Is the private sector more efficient? Big data analytics of construction waste management sectoral efficiency

被引:21
|
作者
Xu, Jinying [1 ]
Lu, Weisheng [1 ]
Ye, Meng [4 ]
Xue, Fan [1 ]
Zhang, Xiaoling [2 ]
Lee, Billy Fook Pui [3 ]
机构
[1] Univ Hong Kong, Dept Real Estate & Construct, Pokfulam, Hong Kong, Peoples R China
[2] City Univ Hong Kong, Dept Publ Policy, Kowloon Tong, Yeung-B7309, Hong Kong, Peoples R China
[3] Carnival Base Co Ltd, Tsuen Wan, Hong Kong, Peoples R China
[4] Southwest Jiaotong Univ, Sch Econ & Management, Chengdu, Sichuan, Peoples R China
关键词
Public-private disparity; Economic efficiency; Construction waste management; Big data; Hong Kong; PERFORMANCE; ORGANIZATIONS; INFORMATION; OWNERSHIP;
D O I
10.1016/j.resconrec.2019.104674
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Efficiency disparity between the public and private sectors is a non-trivial issue that concerns fundamental choices of socio-political-economic systems. Waste management academia and industry also wrestle with issues relating to the choice between public and private sectors. To examine the disparity exclusively caused by "sector", in statistics language, one needs data that is sufficiently big to control many other confounders, e.g., sites, project types, and construction technologies. This paper attempts to ascertain the construction waste management (CWM) efficiency disparity between the public and private sectors by using big data in Hong Kong. The waste disposal records of 132 projects, including 70 public and 62 private projects, were extracted and analysed. By comparing the waste generation flows (WGFs) and accumulative WGFs, it is found that, by and large, there is no significant efficiency disparity in CWM between the two sectors. However, a closer investigation discovered that the private sector outperforms their public counterpart in demolition projects, while the latter performs better in foundation and new building projects. Although there are private projects with higher CWM performance, their divergence between the best and average projects are larger than public ones. Such findings thus reject casual remarks that the private sector is more efficient in CWM. The underlying reasons maybe the waste management index practice promoted in public projects while the private sector is often incentivized to perform better CWM to save waste disposal levies. Future research is recommended to delve into the causes of the efficiency disparity and introduce CWM interventions accordingly.
引用
收藏
页数:11
相关论文
共 50 条
  • [1] Big Data GIS Analytics Towards Efficient Waste Management in Stockholm
    Shahrokni, H.
    van der Heijde, B.
    Lazarevic, D.
    Brandt, N.
    [J]. PROCEEDINGS OF THE 2014 CONFERENCE ICT FOR SUSTAINABILITY, 2014, : 140 - 147
  • [2] Private and Efficient Set Intersection Protocol for Big Data Analytics
    Gheid, Zakaria
    Challal, Yacine
    [J]. ALGORITHMS AND ARCHITECTURES FOR PARALLEL PROCESSING, ICA3PP 2017, 2017, 10393 : 149 - 164
  • [3] Effective and efficient usage of big data analytics in public sector
    Merhi, Mohammad I.
    Bregu, Klajdi
    [J]. TRANSFORMING GOVERNMENT- PEOPLE PROCESS AND POLICY, 2020, 14 (04) : 605 - 622
  • [4] Data analytics and big data in construction project and asset management
    Aibinu, Ajibade A.
    Koch, Fernando
    Ng, S. Thomas
    [J]. BUILT ENVIRONMENT PROJECT AND ASSET MANAGEMENT, 2019, 9 (04) : 474 - 475
  • [5] Healthcare Waste Management and Application Through Big Data Analytics
    Sahni, Poorti
    Arora, Ginni
    Dubey, Ashwani Kumar
    [J]. DATA SCIENCE AND ANALYTICS, 2018, 799 : 72 - 79
  • [6] Big data architecture for construction waste analytics (CWA): A conceptual framework
    Bilal, Muhammad
    Oyedele, Lukumon O.
    Akinade, Olugbenga O.
    Ajayi, Saheed O.
    Alaka, Hafiz A.
    Owolabi, Hakeem A.
    Qadir, Junaid
    Pasha, Maruf
    Bello, Sururah A.
    [J]. JOURNAL OF BUILDING ENGINEERING, 2016, 6 : 144 - 156
  • [7] BIG DATA IN CONSTRUCTION WASTE MANAGEMENT: PROSPECTS AND CHALLENGES
    Lu, Weisheng
    Webster, Chris
    Peng, Yi
    Chen, Xi
    Chen, Ke
    [J]. DETRITUS, 2018, 4 : 129 - 139
  • [8] Adaptive Management Approach for more Availability of Big Data Business Analytics
    Chehbi-Gamoura, Samia
    Derrouiche, Ridha
    Malhotra, Manisha
    Koruca, Halil-Ibrahim
    [J]. ICEMIS'18: PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON ENGINEERING AND MIS, 2018,
  • [9] Use of Big Data Analytics in WASH Sector
    Adhikari, Binod Kumar
    Zuo, Wan Li
    Maharjan, Ramesh
    Yadav, Ram Kumar
    [J]. PROCEEDINGS OF THE 2018 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND CONTROL SYSTEMS (ICICCS), 2018, : 1185 - 1190
  • [10] Benchmarking construction waste management performance using big data
    Lu, Weisheng
    Chen, Xi
    Peng, Yi
    Shen, Liyin
    [J]. RESOURCES CONSERVATION AND RECYCLING, 2015, 105 : 49 - 58