Abstract

AI has gained more attention from the Chinese public sector to improve service and optimize government processes. However, there are a lot of challenges to address when adapting AI technologies into public administration. This study examines the use of artificial intelligence (AI) as a method of technology-driven innovation in the public sector by examining five cases based in China which include the Beijing City Smart Traffic Management System, the Hangzhou City Brain Project, the Nanjing AI-Based Environmental Monitoring System, Shenzhen City AI-Supported Public Security System and the AI-Enhanced Healthcare system in Shanghai. The analysis indicates that there are a number of varied environmental, organizational, and innovation-related factors that impact the implementation of AI, and these factors interplay in ways that extend beyond concerns about data quality. In order to confront this complexity, we provide a framework that outlines the key drivers and challenges to the adoption of AI in the public sector of China, thus extending the scope of the study beyond the data and algorithmic capabilities commonly explored in AI research. With the support of this framework, it is expected that policymakers will be able to identify and address the specific challenges of implementing AI in Chinese public sectors.

Key words: Public administration; AI adaptation in China; Artificial intelligence; AI implementation in public sectors; Chinese AI governance; AI-driven urban management

Introduction

The Chinese public sector’s interest in implementing artificial intelligence (AI) for government procedures and the delivery of public services has been significantly influenced by recent developments in the field of AI. It has been recognized that the application of AI technologies, such as generative AI, chatbots, and narrow AI technologies, has the ability to improve the efficiency and effectiveness of public service delivery in China. These technologies, made in order to enhance data-driven governance, handle urban issues, and streamline service delivery, these technologies can help the nation achieve its national development goals Popescu [1]; Damar [2]. A broader techno-utilitarian perspective that views technology as a tool for improving public governance and satisfying the growing demands of a growing population is reflected in the ways AI is being used in cities like Shanghai to enhance education, urban management, and healthcare Todaro [3]. AI can improve public services through policy innovation, smart applications, and improved data integration, all of which promote more responsive and agile governance (Li et al., 2023). In addition, by customizing government services to each citizen’s needs, AI can increase public satisfaction, but it also raises security and privacy concerns Ladislas [4]. In spite of these advantages, adopting AI presents a number of challenges for Chinese public sector organizations, including questions of ethics, law, and society, as well as barriers pertaining to personnel, organizational processes, and IT systems Damar [1]; Marzouki [5].

Researching how new technologies like AI are being implemented in China’s public sector is crucial as it assists policymakers in overcoming these challenges and ensures that AI is used transparently and responsibly while respecting societal demands and democratic values Manias [6]. We believe that researching the application of AI could help policymakers better understand the factors that are most likely to encourage its implementation in the public sector. In this study, we explore five different AI applications in the Chinese public sector using an exploratory case study methodology. Our research question is, “Which factors enable the adoption of AI innovations in Chinese public sector organizations?”. This exploratory research methodology is suitable for offering an in-depth understanding of new developments in the application of AI, guiding the process of identifying drivers and challenges in this regard. This research adds to the larger scholarly discussion on innovation in the Chinese public sector by investigating particular factors that affect the adoption of artificial intelligence in public administration. Although recent studies are beginning to examine AI’s practical applications and the specific difficulties it faces in the public sector, AI research frequently ignores these applications (Sousa et al., 2019). The article is organized as follows: We start by defining what artificial intelligence (AI) is in the context of our study and discussing factors that encourage innovation in the public sector. After that, we describe our methodology and analyze five AI case studies from Beijing, Shanghai, Nanjing, Hangzhou, and Shenzhen. The study concludes with a discussion of the results and their implications for future research and policy in the Chinese context.

Literature Review

AI in Chinese Public Sector: Concepts, Benefits, and Challenges”

Recent technological advancements have increased the adaptation and development of AI in key sectors, which include the public sector. Important tools like chatbots and generative AI have made public service areas such as education, healthcare, and infrastructure management more efficient and effective. Damar [1]. However, in the absence of a single definition of AI, it complicates its understanding and application. This ambiguity stems from the wide range of AI approaches, having different objectives and applications that vary significantly between sectors and situations Alhosani & Alhashmi [7]. The fragmented nature of AI studies across social sciences and technical fields is reflected in many ways that researchers conceptualize AI, frequently concentrating on its operational, epistemic, and normative dimensions (“artificial intelligence in government: concepts, standards, and a unified framework,2022). In the Chinese public sector, for example, AI technology has been used to boost automation, optimize efficiency, and create a data-driven government. Todaro [3]. AI has the ability to help the Chinese public sector with better decision-making, determine fraud detection, and customize services. Additionally, these capabilities can be helpful in addressing common problems such as inefficient resource allocation and service delays Isagah [8]; Popescu [1].

However, there are possible ethical issues and risks associated with AI implementation, including biases, discrimination, and privacy concerns. These social concerns can affect the future public’s acceptance of AI technologies as well as government operations Padmaja & Lakshminarayana [9]; Popescu [1]. To ensure responsible and ethical development, these risks call for a strategic approach to AI adoption that takes ethical, organizational, and societal implications into consideration Alhosani & Alhashmi [7]; Straub [10]. In conclusion, China has an incredible opportunity to use AI in the public sector; however, its successful integration requires careful evaluation of its various conceptualizations, benefits, and related risks.

Exploring the Factors of Innovation in the Chinese Public Sector

The introduction and adoption of ICT innovations in government operations have been one of the significant challenges (Kamal, 2006; Potts & Kastelle, 2010). In order to define the elements that act as an obstacle or as a driver (also known as antecedents) that affect the diffusion and implementation of innovations in public organizations, many academics have tried to understand the conditions that enhance the possibility that innovation will occur in the public sector. In this article, we constructed a coherent framework, and we use this strategy to uncover the hidden complexities of adopting AI in public organizations, considering it a form of innovation in the Chinese public sector. This strategy is in line with new research by Schedler et al. (2029), which shows that any kind of innovation faces the same challenges when it comes to government adoption. As a result, we investigate the organizational, environmental, innovation-specific, and individual factors that impact the implementation of AI in the public sector.

