AI for Smart Cities

Description

In the past 70 years, AI has been a rapidly growing field and is being applied nowadays in a wide range of applications. Artificial intelligence (AI) plays a crucial role in enhancing smart city initiatives, offering numerous benefits such as improved water distribution, efficient energy management, better waste handling, and alleviation of traffic jams, noise, and pollution. The primary efforts in smart city development have centred on data generation and the acquisition of new insights into the intricate and dynamic nature of urban environments. (H.M.K.K.M.B. & Mittal, 2022). Since 2008, cities have been incorporating artificial intelligence (AI) to enhance decision-making processes and contribute towards the achievement of the Sustainable Development Goals (SDGs). (Ingwersen & Serrano-López, 2018)

What is AI?

Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition and natural language processing. AI technologies involve various techniques, including machine learning, deep learning, and natural language processing, to enable machines to learn and perform tasks without explicit programming.
Essentially, artificial intelligence (AI) is a technology designed to generate results aimed at achieving a specific goal. This goal is a human-devised aim transformed into a mathematical format. The outputs generated by AI can include forecasts, suggestions, or choices. (OECD, 2023). 
The concepts of algorithm, AI system, AI ecosystem, and AI represent varying levels of scale and complexity. An algorithm stands at the most detailed level, acting as a set of instructions that transforms input into output. An AI system refers to a singular application that processes specific input to generate its unique output. An AI ecosystem describes a complex network of interconnected AI systems that communicate and interact with one another. Lastly, AI is the most general term, encompassing the entire spectrum of technologies, methodologies, and systems within the field. (Leal, et al., 2022)
There are two main categories of AI systems: symbolic and statistical. Symbolic AI operates based on explicit rules and logic to reach conclusions, while statistical AI, in contrast, learns from patterns in data, using induction rather than deduction from set rules. Each method has its strengths, depending on the application. However, the recent surge in AI's capabilities and its widespread adoption can be attributed mostly to a subset of statistical AI called machine learning (ML). (Climate Change AI, 2021)

AI for Smart Cities

The potential benefits of AI for city planning and management are manifold. AI offers new tools and methods for analysing, modelling and simulating complex urban systems, thereby having the potential to create more sustainable, resilient and liveable smart cities for their citizens.
By monitoring and analysing data, AI systems can provide recommendations to improve resource usage, help reduce congestion and improve traffic flow, detect potential security threats, reduce energy consumption, promote waste reduction, etc. AI-powered systems can also provide personalized services to citizens such as personalized recommendations for restaurants, events, and activities based on their preferences. However, it is important to ensure that AI is implemented responsibly, with a focus on transparency, accountability and privacy. Such technology can still provide accurate analytics, while remaining non-intrusive.
Implementing AI technologies could be crucial in overcoming worldwide social, economic, and environmental challenges. Although each city has its unique characteristics, urban areas are pivotal in driving societal changes, especially in terms of digital advancements. Cities are hubs for the aggregation of people, employment, research, wealth, and recreational activities. They offer increased access to opportunities for a larger segment of the population while also concentrating both societal problems and environmental concerns. Given their role in international networks, the advantages and disadvantages of adopting AI technologies have implications that reach far beyond their geographical limits. (Leal, et al., 2022)
As urban areas face pressing issues related to resource allocation, governance complexities, socio-economic disparities, and environmental hazards, innovation becomes essential in addressing these evolving challenges. (Yigitcanlar, et al., 2021). To maximize AI's potential for urban improvement, municipal governments need to establish conditions that support sustainable and inclusive growth. AI governance aims to oversee the development of these conditions, ensuring a balanced approach to leveraging opportunities and mitigating risks.

Limitations of AI

In the realm of artificial intelligence, responsible usage is paramount, yet it comes with inherent challenges. These systems, reflective of the biases and assumptions present in their training data and design, often mirror societal prejudices, influencing their decision-making processes. This phenomenon underscores the critical need for vigilance in the AI development phase to avoid encoding negative assumptions. Furthermore, AI lacks the intrinsic ability to evaluate its effectiveness, operating within a narrow set of pre-defined goals without the capacity for human-like judgment. This limitation highlights the fallacy of perceiving AI as capable of independent thought or impartiality, necessitating continuous human oversight to ensure alignment with broader societal values. 
Additionally, the foundation of AI on mathematical principles constrains its understanding to quantifiable metrics, struggling to comprehend the qualitative nuances that define human objectives. In summary, these limitations underscore the need for careful consideration in the design, deployment, and oversight of AI systems to ensure they serve human purposes without perpetuating existing biases or overlooking the complexity of human values. (Leal, et al., 2022)

AI governance

Artificial Intelligence (AI) not only reflects inherent biases but also significantly impacts societies based on the context it's deployed in. Local governments must understand how values are integrated into AI, steering its development towards inclusivity and sustainability. This involves a deep dive into AI governance, which is not limited to digital oversight but includes ethical guidelines, legal regulations, societal norms, and practices. Governance here is a broad concept, designed to capture various interpretations and focus on decision-making and social interactions. (Leal, et al., 2022)

Despite national AI guidelines, cities face challenges within a multi-level governance system, highlighting the need for a nuanced approach that allows them to leverage their unique positions. (Schmitt, 2022) (Taeihagh, 2021) AI also poses accountability challenges, necessitating robust frameworks to balance risk and opportunity throughout the AI lifecycle. A significant hurdle is the limited capacity of cities to navigate the AI governance landscape, amid a high demand for IT and AI specialists. This challenge can be addressed through cross-sector partnerships and a focus on developing local talent, ensuring that cities' core values guide AI initiatives towards a more inclusive and sustainable future.

For that reason, the European Commission has developed the first-ever legal framework on AI, the AI Act. This initial proposal, crafted by the European Commission, was designed to provide clarity and guidance to AI developers and users while minimizing burdens, particularly for small and medium-sized enterprises (SMEs). Key elements of the proposal included a governance structure at both European and national levels to address AI risks and a risk-based classification of AI systems into four categories.

The strictest regulations were reserved for AI practices deemed to pose unacceptable risks, such as social scoring by governments. High-risk applications, notably those in critical infrastructure or law enforcement, were subject to rigorous pre-market requirements including comprehensive risk assessments, data quality control, traceability, and mandatory human oversight. Conversely, AI systems with limited risk were mandated to maintain transparency to enable informed public interactions, such as clear identification of AI-generated content and chatbot disclosures. The least risky applications, like video games or spam filters, were allowed more operational freedom, reflecting their benign usage across the EU.

