While approximately 40% of food is wasted in the United States, about 1 out of every 7 households experiences food insecurity. Food donation is an important avenue for mitigating food waste and redirecting safe, nutritious surplus to households in need. Scaling its impact requires accurate, donor-specific donation forecasts; efficient and equitable logistics that reduce operational costs; and social insight to overcome stigma and regional access barriers. However, two knowledge gaps don’t allow the food donation system to realize its environmental and societal benefits. First, existing forecasting models often lack accuracy and omit key economic and demographic drivers of food donation. Second, forecasting and optimization studies are rarely integrated, resulting in a fragmented understanding of the food donation landscape and limiting the development of coordinated, data-driven redistribution strategies. This project advances the computational basis by integrating machine learning based forecasting, optimization, and social insights to provide a unified, data-driven framework for designing food donation systems that are efficient, equitable, and responsive to real-world conditions.

Using historical donation records from food donation operations in New York State combined with economic, demographic, and contextual indicators, we identify key drivers of food donation supply and develop scenario-based forecasting models that characterize temporal variability across donors and food categories. Scenario-based forecasts are generated using an ensemble forecasting framework that integrates time-series and regression-based models with exogenous social, economic, and demographic covariates. Forecast outputs are translated into multiple operational scenarios representing plausible supply conditions and embedded within a network optimization framework that models food bank logistics, including routing, allocation, transportation capacity, storage limitations, and service coverage constraints. System performance is evaluated across multiple objectives, including operational efficiency, cost, equity of access, and robustness to supply fluctuations.

Our preliminary machine learning analyses suggest that the amount of food donated by a retail store is largely influenced by its historical donation amount and the distance between the retail store and pantries. The number of food stamp users and median household incomes are not the top features for food donation in the current donation networks, showing a possible disconnect between supply and demand. Further analysis is expected to reveal substantial heterogeneity in food donation patterns across donors and food categories, as well as to identify the social, economic, and demographic factors most strongly associated with supply variability. Results will elucidate trade-offs between operational efficiency, cost, and equitable access under alternative donation scenarios, highlighting strategies that remain robust to supply uncertainty. Together, these insights will inform data-driven planning decisions for food banks seeking to balance efficiency with equity under real-world conditions.

As the adoption of electric vehicles (EV) continues to increase, the strategic siting of electric vehicle charging infrastructure (EVCI) becomes critical. However, many existing EVCI installations have been concentrated within high-income and predominantly white neighborhoods, correlating with areas of higher EV adoption. Studies such as Hsu & Fingerman (2021) have highlighted significant disparities in public EVCI placement in California, showing that low-income and minority communities are often excluded from current siting practices. Given this context, our research develops an approach for including additional socio-economic criteria in EVCI siting approaches, as well as explores the role that different input variables and weighting schemes can have on the equitable siting of EVCI.

The literature on optimal EVCI siting has primarily focused on criteria such as economic viability, proximity to freeways (corridor stations), and existing EV uptake. This approach, however, tends to prioritize communities already inclined to own EVs (e.g., higher income, higher access to charging at home), while neglecting communities where strategic installation of EVCI might catalyze EV uptake and where the largest benefits might be realized (e.g., lower income communities, lower access to charging at home). This disparity jeopardizes state goals for achieving high levels of EV uptake and risks deepening existing disparities between marginalized and higher-income communities, emphasizing the need for equity-focused criteria in EVCI planning decisions. To address these disparities, tools like Geospatial Energy Mapper (GEM) have been developed. The GEM tool includes multi-criteria decision analysis capabilities that allow for customizable criteria weighting for inputs such as substation distance, land cover, and population density. However, the impact of varying these criteria weights and the influence of different combinations of input variables on siting recommendations has not been thoroughly explored.

This research aims to fill that gap by analyzing the significance of variable inclusion and weighting adjustments in equity-focused EVCI siting, with a case study focus on the city of Merced, California. Using ArcGIS Pro's Suitability Modeler, I replicated the GEM model while incorporating additional equity layers, such as health equity and pollution burden data that are yet to be analyzed. To assess the influence of additional equity variable considerations, I tested the suitability changes in R by comparing the variable additions to the “baseline” average of the original GEM tool layers. I also tested the weighting scheme sensitivity through random weight assignments. Equity variables that had the highest influence include asthma percentile, PM 2.5 percentile, and minority population considerations. Certain weighting schemes held higher influence and changes from the baseline than others, although top suitability scores stayed true across scenarios. With Merced as a case study, we can assess according to the area (small population, more rural surroundings) and make recommendations for future work on different areas, and their sensitivity to these types of models. Overall, it is important for planners to consider the specialized effects of spatial prioritization tools, especially for EVCI siting to incorporate all community needs for charger placement. Recommendations for Merced include holding stakeholder and community meetings to identify the different priority locations and the benefits they can bring.

By experimenting with different weighting schemes and expanding the criteria set, this work will provide decision-makers and planners with a broader understanding and framework for more equity-informed decisions related to EVCI siting, ensuring that overall decarbonization goals are met and the benefits of transportation electrification are shared across all communities.

The problem addressed is simulation of consumer decisions on what they do with the End-of-Current-Use electronic devices. The goal is to inform future interventions to nudge users to make more sustainable choices. A survey is undertaken of 4,000 U.S. users of their attitudes and knowledge regarding End-of-Current-Use options (resell, recycle, give away, throw away, and their past and planned choices. A Random Forest model is constructed to simulate planned behavior as a function of stated knowledge and attitudes, the latter constructed as an incremental Likert scale (Strongly disagree, Disagree, neutral, Agree and Strongly agree). The model shows reasonable predictive accuracy at 44%. The key result is that incremental shifts in stated attitudes can lead to considerable increases in likelihood of sustainable behaviors. For example, moving the perception of reselling as worthwhile from “neutral” to “agree” increases the probability of resale by 26%. Stronger agreement yields minimal gains, thus relatively small changes in perception may lead to large behavioral changes.

The model identifies behavioral trade-offs, with reselling increases corresponding to decreases in giving away (-14%) and recycling. Feature importance analysis confirms economic considerations are important drivers of reselling decisions, while environmental concerns drive recycling behavior.

