Raveena Marasinghe

Distributed energy resources (DERs), including rooftop solar photovoltaic systems, battery storage, and electric vehicles (EVs), are increasingly recognised as critical components of resilient and sustainable urban energy systems. As cities seek to strengthen energy security and accelerate decarbonisation, understanding the spatial distribution of these resources becomes essential for identifying opportunities for microgrid development. This study presents a spatial assessment framework for evaluating DER readiness and supporting resilience-oriented microgrid planning in South East Queensland, Australia. Publicly available datasets on solar installations, battery capacity, EV registrations, electricity demand, and unplanned power outages were integrated at the postcode level to construct a Microgrid Readiness Index. The index combines demand-normalised measures of renewable generation, distributed storage, and EV adoption to assess local capacity for supporting decentralised energy systems. Spatial clustering was examined using Getis-Ord Gi* hotspot analysis, while outage exposure was analysed to identify locations where microgrid opportunities align with energy vulnerability. Results reveal significant concentrations of DER readiness across the Brisbane metropolitan region and surrounding peri-urban areas. Of the 419 postcodes analysed, 108 were identified as resilience-opportunity areas where relatively high DER capacity coincided with elevated outage occurrence. The findings highlight substantial spatial variation in the composition and concentration of DERs, indicating that resilience potential emerges through diverse local resource configurations. By integrating multiple DER indicators into a scalable spatial assessment framework, this study provides actionable evidence for prioritising microgrid investments and advancing more resilient, sustainable, and decentralised urban energy systems.

Purpose: This study examines how citizens' experiences with public sector AI shape understanding, trust, and risk perceptions, and how these factors influence public support for responsible AI in local government contexts. Design/methodology/approach: Survey data from 1,183 respondents across Australia, Hong Kong, and Saudi Arabia were analysed using structural equation modelling. The framework links experience, perceived understanding, trust, perceived risks and benefits, attitudes toward urban AI and surveillance AI, and the prioritisation of responsible AI principles. Findings: Experience with local government AI significantly enhances perceived understanding, which in turn strengthens trust. Trust increases perceived benefits and favourable attitudes toward AI-enabled urban services. However, perceived risks remain a central driver of responsible AI prioritisation. Positive attitudes toward routine urban AI applications are also associated with greater openness to more contested surveillance AI applications. Cross-context differences indicate that these pathways vary according to governance setting, sample composition, and exposure to AI-enabled services. Research limitations/implications: The findings are constrained by cross-sectional, self-reported data and differences in sampling strategies across contexts. Future research should use longitudinal designs and extend the model across additional institutional settings. Practical implications: Responsible implementation requires trust-building, public engagement, and proactive risk governance. Local governments can adopt an experience-first approach by beginning with visible urban AI services that build familiarity and demonstrate public value, while extensions to surveillance or other sensitive applications require stronger safeguards, transparency, and public justification. Social implications: The findings inform the development of transparent, accountable, and citizen-centred AI governance, supporting socially acceptable and equitable urban innovation. Originality/value: The study advances a comparative, local government-centred model of public sector AI innovation by integrating experiential, cognitive, and perceptual dimensions. It offers novel insights into public support mechanisms for responsible local government AI.

Urban AI is increasingly embedded in the governance of security, mobility, and everyday life through sensors, cameras, and algorithmic systems promoted under smart city agendas. While these technologies promise efficiency and protection, their rapid normalisation raises tensions between security, democratic accountability and individual freedom. This commentary critically examines AI-enabled urban surveillance as a contemporary mode of statecraft, arguing that dominant safety narratives often obscure deeper processes of behavioural regulation, data extraction and power consolidation within urban infrastructures. Drawing on examples from both democratic and authoritarian contexts, it shows how AI-driven monitoring can reshape citizenship, amplify inequality through algorithmic bias and erode privacy and autonomy. To address these challenges, the study advances a multi-level analytical framework tracing how smart city paradigms materialise as surveillance infrastructures, generate social consequences, and produce democratic tensions that diverge into care-oriented or control-oriented governance pathways. The commentary concludes that safeguarding urban freedom requires treating urban AI governance as a political project grounded in transparency, participation, civil liberties and democratic oversight, rather than a purely technical exercise.

