包含能量
具身认知
建筑工程
能量(信号处理)
能量分析
工程类
计算机科学
物理
量子力学
热力学
人工智能
标识
DOI:10.1016/j.rser.2017.05.051
摘要
Approximately half of the annual global energy supply is consumed in constructing, operating, and maintaining buildings. Because most of this energy comes from fossil fuels, it also contributes greatly to annual carbon emissions. When constructing a building, embodied energy is consumed through construction materials, building products, and construction processes along with any transportation, administration, and management involved. Operating energy is used in space conditioning, heating, lighting, and powering building appliances. In order to effectively reduce the carbon footprint of buildings, a comprehensive reduction in both embodied and operating energy is needed. Studies so far have focused on reducing either embodied or operating energy in isolation without realizing the trade-off that exists between them. Also, building energy research has concentrated more on operating energy than embodied energy, and as a result, the operating energy of buildings is gradually decreasing. Due to a variety of issues, however, few efforts have been undertaken to comprehensively minimize embodied energy. Quantifying embodied energy is more tedious, complex, and resource-consuming than measuring operating energy. Furthermore, the reported values of embodied energy vary significantly within and across geographic regions owing to certain methodological and data quality parameters. The literature has repeatedly pointed out a need to standardize these parameters to bring consistency to embodied energy calculations. This paper presents a rigorous review of literature in order to investigate these parameters and their impact on embodied energy calculations. The reported values of initial and life-cycle embodied energy are also presented to highlight variations due to differing parameters. Finally, we suggest a two-step solution to make the process of embodied energy analysis more streamlined and transparent through a set of guidelines and an uncertainty calculation model.
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