Simulation modeling demonstrates targeted attitude "nudges" can produce substantial behavioral shifts. Identifying critical thresholds enables efficient intervention design—modest attitude changes at transition points may prove more effective than maximizing positive attitudes. These findings challenge linear attitude-behavior assumptions and provide evidence-based guidance for electronic waste diversion policies. The framework paves the way for practical tools for policy design through behavioral tipping point identification and pre-implementation intervention simulation.

Understanding how and why climate variability alters agricultural systems remains a central challenge for climate adaptation in Sub-Saharan Africa (SSA). While the prior phase of this research established robust spatiotemporal evidence of climate-driven changes in vegetation dynamics using satellite observations, the next phase advances the work from attribution toward causal discovery. This phase aims to identify and quantify causal pathways linking vegetation stressors to changes in agricultural productivity across heterogeneous agroecological zones in SSA, addressing a key limitation of existing climate impact studies that rely primarily on correlational analyses .

Building on a multi-decadal satellite-derived database of vegetation phenology, precipitation, soil moisture, and temperature, this phase will integrate modern causal inference and discovery frameworks to move beyond association-based methods. Specifically, we will employ methods such as structural causal models, directed acyclic graphs, and invariant causal prediction to uncover stable, interpretable relationships between climate and non-climate drivers and agricultural outcomes, including growing season timing, length, and vigor . These approaches will be complemented by quasi-experimental designs, including difference-in-differences and climate shock analogs, to validate causal mechanisms using naturally occurring climate variability .

The proposed work emphasizes spatially explicit causal heterogeneity, recognizing that identical climate shocks can yield divergent agricultural outcomes depending on local biophysical conditions, land-use practices, and exposure histories . By combining causal discovery with machine-learning-based representation learning, the project will generate region-specific causal models that generalize over time while remaining robust to the confounding, feedback loops, and non-stationarity inherent in climate–agriculture systems .

Key outputs will include a causal impact atlas for SSA agriculture and open-access causal models designed for integration into early warning and decision support systems. This next phase is a critical step toward actionable climate intelligence. By identifying not only where agricultural systems are changing but also the causal processes driving those changes, the research will enable more targeted, adaptive, and equitable climate resilience strategies for smallholder farmers and policymakers across SSA.

Cycling as a mode of transportation is an important tool for lowering emissions and creating sustainable cities. The amount and quality of active transportation infrastructure directly influences how much people walk and cycle. However, we still lack quantitative tools to predict where investments will be most impactful and to causally predict shifts in trips and mode choice as a result of investments in cycling infrastructure. In this work, we model bicycle route choice, a key element in modelling cycling demand. Unlike motor vehicles which are sensitive mainly to distance and travel time, cyclist route choice also involves questions of safety and comfort. Cyclists frequently detour from the shortest path to access safer and more pleasant routes. Quantifying the factors that determine route choice is an essential first step for accurately predicting cycling on both specific road segments and for overall mode choice.

We combine thousands of GPS cycling trajectories from the Toronto Household Activity-Travel Survey in the Greater Toronto Area with road network, cycling network, and land use attributes to estimate bicycle route choice parameters. We quantify cycling infrastructure and road geometry with Level of Traffic Stress (LTS), a widely-used rating system for cycling comfort. LTS is correlated with cycling mode choice and segment-level street usage but has rarely been applied in bicycle route choice modelling. We also model traffic signals and stop signs, elevation, and land use. We estimate route choice parameters using a discrete choice modelling framework and generate probabilistic route predictions for given origin-destination pairs. We find that cycling trips have a median detour rate of 10\% and that cyclists prefer lower-stress (lower LTS) routes. This work is undertaken in partnership with municipalities in the Greater Toronto Area and will support detailed mode choice modelling integrated with larger travel demand models, enabling researchers and practitioners to understand the impact of infrastructure and land use changes on multi-modal traffic volumes at the level of individual road segments and across modal share.

Infrastructure systems are increasingly exposed to uncertainty, complexity, and rapid change. Climate-driven extremes, accelerating technological development, and growing interconnections among infrastructure sectors have made infrastructure failures more difficult to anticipate, model, and manage. While individual infrastructure systems are often well understood, their interactions, particularly the cascading and nonlinear dynamics that emerge during disruptions, remain poorly captured in practice. Many existing assessment approaches continue to treat infrastructures as largely independent systems, limiting their ability to support decision-making in environments characterized by emergence, surprise, and compounding risk.

This paper addresses this gap by identifying, cataloging, and evaluating infrastructure interdependency assessment tools used across research and application contexts for three sectors: water, energy, and transportation. Rather than attempting to list all infrastructure modeling software, the paper focuses on tools explicitly designed for interdependency assessment or those routinely embedded within interdependency analysis workflows. Tool identification followed a structured, multi-stage discovery process.

The first stage consisted of a literature-driven review of peer-reviewed studies on infrastructure interdependencies, cascading failures, and system-of-systems modeling to identify tools used in interdependency analyses. This was complemented by a targeted review of institutional and programmatic sources, including U.S. national laboratories, federal agencies, and federally sponsored research programs, to capture tools commonly used in practice but less visible in academic literature. A third stage examined technical ecosystems and modeling workflows, recognizing that interdependency assessment often relies on coupled toolchains rather than standalone platforms, and identified co-simulation frameworks, sector-specific simulators, network analysis tools, and GIS-based platforms that enable cross-sector modeling. A broad exploratory search further ensures comprehensive coverage of relevant tools and workflows. Finally, the inventory was iteratively refined through cross-comparison with existing interdependency taxonomies, removal of tools outside the scope of interdependency analysis, and consolidation of duplicate or closely related platforms.

Following identification, each tool was evaluated through structured manual coding across technical and practical dimensions, including interdependency breadth, data requirements, validation strength, integration potential, accessibility, and sectoral scope. To translate these evaluations into actionable guidance, the paper introduces a dual comparative framework combining quantitative radar charts with a qualitative decision matrix. Together, these approaches highlight trade-offs among realism, usability, and analytical purposes.