Artificial intelligence (AI) is increasingly embedded in how cities are governed, shaping decisions on mobility, land use, public services, and environmental management. Yet urban AI is predominantly governed through fragmented frameworks designed at national or corporate scales, offering limited guidance for municipal decision-making and overlooking place-specific social and ecological consequences. As the level of government closest to everyday urban life, cities are uniquely positioned to steer AI toward public value, but face persistent tensions between efficiency, equity, accountability, and sustainability. This paper argues that responsible urban AI cannot be governed through top-down or one-size-fits-all approaches. To address this, the study aims to conceptualise and advance a ground-up model of responsible urban AI governance that places cities and local governments at the centre of decision-making. It addresses the following research question: How can municipal authorities translate high-level ethical principles into practical, context-sensitive governance arrangements that respond to local capacities, risks, and public values? Drawing on global governance principles and illustrative city experiences, we propose a locally grounded, stage-based framework for municipal AI governance. The framework addresses institutional capacity gaps, fragmented responsibilities, and algorithmic externalities, advancing a participatory, place-sensitive, and adaptive model that aligns urban AI innovation with democratic legitimacy, social justice, and sustainable urban futures.

Artificial intelligence (AI) is increasingly transforming local government by enhancing data-driven decision-making, service delivery, and administrative efficiency. However, its growing adoption also raises significant governance challenges relating to transparency, accountability, institutional capability, data governance, and public trust. This report examines the emerging role of AI in local government and identifies the governance mechanisms required to support its responsible implementation. The report combines conceptual analysis, policy review, and empirical evidence to examine AI adoption from both institutional and societal perspectives. It reviews the evolution of AI research and policy within local government and maps current and emerging AI applications across key service domains, including urban planning, transportation, environmental management, healthcare, public safety, and citizen engagement. The report further examines the policy and governance landscape supporting AI adoption and develops a responsible AI framework centred on four core principles: transparency, accountability, institutional capability, and citizen engagement. Empirical findings, drawing on cross-national public surveys alongside surveys and interviews with local government practitioners, reveal both shared and divergent perceptions regarding AI adoption. While respondents recognise AI’s potential to improve efficiency, service quality, and evidence-based decision-making, concerns remain regarding fairness, explainability, accountability, regulatory readiness, and institutional capacity. Synthesising these findings, the report identifies six interrelated categories of governance risks encompassing regulatory, institutional, operational, accountability, democratic, and workforce dimensions. The report concludes that realising the benefits of AI in local government requires governance approaches that extend beyond technological implementation. Responsible AI adoption depends on robust policy frameworks, organisational capability, effective data governance, transparent oversight, meaningful public engagement, and workforce preparedness. By integrating these governance principles, local governments can harness AI to improve public services while safeguarding democratic values, institutional integrity, and public trust.

Artificial intelligence (AI) has increasingly been integrated into various domains, significantly impacting geospatial applications. Machine learning (ML) and computer vision (CV) are critical in urban decision-making. However, urban AI implementation faces unique challenges. Academic literature on responsible AI largely focuses on general principles, with limited emphasis on the geospatial domain. This important gap in scholarly work could hinder effective AI integration in urban geospatial applications. Our study employs a multi-method approach, including a systematic academic literature review, word frequency analysis and insights from grey literature, to examine potential challenges and propose strategies for effective geospatial AI (GeoAI) integration. We identify a range of responsible practices relevant to the complexities of using AI in urban geospatial planning and its effective implementation. The review provides a comprehensive and actionable framework for responsible AI adoption in the geospatial domain, offering a roadmap for urban researchers and practitioners. It highlights ways to optimise AI benefits while minimising potential negative consequences, contributing to urban sustainability and equity.

Purpose: This paper examines rent determinants and their relationship with commercial office property rents.Design/methodology/approach: The method adopted in this study differs from that of previous studies on this topic. Firstly, based on the survey of the viewpoints of experts, Relative Importance Index (RII) analysis was used to identify rent determinants and to rank and ensure their relevance and validity in the Sri Lankan context. Secondly, sampling of data related to 115 office properties collected from property tenants and landlords located within the central built-up area of Colombo City was conducted using a multi-methods approach to carry out an objective hedonic analysis of office rents.Findings: This research utilizes RII and hedonic models to provide insights into determinants and relationships. Both analyses confirm that the three top drivers of commercial office rent are distance from the major town center, availability of parking space and the condition of the property. In addition to these three factors, hedonic models reveal that the age of the property and the availability of a conference hall also play a relevant role in explaining office rents. Given the disparities in the findings of the two methods, further examination was able to confirm that factors such as distance from the major town center, parking availability, age of the property, presence of a conference hall, building condition, floor size, business type and type of building are likely to influence commercial office rent. These findings reflect elements such as the quality, newness and better facilities of different office properties.Practical implications: This systematic study and analysis of office rent for the guidance of real estate investors can support sound investment decisions, potentially leading to more financially sound property development, reduced public debt levels and improved public-private financing. Further, the research findings offer valuable insights to real estate investors, developers and planners regarding location decisions for office development quality enhancements in future office developments.Originality/value: This research provides fresh insights into the local scale office market, an area where limited evidence currently exists. Further, the methodology adopted provides evidence that hedonic analysis, supported by a multi-method approach, can mitigate the subjective judgments made by professionals.