Abstract
Although many angles of the smart cities’ movement have been well-studied by academia, a gap remains in our understanding of how municipal policymakers understand and apply the concept in their localized contexts—particularly how community size affects smart city ambitions. In 2017, Infrastructure Canada announced its Smart City Challenge (SCC), asked communities across Canada—municipalities, local or regional governments, and Indigenous communities—to design creative and innovative solutions to address any societal problem using any data and connected technology solution. The application process was a de facto survey that generated a unique, publicly available dataset from the 199 applicant communities, big and small alike. We find that larger communities submit more ambitious proposals to use latest technologies to address social equity and inclusion concerns through expanded operations, whereas smaller, more rural municipalities focus more on basic infrastructure and services. Though there is overlap, their dreams differ.
Introduction
The academic and professional literature on smart cities is growing rapidly, increasing our understanding of the smart city space, but also increasingly raising questions about the role of the public in smart city design (Macke et al. 2018; Vidiasova and Cronemberger 2020; Yeh 2017), the focus of administrators and political actors in procurement (Mora et al. 2019; Pansera et al. 2022) and the place of technology vendors and management consultants in directing the smart city sector (Boltanski and Thevenot 2006; Shelton and Lodato 2019; Soderstrom, Paasche, and Klauser 2014). Much of this research crosses disciplinary and methodological boundaries, creating a rich body of research, but there has yet to be significant survey work completed relating to how municipal public administrators conceive optimal smart city design (Zwick and Spicer 2023). Within this research, there is an emerging pattern that smaller, more rural municipalities are approaching smart cities distinctly different than their larger city counterparts (Spicer, Goodman, and Olmstead 2021). We ask the question: Does variation in the size of communities influence the ambition of thier smart city plans?
Infrastructure Canada’s Smart City Challenge (SCC)—which ran from 2017 to 2019—provided an inimitable perspective into smart city building by creating a forum where municipalities provided their most implementable plans on how to use technology to solve social problems, based upon their existing planning work and on their personalized understanding of smart city frameworks. Although the SCC was not designed to be a research instrument, it accomplished those ends by creating a first-of-its-kind de facto survey process of all municipalities within a single country of their smart city plans. The results are a host of information of the practice of smart city building, including what cities want to do with smart city technology, the problems they hope to solve, which technologies they favor, how they designed their plans and even the level of involvement from the public. This creates a public repository, which standardized questions and requirements for applicants, that inventories detailing planning and design on smart cities—a unique tool found in no other jurisdiction. By utilizing the database, researchers are able to gain insight about how municipalities plan to use smart city technology, for what purposes, with which partners and controlling for certain conditions.
This is not the first study that has looked at the SCC, but rather the first that looks at the entire applicant pool and their applications as a data set. Robinson and Biggar (2021) explored how the SCC promotes municipal innovation, highlighting the opportunity for future research to study federal government intervention into city-building. Goodman et al. (2020) conducted interviews with three applicants in the middle of the competition, finding that cities engagement efforts were genuine but top-down rather than bottom-up. Zwick and Spicer (2023) interviewed finalists after the competition, finding that cities overwhelmingly approached smart cities as a government-driven, data-centric endeavor where technology took a backseat to problem-solving. In contrast to earlier research on the SCC, our research provides a sense of what practitioners, particularly those from smaller municipalities with lower administrative capacities and less developed plans, would like to implement by looking at their initial submissions. 1
This paper analyzes the initial application pool from the 199 communities across Canada that entered the SCC. The competition used a standardized application form, which allows for analysis of different questions across the entire applicant pool including the policy focus, the type of technology utilized, the prospect of partnership, the scope of consultation, and more. The drawback, of course, is that the aim of any competition is to win, and therefore applications were designed with this end goal in mind. The plans were prepared to propel communities to further stages of the competition and, as a result, a wide variety of scope, depth, and quality were submitted: for example, some plans presented were aspirational while others were repackaged existing community plans. This contest design, even with its limitations, effectively demonstrates the types of smart city plans that both large and small communities in Canada are currently prioritizing.
This paper proceeds as follows. First, we ground the study in existing literature, exploring the genesis of smart cities as a theoretical concept and practical focus for city builders. Second, we then provide some contextual background on Canada’s SCC to explain the dataset and our research methods. Third, we present findings using graphs and tables from the data on (1) focus areas, (2) community-serve areas, (3) technologies, and (4) partnerships. Fourth, we discuss (a) how community size determines smart city focus and need, and (b) what co-learning can occur between Infrastructure Canada and local communities. In the final section, we conclude with our paper’s major takeaways and specify avenues for future research.
