Human activities, particularly food production and consumption, contribute significantly to environmental degradation. Footprint indicators are generally used to understand the extent of impacts on the environment. However, relying on individual footprint indicators often leads to conflicting sustainability outcomes, as the reduction of one impact may exacerbate another. This paper applies a framework to holistically evaluate the environmental impacts of food products. By integrating multiple indicators using Multi-Criteria Decision-Making (MCDM) methods, we aim to rank and develop a Relative Aggregate Footprint (RAF) for food products. Considering the uncertainties in the footprints of the food products, we applied a Monte Carlo-based uncertainty analysis to the evaluate the robustness of the results. Sensitivity analysis was conducted to determine the influence of individual indicators on the RAFs. Our findings suggest that this framework offers a reliable approach to evaluating food products, supporting sustainable decision-making by revealing potential trade-offs and providing insights into the variability of environmental impacts. This framework aids policymakers and industry stakeholders in promoting sustainable consumption and production practices aligned with global environmental goals.

Artificial intelligence (AI) is an emerging technology with recent research utilizing large language models (LLMs) to advance climate change. One of the strengths of LLMs in climate research is providing predictions based on probabilistic modeling. However, the potential of AI in predicting embodied carbon in the early building design phase hasn't been explored. This study investigates AI as a prediction tool in early building LCA using existing data sets. We quantitatively analyzed buildings' carbon emissions in the early building designs by comparing various LLMs' scenarios.

Existing literature indicates that the early conceptual design phase affects LCA more than the late stage of design since there is more flexibility to change. Our research emphasizes the timeliness of LCA in early building design to mitigate uncertainty and provides predictions of different design stages. A methodology was developed to predict the potential embodied carbon reduction depending on when the LCA can be implemented. Determining what information needs to be provided at each stage is the next critical step for our process-driven research. Several important decisions are made during the early design stage, such as size and height, shape, materials, and structural system, and those were defined as time frames.

Our knowledge-intensive process provides embodied carbon predictions at each defined time frame. Moreover, the process can suggest different actions representing embodied carbon reduction strategies at the prediction point. These predictions provide insights into customized strategies for early building projects with accumulated data. Following this process, designers are guided in implementing optimized early building designs. This approach reduces reliance on specialized sustainability consultants and streamlines the building system choices and design process. By addressing both the timing and amount of embodied carbon reduction strategies, this study explores the prediction capabilities of AI during the early LCA process. This information can eventually lead to optimized building designs during the early design process.

Digital technology continues to pervade all aspects of modern life, from the way we socially interact, our defense industries, how we find & cure diseases, how we power and heat our homes, and how we transport ourselves in society. The increasing pervasiveness of digital technology also fuels concerns related to the environmental impacts of semiconductor production and design. This paper explores challenges associated with understanding the total environmental impact of digital technologies across their life cycle. The holistic approach addresses difficulties in understanding for instance: (a) the environmental "footprints" created during the production phase and use of digital technologies; (b) the environmental "handprints" from digital technologies as these technologies could help reduce emissions in other industries; (c) uncertainties and difficulties in performing LCA and handprint analysis for digital technologies; and (d) the inherent difficulties in understanding what the future, and specifically, new innovations may bring.

Climate change poses a formidable challenge to humanity, manifesting through both gradual stresses and extreme disasters that threaten efforts to achieve and foster sustainable development. Among its many impacts, average temperature increases and heatwaves are particularly evident examples of stresses and shocks. These phenomena claim tens of thousands of lives annually, even in developed nations, while placing immense strain on energy systems and infrastructure. Moreover, they can exacerbate and trigger other disasters, such as wildfires, amplifying the need for resilience-focused planning and design. Conventional design and assessment approaches often lack the capacity to address these challenges, particularly at the high temporal and spatial resolutions required for robust analyses. Furthermore, existing temperature records and models often fail to capture the impacts of climate change and extreme heatwaves, limiting their utility for long-term resilience and sustainability assessments.

