4.7 Article

The effects of green building on construction waste minimization: Triangulating 'big data' with 'thick data'

Journal

WASTE MANAGEMENT
Volume 79, Issue -, Pages 142-152

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.wasman.2018.07.030

Keywords

Construction waste management; Green building; BEAM Plus; Big data; Hong Kong

Funding

  1. National Natural Science Foundation of China (NSFC) [71273219]
  2. Hong Kong Research Grants Council (RGC) General Research Fund (GRF) [17201917]

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In contrast with the prolific research examining the effects of green building (GB) on property value, energy saving, or indoor air quality, there has been minimal focus on GB's effects on Construction Waste Minimization (CWM), which is also an important aspect of cultivating sustainability in the built environment. To address this significant knowledge gap, this study has two progressive objectives: (1) to ascertain the empirical effects of GB on CWM and; (2) to identify and understand the causes leading to the ascertained effects. This is achieved by triangulating quantitative 'big data' obtained from government agencies with qualitative 'thick data' derived from case studies and interviews. The study found that BEAM Plus, the latest version of the Building Environmental Assessment Method developed by the Hong Kong Green Building Council (HKGBC), gave rise to a 36.19% waste reduction by weight for demolition works, but no statistically significant waste reduction for foundation or building works. It is because CWM, the basis for a demolition project to obtain GB credits, makes up only one of many ways for foundation or building works to earn credits, e.g., site aspects, lighting. In any case, CWM measures typically prove costlier means of acquiring credit, further causing developers to pay less attention to CWM in their GB tactics. The study's results, i.e., CWM in GB significantly influences demolition, but only marginally for foundation and building works, provide useful scientific evidence to inform GB councils and other responsible bodies and encourage continuous improvement in GB practices. While the study in general sheds light on how the triangulation of big, empirical data with conventional, qualitative data, e.g., interviews with GB professionals, helps to better understand the subject of the investigation, i.e., the effects of GB on CWM. (C) 2018 Elsevier Ltd. All rights reserved.

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