In the face of escalating ecological degradation and rapid urbanization, there is an urgent imperative to explore innovative solutions for environmental governance in urban environments. This imperative entails embracing advanced technologies and fostering collaborative models in urban governance to tackle environmental challenges effectively. These challenges encompass resource depletion, energy consumption, transport inefficiency, traffic congestion, infrastructure flaws, waste generation, pollution, biodiversity loss, and climate change. Traditional approaches to environmental governance often fall short of effectively managing these complex issues [1], [2]. In response, the groundbreaking convergence of Artificial Intelligence (AI) and the Internet of Things (IoT)—under the umbrella of AIoT—has emerged as a new frontier for innovative solutions in modern cities. Among the prominent AIoT-powered solutions of smart cities are City Brain, Smart Urban Metabolism (SUM), and platform urbanism [3–7]. These AIoT-driven governance systems offer synergistic opportunities to advance environmental governance in emerging smarter eco-cities. They collectively facilitate real-time monitoring, resource management, data-driven decision-making, and collaborative and participatory approaches to address environmental challenges and promote sustainability. Environmental governance involves coordinating policies, institutions, and actions while actively engaging multiple stakeholders, especially political actors, to shape environmental actions and outcomes. This multifaceted approach encompasses diverse strategies and initiatives to optimize resource management, promote ecological resilience, mitigate environmental impacts, and enhance quality of life. Environmental governance is at the core of smart eco-cities, which prioritize environmental sustainability. These cities integrate data-driven IoT and environmental technologies. However, the evolution towards smarter eco-cities signifies a leap forward, utilizing AI and AIoT technologies to optimize sustainable systems, integrate them with smart systems, and synergize their functions to advance environmental sustainability goals. This progression aims to create intelligent ecosystems that prioritize environmental considerations and leverage cutting-edge technologies to achieve optimal outcomes [8], [9].

In the realm of smarter eco-cities, effective environmental governance by local governments hinges on harnessing AI and AIoT technologies to optimize sustainability initiatives and engage stakeholders in shaping environmentally conscious urban policies and actions. The burgeoning adoption of AI in local governments in smart cities is transforming various domains and addressing complex environmental challenges in smarter eco-city management and planning [10], [11]. In recent years, there has been a noticeable increase in the deployment of AI in smart cities and city governance. This trend underscores a concerted effort to leverage AI technologies in local government settings to bolster efficiency, effectiveness, and innovation across diverse domains. The impact of the smart city movement on local governments, coupled with the increasing adoption of AI and AIoT, has introduced new applications capable of executing tasks with remarkable precision and potentially paving the way for significant societal shifts [12].

However, AI adoption in local governments, aligned with the smart city agenda, still primarily focuses on optimizing efficiency and improving urban services. The main priority continues to be resource allocation and citizens' overall quality of life, with environmental sustainability remaining largely underemphasized in AI adoption. Government agencies worldwide are particularly leveraging AI technologies to streamline operations, enhance citizen services, and make data-driven decisions. Nevertheless, the development of various AI technologies continues to evolve to address specific challenges. This underscores the dynamic nature of AI's role in shaping the future of societal and environmental governance alike [13].

It refers to the management and administration of urban spaces by integrating advanced technologies, data-driven decision-making, and collaborative frameworks to enhance citizens' quality of life. It utilizes digital tools, primarily IoT sensors, data analytics, cloud computing, and AI, to make city operations more efficient, transparent, and sustainable.

In practice, smart city governance encompasses a wide range of domains, including:

I. Infrastructure and public services: ensuring reliable infrastructure for transportation, water supply, waste management, and energy. Smart sensors and data analytics can predict infrastructure needs and maintenance, reducing costs and improving reliability.

II. Transportation and mobility: using IoT data to monitor and manage traffic flow, public transportation, and parking availability. This results in reduced congestion, optimized routes, and improved air quality.

III. Public safety: deploying cameras, sensors, and predictive analytics to improve emergency response, monitor crime hotspots, and enhance overall safety.

IV. Energy management: facilitating efficient energy use through smart grids, sustainable energy sources, and intelligent systems that manage consumption based on demand and environmental impact.

