remote Working, Green Business Model, Knowledge Proximity, Sharing Economy, Smart Industry, Green Economy, Innovation Districts, Green Entrepreneurship, Economic Development and Welfare, Real Estate Valuation, Intelligent Fabrics, Smart Economy, Local Entrepreneurs, Technology Park, Micro-Enterprises, Startups, Accelerators, Open Innovation, Social Entrepreneurship, Digital Nomads, Intelligent Manufacturing System, Ethics, Sustainable Production, Inclusive 3D printing, Advertising, Corporate Social ResponsibilityEconomy and Industry

Appendix B

Table A2. Smart City initiatives organized by city and country.
Table A2. Smart City initiatives organized by city and country.
TopicCitiesCountries
Water and IrrigationInnsbruck (1), Zdarna (1), Aalborg (1), Copenhagen (2), Isted (1), Region of North Jutland (1), Lille (1), Frankfurt (1), Main (1), Skiathos (1), Bay of Pozzuoli (1), Bresso (1), Calabria Region (1), L’Aquila (1), Małopolska province (1), Sosnowiec (1), Aveiro (1), Cávado (1), Alicante (1), Barcelona (1), Huelva (1), Madrid (1), Valencia (2)Austria (1), Czech Republic (1), Denmark (4), France (1), Germany (2), Greece (1), Italy (4), Poland (2), Portugal (2), Spain (6)
Environment and Air QualityGraz (1), Vienna (1), Antwerp (4), Liege (1), Sofia (1), Dubrovnik (1), Brno (1), Aarhus (2), Copenhagen (1), Helsinki (2), Lyon (1), Nancy (1), Berlin (2), Ruhr Valley (1), Stuttgart (1), Athens (1), Elefsina (1), Igoumenitsa (1), Patras (1), Thessaloniki (1), Szeged (1), Dublin (1), Bologna (1), Campania Region (1), Cosenza (1), Florence (1), L’Aquila (1), Osmannoro (1), Pavia (1), Piedmont (1), Rome (1), Syracuse (1), Terni (1), Tuscany Region (1), Amsterdam (1), Krakow (1), Zabrze (1), Paredes (1), Bucharest (1), Lučenec (1), Krvavec (1), Cartagena (1), Coruña (1), Galicia (1), Huelva (1), Jaén (1), Llíria (1), Madrid (4), Malaga (2), Murcia (1), San Sebastián (1), Santander (1), Vitoria-Gasteiz (1), Malmö (2), Örebro (1), Uppsala (1)Austria (2), Belgium (5), Bulgaria (1), Croatia (1), Czech Republic (1), Denmark (3), Finland (2), France (2), Germany (4), Greece (5), Hungary (1), Ireland (1), Italy (12), Netherlands (1), Poland (2), Portugal (1), Romania (1), Slovakia (1), Slovenia (1), Spain (16), Sweden (4)
WasteFlanders Region (1), Hamburg (1), Regensburg (1), Bari (2), Genoa (1), Prato (1), Vidusdaugavas Region (1), Luxembourg (1), Porto (2), Oradea (1), Bilbao (1), Cartagena (1), Malaga (2), Stockholm (1)Belgium (1), Germany (2), Italy (4), Latvia (1), Luxembourg (1), Portugal (2), Romania (1), Spain (4), Sweden (1)
Energy and LightingGraz (4), Linz (1), Salzburg (1), Vienna (2), Villach (1), Leuze (1), Sisak (1), Sveti Križ Začretje (1), Nicosia (1), Jeseník (1), Aarhus (1), Copenhagen (3), Sønderborg (1), Espoo (1), Lorraine Region (1), Lyon (1), Saint-Nazaire (1), Berlin (1), Cologne (1), Dortmund (1), Hamburg (1), Ludwigsburg (1), Trikala (1), Budapest (1), Altavilla Silentina (1), Bagheria (1), Bari (5), Bolzano (3), Campania Region (1), Cesena (1), Cosenza (1), Cuneo (1), Florence (1), Genoa (1), Lazio (1), Milan (3), Naples (1), Padova (1), Palermo (1), Savona (5), Terni (1), Trento (1), Vallelunga Pratameno (1), Luxembourg (1), Amsterdam (1), Apeldoorn (1), Eindhoven (1), Rotterdam (2), Zaanstad (1), Bydgoszcz (1), Gdańsk (1), Krakow (2), Bragança (1), Coimbra (1), Evora (4), Lisbon (1), Porto (1), Bucharest (1), Iasi (2), Ávila (1), Barcelona (4), Bilbao (1), Girona (1), Madrid (1), Malaga (4), Oviedo (2), Santiago de Compostela (2), Soria (1), Valladolid (7), Eskilstuna (1), Gothenburg (2), Malmö (3), Skellefteå (1), Stockholm (2)Austria (9), Belgium (1), Croatia (2), Cyprus (1), Czech Republic (1), Denmark (5), Finland (1), France (3), Germany (5), Greece (1), Hungary (1), Italy (31), Luxembourg (1), Netherlands (6), Poland (4), Portugal (8), Romania (3), Spain (17), Sweden (9)