Organizational Factors

There are a number of organizational factors that are important for the implementation of AI in the public sector of the People’s Republic of China. The strongest technological considerations to be made are to have technology resources and infrastructure in place for the proper application of AI. In order to ensure that industries such as healthcare and smart cities have access to cutting-edge computing and data systems and can use AI technologies effectively, programs supported by the government offer the required financial resources and infrastructure Wolff [11]; Chen [12]. Even though there is currently a shortage of AI specialists, China is working hard to fill this gap by making significant investments in AI education and encouraging foreign collaboration. While the demand for AI skills greatly continues to exceed supply, working with private sector technology firms is important in addressing the problem Chen [12]. Cultural alignment and adaptability are also equally vital due to the fact that AI solutions have to align with the rules and procedures of public sector organizations. While some existing bureaucratic systems may be hard to transform, continuous changes encourage innovation by offering incentives for the successful integration of AI Zhan [13]; Zhang [14].

Additionally, in order to enhance operational effectiveness, Chinese public enterprises frequently copy AI models that have proven successful, whether they be worldwide models or implementations from regional leaders such as Shenzhen and Hangzhou Liang & Qi [15]. This further demonstrates the idea of mimetic isomorphism. These all contribute to the growth of the usage of AI and improve operational efficiency and innovation in the public sector of China. However, when talent development, infrastructure, cultural adaptation, and best practice replication are all considered in terms of their strategic alignment, this presents a holistic approach to the problem of how to use AI and improve public administration with AI in China.

Environmental Factors

Many environmental factors have a profound impact on the public sector innovation ecosystem in China, particularly concerning artificial intelligence (AI). The centralized structure of the Chinese government, along with the policy and directives, is crucial since they play a crucial role in mandating the implementation of AI through top-down initiatives. The “new generation artificial intelligence development plan,” which intends to establish China as a global leader in AI by 2030 and highlights the significance of centralized policy in advancing technical growth, serves as an example of this Roberts [16]. This centralized approach model is complemented by public-private partnerships (PPPs) that involve cooperation between the government and significant private organizations such as Alibaba and Tencent in creating AI solutions. While they have a number of challenges, such as risk management and regulatory restrictions, these collaborations are crucial in order to leverage private sector resources and experience for the achievement of the public sector objectives Wang [17]; Adams [18]. Moreover, key pilot zones and regional innovation hubs such as Shenzhen and Hangzhou serve as AI development testing sites, thereby making it possible for AI technologies to be tested on a small scale before being deployed on a broader scope Roberts [16].

China’s legislative structure, which is less onerous than frameworks like the GDPR in Europe, offers the necessary flexibility for swift AI experimentation and innovation, creating an atmosphere that supports technical progress Roberts [16]. Finally, how the public perceives and engages with AI-driven services is critical. Even though the government maintains a strong role in governance, the growing transparency of environmental information, as demonstrated by the Institute of Public & Environmental Affairs (IPE), emphasizes the significance of public acceptance and trust in the effective implementation of AI solutions Li [19]. Together, these elements provide a dynamic ecosystem that balances adaptability and collaboration with centralized management, therefore promoting and accelerating innovation in China’s public sector.

Innovation-Related Factors

The disruptive potential of AI is one of the key innovation-related factors that impact the effective use of AI in China’s public sector. With their transformational potential, AI technologies have the potential to greatly improve public sector operations by increasing service delivery efficiency and efficacy Chen [12]. Governments that serve as the main regulators and facilitators and want to use AI for the benefit of society as a whole are aware of its potential Jimenez-Gomez [20]. After that, AI technology usability and pilot testing are critical. These factors ensure that AI tools are user-friendly and integrate smoothly within existing systems, thereby minimizing opposition and encouraging smoother adoption processes Liang & Qi [15].

Once pilot testing demonstrates the efficacy of AI tools, an immediate need for rapid technological scaling arises in order to maximize impact and benefits. However, this requires sufficient support from the management as well as the organization’s effective capabilities Chen [12]. Data access and centralization are crucial to these initiatives, as they form the infrastructure needed for AI systems to work effectively. Centralized data systems facilitate improved data management and analytics, which significantly support policy-making and other decision-making processes Misuraca & Viscusi [21]. Lastly, the perception of value derived from AI adoption is crucial because public sector organizations must recognize tangible benefits from AI, such as increased accountability, transparency, and public value, in order to justify investments and maintain long-term adoption Jimenez-Gomez [20]; Misuraca & Viscusi [21]. Together, these elements foster an environment that supports AI-driven innovation, which facilitates the effective use of AI by public sector organizations to improve governance and solve social problems Ghina & Permana [22]. With a detailed understanding of the above elements, China’s public sector will be able to carry AI technology adoption, which will foster innovation and improve public service delivery.

Individual Factors

When it comes to innovation processes inside public companies, especially for the adoption of AI technology, individual antecedents play a significant role. President Xi Jinping is one example of a national leader who has also shown how political vision and leadership can make a difference. He has played a key role in advancing AI as a pillar of China’s aspirations for the world. This top-down method establishes a strategic course and encourages the adoption of AI in a number of industries Moussa [23]. In addition, there are also technocratic leaders and bureaucratic champions who support the application of AI in government systems; these leaders are usually mid-level bureaucrats. These individuals have played an important role in identifying the potential of artificial intelligence and promoting its application in public administration, thereby helping to close the gap between theoretical concepts and practical applications Agolla & Lill [24].

Furthermore, it is technologists and AI experts who are most crucial to this ecosystem, as they possess the necessary technical expertise needed for the development and implementation of AI solutions. The capability of public organizations to innovate is increased when qualified laborers and technicians are available to ensure that AI initiatives are not only Conceptualized but are effectively implemented as well. Kakatkar [25]. The way in which these responsibilities interact creates an environment in which the advocacy and technical skills of those working within the system are able to transform political vision into tangible solutions. This narrative illustrates the need to develop a multifaceted strategy for innovation that combines the necessary technical expertise, leadership, and advocacy to promote an innovative culture within the public sector. Many studies regarding the drivers of both organizational intelligence and innovation have highlighted the importance of such a framework for public organizations hoping to use AI for better service delivery and operational efficiency Stenvall & Virtanen [26]; Balau [27].

Methodological Strategy

We adopted an exploratory case study methodology, focusing on five AI efforts and projects within China’s public sector to answer the research question: “What factors contribute to the successful adoption of AI in Chinese public sector organizations, and what obstacles do they encounter?” Because AI integration in this industry is still in its infancy, we used an experimental approach, following Yin’s (2018) methodological recommendations. This made it possible to analyze new patterns in-depth and made it easier to develop new insights (Flyvbjerg, 2006) (Figure 1). The aim of using multiple case studies was to enhance the reliability, credibility, and generalizability of our study findings, as noted by Baxter and Jack (2008). Because implementation may be shaped by specific political, cultural, and institutional contexts in China, we looked at different AI applications within their specific contexts to ensure such contexts are comparable. Therefore, these unique contexts might have some implications for our findings.

