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The process of industrialization often causes resource depletion and environmental pollution. To shed light on China's resource use and pollution trends in the context of the country's rapid industrial growth, this study analyzes the eco-efficiency of China's industry from 2000 to 2015. We quantify industrial eco-efficiency (IEE) for China and its provinces using data envelopment analysis (DEA) and analyze potential influencing factors at national and regional levels using Tobit regression. IEE in China and in most provinces shows a clear upward trend with some fluctuations, with national scores increasing from 0.394 to 0.704. There is strong regional disparity, with average IEE scores in eastern provinces (0.840) higher than those in central provinces (0.625), which are in turn higher than those in the northeast (0.537) and west (0.438). We next consider potential drivers. Economic development and foreign direct investment (FDI) are positively associated with IEE but appear to show diminishing returns. Environmental enforcement and market for technology are also positively associated with IEE, as expected. The impact of economic development, industrial sector structure, and investment in research and development (R&D) are modified by the stage of industrialization in each region. Targeted measures that can adjust industry structure, enhance environmental enforcement, attract FDI, and increase R&D investment may help further improve IEE in China. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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]