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Purpose Assessing the potential impacts (characterization) of mineral resource use in life cycle impact assessment (LCIA) has long been debated. One of the most crucial challenges in the characterization models for mineral resource use is the consideration of the changing demand and availability of in-use stocks in the future, which is relevant to the global population and economy growth as well as the increasing low-carbon technologies. We propose an extended characterization model to assess the potential impacts for arbitrary time horizons, considering future demand changes and the availability of in-use stock: temporally explicit abiotic depletion potential (TADP). Methods The TADP was developed based on abiotic depletion potential (ADP), which is a widely used characterization model for mineral resource use. While the ADP assesses the potential impacts of mineral resource use based on a natural stock estimate and the current extraction rate, the TADP adopts an average extraction rate for arbitrary time horizons. The average extraction rate was estimated using material flow analysis considering future demand changes and recycling under the five shared socioeconomic pathways (SSPs). TADPs were calculated for six common metals: aluminum, copper, iron, lead, nickel, and zinc. Results and discussion As a result of calculating TADPs for the term by 2050 (TADP2050), compared to iron, all other metals showed larger values of characterization factors for all SSPs than the original ADPs. The TADP2050 of copper exhibited the largest difference with ADP among the six metals (approximately 1.9 times), which is mainly attributed to future demand growth. On the other hand, for the longer time perspective, the TADP2100 of lead and zinc exhibited larger differences with ADP than copper (approximately 2.8 times for zinc), which is mainly due to a relatively shorter lifetime for lead and a lower recycling rate for zinc. This suggests that the relative significance of the characterization factors of metals varies depending on the temporal perspective. Conclusions With the proposed characterization model, the potential impacts of mineral resource use can be assessed reflecting future situations for the selected time horizons. The results demonstrate that the consideration of future situations greatly influences the relative significance of the potential impacts of using different mineral resources in the results of LCIA studies. By expanding the coverage of mineral resources and future scenario analysis to other relevant factors, the TADP model can improve the robustness of the assessment and further support decision-making towards sustainable resource management.

The optimal anode mass fraction of La0.9Sr0.1Cr0.5Mn0.5O3-δ (LSCM) and Gd0.1Ce0.9O2-δ (GDC) is evaluated in this study. The anodes with GDC share of 30 – 100 wt.% are investigated. Initial polarization resistance decreased as the GDC share increased. However, anodes with GDC share over 80 wt.% significantly deteriorated in the degradation tests. Nano-scale cracks were observed in the GDC phase at the grain boundaries after the test. These nano-cracks were not observed in composite anodes, from which it is implied that LSCM has stabilization effect on GDC structure. The mass fraction of LSCM : GDC = 30 : 70 wt.% is found to be optimal in terms of initial electrochemical performance and stability. The optimal LSCM-GDC shows lower polarization resistance than conventional Ni-YSZ at low temperatures, which is comparable to Ni-GDC anode.

Given that artificial intelligence (AI) has been predicted to eventually take on human tasks demanding logical thinking, it makes sense that we should examine psychological responses of humans when their performance is inferior to AI. Research has demonstrated that after people fail a task, whether they reorient their behavior towards success depends on what they attribute the failure to. This study investigated the causal attributions people made in a competition task requiring such thinking. We also recorded whether they wanted to re-challenge the games after they were defeated by AI. Experiments 1 (N = 74) and 2 (N = 788) recruited Japanese participants, while Experiment 3 (N = 500) comprised American participants. There were two conditions: in the first, participants competed against an AI opponent and in the other, they believed they were competing against a human. The results of the three experiments showed that participants attributed the loss to their own and their opponent’s abilities more than any other factor, irrespective of the opponent type. The number of participants choosing to re-challenge the game did not differ significantly between the AI and human conditions in Experiments 1 and 3, although the number was lower in the AI condition than in the human condition in Experiment 2. Besides providing fresh insight on how people make causal attributions when competing against AI, our findings also predict how people will respond after their jobs are replaced by AI. Supplementary Information The online version contains supplementary material available at 10.1007/s12144-021-02559-w.