Literature Review
Our research is keenly interested in asking whether community size affects smart city plans, and from what problems do they wish to use technology to solve to which technologies they wish to employ to solve those problems. We begin by briefly exploring the smart city sector as a concept, before diving into the impact of community size. The term “smart city” arose from the Smart Growth movement in the 1990s (Hollands 2008; Vanolo 2014). Initial Smart City proponents advocated for improved urban planning through policy change to support the use of information and communication technologies and modern infrastructure within cities (Albino, Berardi, and Dangelico 2015; Harrison and Donnelly 2011). Over time, however, the term became synonymous with technology and, eventually technology-infused urbanism.
In 2010, IBM popularized the term Smart City to coincide with their “Smart Cities Challenge”—a pro bono service focused on innovation and opportunity development, widely seen as a business development exercise (Soderstrom, Paasche, and Klauser 2014). IBM was soon joined in this space by other large technology companies, such as CISCO, and management consulting firms, such as McKinsey, who similarly pitched new technology to cities as a solution to pressing urban challenge (Falconer and Mitchell 2012).
Governments became the primary consumers of technology products. The drive for community intelligence accelerated among local decisions-makers, as cities began to label themselves as “smart” after the implementation of functioning ICT infrastructure or e-governance technology (Soderstrom, Paasche, and Klauser 2014). The label also began to be increasingly applied to communities that had attracted high-tech industries (Soderstrom, Paasche, and Klauser 2014). Some countries even seized the idea to create Smart Cities from scratch, rather than integrating ICTs into existing systems of local service delivery.
Cities are often ranked against their global peers for “smartness” and “intelligence,” providing an opportunity for recognition on a global level (Giffinger and Gudrun 2010). The center piece of the smart city pitch from many private firms is generally the local economy, often framed as both a challenge and opportunity. One of IBMs key smart city planning documents argues that workforce skills, aptitude, knowledge, creativity, and innovation are more important drivers of economic growth than traditional drivers, such as natural resources, physical labor, or manufacturing prowess (Dirks, Gurdgiev, and Keeling 2010, 1). Integration of ICTs and smart city technology lays the groundwork for attracting this new type of vital workforce talent. Similarly, an IBM study argues that “quality of life and the attractiveness of a city are profoundly influenced by the core systems of a city . . . therefore these systems are critical for attracting, creating, enabling and retaining this new kind of workforce and the innovation-enabling environment it requires to be productive” (Dirks, Gurdgiev, and Keeling 2010, 6).
The natural conclusion emerging from the private think pieces is that a local government cannot have a robust local economy without laying the groundwork to make the city more attractive to those whose success drives the new economy. Similar narratives are presented by competitors, such as McKinsey (Bouton et al. 2013) and CISCO (Kim, Mitchell, and Villa 2011): harnessing smart city technology creates the conditions for the right kind of economic development, namely the type that captures emerging economic and labor force trends.
“Smart cities” as an applied term extends beyond the “city” itself, with rural municipalities also fitting the bill and applying similar solutions as their larger peers. However, past research has shown that there are distinct needs and preferences between urban and rural municipalities when approaching smart city design and digitization (see Spicer, Goodman, and Olmstead 2021). Rural municipalities operate with steep capacity and scale challenges and must be much more deliberate and thoughtful in their procurement and application of smart city technology (Spicer, Goodman, and Olmstead 2021). These dynamics may influence the decisions of applicants from certain communities.
Throughout the sales and acquisition process municipalities are often confronted with a grim future if smart city technology is not part of their local long-term procurement plan. Urban problems, such as pollution, growth, economic uncertainty are shown to be confronted with inadequate governance tools, such as “broken technologies” and “inadequate systems” (Soderstrom, Paasche, and Klauser 2014). The adoption of smart city technology is then presented as the way to enhance a local economy, increase competitiveness and better manage local resources.
Dataset and Research Methods
In 2017, Amarjeet Sohi, Minister for Infrastructure Canada, launched the SCC by challenging Canadian communities to design creative and innovative solutions to their community problems using data and connected technology (Government of Canada 2017). The SCC was loosely modeled after the United States Department of Transportation (USDOT) Smart City Challenge, which was aimed at mid-sized cities in the US and focused on developing smart transportation systems (US Department of Transportation, 2015). The American competition encouraged partnerships with stakeholders, particularly financial, to leverage prizes into a sustainable solution. Columbus, Ohio was eventually declared the winner on the strength of their plan to better connect marginalized communities to the economic opportunities in the urban core and received $40 million in government seed money (Bliss 2016). 2 The USDOT Smart City Challenge would be more accurately described as a “Smart Mobility Challenge.”
