Consumer electronics are important for global energy use, material extraction, and toxic exposures. Within consumer behavior, End-of-Current-Use (EoCU) decisions (e.g., to resell, donate, recycle, or throw away) particularly are a planetary health concern, not just a waste management problem. The goal of this work is to inform interventions that nudge users toward more sustainable and data-secure choices. We surveyed 4000 U.S. users on their attitudes, knowledge, and planned EoCU choices, and constructed a Random Forest model to simulate planned behavior as a function of reported knowledge and attitudes. The key result is identification of behavioral “tipping points”, i.e., incremental shifts in knowledge or attitude that produce large changes in sustainable behavior. For example, moving the level of agreement relating to “reselling is worthwhile” from “Neutral” to “Agree” increases the probability of resale by 26%, with stronger agreement yielding minimal further gains, suggesting relatively small changes in perception may thus produce large behavioral changes. The model also identifies trade-offs, with increases in agreeing that resale is worthwhile leading to to decreases in giving away (−14%) and recycling (−10%). Economic considerations drive reselling decisions, while environmental concerns drive recycling. Identifying these critical thresholds enables efficient intervention design, as modest attitude changes at transition points may prove more effective than maximizing positive attitudes. These finding challenge assumptions of linearity of behavioral responses, informing circular economy and other policies. They also relate to managing planetary health issues raised by EoCU electronics, such as human and ecological well being, digital equity, and environmental justice. The framework is broadly applicable to identifying behavioral tipping points in other domains, e.g., healthcare, energy efficiency, and data security.

When finished with an electronic device, consumers choose between storing, recycling, giving away, trading-in, reselling, or throwing it away. This choice has environmental and data privacy implications, e.g., reuse of devices is generally environmentally preferable to recycling, which is better than throwing away in the trash. Through a survey of 4000 U.S. consumers and regression analysis, this study analyzes how stated attitude and knowledge connect to consumers’ previous and planned disposition choices. The binomial regression model (pseudo-R2=13%) models the decision to store or not store a device. Important factors leading to increased likelihood of storing are data security concerns when recycling (+14%) or reselling (+9%), lack of knowledge of recycling (+10%), and wanting a backup of data (+11%). Notably, data security concerns when recycling or reselling were significant for past behavior, but not for intended behavior. This suggests consumers take data security more seriously when faced with the actual disposition decision. Multinomial regression (pseudo-R2=15%) is used to model which non-storage option is chosen. Knowledge of (+47%) and perceived convenience (+9%) of recycling programs were important in consumers choosing to recycle, reselling of devices was strongly influenced by knowledge of reuse markets.

The rise in online shopping can be largely attributed to its unmatched convenience, offering consumers the flexibility to shop from any location, at any time. It is widely assumed that online shopping saves time by eliminating the need to physically search for products, queue at checkout counters, and deal with crowds in physical stores. However, the evolving time efficiency of online shopping compared to traditional in-store shopping has not yet been empirically analyzed, leaving a gap in understanding time efficiency and how it has changed. This study aims to bridge that gap by analyzing time spent shopping both in-store and online from 2003 to 2023, utilizing data from the American Time Use Survey. The findings reveal that, in 2003, consumers were able to complete the equivalent of 1 h of in-store shopping in just 35 min online, falling to 15 min in 2023. Over the past decade (2013-2023), the average American has saved a total of 25 h per year due to the time efficiency of online shopping. An experience curve model was applied to characterize past trends and project the future. The resulting learning rate is between 12% and 19%. Using this in a projection suggests that 9.2-11.2 min of online shopping will be needed to replace 1 h of in-store shopping in 2030. Changes in time and money spent online versus in-store have implications for consumer activity patterns, time allocation, purchases, and building use. Increasing efficiency reflects a growing orientation toward speed and immediacy, reshaping shopping as a more instrumental practice, distancing consumers from the social and leisure aspects of shopping, and contributing to ongoing processes of temporal acceleration in everyday life.

Capacity expansion models for electricity grids typically use deterministic optimization, addressing uncertainty through ex-post analysis by varying input parameters. This paper presents a stochastic capacity expansion model that integrates uncertainty directly into optimization, enabling the selection of a single strategy robust across a defined range of uncertainties. Two cost-based risk objectives are explored: “risk-neutral” minimizes expected total system cost, and “risk-averse” minimizes the most expensive 5% of the cost distribution. The model is applied to the U.S. Midwest grid, accounting for uncertainties in electricity demand, natural gas prices, and wind generation patterns. While uncertain gas prices lead to wind additions, wind variability leads to reduced adoption when explicitly accounted for. The risk-averse objective produces a more diverse generation portfolio, including six GW more solar, three GW more biomass, along with lower current fleet retirements. Stochastic objectives reduce mean system costs by 4.5% (risk-neutral) and 4.3% (risk-averse) compared to the deterministic case. Carbon emissions decrease by 1.5% under the risk-neutral objective, but increase by 3.0% under the risk-averse objective due to portfolio differences.