Procedure for Selecting Case Studies

We first obtained an initial database of AI programs from the Ministry of Industry and Information Technology (MIIT) as well as other organizations under the Chinese government. When selecting the cases, the following measures were carried out:

First Identification: Following an extensive analysis of AI projects, we were able to identify 52 projects in many public sector areas in five cities in China: Beijing, Shenzhen, Shanghai, Hangzhou, and Nanjing.

AI Project Shortlisting: A selection of potential projects was made based on factors such as data accessibility, geographical coverage, and AI technological maturity.

Final Case Selection: Cases were selected for in-depth research according to variables such as language accessibility, policy alignment, regional significance, and the availability of relevant data:

Beijing’s smart traffic management system: This AI technology aims to improve traffic flow and reduce traffic congestion.

Shanghai’s AI-driven healthcare system is a new generation of AI-driven innovative measures aimed to standing at the forefront in the field of public health management. Hangzhou uses a well-established artificial intelligence system called City Brain to manage the city’s infrastructure. Shenzhen’s AI-driven public security is an AI-driven public safety management system.

The Nanjing AI-powered environmental monitoring system: AI is used in this system to monitor the environment in real-time.

Methods of Data Collection

The Study Used a Two-Phase Approach to Collect Data

Document analysis was carried out; we reviewed all of the documents and materials made available by government organizations. For example, data about Hangzhou’s city brain came from official publications as well as municipal reports to the extent that it can be found in scholarly papers on websites such as CNKI and Google Scholar. Secondary stakeholder insights: We used secondary sources, such as discussions and interviews posted on government websites and business publications, because we were unable to conduct direct interviews. The sources provided invaluable opinions from key stakeholders, including project managers and government representatives involved in the AI projects.

Examining AI Applications in Chinese Public Sector Case Studies

In this section, we discuss five examples of AI applications in the context of the Chinese public sector, focusing on specific cases from Beijing, Shanghai, Shenzhen, Hangzhou, and Nanjing. These examples include the AI-powered environmental monitoring system in Nanjing, the AI-driven public security system in Shenzhen, the AI-driven healthcare system in Shanghai, the smart traffic management system in Beijing, and the City Brain Smart City project in Hangzhou. These examples highlight the various methods of using artificial intelligence in public administration in China, which will serve as a basis for further research and comparative studies on how AI can improve public services and government operations.

Beijing’s Smart Traffic Management System

Similarly to many other rapidly urbanizing cities, Beijing is facing serious traffic congestion problems associated with an increasing population and number of vehicles on the road. It is necessary to put in place a smart traffic management system in order to enhance urban resilience, as this will efficiently manage traffic, reduce accidents, and enhance effectiveness Santhiya [28]; Kumar [29]. The smart traffic management system of the city employs advanced AI algorithms to merge information derived from a wide network of intelligent traffic lights, sensors, and street cameras. As a consequence, this technology has the capability to process real-time data and predict traffic flows, thereby enhancing the timings of traffic signals and providing real-time routing alternatives Lungu [30]; Shahi and others [31]; Habibullah & Alam [32].

However, the system must integrate a variety of data sources, such as cameras and sensors, and pose challenges of limited privacy associated with excessive surveillance. As a result, the public may become resistant to data gathering and greater monitoring Shahi [31]; Anitha and associates [33]. There has been great support for the advancement of systems and improvement through collaborations with universities in Beijing for AI research, as well as the cooperation of the city’s administration with technology giants such as Alibaba and Huawei Xu [34]. Due to the huge amount of data being collected, a strong technical infrastructure was required. This included cloud-based systems and AI platforms capable of processing massive amounts of data in real time and ensuring the scalability and efficiency of the system Kommineni & Baseer [35]; Khan & Ivan [36]. The Beijing traffic police, and civil servants significantly contributed to the development and training of the system. They were trained to understand the interpretation of the system’s data and predictions, which enhanced confidence in the system’s ability to improve effective enforcement and control traffic congestion E [37]; Alam & Habibullah [32]. The introduction of the smart traffic system has shown very promising results that include a reduction in the number of road accidents and improved traffic congestion, along with an improvement in productivity among the traffic police. To reduce concerns and make the move towards automated traffic management easier, concerns about job loss of traffic police due to automation were resolved by emphasizing the technology’s role in enhancing human skills rather than replacing them Anitha [33]; Khan & Ivan [36]. Beijing’s advancements in traffic management systems are an excellent case study on the role of evolving technologies like artificial intelligence in the development of urban governance and also a good example for other cities facing similar problems.

Shanghai’s AI-Driven Healthcare System

The healthcare organization in Shanghai has developed an AI system that is able to predict healthcare risks and manage resource allocation. This stands as a good example of the potential that artificial intelligence can bring to the world and its future application in improving healthcare services. This system applies AI-based predictive models such as neural networks to analyze large amounts of patient data, including medical records, lifestyle information, and diagnostic history, in order to predict disease and identify high-risk patient conditions Choi [38]; Ghavami & Kapur [39]. There are challenges with the implementation of AI solutions in Shanghai’s healthcare sector, especially when it comes to incorporating AI into China’s heavily regulated healthcare sector. The application of AI technologies in healthcare is highly impacted by China’s strict regulations on data privacy, healthcare reform, and AI regulation, all of which require careful navigation to ensure compliance and effectiveness Secinaro [40]; Siddesh [41]. Important partnerships exist between Shanghai’s academic institutes, local digital behemoths like Tencent and Alibaba, and the healthcare system. Through close Collaboration with medical practitioners and AI researchers, these Collaborations aim to produce accurate and relevant AI models (“Applications of AI in the healthcare sector for enhancement of medical decision-making and quality of service,” [42]; Salehi [43].

