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A variety of energy-efficient and energy-generating products, such as photovoltaics (PV) and electric vehicles, have diffused into the market to reduce greenhouse gas emissions in the residential sector. Understanding future changes in electricity demand and supply is complicated by uncertainties such as lifestyle shifts and national energy policies, and how such changes interact with the diffusion of products. To address this issue, this study adopts a scenario approach to analyze the impact of product diffusion on residential electricity demand under different social circumstances. Two simulation models are employed for the analysis: (i) a model for estimating the diffusion of products based on consumer preferences and (ii) a model to estimate electricity demand in residential sectors considering product diffusion. To demonstrate the proposed method, a scenario analysis case study was conducted, estimating the electricity demand in the residential sector of Toyonaka City, Osaka, Japan, for 2030. The results show that compared to 2012, the net electricity demand in the city in 2030 is projected to decrease by 20–39% depending on the scenarios considered, with changes in demographics and PV diffusion identified as among the most critical factors. [ABSTRACT FROM AUTHOR]

The article explores the use of the Social and Emotional Health Survey-Secondary version (SEHS-S) with a sample of 975 Japanese students in Grades 7-9 attending schools located northwest of Tokyo. A confirmatory factor analysis using half the sample confirmed the four-factor structure of the SEHS-S, and further analyses verified its second-order factor model including Belief-in-Self, Belief-in-Others, Emotional Competence, and Engaged Living, all of which contribute to a latent second-order construct labeled Covitality. Additional SEM validity analyses found that the four identified first-order SEHS constructs and the second-order covitality construct were positively associated with subjective well-being. SEHS-S scores also predicted several variables indicative of positive school engagement for Japanese students, including academic performance, social relationships, and willingness to assist others. Results of the study are discussed in terms of the advantages of using strength-based assessments such as the SEHS-S for Japanese students and in promoting well-being in this population.

Buildings are key in supporting human activities and well-being by providing shelter and other important services to their users. Buildings are, however, also responsible for major energy use and greenhouse gas (GHG) emissions during their life cycle. Improving the quality of services provided by buildings while reaching low energy demand (LED) levels is crucial for climate and sustainability targets. Building sector models have become essential tools for decision support on strategies to reduce energy demand and GHG emissions. Yet current models have significant limitations in their ability to assess the transformations required for LED. We review building sector models ranging from the subnational to the global scale to identify best practices and critical gaps in representing transformations toward LED futures. We focus on three key dimensions of intervention (socio-behavioral, infrastructural, and technological), three megatrends (digitalization, sharing economy, and circular economy), and decent living standards. This review recommends the model developments needed to better assess LED transformations in buildings and support decision-making toward sustainability targets. [ABSTRACT FROM AUTHOR]

Systemic understanding of potential research activities and available technology seeds at university level is an essential condition to promote interdisciplinary and vision-driven collaboration in an attempt to cope with complex sustainability and environmental problems. Nonetheless, any such practices have been hardly conducted at universities due mainly to a lack of appropriate institutional schemes and methodologies to systemically collect, map out, and synthesize individual research activities within a university. In this paper, we present the recent initiative of such systemic and comprehensive understanding of research activities at university level. We carry out a case study, attempting to summarize all the relevant research activities and technology seeds associated with environmental issues and sustainability currently being studied individually at the laboratory level at Osaka University. We collected 138 potential seeds from the university's relevant schools and institutes and sorted them according to Japan's three sustainability visions. The case study demonstrates the university's potential to provide collective knowledge enabling the societal transition to sustainability if these seeds are systematically overviewed and effectively mobilized to mesh with specific social demands and purposes. We highlight the need for a framework and practice that allows synthesizing research activities and promising technologies even at university level to further facilitate providing collective knowledge and discuss challenges and research needs for promoting synthesis practices and interdisciplinary research that are essential to deal with sustainability problems.

