The Japanese government announced its mid-term target of reducing greenhouse gas emissions by 26% from 2013 to 2030 at COP21. In Japan, CO₂ emissions from the transport sector account for 18.5% of total CO₂ emissions, and it is necessary to implement measures in the automobile sector as soon as possible. On the other hand, co-benefit effects such as changes in emissions and noise levels are obtained when global warming countermeasures are implemented. However, previous studies didn't have mentioned co-benefit effects that based on theoretical method. In this study, the authors developed a method for estimating CO₂ emissions of automotive sector to consider integrated approaches, and added the calculation of exhaust emission gas and noise levels. Finally, using the developed method, and assuming four scenarios studied by the authors, the effects of technological progress in automobiles and the effects of measures that may be introduced in the future are considered.

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Distributed energy systems (DES), small scale energy systems established next to consumers, benefit us in various aspects. Especially, the resilience of DES has been paying attention due to the recent catastrophic disasters followed by serious supply disruption incidents. In this research, the authors build up a model to quantitatively analyze the resilience of DES and investigate the optimal system by solving single objective optimization problems, in which stochastic state transition between normal situation and supply disruption situations are incorporated. The results suggest that DES based on CGS (Co-Generation System) is highly efficient both in terms of economic efficiency and resilience against blackouts. Examining CGS based DES, batteries play an important role in adjusting power supply-demand. However, the optimal system is subject to change due to additional conditions taken into account. The risk of city gas supply disruption is related to how much redundancy is required for maintaining the system’s resilient operation without relying on CGS. In addition, the authors conduct analysis focusing on the predictability of disasters such as typhoons. The analysis reveals the impact of disasters’ predictability on the enhancement of storage equipment operation, which results in larger capacity of storage equipment finding its way into the optimal solution, compared to that of unpredictable cases.

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Due to the problems of global warming and energy disruption in the event of a disaster, it is required to reduce the carbon content of energy systems and strengthen their resilience. Therefore, the attention is being paid to a system called "regional smart grid" in which power is interchanged between a plurality of facilities having distributed power sources according to the power demand. By exchanging power between facilities that have distributed power sources, the power generated in the area can be used without waste, and power can be supplied even in the event of a disaster. Thus, it is possible to improve the low carbon performance and resilience of the regional energy system. In this study, we have assumed multiple facilities where power interchange is possible, and tried to optimize the amount of PV / storage batteries installed and the amount of power interchange. Then, the difference in low carbon performance and resilience depending on the presence or absence of power interchange has been quantitatively evaluated.

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In order to facilitate building energy management, this study developed a method to disaggregate the hourly electricity demand of commercial buildings and classify buildings based on the extracted demand characteristics. The developed method was designed to be applicable to a variety of commercial buildings for which hourly electricity demand data is available. The method decomposes hourly electricity demand data into four components, considering the dependency on operation and meteorological conditions. The demand components representing weekly operating conditions extracted by principal component analysis are used for clustering buildings, along with other demand characteristics representing building’s peak demand and the composition of the four components. In a case study of 4947 commercial buildings, the proposed method showed high estimation accuracy with 66.9% of buildings having a CVRMSE of less than 20% between the sum of the four estimated components and the observed demand. It was also confirmed that the components well characterized the demand for end-uses. The clustering identified seven clusters different in temporal and quantitative characteristics of the four components. The demand characteristics of the clusters fitted well with empirical knowledge on subsectors’ demand characteristics. A building energy management method based on demand characteristics was also proposed.

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In order for Japan to achieve carbon zero by 2050, it is necessary to survey greenhouse gas emissions at the municipal level, and to realize and continue a situation in which the results are reflected in the decarbonization PDCA cycle. However, most municipalities think it would be a large-scale survey, so they do not carry it out. This study presents the following conditions for a survey that can be continued by local governments: 1) independent of survey scale, 2) thorough simplification of survey items, and 3) don't stick to random sampling. The method presented in this report, which is a stratified sampling method, consists of a mixture of groups (strata) surveying the total population and groups surveying samples. Since the sample survey layer assumes a normal distribution of the population, the accuracy of the estimation error can be estimated in intervals. It also shows how to calculate the number of surveys when applying this survey method to small municipalities (cities with less than 50,000 inhabitants). Finally, an example of a survey applied to Minamata City is presented to show the accuracy of the survey.

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