V. Health and social services: improving healthcare delivery, social welfare, and public health by enabling remote health monitoring, optimizing resource allocation, and ensuring swift responses to health crises. VI. Citizen engagement and transparency: enabling platforms for real-time communication, feedback, and engagement with citizens, ensuring transparency and building trust between the government and residents. VII. Environmental sustainability: implementing air and water quality monitoring, pollution control, and waste management policies to create an environmentally sustainable urban environment.

The key technologies in smart city governance are as follows:

I. IoT sensors: real-time data collection from various points across the city, feeding information on traffic, weather, air quality, noise levels, and more.

II. Cloud computing: centralized data storage and management, allowing data from IoT devices to be processed, stored, and analyzed by various city departments.

III. Predictive analytics: AI and machine learning algorithms analyze historical and real-time data to anticipate future trends, enabling proactive governance.

IV. Blockchain: enhancing data security and integrity for sensitive transactions, such as property records, while promoting transparency.

V. Geographic Information Systems (GIS): mapping and visualizing spatial data for urban planning and emergency response.

This section introduces and interlinks the essential components of the integrated framework proposed in this study. Fig. 1 depicts the synergistic and collaborative integration of these components. At the core of this dynamic framework resides AIoT, a catalytic force that underpins the functions of City Brain, City Metabolism, and City Platform. These elements collectively drive the governance of smarter eco-cities towards greater environmental sustainability.

These components shape the development trajectory of smarter eco-cities. As illustrated in Fig. 1 and will be exemplified and analytically underpinned, AIoT serves as the linchpin technology, unifying the identified urban computing and development models. Its enabling influence is evident in seamlessly integrating the functionalities of City Brain, SUM, and platform urbanism to enhance environmental governance strategies and practices. While the specifics and nuances of its computational and analytical functionalities may vary across these models, its overarching goal remains consistent: leverage real-time data processing and analysis to generate data-driven insights that inform decision-making in environmental governance. Its integrative potential forms the cornerstone for supporting City Brain's decision-making processes, optimizing SUM's resource management, and enabling the adaptability of platform urbanism. AIoT acts as the connective tissue, orchestrating a harmonious blend of technological advancements and institutional transformations within the dynamic context of smarter eco-cities. This holistic convergence underscores the framework's capacity to propel urban development towards heightened intelligence, sustainability, and resilience.

AI and IoT have traditionally functioned as separate technological domains, with AI focusing on cognitive capabilities and IoT centered on connecting physical objects to the Internet for data exchange and automation. AI simulates human intelligence processes by machines, particularly computer systems. These processes include learning from experience, reasoning, problem-solving, language understanding, and autonomous decision-making. IoT is a network of interconnected physical objects or "things" embedded with sensors, actuators, and communication capabilities. These devices can sense and collect data from their environment, communicate with other devices or systems over the internet or local networks, and actuate or trigger actions based on predefined conditions or commands. IoT enables seamless data exchange between the physical and digital worlds, improving efficiency, automation, and insights across various domains of smart, sustainable cities [3].

AIoT refers to integrating AI models, notably machine learning and deep learning, with IoT devices. AIoT enhances the capabilities of IoT by enabling devices and systems to analyze and interpret data more intelligently, make autonomous decisions, and adapt to changing conditions in real time, particularly through ML and DL techniques. By leveraging AI algorithms, IoT devices can extract meaningful insights from the vast amounts of data they generate, leading to improved efficiency, predictive maintenance, personalized experiences, and innovative applications across various domains and industries [3]. AIoT empowers IoT ecosystems to become smarter, more autonomous, and capable of addressing complex challenges and opportunities in the connected world of smart cities and smarter eco-cities. This synergy marks a pivotal point in the ongoing technological narrative, with profound implications for the future urban landscape.

City Brain, an advanced city management platform, utilizes AIoT to improve and streamline various facets of urban operations and services while providing valuable insights for governance and policy. By employing AI and IoT, City Brain facilitates real-time data collection, processing, and analysis, supporting informed decision-making for urban management and governance [3]. Although City Brain and platform urbanism share essential functionalities of AIoT, they serve distinct purposes and exhibit unique characteristics. SUM represents an AIoT-powered platform that aims to monitor, analyze, and optimize the flow of energy, materials, and resources in cities. It embraces a holistic approach to analyzing cities as complex systems characterized by inputs, processes, and outputs. By viewing cities through this systemic lens, it fosters resource efficiency and minimizes the ecological footprint of urban areas. Overall, AIoT, by integrating AI's capabilities with interconnected IoT devices and sensors across diverse urban systems, enables the extraction of valuable insights derived from AI. It enhances decision-making processes and advances environmental governance practices in smarter eco-cities through City Brain, SUM, and platform urbanism.