The results demonstrate substantial variation in how interdependencies are conceptualized and operationalized and confirm that no single tool fully captures infrastructure as a system-of-system. By consolidating dispersed knowledge into a structured and practitioner-oriented inventory and classification framework, this paper provides practical guidance for selecting interdependency tools aligned with specific resilience objectives, helping bridge the gap between theory and applied infrastructure resilience assessment. In doing so, the work directly contributes to ISSST’s emphasis on systems thinking by offering a generalizable, cross-sector approach for understanding, comparing, and assessing complex infrastructure interdependencies central to sustainability, resilience, and long-term system transformation.

E-fuels, hydrogen, electric vehicles (EVs), and carbon dioxide removal (CDR), along with their supporting infrastructure, will play a crucial role in decarbonizing transportation, heavy industry sectors (e.g., steel, mining, cement), and electricity generation facilities. To inform policymakers, regulators, utility companies, as well as industry plant managers, large-scale energy system models are used in both academia and government to explore emission reduction pathways by combining end-use demand projections to 2050 and beyond with varying degrees of technology representation to inform climate and energy policy. Yet, many modelling efforts struggle to adequately reflect the realistic pace and barriers to their large-scale deployment. Understanding how models currently treat deployment constraints, especially in terms of timing, scaling constraints, and responses to policy shocks, is essential for building more credible scenarios.

Particularly, modelling deployment constraints are challenging since they try to capture any limiting factor that prevents a new technology or fuel from scaling quickly, even when it is cost-effective or policy supported. These may include physical constraints (e.g., capital stock turnover, infrastructure readiness), economic factors (e.g., financing hurdles, cost competitiveness), social or political factors (e.g., acceptance, regulation), and temporal lags (e.g., lead times, permitting delays). Owing to this, modelling simplifications (e.g., assuming smooth or linear adoption) are often relied upon, but may lead to vastly different results creating uncertainty in developing future energy roadmaps, conducting techno-economic analyses, and optimizing energy systems for different climate scenarios.

To improve deployment timelines and constraints for emerging low-carbon technologies like e-fuels, hydrogen, EVs and supporting infrastructure, and CDR, we have developed a survey to gather information about how different energy models handle deployment of these technologies. Target respondents include modelling experts who develop or work with integrated assessment models (IAMs), energy system optimization models, or scenario planning tools. Questions seek to understand the sources of input data used to parameterize deployment assumptions (e.g., peer-reviewed literature, expert judgment, case studies), document current modelling practices, particularly how deployment constraints are represented (e.g., through exogenous caps, learning rates, or infrastructure lag), identify gaps or limitations in how real-world dynamics (e.g., permitting, supply chains, policy uncertainty) are incorporated, and explore opportunities for model improvement to make scenario outputs more robust, credible, and policy-relevant. We anticipate that the results of the survey will help inform energy modellers and academic publication on the state of deployment modelling, contribute to improved parameterization of models under development, and support discussions around best practices for technology diffusion modelling.

The Survey is now live and anyone with knowledge of relevant models is welcome to respond here: https://survey.ucalgary.ca/jfe/form/SV_1Mqpe2bxwGEQ49E. Thank you!

Rural electricity consumption in Sub-Saharan Africa remains persistently low despite large capital investments in grid extension, threatening the financial solvency of utilities and delaying development benefits. A prevailing explanation for this stagnation is the “reliability cliff”: the hypothesis that rural households under-invest in appliances because their grid supply is materially less reliable than in urban centers. This study interrogates that hypothesis in the context of Rwanda, employing a hybrid model that combines Agent-Based Modeling (ABM) with game-theoretic utility optimization to decouple supply-side instability from demand-side liquidity constraints.

We built a residential “digital twin” calibrated to the nationally representative 2022 Multi-Tier Framework (MTF) survey of 5,706 households. Each agent is initialized with empirically observed attributes: connection status, location (urban or rural), financial access (mobile money and loan indicators), and asset ownership. We compute an effective usability metric m(R) from reported daily and evening availability (C029B, C030B), harmonizing records for mini-grids where present (C070D, C071D). Agents are mapped to a granular consumption ladder ranging from Tier 1 (basic lighting) and Tier 2 (television) to latent higher-order tiers, including Tier 3 (medium-load appliances), Tier 4 (refrigeration), and Tier 5 (heavy inductive loads). While higher tiers are rare in current rural data, our model explicitly solves for their emergence under relaxed financial constraints. To quantify the elasticity of demand to financing, we derive a “credit multiplier” from stated willingness-to-pay responses (cash versus 24-month credit in Section H), producing a weighted increase in adoption probability when credit is offered. The simulation creates a Stackelberg game: the utility acts as the leader, optimizing tariff and investment strategies to maximize solvency, while households act as followers, maximizing utility subject to affordability and reliability constraints over a 10-year horizon.

Preliminary results reject the reliability-cliff hypothesis. Rural grid usability m(R) is statistically comparable to urban areas (0.86 versus 0.87), suggesting no structural technical barrier exists. Yet adoption is stalled: 89.4 percent of connected rural households remain trapped in Tier 1, with monthly consumption below 20 kWh. Barrier data (Section MN) identify high upfront appliance cost—not service quality—as the binding constraint. The behavioral response to credit is potent: the estimated credit multiplier is 1.59x, implying that introducing 24-month appliance financing expands the addressable market for productive-use assets from 30.9 percent (cash-only) to 49.2 percent.

Anticipated simulation experiments will quantify trade-offs between reliability upgrades and financial interventions. We expect to show that targeted financial engineering (for example, on-bill appliance financing) yields substantially larger gains in consumption and revenue than marginal grid reinforcement in already serviceable areas. By grounding agent behavior in survey microdata and coupling it with game-theoretic foresight, this work provides a rigorous decision framework for regulators to unlock latent demand while maintaining utility viability.