Strategy and GovernanceVienna (4), Brno (1), Karvina (1), Prague (1), Uherske Hradiste (1), Zlín (1), Helsinki (1), Oulu (3), Tampere (1), Vaasa (1), Lyon (1), Hamburg (1), Leipzig (1), Munich (1), Trikala (1), Dublin (1), Basilicata (1), Milan (3), Turin (4), Amsterdam (3), Rotterdam (1), Utrecht (1), Gdańsk (1), Katowice (1), Sandomierz (1), Warsaw (1), Lisbon (1), Banská Bystrica (1), Barcelona (7), Bilbao (1), Madrid (1), Santander (1), Valladolid (2), Stockholm (1), Växjö (1)Austria (4), Czech Republic (5), Finland (6), France (1), Germany (3), Greece (1), Ireland (1), Italy (8), Netherlands (5), Poland (4), Portugal (1), Slovakia (1), Spain (12), Sweden (2)
Urban PlanningGraz (2), Vienna (3), Antwerp (1), Brussels (1), Wallonia (2), Brno (9), Karlovy Vary (1), Prague (1), Aarhus (2), Sønderborg (1), Talinn (1), Tartu (1), Helsinki (3), Lohja (1), Tuusula (1), Paris (1), Bonn (2), Herrenberg (1), Leipzig (1), Ludwigsburg (1), Nuremberg (1), Athens (1), Mykonos (1), Thessaloniki (1), Dublin (3), Limerick (1), Bergamo (1), Brescia (2), Cagliari (3), Catania (1), Florence (1), Iglesias (1), Matera (1), Milan (1), Naples (1), Pavia (1), Rome (2), Trento (1), Trieste (1), Turin (1), Luxembourg (2), Amsterdam (2), Plock (1), TriCity (1), Żuromin (1), Algarve (1), Braga (1), Lisbon (1), Porto (1), Trenčín (1), Alicante (1), Elda (1), Girona (1), Madrid (1), Rois (1), Santander (1), Vitoria-Gasteiz (1), Zaragoza (1), Stockholm (9)Austria (5), Belgium (4), Czech Republic (3), Denmark (3), Estonia (2), Finland (5), France (1), Germany (7), Greece (3), Ireland (4), Italy (18), Luxembourg (2), Netherlands (2), Poland (3), Portugal (4), Slovakia (1), Spain (8), Sweden (2)
Culture, Tourism and HeritageGraz (1), Varna (1), Usti nad Labem (1), Aalborg (1), Les Orres (1), Hamburg (1), Karlsruhe (1), Athens (1), Budapest (1), Bologna (1), Cagliari (3), Campania Region (1), Cosenza (1), L’Aquila (3), Madonna di Campiglio (1), Milan (1), Naples (1), Salerno (1), Sulcis Iglesiente—Guspinese (1), Sutri (1), Trento (1), Turin (1), Den Bosch (1), Bragança (1), Lisbon (2), Madeira (1), Porto (2), Brasov (1), Bratislava (1), Komárno (1), Lučenec (1), Ávila (1), Barcelona (1), Donostia-San Sebastián (1), Madrid (1), Malaga (1), Tarragona (1), Valencia (3)Austria (1), Bulgaria (1), Czech Republic (1), Denmark (1), France (1), Germany (2), Greece (1), Hungary (1), Italy (17), Netherlands (1), Portugal (6), Romania (1), Slovakia (3), Spain (9)
Rural and AgricultureSchneebergland (1), Nicosia (1), Brandenburg (1), Taranto (1), Granada (1)Austria (1), Cyprus (1), Germany (1), Italy (1), Spain (1)
SportDublin (1), Cagliari (1), Pisa (1)Ireland (1), Italy (2)
Smart City FoundationsVienna (2), Flanders Region (1), Ghent (2), Brno (1), Usti nad Labem (1), Helsinki (1), Berlin (1), Hamburg (2), Main (1), Heraklion (1), Region of Elefsina (1), Trikala (1), Bari (1), Genoa (1), Milan (1), Turin (2), Amsterdam (2), Cascais (1), Barcelona (4), Sant Cugat del Vallès (1), Santander (1), Valladolid (2), Gothenburg (1)Austria (2), Belgium (3), Czech Republic (2), Finland (1), Germany (4), Greece (3), Italy (5), Netherlands (2), Portugal (1), Spain (8), Sweden (1)