To make Shanghai’s AI-centered healthcare programs effective, it is critical to use quality and trusted healthcare data. While addressing the issues of data fragmentation, patient privacy, and integrating data across multiple hospitals and healthcare providers, the Chinese healthcare data rules must be respected. Bhuiyan [44]; S [45]. This healthcare workforce in Shanghai is able to test and validate the predictions made by AI. This is ensured through the existence of well-structured training programs that instill confidence in medical staff and make them use AI tools effectively for diagnosis and patient care, ensuring that AI does not replace their roles but rather enhances their abilities Houfani [46]; Choi [38]. The AI-driven healthcare system developed in Shanghai has significantly elevated the efficiency of hospitals and improved patient treatment through faster and more accurate diagnoses. Furthermore, the use of AI in decision-making assists overworked healthcare professionals and improves public health outcomes, illuminating the field’s revolutionary potential Salehi [43]; Siddesh [41].

Shenzhen’s AI-driven Public Security System

A great example of how AI is transforming public administration, and more specifically, public security, is the AI-driven public security system, which was implemented in the city of Shenzhen. This system is a part of Shenzhen’s smart city initiative, which is about enhancing governance and addressing urban issues through the use of digital technology Grobe-Bley & Kostka [47]. Shenzhen’s public security system is supported by technology such as predictive policing with the help of big data analytics to identify and prevent crimes. This approach has, however, been criticized for maybe exacerbating systemic discrimination against certain groups and for singling out particular groups for special treatment Sprick [48]. This system was developed through Collaboration with several stakeholders, including academic institutes, IT firms, and local government agencies, in order to integrate new data flows and smart governance systems Grobe-Bley & Kostka [47]. However, as is typical with the shift to digital governance, the implementation faced several challenges, including problems with data management as well as administrative issues Grobe-Bley & Kostka [47]. The long-term viability of AI in public administration is also threatened by ethical issues, mainly those pertaining to privacy and transparency Sprick [48]; Андрeевич [49]. The system’s funding and resources were probably provided by the government as well as through alliances with for-profit tech companies that have access to a wealth of public data, which encourages AI advancement Clark [50]. One of the obstacles to data is its centralization, which can cause issues with data security and privacy, and it is also a constraint to the subordinate administrative units of the organization Grobe-Bley & Kostka [47]. Since it affects the performance of the system’s success or failure, staff and public engagement in its development has been very important. The nature of stakeholder interactions has changed, especially the cooperation between private tech companies and public security, which has advanced the system but also raised issues and concerns about data control and accountability Grobe-Bley & Kostka [47]; Clark [50]. The AI-driven public security system in Shenzhen has had mixed results; although it has improved the city’s capacity to handle security, it has also brought attention to the need for stronger governance frameworks to handle moral and data management concerns Sprick [48]. governing AI systems for public values [51]. In summary, the case study of the use of artificial intelligence in public security in Shenzhen offers a positive example of how to grow the use of technology while maintaining a balance between technical advancement, morality, and effective governance. To fully unleash the potential of AI in public administration, future implementations should focus on enhancing transparency and data protection and encouraging inclusive stakeholder engagement (Governing AI systems for public value [51]; Labaki [52].

Hangzhou’s City Brain

The city of Hangzhou is currently promoting its smart cities initiative named the City Brain program, which utilizes the Internet of Things (IoT), artificial intelligence, and real-time data analytics to solve problems in urban areas, such as emergency services and traffic congestion. The initiative, which was started in 2026, is an alliance between the government of Hangzhou and Alibaba. It uses Alibaba’s infrastructure for AI and cloud computing, which allows it to process large volumes of data received from various IoT devices and services integrated within the city. The city brain collects data from millions of cameras and sensors all over the city, which enables quick and efficient decision-making for managing accidents, controlling traffic, controlling signals, and enhancing public services. This program was designed to improve public security and efficiency of municipal operations in accordance with China’s larger smart city and AI development plans, as stated in national policy papers such as the new generation artificial intelligence development plan (2017). Integrating data from many sources and guaranteeing real-time processing presented initial problems for the project. Similar to the difficulties encountered in other intelligent traffic management systems in the past, which employed AI and IoT for traffic management and congestion control, they were resolved by regular testing, input from local government officials, and improvement of AI models Mondal & Rehena [53]; Reza [54]; Lihore [55]. Significant improvements were made with the introduction of a city brain, including a 15% reduction in traffic jams and an improved time to respond to emergencies due to the improved management of traffic light systems. These results demonstrate the potential of artificial intelligence (AI) to improve urban management and public service delivery (Intelligent Transport Systems and Traffic Management [56]. (“Machine learning applications in vehicular traffic prediction and congestion control: A systematic review, [57]; Jinjun [58]. With Alibaba contributing technological know-how and the Hangzhou government providing real-world urban data, the project highlights the importance of public-private partnerships and serves as an example of how to drive AI innovation through cooperation (“smart city intelligent traffic control for connected road junction congestion awareness with deep extreme learning machine,” 2022). the lessons from Hangzhou’s city brain show how essential it is to use artificial intelligence (AI) in urban infrastructure with the aim of improving emergency services or managing traffic. They also highlight the necessity of ongoing development and adaptation to get past early obstacles, a theme that is repeated in other research on intelligent traffic systems and urban management Qi [59].

Nanjing’s AI-Powered Environmental Monitoring System

The AI-powered environmental monitoring system in Nanjing helps the city become more environmentally responsible, and the approach is a revolutionary move in the control and management of the urban environment. The main objective of the system is to monitor and control environmental factors, including air and water quality, by utilizing an advanced technical framework that combines real-time data analytics, machine learning models, and IoT devices. This program is part of China’s larger ‘smart city’ agenda that is funded by both national and local governments, and this program is important for digital transformation in public administration Ye [60]; Kharisova [61]. This includes collaboration between Chinese IT businesses, research institutions, and the Nanjing Environmental Protection Bureau during the development process. In order to guarantee that the system could correctly forecast pollution trends and offer useful insights, these stakeholders collaborated to train AI models using historical environmental data Banach [62]. Kharisova [61]. The technology used in Nanjing’s system includes machine learning algorithms that evaluate data on air quality and forecast pollution patterns, providing environmental monitoring personnel with real-time feedback. This strategy thus enables the city to address pollution violations almost instantly, which in turn reduces the city’s environmental impact and enhances people’s quality of life Evison [63]. (machine learning enabled cognitive approaches for handling IoT-based environmental data [57]). However, at first, there were issues with the systems, including concerns about the reliability and quality of environmental data from sensors and satellite images. Despite these challenges, Nanjing’s AI-driven environmental protection system has been a success. The data correctness and reliability for the success of the system were mandatory; thus, Nanjing’s environmental monitoring team was involved in the testing and verification of AI outputs Ye [60]. (machine learning enabled cognitive approaches for handling IoT-based environmental data [57]). Nonetheless, the system has been very efficient and has been useful in enhancing the efficiency of environmental monitoring since it reduces the need for human involvement and provides real-time information on the quality of air and water. Because of that, it has become possible to make better decisions and practice proactive environmental management Feng [64]; Omar [54].

