Jul 2026· Trends in Social Sciences and Humanities Research· Vol 4, pp. 11-17· 0 citations· 30 references
TL;DR
A shift toward a people-centered governance paradigm is needed, where human-AI collaboration and inclusive governance establish a dynamic balance between efficiency and equity, innovation and regulation.
Abstract
Artificial intelligence is profoundly reshaping the paradigm of urban governance. On one hand, AI brings efficiency gains, resource optimization, and enhanced resilience — automated administration, precise services, and intelligent operations make cities run more efficiently. On the other hand, the accompanying risks cannot be ignored: algorithm optimization, responsible data governance, transparent decision-making, and inclusive digital development. These challenges highlight the importance of balancing technological innovation with public values. To overcome this predicament, a shift toward a people-centered governance paradigm is needed, where human-AI collaboration and inclusive governance establish a dynamic balance between efficiency and equity, innovation and regulation.
Water reservoir governance in Chhattisgarh faces a distinct structural paradox: an abundance of natural hydrometeorological resources coexisting with severe localized seasonal deficits, low creation-to-utilization ratios of irrigation
potential, and complex inter-sectoral competing demands from agriculture, energy, and rapid urban centers. Traditional,
fragmented governance frameworks managed across disconnected administrative bodies lack the institutional agility and
the high-resolution, real-time data architectures needed to insulate the state's vulnerable socio-economic and agrarian
frameworks from escalating climatic variability. From a political economy perspective, this paper evaluates the
contemporary policy challenges, institutional bottlenecks, and distributive inequities plaguing reservoir governance in
Chhattisgarh. We explore strategic pathways for a modern digital transformation by integrating advancements in the
Internet of Things (IoT), Geographic Information Systems (GIS), and Artificial Intelligence (AI). We propose a unified
framework for secure, resilient, and intelligent water management designed to optimize economic allocation efficiency and
institutional accountability. This includes deploying real-time telemetry network layers for dynamic capacity monitoring,
leveraging Geospatial AI (GeoAI) for structural resilience and climate change mitigation, and implementing rigorous
cybersecurity frameworks to protect critical hydrological infrastructures from emerging digital vulnerabilities. Ultimately,
the paper outlines a policy roadmap to shift the state's reservoir management from a reactive, manual operational paradigm
to a predictive, data-driven governance model that secures equitable resource distribution and regional economic stability.
Bharati Sahu, Nusrat Jahan, Deepika Rajwade et al.· International Journal of Inn...· 0 citations
Artificial Intelligence (AI) is a transformative technology, that is reshaping the nature of governance systems, creating an unprecedented playing field for sustainable, efficient, and citizen-centric initiatives. It seems there are many challenges when it comes to the use of AI for governance purposes, with both theoretical and ethical gaps in the implementation process. This article discusses the function of AI in the field of sustainable governance and examines global publication trends, thematic interests, and collaborative patterns from 2000 to 2019. In this study, based on a sample of 1044 Scopus-indexed papers, a bibliometrics- and content analysis-based approach is used to identify the major themes, emerging topics, and geographic considerations of the merger of AI and sustainability. The Results indicate that while AI has been studied extensively in various domains (e.g. smart cities, e-governance, environmental management), ethical governance mechanisms, just policy implementation, and broader social implications of AI adoption have received insufficient attention. Filling these gaps, this study contributes to theory development in governance research and is relevant for policymakers to make the deployment of inclusive and ethical AI a reality. This analysis also underscores the importance of concerted interdisciplinary research efforts to strengthen the resilience, trust, and transparency of governance. One clear area in which the research described in this paper does not cover the broad impact of AI technology on sustainability is in developing countries
The sustained growth of urban areas has increased the complexity of managing services, infrastructure, and mobility, creating a need for advanced technological solutions capable of responding dynamically to rapidly changing environments. In this context, adaptive smart urban systems have emerged as an innovative alternative that integrates artificial intelligence (AI) to optimize real-time decision making. This study presents a systematic literature review and bibliometric analysis of 64 scientific articles focused on the frameworks underpinning these systems. The methodology applied is based on the selection and critical analysis of indexed scientific publications, enabling the identification of predominant approaches such as machine learning, deep learning, multi-agent systems, and reinforcement learning. The findings reveal a strong convergence between AI, the Internet of Things (IoT), and Big Data, as well as significant limitations in terms of interoperability, data governance, and scalability. It is concluded that, while the advances are promising, the consolidation of these systems requires a comprehensive approach that combines technological innovation, appropriate regulation, and social sustainability.