Climate change is an urgent global challenge, and greenhouse gas (GHG) emissions from metal production contribute to a substantial part of total emissions. Metals play an essential role in human life, and their demand will increase with global population and economic growth. Therefore, projecting future GHG emissions associated with metal production and exploring effective measures to alleviate GHG emissions are essential for achieving climate goals. This study projects the future GHG emissions associated with the primary and secondary production of six metals (aluminum, copper, iron, lead, nickel, and zinc) by considering the detailed metal cycles according to the shared socio-economic pathways (SSPs). Additionally, influential factors for GHG emissions in metal cycles are explored using decomposition and sensitivity analyses to reduce future GHG emissions. We show that future GHG emissions from metal production cannot be in line with the climate goal required to maintain the temperature change below 2 °C under any SSP, even though the trends for GHG emissions from metal production are significantly different among the SSPs. Therefore, substantial efforts to reduce GHG emissions are required in addition to the transition to the sustainable socio-economic pathway. From a short-term perspective, lowering the per capita in-use metal stock level and GHG emission intensity of metal production is crucial, especially in middle income groups. From a long-term perspective, improving the recycling rate is a possible step after sufficient in-use metal stocks are accumulated for recycling. To achieve the climate goals for both short- and long-term GHG reductions in metal cycles, a combination of actions on these influential factors without delay is essential.

Freshwater, a limited natural resource, is unevenly distributed worldwide; this limited availability necessitates the virtual water trade in global supply chains, which may induce freshwater overconsumption (beyond carrying capacities). The dependency on remote water resources is imperceptible and barely recognized by consumers indirectly inducing water consumption. Here, we highlight the significance of remotely induced water consumption through supply chains and considerations for assessing and managing the sustainability of water resources through a case study of the Japanese national water footprint. The blue water consumption induced by the Japanese final demand (53.2 Billion m3) is approximately three times the volume saved through virtual water trade. For the compatible achievement of global and local sustainability of freshwater use, the hotspots in the global supply chains must be identified by considering three additional aspects in parallel, i.e., the concentration of overconsumption, pressure of overconsumption, and proportion to the local total overconsumption amount., Resources, Conservation and Recycling, 196, ISSN:0921-3449, ISSN:1879-0658

Trustworthy automation systems can be beneficial in transportation and medical care, thus resulting in broader acceptance of such systems. This study examines the determinants of trust, focusing on...

Artificial intelligence (AI) can provide many benefits in healthcare, including rapid and effective treatment options. However, previous research on human–computer interactions has demonstrated tha...

Objective Autonomous cars (ACs) controlled by artificial intelligence are expected to play a significant role in transportation in the near future. This study investigated determinants of trust in ACs. Background Trust in ACs influences different variables, including the intention to adopt AC technology. Several studies on risk perception have verified that shared value determines trust in risk managers. Previous research has confirmed the effect of value similarity on trust in artificial intelligence. We focused on moral beliefs, specifically utilitarianism (belief in promoting a greater good) and deontology (belief in condemning deliberate harm), and tested the effects of shared moral beliefs on trust in ACs. Method We conducted three experiments ( N = 128, 71, and 196, for each), adopting a thought experiment similar to the well-known trolley problem. We manipulated shared moral beliefs (shared vs. unshared) and driver (AC vs. human), providing participants with different moral dilemma scenarios. Trust in ACs was measured through a questionnaire. Results The results of Experiment 1 showed that shared utilitarian belief strongly influenced trust in ACs. In Experiment 2 and Experiment 3, however, we did not find statistical evidence that shared deontological belief had an effect on trust in ACs. Conclusion The results of the three experiments suggest that the effect of shared moral beliefs on trust varies depending on the values that ACs share with humans. Application To promote AC implementation, policymakers and developers need to understand which values are shared between ACs and humans to enhance trust in ACs.

