The EU aims to accelerate the transition to a circular economy to reduce waste and its environmental impact since the linear economy model imposes significant environmental burdens. Resources are finite, and the consequences of environmental degradation, climate change, and disaster risks cannot be ignored. The construction industry accounts for over 38% of waste in Europe, so it has become inevitable to activate road maintenance as one of the enablers for the transition towards circular economy strategies.

This study aims to explore circular economy (CE) frameworks, indicators, and sustainable maintenance and rehabilitation (M&R) materials and methods. For this purpose, a systematic review has been conducted to explore the transition towards the circular economy and its interconnectivity with the road Pavement Management System (PMS). Scopus and Web of Science (WoS) are the primary databases that have been explored. Google Scholar also has been explored to complete the references. By examining and analyzing 62 papers from the mentioned databases, 26 relevant papers have been selected. By examining the previous practices of CE implementation, the review highlights how pavement maintenance of road have evolved to become more circular. This study highlights different CE frameworks, indicators, and sustainable maintenance materials, methods, and technologies that can facilitate circular PMS practices.

Climate-related failures pose increasing risks to railway operation and maintenance, requiring robust assessments to support effective resilience planning. Accordingly, this study presents a comprehensive risk assessment to identify, quantify, and prioritize climate-related failure modes (CRFMs). CRFMs were identified through text mining of 15-year corrective maintenance records across all five Swedish railway regions. A probabilistic model was then developed to analyze the risk of identified CRFMs, considering both operational and maintenance costs. Risk distributions were derived using Monte Carlo simulation and summarized by Expected Value of Risk (EVoR) and Conditional Value at Risk (CVaR0.90). As a result, 14 CRFMs were identified, accounting for 47% of all failure records. Their annual trend and seasonal distribution align well with periods of increased extreme weather events and dominant seasonal climate hazards. Furthermore, clustering the regional profile of CRFMs reveals regional similarity. The CRFMs were then prioritized, showing that 4 modes (i.e., Track deformation, Snow and ice, Buckling, and Rail breakage/crack) account for 73% of total CVaR0.90. The amplification of total risk in the tail indicates a 74% higher cost burden under extreme conditions. Finally, regional climate vulnerability was assessed using risk metrics normalized by track length and traffic density.

A linear economy is a traditional resource consumption model characterized by a “take-make-dispose” pattern without focusing on sustainable practices and climate change. The circular economy (CE) approach considers the entire life cycle of goods and services, aiming to minimize resource use and environmental impacts while contributing to sustainable development. It involves designing out waste and pollution and keeping high-value use for products and materials.

As the construction industry generates over 35% of Europe’s waste, activating the rail sector as a key driver for advancing circular economy strategies has become essential. So, optimizing resource use and mitigating environmental and societal impacts is important. Therefore, it is important to explore how circularity aspects (e.g., repairability, reusability, recoverability) are applicable for railway systems. This paper evaluates CE in Swedish rail infrastructure. The methods used in this study consists of literature review, interview and survey. Literature review, including circular economy frameworks, indicators, developments in the rail sector, and CE standards documents, have been considered in the study. Thereafter, interview and survey have been conducted with Swedish railway experts to explore the CE in the railway system.

Predicting railway track degradation is essential for ensuring safety, optimizing maintenance strategies, and reducing operation costs. Climate change has a particularly negative impact on regions affected by the phenomenon. This study develops a machine learning-based framework to predict track degradation, considering both environmental and infrastructure-related factors. Nine machine learning models were trained and evaluated on a dataset comprising track geometry, operational data, and environmental conditions from Iran’s eastern railway network. The results indicate that Random Forest outperforms other models, achieving the highest predictive accuracy. The findings reveal that increasing sand coverage leads to rapid track deterioration. This emphasizes the need for adaptive maintenance strategies to mitigate climate change effects. In order to ensure long-term operational reliability, it is essential to integrate climate resilience into railway infrastructure planning.

Railways are one of the most sustainable, safe, and efficient modes of transportation, and play a vital role in the global movement of passengers and goods. However, the linear nature of railway tracks and the increasing frequency and intensity of climate hazards make the railway system highly vulnerable to climate-related adverse risks. Therefore, developing and implementing adaptation strategies to enhance railway infrastructure resilience against those risks is imperative. This study aims to identify and categorize a possible adaptation measure proposed for the railway infrastructure. To this end, we conducted a literature review. Several possible adaptation options were identified and then were categorized them into five main groups. Furthermore, it discusses key challenges, barriers, and maladaptation risks for achieving sustainable railway adaptation planning.

Railway operation is considered as the most sustainable transportation. However, such observation often neglects environmental considerations during the maintenance stage, which is a carbon-intensive phase. This study aims to quantify carbon emissions related to the maintenance stage. In this study, we focused on railway turnout crossings (fixed manganese and movable) as a use-case located on Bandel (track section) 120 near Boden in Sweden for CO2 estimations. We utilize 14 years (2010-2023) of maintenance data collected from the Swedish Transport Administration (Trafikverket or TRV) databases. Results indicate that logistics (transportation) during the maintenance stage are the most significant contributors to fuel consumption and CO2-e emissions. Further, the interrelation between fixed and movable crossings demonstrates that the environmental impact of movable crossings is substantially higher than that of fixed crossings. The insight of this study can be integrated into decision-making models that combine Life Cycle Cost (LCC) and climate impact to optimize railway crossing replacement strategies.

As critical infrastructure, railways are highly vulnerable to climate hazards intensified by climate change. However, awareness of these risks is low, and comprehensive studies outlining these hazards and their impacts on railways are also scarce. To address these gaps, this review aims to identify climate-related risks, clarify the hazard-impact relationships and risk interconnections, and explore strategies to account for the influence of climate change on these risks. To this end, a systematic literature review was conducted in two stages. First, 71 peer-reviewed papers were collected through developing search strings, applying inclusion/exclusion criteria, and performing backward/forward citation searches. Second, the required information was systematically extracted from the papers and critically analyzed to address the research objectives. As a result, 24 climate hazards and 29 associated impacts were identified and categorized. The hazards and impacts having the most and least occurrence in the literature were also highlighted. Informative matrices were developed to correlate hazards with impacts and illustrate the cascading effects of the impacts. Four distinct approaches used to address the effects of climate change on climate risks were identified and critically discussed. The study also highlights critical gaps in the literature to guide future research.

Climate change impacts can escalate the deteriorating rate of infrastructures and impact the infrastructure’s functionality, safety, operation and maintenance (O&M). This research explores climate change’s influence on urban railway infrastructure. Given the geographical diversity of Sweden, the railway network is divided into different climate zones utilizing the K-means algorithm. Reliability analysis using the Cox Proportional Hazard Model is proposed to integrate meteorological parameters and operational factors to predict the degree of impacts of different climatic parameters on railway infrastructure assets. The proposed methodology is validated by selecting a number of switches and crossings (S&Cs), which are critical components in railways for changing the route, located in different urban railway stations across various climate zones in Sweden. The study explores various databases and proposes a climatic feature to identify climate-related risks of S&C assets. Furthermore, different meteorological covariates are analyzed to understand better the dependency between asset health and meteorological parameters. Infrastructure asset managers can tailor suitable climate adaptation measures based on geographical location, asset age, and other life cycle parameters by identifying vulnerable assets and determining significant covariates. Sensitivity analysis of significant covariates at one of the urban railway stations shows precipitation increment reveal considerable variation in the asset reliability.