The challenges of these issues explain why it is important to have good data management procedures and ongoing stakeholder cooperation to preserve system effectiveness. The system gains financial and resource assistance by being a part of China’s “smart city” plan, which offers significant financing for technology innovation in urban settings Feng [64]. The accuracy and reliability of the system have been greatly improved by staff and public participation, with environmental monitoring personnel playing a crucial role in confirming AI alarms and weather forecasts Yunxian [66]. The Nanjing AI-powered environmental monitoring system is expected to have a positive long-term impact. Additional possible options include adding more sophisticated AI algorithms to improve forecasting accuracy and broadening the system’s capability to include additional environmental indicators. In conclusion, Nanjing’s system exemplifies the revolutionary potential of AI in environmental management and provides a paradigm for future applications. Recommendations for future projects include encouraging more stakeholder participation, ensuring the quality of the provided data, and continuously evaluating the effectiveness of the system in addressing environmental issues Ye [60]; Kharisova [61].

Discussion

By examining AI-driven innovations in the Chinese public sector from the perspective of adoption drivers and/or barriers, it helps in understanding the factors that could either help or hinder the broad adoption of AI in public administration. While addressing particular contexts, we determine which factors affect the potential of AI through case study analysis in Beijing, Shanghai, Hangzhou, Shenzhen, and Nanjing. In order to determine whether any other AI-specific factors surfaced in these real-world applications, we address the innovation factors described in the literature review as they relate to these cases. This method expands our knowledge of the factors that encourage the adoption of AI in the Chinese public sector and identifies potential challenges that must be overcome for successful, widespread implementation.

Organizational Factors

From Table 1, we can see how many cities are strategically using their organizational capabilities to promote the kind of AI innovation that meets their objectives. Using public and private sector collaboration, cities can create an ecosystem for the development of AI. Shenzhen, Beijing, and Shanghai have strong corporate or government support for the development of large-scale AI systems, but Nanjing’s emphasis is on environmental aims, depending on outside expertise. Cities collaborate with universities, research institutions, and tech businesses in order to ensure a continuous supply of expertise and knowledge in AI technology because they recognize that knowledge and experience in AI play a crucial role in the development of AI applications. This reliance on external assistance is indicative of a broader trend in which cities are partnering with big industry companies, such as Alibaba and Huawei, to fill the knowledge gaps within their organizations and to accelerate AI project development. Summarized in Table 1).

Cultural adjustment is also crucial; there are differences in the degree of receptivity toward AI technology based on the particular use cases of each city, which is also dependent on whether or not AI is welcome and ready in certain cultures, such as healthcare and urban administration, and it requires high levels of public confidence and government openness in security and surveillance, particularly in Shenzhen and other similar cities. Lastly, it is clear that cities are looking for guidance from global AI leaders for inspiration due to mimetic isomorphism, which is the tendency to use previously successful solutions or the propensity to copy patterns that have been effectively applied elsewhere. Beijing and Shanghai serve as good examples of cities that take a more pragmatic approach to innovation by adapting existing technologies for use in traffic control or healthcare to meet local requirements. Our analysis indicates that the adoption of AI in these cities is influenced not only by technological capabilities but also by the degree to which enterprises are able to match their resources, knowledge, culture, and external models to specific local goals. This greatly improves the efficiency and effectiveness of the AI solutions that are to be employed.

Environmental Factors

The development of AI is fundamentally facilitated by centralized governance and policy directives. In order to prioritize and advance certain AI applications, such as urban management, healthcare, security, and environmental protection, national support is needed. These regulations provide a controlled environment in which cities can focus on their core competencies, such as traffic control in Beijing and healthcare in Shanghai. This is important for all cities to have public-private partnerships. These do not only offer technical solutions, but they also facilitate innovation through resources and experience sharing. Such collaborations foster the deployment and growth of AI applications and enable scalability in sectors like healthcare, urban planning, and security, especially when private players provide cutting-edge technology. Interorganizational data sharing is critical to the AI systems used in public administrations and can benefit both the public and private sectors (Table 2).

Table 1. Based on the case analysis, public-private partnerships and centralized policy support are critical for the adoption of AI in these cities, as the table shows. Each of these cities is a hub of innovation and, therefore, has its own competitive advantage. These regional hubs create an ecosystem that encourages the rapid iteration and deployment of AI development. The reputations of cities such as Shanghai and Shenzhen as leaders in security and healthcare are a good source of talent and investment, which leads to further innovation. For a city to be able to use AI in traffic control, healthcare diagnostics, and surveillance technologies, regulatory flexibility is essential for AI development. While some areas, including data privacy, continue to be problematic, progress has been made because of the ability to modify regulations to suit the demands of AI. The role of the public’s perception and involvement has a huge impact on AI initiative’s level of success. Such Positive perception is largely associated with improvements in the quality of living standards, including improved access to healthcare or better traffic patterns. However, the example from Shenzhen demonstrates that such developments can also lead to privacy concerns, resulting in conflicts, especially when technologies such as monitoring and facial recognition are in use. In order to ensure public support for future advances, it will be necessary to strike a balance between the benefits of AI technology and security and privacy issues (Table 3).

The table indicates how various AI technologies can be deployed by a city to increase efficiency across different sectors while addressing certain problems. Every city makes use of AI in order to upgrade its existing systems, whether they be for environmental sustainability, public safety, traffic management, healthcare, or urban services. Both testing and simplicity of use are crucial steps. And most towns do pilot programs to make sure that the scaling goes smoothly. There are certain sectors, such as health care and public safety, where usability appears to be an issue, and in this case, this approach highlights the role of interface and user training in the implementation of AI. Rapid technology adoption across major infrastructures is another important issue. While some cities, like Beijing and Hangzhou, have had success with this, others, like Nanjing, have struggled with limited infrastructure, especially when it comes to data collection and outreach to rural areas. Improving real-time decision-making requires centralization and data availability. Nonetheless, privacy and data security issues highlight persistent conflicts between privacy and efficiency, particularly in the fields of healthcare and public safety. In conclusion, AI systems are seen as enhancing cities by promoting safety, effectiveness, and overall service delivery. In order to ensure effective long-term adoption, however, further work is needed in the areas of public acceptability and understanding, especially with regard to privacy concerns and environmental AI. Based on the infrastructural preparedness and public opinion, as well as industry-specific requirements, the experiences of the cities together imply that the use of AI is very context-specific.

