**3. Smart and Sustainable Cities: An Urban Focus to Achieve Sustainability**

The aforementioned issues are extremely challenging to tackle, but they are not discouraging many scholars and thinkers from searching for solutions to realize more sustainable futures [53–55]. Today, approximately 55% of the global population lives in cities whose fabric is rapidly expanding across the planet [56]. The figure is over 85% in many countries—such as Australia, the UK, and the Netherlands [57]. This makes urban areas the prime focus of sustainability policy, not only because they house the majority of the world's population, but also because they contain the core of global socioeconomic activities [58,59]. The changing focus from *nation* to *city* has created new and alternative ideas for building sustainable futures by placing cities at the center of policy actions [60].

In recent years, one of the most prominent ideas in urban policy circles has been the imperative to employ information and communication technology (ICT), in order to address major urban and societal challenges [61]. This trend gave birth to the notion of 'smart city'. While the origin of the concept of smart city dates back to centuries ago, the practice of smart urbanism has been made popular only in the 2000s with urban projects led by private companies like IBM and Cisco [62–64]. Since then, many major technology, construction, and consultancy companies, together with policymakers and city planners, have jumped onto the smart city bandwagon [65,66]. This has resulted in a myriad of smart-city initiatives that are reshaping existing cities and building new ones all over the world [67,68]. In a nutshell, a smart city is, in theory, a locality that uses digital data and technology to improve efficiency in different interconnected urban domains (such as energy, transport and safety), eventually resulting in economic development, better quality of life and sustainability [69].

Nevertheless, in practice, this is not always the case. Numerous studies have shown that, actually, existing smart cities are often disproportionately driven by economic objectives and incapable of addressing social and environmental concerns [70–75]. This is why, in recent years, the focus of smart-city research has shifted towards the 'smart and sustainable city', in the attempt to rebalance the economic, social, and environmental dimensions of smart urbanism [76–78]. A conceptual framework is provided in Figure 2. A smart and sustainable city is defined as an urban locality functioning as a robust system of systems with sustainable practices, supported by community, technology, and policy, to generate desired outcomes and futures for all humans and non-humans [79].

This conceptualization utilizes the Input-Process-Output-Impact approach [80]. As the key 'input', we have the city and its indigenous assets. By using this asset base, three 'processes'—i.e., technology, policy, and community—generate strategies, actions, and initiatives. These result in 'outputs' in the economy, society, environment, and governance domains. When these outputs are aligned with knowledge-based and sustainable urban development goals, principles, and practices, they produce the desired 'impact' for a smart and sustainable city [79].

The framework underlines that, despite the prevalent technocentric perspective in the making of smart cities, in order to create cities that are smart *and* sustainable, we actually need a balanced view on the community, technology, and policy trio as the driver of transformation. It also highlights that cities should not be understood and treated as mere technological artefacts, but rather as social processes, and that sustainability should not be approached in a one-dimensional way, but rather holistically as the equilibrium among diverse social, environmental, and economic spheres [81–83]. In other words, technology will only lead to sustainability if its adequateness is thoroughly scrutinized via community engagement, and its implementation is carried out via a sound policy and government monitoring [79].

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**Figure 2.** A conceptual framework of smart and sustainable cities, derived from [79]. **Figure 2.** A conceptual framework of smart and sustainable cities, derived from [79].

