Visualizing energy, material and emission flows to understand scale and connections in the physical global economy - Katrin E. Daehn; Ravi Basuhi; Danika Eamer; Elsa A. Olivetti (Journal of Industrial Ecology, 2026)

Process-based cost assessment of electrochemical metals recovery from municipal solid waste incineration ash - Jacqueline E. Baidoo; Richard Roth; Duhan Zhang; Michael J. Wang; Yet-Ming Chiang; Elsa A. Olivetti (Waste Management, 2026)

Analysis of the impact of automaker strategies on lithium price elasticity using a novel bottom-up demand model - Luke Robert Sullivan; Elizabeth A. Moore; Phuong Ho; Alison A. Wang; Gwyneth Margaux Tangog; Karan Bhuwalka; Elsa Olivetti; Richard Roth (Resources, Conservation and Recycling, 2025)

Strategies for industrial residue valorization through metal recovery, use in construction, or CO2 mineralization - Yixi Tian; Elsa A. Olivetti (Waste Management, 2025)

LLM-empowered literature mining for material substitution studies in sustainable concrete - Yifei Duan; Yixi Tian; Soumya Ghosh; Vineeth Venugopal; Jie Chen; Elsa A. Olivetti (Resources, Conservation and Recycling, 2025)

Understanding key mineral supply chain dynamics using economics‐informed material flow analysis and Bayesian optimization - John Ryter; Karan Bhuwalka; Michelena O'Rourke; Luca Montanelli; David Cohen‐Tanugi; Richard Roth; Elsa Olivetti (Journal of Industrial Ecology, 2024)
The low‐carbon energy transition requires significant increases in production for many mineral commodities. Understanding demand, technological requirements, and prices associated with this production increase requires understanding the supply chain dynamics of many minerals simultaneously, and via a consistent framework. A generalized economics‐informed material flow method, global materials modeling using Bayesian optimization, captures the market dynamics of key mineral commodities. The method relies only on a limited set of widely available historical data as input, enabling quantification of economic relationships (elasticities) for supply chain components where data are sparse, and relationships cannot be obtained via traditional statistical approaches. Building upon established material flow analysis (MFA) and economic modeling techniques, Bayesian optimization was applied to fit an economics‐informed MFA model to global historical demand, supply, and price for aluminum, copper, gold, lead, nickel, silver, iron, tin, and zinc. This approach enables estimates for the evolution of ore grades, mine costs, refining charges, sector‐specific demand, and scrap collection for each commodity. Economic relationships were quantified and compared with a database compiled from the literature, including 1333 values from 213 analyses across 65 publications. Discrepancies in methods and limited coverage make use of these parameters in modeling efforts difficult. This work provides a single, homogeneous, probabilistic approach to identifying economic relationships across mineral supply chains, with uncertainty quantification, a literature database for comparison, and a modeling framework in which to use them. This article met the requirements for a Gold‐Gold JIE data openness badge described at http://jie.click/badges .

Evaluating strategies to increase PET bottle recycling in the United States - R. Basuhi; Karan Bhuwalka; Richard Roth; Elsa A. Olivetti (Journal of Industrial Ecology, 2024)
In the United States, polyethylene terephthalate (PET) bottle collection rates have not increased in a decade. Recycling rates remain abysmal while industry commitments and policy targets escalate the demand for recycled plastics. We investigate the PET bottle recycling system, where collection is a critical bottleneck and recycled PET supply is not meeting the expected demand. We characterize demand for recycled PET (R‐PET), analyze scenarios of expanding deposit return systems (DRS), and quantify cost barriers to improving PET bottle recycling. We find that a nation‐wide DRS can increase PET bottle recycling rates from 24% to 82%, supplying approximately 2700 kt of recycled PET annually. With stability in demand, we estimate that this PET bottle recycling system can achieve 65% bottle‐to‐bottle circularity, at a net cost of 360 USD/tonne of PET recycled. We also discuss environmental impacts, stakeholder implications, producer responsibility, and complimentary policies toward an efficient and effective recycling system.