Digitization & InteroperabilityGraz (1), Vienna (2), Antwerp (1), Brussels (1), Flanders Region (2), Ruse (1), Sofia (1), Dubrovnik (1), Nicosia (1), Brno (1), Moravia Silesian (1), Aarhus (1), Copenhagen (1), Talinn (4), Tartu (1), Espoo (1), Hämeenlinna (1), Helsinki (4), Tampere (2), Lyon (1), Nantes (1), Nice (2), Paris (1), Rennes (1), Berlin (2), Freiburg (1), Hamburg (1), Lübeck (1), Munich (1), Munzingen (1), Ruhr Valley (1), Vaihingen (1), Kavala (1), Papagou (1), Trikala (1), Dublin (4), Bologna (2), Cagliari (1), Catania (2), Florence (2), Lecce (1), Merano (1), Messina (1), Milan (3), South Tyrol (1), Turin (2), Venice (1), Luxembourg (2), Amsterdam (1), Eindhoven (2), Krakow (1), Rzeszów (1), Košice (1), Barcelona (1), Béjar (1), Galicia (2), Granada (1), Valladolid (1), Vitoria-Gasteiz (2), Zaragoza (1), Karlshamn (1), Stockholm (1), Uppsala (1)Austria (3), Belgium (4), Bulgaria (2), Croatia (1), Cyprus (1), Czech Republic (2), Denmark (2), Estonia (5), Finland (8), France (6), Germany (9), Greece (3), Ireland (4), Italy (17), Luxembourg (2), Netherlands (3), Poland (2), Slovakia (1), Spain (9), Sweden (3)
Privacy, Security and SafetyVienna (1), Nicosia (1), Ostrava (1), Tampere (1), Nice (1), Lesvos (1), Budapest (1), Amsterdam (2), Eindhoven (1), Rotterdam (2), Porto (1), Ljubljana (1), Barcelona (2)Austria (1), Cyprus (1), Czech Republic (1), Finland (1), France (1), Greece (1), Hungary (1), Netherlands (5), Portugal (1), Slovenia (1), Spain (2)
Health and WellbeingPrague (1), Aarhus (1), Frankfurt (1), Bologna (1), Lecce (1), Messina (1), Sardinian Region (1), Trento (1), Lisbon (1), Brasov (1), Jaén (1), Stockholm (2)Czech Republic (1), Denmark (1), Germany (1), Italy (5), Portugal (1), Romania (1), Spain (1), Sweden (2)
Infrastructure and Communication NetworksAntwerp (3), Leuven (1), Bol (1), Osijek (1), Brno (2), Aalborg (1), Oulu (1), Grenoble (1), Lille (1), Lyon (1), Dortmund (1), Karlsruhe (1), Mainau-Lake Constance (1), Heraklion (1), Dublin (1), Bologna (1), Florence (1), Padova (2), Palermo (2), Prato (1), Salerno (1), Riga (1), TriCity (1), Warsaw (1), Aveiro (1), Castelo Branco (2), Lagoa (1), Porto (1), Bucharest (1), Bratislava (1), Logatec (1), Coruña (1), Gran Canaria Island (1), Madrid (1), Malaga (1), Pamplona (1), Santander (3), Santiago de Compostela (1), Valencia (2), Valladolid (1), Skellefteå (2)Belgium (4), Croatia (2), Czech Republic (2), Denmark (1), Finland (1), France (3), Germany (3), Greece (1), Ireland (1), Italy (8), Latvia (1), Poland (2), Portugal (5), Romania (1), Slovakia (1), Slovenia (1), Spain (12), Sweden (2)
Buildings and HousingVienna (1), Seraing (1), Havířov (1), Nordhavn (1), Tartu (1), Grenoble (1), Nancy (1), Hannover (1), Munich (1), Vaihingen (1), Miskolc (1), Brescia (1), Gioia Tauro (1), Milan (1), Palermo (1), Pescara (1), Settimo Torinese (1), Turin (1), Amsterdam (1), Lisbon (2), Alba Iulia (1), Galati (1), Betanzos (1), Växjö (1)Austria (1), Belgium (1), Czech Republic (1), Denmark (1), Estonia (1), France (2), Germany (3), Hungary (1), Italy (7), Netherlands (1), Portugal (2), Romania (2), Spain (1), Sweden (1)
TrafficVienna (1), Nicosia (2), Aarhus (1), Odense (1), Oulu (1), Lyon (2), Marseille (1), Nancy (1), Paris (1), Augsburg (1), Cologne (1), Hamburg (2), Patras (1), Thessaloniki (2), Budapest (2), Dublin (4), Catania (1), Florence (2), Milan (2), Modena (1), Turin (1), Jelgava (2), Delft (1), Enschede (2), Bialystok (1), Aveiro (1), Braga (1), Porto (4), Barcelona (1), Madrid (2), Malaga (1), Murcia (1), Navarre (1), Santander (2), Zaragoza (1)Austria (1), Cyprus (2), Denmark (2), Finland (1), France (5), Germany (4), Greece (3), Hungary (2), Ireland (4), Italy (6), Latvia (2), Netherlands (3), Poland (1), Portugal (6), Spain (9)
ParkingAarhus (1), Mainz (1), Milan (1), Jelgava (1), Warsaw (1), Žilina (1), Barcelona (1), Malaga (2), Santander (2), Seville (1), Zaragoza (1)Denmark (1), Germany (1), Italy (1), Latvia (1), Poland (1), Slovakia (1), Spain (7)