The impact of teleworking on residential energy consumption has remained inconclusive due to a lack of empirical evidence. Existing research often relies on scenario-based models with limited empirical evidence, leaving critical gaps in understanding telework's true energy impacts. This study addresses these gaps by analyzing data from the 2020 U.S. Residential Energy Consumption Survey and the 2022 American Time Use Survey to empirically examine the relationship between teleworking frequency, residential energy use, and time-use behaviors. Results reveal that the energy impact of teleworking is not uniform but varies significantly by household income level. For lower-income households and the highest income households, each additional telework day per week is associated with an increase of approximately 871 thousand BTUs in annual energy consumption. However, among upper-middle income households, this effect is significantly reduced, and in some cases, reversed, suggesting a U-shaped relationship between income and teleworking's association with residential energy use. Additional household characteristics, such as the number of adults, children, residence type, size, and thermostat settings also affect energy usage. Time-use analysis reveals that teleworkers spend an average of 5.9 h per day working from home, thereby using more energy for heating/cooling and lighting, which outweighs any energy savings from reduced time spent on activities like grooming, laundry, or online leisure. Clustering analysis identifies distinct time-use patterns among teleworkers and non-teleworkers, further demonstrating how their behaviors drive increased residential energy demand. By linking teleworking frequency with actual energy use and time-use behaviors, this work highlights the importance of addressing teleworking-induced energy demand through energy-efficient home retrofits, sustainable telework policies, and targeted interventions.

When a consumer is finished using an electronic device (End-of-First-Use), they might recycle, resell, donate/give away, trade-in or throw it in the trash. There are security threats if a hostile party obtains the device and extracts data. Data wiping at End-of-First-Use is thus an important security behavior, one that has received scant analytical attention. To explore consumer behavior and reasoning behind data wiping practices, we undertake a survey of the U.S. population. One key result is that 31% of the population did not wipe data when dispositioning a device. When asked why not, 44% replied that they did not find data wiping important or that it did not occur to them. 33% replied the device was broken and data could not be wiped, 12% reported difficulty in wiping and 11% could not find a way to wipe. The 44% who thought data wiping was not important showed lower awareness of the security threat, 23% had heard that data can be recovered from discarded devices, versus 44% for the general population. The most prevalent device types for which data wiping was reported as unimportant are smart TVs, kitchen appliances, streaming, and gaming devices, suggesting that consumers may not be aware that private information is being stored on these devices. To inform future interventions that aim to raise awareness, we queried respondents where they obtained security knowledge. 47% replied that they learned about security threats from a single venue; social media was this single venue 43% of the time. This suggests that social media is a key channel for security education.

Online shopping is widely believed to reduce demand for retail stores and presumably decrease energy consumption in the retail sector, yet this relationship has not been studied empirically. We address this gap by first developing a regression model that empirically links historical retail building space needs to in-store shopping time. The historical online shopping time is taken from the 2003–2023 American Time Use Survey, which is then extrapolated to 2030 under two scenarios: a slower growth scenario based on 2003–2023 trends, and a faster growth scenario based on 2015–2023, reflecting a more recent acceleration of online shopping. Future energy use in retail buildings is estimated by combining predicted building space demand with extrapolated trends in energy intensity. Monte Carlo analysis is used to quantify uncertainty. Results show that by 2030, retail building energy demand will decline by 6–12% under the slower growth scenario and by 11–20% under the faster growth scenario, relative to 2018. These changes correspond to reductions in total U.S. commercial building energy demand of 0.7–1.3% and 1.3–2.2%, respectively. While potential increases in warehouse space, delivery services, and residential energy use are not analyzed here, the findings have significant implications regarding e-commerce for retail space and urban energy demand.