### This conceptualization utilizes the Input-Process-Output-Impact approach [80]. As the key **4. Smart and Sustainable City Technologies: The Increasing Role of Artificial Intelligence**

'input', we have the city and its indigenous assets. By using this asset base, three 'processes'—i.e., technology, policy, and community—generate strategies, actions, and initiatives. These result in 'outputs' in the economy, society, environment, and governance domains. When these outputs are aligned with knowledge-based and sustainable urban development goals, principles, and practices, they produce the desired 'impact' for a smart and sustainable city [79]. The framework underlines that, despite the prevalent technocentric perspective in the making of smart cities, in order to create cities that are smart *and* sustainable, we actually need a balanced view on the community, technology, and policy trio as the driver of transformation. It also highlights that cities should not be understood and treated as mere technological artefacts, but rather as social processes, and that sustainability should not be approached in a one-dimensional way, but rather holistically as the equilibrium among diverse social, environmental, and economic spheres [81–83]. In other words, technology will only lead to sustainability if its adequateness is thoroughly Digital technologies are increasingly offering new opportunities for cities in their journey to become smart and sustainable—especially in relation to issues of community engagement and participatory governance [84]. There is a large variety of smart and sustainable city technologies available today and their list is exhaustingly long [85,86]. For instance, in a recent study, Yigitcanlar et al. [87] have identified the most popular smart and sustainable city technologies in Australia by means of social media analytics. The study concentrated on determining what the key smart city concepts and technologies are, and how they are perceived and utilized in Australia. The results have shown that the concepts of innovation and sustainability, and Internet-of-things (IoT) and artificial intelligence (AI) technologies, are the dominant ones. Unsurprisingly, these top technologies are merging today to form artificial-intelligence-of-things (AIoT) [88] to achieve more efficient IoT operations, improve decision-making and human-machine interactions, and enhance data management and analytics [89].

scrutinized via community engagement, and its implementation is carried out via a sound policy and government monitoring [79]. **4. Smart and Sustainable City Technologies: The Increasing Role of Artificial Intelligence**  Digital technologies are increasingly offering new opportunities for cities in their journey to become smart and sustainable—especially in relation to issues of community engagement and participatory governance [84]. There is a large variety of smart and sustainable city technologies available today and their list is exhaustingly long [85,86]. For instance, in a recent study, Yigitcanlar There is neither a universal definition of AI, nor an established blueprint to build one [4,90]. In simple terms, an AI is a nonbiological intelligence that mimics the cognitive functions of the human mind, such as learning and problem solving [91,92]. More specifically, an artificially intelligent entity is supposed to possess the following capabilities: the ability to learn by acquiring information on the surrounding environment, the capacity to make sense of the data and extract concepts from it, the skill of handling uncertainty, and the power to make decisions and act without being supervised [15]. There are several types of machines and algorithms, which possess the above capabilities at different levels of development, meaning that there are various levels of AI [93]. These levels are illustrated in Figure 3 and described below.

management and analytics [89].

Figure 3 and described below.

et al. [87] have identified the most popular smart and sustainable city technologies in Australia by means of social media analytics. The study concentrated on determining what the key smart city concepts and technologies are, and how they are perceived and utilized in Australia. The results have shown that the concepts of innovation and sustainability, and Internet-of-things (IoT) and artificial intelligence (AI) technologies, are the dominant ones. Unsurprisingly, these top technologies are merging today to form artificial-intelligence-of-things (AIoT) [88] to achieve more efficient IoT operations, improve decision-making and human-machine interactions, and enhance data

There is neither a universal definition of AI, nor an established blueprint to build one [4,90]. In simple terms, an AI is a nonbiological intelligence that mimics the cognitive functions of the human mind, such as learning and problem solving [91,92]. More specifically, an artificially intelligent entity is supposed to possess the following capabilities: the ability to learn by acquiring information on the surrounding environment, the capacity to make sense of the data and extract concepts from it, the skill of handling uncertainty, and the power to make decisions and act without being supervised [15]. There are several types of machines and algorithms, which possess the above capabilities at different

**Figure 3.** Levels of artificial intelligence (Source: Authors). **Figure 3.** Levels of artificial intelligence (Source: Authors).