Mobility and TransportationVienna (2), Flanders Region (1), Mons (3), Split (1), Limassol (2), Brno (1), Lovosice (1), Prague (2), Aarhus (1), Tartu (1), Vaasa (1), Lille (1), Paris (1), Berlin (1), Erfurt (1), Munich (2), Ruhr Valley (1), Heraklion (1), Thessaloniki (1), Trikala (1), Volos (1), Dublin (1), Apulia Region (1), Bologna (1), Cagliari (2), Catania (1), L’Aquila (1), Milan (2), Naples (1), Parma (1), Pavia (1), Piedmont (2), Pisa (1), Rome (1), Siena (1), Trento (2), Turin (4), Tuscany Region (1), Riga (1), Kaunas (1), Amsterdam (1), Krakow (1), Warsaw (4), Wroclaw (1), Zielona Góra (1), Águeda (3), Aveiro (1), Covilhã (1), Lisbon (1), Porto (1), Viana do Castelo (1), Bratislava (3), Žilina (2), Ljubljana (1), Barcelona (3), Bilbao (1), Castelló de la Plana (1), Galicia (1), Madrid (6), Malaga (2), Pamplona (1), Santander (2), Gothenburg (1)Austria (2), Belgium (4), Croatia (1), Cyprus (2), Czech Republic (4), Denmark (1), Estonia (1), Finland (1), France (2), Germany (5), Greece (4), Ireland (1), Italy (23), Latvia (1), Lithuania (1), Netherlands (1), Poland (7), Portugal (8), Slovakia (5), Slovenia (1), Spain (17), Sweden (1)
LogisticsBrussels (1), Region of North Jutland (1), Helsinki (1), Baden-Württemberg (1), Cologne (1), Erfurt (2), Heilbronn (1), Basilicata (1), Calabria Region (1), Milan (1), Helmond (1), Covilhã (1), Pamplona (1)Belgium (1), Denmark (1), Finland (1), Germany (5), Italy (3), Netherlands (1), Portugal (1), Spain (1)
Community, Participation & InclusionVienna (2), Brussels (1), Ghent (1), Knokke-Heist (1), La Louvière (1), Namur (1), Koprivnica (1), Rijeka (1), Limassol (1), Brno (1), Usti nad Labem (1), Zlín (1), Aarhus (2), Copenhagen (1), Espoo (1), Helsinki (2), Oulu (3), Region of Häme (1), Tampere (1), Lyon (3), Cologne (1), Darmstadt (1), Essen (1), Hamburg (2), Munich (1), Stuttgart (1), Heraklion (1), Island of Kos (1), Kavala (1), Korydallos (1), Samos Island (1), Thessaloniki (1), Trikala (2), Budapest (1), Cork City (1), Dublin (4), Ennis (1), Bologna (1), Cagliari (4), Catania (1), Lombardy Region (1), Milan (3), Montieri (1), Pavia (2), Rome (1), Rovereto (1), Syracuse (1), Turin (3), Riga (1), Amersfoort (1), Amsterdam (4), Eindhoven (3), Schiedam (1), Utrecht (1), Poznań (1), Silesian Province (1), Warsaw (1), Wroclaw (2), Evora (1), Lisbon (3), Paredes (1), Cluj (1), Oradea (1), Bratislava (1), Poprad (1), Žilina (1), Alicante (1), Barcelona (3), Bilbao (1), Guadalajara (1), Huesca (1), Madrid (3), Malaga (1), Murcia (1), San Sebastián (1), Santander (2), Valencia (1), Vitoria-Gasteiz (1), Linköping (1), Luleå (1)Austria (2), Belgium (5), Croatia (2), Cyprus (1), Czech Republic (3), Denmark (3), Finland (8), France (3), Germany (7), Greece (8), Hungary (1), Ireland (6), Italy (19), Latvia (1), Netherlands (10), Poland (5), Portugal (5), Romania (2), Slovakia (3), Spain (17), Sweden (2)
EducationVienna (1), Prague (1), Aarhus (1), Espoo (1), Düsseldorf (1), Limerick (1), Brescia (1), Genoa (1), Glurns/Glorenza (1), Turin (1), Alba Iulia (1), Barcelona (1), Jaén (1)Austria (1), Czech Republic (1), Denmark (1), Finland (1), Germany (1), Ireland (1), Italy (4), Romania (1), Spain (2)
Economy and IndustryFlanders Region (1), Prague (1), Aarhus (1), Paris (1), Berlin (1), Heraklion (1), Trikala (1), Dublin (2), Emilia-Romagna Region (1), Liguria (1), Rende (1), Turin (1), Utrecht (1), Bialystok (1), Bydgoszcz (1), Czestochowa (1), Águeda (1), Cluj (1), Žilina (1), Ljubljana (1), Madrid (1)Belgium (1), Czech Republic (1), Denmark (1), France (1), Germany (1), Greece (2), Ireland (2), Italy (4), Netherlands (1), Poland (3), Portugal (1), Romania (1), Slovakia (1), Slovenia (1), Spain (1)

Appendix C

Table A3. Number of Smart City initiatives divided by country and category.