To address these critical gaps, we have developed a suite of tools for generating synthetic temperature data scenarios. These tools, based on 30 years of monthly data averages and 19 years of daily data averages, enable the creation of high-resolution temperature datasets that are both realistic and flexible. The tools can generate synthetic daily temperature scenarios spanning decades, from present-day conditions to the end of the century, while accounting for gradual temperature increases derived from observed or projected climate change scenarios. Additionally, they incorporate the capacity to simulate heatwaves at chosen rates or randomly, with probabilities and durations increasing over time in alignment with climate change models. This novel integration of stochastic processes ensures realistic variations while aligning with observed data, providing a robust foundation for resilience assessments. The methodology assumes that monthly temperature distributions follow a normal pattern, with summer maximums and winter minimums situated at two standard deviations from the mean, in their respective seasons. Randomly generated monthly averages are compared with reference averages derived from daily temperature records, and daily averages are then adjusted to ensure alignment. This process allows the tools to produce datasets that are representative of real-world conditions while maintaining sufficient variability for meaningful scenario generation. The average temperatures align with real data, and the day-to-day and year-to-year variations remain meaningful and realistic.

This innovative approach enables the assessment of resilience and sustainability across diverse scales, such as pavements, the energy-water nexus, and buildings. Applications include forecasting future demand under various scenarios, testing the flexibility and preparedness of infrastructure, and enhancing disaster planning and operational practices. Moreover, the tools’ potential for further refinement, including higher temporal resolution (e.g., hourly data) and the incorporation of spatial dimensions, enhances their relevance for urban climate studies. This could support investigations into urban heat islands, albedo effects, and the interplay between urban design and infrastructure. By bridging the gap between climate data generation and practical resilience assessments, this work represents a significant advancement in climate adaptation tools. The ability to generate synthetic yet representative temperature data with high temporal resolution addresses critical needs for planning sustainable and resilient systems in the face of climate change.

There are numerous initiatives towards envisioning better cities for the future. Three intertwined initiatives are sustainability, resilience, and smart cities. Sustainability means the ability to sustain; the goal of sustainability is maintaining a state at a certain level. Resilience has been typically defined in technological domains (such as engineering and disaster response) as robustness, rapid recovery and resourcefulness – where a system should withstand the force of a disturbance and, should it fail, return to its original performance levels quickly with readily available resources. And, the smart city is deeply rooted in the usage of information and communication technology in urban policies to solve diverse problems (e.g., energy, parking, safety, etc.). Oftentimes, research on these initiatives will reference one or two of the other initiatives. Therefore, we set out to describe how are sustainability, resilience, and smart city concepts converging (or not). We conducted a scoping literature review to clarify the relationships between sustainability, resilience, and smart cities in urban systems. We followed the process suggested by Pham et al. (2014) and refined our literature pool using the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA). For each article, we coded the title, author, year, and keywords. This coding was conducted by each of the three authors independently and then validated through consensus. Combining this literature review and coding, we established an integrated framework for sustainability, resilience, and smart cities in urban systems.

Unsurprisingly, the topics of sustainability, resilience, and smart cities show alignments and contradictions. It appears that resilience is a key component of both urban sustainability and smart cities, but there are not agreed upon approaches to this assimilation. Both resilience and smart city concepts are distinctive yet can share similar goals of having sustainable development. We discuss and map these interconnections to help navigate the concepts. The review led to a few interesting discussion points, including the complexity of urban systems and how this complexity can lead to lock-in and create difficulties in pursuing physical or institutional adaptation and transformation within any of these three initiatives, the increasing amount of information generated in interconnected systems and its exchange can bring opportunities and challenges (such as demand on the environment for data storage, AI, etc.), and the role of the citizen within the urban system. There is not a silver bullet for urban planning to address sustainability, resilience, and smart cities. By understanding how these three topics interrelate, decision-makers can avoid over-emphasizing one topic over another in the design process. By understanding how each topic impacts the built environment, they can also strategically place resources based on their local context.

Road-stream crossings (RSCs) are critical infrastructures for stream ecosystems and transportation networks. Many RSCs are aging, in disrepair, and/or undersized, threatening the resilience of freshwater habitats and transportation systems. Given the limited resources of state agencies and municipalities that manage these assets, it is essential to prioritize these RSCs for replacement effectively. However, there is a significant lack of coordination among stakeholders. This research develops a multi-objective optimization (MOO) framework that incorporates the interests of multiple stakeholders and compares its results with conventional scoring and ranking (S&R) methods. We employed the non-dominated sorting genetic algorithm (NSGA-II) to optimize environmental, transportation, and replacement cost objectives, achieving optimal solutions at the watershed scale. To determine the optimal population size, initialization method, and termination criterion, we utilized the modified inverted generational distance (IGD+) performance indicator. Our custom-seeded initialization method demonstrated superior performance and faster convergence compared to other initialization methods. Compared to S&R methods, MOO consistently resulted in higher scores for environmental and transportation objectives across various budget limitations, with increases of at least 19.57% and 37.68%, respectively. The frequent selection of certain RSCs among Pareto optimal solutions highlights the significant impact of their replacements. Analysis of this selection frequency in relation to RSC characteristics revealed that structural condition had the highest correlation (Pearson correlation of 0.60), indicating it as the most significant factor. This systematic approach promotes more comprehensive and effective infrastructure management by aligning multiple objectives and addressing the diverse priorities of stakeholders.

