Abstract
The low efficiency of the closed-loop supply chain in construction and demolition waste (CDW) recycling has restricted the green development of China’s construction industry. Additionally, the government’s reward–penalty mechanism has a huge influence on green development. This study aimed to investigate the effect of green development performance (GDP) and the government’s reward–penalty mechanism on the decision-making process of production and recycling units, as well as to reveal the optimal strategies under different conditions. Therefore, the strategies’ evolutionary paths of production and recycling units were investigated by using evolutionary game theory. Firstly, an evolutionary game model between production units and recycling units was proposed under the government’s reward–penalty mechanism. Then, the evolutionary stability strategies in different scenarios were discussed. Finally, the effects of the relevant parameters on the evolutionary paths of the game model were analyzed using numerical simulations. The main conclusions are as follows. (1) When the range of GDP changes, the evolutionary stable strategy changes accordingly. GDP plays a positive role in promoting the high-quality development of the CDW recycling supply chain, but an increase in GDP can easily lead to the simultaneous motivation of free-riding. (2) The government’s reward–penalty mechanism effectively regulates the decision-making process of production and recycling units. An increase in the subsidy rate and supervision probability helps to reduce free-riding behavior. Moreover, the incentive effect of the subsidy probability on recycling units is more obvious, while the effect of the supervision probability on improving the motivation of active participation for production units is more remarkable. This paper not only provides a decision-making basis to ensure production and recycling units to make optimal strategy choices under different conditions but also provides a reference for the government to formulate a reasonable reward–penalty mechanism that is conducive to a macro-control market.
Highlights
The concept of green development is widely accepted due to increasingly serious global environmental issues [1]
How does Green development performance (GDP) and the government’s reward–penalty mechanism affect the strategic choices of production and recycling units? This study aimed to investigate the effect of GDP and the government’s reward–penalty mechanism on the decision-making process of production and recycling units, revealing the optimal strategies for these units under different circumstances
To the best of the authors’ knowledge, for the first time, GDP was introduced into the evolutionary game model to study the optimal decision-making process of construction and demolition waste (CDW) recycling players under the Chinese government’s reward–penalty mechanism
Summary
The concept of green development is widely accepted due to increasingly serious global environmental issues [1]. Lu and Huang [13] constructed game matrixes for production and recycling units within the government, analyzing their decision-making behavior Both of these studies suggested that government subsidies can effectively promote the development of the CDW recycling supply chain. To the best of the authors’ knowledge, for the first time, GDP was introduced into the evolutionary game model to study the optimal decision-making process of CDW recycling players (i.e., production and recycling units) under the Chinese government’s reward–penalty mechanism. This paper provides a decision-making basis for CDW production and recycling units to make optimal strategy choices under different conditions It provides a reference for the government to formulate a reasonable reward–penalty mechanism, which is conducive to a macro-control market. The remainder of this paper is structured as follows: Section 2 provides a relevant literature review; Section 3 constructs a game model between production and recycling units under the government’s reward–penalty mechanism; Section 4 analyzes the stability of each equilibrium point and determines the evolutionary stability strategy (ESS) in different scenarios; Section 5 presents the numerical simulation and discusses the influence of relevant parameters on the game’s evolutionary path; Section 6 summarizes the conclusions and limitations of this paper
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