This brief report documents the results of a survey that measured the public’s and doctors’ perceived effectiveness of preventive behaviors against COVID-19, in Japan. Medical doctors ( n = 117) and the general public ( n = 1086) participated in our online survey. The results of the analysis of mean scores indicate that there were only slight differences in perceived effectiveness between the two groups, while the differences in distributions were remarkable. The results of Silverman’s test suggest the unimodality of doctors’ responses and multimodality of the public’s responses. Implications of the findings to combat the risk of infection are discussed.

Summary Metal-consuming countries depend on mining activity in other countries, which may impose potential pressure on sustainable metal supply. This study proposes an approach to analyze the responsibility of consuming countries for mining activities based on the decomposition analysis of scarcity-weighted metal footprints (S-MFs) of Japan. The application results to the Japanese final demand (iron, copper, and nickel) demonstrate the significance of country- and metal-specific conditions in terms of metal footprints and mining capacity in assessing the responsibility of consuming countries. Consuming countries can identify influential factors to reduce their S-MFs based on the decomposition analysis by discriminating the directly controllable and uncontrollable factors for consuming countries, which can help to plan different countermeasures depending on the types of the identified influential factors. The proposed approach supports metal-consuming countries to determine the effective options for reducing the responsibility for the sustainability of metal supply., Graphical abstract, Highlights • Scarcity-weighted metal footprints (S-MFs) of Japan are analyzed for Fe, Cu, and Ni • S-MFs demonstrate the consuming country-specific responsibility for mining capacity • Decomposition analysis can identify key factors for the consumer's responsibility • The analysis provides the insights on metal supply risks and their alleviation, Earth Sciences; Mining Geology; Environmental Policy; Materials Science; Metals

A concise, highly stereoselective synthesis of 2,4- and 2,5-disubstituted thiazole amino acids was developed. These are important building blocks for various biologically active thiazole-containing natural peptides and their regioisomeric analogues. The fundamental reactions are diastereoselective addition of (4- or 5-bromothiazol-2-yl)lithium to N-tert-butanesulfinyl imine with subsequent Pd-catalyzed phenoxycarbonylation.

This study examined the determinants of trust in artificial intelligence (AI) in the area of asset management. Many studies of risk perception have found that value similarity determines trust in risk managers. Some studies have demonstrated that value similarity also influences trust in AI. AI is currently employed in a diverse range of domains, including asset management. However, little is known about the factors that influence trust in asset management-related AI. We developed an investment game and examined whether shared investing strategy with an AI advisor increased the participants’ trust in the AI. In this study, questionnaire data were analyzed (n=101), and it was revealed that shared investing strategy had no significant effect on the participants’ trust in AI. In addition, it had no effect on behavioral trust. Perceived ability had significantly positive effects on both subjective and behavioral trust. This paper also discusses the empirical implications of the findings.

The sustainability of metal use is threatened by increasing global demand. The concept of scarcity in mining capacity is of particular relevance to the sustainability of metals; it represents an imbalance between availability and mine production and is determined by socio-economic conditions. Although socio-economic conditions vary by region or country, few studies have assessed the scarcity in mining capacity of metals on such regional scales, which fails to assess potential risks for local sustainability. Here, we propose a country-specific indicator for the assessment of scarcity in mining capacity of metals that reflects differences in national conditions. We apply our methodology to 16 representative metals for 2000, 2010 and 2017. The results demonstrate our indicator of country-specific scarcity varies by 1-3 orders of magnitude for the metals considered, depending on reserves and mine production in producing countries. Furthermore, an indicator that represents the degree of concentrated production in high-scarcity countries is introduced, which reveals the dependency on high-scarcity countries for Al, REE, Li, and Sn. For the assessment of the effects of specific demand on the potential risks of threatening local sustainability, we define the severity of mine production that represents the magnitude of the potential risks determined by both country-specific mine production and scarcity. Analysis of metal scarcity considering country-specific conditions can reveal the potential risks for local sustainability associated with metal production and support metal users to seek effective solutions to reduce the potential risks, which is not considered in existing criticality assessments.