Furthermore, the interconnected relationship between environmental governance and smarter eco-cities emphasizes the importance of their collaborative efforts in shaping sustainable urban futures through data-driven approaches enabled by AIoT. Together, they work hand in hand and synergize, leveraging each other's strengths and capabilities to create environmentally conscious, efficient, and resilient cities. This collaboration signifies a shared commitment to addressing the challenges of urbanization and ecological degradation while promoting sustainable development and improving the quality of life for city dwellers.

Urban governance is the system of values, practices, and institutions governing urban areas' planning, management, and regulation. Hence, it involves various decisions, policies, and actions, engaging diverse stakeholders such as government authorities, institutions, community organizations, and citizens. In this process, "conflicting or diverse interests may be accommodated, and cooperative action can be taken". However, the landscape of urban governance has undergone substantial transformation due to AI and IoT and their impactful convergence under the umbrella of AIoT [10]. As AI and AIoT evolve, their integration within environmental governance frameworks promises to redefine urban sustainability paradigms and promote more inclusive, resilient, and responsive cities through integrating City Brain, SUM, and platform urbanism.

The primary aim of this study is to explore the linchpin potential of AIoT in seamlessly integrating City Brain, SUM, and platform urbanism to advance environmental governance in smarter eco-cities. Specifically, it seeks to develop a pioneering framework that effectively leverages the synergies among these emerging AI or AIoT-driven governance systems to enhance environmental sustainability practices in smarter eco-cities. This exploration process adheres to a systematic approach encompassing five stages (Fig. 2). Significantly, the comprehensive literature review and empirical analysis ensure that this research endeavor is grounded in both theoretical insights and practical foundations.

To establish a solid groundwork for developing the conceptual framework, a comprehensive review of the existing literature was conducted, focusing on City Brain, SUM, platform urbanism, and their interlinkages. In this context, various literature was drawn to establish a basis for structuring the research, including theoretical literature, empirical studies, review articles, policy documents, historical literature, and interdisciplinary literature. These types of literature provided insights into the development, implementation, impact, and dynamic interplay of City Brain, SUM, and platform urbanism.

Fig. 3 illustrates the three-phase flowchart associated with the PRISMA approach. The selection of academic databases Scopus, Web of Science (WoS), and ScienceDirect was deliberate due to their reputation for comprehensive coverage of the 151 high-quality, peer-reviewed studies relevant to the multifaceted topic of this study. A meticulous selection of keywords related to the core topics and their combinations was employed to ensure specificity and diversity in the search and retrieval of pertinent scholarly literature. These keywords were chosen to accurately reflect the various facets and components of the data-driven governance systems under investigation about environmental governance in the context of smarter eco-cities, thereby optimizing the relevance and precision of the search results. These keywords included "City Brain," "City Brain and Artificial Intelligence or Artificial Intelligence of Things," "City Brain and Platform Urbanism," "Smart Urban Metabolism," "Smart Urban Metabolism nd Artificial Intelligence or Artificial Intelligence of Things," "Smart Urban Metabolism and Platform Urbanism," "Platform Urbanism," "Platform Urbanism and Artificial Intelligence," "Platform Urbanism and Governance," "Smart Eco-cities and Governance," "Smart Eco-cities and Artificial Intelligence or Artificial Intelligence of Things," "Smart Cities and Artificial Intelligence or Artificial Intelligence of Things," and "Environmental Sustainability and Artificial Intelligence or Artificial Intelligence of Things." These were used to search against the title, abstract, and keywords of articles to produce initial insights.

The inclusion criteria filtered studies based on their relevance, reliability, language, publication date, and publication type (article, conference paper, or book chapter), providing definitive primary information. Exclusion criteria were applied to remove studies unrelated to the focal topics and their interlinkages and irrelevant to the research aim and questions. Based on these predefined inclusion/exclusion criteria, the selection process involved initial screening based on titles and abstracts, followed by a detailed full-text review to determine eligibility for inclusion in the analysis. The search query retrieved a total of 375 records from three databases. After removing duplicates, 129 records were eliminated. Subsequently, titles and keywords were scrutinized, leading to the exclusion of 52 more records. The remaining 194 records underwent abstract screening against the inclusion and exclusion criteria, removing 33 records. The full-text screening of the remaining 161 records led to an additional 22 records being excluded. Ultimately, this process yielded a final selection of 139 publications.