On March 6, this foundational proposal transitioned into enforceable law as the European Parliament approved the Artificial Intelligence Act. The legislation, which had been refined through negotiations with member states since December 2023, was robustly supported by MEPs, passing with 523 votes in favor, 46 against, and 49 abstentions.

The ratified Act continues to protect fundamental rights, democracy, the rule of law, and environmental sustainability, shielding them from the risks associated with high-risk AI technologies. It aims to foster innovation and secure Europe's leadership in AI by setting tailored obligations based on the risk and impact level of different AI systems. The Act specifically prohibits AI applications that could infringe on citizens' rights, such as biometric categorization based on sensitive characteristics, indiscriminate scraping of facial images for recognition databases, emotion recognition in workplaces and schools, social scoring, profiling-based predictive policing, and any AI that manipulates human behaviour or exploits vulnerabilities.

Law enforcement use of biometric identification systems, although generally prohibited, is permitted under strictly defined and narrowly applicable circumstances, such as locating a missing person or preventing terrorist attacks, subject to stringent temporal, geographical, and approval constraints. High-risk AI systems now face stringent obligations to evaluate and mitigate risks, maintain transparency, ensure human oversight, and log usage. General AI systems are required to adhere to transparency standards, including compliance with EU copyright laws and the publication of detailed summaries of their training content. Noteworthy innovations include the introduction of labelling requirements for manipulated content, known as "deepfakes", and the establishment of national-level regulatory sandboxes to foster the development of innovative AI technologies.

As the Act moves towards formal adoption and implementation, it embodies the principles and recommendations from the Conference on the Future of Europe, aiming to create a competitive, safe, trustworthy society with transparent and responsible AI usage, while improving digital accessibility for all, including those with disabilities. This legislative milestone marks the beginning of a new era in AI governance, aligning closely with European values and strategic ambitions. (European Commission, 2024).

The AI Life Cycle

The development of an artificial intelligence (AI) system is segmented into five interconnected stages that outline its interaction with the external environment. This model aids in comprehending AI's architecture and its progression, promoting strategic analysis. The stages form a cycle, each linked to the others, influencing aspects ranging from algorithm creation to final deployment.

1. Beginning Phase
   The journey starts with the beginning phase, focusing on identifying the problem. This initial step lays the foundation for the rest, as everything that follows is tied back to the original problem identified. It includes critical assessments and identification of potential risks in the AI system's intended use.
2. Design Stage
   Next, the design stage emphasizes planning the algorithm's structure before any coding begins. It builds on the groundwork laid in the beginning phase, taking into account team dynamics and the potential impact of introducing AI, such as changes in power dynamics and economic impacts.
3. Technical Creation Stage
   The technical creation stage shifts towards the practical aspect of AI development, concentrating on the specifics of the algorithm and the technical decisions involved.
4. Implementation Phase
   After development, the implementation phase transitions the algorithm from a controlled setting to its real-world application. This phase is critical as it exposes the algorithm to the complexities of its operational environment.
5. Maintenance Phase
   Lastly, the maintenance phase focuses on the period after the AI system has been deployed, up until it is decommissioned. This phase is crucial for ensuring the algorithm's continued effectiveness and relevance through regular updates and maintenance.
6. In urban contexts, AI systems typically function within a complex ecosystem, necessitating proactive risk identification and management. Integrating AI development stages with local project management methods offers strategic opportunities for intervention and adaptation, ensuring the AI systems align with urban dynamics and challenges. (Leal, et al., 2022)

Stages of implementation of AI solutions in smart cities

1. Understand the Difference Between AI and ML for Smart Cities: Begin by educating stakeholders on the nuances of AI and ML. Understanding these technologies' capabilities is crucial for identifying how they can be applied to enhance urban living, such as through traffic flow optimization or predictive infrastructure maintenance.
2. Define Urban Challenges and Opportunities: Clearly identify the specific problems or opportunities within the city that AI can address. Ask critical questions about desired outcomes, existing obstacles, the role of AI in overcoming these challenges, and the data available to support these efforts.
3. Prioritize Value-Driven AI Initiatives: Select AI projects based on their potential to deliver tangible benefits to the city and its residents, focusing on near-term goals. This might include improving public transportation efficiency or enhancing energy sustainability in public buildings.
4. Evaluate Capabilities and Approach for AI Development: Assess whether to develop AI solutions in-house, purchase existing technologies, collaborate with external partners, or outsource development, based on the city's internal capabilities and strategic objectives.
5. Consult with Smart City AI Specialists: Engage with domain experts who have experience in applying AI within urban environments. This could include academic researchers, private sector innovators, and technologists from other smart cities.
6. Prepare and Secure Urban Data: Organize and clean the city's data to ensure it is ready for AI applications. Invest in robust security measures to safeguard this data, respecting privacy and ethical considerations.
7. Start Small and Scale Thoughtfully: Launch pilot projects targeting specific urban challenges to demonstrate the potential of AI. Use the insights gained from these initial efforts to guide the strategic expansion of AI applications across different city domains. (Majewski, 2023)

Application Areas

Mobility
Artificial intelligence (AI) offers significant improvements for public transport systems, enhancing their efficiency and user-friendliness. AI technologies have proven effective in forecasting bus schedules and the variability in their arrival times. (Mazloumi, Geoff, Currie, & Moridpour, 2011) Additionally, AI analysis of survey data provides insights into public perceptions of shared mobility options, including dockless bike-sharing schemes. (Taleqani, Hough,, & K. E) Furthermore, AI enables the development of platforms that allow for the interactive examination and prediction of traffic patterns, facilitating the evaluation of urban planning strategies. (Leal, et al., 2022)

Another innovative application of AI is in smart parking solutions, where sensors installed in parking areas relay availability information to commuters via mobile applications. This approach has the potential to reduce congestion in densely populated urban centres significantly. ((n.d)) AI technologies support various aspects of delivery, logistics and transportation, facilitating efficient and innovative urban transport solutions. Furthermore, AI revolutionises urban mobility by enabling the control of autonomous vehicles and aircraft, including drones, helicopters and planes. (33A, 2024)

Environmental and Risk Management
Within urban contexts, AI offers real-time monitoring of environmental conditions, such as air quality, through pollution sensors. Moreover, AI's predictive capabilities support city risk management, enabling the forecasting of natural disasters and identifying areas vulnerable to flooding and earthquake aftershocks, thereby enhancing urban safety and resilience. (33A, 2024) Generative AI can also contribute to environmental sustainability by analysing data on pollution levels, climate change, and the impact of urban activities on the environment. (Arkara, 2023)
Artificial intelligence (AI) possesses the capability to assess the environmental impact of products throughout their entire lifecycles and supply chains.

