Conference Agenda

The purpose of this work is to improve the accuracy, functionality, and geographic scope of a key life cycle analysis (LCA) tool used to evaluate greenhouse gas (GHG) emissions from energy and transportation systems within MENA (Middle Eastern and North African) countries. MENA GREET is an LCA tool developed in collaboration with Argonne National Laboratory to model the life cycles of various gasoline, diesel, and electric vehicle technologies (or other energy consumption applications) in MENA countries. The tool is adapted from the well-established US-based R&D GREET LCA model, and it covers 12 Middle Eastern and 5 North African countries. While MENA GREET is a useful tool with valuable LCA applications, it has not undergone updates to data or interface with recent years, with the last refresh occurring in 2021 using 2019 data. Thus, this work expands upon and updates the MENA-GREET model using the latest country-specific data and methodologies to better evaluate the fuel cycle GHG emissions of different vehicle technologies and fuel pathways in the MENA region. This includes MENA-specific upstream data, energy and fuel production pathways, vehicle classes, vehicle fuel consumption rates, and other relevant key parameters. The work highlights notable improvements in the model through country-specific GHG emission comparisons for various passenger vehicles.

Circular economy principles have led to the rapid development of metrics intended to evaluate material circularity in the built environment. While these metrics are increasingly referenced in research and practice, their applicability to real-world building products remains insufficiently tested. In this study, we evaluate the practical performance of circularity metrics through their application to commercially available façade systems, with a focus on identifying information gaps and assessing whether design modifications can measurably improve circularity outcomes.

We apply two existing circularity assessment approaches: the Material Circularity Index (PCI/MCI), a widely used metric which quantifies material flows and durability through mass-balance and utility factors, and the Circular Construction Evaluation Framework (CCEF), which evaluates component-level circularity through qualitative criteria such as durability, material inventory, finishes, and design-for-disassembly. Two façade products are selected for analysis: the Sky-Frame 3 sliding façade system and the Riventi R70ST low-carbon curtain wall. The assessment is based exclusively on publicly available manufacturer's documentation, including Environmental Product Declarations (EPDs), technical specifications, and performance reports.

First, we conducted baseline circularity assessments for each product using PCI/MCI input tables and CCEF component-level scoring. The analysis reveals substantial variability in data completeness across products, particularly regarding mass breakdowns per unit area, recycled content disclosure, end-of-life recovery assumptions, and operational parameters. These gaps directly affect the computability, comparability, and robustness of circularity metrics. Second, we proposed targeted redesign scenarios for each product, focusing on changes that circularity frameworks identify as high-impact, such as increased recycled aluminum content and improved design-for-disassembly strategies. We then re-evaluated the redesigned configurations using the same metrics to test their sensitivity to design changes. To complement the circularity indicators and examine potential trade-offs between circularity and environmental performance, a comparative LCA is conducted for both baseline façade products and their respective redesign scenarios.

The results demonstrate that circularity metrics can detect improvements resulting from design modifications, but only when sufficient and transparent product data is available. In cases where critical inputs are missing or ambiguous, the metrics produce incomplete or weakly supported results. This finding highlights a persistent disconnect between the data requirements of circularity assessment methods and the information typically disclosed by manufacturers. Finally, preliminary LCA results will be presented, comparing baseline and redesigned configurations.

This study contributes to sustainable systems evaluation by clarifying both the strengths and limitations of current circularity metrics. We emphasize the need for improved data transparency and standardized product documentation to enable meaningful circularity assessment and informed design decisions in façade systems.

Industrial activities have significant environmental challenges across the entire value chain. The circular economy (CE) offers a promising approach to advancing sustainable development by reducing resource use, waste generation, and environmental impacts. However, implementing CE within complex industrial value chains is difficult due to multiple stakeholders with different values and interests, as well as a lack of a holistic view of the interrelations among various stages of the chain. Therefore, effective CE transition requires a shared strategic vision, prioritized strategies that account for stakeholders’ values, and quantitative insights into material flows and system interconnections. This study proposes a system-based theoretical framework that integrates qualitative and quantitative methodologies to facilitate informed decision-making during the industry-scale CE transition.