Canada’s SCC was built on similar foundations, but was designed to encourage all municipalities to apply, to tackle any policy problem. Unlike the American challenge, the policy area, technology use, and community engagement requirements were left open. Another key differing point in Canada’s SCC is that its design and recruitment actively encouraged participation from small, rural, remote and Indigenous communities, which makes the resulting data set novel. As the initial application used a standardized template, it served as a de facto survey of Canadian municipalities’ smart city plans. Infrastructure Canada received 133 applicants, representing 199 municipalities and made this data publicly available. 3 The inclusion of standardized criteria for technologies, focus area and community systems/service area create a unique view into the structure of the applications for researchers.
The SCC had three different prize categories: a $5 million dollar prize for communities with populations not exceeding 30,000, two $10 million prizes for communities with populations up to 500,000 and one $50 million prize, available to all communities regardless of population. Applicants could select only one prize category, based upon their individual eligibility. For instance, a community of 1 million residents may select the $50 million prize, but not the $10 or $5 million, while communities of 100,000 could select the $50 million or the $10 million prize. A community of 10,000 residents would be eligible to select any of the prize categories, as were Indigenous communities of any size. Despite the eligibility to all categories for small and midsize communities, we found that they self-sorted into $5 million and $10 million prize pools respectively to increase their odds of winning. 4 In later phases of the competition, twenty applicants were selected as finalists and four were chosen to win the various prize categories. 5 Finalist communities would receive cash awards to further their smart city planning process, and winners received greater sums intended to be seed money complemented by committed partners. This contest design uniquely enables research on how cities of different magnitudes approach smart city planning.
The application defined a “smart cities approach” for applicants, describing the approach as one that “aims to achieve meaningful outcomes for residents by leveraging the fundamental benefits that data and connected technology have to offer” (Infrastructure Canada 2017). Along these lines, Infrastructure Canada provided four key themes, which are shared below, in Table 1. Following this framework, applicants were encouraged to engage and consult with residents when preparing their applications to identify “the most pressing issues their community faces” (Infrastructure Canada 2017). The belief was that consultation would help applicants better define the scope of their projects.
Smart City Definitions (Infrastructure Canada).
Each applicant was asked to design a “challenge statement,” which is a single sentence that defines the outcome a community aims to achieve by implementing its smart city proposal. The statement was intended to be “measurable, ambitious and achievable through the proposed use of data and technology” (Infrastructure Canada 2017). For instance, a community could propose to use technology to reduce crime below the national average or convert a derelict neighborhood into a center for economic growth in Canada—the purpose of the challenge statement was to provide a goal and a benchmark for the project. Essentially, these statements are needed to operationalize the proposal.
In addition to the challenge statement, applications had to include a brief summary of their project and describe how it would benefit members of the community. Applicants also needed to select from a list of six “focus areas,” which matched the intended goals of the project: economic opportunities, employment and inclusion, environmental quality, healthy living and recreation, mobility, and safety and security. Applicants could choose more than one focus area if it applied to their project. The “focus areas” were intended to be general in nature but still provide a policy scope to the project that would help jury members categorize the submitted projects.
Applicants also needed to select from a set list of technologies and community systems/service areas. The categories that communities could select are provided below, in Table 1. These were essentially more aligned with traditionally delivered areas of municipal public service, such as social services and public health. Of note, both had the option to select “other,” which would allow a municipality to add in a component not available in the standardized list. Seventy-one municipalities selected “other” for the community systems/service areas and thirty-six selected “other” for technologies, thereby writing in additional information beyond the pre-selected categories for each (Table 2).
Additional Standardized Selection Criteria for SCC Applications.
Those applying were also asked to respond to a series of questions about the anticipated outcome of the project, how consultation with the community shaped the proposal, how the application supports the medium and long-term goals of the community, the community’s readiness and ability to implement the project, and where potential partners are involved in the project and how the applicant anticipates partners taking part in the future (Infrastructure Canada 2017). Applicants were also encouraged to include letters of support from community leaders and partners, and to place their proposal online for review by the community (Infrastructure Canada 2017).
