Measuring the impact of prefabrication on construction waste reduction: An empirical study in China

Prefabrication has been widely regarded as a sustainable construction method in terms of its impact on environmental protection. One important aspect of this perspective is the influence of prefabrication on construction waste reduction and the subsequent waste handling activities, including waste sorting, reuse, recycle, and disposal. Nevertheless, it would appear that existing research with regard to this topic has failed to take into account its innate dynamic character of the process of construction waste minimization; integrating all essential waste handling activities has never been achieved thus far. This paper proposes a dynamic model for quantitatively evaluating the possible impacts arising from the application of prefabrication technology on construction waste reduction and the subsequent waste handling activities. The resulting model was validated based on an actual building project in Shenzhen, China. The simulation results of the design scenarios indicate that the policy on providing subsidy for each square meter of the prefabrication adopted in the construction would have more significant effect on promoting the use of prefabrication and improving the performance of construction waste reduction compared to the increase of income tax benefits. The results also show that (1) interaction exists among different management measures, and (2) the combined effect of multiple policies is larger than the simple sum of their individual impacts, indicating the need for comprehensive consideration on the combined effect of these potential polices. This paper demonstrates the potential benefits of using a system dynamics approach in understanding the behavior of real-world processes. The developed model not only serves as a practical tool for assessing the impact of off-site prefabrication on construction waste reduction and the corresponding waste handling activities, but also help provide a valuable reference to policy makers through the comparison of simulation results generated under various scenarios such that the best policy mix can be identified prior to production.