Wetlands are essential for humanity’s continued existence. They offer significant “ecosystem services”, including freshwater supply and climate change mitigation. They are also among the most biologically diverse ecosystems on Earth, supporting a myriad of plant, animal, and microbial life. However, the Ramsar Convention of Wetlands reports that since 1970, approximately 35% of wetland area has been lost, disappearing almost three times faster than forested areas. The loss of these areas of global importance is valued at almost $50 trillion a year. This economic toll directly translates into diminished health and well-being for billions who rely on wetlands for water, food, and livelihoods, hampering progress toward critical Sustainable Development Goals (SDGs) - including those focused on clean water, zero hunger, and climate resilience.

Africa has 431 Ramsar sites covering approximately 1,000,000 km2 of Africa’s 30,000,000 km2, the largest combined area of sites in the world. The Ramsar Convention, in their 2021 Global Outlook Report, notes that integrated water resource management will aid in safeguarding freshwater ecosystems. However, the World Meteorological Organization estimates that by 2030, almost 80% of Africa will not have an integrated water resource management plan. The Sixth Assessment Report of the Intergovernmental Panel on Climate Change notes that Africa’s lack of progress towards adopting integrated water resource management plans is hindered by the lack of data.

In this study, we leverage remote sensing products to provide a holistic characterization of the spatial and temporal changes of Ramsar-listed wetlands across Africa, specifically between 2003 and 2022. Recognizing that gradual and abrupt change can occur simultaneously, we focus on the following change definitions:

1. Identifying monotonic trends in the surface water extent of water bodies over the 20-year period.

2. Examining altered seasonal patterns in surface water extent over time.

3. Detecting evidence of changes in the underlying data-generating processes that drive surface water dynamics.

4. Assessing whether extreme occurrences in surface water extent - both high and low—have become more frequent or severe.

We perform our analysis for each change definition on each Ramsar listed site and aggregate by basin and then by country to report the loss or gain in surface area over time. We also anticipate providing a priority list of Ramsar sites from most concerning (necessitating closer monitoring) to least concerning using multi-criteria decision analysis.

Our study extends beyond the conventional approaches of investigating long-term inter-annual and intra-annual dynamics of surface water to explore surface water dynamics through three additional change definitions. Furthermore, we anticipate the insights gleaned from our study will help guide targeted interventions – guiding investments in infrastructure and conservation efforts, informing regional water resource management policies, and aiding in evaluating the effectiveness of water management strategies, enabling policymakers to assess whether existing strategies have mitigated risks or exacerbated challenges.

Legacy forms of electricity generation developed in the way they did according to the demand that civilization presented to them in terms of both magnitude and in changes to that magnitude at various time scales and they did so with essentially no energy storage involved. Wind and solar electricity generation could at least in theory supply all of a region’s electricity needs under business-as-usual conditions. However, because the available power from those forms of primary generation varies with the weather and with natural annual and daily cycles, it would be necessary to incorporate energy storage in some form and this presents a challenge when considering energy generation regimes that depend primarily or entirely on these fuel-less forms of generation. In this presentation, an all-solar-photovoltaic generation regime servicing a North American region of approximately 150,000km2 and a population of approximately 11M persons is imagined. Realistic proxies for an electricity demand time series and an insolation time series are established for the same one-calendar-year period. The proxied demand time series is scaled such that the integral of the scaled timed series is equal to the actual annual demand for the region for that year and based upon that result, the proxied insolation time series is scaled given the need to oversupply to compensate for transmission and distribution losses and for losses within a hypothetical energy storage system with the capacity to make up the difference between electricity supply and demand at any given moment. The presentation then characterizes the solar generation system that would be required and the associated storage system that would be required in two ways: as an electrochemical battery apparatus and as an electromechanical apparatus that uses in situ material as an inertial mass. In so doing, the presentation aims to communicate the enormity of the storage problem associated with high-proportion or total solar electricity generation regimes and the targets society must set for itself to achieve such a regime.