Small Island Developing States (SIDS) face unique challenges, including limited resources and disproportionately high exposure to natural hazards, which complicate the management of their critical road networks. While various risk-based asset management frameworks have been proposed, they are often conceptual and lack operational procedures or require elaborate data. Using a weighted objective function, this paper implements a practical framework that integrates current and forecasted risk, physical condition, and cost data. The framework also accounts for the remaining service life of investments, uses a simple greedy algorithm for work planning, and benchmarks plans against different budget scenarios. The methodology's flexibility was demonstrated through a case study on the road network in the Commonwealth of Dominica, which showed the tool's effectiveness with limited data. Despite this success, limitations such as missing data and subjective parameters highlight areas for future research, including sensitivity analysis and the incorporation of more advanced analytical models. This work provides a valuable, actionable approach for road management in low and middle-income countries.
This paper investigates the relationship between public infrastructure and economic growth in Assam, India, using annual data from 1990–1991 to 2019–2020. The study adopts a broader classification of public infrastructure comprising economic, social, and financial infrastructure. Composite infrastructure indices are constructed using principal component analysis, while the autoregressive distributed lag bounds testing approach and its error correction specification is used to differentiate between long- and short-run effects. The empirical findings indicate the existence of a long-run relationship among economic growth, labour, capital, and infrastructure variables. Financial infrastructure is found to have a significant negative relationship with long-run economic growth, whereas economic and social infrastructure exhibit statistically insignificant long-run effects. In the short run, social infrastructure is found to stimulate economic growth, while economic infrastructure appears to impede it. The causality analysis further indicates that social infrastructure Granger-causes economic growth, whereas the direction of causality for economic and financial infrastructure appears to run from economic growth to infrastructure development. These findings raise serious concerns about the efficacy of public infrastructure development for long-term economic growth of Assam. Secondly, the demand-driven character of economic and financial infrastructure limits its development to the level required to contribute towards economic growth.
Understanding the spatial distribution of water infrastructure heritage is crucial for its conservation in large regions. This study analyses 147 water infrastructure heritage sites in the Shandong section of the Yellow River Basin using an integrated nearest neighbour distance index (NNDI)-kernel density estimation (KDE)-GeoDetector framework. Spatial techniques, including NNDI and KDE, reveal that sites display a statistically significant clustered pattern described as 'large aggregation, small dispersion', forming a spatial layout of 'three cores and one line'. GeoDetector analysis quantitatively shows that anthropogenic engineering factors, especially water infrastructure systems (q = 0.92), are the primary determinants of distribution, while natural geographical elements serve as secondary constraints. This integrated framework outperforms traditional single-method analyses, offering a robust tool for conservation planning, sustainable tourism, and heritage management in complex river basins. The methodology also provides a transferable model for analysing water infrastructure heritage in other major river systems globally.
This study focuses on the optimisation of the urban waterlogging disaster warning system. In view of the limitations of traditional warning systems in complex urban environments, deep reinforcement learning () technology is introduced. The proposed DRL-EWS model integrates a spatiotemporal graph convolutional network for spatial feature extraction, a gated recurrent unit for temporal sequence modelling, and a policy gradient-based decision module for adaptive warning generation. By innovatively combining these components, the risk of urban waterlogging is accurately predicted. The results show that the waterlogging disaster warning system (DRL-EWS) based on is superior to traditional and other machine learning warning models in terms of warning accuracy, false alarm rate, and false alarm rate, and has good adaptability to different terrains, pipe network density, rainfall patterns, and other conditions. This study not only enriches the waterlogging warning technology system in theory, but its practical results are expected to enhance the urban waterlogging defence capabilities and provide a strong guarantee for urban safety. At the same time, it also points out the direction for further improvement in subsequent research.
This study assesses the asset management (AM) maturity, implementation challenges, and capacity-building needs of local governments in New Brunswick, Canada, following updated provincial AM reporting requirements introduced alongside the 2023 Local Governance Reform. Using a mixed-methods approach, data were collected through a province-wide survey of municipalities (53% response rate) and eight follow-up interviews with municipal staff. The findings indicate that while AM is widely endorsed in principle, most municipalities, particularly smaller and rural governments, remain at early stages of AM maturity and report limited organisational capacity to meet evolving provincial expectations. Key barriers include weak integration between AM plans and capital budgeting, low AM awareness among elected officials, fragmented guidance, and constraints in financial and human resources. Municipal respondents identified a strong need for practical and scalable support mechanisms, including standardised core asset management plan components, targeted funding for external expertise, regional shared-service models, and short-format, risk- and climate-informed training. Based on these findings, the paper advances evidence-based recommendations aimed at strengthening provincial AM support structures and improving the conditions for effective AM implementation across municipalities. The results underscore the importance of aligning diagnostic assessments of AM capacity with proportionate, context-sensitive policy interventions to advance sustainable municipal infrastructure management.
