Discussion on the effects of carbon trading for construction-related activities based on hybrid defuzzification strategy

Abstract

This paper constructs and develops a bi-layer programming model (BPM) in order to validate the various advantages of combining construction-related activities (CRAs) with carbon trading mechanisms under uncertainty. Besides, a hybrid defuzzification strategy (HDS) is applied to manage the uncertainties; for example, the uncertainties regarding with carbon price are mitigated by machine learning. As well, the uncertainties surrounding construction-related costs and carbon emissions are quantified by introducing the subjective risk propensity of decision makers. Subsequently, the effectiveness of the HDS is demonstrated with sensitivity indicators by conducting simulations in different scenarios based on real data. The results show that carbon trading can cut emissions from CRAs by up to 31.9234 %, 8.1393 %, and 8.7023 % during procurement, transportation, and usage respectively, while lowering the costs by up to 0.9866 %, 0.2004 %, and 1.1267 % respectively. While carbon trading can effectively facilitate emission reduction from CRAs, the profits are not burdened. Furthermore, the key factors in the green transition process of CRAs are summarized, and it is argued that the implementation of carbon trading can lead to higher affordability of CRAs. Finally, the paper suggests that the level of carbon prices affects the schedule of CRAs, with higher construction desire in periods of low carbon prices.