Gary Reyes, Roberto Tolozano-Benites, Jorge Reyes et al.· Information· 0 citations
Optimizing the allocation of national land resources is a crucial measure for safeguarding food security and ecological security, as well as a key pathway for achieving urbanrural integration and coordinated regional development. Against the backdrop of the global digital transformation, artificial intelligence offers new opportunities for intelligent and refined land resource management. However, existing research has not sufficiently explored the impact of AI on land resource allocation. Using panel data of 265 Chinese cities from 2010 to 2023, this study investigates the effect of artificial intelligence level (AIL) on land resource misallocation (LRM) and its underlying mechanisms. The findings demonstrate the key conclusions: (1) AIL significantly reduces urban LRM. This result remains robust after adjusting the sample, accounting for province–time interactions, controlling for other policy effects, excluding outliers, and addressing endogeneity concerns. (2) Mechanism analysis indicates that AIL reduces urban LRM by enhancing government environmental concerns, promoting land transfer marketization and industrial structure upgrading. (3) Heterogeneity analysis indicates that the inhibitory effect of AIL on LRM is more pronounced in non-old industrial base cities, low-population-density cities and strong government intervention cities. These findings suggest that local governments should tailor AI research, development, and application strategies according to their cities’ resource endowments and developmental foundations. This study not only enriches empirical evidence on AI’s role in reducing LRM but also offers practical pathways and decision-making references for other countries to optimize territorial governance and achieve sustainable land resource utilization through digital and intelligent technologies.
Long Xin, Yidan Liu, Yuyao Wang et al.· Land· 0 citations
To manage complex disaster risks, it is important to develop a response that spans multiple areas of expertise. Bridging the gap between law, social sciences, and natural sciences is an important part of any disaster risk reduction. It helps systems adapt quickly as AI technologies are constantly changing and have significantly impacted where law and the natural environment intersect, influencing legal systems and environmental policies, and how legal and environmental issues can prevent AI from having a greater impact on society and the economy. As part of a participatory assessment of production, with the assistance of legal experts, social and environmentalists, the key principles of responsible data analysis are proposed. These principles focus on security, transparency, fairness, accountability, and the ability to challenge or challenge decisions. This conversation describes how different disciplines can work together to create flexible legal systems that use AI, while drawing knowledge from the environmental and social sciences. The way environmentalists and decision-makers talk about useful and accurate information leads to differences that make the use of artificial intelligence difficult due to legal issues related to the reliability, reliability, and contentiousness of disaster management systems. If social media is useful for disaster risk reduction through AI, it's important to consider legal issues related to who is responsible and how sensitive the information used in disaster management is. Ideas for a fair and responsible process focus on the environment and encourage discussions on social and economic issues related to public participation. AI is also very important in education, as it brings together the next generation of law, social and natural sciences to jointly find solutions that bring together different disciplines in a balanced way. AI tools can be very useful in emergency management, but it's important to use them fairly and responsibly. You need to think about things like possible unfair benefits, clear explanations, and protecting people's personal data. Careful use of AI techniques, considering how AI, laws, and environmental risks interact with each other, helps create equitable and sustainable ways to collect and use data.
R. Paper, Research Supervisor Prof, Dominique Ferraro· The social science· 0 citations
Rapid urbanization and escalating climate-related risks have significantly heightened the disaster vulnerability of cities worldwide. Traditional urban planning methods, constrained by their reliance on static historical data and reactive strategies, are increasingly inadequate for addressing contemporary hazard complexities. Artificial Intelligence (AI) presents a transformative opportunity for disaster-resilient urban planning through data-driven insights and dynamic decision-making capabilities. This paper evaluates the role of AI in enhancing urban resilience and proposes a conceptual framework grounded in a systematic review of peer reviewed literature and comparative global case studies. The study employs a qualitative review methodology encompassing an examination of academic publications, policy reports, and case studies from disaster prone metropolitan areas. Secondary data were sourced from Scopus, Web of Science, institutional databases, and publicly accessible urban and climate datasets. Results indicate that AI-enabled approaches demonstrate superior capabilities in risk recognition, integrated data analytics, and the formulation of proactive, adaptive planning strategies compared to conventional methods. Comparative case study analysis of Chennai, Rotterdam, Tokyo, and Singapore reveals that the efficacy of AI applications is contingent upon data availability, technological infrastructure, and institutional governance capacity. The study concludes that while AI holds substantial potential to transform disaster-resilient urban design, its effective implementation necessitates robust institutional frameworks, ethical governance, and context-specific adaptation, particularly in developing regions.
Senthil M· 2026 11th International Conf...· 0 citations