Behaviour and big data in construction waste management: A critical review of research

Abstract

In recent years, there has been a more widespread application of big data analytics to researches on marketing. Nevertheless, the use of big data which is a more reliable tool in comparison to traditional research methods is still at its infant stage in the academia. This review contributes to the understanding of the current status of construction waste management in Hong Kong, the way in which big data analytics had been applied to construction waste management behavioural research as well as related areas of big data research which are not yet addressed by existing literature. This chapter identifies certain gaps for further research, including the future directions in conducting research on construction waste management (CWM) in green building projects, fly-tipping as well as promotion of better CWM practices. Big data analytics have been gaining popularity in both academic and industrial researches on marketing and consumer behaviours. CW includes any abandoned surplus materials generated from construction activities such as excavation, construction, demolition and refurbishment. CW can be classified into two categories, namely, inert and non-inert wastes. CW being dumped at landfills not only depletes valuable land in Hong Kong, but its anaerobic degradation also causes air pollution and contamination to ground water and soil. W. Lu et al. had conducted a comparative study of CWM behaviours of the same pool of contractors engaging in both public and private projects using big data. The usefulness of the trip-ticket system being introduced in 1999 and enhanced in 2004 in preventing illegal dumping of CW is highly questionable.