Mixed-species forests are increasingly recognized for their role in buffering ecosystems against climate stressors; however, the effects of species mixing ratios and tree sizes on drought resilience remain insufficiently explored, particularly in temperate regions. This study investigates the impact of species composition and structural diversity on drought resilience in mixed Pinus tabuliformis and Quercus variabilis stands. We collected 180 tree core samples (60 per species ratio) from three specific mixing ratios—90% P. tabuliformis and 10% Q. variabilis (P9Q1), 60% P. tabuliformis and 40% Q. variabilis (P6Q4), and 20% P. tabuliformis and 80% Q. variabilis (P2Q8)—and further stratified samples by dominant, intermediate, and suppressed size classes. Field sampling at breast height utilized increment borers to obtain tree cores with minimal impact, which were subsequently air-dried, mounted, and polished in the lab to enhance ring clarity. Growth ring widths were measured using a high-precision system, with cross-dating techniques ensuring chronological accuracy. To evaluate drought resilience, we calculated resistance (Rt), recovery (Rc), and resilience (Rs) indices and employed the Palmer Drought Severity Index (PDSI) to analyze growth sensitivity across the ratios and size categories. Mixed-effects models were applied to assess the effects of species composition, tree size, and climate factors on drought resilience. Results showed that the P6Q4 ratio optimally supported Q. variabilis resilience during extended droughts by fostering hydrological niche benefits, while P. tabuliformis showed declining Rt, Rc, and Rs values as Q. variabilis proportions increased. PDSI analysis revealed dominant trees had stronger responses in P6Q4 and P2Q8, while intermediate and suppressed trees responded more in P9Q1. These findings underscore the importance of species-specific mixing ratios and structural diversity for enhancing forest resilience under climate change, supporting a framework for sustainable forest management that prioritizes mixed-species configurations to reduce climate vulnerability and promote long-term ecosystem stability.

Background/Motivation:

Of growing concern is the presence of microplastics (MPs), defined as plastic particles ranging from one micron to five millimeters in diameter, and have emerged as a significant and pervasive environmental contaminant. Microplastics pose unique environmental and health challenges due to their persistence, mobility, and ability to sorb environmental contaminants, which raise serious concerns regarding ecotoxicity and bioaccumulation[1]. The behavior of MPs can vary by polymer type, size, and density, influencing residence times in aquatic and terrestrial systems. With the increase in use and production of synthetic textiles, microfibers (MFs) have been a large proponent in the influx of MPs. It is estimated 60-90% of MPs identified in wastewater treatment plants (WWTP) were MFs which, largely come from laundry washing machines[2]. Even though properly equipped WWTPs can retain ~ 85% of MPs from the influent wastewater, retained MPs build up in sewage sludge and are reintroduced into the environment upon removal of the sludge on to agricultural fields[3]. Given plastic’s integral role in modern life, complete eradication of microplastics is unlikely. Instead, mitigation strategies focused on source reduction, improved waste management, and removal technologies are critical.

Significance:

A way to reduce the amount of MFs going into WWTP is by filtering such fibers from laundry water discharge. Current methods include membrane filtration and mechanical filters (e.g. Coraball or Guppyfriend washing bag) that can be used in-drum or add-on. The greatest disadvantages of these methods are clogging and the waste associated with renewing the filter, and user error. Life Cycle Analyses (LCAs) are critical to fast-track new filtration devices by understanding their environmental impacts and how they compare to conventional filtration methods. A low-cost, scalable, reusable, and easy to use filtration method is needed.

Method:

In this work, we have developed a spherical cage design that utilizes rotational flow of MP laden water to capture and collect MFs. This design is fabricated using 3D printing technologies and can be easily scaled to fit its environment. The capture of MFs relies on the alignment of and mechanical interlocking between the smaller MFs and the larger branches of the cage. As such, we will develop a framework to integrate experimental results of the developed method with LCAs. The model will include fabrication, capture and release, and end-of-life of the technology. The model will be fed with the performance data acquired from experiments, which includes fibers captured, capture rate, capture efficiency, and removal efficiency. The LCA model will help elucidate areas for improvement, which can improve cost of production, environmental impact, or the efficiency of the tool. The model will be grounded by direct performance insights with the LCA results informing the tuning of the spherical cage design and method. Our approach is to combine experimental research and system analysis to provide competitive technology for an emerging concern.

References:

[1] Zarus et al., 2020. The Science of the Total Environment, 756, 144010.

[2] Liu et al., 2022. Reviews of Environmental Contamination and Toxicology, 260

[3] Hamann et al. 2025. Npj Emerging Contaminants, 1

Eucalyptus Camaldulensis is one of the most extensively planted, highly productive and best adapted species for firewood. Large scale plantation of Eucalyptus in the non-native regions has brought many problems to local environment such as loss of biodiversity and soil erosion, which is attributed to its allelopathic effect. The present study was conducted in sub-tropical forest of Khyber Pakhtunkhwa to check the inhibitory effects of E. camaldulensis on native plant species and soil quality. The data was collected through field survey, growth chamber experiment and soil analysis in the laboratory. The field survey results revealed that the species composition and richness of native vegetation under E. Camaldulensis was suppressed than open area. Similarly, germination percentage and root growth of the species was more suppressed in leachate treatment than in control in growth chamber studies. The germination percentage of the species (Acacia modesta, Dalbergia sissoo, and Ailanthus altissima) was maximum in control, while the germination percentage of Dodonea viscosa was maximum in leachate treatment and the Olea ferruginea was non-significant. The seedling vigor Index of the species, A. modesta, D. sissoo, A. altissima, and O. ferruginea were maximum in control, while the D. viscosa was maximum in leachate treatment. The soil analysis in the laboratory indicated that soil under the E. camaldulensis had increased Moisture content, pH, Electrical conductivity, Organic matter and also macro nutrients (Nitrogen, Phosphorus, Potassium) at the depth of 0-15 cm and 15-30 cm than open area with native vegetation in all selected sites. The Moisture content, pH, Organic matter, Phosphorus, Potassium was significant under the canopy of E. camaldulensis and open area, whereas Electrical conductivity and Nitrogen was non-significant under the canopy of E. camaldulensis and open area with native vegetation at the depth of 00-15 cm and 15-30 cm. The results represented that the E. camaldulensis had more effect on understorey plant communities than an open area. Our results suggested that the plant of E. camaldulensis produced the allelochemical compounds which inhibited the growth of native plants. Further studies are necessary to clarify the possible physiological mechanism related to the allelopathic effects of Eucalyptus species specially in association with D. viscosa.