The SCC attracted a lot of interest from municipalities, with applicants spanning communities with as many as 2.7 million residents (Toronto, Ontario) to one with just 185 (Riverhead, Newfoundland). The bulk of the applications came from the provinces of Ontario (30), Quebec (28), and British Columbia (24). Forty-nine communities applied in the $5 million prize category, one hundred twenty-seven in the $10 million category, and twenty-two in the $50 million category. Many of the communities could be classified as small, with only 44 having populations over 100,000. With that said, Canada’s largest cities were well represented, with Toronto, Montreal, Calgary, Ottawa, Edmonton, Winnipeg, and Vancouver all submitting applications. Twenty applications were from Indigenous communities, or the bids were focused on addressing the Indigenous populations within the bidding community.
We began our research by downloading the summary application data available on the SCC website. 6 We then cleaned the file by making sure terminology was used consistently and that unique variables were separated. We then categorized the data by how far cities made it through the competition (separating data by all applicants, finalists, and winners) and by the prize category ($5 million, $10 million, $50 million) in which each municipality applied to derive descriptive results for how successful/developed applications became and how smart cities applications varied by small, midsize, and large communities respectively. The results are found in tables and charts in the next section.
Findings
Applicants were asked to select from six focus areas, aligning their projects with the pre-determined focus areas designed by Infrastructure Canada. If the submitted project fit into more than one category, applicants could select more than one focus area. The focus areas are listed below, in Figure 1.
Focus areas by prize category.
Empowerment and inclusion was the most frequently selected focus area at 54% for small communities and 70% for midsize and large communities. 7 Projects in this area covered a wide a range. Some, such as the City of Regina’s application, focused on reducing Indigenous youth incarceration rates by using technology to increase supports for at-risk youth. 8 As another example, the Town of Yarmouth planned to increase housing security by using technology to match those at risk for homelessness or housing insecurity with local resources to assist them. Many of the applications in this category took on similar themes, which may be because the US SCC was won by Columbus, Ohio who centered equity and inclusion in their plan (Bliss 2016), even though new research has found that the overall narrative of inclusion in the US challenge proposals was largely performative and paid insufficient attention to marginalized populations (see Wang et al. 2021). Because of the narrative around Columbus’ victory there was a precedent set to use technology to overcome pressing social problems that policy or fiscal tools have solved in the past. Considering the emphasis the SCC placed on this criterion as an integral part of the challenge, it is almost surprising that empowerment and inclusion were not even higher. However, it does show that small communities put less emphasis on this focus area.
In contrast, 56% of small communities emphasized economic development while only 35% of large ones did, illustrating that economic development is more of a central concern in smaller municipalities. The economic opportunity applications pursued small business supports, improved infrastructure, or enhanced sustainability efforts. At the second highest category at 48% overall and not being integral to the challenge, this indicates that economic opportunity is central to the thinking of most communities’ planners. This aligns with prior literature on how cities appreciate smart city technology for its potential economic benefits (Odendaal 2021).
Those in the healthy living and recreation category generally centered around food security, agriculture and shared sport or recreation facilities; a category of much more interest by large communities (43%) than midsize (21%) or small ones (28%). “Mobility” applications tended to focus on active transportation networks or sensor-enabled transportation systems, such as on-demand transportation or bus tracking systems. 9 Those in the environmental quality area focused on sustainability, food systems and using technology to measure environmental indicators, such as air quality. Certain applications in this section also focused on small-scale climate change mitigation. Finally, safety and security focused on a broad range of projects, including those similarly categorized in the empowerment and inclusion section, such as reducing Indigenous youth incarceration, and those listed in the environmental quality section, including food security. Very few of the applications in this area focused on community policing or crime.
As shown in Figure 2, the focus areas for the winners and finalists were similar to the general application pool, with 68% of finalists listing the “empowerment and inclusion” category and the winners were slightly higher (80%). However, half of the finalists (50%) listed “health living and recreation,” with the applicant pool (26%) and winners (20%) more in line. “Environmental quality” was the least selected focus area among the finalists, with only 12% choosing it as a focus. However, 40% of the winners selected this category. Giving an impression that Infrastructure Canada favored “Healthy Living and Recreation” to advance to the finalist round, while favoring “Environmental Quality” for the winning prizes.
Focus areas by all applicants, finalists, and winners.
Turning attention to the community system and service areas categories in Table 3, we see similar direction as the focus areas above. The three most frequently selected areas are economic development, education and training, and social services; which remained consistently high for communities of all sizes. The projects in these categories all tended to center on equity and inclusion, namely empowering marginalized communities or increasing opportunities for certain community groups. While many were interested in projects that developed Recreation and Parks (60%), Emergency Services and Enforcement (38%), and Water and Wastewater (24%), the jury did not select any of these projects as winners. Similarly, Arts and Culture was selected by over half of the applicants (55%) but was significantly less likely to be chosen to be winners (20%). All in all, infrastructure Canada’s jury selected finalists largely in line with the applicant pool.