Individual Factors

China has been successful in adopting AI because of a multi-layered approach that is a combination of technical skills, bureaucratic advocacy, and political leadership. As seen by the new generation artificial intelligence development plan of 2017, which aims to make China a global AI leader by 2030, President Xi Jinping has positioned AI as a strategic priority through his top-down vision. Local bureaucrats in cities such as Hangzhou and Shenzhen have helped facilitate the use of artificial intelligence (AI) in public services by working with Tencent and Alibaba on security and smart city projects. This is an indication of the support that local bureaucrats have for AI integration into governance. At the same time, companies such as Tencent, Baidu, and Alibaba develop large-scale AI solutions for use in healthcare and smart cities, working with the government on the implementation of the solutions. For instance, projects like the smart traffic management system of Beijing demonstrate this integrated effort between national policy, local administration, and IT leaders. There are, however, still challenges in China, especially with regard to ethical issues of privacy concerns and AI monitoring (Figure 2).

Strategic Approach

China’s strategic approach to innovation is well-planned and complemented by national long-term goals and objectives, with artificial intelligence being one of the aspects that enhance international and domestic competition. Under the leadership of Xi Jinping, China has set out on an innovation-driven growth path with the goals of becoming an inventive country by 2020, rising to the top of innovative nations by 2030, and becoming an innovation powerhouse by 2050 Litao [67]. A key component of this goal is the “new generation artificial intelligence development plan,” which sets the stage for China to establish international ethical norms and standards for AI by outlining the country’s ambition to become a global leader in AI by 2030 and turn the field into a trillion-yuan industry Roberts [16]. This strategic focus on AI is not just a technology to be developed but also a means to promote data governance and interoperability among different departments in the public sector. With the inclusion of artificial intelligence in public administration, China seeks to enhance governance and service delivery, close the digital gap, and promote rural information Hanna & Qiang [68].

According to the People’s Republic of China, which considers artificial intelligence vital for competitiveness and national security, the technology’s potential to offer a military advantage further strengthens its strategic importance Waltzman [69] (Figure 3). China’s rapid advancements, strong national ambitions, and substantial government support are enabling the country to make strategic advancements in terms of their AI industry. In addition, the goals set by China aiming for technical independence, economic transformation, and global leadership by 2030, are all dependent on its strategic concentration on AI. All these elements form a coherent narrative of China’s strategic antecedents, where the nation’s goals, data governance, objectives for global leadership, and application of AI into public administration to improve governance and service delivery are all closely related to the country’s technological ambition, especially in AI, which enhances its agenda for innovation. Even though China is growing rapidly, it faces issues such as a lack of skilled people and fundamental AI research, including algorithms and hardware. These issues are being addressed via international cooperation, innovative ecosystems, and greater investment in education [70-75].

Conclusion

AI adoption in China’s public sector shows a lot of promise, although there are some challenges that must be overcome for integration to be sustainable. This study highlights crucial gaps in the complete applicability of AI technologies, particularly with regard to long-term sustainability in complex settings such as public safety, smart city development, and healthcare. Even though early implementations of AI at different levels have their advantages, a more comprehensive strategy that focuses on operational viability, regulatory flexibility, and the creation of strong data ecosystems is necessary to achieve long-lasting effects.

Organizational factors such as workforce development, public-private partnerships, and investments in technology infrastructure are very critical. Cities such as Hangzhou, Shenzhen, and Beijing have managed to address these factors effectively. Regardless, there are still challenges with overcoming bureaucratic opposition, ensuring that public institutions are culturally compatible, and carefully copying effective models like Hangzhou’s city brain.

The process of modifying organizations is still essential in order to increase the applications of AI in the public sector. Environmental factors play a very significant role as well. For instance, the “New Generation Artificial Intelligence Development Plan,” which is an example of centralized governance, offers strategic guidance and promotes the advancement of AI. The development of technology is greatly aided by public-private partnerships, such as those between local governments, Tencent, and Alibaba. However, problems concerning data privacy, transparency, and public trust issues still present difficulties, especially for security-related applications like Shenzhen’s public security system. For wider public acceptance, these issues must be solved through stronger governance. Innovation-related elements such as data centralization, scalability, ease of use, and AI’s disruptive potential have produced noticeable advancements in fields like environmental monitoring and traffic management. However, as shown in the examples of Beijing’s smart traffic management system as well as Nanjing’s environmental monitoring system, these technologies cannot be effectively implemented without strong organizational support and developed data ecosystems.

Individual preconditions such as technical expertise as well as leadership have been critical. The adaptation of AI into government services in China has been further accelerated by a combination of the central government’s top-down strategic vision as well as the active participation of regional technocrats and private sector specialists. The way artificial intelligence is adopted into China’s public sector is largely influenced by strategic antecedents. This strategic vision is made possible through China’s long-term national policies and places the development of AI technologies as one of the key components of its innovation strategy, as indicated in the “New Generation Artificial Intelligence Development Plan.” With the help of central leadership, this top-down strategy gives an intelligence development plan.” With the help of central leadership, this top-down strategy places AI as a primary goal among many sectors and aims to position China as a global leader in AI by 2030. Through the alignment of technical advancement in relation to national goals, including public health, urban management, and economic reform, China’s approach makes sure that AI improves its competitiveness both domestically and globally. However, there are still weaknesses in the area of fundamental AI research and talent development, which need further international cooperation as well as more investments in research and educational institutions.

Research Limitations

There are some limitations to the results of this research. To begin with, this study was conducted in only five case studies from China’s major cities, which might not provide an adequate sample of AI adoption in other regions or rural areas. Secondly, the use of secondary data sources limits the depth of understanding internal government processes as well as the views of stakeholders. There is one notable gap, which is the absence of direct interviews with public administering officials or technologists directly involved in these initiatives. Lastly, Due to the continuous enhancement of AI solutions, some of the perspectives may become outdated. This is a characteristic of AI that inspires constant change. Future research should expand its scope to include more diverse geographic and sectoral contexts and also engage with more relevant firsthand information from AI specialists as well as public sector organizations.