In 1997, IBM's Deep Blue defeated the then World Chess Champion Garry Kasparov—that was a remarkable twist in the story of AI and intelligent machines. However, it is more appropriate to classify Deep Blue as a 'reactive machine' (Level 1), since this AI is programmed to undertake one single task, and it does not have the capacity to learn and improve itself [94]. Above all, this type of AI does not take the initiative. It mostly *reacts* to human inputs, rather than planning and pursuing its own original agenda. Its actions and ideas are derivative and are triggered in response to external In 1997, IBM's Deep Blue defeated the then World Chess Champion Garry Kasparov—that was a remarkable twist in the story of AI and intelligent machines. However, it is more appropriate to classify Deep Blue as a 'reactive machine' (Level 1), since this AI is programmed to undertake one single task, and it does not have the capacity to learn and improve itself [94]. Above all, this type of AI does not take the initiative. It mostly *reacts* to human inputs, rather than planning and pursuing its own original agenda. Its actions and ideas are derivative and are triggered in response to external stimuli.

stimuli. The next level (Level 2) is the 'Independent AI'. In 2016, Google's AlphaGo beat the international Go champion Lee Sedol. Go is arguably the most complex board game ever invented by mankind, and AlphaGo won thanks to its learning ability and capacity to take original actions that its human opponent could not foresee. This victory was an extraordinary outcome and boosted AI research world-wide. A similar, although less spectacular example, are now common AI chatbots which today many companies are using to interact with their customers on their websites. Other examples range from apps that regulate our phones and homes, to autonomous vehicles that are capable of determining and executing complex routes in chaotic urban environments [95–97]. What these AIs The next level (Level 2) is the 'Independent AI'. In 2016, Google's AlphaGo beat the international Go champion Lee Sedol. Go is arguably the most complex board game ever invented by mankind, and AlphaGo won thanks to its learning ability and capacity to take original actions that its human opponent could not foresee. This victory was an extraordinary outcome and boosted AI research world-wide. A similar, although less spectacular example, are now common AI chatbots which today many companies are using to interact with their customers on their websites. Other examples range from apps that regulate our phones and homes, to autonomous vehicles that are capable of determining and executing complex routes in chaotic urban environments [95–97]. What these AIs have in common is that they all operate independently. Human actions do not dictate their actions. Independent AIs proactively come up with their own agenda and implement it without humans leading the way.

The above categories constitute what is commonly referred to as 'artificial narrow intelligence'. This is the AI level that we have reached to date in practice, and that is becoming a common sight in contemporary cities and societies. However, R&D efforts are constantly leading to bolder and more innovative theories such as the 'theory of mind AI', which pictures an AI system that has beliefs, desires, and emotions [98]. A 'self-aware AI' is likely to be the next level of AI, thereby producing machines which actually function like us [99]. We call this level 'Mindful AI' (Level 3) to denote artificial intelligences which not only have a mind and are capable of thinking. They are also conscious of their own mind and thoughts which they apply to multiple domains of knowledge. This is the level of 'artificial general intelligence' at which machine behavior is almost indistinguishable from human behavior.

Mindful AIs, and artificial general intelligence more in general, are hypothetical stages of development, which could become the steppingstone to further technological progress in the field of AI. The ultimate level of AI that has so far been imagined is the 'artificial super intelligence'. Here at the 'Super AI' level (Level 4), the AI does everything and anything better than us humans [100]. The opinions of scholars on superintelligence are mixed. While some believe that this could be mankind's last invention leading to the end of human civilization, others posit that this technology could be the from human behavior.

leading the way.

beginning of a new era as our only chance of leaving this planet and establishing an interplanetary or interstellar civilization [101–103]. last invention leading to the end of human civilization, others posit that this technology could be the beginning of a new era as our only chance of leaving this planet and establishing an interplanetary

opinions of scholars on superintelligence are mixed. While some believe that this could be mankind's

Mindful AIs, and artificial general intelligence more in general, are hypothetical stages of development, which could become the steppingstone to further technological progress in the field of

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have in common is that they all operate independently. Human actions do not dictate their actions. Independent AIs proactively come up with their own agenda and implement it without humans