This allows both companies and consumers to make decisions that are well-informed and impactful. However, it's important to recognize that while data and AI play crucial roles in improving environmental surveillance, they also come with their own environmental implications. Specifically, the information and communication technology (ICT) sector is responsible for approximately 3-4% of global emissions. Additionally, data centers consume significant amounts of water for cooling purposes, highlighting a critical aspect of the environmental footprint of data processing that must be considered. (UN Environment Programme, 2023)

The application of Artificial Intelligence (AI) and Machine Learning (ML) technologies is becoming increasingly crucial in environmental risk management, particularly in the context of climate change adaptation. These technologies enhance the efficiency and accuracy of climate models, enable detailed analysis of uncertainties, and make use of expansive geospatial data sets for improved predictions. AI and ML facilitate the development of flexible, modular modeling platforms that streamline the process of creating and deploying models for various climate and weather-related applications. This approach addresses the complexities of modeling in uncertain conditions and supports the integration of diverse impacts, thereby improving collaboration between scientists and stakeholders to ultimately accelerate the solutions to mitigate environmental risks. (Jones, et al., 2023)

City Infrastructure 
AI empowers cities to manage their infrastructure. It can be implemented in the development of predictive maintenance strategies for critical urban infrastructure such as roads, bridges, pipes, sewer lines, power grids and public buildings. AI-driven systems can gather and analyse data to accurately forecast maintenance requirements, thereby preventing accidents and minimizing operational disruptions.

For example, cities can employ AI applications to develop Digital Twins for urban road networks, facilitating advanced damage assessment through mobile mapping, laser scanning and ground-penetrating radar measurements. This approach allows for precise detection and forecasting of road maintenance needs, including pothole identification.

Resources Management (Energy, Waste and Water)
AI can play a significant role in improving the management of a range of resources in cities, including Energy, Waste and Water. Its technologies enable better decision-making, optimisation and automation of various processes. Cities can leverage AI to analyse data from diverse sources, including sensors, to predict energy demand and supply, enabling more efficient management of energy grids, reducing waste, and achieving cost savings with lower carbon emissions. Additionally, AI monitors energy consumption patterns in smart buildings and anticipates electricity load, generation and transmission capacity in the energy sector. (33A, 2024). This ensures a stable, efficient and low-carbon electricity grid, enhancing urban energy sustainability.

Additionally, AI can learn and refine its decision-making capabilities over time, enabling autonomous solutions for complex tasks. This technology is applied across various domains, such as optimizing heating and cooling systems in buildings, to boost efficiency and decrease energy use, showcasing its potential to significantly improve urban resource management. (33A, 2024). A similar approach can also help cities optimise water usage with sensors, weather forecasts or water supply systems. It allows for better management of water resources, detecting leaks and reducing water waste, leading to improved water sustainability. Furthermore, AI can analyse data from waste collection routes or recycling facilities, thus helping city authorities to better manage waste collection, reduce the amount of waste going to landfills and increase recycling rates.

More recent applications of AI included the analysis of data patterns to predict the amount of power generated from wind and water to plan infrastructure and manage disasters. (Mathe, Miolane,, Sebastien,, & Lequeux, 2019) (Mathe, Miolane,, Sebastien,, & Lequeux, 2019)It has also been used to help research the development of new materials that are more efficient in storing energy and use energy from low-carbon sources. (Butler, Davies, Isayev, & Walsh, 2018) Artificial intelligence (AI) has significantly improved the efficiency of energy distribution systems by enabling precise predictions of system status with minimal sensor data. (Donti, et al., 2018) By merging AI with physical models, it's now possible to simulate energy flows in specific areas like university campuses or residential zones. (Nutkiewicz, Yang, & Jain, 2018) Moreover, AI-driven algorithms play a pivotal role in demand response optimization, smartly scheduling power-intensive activities such as cement grinding and powder coating during times when electricity is cheaper, thereby optimizing energy use and reducing costs. (Zhang, et al., 2018)

Citizen Engagement
AI can help cities engage with their citizens by analysing social media, surveys and other data to understand their needs and preferences. AI techniques like natural language processing (NLP) and machine learning make it possible to organise and decipher unstructured data, enabling cities to design services that more effectively meet citizen needs. Cities can also employ AI-powered chatbots and virtual agent networks (VAN) that offer automated assistance. These conversational AI tools interact with individuals, responding to a wide array of inquiries and issues efficiently, without the need for human intervention (33A, 2024). By utilizing chatbots, cities can significantly enhance their citizen engagement strategies, providing prompt and accessible support to their residents.

The integration of Artificial Intelligence (AI) into citizen engagement facilitates more inclusive governance by leveraging AI and Large Language Models (LLMs) to enhance accessibility and analyze citizen feedback efficiently to exemplify AI's potential to democratize access to government services and AI's ability to unearth community priorities through social media data analysis. By institutionalizing feedback mechanisms and utilizing AI for comprehensive data analysis, governments can ensure development interventions are closely aligned with community needs. This streamlined approach highlights AI's pivotal role in transforming citizen-government interactions, ensuring policies and services are both responsive and reflective of the diverse voices they aim to serve. (Rahim, Mahony, & Bandyopadhyay, 2024)

In addition to optimizing urban systems, AI can also enhance citizen experiences and provide personalized services. By analysing data on citizen preferences and behaviour, AI-powered systems can offer personalized recommendations for restaurants, events, and activities. This enhances citizen engagement and satisfaction and contributes to a vibrant urban culture. (Nolle, 2023)

Security and Safety
AI can assist cities in enhancing their security and safety solutions. For instance, AI-powered video analytics can be used to automatically detect potential threats or monitor social movements and behaviours, differentiating between various groups like couples or adults with children. Additionally, cities can employ this technology to assess compliance with government rules and policies in public areas, providing immediate feedback to government authorities and businesses. This application was particularly evident in tools developed for the public response to the COVID-19 pandemic. For example, AI technologies were capable of identifying adherence to hygiene mask usage and social distancing policies in public spaces.

