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期刊论文 2

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2018 2

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Expert networks as science-policy interlocutors in the implementation of a monitoring reporting and verification (MRV) system

Remi CHANDRAN, Tsuyoshi FUJITA, Minoru FUJII, Shuichi ASHINA, Kei GOMI, Rizaldi BOER, Muhammad ARDIANSYAH, Seiya MAKI

《能源前沿（英文）》 2018年第12卷第3期页码 376-388 doi: 10.1007/s11708-018-0559-x

摘要：

The Paris Agreement, which entered into effect in 2016, emphasizes a definite timeline for communicating and maintaining successive nationally determined contributions (NDCs) that it plans to achieve in addressing climate change. This calls for the development of a measurement, reporting and verification (MRV) system and a Capacity-building Initiative for Transparency (CBIT). Though such actions are universally accepted by the Parties to the Paris Agreement, earlier studies have shown that there remain technological, social, political and financial constrains which will affect the development and deployment of such a system. In this paper, using a case study on MRV implementation in Bogor City in Indonesia, how the above-mentioned challenges can be overcome is outlined through a technological and policy innovation process where scientists and technologists (collectively referred as expert networks) can join hands with local governments and national policy makers in designing, development and implementation of an MRV system that meets the local, national and global requirements. Through the case study it is further observed that expert networks can act as interactive knowledge generators and policy interlocutors in bridging technology with policy. To be specific, first, a brief history of the international context of MRV and CBIT is outlined. Next, the theoretical underpinning of the study is contextualized within the existing theories related to public policy and international relations. Finally, the case study is outlined and investigated where the engagement of an expert-network and policy makers in the design, development and implementation of an MRV tool is showcased.

关键词： MRV CBIT UNFCCC Indonesia Japan ICT based monitoring climate policy

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electricity-consumption monitoring systems and integrative time-series analysis models: A case study in Bogor, Indonesia

Seiya MAKI, Shuichi ASHINA, Minoru FUJII, Tsuyoshi FUJITA, Norio YABE, Kenji UCHIDA, Gito GINTING, Rizaldi BOER, Remi CHANDRAN

《能源前沿（英文）》 2018年第12卷第3期页码 426-439 doi: 10.1007/s11708-018-0560-4

摘要：

The Paris Agreement calls for maintaining a global temperature less than 2°C above the pre-industrial level and pursuing efforts to limit the temperature increase even further to 1.5°C. To realize this objective and promote a low-carbon society, and because energy production and use is the largest source of global greenhouse-gas (GHG) emissions, it is important to efficiently manage energy demand and supply systems. This, in turn, requires theoretical and practical research and innovation in smart energy monitoring technologies, the identification of appropriate methods for detailed time-series analysis, and the application of these technologies at urban and national scales. Further, because developing countries contribute increasing shares of domestic energy consumption, it is important to consider the application of such innovations in these areas. Motivated by the mandates set out in global agreements on climate change and low-carbon societies, this paper focuses on the development of a smart energy monitoring system (SEMS) and its deployment in households and public and commercial sectors in Bogor, Indonesia. An electricity demand prediction model is developed for each device using the Auto-Regression eXogenous model. The real-time SEMS data and time-series clustering to explore similarities in electricity consumption patterns between monitored units, such as residential, public, and commercial buildings, in Bogor is, then, used. These clusters are evaluated using peak demand and Ramadan term characteristics. The resulting energy-prediction models can be used for low-carbon planning.

关键词： electricity monitoring electricity demand prediction multiple-variable time-series modeling time-series cluster analysis Indonesia