Table A3. Number of Smart City initiatives divided by country and category.
ABCDEFGHIJKLMNOPQRSTUVTotalCategories
Austria12094511023100110202103615
Belgium05110400034004100415013412
Bulgaria010000100020000000000043
Croatia010200000010020001020096
Cyprus000100010011000202010097
Czech Rep.11015310022112100403113016
Denmark43050310002011121113113116
Estonia000002000050001001000094
Finland02016500018101010118103713
France12031110006103250203013214
Germany24253721049013341557117020
Greece15011310033101030408023714
Hungary010100100001001200010087
Ireland01001400104001040106122611
Italy4124318181712517058761233194419921
Latvia001000000000010211010076
Lithuania000000000000000001000011
Luxembourg001102000020000000000064
Malta 1000000000000000000000000
Netherlands010652100235001301110014214
Poland22044300002002011705033612
Portugal21281460010115260815015517
Romania01130010000011200002111410
Slovakia01001130001001001503011810
Slovenia010000000001010001000155
Spain616417128910892112197171172116021
Sweden04192200013022100102003012
Total24671710954774653328716135125511393141131322
Countries1021919141814521120118181415722821915
Legend: (A)—Water and Irrigation; (B)—Environment and Air Quality; (C)—Waste; Energy and Lighting; (D)—Strategy and Governance; (E)—Urban Planning; (F)—Culture, Tourism and Heritage; (G)—Rural and Agriculture; (H)—Sport; (I)—Smart City Foundations; (J)—Digitization & Interoperability; (K)—Privacy, Security and Safety; (L)—Health and Wellbeing; (M)—Infrastructure and Communication Networks; (N)—Buildings and Housing; (O)—Traffic; (P)—Parking; (Q)—Mobility and Transportation; (R)—Logistics; (S)—Community, Participation & Inclusion; (T)—Education; (U)—Economy and Industry. 1 As mentioned in the Methodology section “If there was not any location in the title, abstract and keywords, the paper’s contribution was considered as of the country. The same happened if it were a generic study of numerous cities in the country. These results were excluded from the final detailed analysis”. Malta’s results did not mentioned any city in particular.

Appendix D

The methodology and analysis are the same of the one described in Section 4.2.4. The difference of the Country Cluster Analysis (dimensionality reduction) performed in this appendix is that instead of the typology of the initiatives, was considered the relative percentage of the categories in each country (spatial patterns according to the percentage of the initiatives). Furthermore, the goal was to find patterns according to the weight of each category compared to the total number of initiatives (and categories) a specific country had. Thus, the categories without projects were excluded in this analysis. The aim was to find if there were similarities between countries when considering only the categories that they contained smart city initiatives.
Figure A1. Geographic representation of the countries by category.
Figure A1. Geographic representation of the countries by category.
Smartcities 05 00089 g0a1
Table A4. Loadings of the principal component analysis (% of initiatives).
Table A4. Loadings of the principal component analysis (% of initiatives).
Component
1234
Economy and Industry(%)0.850
Buildings and Housing(%)0.769
Mobility and Transportation(%)0.761
Strategy and Governance(%)−0.558
Culture, Tourism and Heritage(%)−0.456
Energy & Lighting(%)
Urban Planning(%) 0.856
Digitization & Interoperability(%) 0.652
Waste(%) 0.616
Environment and Air Quality(%) −0.521
Community, Participation & Inclusion(%) −0.439
Water and Irrigation(%)
Parking(%) 0.978
Smart City Foundations(%) 0.954
Traffic(%) 0.690
Health and Wellbeing(%) −0.671
Infrastructure and Comm(%) −0.575
Rural and Agriculture(%) 0.549
Privacy, Security and Safety(%) 0.505
Education(%) −0.503
Logistics(%) −0.443
Sport(%)
Total Variance Explained14.35313.88311.87410.354
Figure A2. Geographic representation of the scores of the factors.
Figure A2. Geographic representation of the scores of the factors.