As we begin to incorporate Artificial Intelligence (AI) into our surveillance systems, the role of Deep Learning (DL) analytics is quickly becoming evident as a valuable tool for many organizations. A prime example of its utility is in conducting forensic searches, which involve combing through extensive video archives to pinpoint specific items or occurrences. With the growing integration of DL and analytics in surveillance technology, it's critical to approach this expansion thoughtfully. Understanding the full scope of its applications, acknowledging the technology's limitations, and engaging in thorough testing and evaluations are key steps in ensuring the successful realization of its benefits.

The foundation of successful video analysis lies in the quality of the surveillance footage. The principle of 'image usability' underscores the fact that the effectiveness of video analytics is heavily dependent on both the camera's quality and the clarity of the images it captures. Surveillance cameras are expected to operate around the clock, under a variety of environmental conditions and lighting situations, while still maintaining the ability to accurately process images in real time. (AXIS Comunications, 2023)

Informed Urban Planning
AI can improve urban planning by guiding cities with data-driven insights. It can be applied to land use planning, identifying the best locations for residential, commercial and recreational purposes. AI achieves this by analysing patterns of urban growth, migration and demographic changes, which are key to accurately predicting the immediate and long-term requirements for housing, transportation and services.

Furthermore, AI can be applied to model and simulate urban spaces, for instance, by being integrated into Digital Twins systems, enabling more dynamic and accurate representations. This allows planners to visualise future developments and evaluate their potential effects on e.g. the built environment, including aspects like sunlight exposure in public spaces and building density. Traditional urban planning often relies on static models and limited data, which can lead to inefficiencies and suboptimal decision-making. With the creation of digital twins, internal data with external variables can be used to simulate possible future decisions regarding the optimisation of resources, the creation of infrastructure or the implementation of a new system.

Artificial Intelligence (AI) revolutionizes urban planning by harnessing vast data to inform decisions, leading to more sustainable and efficient city development. It identifies trends and forecasts changes, enabling planners to make informed choices on zoning and infrastructure. AI automates routine tasks, allowing planners to focus on complex urban challenges. This technology not only improves decision-making but also shifts planners' roles towards deeper engagement with urban issues, enhancing research and planning practices. Ultimately, AI promises to create better-designed cities that meet the evolving needs of their populations, driving towards more strategic, equitable, and responsive urban development. (Peng, Lu, Liu, & Zhai, 2023)

City Administration
AI can aid city administration by streamlining the management of information from diverse sources such as scientific and legal documents, digital platforms and databases. It efficiently converts unstructured data, like emails, into structured formats, such as Excel sheets, facilitating easier data handling. Furthermore, AI enhances data visualisation through dashboards and reports, incorporating charts, interactive maps and apps, to support more informed decision-making and improve operational efficiency. (33A, 2024)

The integration of AI into city administration offers the promise of streamlined workflows and improved efficiency, automating routine tasks to enhance service quality and access to benefits. However, the adoption of AI necessitates rigorous ethical and legal frameworks to mitigate biases and ensure transparency and fairness. The dynamic nature of digital governance requires ongoing adaptation of laws and standards, fostering collaboration between legal experts, technologists, and policymakers. This balanced approach ensures that AI not only optimizes administrative processes but also upholds the principles of democracy and equitable treatment, underpinning the sustainable evolution of smart cities. (Parycek, Schmid, & Novak, 2023)

Education
In recent years, the educational sector has significantly evolved, thanks to the adoption of Artificial Intelligence (AI) and Information Technology (IT). This shift towards smart education involves leveraging IoT and AI to foster continuous, engaging learning environments and connect educational institutions nationwide through an intelligent, multidisciplinary approach.

Key innovations include smart devices and robotics in education, exemplified by a smartphone-controlled robot within a smart city simulation, enhancing practical learning in smart classrooms. Moreover, there's a growing focus on personalized learning, with new tools being developed to assess and adapt to individual learning styles using AI. This approach aims to optimize educational outcomes by matching students with the most effective learning strategies.

Additionally, the expansion of e-learning platforms underscores the importance of tailoring education to individual needs, offering students a broader spectrum of online learning opportunities and marking a shift towards more accessible, customized education experiences. (H.M.K.K.M.B. & Mittal, 2022)
 

Value Model

City Context

The success of AI solutions relies on the availability of comprehensive, high-quality data. Cities that have systems in place to collect, store and analyse urban data can more easily implement and benefit from AI technologies. To effectively incorporate generative AI into smart city initiatives, it is essential to blend technical know-how, forward-thinking leadership, and comprehensive stakeholder involvement.

Technical Expertise and Forward-Thinking Leadership: For generative AI to positively impact smart cities, leaders and stakeholders must envision its integration into current infrastructures and services. They need to stay updated on AI advancements and pinpoint where generative AI could be most beneficial. Promoting a culture of innovation and collaboration is key, encouraging diverse groups to come together to craft and deploy AI-enhanced solutions.

Engaging Stakeholders: The success of generative AI projects in smart cities hinges on the active participation of all relevant parties. This means assembling government bodies, private enterprises, academic circles, and the general populace to co-create AI initiatives. By incorporating a range of viewpoints and expertise, cities can devise AI applications that address the unique needs and hurdles of their populations.

Effective implementation must adhere to the principles of smart cities, focusing on people-centric solutions, partnerships across sectors, and governance driven by data. Generative AI should aim to improve living standards and sustainability, ensuring that innovations are transparent, ethical, and centered around the well-being of citizens. (Arkara, 2023)

Government Initiatives

The European Union's strategy on artificial intelligence (AI) emphasizes excellence and trust, with policies aimed at ensuring AI's safe, ethical, and innovative development. Key components of this strategy include:

• AI Package (April 2021): Introduced a comprehensive set of initiatives, including a communication on advancing a European AI approach, a review of the Coordinated Plan on AI with Member States, and a proposal for a regulatory framework addressing AI's impact.
• AI@EC Communication (January 2024): Outlined strategies to enhance the Commission's AI capabilities, focusing on safe, transparent, and human-centered AI use.
• Investment and Collaboration: A commitment to significantly fund AI development through Horizon Europe and Digital Europe programmes, aiming for a €20 billion annual investment from both public and private sectors. The Recovery and Resilience Facility further supports this with €134 billion earmarked for digital initiatives.
• Legal Framework for Trustworthy AI: Proposes a nuanced legal framework to manage AI risks, introducing categories (minimal, high, unacceptable, and specific transparency risks) and establishing dedicated regulations for general-purpose AI models.

These policies reflect the EU's dedication to becoming a global leader in trustworthy AI, balancing innovation with fundamental rights and safety. (COMMISSION E. , n.d.)
 