California, alongside the rest of the world, has been expanding the use of zero-emission vehicles (ZEVs) to address energy, environmental, and climate challenges. However, the uptake has been uneven, with relatively high adoption in some areas and relatively low adoption in other areas. If ambitious targets like California’s 2035 and 2045 goals are to be met, then action and buy-in will be needed across all locations and types of communities. To improve understanding of the nuances associated with ZEV adoption, this work seeks to build a general understanding of the relationship between socio-economic factors and ZEV uptake, determine which socio-economic factors have an influence on ZEV uptake, and identify outlier communities whose strategies can be studied and learned from in order to help improve uptake across similar communities. Initially, this project looks to determine the relative and combined impact of different socio-economic factors on zero-emission vehicle (ZEV) adoption, as well as the magnitude of influence across each factor. This analysis will be completed using computational methods in R, including 1) correlation tests, 2) single factor regression, and 3) multi-factor regression. The regression analysis will include ZEV registration per capita as the dependent variable and income, poverty level, housing burden, and educational attainment as the independent variables. The analysis utilizes publicly available zip-code level data from various California state agencies such as the California Energy Commission and the Office of Environmental Health Hazard Assessment. We hypothesize that income will have the most impact on ZEV uptake. After determining the nature of the relationship between the selected socio-economic factors and ZEV adoption, we will then look to identify outlier communities. Specifically, where communities have higher or lower adoption rates than other similar communities based on the four socio-economic factors. Identifying these communities will open new areas for research and determine what steps can be taken in similar communities to boost adoption. For example, what policies and programs might lead to a lower-income area having relatively high ZEV uptake? And can these policies and programs be put into practice in other areas in pursuit of similar outcomes? The focus area for this research is California, however, the findings, particularly regarding outliers and improving ZEV adoption, will be widely applicable and relevant as the world works to electrify transportation systems and reduce climate and environmental impacts.

Existing research primarily uses census data to identify the vulnerability of communities to hazards. These indices are provided at an aggregated scale and are not hazard-specific nor validated with post-event data. In contrast, our study uses household survey data (n= 1,065) to understand which Texan households suffered the greatest loss of their capabilities due to power outages and other utility service disruptions during Winter Storm Uri. Inspired by the Capabilities Approach, our measures of burden include the number of household capability types disrupted during the outages (e.g., cooking, heating, refrigeration), the severity of impact for each disrupted capability, as well as the additional time and financial costs of coping with these disruptions. We perform a clustering analysis, and find two distinct groups in our data, consisting of ‘lesser burden’ and ‘heavier burden’ households. Results indicate that the households experiencing the heaviest capabilities burden were most likely to experience longer power outages and the loss of water services. They were also more likely to have a Hispanic-Latino household member, lack access to a generator, live in a rented home, have more family circumstances that made the power outage particularly difficult, be impacted by the COVID-19 pandemic, and report larger declines in life-satisfaction during the outages. We also fit a logistic regression model to assess the role of household characteristics in explaining differences in capabilities burden. Our results offer insights for enumerating the consequences of utility service disruptions on vulnerable households, which can inform more targeted and equitable resilience strategies.

The accelerating transition toward sustainable materials has intensified interest in bio-based polymers as alternatives to fossil-derived plastics. Polylactic acid (PLA), derived primarily from fermented agricultural feedstocks, is one of the fastest-growing bioplastics and is frequently promoted as a lower-carbon substitute for Polyethylene terephthalate (PET), a dominant polyester used in packaging and textiles. While prior life cycle assessment (LCA) studies emphasize renewable feedstocks and biogenic carbon uptake, fewer provide a technically rigorous comparison that integrates production impacts with end-of-life management pathways. Because production and disposal stages often dominate the environmental burdens of short-lived plastic products, a focused assessment of these stages can generate decision-relevant insights for material selection and waste policy.

This study presents a cradle-to-gate assessment extended to include end-of-life scenarios for PLA and PET, excluding transport and use phases due to functional equivalence and negligible direct energy demand during use for typical packaging applications. The functional unit was defined as 1 kg of polymer resin entering the market and managed through representative waste pathways. System boundaries include feedstock extraction and cultivation, monomer synthesis, polymerization, and modeled end-of-life scenarios: mechanical recycling, chemical recycling (e.g., methanolysis), industrial composting (PLA), and landfill disposal. Life cycle inventory data were compiled from peer-reviewed literature and established LCI databases. Impact assessment focused on global warming potential (GWP) as the primary comparative metric, with additional evaluation of fossil resource use, acidification, ecotoxicity, human toxicity, land use, particulate formation, and water use. A 1,000-iteration Monte Carlo analysis quantified uncertainty across impact categories. Three supplementary case studies examined: (1) blending virgin resin into mechanically recycled polymers to maintain quality, (2) alternative PLA feedstocks, and (3) conceptual inclusion of plastic pollution impacts in LCA.

Results show that biogenic carbon accounting is the dominant driver of comparative climate outcomes. When end-of-life carbon credits are excluded, PLA exhibits higher GWP than PET across modeled disposal pathways due to upstream agricultural inputs and processing energy. When carbon credits are included, PLA’s GWP becomes comparable to or lower than PET in most scenarios, particularly under landfill or composting conditions. Multi-life-cycle modeling demonstrates that linear pathways (landfill and composting) yield higher cumulative GWP over successive product systems than circular options such as mechanical and chemical recycling.

Blending analysis indicates a near-linear increase in GWP with increasing virgin resin addition to mechanically recycled material. However, for both PET and PLA (with carbon credit applied), mechanical recycling remains lower in GWP than chemical recycling. Feedstock sensitivity analysis further shows that PLA derived from food waste feedstocks yields the lowest impacts across most categories, reflecting avoided burdens associated with waste valorization.

