エネルギー・資源学会論文誌
Online ISSN : 2433-0531
ISSN-L : 2433-0531
38 巻, 1 号
選択された号の論文の3件中1~3を表示しています
研究論文
  • 秋元 圭吾, 佐野 史典
    原稿種別: 研究論文
    2017 年 38 巻 1 号 p. 1-9
    発行日: 2017年
    公開日: 2019/02/08
    ジャーナル フリー
    The Paris Agreement describes “To hold the increase in the global average temperature to well below 2 °C above pre-industrial levels.” The Plan for Global Warming Countermeasures by the Japanese Government includes the emission reduction target of 80% by 2050 corresponding to the 2 °C target although several conditions including compatibility with its economic growth are imposed. This paper firstly estimated the global emission pathways for the 2 °C target considering scientific and policy uncertainties. Then, based on the global emissions in 2050, emission reductions required of Japan were estimated under the condition of equalized marginal abatement costs across countries in 2050 by using a global energy and climate change mitigation model having regionally disaggregated. The result shows emission reduction required of Japan varies widely ranging from +0 to -77% relative to 2010. In addition, for the 80% reductions by 2050, the mitigation measures and costs were estimated. The CO2 marginal abatement costs were found around six thousand $/tCO2 and the annual mitigation costs were 29−72 trillion JPY. The 80% reductions by 2050 in Japan are very difficult to be achieved.
  • 磐田 朋子, 野村 昇, 田中 加奈子, 松橋 隆治
    原稿種別: 研究論文
    2017 年 38 巻 1 号 p. 10-19
    発行日: 2017年
    公開日: 2019/02/08
    ジャーナル フリー
    It's more important than ever to take control electricity consumption in residential sector towards low carbon society. We started supplying an advisory system on “i-cosmos” which is experimental field about 230 households participating in. The advisory system is useful for electricity saving and well-reflecting reality, based on the social psychological approach. The system supplies the daily targets for electricity use in each households calculated by the linear regression model based on the past 8 weeks electricity consumption, temperature and dummy variables (0=weekday 1=weekend & holiday). In addition, the grouped household target was also showed in the system aimed to stimulate a social responsibility norm and self-efficacy based on psychological measure. The purpose of this study is to test the effectiveness of the advisory system and the daily targets for electricity use. The electricity consumption in 7 household accessing to the advisory system everyday was compared with that in 70 households having no access to it as a preliminary experiment. The result showed the effect of advisory system was an average 6-7% for saving. Especially the effect of grouped target was more than that of individual target. A questionnaire survey indicated that the social responsibility norm especially contributed to the electricity-saving action.
  • 森田 圭, 真鍋 勇介, 加藤 丈佳, 舟橋 俊久, 鈴置 保雄
    原稿種別: 研究論文
    2017 年 38 巻 1 号 p. 20-29
    発行日: 2017年
    公開日: 2019/02/08
    ジャーナル フリー
    Japanese electricity retail market is fully liberalized in April 2016. New entry electricity suppliers have started to supply electricity targeting hundreds or more of households and they need to plan supply and demand schedule in daily operation. However, there are not many studies which are subjected aggregated households’ electricity demand characteristics so that new entry electricity suppliers are not able to construct an accurate demand forecast model for daily operation. Therefore, this paper presents characteristics of households’ electricity demand targeting hundreds of households using about 700 households’ electricity demand. We evaluate electricity demand characteristics with different time span such as monthly, daily and hourly. We also evaluate electricity demand difference between weekday / weekend and holiday, or singularity in days such as a new year’s day or the bon festival season. We compare those characteristics with some of earlier studies to define generality of household’s data which we use. In addition, we calculate electricity demand fluctuation in different number of households to find out smoothing effect so that our data is large enough to define hundreds or more of households’ demand characteristics.
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