Quantifying the drivers of long‐term prices in materials supply chains - Karan Bhuwalka; Randolph E. Kirchain; Elsa A. Olivetti; Richard Roth (Journal of Industrial Ecology, 2023)
Raw materials costs form an increasingly significant proportion of the total costs of renewable energy technologies that must be adopted at unprecedented rates to combat climate change. As the affordable deployment of these technologies grows vulnerable to materials price changes, effective strategies must be identified to mitigate the risk of higher input costs faced by manufacturers. To better understand potential threats to deployment, a market modeling approach was developed to quantify economic risk factors including material demand, substitutability, recycling, mining productivity, resource quality, and discovery. Results demonstrate that price changes are determined by interactions between demand growth, mining productivity, and resource quality. In the worst cases with high demand and low productivity, development of material substitutes and large recycling rates help reduce the prevalence of price risk from over 90% to under 10%. Investing in these strategies yields significant benefits for manufacturers and governments concerned about costs of materials critical to decarbonization and other advanced technologies.

A hierarchical Bayesian regression model that reduces uncertainty in material demand predictions - Karan Bhuwalka; Eunseo Choi; Elizabeth A. Moore; Richard Roth; Randolph E. Kirchain; Elsa A. Olivetti (Journal of Industrial Ecology, 2023)
Predictions of metal consumption are vital for criticality assessments and sustainability analyses. Although demand for a material varies strongly by region and end‐use sector, statistical models of demand typically predict demand using regression analyses at an aggregated global level (“fully pooled models”). “Un‐pooled” regression models that predict demand at a disaggregated country or regional level face challenges due to limited data availability and large uncertainty. In this paper, we propose a Bayesian hierarchical model that can simultaneously identify heterogeneous demand parameters (like price and income elasticities) for individual regions and sectors, as well as global parameters. We demonstrate the model's value by estimating income and price elasticity of copper demand in five sectors (Transportation, Electrical, Construction, Manufacturing, and Other) and five regions (North America, Europe, Japan, China, and Rest of World). To validate the benefits of the Bayesian approach, we compare the model to both a “fully pooled” and an “un‐pooled” model. The Bayesian model can predict global demand with similar uncertainty as a fully pooled regression model, while additionally capturing regional heterogeneity in income elasticity of demand. Compared to un‐pooled models that predict demand for individual countries and sectors separately, our model reduces the uncertainty of parameter estimates by more than 50%. The hierarchical Bayesian modeling approach we propose can be used for various commodities, improving material demand projections used to study the impact of policies on mining sector emissions and informing investment in critical material production.

Life cycle assessment of CO 2 conversion and storage in metal–CO 2 electrochemical cells - Olivia Pfeiffer; Aliza Khurram; Elsa A. Olivetti; Betar M. Gallant (Journal of Industrial Ecology, 2022)
Researchers have proposed a family of electrochemical technologies that incorporate CO 2 as a gas cathode and alkali or alkaline earth metal anodes to exploit the high reactivity of CO 2 with these metals. Such proposed cells operate by converting gaseous CO 2 into solid carbonate, extracting value from CO 2 in the form of electricity, and fixing the CO 2 in solid form. Given the increasing number of these proof‐of‐concept studies, it is timely to critically examine the potential of such emerging technologies to reduce stationary point source emissions. Environmental life cycle assessment (LCA) during early research stages can inform important design decisions. In this study, we perform LCA on electrochemical conversion processes (ECPs) involving Li, Na, Mg, or Ca as a co‐reactant with CO 2 sourced from a coal plant, evaluating differing scenarios. We find that a Na‐based primary ECP configuration that integrates capture with conversion of coal plant CO 2 emissions to Na 2 CO 3 can avoid almost 28% of emissions (and 74% under best‐case conditions) that are typically associated with conventional production of the same carbonate and electricity. This represents a net emissions decrease of 435 kg CO 2 ‐eq per 1 MWh of electricity delivered by the ECP, from a baseline of 1,105 kg CO 2 ‐eq emitted without any mitigation strategies. In contrast, all ECPs in a rechargeable configuration result in increased emissions compared to the conventional baseline, unless the ECP system energy requirements are provided by renewables and the conventional system is assumed to have high emissions intensity.

