An agent based environmental impact assessment of building demolition waste management: Conventional versus green management

Waste materials generated from building demolition have become a great challenge to sustainable urban development due to its consumption of the limited landfill spaces, water pollution, energy consumption and harmful gas emissions. Proper management of demolition waste (DW) is a complex process and requires systematic thinking and analysis. Many methods have been proposed to study the environmental impact assessment of demolition waste management (DWM). However, it is found that the currently available studies pay little attention from the perspective of complex adaptive system (CAS) to consider the attitude and interaction of the heterogeneous stakeholders as well as the importance of green DWM which has a great influence on the effectiveness of DW management. The aim of this research is to simulate and explore how the change of attitude and the dynamic interaction among heterogeneous stakeholders can influence the environmental performance of DWM. To achieve this aim, a model for evaluating the environmental impact of DWM was developed by using an agent-based modeling (ABM) approach. The main factors considered in the model are the ratio of green deconstruction (i.e., building deconstruction) managers vs. conventional demolition (i.e. building destruction) managers, the ratio of green design managers (i.e. design for deconstruction) vs. conventional design managers, and the interaction behavior of heterogeneous stakeholders following the herd theory. In the model, the environmental impact assessment was quantified into four categories i.e. land resources, water resources, air resource and energy resources. The proposed model is demonstrated by using the data drawn from the Chinese construction industry. The results reveal that if the deconstruction method and the deconstruction oriented design are widely adopted by architects and engineers, the negative environmental impacts generated by DW can be reduced by at least 50%. Furthermore, the results provide valuable information for government departments to make decisions on how to improve environmental performance of DWM. (C) 2016 Elsevier Ltd. All rights reserved.