The goal of this study is to find patterns in how consumers disposition electronic devices at End-of-First-Use, i.e. store, recycle, resell, trade in, donate/give or and throw in the trash. K-means clustering was used on survey data from 3,747 U.S. respondents across 10 device categories to divide the population into three clusters of consumers based on stated attitudes and knowledge of data privacy, environmental benefits, convenience and other aspects of End-of-First-Use options. We then measure the reported intended disposition of devices for each cluster and compare with the general population. Cluster 1 has higher data security concerns when recycling, reselling or donating, and less knowledge and trust in End-of-First-Use options overall. The intended behavior of cluster 1 shows higher than average uncertainty in what to do at End-of-First-Use and more intent to store (lower values for other options - recycling, reselling and donating). Cluster 2 shows higher knowledge and trust in recycling, reselling, and donation, and slightly higher than average concern about data security of these options. The intended behavior of cluster 2 shows higher intent to resell, trade-in or donate, and lower levels of being uncertain of what to do and of storing. Cluster 3 expresses much less concern about data security, and lower utility of a stored device. Their intended behavior shows less storage and higher levels of other End-of-First-Use options. While cluster analysis does not yield causal connections, the groups show consistent trends in stated knowledge and attitudes towards different End-of-First-Use options and corresponding planned behaviors. These results indicate there are subgroups of the general population with similar reported attitudes, knowledge and behaviors. The three subgroups do not have distinct demographic characteristics, i.e. knowledge and attitudes regarding disposition of electronics does not depend strongly on age, education level, income and similar factors. Understanding segmentation is useful to investigate more effective interventions to influence behavior for better sustainability outcomes.

Expanding use of rare earth elements (REEs) necessitates characterizing deposits. Challenges include variable REE concentrations (e.g., coal ash ranges from 267 to 843 ppm) and price volatility. For a range of sources, we estimate distributions in the REE value per tonne of material, by collecting multiple data points for each type and using mean-reversion price forecasting. The study covers primary ores (e.g., bastnaesite), industrial wastes (e.g., red mud) and consumer wastes (e.g., NiMH batteries). Electronic wastes have highest value, driven by neodymium, industrial waste value is driven by scandium. Variability exists within resource types, e.g., the value of Australian monazite > Bayan Obo bastnaesite > Malaysian monazite. Using a power-law relationship, the total REE value of a sources correlates well with its ore grade. These results inform investment decisions to develop primary and secondary sources by clarifying potential variability and providing a useful rule of thumb to estimate revenues.

As office workers shift to telework, office building space requirements should decrease, but this relationship has not been empirically studied. We construct a dataset describing historical office building space, number of office workers, and number of teleworkers from 2003-2019 in the US, and use linear regression to estimate the effect of telework on office building space. The results show that the average office building space required for an additional office worker and teleworker is 32 and 18 square meters (340 and 191 square feet), respectively, suggesting an average 44% reduction in office building space when an office worker transitions to telework.

Understanding the adoption patterns of clean energy is crucial for designing government subsidies that promote the use of these technologies. Existing work has examined a variety of adoption models to explain and predict how economic factors and other technology and demographic attributes influence adoption, helping to understand the cost-effectiveness of government policies. This study explores the impact of adoption modeling choices on optimal subsidy design within a single techno–economic framework for residential solar PV technology. We applied identical datasets to multiple adoption models and evaluated which model forms appear feasible and how using different choices affects policy decisions. We consider three existing functional forms for rooftop solar adoption: an error function, a mixed log-linear regression, and a logit demand function. The explanatory variables used are a combination of net present value (NPV), socio-demographic, and prior adoption. We compare how the choice of model form and explanatory variables affect optimal subsidy choices. Among the feasible model forms, there exist justified subsidies for residential solar, though the detailed schedule varies. Optimal subsidy schedules are highly dependent on the social cost of carbon and the learning rate. A learning rate of 10% and a social carbon cost of USD 50/ton suggest an optimal subsidy starting at USD 46/kW, while the initial subsidy is 10× higher (USD 540/kW) with a learning rate of 15% and social carbon cost of USD 70/ton. This work illustrates the importance of understanding the true drivers of adoption when developing clean energy policies.

Behavioral changes due to digitalization, such as telework, shifted energy demand, especially during COVID-19. Behavioral changes are often overlooked in macro-demand forecasts. This study forecasts macro-energy use to 2030 using American Time Use Survey and national energy data from 2003-2019. It examines and explains residential, non-residential, and transportation sectors through efficiency, technology characteristics (e.g., home floor area), and usage (time-use). Results showed that improved efficiency had the largest effect on energy demand per capita in all sectors from 2003-2019. The time-use (behavior) effect was strongest in non-residential buildings, resulting in a net energy reduction of -9%, decomposed into increased in floor area (+24%), improved efficiency (-26%), and reduced time-use (-7%). In forecasting, two potential effects of telework on energy use in 2030 were explored: (1) temporary shift in telework due to COVID-19 (14% teleworking in 2030) and (2) permanent shift in telework that increases with historical trends (34%). The permanent shift resulted in 3.6% less energy demand per capita in 2030. Reduced time-use of non-residential buildings had the largest effect on decreasing energy demand, -29%, with +8.5% from residential energy use, -7.5% from transportation, yielding a net -28% energy use per teleworker. Alternate perspectives are needed to corroborate results.