Stakeholder Mapping

Market Potential

Market Potential 

On the innovation front, AI developers are actively focusing on crafting AI-powered capabilities, products, and services tailored for smart cities and communities. Those innovators who are clients of TEF are poised to evolve into service providers for urban and communal settings. The subsequent sections delve into estimates of the AI market's size both in Europe and globally, offer insights into the EU's AI innovation landscape, highlight the hurdles AI developers face within the smart cities and communities framework, discuss market trends and demands, analyze the competition for CitCom.ai, and conclude with overarching observations.

AI Market Size Estimates

This segment offer an overview on the size of the AI market in Europe and around the globe. Despite discrepancies among analysts regarding exact figures and market definitions, a consensus on market size and future trends emerges, providing valuable indicators. Data related to the AI sector reveals insights into the number of AI entities in Europe, global and European revenue sizes, regional AI investments, and projections for the AI market in the public sector. Initially, the AI Watch Index 2021 reported approximately 5,776 AI stakeholders in the EU28 in 2020, encompassing research institutes, companies, and governmental bodies. Within this ecosystem, the JRC report classifies AI companies into three categories: those primarily focused on AI without patenting, those filing AI-related patents, and those both centred on AI and engaging in patenting, noting that 43 firms (0.7% of all EU AI companies) fall into the latter category. Expanding the scope, a study on AI and Blockchain for Europe's future, drawing from Crunchbase data, identified 950 AI SMEs in the EU27.

A 2023 report by Precedence Research highlighted the global AI market's value at USD 454.12 billion in 2022, with projections of reaching USD 2,575.16 billion by 2032, indicating a CAGR of 19% from 2023 to 2032. Europe's share of this market in 2022 stood at 25%, with expectations for the European AI market revenue to hit approximately USD 712.61 billion by 2032. Investment trends, as per Pitchbook, show that AI startups garnered over $115 billion in 2021, marking an 87.2% increase from the previous year. OECD.AI Policy Observatory's live data sheds light on AI development, usage rates, and sector-specific investments, with Venture Capital investments in AI industries within the EU27 from 2012 to 2023 amounting to about USD 51 billion. Analysis suggests that sectors related to CitCom.ai—such as Mobility & Travel, Energy, and digital development support—receive 10-15% of Europe's total AI funding.

EUROSTAT, Spintan, and Intan-Invest data reveal that Europe's investment in AI development and adoption exceeded €15.9 billion in 2020, with the private sector contributing €10.7 billion (67%) and the public sector €5.2 billion (33%). Future projections are even more ambitious, with the JRC's AI Watch Report 2021 setting a target of €22 billion by 2030, a figure potentially rising to over €30 billion by 2025 if current growth trends persist, according to a JRC publication. In examining the AI market specifically for the public sector within the EU, the available data is limited, particularly regarding smart cities and communities. Nonetheless, an analysis by GovReport suggests that AI will become indispensable for managing the dynamic needs of government services, despite the challenge of accurately sizing this market segment due to a lack of detailed sectoral data.

This comprehensive overview paints a picture of a rapidly expanding AI landscape, underscored by robust investment and innovation, with specific implications and opportunities for smart cities and communities.

Competitors offering TEF services.

The TEF (AI testing and experimentation facility) stands out for its comprehensive approach, spanning a wide array of disciplines, offering extensive geographical reach across Europe, and providing a diverse suite of services to AI developers focusing on Smart Cities and Communities. Despite its unique positioning, CitCom.ai faces potential competition from several organizations that provide related or comparable services. To maintain its competitive edge, CitCom.ai will monitor these entities closely to ensure its offerings continue to differentiate in the market. Potential competitors include:

•    Research and Technology Organizations (RTOs): Across 32 countries, approximately 350 RTOs offer support to SMEs, participating actively in the Horizon Europe programme. Notably, every second project under this program involves at least one RTO.
•    Other TEFs: There are other Testing and Experimentation Facilities, such as those focusing on health, agriculture, and AI-specific sectors like AI-Matters. Future TEFs could expand into the energy sector and public services.
•    AI4Cities: This initiative aims to expedite cities' transition to carbon neutrality by employing AI across six key areas: mobility, energy, construction, climate change adaptation, the circular economy, and engaging citizens.
•    AI4EU: This project is developing a European AI on-demand platform and ecosystem to facilitate AI development and testing across various fields, including healthcare, media, agriculture, robotics, and manufacturing.
•    AI4Copernicus: Utilizing the Copernicus Earth Observation Programme alongside the European AI on-demand platform, this project delivers AI-driven solutions to tackle environmental and societal issues, covering areas like energy, security, healthcare, and agriculture.

Founding 
This section outlines a variety of funding programs within the European Union aimed at supporting the growth of digital and smart city initiatives, as well as artificial intelligence (AI) development. These programs are diverse, ranging from digital infrastructure implementation to research and innovation projects, with the overarching goal of driving digital transformation, sustainability, and competitiveness across European cities, regions, and economies. The funding focuses on several key areas, including the development of smart cities and communities, AI advancement, enhancement of digital infrastructure and platforms, and support for small and medium-sized enterprises (SMEs). Here, we present an overview of the main funding mechanisms:

