Research on the Impact of New Parts Price Increase on the Stability of Closed-Loop Supply Chain

Abstract

In the closed-loop supply chain system of component remanufacturing, new parts suppliers are likely to be affected by certain factors that lead to sudden increases in supply prices, and this sudden increase in new parts prices may trigger the chain collapse of the closed-loop supply chain system and impact the stability of the closed-loop supply chain. Therefore, this paper combines closed-loop supply chain theory, evolutionary game theory, and system dynamics theory, which are internalized to construct a complex system model. Furthermore, the impact of different price increases of new parts on the stability of the closed-loop supply chain is analyzed through scenario simulation. The results show that a 25% increase in the price of new parts will delay the time for remanufacturers and retailers to reach a stable state of product flow, reduce the profitability of remanufacturers and retailers, and inhibit the willingness of remanufacturers to carry out high-green production in the early stage. A 50% increase in the price of new parts can break the closed-loop supply chain from the remanufacturer, severely undermining the stability of the closed-loop supply. According to the simulation results, this paper presents a timely government dynamic reward and punishment optimization scheme for remanufacturers to improve the tolerance level of the closed-loop supply chain for the price increase of new parts and to provide a reference for enhancing the stability of the closed-loop supply chain and optimizing the government supervision strategy.

1. Introduction

In recent years, the average annual growth rate of the global remanufacturing industry has reached 10%, and remanufactured products can replace new products in terms of quality, durability and functionality, and the unit cost of remanufacturing is only 40–60% of new products [1,2]. As early as 2019, the global remanufacturing industry was valued at more than USD 200 billion, but China only accounted for about 7.5% of this [3]. In the United Kingdom, around 10 million tons of CO₂ emissions can be reduced per year through remanufacturing [4]. China’s remanufacturing industry is still in the development stage, and there are fewer remanufacturing pilot enterprises in China compared with the 70,000 remanufacturing enterprises in the United States. It can be seen that product remanufacturing has great potential and significant economic and environmental benefits [5]. Currently, product remanufacturing at the component level through the use of new parts and recycled parts that have been properly disassembled and disposed of is a viable avenue [6]. Closed loop supply chain is an important way to realize product remanufacturing at the component level, which is regarded by some companies as the their operation strategy [7,8]. The so-called closed-loop supply chain refers to the integration of reverse logistics on the basis of the traditional positive supply chain, which tries its best to make materials circulate within the supply chain to reduce the adverse impact of supply chain activities on the outside world [9]. In the closed-loop supply chain, this mainly includes raw material suppliers (new parts suppliers), recyclers (recycled parts suppliers), remanufacturers, retailers, consumers, and many other entities, comprising a complex system [10,11].

Therefore, product remanufacturing at the component level can be guided by research into closed-loop supply chain systems, thereby contributing to the reduction in waste and unidirectional flow of non-renewable resources [12]. In reality, many industries have implemented closed-loop supply chain strategies for component remanufacturing, such as the electrical and electronic industries, automotive industries, construction machinery industries, etc. Taking the electrical and electronic industry as an example, the current global annual growth rate of electronic waste is about 10–20% [13], and governments and enterprises in many countries are focusing on promoting the effective recycling and remanufacturing of waste electrical and electronic products from the perspective of developing recycling infrastructure, stimulating re-product consumption, and expanding corporate responsibility [14]. However, the supply of new parts is another important basis for component remanufacturing, and governments and companies have given this aspect little attention [15]. In fact, not all recycled parts are used in the component remanufacturing process, and when some recycled parts cannot fully recover their performance through technical means, they need to be replaced by new parts [16]. The main purpose of mixing new parts with recycled parts is to ensure that the remanufactured products can reach the same level as the original products in terms of the quality and performance [17]. In addition, cost control is always the focus of remanufacturing because the biggest advantage of remanufactured products over new products is the lower manufacturing cost and selling price [18,19]. The closed-loop supply chain is a complex system with multi-party participation, and brittleness, as a basic attribute of complex systems, always exists within complex systems [20]. Therefore, on the basis of affirming the important role of new parts, it is particularly important to explore the impact of their price increases on the stability of closed-loop supply chain systems.

