*2.1. Construction Waste Recycling and Management*

In recent years, with the rapid development of the economy and the acceleration of urbanization, construction and demolition waste (C&D) has increased dramatically recent years, accounting for 30–40% of city waste in China and more than 40% of all municipal waste in Europe [7–9]. However, the recycling of C&D waste is not optimistic. According to the National Bureau of Statistics of China, 1.3 billion tonnes of construction waste were produced in China in 2017, which is five times the total quantity of residential waste produced in the same year [3]. According to Ma et al. [1], 80% of the construction waste can be recycled. However, the construction waste recycling rate in China is less than 10%, which is much lower compared with 94% for the Netherland and 95% for Japan. A large gap is observed between China and developed countries in the construction waste recycling industry. In other words, construction waste recycling and management have received considerable attention from scholars both at home and abroad. Duan et al. [17] and Yang et al. [18] said that the traditional method of processing construction waste is landfill and 84% of the construction waste is landfilled in recent years in Shengzhen City, China. However, there is insufficient capacity in this area to landfill construction waste. As a result, construction waste recycling and resourcing have become a national primary objective for improving environmental effects, and the question about how to process construction waste effectively and rationally has become an urgent one. Lately, Kabirifar et al. [19] presented a framework to assess the effectiveness of construction and demolition waste management (CDWM) using construction and demolition waste stakeholders' attitudes (CDWSA), CDWM within project life cycles (CDWPLC), which pointed out that CDWAS was the most effective factor in CDWM and CDWPLC was the least effective factor in CDWN. Finally, it was stated that the most effective CDWM strategies were recycle, reuse, and reduce. Furthermore, motivated by sustainability concepts, Ghafourian et al. [20] investigated the sustainable construction and demolition waste management (SCDWM) by introducing sustainability dimensions in CDWM, which further analyzed the impacts of factors that contribute to sustainability aspects of CDWM on waste management hierarchy, such as reduce, reuse, recycle, and disposal strategies.

Recently, Bao et al. [21] treated Shengzhen as a case study and provided a decisionsupport framework for construction waste recycling planning. This framework intends to assist in the planning of on-site and off-site construction waste recycling in Shenzhen, China, using qualitative research methodologies such as case studies, site visits, and semistructured interviews. Lu et al. [22] investigated a data-driven approach to obtain the bulk densities of inert and non-inert construction waste by analyzing a big dataset of 4.9 million loads of construction waste in Hong Kong in the years 2017 to 2019. Hoang et al. [23] studied the financial and economic evaluation of construction and demolition waste recycling in Hanoi, Vietnam from the supply and demand perspective. However, informal

processing the construction waste, e.g., land-filling, has increased the government costs. Ma et al. [1] constructed an evolutionary game model including construction enterprises and recycling enterprises and analyzed the behavior evolution trajectory of participants in the construction waste recycling management system. Moreover, Su [2] studied the multi-agent evolutionary game, including government agencies, waste recycles, and waste producers, in the recycling utilization of construction waste. Most of the above literature analyzes the importance of recycling construction waste. Moreover, it only considers the deterministic replicator dynamics equations, without further consideration that environmental uncertainty on the behavioral decision of participants, which plays an essential role in constructing the evolutionary game theory model. Compared with the deterministic model, which assumes that parameters are deterministic, Yazdani et al. [24] studied a waste collection routing problem by considering uncertain and proposed a novel simheuristic approach based on an integrated simulation optimization. In particular, an efficient hybrid genetic algorithm is used to optimize vehicle route planning for construction and demolition waste collection from construction projects to recycling facilities.

#### *2.2. Evolutionary Game Theory for Construction Waste*

Evolutionary game theories are flexible and powerful tools for understanding evolutionary dynamics of group interactions [25]. Many significant efforts have been made towards using evolutionary game theory to manage construction waste recycling. Ma et al. [1] developed a dynamic evolutionary game model on construction waste recycling to analyze the symbiotic evolution between the behavior of construction enterprises and recycling enterprises, in situations with or without government incentives. Moreover, the authors also studied how government incentive policy affects the dynamic evolution process of construction waste recycling. Lately, Su [2] further studied the multi-agent evolutionary decision-making process and stable strategies among three stakeholders, including government agencies (GA), waste recycles (WR) and waste producer (WP), in the recycling utilization of construction waste. In particular, Su analyzed the main factors that affected the strategies of the stakeholders and provide the tripartite evolutionary game model.

However, considering the existence of uncertainties, in reality, it is difficult to reflect the actual situation of construction waste recycling in reality only by using the general deterministic evolutionary game model. So it is necessary to introduce the random disturbance for analysis [15] and judge the stability of stochastic evolution [26]. Li et al. [16] constructed a multiplayer stochastic evolutionary game model to study the impact of innovation subsidy on enterprise innovation development. Liu et al. [27] introduced Gaussian white noise to analyze the corporate governance issues, and found that random interference factors can affect the trajectory of the equilibrium strategy.
