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Purpose: Heavy and light rare earth elements (REEs) are critical to clean energy technologies, and thus the environmental impacts from their production are increasingly scrutinized. Most previous LCAs of REE production focus on sites producing light REEs. This research addresses this gap by collecting primary data from sites producing heavy rare earth oxides (HREOs) from ion-adsorption clays, conducting an LCA, and providing open-source life cycle inventory (LCI) datasets of HREO production for the LCA community. Methods: This study conducts a LCA based on acquired primary data from four mining sites in Jiangxi Province, China. The functional unit is 1 kg of mixed HREOs of 90% purity from ion-adsorption clays using the technology of in situ leaching. Previous studies have used the Ecoinvent database, relying mostly on European or global life cycle inventories (LCIs). Here, the Chinese Life Cycle Database provided China-specific reference life cycle inventories (LCIs) for all inputs and processes with the exception of electricity generation LCIs used in a scenario analysis, which were provided by Ecoinvent 3. Twelve impact categories were examined using Impact 2002+, USEtox 2.01, and IPCC methods. Results are provided as a bounded range, reflecting low and high estimates based on collected primary data. Results and discussion: Results show 1 kg of mixed HREOs emit 258–408 kg CO2e, and consume 270–443 MJ primary energy. These values fall within the range of previous LCAs that examined both bastnaesite/monazite deposits and ion-adsorption clays using literature values. Other impact categories considered are not similar across studies, however. Differences are due to variability in resource type and quality, technology, and modeling choices, such as reference LCI sources. Mining and extraction contribute most to impacts due to large quantities of chemicals for leaching and precipitation of REOs, and electricity consumption. Among chemicals, ammonium sulfate is the largest contributor to many impact categories. When China’s electricity grid mix change over time is included, environmental impacts for the whole production process can change up to 12%. Conclusions: The primary contributions of this study are the collection and publication of primary data from mining companies in Jiangxi Province, China; the provision of open-source LCI datasets for mixed HREOs from ion-adsorption clays; and a comparison of results between this study and previously published studies. While the scope of this study concludes at the production of mixed HREO, which is a limitation, it provides a foundation for development of LCIs for refined heavy REEs.

In the United States, the transportation sector is responsible for 36% of greenhouse gas (GHG) emissions, with light-duty vehicles (LDVs) comprising the largest contribution. Globally, transportation is responsible for approximately 24% of energy-related GHG emissions, of which road transport constitutes over 70%. In addition to other measures, rapid and extensive deployment of renewable and energy-efficient technologies is seen as a crucial intervention necessary to reduce transportation sector emissions in coming decades. This report documents the cumulative results of the project and presents both published findings and ongoing research. To understand the potential for developing and implementing life cycle-based policies for LDVs we must first develop the appropriate modeling tools, and we must understand how LCA or life cycle thinking has been implemented in policy contexts in the past. Thus, the rest of this report is divided into sections that summarize the work conducted on (i) developing LCA sub-models that will be integrated in the coupled system dynamics and LCA model, (ii) a review of the global market for PEVs with a focus on U.S. and China and implications for materials and manufacturing, (iii) a review of LCA and life cycle thinking in policy in the United States and around the world, and implications for life cycle-based vehicle policy, and (iv) the development of a new life cycle inventory to demonstrate the feasibility of a summary of findings from a Transportation Research Board (TRB) workshop on this topic conducted in January 2017.View the NCST Project Webpage

This paper investigates the impact of several factors on the adoption of telecommuting, using empirical data collected from 1,864 commuters in Northern California. We distinguish three types of telecommuting – full-day, part-day, and overtime – as they might affect travel behavior in very different ways. We develop binary logit models and a trivariate probit model of the adoption of each form of telecommuting, using non-centered factor scores to identify pro-high- vs. pro-low-density workers. We test the hypothesis that the impact of the built environment on the decision to adopt telecommuting differs between those two segments. The results support the hypothesis, with numerous variables significant to one of those segments but not the other; in the case of the binary logit model for overtime telecommuting, employment density in the home area even has opposite influences for pro-high-density (positive) compared to pro-low-density people (negative). Other groups of variables, including personal attitudes and demographic traits, also significantly affect individuals’ decisions on whether and how to telecommute, if at all. For example (consistent with other studies), education and household income are strong (positive) predictors of the decision to telecommute. Compared to the separate binary logit models, the trivariate probit model better predicts the joint probabilities of adopting the various forms of telecommuting, and better models the correlated adoption of the three types of telecommuting.

Twitter is an emerging form of news media with a wide spectrum of participants involving in news dissemination. Owing to their open and interactive nature, individuals, non-media, and non-commercial participants may play a greater role on this platform; thus, it is deemed to disrupt conventional media structures and introduce new ways of information flow. While this may be true in certain aspects in news dissemination such as allowing a broader range of participants, the authors' analysis of the involvement and influence of the different participant types, based on a large tweets dataset collected during the Ukraine's conflict event (2013-2014), portrays a different picture. Specifically, the results unveil that while non-commercial participants were the most "involved" in generating tweets about the news event, the retweets they attracted, a common measure of influence, were among the lowest. In contrast, mass media and sources related to journalists, professional associations and commercial organizations garnered the highest retweets. [ABSTRACT FROM AUTHOR]