Smartcities 05 00089 g0a2
Figure A3. Cluster analysis of the countries by the percentage of the initiatives.
Figure A3. Cluster analysis of the countries by the percentage of the initiatives.
Smartcities 05 00089 g0a3

Appendix E

Table A5. Socio-economic characteristics of clusters.
Table A5. Socio-economic characteristics of clusters.
Descriptions
NMeanMinimumMaximum
Number of Municipalities1410,777.82098.0035,357.00
28322.031.00589.00
3111235.160.006258.00
4111,054.011,054.0011,054.00
517960.07960.007960.00
61380.0380.00380.00
718124.08124.008124.00
Total273214.531.0035,357.00
Area (km2)14282,356.582,409.00647,795.00
2896,037.95695.00303,892.00
31194,445.5315.00407,340.00
41357,376.0357,376.00357,376.00
51295,114.0295,114.00295,114.00
6133,688.033,688.0033,688.00
71504,712.0504,712.0050,4712.00
Total27152,871.1315.00647,795.00
Inhabitants1430,883.38773.0066,989.00
285808.5855.0011,352.00
3116272.1460.0019,644.00
4182,522.082,522.0082,522.00
5160,589.060,589.0060,589.00
6117,082.017,082.0017,082.00
7146,528.046,528.0046,528.00
Total2716,508.0460.0082,522.00
GDP (billion EUR)14811.5123.502287.60
28160.019.20437.20
311145.411.10477.90
413263.43263.403263.40
511716.91716.901716.90
61733.2733.20733.20
711163.71163.701163.70
Total27481.511.103263.40
GDP per capita (EUR)1425,275.312,261.0042,086.00
2826,575.47101.0061,900.00
31130,096.89563.0093,754.00
4139,545.039,545.0039,545.00
5128,337.028,337.0028,337.00
6142,922.042,922.0042,922.00
7125,010.025,010.0025,010.00
Total2728,910.57101.0093,754.00
Public Budget Balance (billion EUR)14−174.2−593.00−2.60
28−3.2−13.001.50
311−8.7−57.008.00
41412.0412.00412.00
51−397.0−397.00−397.00
6180.080.0080.00
71−362.0−362.00−362.00
Total27−40.2−593.00412.00
Public Budget Balance (% GDP)140.0−0.03−0.01
280.0−0.010.02
3110.0−0.030.04
410.00.010.01
510.0−0.02−0.02
610.00.010.01
710.0−0.03−0.03
Total270.0−0.030.04
Debt (billion EUR)14709.790.502218.40
28303.010.801373.00
31171.22.10242.60
412092.62092.602092.60
512263.12263.102263.10
61416.1416.10416.10
711144.31144.301144.30
Total27443.02.102263.10
Debt (% GDP)140.70.510.97
280.80.251.79
3110.50.091.26
410.60.640.64
511.31.321.32
610.60.570.57
711.00.980.98
Total270.70.091.79
Public Investment (EUR billion)1428.35.1079.60
284.10.509.80
3115.00.2021.20
4168.968.9068.90
5134.334.3034.30
6123.623.6023.60
7124.024.0024.00
Total2713.00.2079.60
Public Investment (EUR per capita)14857.5450.001275.00
28703.4124.001523.00
3111076.4270.003758.00
41834.0834.00834.00
51566.0566.00566.00
611380.01380.001380.00
71516.0516.00516.00
Total27896.0124.003758.00
Public Investment (% GDP)140.00.030.04
280.00.020.04
3110.00.020.06
410.00.020.02
510.00.020.02
610.00.030.03
710.00.020.02
Total270.00.020.06
Public Revenue (EUR billion)14412.655.001232.60
2870.67.70224.50
31165.04.50240.60
411474.61474.601474.60
51799.9799.90799.90
61320.0320.00320.00
71441.1441.10441.10
Total27221.04.501474.60
Public Revenue (EUR per capita)1412,305.84850.0020,358.00
2810,931.62567.0021,592.00
31113,397.62913.0041,599.00
4117,869.017,869.0017,869.00
5113,202.013,202.0013,202.00
6118,735.018,735.0018,735.00
719480.09480.009480.00
Total2712,716.12567.0041,599.00
Public Revenue (% GDP)140.50.400.54
280.40.260.53
3110.40.310.53
410.50.450.45
510.50.470.47
610.40.440.44
710.40.380.38
Total270.40.260.54
Public Tax Revenue (EUR Billion)14297.798.00681.00
2842.94.90134.70
31145.73.00196.10
41773.3773.30773.30
51502.6502.60502.60
61180.2180.20180.20
71259.4259.40259.40
Total27139.03.00773.30
Public Tax Revenue (EUR per capita)146846.02580.0011,424.00
286759.81511.0012,650.00
3118982.81563.0025,771.00
419371.09371.009371.00
518296.08296.008296.00
6110,551.010,551.0010,551.00
715574.05574.005574.00
Total277928.31511.0025,771.00
Public Tax Revenue (% GDP)140.30.210.30
280.30.190.31
3110.30.160.46
410.20.240.24
510.30.290.29
610.20.250.25
710.20.220.22
Total270.30.160.46
Public Tax Revenue (% Public Revenue)140.60.530.57