In-use stocks of metals underpin essential services in the world's economy and drive long-term anthropogenic metal cycles. Given this fact, the global distribution patterns of metal stocks provide a fundamental basis for examining ways to satisfy the basic needs of an expanding global population within the Earth's carrying capacity. Here we show that the in-use stocks of six major metals (steel, aluminum, copper, zinc, lead and nickel) are distributed very unevenly across 231 countries and regions. According to our analysis, the highest 20% of the world's population (ordered in terms of metal stock per capita) accounts for approximately 60-75% of the world's total metal stock, while the lowest 20% accounts for only about 1%. International inequality in metal stocks has been decreasing over time due to the strong growth in developing countries, mainly those in Asia. However, our analysis shows that the continued reduction of metal stock inequality through this growth-led pathway will put heavy pressure on the environment, with a 2-3-fold increase in global metal demand by mid-21st century. These findings imply the need for alternative stock evolution patterns involving contraction and convergence, where global per capita metal stock growth stabilizes at a level below that current for developed countries.

Recycling has become increasingly important as a means to mitigate not only waste issues but also problems related to primary resource use, such as a decrease in resource availability. In order to promote and plan future recycling efficiently, detailed information on the material stock in society is important. For a detailed analysis of material stocks, quantitative information on flows of a material, such as its accumulation pathways, final destinations, and its processing forms, are required. This paper develops a model for tracking accumulation pathways of materials using input–output tables (IOTs). The main characteristics of the proposed model are as follows: (1) accumulations in sectors other than the final demand sectors (i.e., endogenous sectors) are explicitly evaluated, (2) accumulations as accompaniments to products, such as containers and packaging, are distinguished from the products, and (3) processing forms of materials are considered. The developed model is applied to analyze copper flows in Japan using the Japanese IOTs for the year 2011. The results show that accumulations of copper in endogenous sectors were not negligibly small (9.24% of the overall flow). Although accumulations of copper as accompaniments were very small, they may be larger for other materials that are largely used as containers or packaging. It was found that the destinations of copper showed different characteristics depending on the processing forms.

The future availability of mineral resources has attracted much attention; therefore, a quantitative evaluation of the potential impacts of resource use on future availability is important. Although the surplus cost model is often recommended among the existing endpoint characterization models of mineral resources, it has a shortcoming as it does not consider the changes in future primary resource use. This paper introduces a new characterization model considering future primary resource use changes, due to future changes in total demand and secondary resource use. Using material flow analysis, this study estimated time-series primary resource use for iron and copper for five shared socioeconomic pathways (SSPs) and a constant total demand scenario. New characterization factors, i.e., demand change-based surplus costs (DCSC), are calculated for each resource. In all of the SSPs, the calculated DCSCs are larger than the conventional surplus costs (SC) for both iron and copper. The DCSC, relative to the SC of copper, is larger than that of iron for all of the SSPs, which suggests that the potential impacts of copper use, relative to iron, will be underestimated, unless future primary resource use changes are considered. In calculating DCSC for other resources, it is important to choose an appropriate approach for forecasting future total demands.

Though solid modeling becomes essential to research present CAD systems, a great many small companies still use 2D CAD systems. So, automatic conversion systems between them have been desired for many years. Most of proposed methods could apply to only correct 2D drawings in geometry. However, simplified drawings are usually used in actual design. In our method, the relationships of lines in 2D drawings could be acquired as properties. So, when a user operates broken lines to make correct lines, the processes of operations can be learned and generalized by using the properties, and generalized processes can be applied to new broken lines of 2D drawings.

Presently solid modeling is essential for CAD and its applications. However, most of small companies still use 2D CAD systems mainly, and ordinarily solid models are made manually from 2D drawings because they are usually incorrect in geometry and simplified. If 2D drawings are correct, solid models would be automatically generated by existing systems. The authors have attempted to develop methods to automatically restore incorrect 2D drawings into correct 2D drawings, and repetitive features in partial omissions were already automatically restored. In this paper, a method is proposed that isolated line segments made by the partial omissions can be restored by using learning systems. In this method, various patterns of isolated line segments can be learned and be automatically restored.

Doctor of Philosophy in Psychology, Doshisha University