Most of the current research reflects the operation of the closed-loop supply chain system from the perspective of profit acquisition ability [21,22]. Unlike these studies, this paper will consider the stability of closed-loop supply chain systems from the perspectives of material flow, capital flow, and information flow [23]. Specifically, in terms of material flow, this paper will analyze the product circulation status of remanufacturers, retailers, and recyclers in the closed-loop supply chain when the price of new parts rises. In terms of capital flow, the profits of the three main enterprises are explored, and the profits of enterprises can achieve sustained and stable growth, as well as increased profits, to a certain extent, which reflects the good operation of the closed-loop supply chain. In terms of information flow, the choice of corporate strategy represents the potential development direction of the supply chain. Therefore, in order to explore the above content, this paper uses system dynamics theory to construct a closed-loop supply chain system dynamics flow diagram module to reflect the stability of the supply chain from the perspectives of material flow and capital flow, and combines evolutionary game theory and system dynamics theory to construct an evolutionary game system dynamics flow diagram module to reflect the stability of the closed-loop supply chain from the perspective of information flow.

Systems dynamics theory, first proposed by Professor Forrester [24], is a discipline that analyzes and studies the feedback of complex systems. It aims to look at the operation of things from the perspective of the system rather than the local perspective. The behavior of the system is mainly manifested through internal dynamic structures and feedback. Due to the complex nature of the feedback structure, nonlinear changes to the system over time often need to be realized using computer simulation technology [25,26]. The closed-loop supply chain involves a large number of participants, the relationship between the participants is complicated, and the government policy and social values are integrated into it; therefore, system dynamics is applicable to the study of closed-loop supply chain systems [27]. Evolutionary game theory was gradually developed by the application of game theory to the field of biology [28]. The theory claims that people are bounded rationality, focusing on the fact that individuals in a group can learn and imitate in the process of evolution, and the behavior strategy of the group will gradually modify, and eventually reach a stable equilibrium state in the process of evolution [29]. Remanufacturers, retailers, and recyclers in a closed-loop supply chain are in one system, and their decisions influence each other. Therefore, with the help of evolutionary game theory, it can reflect the process of each agent constantly modifying and improving their behavior and eventually forming a stable set of strategies.

The remainder of this paper is organized as follows: Section 2 is a literature review, which mainly points out the current research gaps and research methodological deficiencies. Based on the problem description described above, this article directly designs and builds the model in Section 3. Section 4 conducts benchmark scenario analysis and rise scenario setting. In Section 5, the government’s reward and punishment strategy is optimized. Section 6 draws conclusions and makes recommendations based on the results of the analysis.

2. Literature Review

2.1. Study on the Influence of Internal and External Factors on Closed-Loop Supply Chain System

Changes in the internal and external factors in the closed-loop supply chain are an important component affecting the closed-loop supply chain system, and many scholars have conducted relevant research on the closed-loop supply chain system when the internal and external factors change.

In regard to research on the influence of internal factors on closed-loop supply chain systems, in terms of the impact of quality control of recyclables on the closed-loop supply chain, Lu and Yang discussed decision-making in the manufacturing closed-loop supply chain in the context of the controllable quality of recyclables, and explored the impact of the quality of recyclables on the pricing, production decisions, and profits of closed-loop supply chain enterprises by constructing a Stackelberg game model led by remanufacturers under centralized and decentralized decision-making [30]. In terms of the impact of recycling costs on closed-loop supply chains,, Chen and Tian, on the basis of considering the cost of recycling management and remanufacturing, found that with the increase in recycling prices, remanufacturers transitioned from the indirect recycling mode to the direct recycling mode [31]. In reference to the impact of recycling channels on closed-loop supply chains, Li et al. designed three recycling channel decision models for retailers, manufacturers, and dual recycling, and analyzed and discussed the impact of manufacturers’ fairness preference on closed-loop supply chain decisions under different recycling channels [32]. In their study on the impact of consumer preferences on closed-loop supply chains, Sun et al. constructed single-channel and dual-channel closed-loop supply chain decision-making models to explore the impact of consumers’ remanufactured product preferences and fairness concerns on members’ decision-making and profits, and found that with the increase of consumers’ remanufactured product preferences, manufacturers’ and retailers’ profits and waste product recovery rates increased [33]. In regard to the impact of Corporate Social Responsibility (CSR) on closed-loop supply chains, Yan et al. considered a closed-loop supply chain system with CSR awareness, and found that manufacturers’ CSR awareness and retailers’ CSR investment have mutual incentives, and with the enhancement of manufacturers’ CSR awareness, the total profits of the closed-loop supply chain under the four main enterprise strategy models increase [34]. In regard to the impact of information sharing on closed-loop supply chains, Huang and Wang set up three remanufacturing scenarios to explore the impact of information sharing and learning effects on the optimal decision-making and revenue of closed-loop supply chain members, and found that information sharing will enable OEMs to make more accurate pricing decisions, but will reduce the efficiency of retailers [35].