Future Directions

This study highlights the importance of understanding the sustainability of the use of AI applications in the public sector. Furthermore, future research should focus on tracking how AI systems change with regard to organizational changes, changes in public opinion, and changes in regulations. It is also essential to address AI’s impact on governance systems, levels of public trust, and service effectiveness in wider contexts, particularly in less developed regions. Finally, to leverage the potential of AI in public administration, more innovation in data governance, ethical frameworks, and public engagement will be required.

References

1. Popescu R, Corbos R-A, Bunea O-I (2024) From bytes to insights through a bibliometric journey into AI's influence on public services. Applied Research in Administrative Sciences.
2. Damar M, Özen A, Çakmak ÜE, Özoğuz E, Erenay FS (2024) Super AI, Generative AI, Narrow AI and Chatbots: An Assessment of Artificial Intelligence Technologies for The Public Sector and Public Administration. Journal of AI 18(1): 83-106.
3. Todaro D (2024) Approaching China’s “Artificial Intelligence Development Highland. The Use of Artificial Intelligence in the Public Sector in Shanghai 1-17.
4. Ladislas ES (2024) Personalizing Government Services through Artificial Intelligence: Opportunities and Challenges. Indian Journal of Artificial Intelligence and Neural Networking (IJAINN) 3(5): 2023.
5. Marzouki A, Chouikh A, Mellouli S, Haddad R (2023) Barriers and actions for the adoption and use of Artificial Intelligence in the public sector 94-100.
6. Manias G, Apostolopoulos D, Athanassopoulos S, Borotis SA, Chatzimallis C, et al. (2023) AI4Gov: Trusted AI for Transparent Public Governance Fostering Democratic Values.
7. Alhosani K, Alhashmi SM (2024) Opportunities, challenges, and benefits of AI innovation in government services: a review. Discover Artificial Intelligence 4: 18.
8. Isagah T (2023) Problem Formulation and Use Case Identification of AI in Government: Results from the Literature Review. Digital Government Research 434-439.
9. Padmaja CVR, Lakshminarayana S (2024) The rise of AI: a comprehensive research review. IAES International Journal of Artificial Intelligence 13(2).
10. Straub VJ, Morgan D, Bright J, Margetts H (2023) Artificial intelligence in government: Concepts, standards, and a unified framework. Government Information Quarterly 40(4): 101881.
11. Wolff J, Pauling JK, Keck A, Baumbach J, Baumbach J (2021) Success Factors of Artificial Intelligence Implementation in Healthcare 3: 594971.
12. Chen H (2019) Success Factors Impacting Artificial Intelligence Adoption --- Perspective from the Telecom Industry in China.
13. Zhan Y, Wang P, Xia S (2011) Exploring the Drivers for ICT Adoption in Government Organizations in China. International Conference on Business Intelligence and Financial Engineering.
14. Zhang C, Cui L, Huang L, Zhang C (2007) Exploring the Role of Government in Information Technology Diffusion. Organizational Dynamics of Technology-Based Innovation: Diversifying the Research Agenda 393-407.
15. Liang Y, Qi G (2017) The determinants of e-government cloud adoption: multi-case analysis of China. International Journal of Networking and Virtual Organisations 17: 2-3.
16. Roberts H, Cowls J, Morley J, Taddeo M, Wang V, et al. (2019) The Chinese Approach to Artificial Intelligence: An Analysis of Policy and Regulation. Social Science Research Network 36: 59-77.
17. Wang S, Ke Y, Xie J (2012) Public Private Partnership Implementation in China.
18. Adams J, Young A, Zhihong W (2006) Public-private partnerships in China: rational system and constraints.
19. Li S (2016) Does environmental information transparency lead to more collaborative governance in China? an analysis of the IPE information database's function in boundary spanning.
20. Jimenez-Gomez CE, Cano-Carrillo J, Lanas FF (2020) Artificial Intelligence in Government. IEEE Computer.
21. Misuraca G, Viscusi G (2020) AI-Enabled Innovation in the Public Sector: A Framework for Digital Governance and Resilience. Electronic Government 110-120.
22. Ghina A, Permana D (2017) Fostering Innovation Within Public Sector: Antecedents and Consequences of Public Sector Innovation.
23. Moussa M, McMurray A, Muenjohn N (2018) Innovation and Leadership in Public Sector Organizations. Journal of Management and Research 10(3).
24. Agolla JE, Lill JBV (2016) An empirical investigation into innovation drivers and barriers in public sector organizations. International Journal of Innovation Science.
25. Kakatkar C, Bilgram V, Füller J (2018) Innovation Analytics: Leveraging Artificial Intelligence in the Innovation Process. Social Science Research Network.
26. Stenvall J, Virtanen P (2017) Intelligent Public Organizations. Public Organization Review 17: 195-209.
27. Balau G, Faems D, Bij H van der (2012) Individual Characteristics and Their Influence on Innovation: A Literature Review.
28. Santhiya P, Jebadurai IJ, Paulraj GJL, Jenefa AAGR, Karan SK (2024) ITS-AI Integration: Enhanced Strategies for Mitigating Urban Traffic Congestion.
29. Kumar A, Batra N, Mudgal A, Yadav AL (2024) Navigating Urban Mobility: A Review of AI-Driven Traffic Flow Management in Smart Cities.
30. Lungu MA (2024) Smart Urban Mobility: The Role of AI in Alleviating Traffic Congestion.
31. Shahi Mr G, Gupta M, Sharma M, Khare M (2024) Smart traffic management system using networked CCTV smart cameras.
32. Alam M, Habibullah Md (2024) Enhancing Traffic Management in Smart Cities: A Cyber-Physical Approach. International Journal For Multidisciplinary Research.
33. Anitha C, Sharma S, Nassa VK, Agrawal SK, A R (2024) Artificial Intelligence Powered Congestion Free Transportation System Through Extensive Simulations. Journal of Machine and Computing 4(1): 250-260.
34. Xu Y, Cugurullo F, Zhang H, Gaio A, Zhang W (2024) The Emergence of Artificial Intelligence in Anticipatory Urban Governance: Multi-Scalar Evidence of China’s Transition to City Brains. Journal of Urban Technology 32(3): 9-33.
35. Kommineni M, Baseer KK (2024) An Architecture and Review of Intelligence Based Traffic Control System for Smart Cities. EAI Endorsed Transactions on Energy Web.
