Road-stream crossings (RSCs) represent a critical nexus of infrastructure resilience and ecosystem health, yet fragmented governance and institutional silos hinder effective management. This study used a co-produced survey to assess stakeholder priorities, map the stakeholder collaboration network, and characterize non-financial barriers to RSC decision-making in New Hampshire, USA. Analyses included the Kruskal-Wallis and Dwass-Steel-Critchlow-Fligner tests to evaluate differences in priorities across stakeholder groups, social network analysis to identify central actors, and inductive content analysis for non-financial challenges. Flood vulnerability was the most widely supported goal, offering common ground for collaboration. However, divergences in wildlife conservation, environmental quality, structural risk, and road criticality highlighted persistent tensions between conservation and transportation stakeholders. Socioeconomic goals, including economic impact and environmental justice, received lower ratings and minimal divergence, indicating systemic neglect rather than conflict. Social network analysis identified the New Hampshire Departments of Transportation and Environmental Services as central actors, enabling coordination but concentrating decision power. Content analysis revealed key non-financial barriers: lack of prioritization, project complexity, regulatory burdens, and limited municipal capacity. These findings highlight opportunities for inclusive, multi-benefit decision frameworks, regulatory streamlining, and investments in local technical capacity to better align infrastructure planning with ecological and community needs.

Flood damage repairs to the built environment generate substantial greenhouse gas (GHG) emissions, yet these indirect climate impacts are rarely integrated into flood consequence assessments. In this study, we present a fragility-based modeling framework to estimate material replacement needs for building components damaged at specific flood depths. We develop fragility curves for each building component using a triangular cumulative distribution derived from expert judgment due to the lack of empirical data on flood losses, especially at the component level. By combining these estimates with life cycle GHG emissions for each component in a Monte Carlo simulation, we derive probabilistic, emissions-based damage curves for single-family residential structures which comprehensively account for uncertainty in the estimates. We then applied these damage curves to estimate the GHG emissions caused by a 100-year flood in two testbed regions in the Mississippi River Valley. Our results show that including the social costs of GHG emissions can increase the valuation of total flood damages by over 6%. Our results also show that flood impact estimates are highly uncertain our model can be used by planners in cost-benefit analyses of flood risk management projects to show that such projects are more economically efficient than current methods would report.

This paper presents a serious game that simulates a water crisis triggered by the spill of an unregulated chemical. The game includes five stakeholder roles representing a chemical manufacturer, resident, water treatment plant, environmental agency, and health department, in addition to a facilitator role. The game seeks to provide players with practical experience of the communication and collaboration needed among different stakeholders to prepare for and respond to water contamination emergencies. Initial findings from game sessions with 41 participants suggest that frequent, proactive, and transparent communication can expedite the decision-making process and resolve the crisis more effectively. The game results also reveal challenges in inter-organizational coordination and communication, highlighting the need for training and standardized communication terminologies and protocols.

Effective response to drinking water contamination is critical to minimizing its harm, yet its outcomes often depend not only on technical preparedness but also on stakeholder behavior. This study investigates how behavioral preferences (i.e., individual tendencies in how people value future outcomes, perceive and respond to risk, and interact with others) and prior knowledge influence decision-making in a simulated water crisis. A total of 130 participants completed a behavioral preference survey and engaged in a five-party serious game simulating an acute drinking water contamination incident. Analyses at individual, group, and role levels revealed that decisions related to communication, action-taking, and satisfaction were largely driven by role responsibilities, with limited behavioral preference effects. However, aggregated behavioral preferences, e.g., group-averaged probability weighting and loss aversion, explained collective decision-making patterns. The role-specific analysis further revealed the interactions between certain behavioral preferences and role performance: for instance, altruism reduced residents' likelihood of demanding compensation but increased the contamination exposure rate. Taken together, these findings suggest that behavioral preferences are a modifiable component of emergency preparedness. These findings underscore the importance of institutional clarity, leveraging behavioral diversity, and training that integrates behavioral assessment and cross-role learning to enhance the water emergency response.

Bridges are critical transportation infrastructure vulnerable to deterioration, aging, and extreme events. Maintaining bridges cost-effectively while considering social equity is crucial. This study proposes a deep reinforcement learning (DRL) algorithm to optimize bridge maintenance, considering time deterioration, flood degradation, and social vulnerability. Using Suffolk County, MA, as a case study, this paper conducts flood simulations to identify vulnerable bridges. Bridge condition is modeled using scour depth, and a Social Impact Index (SII) is developed to capture each bridge's importance regarding surrounding vulnerable communities. The DRL algorithm learns to minimize economic costs and promote social equity over a 20-year planning horizon. The algorithm outperforms traditional management strategies, adapting to different prioritization metrics and highlighting the importance of social vulnerability (SV) in decision-making. Integrating SV leads to a socially optimized bridge portfolio with lower failure risk but increased costs. The study quantifies the tradeoffs between economic costs and social equity, providing insights for balancing financial investments and societal welfare. This research contributes to the development of sophisticated, equitable, and effective approaches for managing critical infrastructure in vulnerable regions, paving the way for resilient and sustainable cities in the face of climate change and urbanization.

