The carbon emission pattern of the built environment is closely associated with its morphological and functional structures. Based on the online volunteered geographic information and some publicly available official data sources, this study intends to provide a standardized framework for estimating the indirect building carbon emissions within the boundaries of various types of Local Climate Zones (LCZs), to better forecast the LCZ carbon emission patterns and assist district wide energy management. The whole research is devised into four sequential sections: First, the statistics of energy use intensity of different building uses (including residential and non-residential buildings) are retrieved from official data sources using a down-scaled approach; then a random forest machine learning method is applied to automatically identify building uses based on the training samples; next, a GIS method is developed to delineate the LCZs in Shanghai utilizing calculated urban form and land cover parameters; finally, the building carbon emission values are linked to the LCZs to determine the emission coefficient of different LCZ categories in Shanghai.
|出版ステータス||Published - 2018|
|イベント||2018 Applied Energy Symposium and Forum, Carbon Capture, Utilization and Storage, CCUS 2018 - Perth, Australia|
継続期間: 2018 6月 27 → 2018 6月 29
ASJC Scopus subject areas