Overall, results underscore that sustainability performance is not intrinsic to bio-based or fossil-derived origin alone but depends on carbon accounting conventions, achievable recycling performance, and feedstock selection. By integrating production and end-of-life stages with uncertainty analysis, this study strengthens the completeness and technical clarity of comparative biopolymer assessment and provides a quantitative foundation for aligning polymer selection with climate and circular economy objectives.

Winter cover crops (WCCs) are increasingly promoted as a sustainable agricultural practice due to their potential to improve soil health, reduce nutrient and sediment losses, increase soil organic carbon (SOC) sequestration, and enhance ecosystem services. The adoption of cover crop in the United States has increased 75% from 2012 to 2022 due to policy, economic and technical supports. Besides, the U.S. government has actively promoted cover crops as part of climate-smart agricultural policy, including targeted investments of approximately $38 million through the Environmental Quality Incentives Program to support cover crop adoption across multiple states. To support sustainable agriculture, it’s essential to quantify the spatially explicit environmental impacts of WCC influenced by soil, weather and farming practices (i.e. crop rotation). Such evaluations will assist in identifying where and how to grow crops for maximizing environmental impacts of WCCs.

The existing life cycle assessment are limited and presented conflicting results regarding if growing cover crop reduces life cycle global warming potential (GWP). Some studies report lower GWP with cover crops, whereas others find that specific species can increase GWP compared with no-cover systems. A few assessments even suggest negative GWP values across termination methods, though these analyses typically do not account for herbicide termination. Moreover, the existing LCA studies focused on a few farms or fields, which are incapable of identifying spatially heterogeneous life cycle environmental impacts as influenced by distinct soil, weather, and farming practices. This study addresses these two knowledge gaps by quantifying spatially explicit life cycle environmental impacts and revealing key influential factors driving spatial variability of life cycle impacts.

This study evaluates the spatially explicit environmental impacts of WCC adoption in the Tuckahoe Watershed (TW), Maryland, using scenarios that include a baseline without cover crops and systems with barley, rye, and wheat planted at early and late dates. LCA quantified GWP, acidification, eutrophication, and SOC changes by integrating SWAT simulation outputs with life cycle inventory data from databases such as ecoinvent and openLCA. Machine learning models (Random Forest and XGBoost) were then used to identify and rank key environmental drivers. SWAT supplies spatially explicit inputs for more realistic inventories, while machine learning reveals key drivers and nonlinear relationships. Together, this integrated framework strengthens the accuracy, interpretability, and policy relevance of WCC environmental assessments.

Preliminary results indicate that WCC systems consistently outperform the baseline. Early-planted rye and barley remain net carbon sinks, while the baseline shows a net positive climate impact. Across cover crop types, WCC systems exhibit improved climate performance and greater SOC sequestration relative to the no-cover scenario. Variations in eutrophication and acidification potentials also reveal environmental trade-offs among management options. Machine learning results show that impacts are mainly driven by cover crop management and fertilizer assumptions, followed by soil texture and weather, with crop rotation having a more moderate influence.

Overall, this research provides a transparent, systems-based evaluation of winter cover crops and highlights the conditions under which environmental benefits are maximized, offering insights to support sustainable agricultural decision-making from a life cycle perspective.

The rapid expansion of utility-scale solar photovoltaics in the United States (US) has created a need to understand the spatial distribution and material intensity of these deployed technologies. Thin-film cadmium telluride (CdTe) systems account for a disproportionate share of utility-scale capacity; according to the Ground-Mounted Solar Energy in the US spatiotemporal dataset (GM-SEUS), only 441 CdTe arrays are identified nationwide, yet these systems are typically deployed at large capacities, which concentrates a significant portion of utility-scale capacity within a relatively small number of installations. Although GM-SEUS provides detailed spatial and temporal information on solar arrays, it does not explicitly identify module series or associated material composition. Thus, there is an opportunity to expand its applicability for material stock and recycling analyses. This study developed a data-integration framework to infer CdTe module series type and estimate embodied material inventories at the array-level across the contiguous US.

The GM-SEUS array and panel datasets contain array-level metadata including total row area, average row width, row length, mount, and orientation. Thin-film arrays were classified into CdTe module First Solar series (Series 2–7) using a hybrid inference approach that integrated installation year ranges with spatial characteristics derived from panel layouts. We reviewed First Solar CdTe module datasheets to extract series specific module dimensions and material compositions and calculate per-module material intensities for CdTe, aluminum, steel, EVA, and other components. A validation dataset of confirmed CdTe projects, compiled from public records, manufacturer disclosures, and developer project lists was used to refine and evaluate the series classification methods.

We estimated module count and embodied material inventory using series-specific module dimensions and material intensity. Results revealed substantial spatial heterogeneity in CdTe material distribution, driven by regional differences in the age and type of installed module series. States with higher shares of earlier generation CdTe modules exhibited substantially greater steel mass per unit capacity, whereas states dominated by newer modules showed reduced steel intensity, with aluminum contributing a smaller share of the variation. California alone accounts for approximately 41% of total installed CdTe modules nationwide, with deployment concentrated in the Central Valley and southeastern desert regions, particularly in Kern, Riverside, and Imperial counties. Despite this concentration, California contributes only 22% of steel and 23% of aluminum inventories reflecting its higher prevalence of newer generation modules. In contrast, Texas represents 9% of installed CdTe modules but contributes nearly 24% of aluminum, and 14% of steel, demonstrating that similar installed capacities can mask substantial differences in material inventories. Because recycling capacity, policy requirements, and material recovery practices vary by region, inaccurate estimates of when and how much material will reach end of life availability can lead to poorly sited or sized infrastructure and missed opportunities to recover valuable materials leading to greater global warming potential. By evaluating CdTe material stocks at the array- and module-series level, this project addresses these issues, enabling more effective recycling infrastructure planning, improving projections of secondary material supply, and supporting policies that strengthen supply-chain resilience and advance circular-economic goals for utility-scale solar.