The flexible polyurethane foam (FPUF) in the mattress industry value chain serves as a case study due to high production volume and end-of-life challenges. FPUF, a petroleum-based material with a complex chemical structure, presents significant recycling difficulties, and landfilling remains the dominant end-of-life pathway in the U.S. Although some circular solutions exist, such as mechanical recycling and EPR programs, substantial volumes of FPUF material continue to be landfilled annually. Therefore, advancing circularity systematically across the value chain is a priority.

The proposed framework builds upon two prior works and systematically integrates quantitative and qualitative methodologies, including participatory backcasting, Material Flow Analysis (MFA), and scenario analysis, to align stakeholders’ strategies with the system's potential to advance circularity. Participatory backcasting is employed to co-develop strategies for CE transitions across the industry value chain, while MFA serves as a tool in industrial ecology for quantifying material flows and stocks and clarifying the interrelations, which help to identify hotspots for circularity. In addition, scenario analysis is applied to clarify how alternative CE interventions can affect future system performance. Within the proposed framework, the quantitative MFA results are explicitly linked with co-developed strategies through stakeholder-driven participatory backcasting. This quantitative output is then mapped against stakeholder-prioritized strategies to assess feasibility, scale, and potential system-wide impacts.

Anticipated results are expected to demonstrate that this integrated approach, which is employed through the proposed framework, will facilitate CE decision-making for stakeholders at the industry level in several ways. The strategies co-developed through backcasting enable stakeholders to meaningfully engage in the decision-making process and to identify and prioritize strategies aligned with shared values. MFA adds quantitative resolution to qualitatively defined strategies by clarifying trade-offs, timing, and material constraints. Scenario analysis further supports the comparison of alternative transition pathways, thereby enabling the prioritization of strategies that are both system-effective and stakeholder-aligned. This integrated approach highlights synergies and tensions between stakeholder priorities and material constraints, thereby providing a structured method for aligning vision-driven planning with data-driven analysis.

The proposed framework advances industrial ecology research by demonstrating how participatory and quantitative methods can be systematically combined to bridge the gap between vision-driven planning and data-driven analysis. Beyond the PU foam case, the approach is transferable to other complex industrial systems seeking coordinated, evidence-based pathways toward circular economy transitions.

Product-as-a-service is an emerging model that can advance material efficiency and reduce environmental burdens. Baby gear—characterized by short use periods and frequent underutilization—offers a compelling case for leasing alternatives, yet rigorous lifecycle assessments comparing ownership and leasing models remain limited. This study examines lifecycle environmental benefits of localized baby gear leasing services in the U.S., drawing on over 2,000 transaction records from a service provider across five locations over five years.

We developed cradle-to-grave lifecycle models for five focus items selected based on leasing frequency, duration, and revenue: regular stroller, travel stroller, sleeping tent, bottle sterilizer, and toy. Each item was modeled using a representative product within its category. Bills of materials were constructed from manufacturer specifications and direct measurement, while cleaning data were provided by the service provider. End-of-life treatment accounts for material-specific recycling rates and landfill disposal. The conventional ownership scenario assumes users acquire items either new or second-hand, characterized by a distribution of lifetime owners per item. The number of items required to serve equivalent users under leasing was derived from historical leasing patterns. Default round-trip transportation distance was 8 km based on service records.

Results show that environmental benefits vary substantially across product categories depending on embodied emissions intensity, transportation distance, transport mode, and number of items transported per trip. Lifecycle greenhouse gas emissions for travel strollers, for instance, are reduced by 55–75% when leased using a large SUV, and by 70–80% when transported with a battery electric vehicle. Emissions reductions are smaller for regular strollers (as low as 20%) due to higher embodied emissions per item and fewer items displaced per lease cycle. These findings highlight that transport logistics—distance, mode, and load consolidation—are critical levers for leasing providers seeking to maximize environmental benefits.

Deforestation remains a major driver of biodiversity loss and greenhouse-gas emissions, yet many operational monitoring systems are fundamentally reactive since they detect forest disturbance only after it becomes visible in satellite imagery. For sustainable systems decision-making, a key gap is forecasting where deforestation risk is likely to emerge next, especially when models must generalize across temporal and spatial shift (e.g., changing land-use dynamics, policies, and frontier expansion). This work evaluates whether AlphaEarth annual embeddings can support reliable, forward-looking deforestation risk forecasting under realistic distribution shift, and it proposes a practical pipeline for annual, explainable risk mapping.

