Summary The extension of the waste input‐output (WIO) model to analyze households' sustainable consumption patterns is presented in this article. We estimate direct and indirect emission loads induced by household consumption by the WIO model. The WIO model is much more suitable for the analysis of sustainable consumption than the conventional input‐output model because it can deal with the disposal stage of consumed goods as well as the purchase and use stages. A simple method for evaluating income rebound effects is also introduced. As indicators of environmental loads, we estimate carbon dioxide (CO2) emissions and landfill consumption induced by household consumption. The model is applied to some typical sustainable consumption scenarios: shifting transportation modes from a private car to public transportation, the longer use of household electric appliances, and eating at restaurants instead of cooking at home. We found that the income rebound effects should be considered to evaluate environmental loads induced by different consumption patterns.

No abstract

Residential demand-side management (DSM) of electricity has been gaining attention as a way to reduce energy consumption at home and as a way of maximizing the utilization of fluctuating solar power generation. To promote the smooth introduction of DSM into homes, power usage trends according to the time of the day should be examined for individuals in relation to their lifestyles. The analyses of power usage trends can identify the types of home appliances that should be utilized differently in order to increase energy efficiency. Such analyses can also predict the individual behavioral changes that should result in home appliances being used in the time slots in which solar power is more conveniently available. The purpose of this research was to estimate and observe the amount of power saving potential for each daily time slot with respect to an individual’s particular attributes, and to derive the power saving potential of the whole country by accumulating these data on individuals. This was achieved by using the Survey on Time Use and Leisure Activities (STULA) and Energy-Saving Performance Catalog (ESPC) in Japan. According to the results of our estimation, a meaningful power saving potential is sufficient to address a power supply shortage after a disaster such as an earthquake. It is possible to save power by replacing existing home appliances with more energy efficient ones, by making environmentally conscious choices when using home appliances, and by discontinuing the use of home appliances during electricity shortages within the community as a whole. Using the estimated power saving potentials, we examined the effects of two DSMs: (1) adjusting the time for which home appliances are used; and (2) aggregating the power demand of households with different attributes. The results showed that these DSMs would contribute to a more stable power system operation. Future research might address the rapid penetration of community energy management systems and demand response systems.

This study evaluates the acceptability of home energy management systems (HEMS) in New York and Tokyo using a questionnaire survey. We investigated three basic functions of HEMS: money saving, automatic control, and environmental impact, and then quantified people’s propensity to accept each of these three functions by measuring their willingness to pay. Using the willingness to pay results, we estimated the demand probability under a given usage price for each of the three functions of home energy management systems and analyzed how socio-economic and demographic factors influence the demand probability. The demand probability related to a home energy management system function decreases as the usage price of the function increases. However, depending on people’s socio-economic characteristics, the rate of decrease in demand probability relative to the rate of increase in usage price varies. Among the three functions of home energy management systems, we found that the automatic control function showed the highest demand probability in New York and Tokyo, emphasizing the significance of an automatic control function. In New York, when the home energy management system has an automatic control function, its demand probability increases, which is further enhanced if people trust their utility company. In Tokyo, when a home energy management system has an environmental impact function, its demand probability increases at a given price. People in Tokyo have anxieties related to new technologies such as home energy management systems. Therefore, it is necessary to enhance their comprehension of a home energy management systems to address this anxiety.

No abstract