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.

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.

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.

Mining is becoming increasingly vulnerable to the effects of climate change (CC). The vulnerability stems from changing weather patterns, leading to extreme weather events that can cause damage to equipment, infrastructure, and mining facilities and disrupt operations. The new demand from governments and international agreements has placed additional pressure on mining industries to update their policies in order to reduce greenhouse gas emissions and adapt to CC. This includes implementing carbon pricing systems, utilizing renewable energy, and focusing on sustainable development. Most mining and exploration industries prioritize reducing mining’s impact on climate change rather than adapting to extreme weather events. Therefore, it is important to study and investigate the impacts of climate change on the mining sector. This paper aims to investigate the challenges and strategies for adapting to and mitigating the impacts of climate change on mining through a systematic literature review. The results indicate that the majority of proposed models and strategies in the mining field are still in the conceptual phase, with fewer practical implementations. It has been identified that there is a requirement for long-term planning, improved risk management plans, and increased awareness and education within the industry. Practical strategies such as integrating renewable energy, enhancing operational safety, and improving water and tailings management have been recognized as crucial for effective climate change adaptation and mitigation.

Climate change impacts pose challenges to a dependable operation of railway infrastructure assets, thus necessitating understanding and mitigating its effects. This study proposes a machine learning framework to distinguish between climatic and non-climatic failures in railway infrastructure. The maintenance data of turnout assets from Sweden’s railway were collected and integrated with asset design, geographical and meteorological parameters. Various machine learning algorithms were employed to classify failures across multiple time horizons. The Random Forest model demonstrated a high accuracy of 0.827 and stable F1-scores across all time horizons. The study identified minimum-temperature and quantity of snow and rain prior to the event as the most influential factors. The 24-hour time horizon prior to failure emerged as the most effective time window for the classification. The practical implications and applications include enhancement of maintenance and renewal process, supporting more effective resource allocation, and implementing climate adaptation measures towards resilience railway infrastructure management.

Life cycle cost (LCC) analysis is an important tool for effective infrastructure management. It is an essential decision support methodology for selection, design, development, construction, maintenance and renewal of railway infrastructure system. Effective implementation of LCC analysis will assure cost-effective operation of railways from both investment and life-cycle perspectives. A major setback in the successful implementation of LCC analysis by infrastructure managers is the availability of relevant, reliable, and structured data. Different cost estimation methods and prediction models have been developed to deal with this challenge. However, there is a need to include condition degradation models as an integral part of LCC model to account for possible changes in the model variables. This article presents an approach for integrating degradation models with LCC model to study the impact of change in design speed on key decision criteria such as track possession time, service life of track system, and LCC. The methodology is applied to an ongoing railway investment project in Sweden to investigate and quantify the impact of design speed change from 250 to 320 km/h. The results of the studied degradation models show that the intended change in speed corresponds to correction factor values between 0.79 and 0.96. Using this correction factor to compensate for changes in design speed, the service life of ballasted track system is estimated to decrease by an average of 15%. Further, the expected value of LCC for the route under consideration will increase by 30%. The outcome of this study will be used to support the design and requirement specification of railway track system for the project under consideration.