The annual deforestation risk is formulated as a binary prediction problem, using AlphaEarth embedding vectors as fixed, high-dimensional representations of landscape dynamics. The study focuses on a Brazilian Amazon case study with labeled samples spanning 2020–2024. To reduce spatial leakage and better approximate deployment conditions, model development uses spatially buffered splits (10 km) and separates internal evaluation (random splits) from a temporal challenge test designed to stress generalization. The gradient-boosted decision trees (XGBoost) are compared with a Random Forest baseline, and their performance is evaluated with threshold-free metrics (AUROC) as well as thresholded metrics aligned with screening use-cases (precision/recall at a fixed operating threshold). To support interpretability for sustainability stakeholders, SHAP-based attribution is applied to identify which embedding dimensions and spatial patterns are most associated with elevated risk.

Results show that internal random-split evaluation can overestimate real-world performance (AUROC 0.9866 on internal validation; 0.9984 on internal test), underscoring the importance of shift-aware evaluation. On the external temporal challenge test spanning 2022–2024 (N=340), the XGBoost model achieves AUROC 0.8822 (95% CI 0.8463–0.9181) with precision 0.8579 and recall 0.7811 at a 0.5 threshold, outperforming the Random Forest baseline (AUROC 0.8340, 95% CI 0.7906–0.8774). These findings indicate that embedding-based representations can retain meaningful risk signal under temporal shift while enabling an operationally simple forecasting workflow.

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.

Boron and lithium are considered critical materials in many nations due to their importance in the energy transition(1,2). Both elements are documented in geothermal fluids used in renewable energy production, at reported concentrations potentially exceeding 500 mg/L for boron and 200 mg/L for lithium(3,4). To date, peer-reviewed life-cycle assessments (LCAs) specifically evaluating the recovery of boron from geothermal waters or the co-recovery of boron and lithium appear to be lacking (3,5,6). This study presents an integrated thermodynamic and LCA framework to assess the process performance and environmental impacts of co-recovering technical-grade lithium carbonate (Li2CO3) and boric acid (H3BO3) from high-salinity geothermal fluids. The Cesano C-1 geothermal well in Italy was selected as a case study due to its promising resource potential, with model scenarios expanded to represent global geothermal conditions(7). Thermodynamic analysis was conducted using PHREEQC, a U.S. Geological Survey (USGS) geochemical modelling tool and the Pitzer database to model the thermodynamic performance of an industry-standard brine recovery system proposed by an Australian-based firm (Altamin Limited) for the Cesano field(8). The Pitzer database was used as it is suitable for modelling high-salinity fluids, such as the Cesano geothermal fluid (total dissolved solid up to 350 g/L) (9–12). The baseline thermodynamic model of the recovery system used literature pH and temperature conditions to selectively form boric acid (15°C, pH < 4.0) and lithium carbonate (90°C, pH > 9.0) (13–18). Product purification was modelled through extraction, washing and drying steps. The life cycle inventory was based on a combination of data collected from thermodynamic modelling, literature research and datasets from ecoinvent v3.10. A cradle-to-gate LCA was conducted using a functional unit of 1 kg battery-grade Li2CO3, employing a system expansion approach to consider the H3BO3 co-product. Recovery system optimizations were evaluated using the ReCiPe 2016 midpoint (H) method of life cycle impact assessment to inform iterative process design. Sensitivity analyses were conducted to consider variability of global geothermal fluid compositions and process trade-offs between recovery yield and environmental impacts.

The baseline thermodynamic model showed yields of 37% and 83% for lithium and boron, respectively, which could be increased by varying process conditions. LICA results suggest that Li2CO3 extraction from a combined geothermal energy and mineral recovery system represent a competitive alternative to traditional lithium production pathways, exhibiting lower relative contribution across most environmental impact categories. The geothermal recovery system reduced relative contributions per kg Li2CO3 across 14 and 17 of 18 impact categories compared to solar evaporation ponds or spodumene-mining, respectively. For example, climate change and water use impacts per kilogram of Li2CO3 were reduced by 4% and 57%, respectively, compared to solar evaporation ponds, and by 80% and 84%, respectively, compared to spodumene mining. Sensitivity analysis indicated that chemical inputs for pH adjustment and precipitation are the main drivers for environmental impact across most categories. This study suggest that geothermal fluids have the potential to provide a lower-impact source of lithium and boron compared to traditional production, with variability in geothermal fluid compositions playing a role in global contexts.

References

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