Nitrogen (N), phosphorus (P), and potassium (K) commonly referred as NPK are primary nutrients in fertilizers. They are derived through carbon-intensive process and rely on critical minerals such as phosphate and potash. An alternative source for NPK is human urine (HU). A transition to a circular economy which recovers NPK as fertilizers from HU will reduce the reliance on critical minerals and environmentally intensive processes. Existing research primarily focuses on increasing technical efficiency and recovery rates of extracting NPK from HU on a lab scale. However, there has been a lack of analysis on quantifying HU from potential building types in the US, locating resource recovery facilities (RRF) that recovers NPK as fertilizers from HU on a commercial scale and identifying highest contributing parameters for NPK recovery in RRF through sensitivity analysis.

To address this knowledge gap, we present the first study that geospatially optimizes and minimizes the environmental burden of locating RRF based on HU generation in public school buildings and available sites for establishing RRF in the US. Potential sites for RRF depend on the zoning laws in US. We explore two options for RRF: centralized and decentralized. For centralized RRF, HU is transported from multiple buildings to a centralized facility and NPK recovery occurs at the facility. In a decentralized RRF, NPK recovery occurs at the building itself and therefore the burden of transportation is avoided. We quantify the HU generated in 420 public schools across Phoenix housing 217,000 students and 26,000 teachers and school staff. For Phoenix, 16 land parcels were identified as potential sites to establish centralized RRF. A geospatial optimization model is developed to determine public schools with decentralized RRF and public schools connected to a centralized RRF along with the location of the centralized RRF. The geospatial optimization model utilizes an anticipatory LCA framework to account for the transport of HU and recovery of NPK as fertilizers in RRF while incorporating the benefit of economies of scale. Furthermore, to identify inputs in RRF with the biggest contribution in global warming and water consumption impact categories, Global Sensitivity Analysis (GSA) is utilized.

The results showed that the transition to a circular economy for HU in Phoenix reduces global warming and water consumption by 127% and 198% respectively, compared to the conventional fertilizer production and toilet-use without RRF. This significant reduction is driven by the avoidance of flush water and wastewater treatment. Acetic acid and calcium chloride were the largest contributors to global warming while sulfuric acid and acetic acid were the largest contributors to water consumption. In addition, the analysis will present a geospatial map of Phoenix with location of centralized and decentralized RRF along with a result of GSA showing the highest-contributing input for global warming and water consumption impact categories.

Hydrogen, because of its less harmful by-products and versatility, promotes a great potential for decarbonization of energy in intense consumption sectors. The extensive infrastructure of natural gas steel pipelines in the United States makes hydrogen integration an attractive and cost-effective transition pathway from fossil fuel-based energy. However, steel pipelines are susceptible to hydrogen embrittlement depending on the application conditions, e.g. hydrogen pressure. These effects have created the need of safety measures for hydrogen transportation systems be prioritized, driving standards to implement design penalties known as “materials performance factors”, as seen in the American Society of Mechanical Engineers (ASME) B31.12 code for Hydrogen Piping and Pipelines. The material performance factors penalize materials with a specified minimum yield strength greater than 52 ksi, there are ongoing initiatives to develop higher strength alloys that are resistant to hydrogen embrittlement and thus would not be subjected to severe design penalties.

Our research studies the influence of microstructure on high-strength steels, such as X65 and X70 steels, to determine if the application of higher-strength steels in pipeline design can reduce materials use and environmental emissions by increasing fuel delivery. Thus, this study aims to compare the life cycle emissions and environmental impacts (EI) of traditional steel pipelines made from X52 alloys to those made from higher-strength steel alloys (X65 and X70) for commonly used pipe diameters (12”, 24”, 36”). Using life cycle assessment (LCA), this study seeks to inform the implications of current standard design penalties on the life cycle emissions of hydrogen transportation and the potential environmental advantages of the application of higher-strength steels without a design penalty. Our preliminary results indicate pipelines made from X70 alloy with 36” diameter produces less environmental impacts overall, across all impact categories, where the processes of steel production and mineral extraction are identified as the major contributors.

Sustainable aviation fuels (SAF) are widely viewed as essential to decarbonizing aviation, yet uncertainty surrounding production costs and long-run cost trajectories continues to limit investment and policy confidence. Existing techno-economic assessments (TEAs), including those embedded in tools such as ASCENT, provide internally consistent and regionally sensitive cost estimates for major SAF pathways, including hydroprocessed esters and fatty acids (HEFA), Fischer–Tropsch (FT), and alcohol-to-jet (AtJ), but are largely static and offer limited insight into how low-level process improvements translate into high-level cost change, learning, and policy-relevant outcomes.

This paper develops a generalized cost-equation framework derived directly from ASCENT’s underlying assumptions, preserving its empirically grounded parameter values while making explicit the mathematical structure that is often implicit in spreadsheet-based models. We reformulate ASCENT-style cost accounting into transparent, pathway-specific cost equations that assign inputs to interpretable “buckets” (such as feedstock, energy and hydrogen, capital, operations, and conversion efficiencies). This structure enables systematic examination of how process-level mechanisms, particularly efficiency metrics such as jet fuel cut, carbon efficiency, and energy efficiency, propagate through total production costs.

Recognizing the centrality and fragility of efficiency assumptions in SAF modeling, the framework emphasizes careful definition and separation of efficiency concepts rather than claims about industrial relevance. Relying on the empirical basis of the parameters in ASCENT that would be difficult to re-estimate due to data accessibility issues, the value added by this study lies in linking low-level engineering parameters to high-level mechanisms of cost change, including research and development (R&D), economies of scale, and learning-by-doing. These linkages are formalized analytically (e.g., in a series of well-defined mathematical equations), allowing inspection of relationships that are otherwise hidden within the spreadsheet-based model.

We apply a data-informed sensitivity analysis that evaluates the relative importance of cost drivers by uniformly perturbing key parameters across two time points: a present-day baseline and a stylized future state. This approach identifies which high-level mechanisms exert the greatest influence on costs, highlighting where learning, R&D investment, or market expansion are most likely to yield meaningful cost reductions. Preliminary results suggest that different SAF pathways are sensitive to distinct mechanisms, implying divergent policy levers, such as targeted R&D support versus market-based scale incentives.

By making cost structures and sensitivities explicit, this work contributes an interpretable bridge between TEA, learning analysis, and policy design. The framework supports more transparent investment guidance and helps align sustainability modeling with decision-making under uncertainty, advancing ISSST’s goals in sustainable energy systems and innovative sustainability assessment methods.