Additionally, 10 extra records were included through other sources and alerts, bringing the total number of records included in the final analysis to 149. Through this selection process, the study compiled a diverse set of multiple studies offering varied perspectives. This approach ensured the generation of nuanced and cohesive insights, contributing significantly to the overarching aim and objectives of the study.

The literature search was conducted with a focus on studies published between 2018 and 2023. This timeframe was selected to align with the widespread emergence of City Brain, SUM, platform urbanism, and smart(er) eco-cities. The period between 2018 and 2023 marked the foundational phase for integrating these concepts into sustainable urban development strategies to advance environmental goals. It provided a comprehensive view of the evolution of smart eco-cities to smarter eco-cities in the context of AIoT, City Brain, SUM, and platform urbanism. It allowed for an in-depth exploration of the interconnections between these data-driven smart and sustainable city systems and the evolving synergies among smart cities, smart eco-cities, environmental sustainability, and AI and AIoT technologies. In particular, the studies published between 2021 and 2023 made it possible to capture the latest advancements, trends, and insights related to integrating AI and AIoT technologies and urban development approaches. These include environmentally sustainable smart cities and smarter eco-cities. Overall, the decision to conduct the literature search between 2018 and 2023 ensures that the research findings are grounded in the most current and relevant scholarly discourse, enriching the analysis and advancing knowledge in the field.

Guided by an inductive approach, a content analysis was conducted on the included studies to collect the necessary data to analyze and synthesize the existing literature. This process identified key themes, patterns, and variations, ensuring a thorough analysis and synthesis. The study focused on the fundamental components of the conceptual framework, examining their technical and functional similarities and distinctions, their role in and impact on environmental governance, and the challenges and obstacles associated with their integration.

Both configurative and aggregative synthesis methods were employed as complementary approaches to develop the conceptual framework. Configurative synthesis involves identifying common themes across the synthesized studies and developing a conceptual framework to elucidate the observed interconnections, variations, and contextual nuances. Thematic synthesis, in this context, aims to understand the underlying patterns, arrangements, or relationships among research findings, focusing on interpretation throughout the synthesis process to derive an overarching meaning or provide a nuanced understanding of the subject under review. On the other hand, aggregative synthesis, as Cooper (2017) described, entailed summarizing and consolidating multiple studies to generate an overall thematic summary of the findings. This approach offered a comprehensive assessment of research evidence, interpreting the results after the synthesis process within specific conceptual frameworks and leveraging evidence to make informed statements. Through these complementary synthesis methods, the conceptual framework was developed to provide a robust understanding of integrating AIoT-driven governance systems in smarter eco-cities in the context of environmental sustainability.

The technology underlying wireless sensor networks is far from perfect at the moment. That requires more study and development, and innovative work will substantially ease the progress of the IoT. Multi-hosted network transmission method: the IoT relies on wireless sensor network technology. Attempts can be made to leverage multi-homed network transmission to boost the IoT's resiliency. It allows for many links to transfer commands from the top down and collect data from several sensors to be relayed to the upper network. This method could speed up data transfer via networks and make them more reliable.

While the smart city governance model offers numerous benefits, it also presents challenges. Data privacy and security are critical concerns, as cities must ensure citizen data protection while using it effectively for governance. Technical scalability and interoperability of systems are also significant challenges, as cities must ensure that new technologies can integrate with existing infrastructure and expand as needed. Additionally, policies to address digital inequality are needed, ensuring that smart city benefits are accessible to all citizens. In summary, smart city governance aims to create a responsive, efficient, and inclusive urban ecosystem where technology and data empower city officials and citizens. By implementing robust frameworks and carefully managing data, smart cities can evolve to meet the demands of urban populations sustainably and transparently.