Community Systems and Service Areas split by competition results and prize category.
Small communities’ applications, compared to large ones, put more emphasis on upgrading waste (38% compared to 9%), water and wastewater systems (32%–9%), and the environment (72%–39%). Whereas the needs of large communities compared to small ones contrasted in Land Use Planning and Development (91%–58%), Public Health (87%–58%), and Social Services (83%–60%). Small and large cities both favored “Roads and Transportation” projects; however we find that smaller cities focused on the prior while larger cities focuses on the latter. We conclude that smaller municipalities were relatively more focused on basic services and infrastructure, while larger communities were relatively more focused on operations and human services. What is clear as well is that few communities were interested in systems that were not necessarily visible to the public, as most applicants chose areas that had high visibility. This makes sense considering that public input in the selection of what to bid on and the public is more likely to ask for more palpable outcomes.
As part of their applications, communities were asked to identify the types of technologies they planned to incorporate into their proposed projects. Much like the thematic areas described above, applicants could select more than one technology if necessary. 10 This information is presented below, in Table 4.
Technology Choice split by competition results and prize category.
The categories provided by Infrastructure Canada were quite broad, with most applicants coupling several technologies together. For instance, “assistive technology” was a category that applicants believed encompassed everything from drones to power-assisted scooters to digital speech software. Some were selected, but not described in the summary or challenge statement. In fact, with many applications, there was little indication about how the specific technology selected would be integrated or power the project that was being proposed.
The finalists overwhelmingly selected multiple technologies. Ninety-four percent indicated that they intended to incorporate “mobile applications” and “internet of things.” On average, the finalists selected at least four technologies, demonstrating the breadth of their planning even at the earlier stages of the competition. In fact, every winner (100%) involved a “mobile application,” while “sensors,” “internet of things,” and “open data platforms” were all by 80% of winners. This demonstrates a preference for traditional smart cities technologies that could potentially be sourced from a number of vendors, rather than novel, cutting-edge technologies.
Community size appears to play some role in determining the type of technology applications incorporated. For instance, larger communities tended to emphasize open data platforms (100%–72%), big data analytics (100%–70%), and augmented/virtual reality programs (78%–26%). Traditional methods of technology (i.e., big data analytics, cloud computing, enterprise solutions) remained the most common forms of engagement, likely due to the nuance of technical implementation in municipal frameworks (Johnson, Acedo, and Robinson 2020). The vast majority, however, were incorporating some type of mobile application, which would be highly visible for residents. Health and Medical Technology was also seen to be more important in larger communities (52%) compared to medium sized (31%) and small sized communities (26%), most likely due to a greater demand and a variety of needs among larger smart city populations.
Overall, we see that large and midsize communities used their proposals to check more technology boxes than small ones did. There were only a couple technologies where small communities showed more interest than larger ones. Small communities emphasized payment platforms (46%) compared to large cities (26%), which we interpret as larger communities having already implemented electronic tax and bill payment systems, whereas smaller cities still lag behind on this. Smaller communities also tended to identify environmental monitoring as a priority (70%), while comparatively few large cities did the same (39%). Across the board though, smaller communities seemed to favor proven, off-the-shelf technologies that they wish to apply to their home communities, whereas large and midsize communities preferred more experiential ones.
Applicants were provided limited text-space to explain their project using the challenge statement and summary.
11
Many communities failed to articulate the centrality of technology in those statements. The technology was highlighted using the standardized options above, but most applicants supplied a vision of their project where the place or need for technology was not readily apparent. For instance, some applications aimed to create an active transportation network or a community farming or gardening project and only made passing reference to technology (e.g., the incorporation of sensors or mobile devices) but did not make mention of how that technology would enhance the proposal or plan. The novelty of technology was on full display, but the use case or end goal of the incorporation of the technology was rarely present. Some examples are provided below
12
: Our agricultural community will revitalize and grow through the connection of people to the land and food while attracting citizens to share in its prosperous, innovative and resilient way of life [City Name] will continue to blend its five historic communities into one with connected and engaged citizens to strengthen social cohesion and increase a sense of belonging by 10 per cent while creating new levels of accessibility to services, information and events, thereby supporting mental well-being for all Canadians. [City Name] will become a community focused on building mental wellness and resilience among youth, reducing the prevalence of mood and anxiety disorders, reducing the costs of mental health care and reducing tobacco use, heavy drinking and cannabis use through increasing physical activity and improving opportunities for social inclusion and empowerment.