36. Khan ABF, Ivan P (2023) Integrating Machine Learning and Deep Learning in Smart Cities for Enhanced Traffic Congestion Management: An Empirical Review.
37. E L, Kanailal PS, Selvi GA, Senthamilarasi N (2024) Smart Traffic Control System Using AI. International Journal for Multidisciplinary Research.
38. Choi E, Bahadori MT, Schuetz A, Stewart WF, Sun J (2016) Doctor AI: Predicting Clinical Events via Recurrent Neural Networks 56: 301-318.
39. Ghavami P, Kapur KC (2012) Artificial neural network-enabled prognostics for patient health management. IEEE Conference on Prognostics and Health Management.
40. Secinaro S, Calandra D, Secinaro A, Muthurangu V, Biancone P (2021) The role of artificial intelligence in healthcare: a structured literature review. BMC Medical Informatics and Decision Making 21(1): 125.
41. Siddesh GM, Krutika S, Srinivasa KG, Siddiqui N (2021) Healthcare Data Analytics Using Artificial Intelligence.
42. Applications of AI in Healthcare Sector for Enhancement of Medical Decision Making and Quality of Service. 2022 International Conference on Decision Aid Sciences and Applications (DASA).
43. Salehi AW, Gupta G, Sonia S (2021) A prospective and comparative study of machine and deep learning techniques for smart healthcare applications.
44. Bhuiyan Md AR, Ullah Md R, Das AK (2019) iHealthcare: Predictive Model Analysis Concerning Big Data Applications for Interactive Healthcare Systems †. Applied Sciences 9(16).
45. S N, R K, G J, M S, K S (2021) Smart Health Prediction Using Machine Learning. Journal of Emerging Technologies and Innovative Research 10(2).
46. Houfani D, Slatnia S, Kazar O, Saouli H, Merizig A (2021) Artificial intelligence in healthcare: a review on predicting clinical needs. International Journal of Healthcare Management 15(3)267-275.
47. Große-Bley J, Kostka G (2021) Big Data Dreams and Local Reality in Shenzhen: An Investigation of Smart City Implementation in China. Social Science Research Network.
48. Sprick D (2019) Predictive Policing in China: An Authoritarian Dream of Public Security. Social Science Research Network.
49. Андреевич КА (2023) Technological development of modern states: artificial intelligence in public administration. Государственное и Муниципальное Управление.
50. Clark RB (2022) Data-intensive Innovation and the State: Evidence from AI Firms in China. The Review of Economic Studies 90(4): 1701-1723.
51. Governing AI Systems for Public Values (2022).
52. Labaki G (2022) Artificial intelligence and the reimagining of public administration / արհեստական բանականությունը եվ հանրայինկառավարման վերաիմաստավորումը.
53. Mondal MA, Rehena Z (2021) An IoT-Based Congestion Control Framework for Intelligent Traffic Management System 1287-1297.
54. Reza S, Oliveira HS, Machado JJM, Tavares JMRS (2021) Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System. Sensors 21(22): 7705.
55. Lilhore UK, Simaiya S, Ghosh P, Garg AK, Trivedi NK, et al. (2022) Role of Swarm Intelligence and Artificial Neural Network Methods in Intelligent Traffic Management. Machine Learning and Autonomous Systems 209-222.
56. Intelligent Transport Systems and Traffic Management (2022).
57. Machine learning-enabled cognitive approaches for handling IoT-based environmental data (2022).
58. Jinjun L, Haohao L, Yazhong M, Bin H, Baosong G, Jianmeng L (2020) AI algorithm and AI model deploying system and method applied to the urban brain.
59. Qi W, Liran Z, Xian D, Yinle W (2021) Urban brain intelligent big data system.
60. Ye X, Newman GD, Lee C, Zandt SV, Jourdan D (2023) Toward Urban Artificial Intelligence for Developing Justice-Oriented Smart Cities. Journal of Planning Education and Research.
61. Kharisova R (2022) Artificial Intelligence in Pollution Control and Management: Status and Future Prospects. Artificial Intelligence and Environmental Sustainability 22-43.
62. Banach M, Dlugosz R, Talaska T, Pedrycz W (2022) Air Pollution Monitoring System with Prediction Abilities Based on Smart Autonomous Sensors Equipped with ANNs with Novel Training Scheme. Remote Sensing 14(2).
63. Evison F (2022) IoT-enabled environmental toxicology for air pollution monitoring using AI techniques. Environmental Research 205: 112574.
64. Feng Z (2021) Optimization and Simulation of Atmospheric Environment Monitoring System Based on Wireless Sensor. Journal of Sensors.
65. Omar DSS, Binti A, Hayder G, Hung Y-T (2023) Monitoring and modelling of water quality parameters using artificial intelligence. International Journal of Environment and Waste Management 31(4).
66. Yunxian Z (2019) Environmental artificial intelligence monitoring system and environmental artificial intelligence monitoring method.
67. Litao Z (2016) China’s Innovation-Driven Development under Xi Jinping. East Asian Policy 8(4): 55-68.
68. Hanna NK, Qiang CZ-W (2010) China’s Emerging Informatization Strategy. Journal of The Knowledge Economy 1: 128-164.
69. Waltzman R, Ablon L, Curriden C, Hartnett GS, Holliday MA, et al. (2020) Maintaining the Competitive Advantage in Artificial Intelligence and Machine Learning.
70. Hamirul (2023) The Role of Artificial Intelligence in Government Services: A Systematic Literature Review. Open Access Indonesia Journal of Social Sciences 6(3).
71. Straub VJ, Morgan D, Bright J, Margetts H (2022) Artificial intelligence in government: Concepts, standards, and a unified framework. arXivOrg.
72. The National Artificial Intelligence Research and Development Strategic Plan (2018).
73. Johny C, Dahiya V (2022) Machine Learning Applications in Vehicular Traffic Prediction and Congestion Control: A Systematic Review. 6^th International Conference on Electronics, Communication and Aerospace Technology.
74. Muhammad Hassan, Asma Kanwal, Muath Jarrah, Manas Pradhan, Arshad Hussain (2022) Smart City Intelligent Traffic Control for Connected Road Junction Congestion Awareness with Deep Extreme Learning Machine. International Conference on Business Analytics for Technology and Security (ICBATS) 1-4.
75. Zubairi JA, Idwan S, Haider SA, Hurtgen D (2022) Smart City Traffic Management for Reducing Congestion. International Symposium on High-Capacity Optical Networks and Enabling Technologies.