597 / 2019-03-01 13:17:24
Carbon Emission Intensities of Each Building Typology: Towards a Standardized Framework
CO2 emissions,building,typology
终稿
Takahiro Yoshida / National Institute for Environmental Studies
Tobey Michael / National Institute for Environmental Studies
Chang Soowon / National Institute for Environmental Studies
Yoshiki Yamagata / National Institute for Environmental Studies
Buildings account for the biggest part of carbon dioxide (CO2) emissions in almost countries. In the United States, the commercial and residential building sector accounts for about 40% of CO2 emission per year (Nejat et al., 2015). This clearly indicates the integral role that buildings should play in reducing global CO2 emissions and meeting the ambitious targets of the Paris Agreement that are aimed at limiting global warming to well below 2°C above pre-industrial levels. Significant reduction of emissions in buildings hinges on having adequate understanding of the typologies of emissions. Better understanding of building CO2 emissions and their dynamics is essential for achieving climate-compatible urban development (Dascalaki et al., 2015; Sharifi et al., 2018). Poorly designed urban pat-terns can introduce inertia into climate change mitigation efforts and lock us into undesirable patterns for decades to come. Furthermore, it is essential to develop consistent methods for what extent each building-typology accounts CO2 emissions. Mapping on building CO2 emissions at micro- and meso-levels is hampered by limitations regarding data availability and accessibility. As significant differences exist at these scales, it is not easy to develop consistent mapping methods. However, consistent methods and approaches are essential for overcoming scalability and reproducibility challenges. Development of consistent and standardized methods for CO2 mapping at micro- and meso- levels warrants further investigation. Developing consistent methods for dividing cities into homogeneous morphometric zones can provide opportunities for addressing these challenges. It may be possible to predict emission trends and pathways of a zone based on emission trends and pathways of other zones with similar morphological and socio-economic characteristics. This will enhance scalability of mapping efforts. In addition, such consistent frameworks should facilitate accounting for building emissions with minimum data requirements. They should also be applicable to different cities across not only the same country but also the world. Towards this direction, this study introduce a framework for synthesizing building typologies and CO2 emission. Firstly, we estimate CO2 emissions of all individual building by using basic unit of energy consumptions of each building use, building physical information of building, and energy-measuring sensors data (Yamagata et al., 2018; Yoshida et al., 2018). Secondly, we divide buildings based upon a set of four criteria categories: form/shape, context, use, and structure. From the four categories with detail sub-categories, we get 224 building-typologies. Finally, to facilitate to standardize/simplify the relationships between building-typologies and its CO2 emission and simultaneously to minimize loss of information, we use dimension reduction techniques (Van der Maaten et al., 2009), so that we get 20 categories. Drawing in results from applying the typology to Sumida ward, Tokyo, Japan, we explain how this approach can provide opportunities for standardizing CO2 emission intensity and developing a global dataset of fine-scale building CO2 emission data. Our standardized and consistent method will make comparison across cities possible. Buildings can use such a consistent and standardized approach to share lessons and emulate effective mitigation strategies adopted by other cities with similar conditions. This is particularly useful for predicting emission patterns of cities in developing countries, where data availability is still a major challenge.
重要日期
  • 会议日期

    07月08日

    2019

    07月12日

    2019

  • 06月28日 2019

    初稿截稿日期

  • 07月12日 2019

    注册截止日期

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