280.60.570.73
3110.60.460.88
410.50.520.52
510.60.630.63
610.60.560.56
710.60.590.59
Total270.60.460.88
European Structural and Investment Funds (2014–2020 EUR billions)1435,830.84923.0086,112.00
287300.6917.0021,382.00
31110,797.6140.0030,883.00
4127,935.027,935.0027,935.00
5144,656.044,656.0044,656.00
611947.01947.001947.00
7139,835.039,835.0039,835.00
Total2716,106.5140.0086,112.00
European Structural and Investment Funds (2014–2020 EUR per capita)141447.3407.002553.00
281443.8242.002889.00
3111820.6237.003362.00
41339.0339.00339.00
51737.0737.00737.00
61114.0114.00114.00
71856.0856.00856.00
Total271459.7114.003362.00
Expenditure (Education)140.20.150.28
280.20.000.41
3110.20.000.40
410.20.210.21
510.10.060.06
610.30.310.31
710.20.190.19
Total270.20.000.41
Expenditure (Social Protection)140.20.120.23
280.20.000.33
3110.20.050.56
410.30.280.28
510.00.050.05
610.20.240.24
710.10.070.07
Total270.20.000.56
Expenditure (General Services)140.20.100.25
280.20.050.41
3110.20.040.37
410.20.230.23
510.20.150.15
610.10.070.07
710.20.220.22
Total270.20.040.41
Expenditure (Health)140.10.010.25
280.10.000.26
3110.10.000.27
410.00.020.02
510.50.480.48
610.00.030.03
710.30.270.27
Total270.10.000.48
Expenditure (Economic Affairs)140.20.120.20
280.10.000.21
3110.10.040.18
410.10.110.11
510.10.130.13
610.10.130.13
710.10.100.10
Total270.10.000.21
Expenditure (Others)140.20.080.27
280.20.050.59
3110.20.040.46
410.20.150.15
510.10.130.13
610.20.220.22
710.10.150.15
Total270.20580.040.59

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Figure 1. Methodological approach to analyze smart city initiatives in the EU.
Figure 1. Methodological approach to analyze smart city initiatives in the EU.
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Figure 2. Number of Scopus results per European Union country after applying exclusion criteria.
Figure 2. Number of Scopus results per European Union country after applying exclusion criteria.
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Figure 3. Detailed methodological approach to analyzing the literature.
Figure 3. Detailed methodological approach to analyzing the literature.
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Figure 4. Year of existing studies in the literature by country.
Figure 4. Year of existing studies in the literature by country.
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Figure 5. Clusters of the countries by their number of literature results.
Figure 5. Clusters of the countries by their number of literature results.
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Figure 6. Distribution of the smart city initiative results by category.
Figure 6. Distribution of the smart city initiative results by category.
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Figure 7. Countries’ Smart City initiatives divided by category.
Figure 7. Countries’ Smart City initiatives divided by category.
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Figure 8. Categories with the widest presence among countries.
Figure 8. Categories with the widest presence among countries.
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Figure 9. Geographic representation of the scores of the factors.
Figure 9. Geographic representation of the scores of the factors.
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Figure 10. Loadings of the principal component analysis.
Figure 10. Loadings of the principal component analysis.
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Figure 11. Socio-economic profile of territorial clusters of categories typologies (initiatives).
Figure 11. Socio-economic profile of territorial clusters of categories typologies (initiatives).
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Table 1. Inductive analysis and originating keywords of smart city categories.
Table 1. Inductive analysis and originating keywords of smart city categories.