Similarly, 72% of communities selected artificial intelligence (AI) as a form of technology in their project proposals, while 44% selected augmented reality (AR) or virtual reality (VR)—technologies that are certainly novel but have been shown to have limited broad application in government, and even raise concerns about “algorithmic bias” (Valle-Cruz et al. 2019). In fact, it has been argued that AI will likely not solve systemic problems in government and may even exacerbate troubles in service delivery, privacy, and ethics (Mehr 2017). This further leads to questions about how thoroughly applicants considered the incorporation of this technology in their projects.
While some failed to articulate the centrality of certain technologies to their applications, others described the technological ends, such as the creation of “connected communities” or “digitally enhanced services” without articulating the end policy goal. For instance, some applications discussed the need to connect citizens, places and communities together both internally and externally, but failed to mention the purpose of this connection or the goal of the technology enablement.
Many applicants also fell short on articulating how data would be used and how privacy would be protected in their plans. Only four applicants made mention of privacy. Of those four, two pledged to anonymize data generated through their projects, while one mentioned a procedure to ensure data security and the final applicant made mention of a collective, transparent decision-making process to release information generated by the project. Some applications indicated their municipalities reached out to external privacy experts through consultation to address data governance components in their proposals. Even though very few applicants made even limited mention of privacy and data governance, 52% of communities indicated that their projects would collect citizen data. Given the range of technology options selected, it is safe to assume that virtually all projects would have collected and stored citizen-generated data in some shape or form. Data governance and privacy is a long-standing blind spot for the smart city sector and even became a central facet in community activism against Toronto’s Quayside project that was proposed by Google. Data can be considered the fuel for smart cities and how that data is collected, stored and accessed by private firms and other third parties has consequences for the public (Haggart and Spicer 2022). The near absence of meaningful data governance mentions and privacy protections should be considered alarming, but part of a trend to treat these important safeguards as an afterthought by policy makers.
No question on the initial SCC form asked communities specifically about their partnerships, however it was indicated as a priority area for the SCC. In the limited text-space provided for vision and summary, most of the communities (72%) mentioned partnerships in some form, which should not be surprising given that the application framework specifically asked communities to identify any potential partners. These partnerships included a range of actors and are displayed below, in Figure 3.
Partnerships by prize category.
Forty-three percent of communities indicated that they were partnering with private firms, greater for large communities (65%) and less for small ones (32%), however only 8% of applications specified the name of the private firm. Two communities pledged to partner with telecommunications companies (namely large Canadian telecommunications firms Telus and Bell, respectively), while the remainder were intending to partner with local economic development corporations. 13 Attention on innovation has shifted toward private sector development and the use of public-private partnerships (Shearmur and Poirier 2017), which makes these relationships particularly valuable.
Out of the 25% of applications that included partnerships with other municipalities, most are done so by large communities (43%) rather than small ones (8%). This is most likely due to proximity, with large municipalities interested in partnering with smaller municipalities (e.g., suburbs) within their metropolitan region, while small, rural communities felt as if it was easier to go it alone. Partnering with local community development organizations, was the most popular organization type to work with and was more popular in large communities (65%) than small ones (32%) despite small communities have a higher emphasis on economic development in general. We can only assume that many smaller communities do not have such a local organization. All of the winners identified a community development organization partnership in their proposals. Collaborative efforts are central in smart city implementations and focus tends to be centered on developing productive interactions between networks of urban actors (Nilssen 2018). Of note, those who won used their project descriptions to detail which groups they were partnering with and how that partnership would ultimately lead to their project’s success, emphasizing that the jury appreciated understanding the nature of the partnership in question. Additionally, 11% of communities included partnerships with universities or other post-secondary institutions, which did not vary significantly based on city size. We can speculate, particularly for rural areas, that none exist in many communities, whereas larger communities did not see how they were an important player in their smart city plans. However, none of the winners intended to work with a post-secondary institution despite having one or more all within their regions. Together, this exhibits how the knowledge of university ecosystems is not well integrated into municipal planning and implementation. Local non-profits were not mentioned a lot; however, we speculate that many communities did not see a role for them in their applications.