Assessing recycling, displacement, and environmental impacts using an economics‐informed material system model - John Ryter; Xinkai Fu; Karan Bhuwalka; Richard Roth; Elsa Olivetti (Journal of Industrial Ecology, 2022)

High‐Resolution Insight into Materials Criticality: Quantifying Risk for By‐Product Metals from Primary Production - Xinkai Fu; Adriano Polli; Elsa Olivetti (Journal of Industrial Ecology, 2019)
Summary Many advanced energy and environmentally relevant technologies rely on metals that have been identified as critical, or whose availability may be limited. Several of these elements are produced mostly as by‐products of mining other base metals (carriers). This by‐product dependence has been proposed as a significant supply‐risk indicator by the materials criticality community. This article provides new quantitative evidence that, in several cases, by‐product metals’ availability may not be directly limited by carrier supply. We perform an assessment based on characteristics essential to by‐product metals, including physical concentration, market value of metals, and extraction technology efficiency. We analyze 40 carrier/by‐product pairs and identify five ‘high‐by‐product’ pairs. We assess the supply responsiveness of these metals. Our analysis suggests that rather than limited primary production of carrier, lack of incentive for improving recovery efficiency may limit availability of the by‐product. This behavior is found in the zinc‐indium and copper‐selenium systems. For germanium, on the other hand, we instead propose influence from the by‐product market itself leading to price inelasticity of supply. As a complement to other quantitative methods developed for material systems, such as material flow analysis, we provide an essential technoeconomic analysis of the by‐product metals problem by employing cluster analysis and econometric modeling. This approach provides insight into supply‐risk mitigation strategies related to extraction efficiency and supply‐chain structure.

Streamlining the Life Cycle Assessment of Buildings by Structured Under‐Specification and Probabilistic Triage - Paolo Tecchio; Jeremy Gregory; Elsa Olivetti; Randa Ghattas; Randolph Kirchain (Journal of Industrial Ecology, 2019)
Summary Life cycle thinking plays an important role in sustainable development in the building sector. However, the complexity of data collection and scope definition limits life cycle assessment (LCA) applications. Even if the inventory data have already been collected, tabulated, and indexed, the method is still time‐consuming, which may be discouraging for designers. This study demonstrates how the LCA of buildings can be robustly streamlined using structured underspecification of impact data combined with an effective and efficient triage of the data collection. Tests were conducted with a series of building typologies that were analyzed with a cradle‐to‐gate approach. The probabilistic triage approach was tested to identify selected activities requiring detailed specification because they contribute most to total impact, thereby reducing data gathering effort. Impacts such as global warming, acidification, eutrophication, and smog creation were assessed, and results showed that 40% to 46% of the bill of materials components represent 75% of total impacts of single‐family houses and multifamily buildings. By specifying only a prioritized subset of the bill of materials to the highest level of specificity, results proved to be reasonably accurate and obtainable with less effort.

Taking the Circularity to the Next Level: A Special Issue on the Circular Economy - Nancy M. P. Bocken; Elsa A. Olivetti; Jonathan M. Cullen; José Potting; Reid Lifset (Journal of Industrial Ecology, 2017)

Design for Recycling - Gabrielle Gaustad; Elsa Olivetti; Randolph Kirchain (Journal of Industrial Ecology, 2010)
Summary As design for recycling becomes more broadly applied in material and product design, analytical tools to quantify the environmental implications of design choices will become a necessity. Currently, few systematic methods exist to measure and direct the metallurgical alloy design process to create alloys that are most able to be produced from scrap. This is due, in part, to the difficulty in evaluating such a context‐dependent property as recyclability of an alloy, which will depend on the types of scraps available to producers, the compositional characteristics of those scraps, their yield, and the alloy specification itself. This article explores the use of a chance‐constrained based optimization model, similar to models used in operational planning in secondary production today, to (1) characterize the challenge of developing recycling‐friendly alloys due to the contextual sensitivity of recycling, (2) demonstrate how such models can be used to evaluate the potential scrap usage of alloys, and (3) explore the value of sensitivity analysis information to proactively identify effective alloy modifications that can drive increased potential scrap use. These objectives are demonstrated through two cases that involve the production of a broad range of alloys utilizing representative scraps from three classes of industrial end uses.