• Digital Europe Programme (DIGITAL): With a budget of €7.6 billion, DIGITAL aims to fast-track Europe's digital transformation by funding relevant projects, particularly those related to smart cities and AI. It addresses challenges like infrastructure gaps and digital transformation management, allocating €9.2 billion to advance critical technologies like AI, cybersecurity, and supercomputing. It also emphasizes digital skills acquisition and the use of interoperable digital technologies, with €2.1 billion dedicated to fostering common European data spaces and supporting ethical AI. The program also establishes a network of European Digital Innovation Hubs (EDIHs) to enhance technological capacity and innovation across the EU.
• Horizon Europe: With a massive investment of €94.1 billion, Horizon Europe supports research and innovation to strengthen the EU's technological and scientific foundations, including smart cities and AI development. A portion of the budget, €13.6 billion, is specifically earmarked for 'Digital, Industry and Space' sectors. Horizon Europe also aligns with strategic initiatives like the European Green Deal and Digital Agenda to promote scientific progress, address global challenges, and encourage economic growth and job creation.
• InvestEU: Offering a €26.2 billion budget guarantee, InvestEU aims to leverage additional private and public investments totaling €372 billion to support innovation, digitization, and socio-economic growth, particularly in response to the COVID-19 pandemic. It focuses on strategic EU goals, including the enhancement of digital cities and AI.
• Single Market Programme: With a €4.2 billion budget, this program seeks to streamline the internal market, support SMEs using digital technologies, enhance consumer protection, and improve EU statistics, thereby reinforcing the single market's competitiveness and digital dimension.
• Connecting Europe Facility (CEF): CEF's €33.7 billion budget supports the expansion of trans-European networks in transport, energy, and digital infrastructure, facilitating the adoption of smart city solutions and AI through a mix of grants, blended finance, and procurement strategies.
• European Regional Development Fund (ERDF): The ERDF allocates €226 billion towards making Europe more competitive and smarter, with a focus on infrastructure, SMEs, and fostering intra-EU cooperation through programs like Interreg and the Cohesion Fund, which prioritizes less developed regions.
• Recovery and Resilience Facility: This facility, part of the COVID-19 pandemic response, dedicates €672.5 billion (both non-repayable and repayable) to support the green and digital transitions of EU economies, with a significant portion earmarked for digital initiatives.
• Just Transition Fund: With €17.5 billion, this fund aims to mitigate the socioeconomic impacts of transitioning towards EU energy and climate objectives, offering support to workers and communities affected by the shift away from fossil fuels and carbon-intensive industries.
• Innovation Fund: Approximately €40 billion from 2020 to 2030 is allocated to support the deployment of innovative low-carbon technologies and processes, funded by the auction of EU ETS permits, emphasizing energy-intensive sectors, carbon capture, renewable energy, and storage technologies.
• LIFE Programme: With a budget of €5.4 billion, the LIFE Programme supports projects that address environmental and climate issues, promoting the adoption of sustainable, circular, and climate-resilient practices and technologies.
  These programs collectively aim to foster innovation, support sustainable and digital transitions, and enhance the competitiveness of the European Union on the global stage.

Market opportunities/potential

This analysis delves into the structured organization and strategic focus of three distinct regional initiatives, each championing specific areas of innovation and represent market opportunities for the developments of AI based project for smart city solutions. These initiatives are orchestrated around central hubs, referred to as supernodes, which spearhead the innovation efforts within their respective regions. Each supernode is a coalition of a primary node, guiding the overarching regional strategy, and subsidiary nodes, which specialize in particular thematic areas under the broader innovation umbrella. The ensuing sections offer an in-depth market analysis of these thematic and sub-thematic areas as delineated by the supernodes.

The Nordic Supernode: Emphasizing 'POWER' in Energy

The Nordic supernode, themed 'POWER', explores the multifaceted domain of energy, encompassing a variety of sources such as gas, wind, solar, hydro, nuclear, and biomass, among others. The focus is on the complex market structure comprising Transmission System Operators (TSOs), Distribution System Operators (DSOs), and the supply chain. While TSOs and DSOs, being monopolies, traditionally focus on grid stability and energy provision, there is a burgeoning emphasis on innovation in sustainable energy production, storage, and conversion. This includes advancements in environmental monitoring for a more informed and environmentally conscious approach, underpinned by digital technologies for data collection, analysis, and dissemination. Moreover, the criticality of cybersecurity in safeguarding energy infrastructure from cyber threats is underscored, along with the European agenda for reducing fossil fuel dependence through electrification and the integration of renewable energy sources.

Subthemes: 

• Energy
• Environmental Solutions
• Cybersecurity

Regional insights

• Cyber security for energy devices connected to internet
• Dynamic rating of transformers
• Temperature optimization of district heating centrals
• Intelligent heat pump control
• Load profiling in the DSO-grid for future tariffs
• Predictive and preventive maintenance for large-scale heat pumps

The Central Supernode: 'MOVE' for Transportation and Mobility

'MOVE', the central supernode, targets the enhancement of transportation and mobility within urban settings. It aims to foster the development and application of AI systems for safer, more sustainable urban mobility solutions. This initiative acknowledges the dynamic nature of cities as ecosystems that facilitate and shape the movement of citizens, emphasizing the potential of digital technologies like AI, IoT, and big data analytics in revolutionizing urban mobility. By promoting collaborative ecosystems among various stakeholders, including local governments, industry, and civil society, MOVE seeks to expedite the digital transformation of transportation, thereby improving the quality of life, safety, and environmental sustainability in urban areas.

Subthemes: 

• Urban mobility algorithms and smart intersections
•  Electromobility
• Autonomous driving

Regional insights

• Automated parking control system
• SAM Project
• Local Digital Twins for Energy
• Vehicle-Integrated photovoltaic
• Battery-enabled EV chargers assessment
• Optimization of EV charging networks
• EV-related emissions

The Southern Supernode: 'CONNECT' for Integrated Urban Solutions

Lastly, the 'CONNECT' theme underpins the southern supernode's mission to synergize citizens, infrastructure, and AI-driven services for smarter, more sustainable urban living. This theme encapsulates six sub-themes targeting pollution control, urban development, water management, facility management, drone delivery, and tourism management. Through a collaborative framework, CONNECT aspires to advance the digital transformation of cities by leveraging AI and other digital technologies to enhance urban life and sustainability. It involves a broad spectrum of stakeholders across the public and private sectors, civil society, and academia, focusing on leveraging data and AI experimentation to innovate and address urban challenges.
Subthemes: 

• Pollution, greenhouse gas emissions and noise management
• Urban development management
• Water and wastewater management
• Integrated facility management
• Drone delivery management
• Tourism management.

Regional Insights

• AI to drive the benefits of green infrastructure in society
• Brain4it
• Connecta València: Smart and sustainable tourism territory
• AI4water
• The Water Innovation Network (WIN)
• Development of a prediction model for the development of the COVID-19
• Air quality monitoring system using IoT sensors and LoRa system
• Video surveillance system with proprietary AI tools enabling edge computing
  (consortium, 2024)
   

Cost Structure

Content Update Pending, stay tuned!

Operating Models

Content Update Pending, stay tuned!

Legal Requirements

At the local level, the fast-paced evolution of technology presents challenges in regulating AI. Amidst uncertainties about future AI capabilities and the influence of major tech companies, cities are increasingly taking the lead in adopting and regulating these technologies. They are compelled to adopt a pragmatic and agile response to the rapidly changing tech landscape, emphasizing the importance of establishing dynamic, decentralized guidelines for responsible AI usage. This is crucial to safeguarding residents' well-being and privacy. (Garner, 2024)

The European Union is dedicated to advancing the development and application of artificial intelligence (AI) that prioritizes human interests and sustainability. This approach emphasizes the importance of data integrity, openness, transparency, accountability, and human oversight. The aim is for European AI to enhance innovation across all economic sectors and boost the EU's single market competitiveness, all while respecting fundamental rights and adhering to essential EU principles. To avoid fragmentation within the EU, a proposed Europe-wide, risk-based regulatory framework focuses on high-risk AI applications, updating existing regulations on product safety to address issues like cybersecurity and human autonomy. This ensures individuals have the right to compensation for any harm and provides legal clarity for organizations.