This study investigated various drinking water emergency countermeasures, evaluating their economic, environmental, and public health tradeoffs during contamination events. Employing process-based dynamic modeling and life cycle assessment methodologies, we assessed the effectiveness of 10 countermeasure deployment scenarios applied to a surrogate drinking water system. Our analysis indicates that the impacts of deployed countermeasures during emergency contamination can vary dramatically depending on the conditions of water supply, water demand, and the response time taken. While facility shutdown presents high effectiveness from all three aspects under the low demand and high supply condition, tradeoffs were found between economic and health/environmental impacts under the high demand and low supply condition, indicating that it is more costly to achieve the desired public health protection under the latter condition. Decision-makers must fully comprehend the impacts and benefits of countermeasure deployment, recognizing the tradeoffs between health, economic, and environmental considerations.

Islanded microgrids often struggle with limited resources and heavy reliance on fossil fuels. This study optimizes an island energy-water microgrid using reinforcement learning (RL) to schedule the water system as a virtual battery. Using the Shoals Marine Laboratory microgrid as a testbed, a dynamic model simulating water and energy interactions was integrated with an RL algorithm to improve profit, sustainability, and reliability. The RL scenario outperformed the status quo, leading to a 7.04 % overall improvement in the equally weighted total score. It also exceeded a single-objective, heuristic management approach in profit and reliability, though slightly reducing sustainability. The RL model strategically reserves battery storage for peak renewable energy generation and extends water system operation to circumvent pumping constraints. Increasing desalination rates further improved performance, while larger water tank capacity offered minimal advantages. Future research may expand RL decision space to incorporate energy system actions for enhanced benefits.

The electric vehicle (EV) market has expanded rapidly over the last decade, raising concerns about the sustainable supply of raw materials for EV batteries (EVBs) worldwide. Therefore, establishing an effective and efficient reverse logistics system for EVBs is essential. This paper has two main objectives. First, we present a comprehensive literature review on reverse logistics for EVBs, synthesizing findings from 165 articles published between January 2010 and March 2025. Second, we identify future directions by addressing specific gaps, real-world challenges, and research questions within the reverse logistics system for EVBs. In particular, this review categorizes the literature into three main areas: (1) manufacturing-oriented end-of-life (EoL) EVB processes, including disassembling, repurposing, remanufacturing, and recycling; (2) logistics-oriented operations, covering collection, transportation, and facility location; and (3) assessment-oriented activities, including life cycle assessment (LCA), and policy & regulation. Through a gap analysis, we propose future research directions that are expected to grow in the next decade, including understanding economic dynamics among supply chain partners in EVB reverse logistics system, studying the critical material supply uncertainty, EoL EVB stockpile prediction, planning and modeling on facility location and transportation, developing a resilient supply chain for EoL EVBs, and integrating LCA in the design of a reverse logistics system for EVBs. Overall, this study provides a systematic review of the critical areas of the reverse logistics system for EVBs, identifies gaps requiring diverse industrial engineering expertise, and outlines promising research directions in logistics, circular economy, energy, and sustainable systems related to EoL EVBs.

Road–stream crossings (RSCs) are vital for the sustainability of both stream ecosystems and transportation networks, yet many are aging, undersized, or failing. Limited funding and lack of stakeholder coordination hinder effective RSC management. This study develops a multi-objective optimization (MOO) framework utilizing the non-dominated sorting genetic algorithm (NSGA-II) to maximize and balance diverse stakeholder interests (i.e., environmental and transportation agencies) while minimizing management costs. MOO was used to identify optimal RSC management scenarios at a watershed scale, using the Piscataqua–Salmon Falls watershed, New Hampshire, as a testbed. It was found that MOO consistently outperformed the currently used scoring and ranking method by the environmental and transportation agencies, improving the environmental and transportation objectives by at least 19.56% and 37.68%, respectively, across all evaluated budget limits. These improvements translate to a maximum cost saving of USD 19.87 million under a USD 50 million budget limit. Structural conditions emerged as the most influential factor, with a Pearson coefficient of 0.60. This research highlights the potential benefits of a data-driven, optimization-based approach to sustainable RSC management.