The application procedures also asked specifically about the nature of community engagement prior to developing the proposal. Specifically, applicants were provided with a maximum of 1,500 words to describe “how your community residents have shaped your challenge statement . . . describe your plans for continuing to engage and involve them in your final proposal going forward” (Infrastructure Canada 2017). Only 10% of communities made mention of any prior community engagement. Of those, half did not specify how the community was consulted, only that consultation had taken place. One community made mention of a prior study on “community health” that helped to shape their proposal. Three communities made note of public consultation sessions that occurred while their proposals were being developed. One community made mention of a survey, while another described a “health co-op” program with students that aided in shaping their proposal. In terms of higher-level engagement, one community described a co-design process that incentivized participation from the community, including visioning and process mapping exercises, while another described an elaborate process involving digital, social media, radio, print and online engagement forums, dedicated events, a telephone town hall and in-person meetings to allow the community to provide input on the proposal.
The fact that very few communities opted for consultation can potentially be attributed to the compressed timeline of the contest. Those communities that already had a planning and engagement process underway could make mention of their consultation efforts in their application, and then fit that process into their SCC bid. This head start on planning before the competition launch allowed time to consult with community members. However, as detailed previously (see Goodman et al. 2020) many communities do not generally consider consultation as a key element of smart city planning. Those that did engage in consultation already had a cultural convention within their organizations to routinely seek the input of the community (Goodman et al. 2020). While the timeline to submit applications was relatively short, less than a year, for those that had no existing plan in place, some communities did develop consultation plans, as demonstrated above. It would have been possible to consult if the municipality itself had existing resources and experience to execute a consultation process quickly.
Of note as well is that only 7 out of 20 of the finalists conducted any sort of consultation with the community prior to the contest. Those that did consult provided fairly engaged plans, including in-person and digital consultation. Most of these consultation efforts appear to have been conducted prior to the design of the SCC proposal. Out of the finalists, 80% included partners in their proposals. Most of these partnerships were with other municipal governments instead of not-for-profit, private, or academic organizations.
Discussion
Even with its limitations, studying the entire SCC applicant pool has provided a useful view into how communities of different sizes view smart city planning. The prize categories provide proxies for city size, which allows researchers to determine the types of priorities emphasized by each.
The most noticeable pattern throughout the applications, noted here as focus area, was that proposals could be generally categorized into those seeking to address equity and empowerment related community concerns (large communities), and those hoping to drive efficiencies, create economic opportunity, and better manage resources through the adoption of technology (small communities)—a finding consistent with past work exploring how community size impacts smart city adoption (Spicer, Goodman, and Olmstead 2021). For community systems and service areas, smaller communities prioritized basic services and infrastructure, while large cities focused their attention on operations and human services. For technology, large cities also gravitated toward much more experimental technologies, such as artificial intelligence, video analytics, and augmented and virtual reality technology, while small communities gravitated to proven off-the-shelf technological solutions. This mirrors the greater technology skillset and capacities available in larger communities than small ones. Consistent with their environmental focus, small communities leaned heavily into incorporating environmental monitoring technology—an aspect that barely registered for large cities. Those in larger communities also appeared more willing to partner with private firms, while smaller communities seemed less open to such arrangements, which may be due to a lack of established smart city private sector entities in small communities.
That said, there were some commonalities among communities of all sizes in both what they emphasized and what they did not. For focus areas, empowerment and inclusion was the most selected choice. The literature (see Baldi, Megaro, and Carrubbo 2022; Spicer, Goodman, and Olmstead 2021) tells us that is a key arena of smart city development, however it was also a requirement under the rules of the competition, therefore it’s difficult to read much into this.
Under community systems and service areas, nearly all communities selected both Economic Development and Education and Training, showing that these are universal characteristics of smart city projects. The focus on economic opportunity should likely not be surprising, given that economic incentives have been so tied to smart city design in past literature (see Odendaal 2021; Vanolo 2014). Applicants tended to focus less on core municipal services areas, such as waste, water and wastewater services and emergency services and enforcement. The focus here is deliberate given how much advanced resource monitoring and policing technologies have become in smart city design over the past decade (see Joh 2019; Lynggaard and Skouby 2016). Other core priorities for municipalities, such as safety and security, barely registered, indicating that municipalities are largely looking to smart city technology in an aspirational sense to create more equal and vibrant communities.
All cities favored established technologies, including sensors, open data platforms, data analytics, and mobile applications. These technologies would be relatively easier to implement in smart city projects as they overlap with many different public administration services and infrastructure projects. A prevailing trend throughout many of the applications in the SCC is that technology took a backseat in the process—a feature highlighted in much of the critical urban research around smart cities and a prevailing trend in the smart city sector to focus less on technology and more on planning and prioritization (McFarlane and Soderstrom 2017). While applicants had to select which technologies they planned to utilize in their projects, few mentioned how technology would be integrated or power the proposal in their summary or challenge document, even fewer thought through the data security and data privacy aspects of their proposals.