In essence, the EU's goal is to create a thriving ecosystem that combines excellence with trust, positioning itself as a leader in ethical and people-focused AI development.
We will also explore the proposals for the Artificial Intelligence Act and the Artificial Intelligence Liability Directive. (consortium, 2024)

Artificial Intelligence Act Proposal: This proposal categorizes AI systems according to their risk levels—from unacceptable to minimal—to guarantee compliance with ethical standards, legality, and fundamental rights within the EU. Certain high-risk AI applications, such as social scoring, are banned outright, while others must meet strict requirements for assessment, registration, and oversight. The proposal particularly considers the needs of SMEs, including access to AI regulatory sandboxes, to ensure competitive fairness and trust, balancing economic prospects with ethical and safety concerns. (COMMISSION E. , LAYING DOWN HARMONISED RULES ON ARTIFICIAL INTELLIGENCE (ARTIFICIAL INTELLIGENCE ACT) AND AMENDING CERTAIN UNION LEGISLATIVE ACTS, 2021)

Artificial Intelligence Liability Directive Proposal: This directive seeks to harmonize liability rules across the EU for civil law claims resulting from AI outputs, aiming to provide fair protection for individuals harmed by AI and sustain confidence in technology use. It outlines measures for claimants to obtain documentation for high-risk AI systems through courts and establishes conditions under which the blame for harm can be attributed to AI system negligence. This directive sets baseline rules for the EU, allowing member states the flexibility to enforce stricter laws if desired. (COMMISSION E. , DIRECTIVE OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL on adapting non-contractual civil liability rules to artificial intelligence, 2022)

Ethical Considerations

The ethical integration of Artificial Intelligence (AI) into urban systems is a cornerstone for advancing smart city initiatives that respect and enhance the fabric of society. At the heart of this ethical framework is the commitment to privacy, security, fairness, and transparency, ensuring that AI solutions foster public trust and that their decision-making processes are accessible and understandable to all citizens.

Social ethics demand that AI implementations honour human dignity, ensuring that individuals are treated with respect and that technology serves to enrich rather than undermine personal relationships. Equity and fairness are paramount, requiring systems to operate free from racial, gender, or other biases, and to actively promote equality. This includes a focus on empowering those who are often left behind, demonstrating a tangible positive impact on underserved communities and contributing to the common good. Moreover, maintaining human dominance over AI is critical, ensuring that humans remain in control, with strategies in place to retrain workers displaced by automation.

From an environmental perspective, AI applications must strive to be green, exerting either neutral or positive effects on nature. This aligns with broader goals of sustainability and environmental stewardship, reflecting a commitment to the planet that matches the commitment to people. 

In terms of safety and security, ensuring the physical and mental safety of individuals is essential. Privacy rights must be safeguarded, alongside the protection of data and system security to prevent misuse or harmful overuse. Ethical AI solutions should also eliminate incentives for misuse, ensuring that technology cannot be exploited for negative purposes.

Governance ethics emphasize accountability, regular monitoring, and transparency in AI applications to ensure responsible use and clear oversight. They mandate that AI systems identify themselves when interacting with humans and include human review in decision-making processes. These principles form a foundation for deploying AI in urban settings that respects privacy, security, and fairness, ultimately enhancing city life and safeguarding resident welfare.

Data and Standards

Content Update Pending, stay tuned!