Carbon capture and storage (CCS) is a critical technology for mitigating climate change by reducing greenhouse gas emissions. This paper explores the current global landscape of CO2 storage projects, leveraging data from the Global CCS Institute’s CO2RE platform, which tracks the development and operational status of CCS initiatives. As of October 2024, approximately 356 CO2 storage projects have been identified, with significant concentrations in North America and Europe. This analysis highlights the growth rates required to meet the Intergovernmental Panel on Climate Change's (IPCC) 2050 CO2 storage rate targets, emphasizing the need for exponential increases in storage project development rate. Furthermore, the paper discusses the importance of investing more in screening, ranking, and characterizing storage complexes to maximize pore space available for utilization and improve project development timelines. The paper underscores the urgency of accelerating CCS deployment to achieve necessary CO2 storage rates and support global climate objectives by addressing these critical factors.

Due to its light-weighting potential and utility in fuel cells, scandium incorporation is important to achieving decarbonization and energy efficiency. Forecasting scandium supply and demand is complex due to lack of public data and uncertainty in potential new market sectors such as automobiles and commercial airplanes. Consultants provide forecasts, but the underlying assumptions are unclear, and results differ by firm. We explore global supply and demand of scandium oxide in 2030 using public data and information from government, consultants, and literature to analyze two possible scenarios: business-as-usual share of scandium products in sectors and an assumed additional 10% share, including new applications. For supply, in addition to current producers, planned and proposed scandium oxide suppliers are assessed and ranked to match demand scenarios. Current production is 14–23 tonnes per annum, if proposed projects are built by 2030 the maximum total is approximately 1,800 tonnes. The business-as-usual scenario would result in 38 tonnes of production per annum in 2030. The assumed additional 10% share scenario suggests supply would be sufficient to meet all demand at 260 tonnes in 2030, except in automobiles, which could use 5,300 tonnes. Scandium oxide production would need to expand by 3,700 tonnes in 2030 over proposed projects to meet the additional 10% share. Adoption is not ensured if price and supply volatility remain. The most critical drivers of future scandium oxide demand are its price and availability.

The economic and policy justifications for clean energy subsidies are complex and difficult to internalize. A subsidy induces additional consumers to buy, reducing carbon emissions through reduced fossil fuel consumption. Over the long term, subsidies encourage industry investment and cost reductions. Ideally, subsidies can be removed when the technology is broadly competitive. Deciding on an appropriate level of government subsidy is complex because a decision-maker should balance government expenditures and benefits over both time and space. A subsidy can be excessive when government costs are too high and/or many consumers would have purchased the unsubsidized product, but can also be too low if insufficient to encourage adoption. In order to educate non-experts on these ideas, we created a case study about the topic, consisting of teaching materials and a cooperative multiplayer game that is playable in small groups in 15–30 min. We have used the case study in both university courses and public-facing events and believe that it would be of interest as teaching material for cost-benefit analysis, government subsidy design, clean energy policy, and science and technology policy education or training. After 15 min with the game, players have a basic understanding of all the important factors and dynamics of subsidy design. For the reader, this article offers a specific example of how to translate a sophisticated technoeconomic decision model into an educational game and includes rules and supplementary materials needed to cover the topic of subsidy design and to try the game in courses and general public settings.

•Single sample Monte Carlo inflates price-driven uncertainty.•Time-interval of historical data used significantly affects uncertainty outcomes.•Understanding price uncertainty requires knowledge of purchase/sales frequency.•Dynamic sampling Monte Carlo could offer simple alternate uncertainty analysis.•Analysts should use conservative assumptions on shorter time-intervals.

In this paper we develop an integrated model to identify optimal subsidy schedules for clean energy technologies that maximize social benefits less subsidy costs. The model uses historical cost, adoption, and emissions data and accounts for both environmental and technological progress benefits of the subsidy. An alternative analytical model is also presented to analyze key technological features affecting subsidy design. We focus on three important factors in determining the social benefits of subsidizing the use of clean energy technology: the price (or cost) sensitivity of adoption, induced cost reductions through learning, and environmental benefits. We quantify how distinct profiles of these three factors result in qualitatively different optimal subsidy plans for utility wind and residential solar power in 13 electricity grid regions in the US. Results show that optimal subsidy schedules for utility wind depend on the region, starting at $20–60/MWh, and are roughly constant over time. In contrast, optimal residential solar subsidies either decline over time (starting from $8–70/MWh) or are not desirable (subsidy of zero). The results imply that the optimal subsidy for utility wind is justified mainly through the direct environmental benefits, unlike residential solar PV in which the subsidy is primarily justified by indirect technological progress benefits.