KeywordsCategory
Leaks, floods, anomaly detection, infrastructure, wastewater, water quality, irrigation, smart meters, residential consumptionWater and irrigation
Air quality, sound and geo-sensing, green roofs, noise, climate change, green spaces, indoor and outdoor monitoring, crowdsourcing monitoringEnvironment and air quality
Circular economy, waste management, fill-level sensing, route optimization, selective waste collection, organic wasteWaste
Smart districts, energy systems, renewable energy, green solar cities, intelligent streetlight management, electric car, smart grid, residential microgrid, geothermal, wind power plants, green energy, biomass, biogas, fast charging Energy and lighting
Governance framework, development strategy, smart municipality, crisis management systems, public procurement, open strategy, smart city control rooms, policy process standardization, city marketing, smart regions, advanced e-governmentStrategy and governance
3D city model, spatial data population distribution, land use, sustainable urban planning, spatial planning, modelling tool, urban computing, virtual reality, geology, urban accessibilityUrban planning
Smart tourism destination, tourism and hospitality, travel behavior, heritage, preservation, sustainable tourism, smart museum, augmented reality, rural tourismCulture, tourism and heritage
Rural areas, rural contexts, small cities, cow sharing, smart town, urban-rural relationships, smart villageRural and agriculture
Smart stadium, sport and smart communities, sport activitySport
Driving elements, challenges, priorities, barriers, benefits, value proposition, ideology, myths and utopias, evaluation system and model, national scale, practices, case studies, characteristics, understandings, findings and lessons, KPIs and index, rankings, trends and opportunitiesSmart city foundations
Standards, data interoperability, open data policy, urbanAPI, information modeling, big data analytics, mobile sensor, crowdsensing and crowdsourcing, living labs, integrated dashboardDigitization and interoperability
Digital rights, surveillance, cybersecurity education management, general data protection regulation, blockchain, risk management, crime prevention, emergency response, cameras and LiDAR sensors, digital rights, urban safety, smart securityPrivacy, security and safety
Senior care system, urban health, wellbeing, disease control, hospital information systems (his), e-health, e-prescription system, quality of life, emotion, stress mapping, Covid-19, predicting patients’ urgency demand, active mobile phone intervention, health monitoring systemHealth and well-being
Wireless sensors, mobile sensors, LoRaWAN, internet of things, 5G networks, communication technologies, LoRa network, Wi-Fi and interactive displays, cyber physical systems, Zigbee wireless communication, narrowband IoT (NB-IoT)Infrastructure and communication networks
Sustainable urban regeneration, utilities, smart building, buildings renovation, green building, thermal modelling, smart home, municipal building regulation, building information modellingBuildings and housing
Traffic forecasting and route guidance, traffic management system, road marking, road damage detection, road safety, crowdsensing urban transit, dynamic obstacle detecting, intelligent traffic light, automatic road sign inventory, road safety, intelligent crosswalksTraffic
Smart parking, off-street parking, parking management, pay-as-you-go, controlling system, applications, mobile payment, social parking, real-time parking prediction systemParking
Intelligent transportation system, electric bike sharing, carpooling and electro-mobility, smart mobility, light rail, public transport demand, pedestrian movement, green transportation, electric carsharing, multimodal sustainable mobility, shared micromobility, inclusive mobility, soft mobility, autonomous vehicles, mobility-as-a-service (MaaS), crowd mobility, connected carsMobility and transportation
Sustainable urban freight transport, logistics, last-mile, intelligent offloading distribution, logistics freight traffic, short-distance freight, autonomous vehicle fleet, city logistics, grocery distributionLogistics
Participatory governance, collaborative data platforms, citizen participation, human-centric approach, co-creation, smart community, e-governance, democratization, urban experimentation, social policy, inclusion, collaborative decision-making processes, stakeholder involvementCommunity, participation and inclusion
Smart campus, hybrid learning, mobile education, public libraries, school as a service, human capital, knowledge and intellectual capital, role of universities, gamification educational strategy, training, employment future skillsEducation
Public private partnerships, Industry 4.0, remote working, sharing economy, smart industry, green economy, local entrepreneurs, startups, accelerators, digital nomads, intelligent manufacturing system, ethics, sustainable production, inclusive 3D printing, corporate social responsibilityEconomy and industry
Table 2. Loadings of the principal component analysis.
Table 2. Loadings of the principal component analysis.
Component
1234
Parking0.892
Infrastructure and Communication Networks0.869
Smart City Foundations0.778
Environment and Air Quality0.757
Waste0.669
Traffic0.654
Water and Irrigation0.651
Mobility and Transportation0.626
Strategy and Governance0.592
Sport 0.899
Health and Wellbeing 0.738
Education 0.737
Culture, Tourism and Heritage 0.670
Urban Planning 0.632
Energy and Lighting 0.557
Rural and Agriculture 0.837
Logistics 0.732
Buildings and Housing 0.708
Digitization and Interoperability 0.614
Economy and Industry 0.541
Privacy, Security and Safety 0.901
Community, Participation and Inclusion 0.565
Total Variance Explained64.9709.6335.3164.303
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Correia, D.; Marques, J.L.; Teixeira, L. The State-of-the-Art of Smart Cities in the European Union. Smart Cities 2022, 5, 1776-1810. https://doi.org/10.3390/smartcities5040089

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Correia D, Marques JL, Teixeira L. The State-of-the-Art of Smart Cities in the European Union. Smart Cities. 2022; 5(4):1776-1810. https://doi.org/10.3390/smartcities5040089

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Correia, Diogo, João Lourenço Marques, and Leonor Teixeira. 2022. "The State-of-the-Art of Smart Cities in the European Union" Smart Cities 5, no. 4: 1776-1810. https://doi.org/10.3390/smartcities5040089

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