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Benchmarking construction waste management performance using big data

Author

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  • Lu, Weisheng
  • Chen, Xi
  • Peng, Yi
  • Shen, Liyin

Abstract

The waste generation rate (WGR) is usually used as a key performance indicator (KPI) to benchmark construction waste management (CWM) performance, with a view to improving the performance continuously. However, existing researches, for different reasons, only investigated a relatively small amount of construction projects, whose WGRs cannot be confidently accepted as KPIs. This study develops a set of more reliable KPIs/WGRs using an available big dataset on CWM in Hong Kong. By mining the 2,212,026 waste disposal records generated from 5764 projects in two consecutive years of 2011 and 2012, the WGRs/KPIs are revisited and refined. Demolition is found the most wasteful works. New building, and maintenance and renovation (M&R) works individually produce the least waste amount but by accumulating all M&R works, their contribution to the total amount of construction waste could be phenomenal. Based on the more reliable WGRs from the big data, CWM performance benchmarks for different categories of projects are set up. A contractor can benchmark its CWM performance against its counterparts or its past performance as ‘Good’, ‘Average’, and ‘Not-so-good’, and thus identify better CWM practices that induce superior performance. Based on the benchmarks, the government may consider setting up a WGR-step toll system to encourage those ‘Not-so-good’ contractors to perform well in the future, and initiate incentives to the companies conducting ‘Good’ projects to spur better CWM performance. Overall, the WGRs derived from the big data and more robust analyses provide a very powerful and handy tool for CWM.

Suggested Citation

  • Lu, Weisheng & Chen, Xi & Peng, Yi & Shen, Liyin, 2015. "Benchmarking construction waste management performance using big data," Resources, Conservation & Recycling, Elsevier, vol. 105(PA), pages 49-58.
    • Handle: RePEc:eee:recore:v:105:y:2015:i:pa:p:49-58
    DOI: 10.1016/j.resconrec.2015.10.013
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    References listed on IDEAS

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    Cited by:

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    2. Truong Dang Hoang Nhat Nguyen & Hyosoo Moon & Yonghan Ahn, 2022. "Critical Review of Trends in Modular Integrated Construction Research with a Focus on Sustainability," Sustainability, MDPI, vol. 14(19), pages 1-23, September.
    3. Nehal Elshaboury & Abobakr Al-Sakkaf & Eslam Mohammed Abdelkader & Ghasan Alfalah, 2022. "Construction and Demolition Waste Management Research: A Science Mapping Analysis," IJERPH, MDPI, vol. 19(8), pages 1-25, April.
    4. Isaac Akomea-Frimpong & Xiaohua Jin & Robert Osei-Kyei, 2022. "Mapping Studies on Sustainability in the Performance Measurement of Public-Private Partnership Projects: A Systematic Review," Sustainability, MDPI, vol. 14(12), pages 1-20, June.
    5. Johanna Karina Solano Meza & David Orjuela Yepes & Javier Rodrigo-Ilarri & María-Elena Rodrigo-Clavero, 2023. "Comparative Analysis of the Implementation of Support Vector Machines and Long Short-Term Memory Artificial Neural Networks in Municipal Solid Waste Management Models in Megacities," IJERPH, MDPI, vol. 20(5), pages 1-20, February.
    6. Søren Munch Lindhard & Simon Wyke & Hadi Mahami & Seyyed Saeed Vaezzadeh & Kjeld Svidt, 2023. "Waste Generation Predictions and On-Site Waste Management: A Danish Perspective," Sustainability, MDPI, vol. 15(5), pages 1-18, February.
    7. Jianguo Chen & Yangyue Su & Hongyun Si & Jindao Chen, 2018. "Managerial Areas of Construction and Demolition Waste: A Scientometric Review," IJERPH, MDPI, vol. 15(11), pages 1-20, October.
    8. Li Wang & Yanhong Lv & Siyu Huang & Yu Liu & Xinrong Li, 2023. "The Evolution of Research on C&D Waste and Sustainable Development of Resources: A Bibliometric Study," Sustainability, MDPI, vol. 15(12), pages 1-20, June.
    9. Esa, Mohd Reza & Halog, Anthony & Rigamonti, Lucia, 2017. "Strategies for minimizing construction and demolition wastes in Malaysia," Resources, Conservation & Recycling, Elsevier, vol. 120(C), pages 219-229.

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