Kai Chen Goh

This paper discusses the urgency of proactive approaches to urban resilience in the context of deepening climate change impacts and the rapid pace of urbanization. Due to the rising sea levels, extreme weather events, and other challenges urban areas face today, such as aging underground infrastructure that was not built to withstand stressors such as climate change, pollution from industrial activities, which have been distorted by overdevelopment. The analysis draws attention to the contribution of intelligent grid solutions, climate‐resilient materials, and renewable energy integration in promoting urban resilience. Smart grid implementation alone is expected to reduce electricity use by 12%–15%, while self‐healing concrete (a climate‐resilient material) reduces maintenance costs by 50%–70% while extending the life spans of infrastructure assets. Moreover, this study underscores how digital twin technology combined with artificial intelligence‐led predictive analytics to predict and avert infrastructure failures, while reducing downtime and costs by up to 30%–50%, respectively. The findings highlight the requirement for policymakers, urban planners, engineers, and communities to work together in implementing these mitigation strategies. Key enablers include public‐private partnerships to close the funding gap and hasten technology adoption. This article concludes that investing in research, development, and strategic planning is the key to creating resilient, sustainable urban environments.

As cities increasingly adopt digital transformation strategies, Artificial Intelligence (AI) is emerging as a pivotal force in reshaping public sector operations and workforce dynamics. This study investigates the influence of AI on employee performance within Sharjah City Municipality (SCM). Employing a quantitative research design, the study aims to establish the structural relationship between AI component usage factors and their impact on employee performance. Data were collected using a structured questionnaire distributed to SCM employees through a purposive convenience sampling method. A total of 233 valid responses were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) via SmartPLS software. The analysis confirmed statistically significant and positive relationships between AI components and employee performance. Notably, the standardized beta coefficient of 0.818 indicates a strong influence of AI integration on performance outcomes. These findings provide valuable insights for SCM in enhancing workforce efficiency by promoting AI awareness, delivering targeted training, integrating AI tools into routine operations, and fostering a culture of continuous innovation and upskilling.

Recently, landfill mining (LFM) has emerged as a promising strategy for addressing the challenges of waste management, resource recovery, and climate change mitigation. This work explores the potential of landfill mining to transform traditional landfills from environmental liabilities into assets. By recovering nutrients, energy, and materials from landfill leachate, landfill mining can reduce greenhouse gas (GHG) emissions, particularly methane (CH ₄ ), while contributing to the circular economy. This study evaluates the technologies applied in landfill mining, such as bioreactors, anaerobic digestion, and leachate recirculation, based on published literature from 2000 to 2025, focusing on their value in resource recovery. More specifically, this study aims at recovering renewable CH ₄ energy from leachate and extracting macro-nutrients such as nitrogen (N), phosphorus (P), and potassium (K) which can be turned into commercial fertilizers. This study further analyzes the advantages of landfill mining, which include reducing CH ₄ emissions by up to 30% and the potential energy value of 15 GWh from 1.5 million m ³ of CH ₄ . The study also explores the socio-economic consequences of landfill mining, focusing on employment opportunities, improved waste management systems, and enhanced local community welfare. Additionally, this work discusses the technological, financial, and regulatory challenges that hinder the widespread adoption of landfill mining for promoting circular economy. Finally, this work calls for further investment, research, and policy development to unlock the full potential of landfill mining as a sustainable waste management strategy and a key contributor to resource recovery in the circular economy paradigm.

Hospital waste management (HWM) is critical to advancing environmental sustainability, particularly as Germany and the European Union (EU) pursue carbon neutrality by 2050. This review examines the potential of Industry 5.0 technologies to transform waste management through intelligent, human-centric systems. The study adopts a mixed-methods approach, including a comprehensive literature review, site visits to five German hospitals and interviews with 20 key stakeholders such as hospital staff, policymakers and waste service providers. Germany generates an estimated 210,000 tonnes of hospital waste annually, with 25–30% classified as hazardous. However, only 17% is currently recycled or recovered. The integration of Industry 5.0 tools – Artificial Intelligence (AI)-driven sorting, robotics and Internet of Things-enabled tracking – combined with staff training and participatory design, can improve segregation accuracy by 40%, reduce landfill reliance by 35% and cut waste-related carbon emissions by 22% within 10 years. Pilot technologies such as plasma gasification and autoclaving have further reduced CO ₂ emissions by 28% compared to traditional incineration. Beyond technical innovation, the study emphasizes the importance of worker involvement, ethical AI and feedback systems in fostering responsible waste practices. It also identifies policy enablers like the EU Green Deal and Germany’s Hospital Future Act, which support the digital and sustainable transformation of healthcare. The findings highlight the need for a collaborative ecosystem connecting healthcare professionals, technologists and regulators. Such synergy is essential to achieving a circular, low-carbon and socially inclusive HWM system aligned with Industry 5.0 values and climate goals.

Swift urbanization and technical progress in Cambodia, specifically in Phnom Penh, require underground transmission lines (UGTL) as a viable substitute for overhead transmission lines (OHTL). However, the substantial cost of UGTL has prevented its extensive integration. In this respect, we identified the most cost-effective technological route for an underground transmission line between substations. Using geographic information system (GIS) data, we generated algorithms to define the optimal route for the installation of a UGTL and minimize the costs of the material and labor required. The research results presented an automated tool for route optimization which simplifies the planning of energy projects and partially relieves the financial burden of UGTL integration. The proposed method radically changes the planning of urban energy infrastructure, as it provides a technology-based, cost-efficient, and environmentally favorable decision for UGTL routing. It also fosters the development of sustainable and resilient urban energy systems in similar urban locations.