References

• (n.d), B. (n.d.). Intelligent cities case study: Saving money and reducing emissions in Milton Keynes using smart parking.
• 33A. (2024). Retrieved from This is AI Design Sprint™: Process Automation: https://www.33a.ai/ai-design-sprint
• Abhimanyu, C., Abhinav, P., & Chandresh, S. (2013). Natural Language Processing. International Journal Of Technology Enhancements And Emerging Engineering Research, Vol 1, Issue 4.
• Arkara, N. (2023, August 6). Medium. Retrieved from ‘SmartCityGPT’: How Generative AI Creates Smart and Sustainable Cities: https://nonsmartcity.medium.com/smartcitygpt-how-generative-ai-creates-smart-and-sustainable-cities-4d00ce73da10
• AXIS Comunications, Smart Cities World. (2023). Following the path to smart and sustainable cities. Smart City Magazine 2023/2024 | Global Ebrochure.
• Butler, K., Davies, D., Isayev, O., & Walsh, A. (2018). Machine learning for molecular and materials science. Nature.
• Climate Change AI, t. C. (2021). Climate Change & AI: Recommendations for Government. GPAI.
• Consortium, CitCom.ai. (2024). Market report: Mapping of the current EU context for the AI testing and experimentation facility (TEF) in support of smart and sustainable cities and communities. 
• Cugurullo, F. (2020). Urban Artificial Intelligence: From Automation to Autonomy in the Smart City. Department of Geography, Trinity College Dublin, Dublin, Ireland.
• Deep, K., Aayushi, C., Ruchita, M., Krishna, P., & Chintan, B. (2021). The Convergence of Deep Learning and Computer . Atlantis Highlights in Computer Sciences, volume 4.
• Donti, P., Liu,, Y., Andreas J, S., Andrey, B., Rui, Y., & Yingchen, Z. (2018). Matrix Completion for Low-Observability Voltage Estimation. arXiv:1801.09799.
• European Commission. (n.d.). Retrieved from European approach to artificial intelligence: https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence
• European Commission. (2021). Retrieved from Laying Down Harmonised Rules On Artificial Intelligence And Amending Certain Union Legislative Acts: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A52021PC0206
• European Commission. (2022). Retrieved from Directive Of The European Parliament And Of The Council On Adapting Non-Contractual Civil Liability Rules To Artificial Intelligence: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52022PC0496
• European Commission. (2024). Retrieved from Shaping Europe’s digital future: https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai
• European Commission. (2024). Shaping Europe’s digital future. Retrieved from https://digital-strategy.ec.europa.eu/en/policies/ai-office
• Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024). Generative AI. Business & Information Systems Engineering.
• Garner, C. (2024). SmartCitiesWorld. Retrieved from AI to digital twins: trends you need to know about for 2024: https://www.smartcitiesworld.net/ai-and-machine-learning/ai-and-machine-learning/ai-to-digital-twins-trends-you-need-to-know-about-for-2024
• Golubchikov, O., & Thornbush, M. (2020). Artificial Intelligence and Robotics in Smart City Strategies and Planned Smart Developmen. MDPI & SmartCities.
• H.M.K.K.M.B. , H., & Mittal, M. (2022). Adoption of artificial intelligence in smart cities: A comprehensive review. International Journal of Information Management Data Insights.
• Harshit , V., Rizwan , A., Uzair , K., & Rajat , V. (2020). Approaches of Artificial Intelligence and Machine Learning in Smart Cities: Critical Review. IOP Conference Series: Materials Science and Engineering, Volume 1022, 1st International Conference on Computational Research and Data Analytics (ICCRDA 2020).
• Hurwitz, J., Kaufman, M., & Bowles, A. (2012). Chapter 12 Smarter Cities: Cognitive Computing in Government. In Cognitive Computing and Big Data Analytics. 
• Ingwersen, P., & Serrano-López, A. (2018). Smart city research 1990–2016. Scientometrics.
• Jones, A., Kuehnert, J., Fraccaro, P., Meuriot, O., Ishikawa, T., Edwards, B., . . . Assefa, S. (2023). AI for climate impacts: applications in flood risk. npj Climate and Atmospheric Science.
• Kinza, Y., Gillis, A., & Botelho, B. (n.d.). cognitive computing. Retrieved from Tech Target: https://www.techtarget.com/searchenterpriseai/definition/cognitive-computing
• Lea, R. (2023). LinkedIn. Retrieved from Generative AI: How are cities using it: https://www.linkedin.com/pulse/generative-ai-how-cities-using-rodger-lea-bqa9e/
• Leal, A., de Bezenac, C., Occhini,, G., Lefebvre,, H., Gallego-Posada, J., Chehbouni,, K., . . . Téhinian, S. (2022). AI & Cities: Risks, Applications and Governance. UN-Habitat.
• Majewski, S. (2023). D.Labs. Retrieved from 7 Key Steps To Implementing AI In Your Business: https://dlabs.ai/blog/how-to-implement-ai-in-your-company/#:~:text=Seven%20key%20steps%20to%20implementing%20AI%20in%20your,You%E2%80%99re%20ready%20to%20start%2C%20but%20start%20small.%20
• Mathe, o., Miolane,, N., Sebastien,, N., & Lequeux, J. (2019). PVNet: A LRCN Architecture for Spatio-Temporal Photovoltaic PowerForecasting from Numerical Weather Prediction. arXiv:1902.01453.
• Mazloumi, E., Geoff, R., Currie, G., & Moridpour, S. (2011). Prediction intervals to account for uncertainties in neural network predictions: Methodology and application in bus travel time prediction. Volume 24, Issue 3.
• Miller, G. (2022). Stakeholder roles in artificial intelligence projects. Project Leadership and Society.
• Nemika, T., & Bharat, B. (2023). Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions. Wireless Personal Communications.
• Nolle, M. (2023). How can AI apply to Smart Cities? LinkedIn BABLE Newsletter & Insights.
• Nutkiewicz, A., Yang, Z., & Jain, R. (2018). Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow. Applied Energy Volume 225.
• OECD. (2023). Recommendation of the Council on Artificial Intelligence. OECD.
• Padilha França, R., Borges Monteiro, A., Arthur, R., & Iano, Y. (2020). An Overview of the Machine Learning Applied in Smart Cities. Smart Cities: A Data Analytics Perspective. Lecture Notes in Intelligent Transportation and Infrastructure. Springer.
• Papandreou, T. (2024). Forebes. Retrieved from Generative Urban AI Is Here. Are Cities Ready?: https://www.forbes.com/sites/timothypapandreou/2024/02/18/generative-urban-ai-is-here-are-cities-ready/?sh=2fc6aa449613
• Parycek, P., Schmid, V., & Novak, A.-S. (2023). Artificial Intelligence (AI) and Automation in Administrative Procedures: Potentials, Limitations, and Framework Conditions. Journal of the Knowledge Economy.
• Peeler, R. (2023). The Hidden Costs Of Implementing AI In Enterprise. Retrieved from Forbes: https://www.forbes.com/sites/forbestechcouncil/2023/08/31/the-hidden-costs-of-implementing-ai-in-enterprise/?sh=f7559e64d5c1
• Peng, Z.-R., Lu, K.-F., Liu, Y., & Zhai, W. (2023). The Pathway of Urban Planning AI: From Planning Support to Plan-Making. Journal of Planning Education and Research.
• Rahim, A., Mahony, C., & Bandyopadhyay, S. (2024). World Bank Blogs. Retrieved from Generative Artificial Intelligence as an Enabler for Citizen Engagement: https://blogs.worldbank.org/governance/generative-artificial-intelligence-enabler-citizen-engagement
• Schmitt, L. (2022). Mapping global AI governance: a nascent regime in a fragmented landscape. AI and Ethics.
• Taeihagh, A. (2021). Governance of artificial intelligence. Policy and Society.
• Taleqani, R., Hough,, A., & K. E, N. (n.d.). Public Opinion on Dockless Bike Sharing: A Machine Learning Approach. Transportation Research Record, 2673(4), 195-204. 
• Ullah, Z., Al-Turjman, F., Mostarda, L., & Gagliardi, R. (2020). Applications of Artificial Intelligence and Machine learning in smart cities,. Computer Communications, Volume 154.
• UN Environment Programme. (2023). Retrieved from How artificial intelligence is helping tackle environmental challenges: https://www.unep.org/news-and-stories/story/how-artificial-intelligence-helping-tackle-environmental-challenges
• Upadhyay, M., & Upadhyay, P. (2020). The Upadhyays. Retrieved from 8 Parameters to Qualify AI Solutions: https://www.theupadhyays.com/post/8-parameters-to-qualify-ai-solutions
• Yigitcanlar, T., Corchado, J., Mehmood, R., Yi Man Li, R., Mossberger, K., & Desouza, K. (2021). Responsible Urban Innovation with Local Government Artificial Intelligence (AI): A Conceptual Framework and Research Agenda. Journal of Open Innovation: Technology, Market, and Complexity.
• Zhang, W., Robinson, C., Guhathakurta, S., Garikapati, V., Bistra Dilkina, M., Brown, M., & Pendyala, R. (2018). Estimating residential energy consumption in metropolitan areas: A microsimulation approach. Energy, Volume 155,.