This section presents the results of exploring the linchpin potential of AIoT in seamlessly integrating City Brain, SUM, and platform urbanism to advance environmental governance in smarter eco-cities. Specifically, it introduces a pioneering framework derived from a thorough analysis and synthesis of an extensive literature review and several case studies. It focuses on how AIoT can be effectively leveraged to synergize City Brain, SUM, and platform urbanism for enhancing environmental sustainability practices in smarter eco-cities. Through detailed analysis and interpretation, this section offers profound insights into the transformative influence of AIoT, City Brain, SUM, and platform urbanism on the trajectory of environmental governance in the complex ecosystem of smarter eco-cities.

I want to express my sincere gratitude to all those who supported me throughout the development of this research paper on Cloud-IoT Integration for Predictive Analytics in Smart City Governance. First and foremost, I am deeply thankful to my academic advisor, Mr. Hitesh Mohapatra, for their invaluable guidance, insightful feedback, and unwavering support during the research process. Their expertise and encouragement were instrumental in shaping my understanding of this complex subject.

I also want to thank my colleagues and peers for their collaboration and discussions that enriched my research experience. Their diverse perspectives and constructive critiques were essential in refining my ideas.

Special thanks go to the institutions and researchers whose work has significantly influenced this paper. The literature and case studies provided a solid foundation for my analysis and conclusions.

Finally, I am grateful to my family and friends for their encouragement and patience during this research journey. Their belief in my abilities motivated me to persevere through challenges.

Thank you all for your support and contributions to this research endeavor.

Conceptualization and research design: the author initially proposed and developed the primary concept of the study on energy-efficient IoT networks through AI-driven approaches. They designed the study structure, established research objectives, and outlined the theoretical framework.

Methodology and model development: the author led the development of AI-based models and energy optimization strategies, designing novel algorithms and conducting initial testing to ensure the models aligned with the study's energy efficiency goals. They contributed to refining the model architecture, mainly integrating IoT network-specific requirements and constraints.

Data collection and analysis: the author managed the data collection processes, ensuring the quality and relevance of the data sets used for training and testing the AI models. They also implemented the analysis pipeline. They contributed by refining the preprocessing steps and conducting statistical validation of model outputs to enhance reliability.

Software development and testing: the Author developed the implementation code for AI-driven optimization, managed software integration, and supported the software testing phase, troubleshooting, and debugging to ensure reliable performance across different IoT network conditions.

Manuscript preparation and revision: the author wrote the initial draft of the manuscript, with contributions from all authors to various sections. They also provided substantial revisions, particularly in the technical and methodological sections. Final proofreading and review were conducted.

Funding and project administration: the author managed Project funding and resource allocation. They also oversaw the project's administrative aspects, coordinating between different teams to maintain the study timeline.

Each author has read and approved the final version of the manuscript, ensuring an accurate and comprehensive presentation of the research findings.

Kalinga institute of industrial technology supported this research. The funding was instrumental in facilitating the research process, enabling access to essential resources, and supporting the dissemination of findings. I want to acknowledge Dr. Hitesh Mohapatra for their assistance throughout the project. Their support and expertise greatly contributed to the successful completion of this research paper.

Any opinions, findings, or conclusions expressed in this work are those of the author and do not necessarily reflect the views of the funding organization.

The data sets used in this study on cloud-IoT integration for Predictive Analytics in Smart City Governance were sourced from publicly accessible repositories and, where applicable, proprietary IoT network datasets to model real-world scenarios. Specific data on IoT network traffic, energy consumption, device usage patterns, and network performance were anonymized to protect privacy and preprocessed for the study.

Public datasets: the research relied on publicly available datasets, including those related to IoT network performance, energy consumption metrics, and simulation models that provide general patterns applicable to energy-efficient IoT applications.

Proprietary datasets: where necessary, proprietary data from specific IoT network case studies were employed under restricted use conditions to validate model performance. Access to proprietary data may be subject to institutional permissions and data-sharing agreements.

Generated data: additional synthetic data were generated to model edge cases and optimize the AI models. These synthetic datasets have been made available in the project repository for reproducibility.

All relevant data and code are available for further research and reproducibility. Researchers interested in proprietary or sensitive data used in this study may request access by contacting the corresponding author, subject to institutional approvals and confidentiality agreements.

The author declares no conflicts of interest in this research paper on energy-efficient IOT networks using AI-driven approaches. The research was conducted independently, and no financial or personal relationships influenced the outcomes or interpretations presented in this study.

If any potential conflicts arise in the future, the author is committed to disclosing them transparently to uphold the integrity of the research.