The validity of the Multidimensional School Anger Inventory (MSAI) was examined with adolescents from 5 Pacific Rim countries (N = 3,181 adolescents; age, M = 14.8 years; 52% females). Confirmatory factor analyses examined configural invariance for the MSAI's anger experience, hostility, destructive expression, and anger coping subscales. The model did not converge for Peruvian students. Using the top 4 loaded items for anger experience, hostility, and destructive expression configural invariance and partial metric and scalar invariances were found. Latent means analysis compared mean responses on each subscale to the U.S. sample. Students from other countries showed higher mean responses on the anger experience subscale (ds = 0.37-0.73). Australian (d = 0.40) and Japanese students (d = 0.21) had significantly higher mean hostility subscale scores. Australian students had higher mean scores on the destructive expression subscale (d = 0.30), whereas Japanese students had lower mean scores (d = -0.17). The largest latent mean gender differences (females lower than males) were for destructive expression among Australian (d = -0.67), Guatemalan (d = -0.42), and U.S. (d = -0.66) students. This study supported an abbreviated, 12-item MSAI with partial invariance. Implications for the use of the MSAI in comparative research are discussed.

Occupant behaviour models play an important role in building energy demand modelling. Useful simulation algorithms have been developed in previous studies; however, the pre-simulation process to prepare modelling parameters for simulated occupants has received less attention. This study elaborated on the pre-simulation process and evaluated how it may alter model performance. We selected the activity-starting probability using American time use survey data as an example. The model performance was compared under three cases representing different numbers and types of variables together with three parameter preparation methods: multinomial log-linear regression, support vector machine, and artificial neural network. All the methods considering basic demographic and time-related variables performed well in reproducing the average probabilities. An increase in significant variables contributed to the reproduction of inter-occupant diversity. All the methods showed similar performances within the given dataset, although they were practically different. The results offer practical guidance for shaping the pre-simulation process. [ABSTRACT FROM AUTHOR]

With growing attention to sustainability and recognition of the impact of global warming problems, energy supply and consumption have become critically important. This paper presents the construction of a modeling platform accommodating cooperative energy management systems (EMSs), which virtually produces the model of a smart city with a distribution network (DN) by using a wide range of data obtained from the real world. The platform involves models of various EMSs, governing the operation of a power system or controlling consumer-installed devices, and simulating the power flow, electrical losses, and voltage in the DN. In addition, indices measuring the sustainability of the model city, such as CO2 emission, are estimated from scenarios, for example, photovoltaic system installation, electric vehicle penetration, etc. The results can be visually displayed and the platform is highly versatile and applicable to various types of issues associated with smart cities. Two case studies are presented in detail. [ABSTRACT FROM PUBLISHER]

The present study aimed to investigate the reciprocal relations between experiential avoidance, stressor, and psychological stress response (which consist of anger, depression, anxiety, helplessness, and physical complaints). In this study, 688 Japanese junior high school students (353 boys, 334 girls, 1 unidentified; mean age 13.28 years) completed three waves of questionnaires on experiential avoidance, stressor, and psychological stress response, with one-week intervals between measurement waves. Results from cross-lagged panel analyses showed that experiential avoidance predicted subsequent stressor and psychological stress response. Furthermore, the stressor and psychological stress response influenced by prior experiential avoidance affected subsequent occurrence of experiential avoidance. The findings suggest that reciprocal relations exist among the variables, and that the interaction between experiential avoidance and psychological stress was possible in adolescents. [ABSTRACT FROM AUTHOR]

This paper proposes a stochastic discrete-event model to predict occupants’ activities at home to be used in community-/urban-scale energy demand modelling. The model is designed to consider interactions among household members and generate consistent behaviour across simulated days and times of day with specific time-dependent characteristics. Routine behaviours undertaken routinely everyday such as working and eating meals are placed on the timeline while considering interactions among household members. Gaps between routine behaviours are then filled by non-routine behaviours. Individual specificity and consistency are enhanced by (1) providing input data representing intrapersonal variation instead of interpersonal variation, (2) using relative time to time of routine behaviours, instead of clock time and (3) adjusting the input dataset affecting the selection of non-routine behaviours and their duration. Case studies demonstrated that interactions among household members are reproduced as observed in empirical data and the individual specificity and consistency are enhanced. [ABSTRACT FROM AUTHOR]