The longest river in India is the Ganges (Ganga in Hindi). This river is particularly significant in this nation and in South Asia more generally because the river occupies a central place in the Hindu religion (Hammer 2007, Conaway 2015). As the Ganges traverses the densely populated and agriculturally intensive Indo-Gangetic Plain, it faces severe pollution from a confluence of urban, industrial, and agricultural sources (Markandya and Murty 2000).

A primary contributor is the vast volume of untreated or partially treated sewage discharged from hundreds of cities and towns along its course, including major urban centers like Kanpur, Varanasi, and Patna (Hamner et al. 2006). At the same time, industrial effluents from numerous clusters of tanneries, textile mills, distilleries, and chemical plants add a toxic layer of pollution (Rawat et al. 2009). These discharges introduce heavy metals like chromium, arsenic, and lead, along with synthetic dyes, acids, and other hazardous chemicals, which are not only poisonous to aquatic life but also bioaccumulate in the food chain.

Despite the severity of the water pollution problem along many stretches of the Ganges and the dire implications of this problem for the sustainable use of the goods and services provided by this river, the literature about pollution cleanup in the Ganges and how political-economy factors affect this cleanup is sparse. Therefore, we focus on a single location or city along the Ganges such as Kanpur or Varanasi and suppose that the decision to provide pollution cleanup is decentralized. In this setting, we extend the extant literature by analyzing, for the first time, the implications of two kinds of taxation and majority voting for water pollution cleanup.

First, we examine how a poll or head tax on citizens of the locality under study and majority voting together affect how much pollution is cleaned up. Second, we study how a proportional tax on the citizens of the relevant locality and majority voting together impact the magnitude of pollution cleanup. Third, for alternate income distributions, we discuss which of the two preceding cleanup amounts is closer to the efficient amount of pollution cleanup. Finally, we discuss what our findings tell us about the sustainable use of the many goods and services that the Ganges provides to the citizens of India in particular and to those of South Asia more generally.

As the climate changes in Western New York, extreme temperature events will become more prevalent. Future climate projections show that heat waves are expected to occur with more frequency and intensity, causing heightened risk to human health and wellbeing (NYS Climate Impacts Assessment, 2026). At the same time, the region will continue to experience episodes of extreme cold and lake-effect snow events, like the deadly 2022 Buffalo Blizzard. Erie County, accustomed to preparing for cold weather, lacks the same experience with extreme heat, and is considered highly vulnerable due to its old housing stock, aging population, and limited access to air conditioning (U.S. Census Bureau, 2023). To help address vulnerability to extreme temperature, the Erie County Department of Environment and Planning, in partnership with University at Buffalo (UB), is formulating an Extreme Temperature Emergency Plan (ETEP), funded by NYS Climate Smart Communities program. Using meteorological, demographic, and health data, the ETEP will provide countywide guidance to mitigate the harm of extreme temperatures through the development of planning protocols, as well as the coordination of communication and outreach methods before and during extreme temperature events.

This research focuses on the community outreach component of the ETEP project through the collection of individual narratives from Erie County residents. The objective is to gain a deeper understanding of the experiences of households during extreme temperature events. Key research questions include: 1) What are the key challenges or barriers that individuals face during extreme temperature events?; 2) What services or resources do individuals rely on to cope?, and 3) What additional resources or services do they need to better respond to future events? These research questions will allow the integration of personal experiences into the data collection and planning process of the ETEP.  

This study, approved by the Institutional Review Board at UB, collected personal narratives of 20 individuals from diverse households in Erie County through a semi-structured interview process, each interview lasting 120-minutes. This method allowed for open-ended inquiry and space for follow-up discussion with residents. Participants were recruited with the help of key community partners, including the Erie County Department of Environment and Planning, and community organizations such as WNY Environmental Alliance and P.U.S.H Buffalo. Residents who were eligible for the study were compensated for their time.

Interviews were transcribed, and a thematic narrative analysis was conducted using NVivo to identify patterns and themes that align with the research questions of key challenges, coping strategies, and needs. Anticipated results include rich qualitative data at the individual scale to supplement the generalized demographic and socioeconomic data at the county level. The narratives reveal lived experiences and identify gaps in policy and planning that are crucial to community resilience. Harnessing and sharing these lived experiences humanizes extreme temperature events, allowing for a planning approach that holistically addresses risks and adaptation opportunities that are local and specific to the Erie County population.

Life cycle assessment (LCA) is an internationally standardized approach for modelling the environmental impacts of product systems, which ultimately supports informed decision-making for sustainability. The data-intensive nature of LCA – primarily in the life cycle inventory (LCI) step – is a longstanding methodological and practical challenge, especially for industries like mining and metallurgy with widely varied production technologies. Through an application to the production of special high grade (SHG) zinc, the fourth-most produced metal globally by tonnage and widely used in galvanized steel, we demonstrate how process simulation can facilitate and improve LCI data generation. In our demonstration, we use HSC Chemistry to model SHG zinc production via roast-leach-electrowinning (RLE). We validate our model based on zinc production operations in Trail, British Columbia, Canada. Our model draws on published flowsheets and integrates mass and energy balances with unit-process-level flows to generate a gate-to-gate LCI from concentrate to SHG zinc. The model allows systematic variation of feed and operating parameters. LCI and LCA results are compared to those obtained with generic and industry-association data from the ecoinvent database. Our zinc demonstration illustrates three ways in which process simulation can improve LCA. First, process simulation supports specific and detailed modelling of unit processes (e.g., roasting, primary and secondary leaching, iron removal, purification, and electrowinning processes in SHG zinc production), thereby increasing the transparency, granularity, and representativeness of LCI data. Second, process simulation facilitates evaluation of how variations in model inputs (e.g., differences in composition of mineral concentrates) affect model outputs (i.e., the LCI and LCA results). Finally, and arguably most importantly, process simulation ensures the scientific plausibility of LCA models by adhering to fundamental scientific principles – namely the laws of thermodynamics and conservation of mass and energy.