Recent advancements have significantly enhanced the capabilities for in-space servicing, assembly, and manufacturing (ISAM), to develop infrastructure in orbit and on the surface of celestial bodies. This progress is a departure from the traditional sustainability paradigm focused solely on Earth, highlighting the urgent need to define and operationalize the concept of “space sustainability” along with the development of an evaluation framework. The expansion of human activity into space, particularly in low-earth orbit, cis-lunar space, and beyond, underscores the critical importance of considering sustainability implications. Leveraging space resources offers economic growth and sustainable development opportunities, while reducing pressure on Earth’s ecosystems. This paradigm shift requires responsible and ethical utilization of space resources. A space sustainability assessment framework is essential for guiding ISAM capabilities, operations, missions, standards, and policies. This paper introduces an initial framework encompassing (1) pollution, (2) resource depletion, (3) landscape alteration, and (4) space environmental justice, with potential metrics (resources use and emissions, midpoint, and endpoint indicators) to measure impacts in the four domains.

Bridges play a critical role in transportation networks; however, they are vulnerable to deterioration, aging, and degradation, especially in the face of climate change and extreme weather events such as floodings. Furthermore, bridges can significantly affect social vulnerability; their damage or destruction can isolate communities, inhibit emergency responses, and disrupt essential services. Maintaining critical bridges in a cost-effective and sustainable manner is crucial to ensure their longevity and protect vulnerable communities. To address the maintenance optimization problem of bridge systems considering the effects of time deterioration, flood degradation, and social vulnerability, this study proposes a deep reinforcement learning algorithm to optimally allocate resources to bridges that are at expected cost of failure due to scour. The algorithm considers the effects of flood degradation with different return periods and is trained using a Markov Decision Process as the environment. The study conducts four flood simulation scenarios using Geographic Information System data. The findings suggest that the deep reinforcement learning algorithm proposes a sequence of repair actions that outperforms the status quo, currently employed by bridge managers. The significance of this study lies in its valuable insights for cities worldwide on how to effectively optimize their limited resources for the maintenance and rehabilitation of critical infrastructure systems to decrease portfolio cost and increase social equity.

Large residential solar photovoltaic (PV) penetration has a compound effect on the grid load reductions, PV hosts’ economic savings, and the achievable environmental benefits, which is not fully understood. This study combines process-based energy balance modeling, life cycle assessment, regression analysis, and stochastic demand simulations to assess the technical, economic, and environmental tradeoffs under increased residential solar PV adoption, using Boston, MA as a testbed. It was found that increased PV adoption may lead to a steeper ramp-up in the grid during winter months, but a flattened peak load curve during summer months, emphasizing the need for seasonal time-of-use rates and energy storage. It also reduces electricity wholesale prices, lowering PV hosts' economic benefits by about $15 million under 100 % adoption. The largest buildings present the highest load reduction (top 5.5 %) and environmental benefits (top 16.6 %), but they are the least cost efficient (top 14.5 %), requiring tradeoff balance.

Chemical spills in surface waters pose a significant threat to public health and the environment. This study investigates the public health impacts associated with organic chemical spill emergencies and explores timely countermeasures deployable by drinking water facilities. Using a dynamic model of a typical multi-sourced New England drinking water treatment facility and its distribution network, this study assesses the impacts of various countermeasure deployment scenarios, including source switching, enhanced coagulation via poly‑aluminum chloride (PACl), addition of powdered activated carbon (PAC), and temporary system shutdown. This study reveals that the deployment of multiple countermeasures yields the most significant reduction in total public health impacts, regardless of the demand and supply availability. With the combination PAC deployed first with other countermeasures proving to be the most effective strategies, followed by the combination of facility shutdowns. By understanding the potential public health impacts and evaluating the effectiveness of countermeasures, authorities can develop proactive plans, secure additional funding, and enhance their capacity to mitigate the consequences of such events. These insights contribute to safeguarding public health and improving the resilience of drinking water systems in the face of the ever-growing threat of chemical spills.

Appledore Island, ME, USA, Microgrid Modeling and Optimization

Enhancing drinking water resilience has become increasingly important. However, a comprehensive analysis of drinking water emergency countermeasures is lacking. This study evaluated eight countermeasures including monitoring, local alternatives, reclaimed water, interconnection, bulk water, pre-packaged water, emergency treatment, and isolation valves from resilience and sustainability (i.e., life cycle cost) perspectives. While countermeasures such as interconnections perform relatively well from both perspectives, there is a clear trade-off between resilience and cost. Local alternatives and emergency treatment respond quickly and provide sustained supply during emergencies but may incur higher costs. Bulk water and pre-packaged water are typically inexpensive but have limited supply capacity and take time to distribute. As future threats are likely to become more frequent and prolonged, it is prudent for service providers to invest in countermeasures that perform well in both resilience and cost and use an integrated approach that combines high capital projects with bulk/pre-packaged water contracts.