This may be attributed to a broader trend pushing technology to the background in smart city building, which springs from a belief that smart cities were too focused on technology for far too long and that technology should be dislodged from the center of analysis on smart city development (Chang, Jou, and Chung 2021). Even though some have argued that the definition of a smart city ought to be broadened where possible to include more than technology (see Albino, Berardi, and Dangelico 2015; Nam and Pardo 2011; Neirotti et al. 2014), technology is a core feature of smart city design and ought not to be ignored (James et al. 2021). In fact, what the SCC applications demonstrate as well is that there is little emphasis on the back-end service delivery platforms, something that has also been addressed elsewhere in literature (see Kuk and Janssen 2011). Instead the focus through the challenge was on large, visible projects with an ambiguous focus on technology instead of the often unseen platforms that drive digitization and technology products across an organization.
Conclusion
Infrastructure Canada’s SCC launched to significant fanfare among municipal leaders. The smart city space is populated by a range of contests and rankings, measuring which communities are “smartest,” better equipped with technology and innovative strategies (Giffinger and Gudrun 2010). Canada’s SCC was built on similar foundations in that communities from across the country were competing with each other to win, but also gain resources to implement their smart city visions. As mentioned above, Canada’s SCC was designed as a de facto survey into the state of smart city building. The competition was intentionally designed to recruit communities of all sizes, from large cities to indigenous nations, from urban to rural. Municipalities across Canada submitted applications for millions in prize money that would be awarded on the strength of their ideas for utilizing smart city technology in their own communities. For the first time, a de facto survey of communities of all shapes and sizes asked what their smart city plans would be if they had funding. This allows us to answer the research question: Does variation in the size of communitiesinfluence the ambition of their smart city plans technology?
In answering the call, municipalities provided their best applications for a smart city project, expressing their ambitions, philosophy on development and smart city design and conceptualizing scale, partnership, and implementation. Each of their responses highlights what challenges they identified, what focus areas they would address, which technologies they would use, and what partnerships would make it possible. Most who applied were unsuccessful, but in applying have left deep insight into their motivations to adopt certain smart city technologies and processes. This was a unique opportunity for Canadian municipalities, creating an equally unique opportunity for researchers hoping to gain deeper insight into how, why and under what conditions municipalities are adopting (or not adopting) certain smart city technologies.
By analyzing the results, it becomes clear there are both similarities and differences in the appetites and approaches of small and large communities. Smaller municipalities prioritized basic services, economic development, and infrastructure, while larger cities focused their attention on operations, community empowerment, and human services. Large cities also gravitated toward much more ambitious projects with experimental technologies, such as artificial intelligence, while small communities preferred tried-and-true technological solutions that could be more easily implemented to address a specific need. Many applications—from both larger and small municipalities alike—lacked basic elements, such as privacy and security, that would be expected of them in their pursuit of becoming smart cities. Small communities may not have big smart city dreams, but they do wish to use technology to help solve local challenges.
Now that the challenge has ended, future research to conduct a post-hoc analysis would be valuable. Interviews with applicants would give insight into the extent to which the SCC changed their long-term planning and determine how many applicants used this initial exercise to pursue smart city plans even if their applications were not successful. It would, therefore, be useful to evaluate how communities approached the SCC with the comparison of other funding envelopes in-mind. Given the increasing popularity of funding-based challenges for municipal projects, it would be beneficial to examine the utility of the competition model itself. While adjudication by a jury does remove some political elements from the traditional multi-level infrastructure funding model between national and local governments, it also creates challenge for non-winning communities who devoted resources to the competition and ultimately came up short without anything to show for their participation. Finally, further research is needed to determine how successful winning applications were in practice. The prize money was substantial, but the aspirations of each community were even more so. In some cases, it is difficult to understand how the Infrastructure Canada prize money would be sufficient to complete the projects. Examining how these communities scaled their project based upon the resources available should be another component in evaluating the policy and governance value of the SCC.
Through the SCC, smart city researchers have gained a unique insight. Municipalities had to publicly share their smart city aspirations, something that some have been loathe to do in the past before they come to fruition. In this case, researchers have information on what communities would hope to do if they had resources, rather than what they are currently pursuing in an environment of resource constraint. In this sense, the SCC provides a novel view into smart city building of communities of all shapes and sizes. It could be used to answer future research questions about technology adoption, service design and thematic focus, especially if repeated in the future by Infrastructure Canada, where comparisons over time would be made possible.
Footnotes
Data Availability Statement
Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