The above categories constitute what is commonly referred to as 'artificial narrow intelligence'. This is the AI level that we have reached to date in practice, and that is becoming a common sight in contemporary cities and societies. However, R&D efforts are constantly leading to bolder and more innovative theories such as the 'theory of mind AI', which pictures an AI system that has beliefs, desires, and emotions [98]. A 'self-aware AI' is likely to be the next level of AI, thereby producing machines which actually function like us [99]. We call this level 'Mindful AI' (Level 3) to denote artificial intelligences which not only have a mind and are capable of thinking. They are also conscious of their own mind and thoughts which they apply to multiple domains of knowledge. This is the level of 'artificial general intelligence' at which machine behavior is almost indistinguishable

As urbanists interested in the present and near future of urban development, we deal with those existing technologies that are already in the process of altering the sustainability of cities. The rest of the viewpoint will, therefore, focus on artificial narrow intelligence. This vast field of AI includes technologies with at least one of the following capabilities: (a) *perception* including audio/visual/textual/tactile (e.g., face recognition); (b) *decision-making* (e.g., medical diagnosis systems); (c) *prediction* (e.g., weather forecast); (d) *automatic knowledge extraction and pattern recognition* (e.g., discovery of fake news); (e) *interactive communication* (e.g., social robots or chat bots); (f) *logical reasoning and concept extraction* (e.g., theory development from premises) [104]. Mapping out the state of the art in AI is highly useful to better understand the capacities and impact of artificial narrow intelligence. Figure 4 illustrates the key AI problem domains and paradigms. or interstellar civilization [101–103]. As urbanists interested in the present and near future of urban development, we deal with those existing technologies that are already in the process of altering the sustainability of cities. The rest of the viewpoint will, therefore, focus on artificial narrow intelligence. This vast field of AI includes technologies with at least one of the following capabilities: (a) *perception* including audio/visual/textual/tactile (e.g., face recognition); (b) *decision-making* (e.g., medical diagnosis systems); (c) *prediction* (e.g., weather forecast); (d) *automatic knowledge extraction and pattern recognition* (e.g., discovery of fake news); (e) *interactive communication* (e.g., social robots or chat bots); (f) *logical reasoning and concept extraction* (e.g., theory development from premises) [104]. Mapping out the state of the art in AI is highly useful to better understand the capacities and impact of artificial narrow intelligence. Figure 4 illustrates the key AI problem domains and paradigms.

**Figure 4.** Artificial intelligence knowledge map, derived from [105].

Artificial narrow intelligence is increasingly becoming part of our lives, and an integral element of our cities. For instance, in many parts of the world, states are trialing AI-driven cars to prepare their cities and citizens for the disruptions that autonomous driving will generate [97,106–108]. Robotic dogs are employed in places like Singapore for monitoring social distancing in the era of COVID-19 [109]. A couple of years ago, Dubai has started robot police services meant to stop petty crime [110]. Hospitals in a number of countries, such as Japan, are employing robot doctors [111]. Many homes are getting safer and more energy efficient due to smart home technology and services, and home automation, or *domotics*, is becoming a big part of the construction industry [112]. Websites of both major corporations and ordinary companies have now chatbots to respond to clients' inquiries [113]. In China and Malaysia, large-scale urban artificial intelligences called *city brains* are managing the transport, energy and safety systems of several cities [15].

Additionally, AI is an integral part of environmental research in a number of countries such as Australia, where autonomous drones are detecting via machine learning environmental hazards and animals in danger of extinction [114,115]. Today, most smart phones offer an AI as a personal assistant [116]. Overall, these examples are only the tip of the AI iceberg, as the largest application of AI technology is in analytics. Many of the decisions impacting our life are being made as a result of descriptive, predictive, and prescriptive analyses of data collected and processed by AI [117,118]. In other words, AI-aided urban data science is being extensively used today in cities across the globe, to address the uncertainties and complexities of urbanity [119,120].