Although many studies analyze the impact of internal factors, such as recycled product quality, recycling cost, recycling channels, consumer preferences, CSR, and information sharing, on the closed-loop supply chain system, most of them are studied from the operation status of some enterprises or the overall operation of the closed-loop supply chain, and rarely consider the interaction between the operation status of multiple enterprises. In a closed-loop supply chain, companies are not isolated and their actions are influenced by their own interests, the actions of other companies, and the supply chain environment. Therefore, this paper considers the complex connections between different enterprises, such as material, financial and information, when building the model.

In addition to the internal factors, the impact of external constraints on closed-loop supply chain systems cannot be ignored. In their study of the impact of government regulation on closed-loop supply chains, Wang et al., considering market segmentation, compared and analyzed the impact of government reward and punishment strategies on the closed-loop supply chain system, and further discussed the impact of different reward and punishment intensities and consumer preferences on the behavior of recyclers and remanufacturers [36]. Ma and Li used dangerous goods as an example to construct a two-stage stochastic model of the closed-loop supply chain of dangerous goods, and the risk constraint and reward and punishment mechanism were considered in the model. It is found that the promotional effect of reward and punishment strategies on improving the average utilization rate of waste products under different risk thresholds is different [37]. In regard to the impact of carbon emission constraints on closed-loop supply chains, Jiao et al. combined game theory and system dynamics to explore the impact of carbon trading strategies and technological progress on the closed-loop supply chain system of power batteries, and found that with the reduction in free carbon quotas and the rise of carbon trading prices, the recycling of waste power batteries will be greatly promoted, but the production and social welfare of power batteries will be greatly affected [38]. In reference to the impact of force majeure events on closed-loop supply chain, Katsoras and Georgiadis constructed a system dynamics model suitable for closed-loop supply chain disaster situations, and used the total supply chain profit and demand backlog as measures of the strategic performance to explore the response of the closed-loop supply chain to two different mitigation policies under three demand modes [39].

In terms of the research content, there are still some gaps in the current research. Although there are many studies that explore the impact of internal and external factors on closed-loop supply chain systems, few have studied the impact of external raw material supply on component remanufacturing or closed-loop supply chain systems, such as new parts required for component remanufacturing. The existing studies focus on reflecting the operation advantages and disadvantages of closed-loop supply chain systems from the perspective of profit acquisition ability, for example, literature [30,33,34,39] However, the existing works rarely analyze the system operation from multiple perspectives. At the same time, the above literature also ignores the dynamic adjustment of government reward and punishment strategies when considering government reward and punishment strategies, for example, literature [36,37].

2.2. Research Methods of Closed-Loop Supply Chain System

In terms of research methods, many scholars choose to study closed-loop supply chain systems through game or system dynamics. For example, Das et al. used Stackelberg game theory to treat retailers as leaders and manufacturers as followers, and to maximize the profit of the supply chain by optimizing the selling price of channel members and the length of time for the remanufacturer to reach zero inventory [40]. Guan et al. constructed a tripartite evolutionary game model of manufacturers, third-party recycling enterprises, and third-level utilization enterprises, and analyzed how the equilibrium strategies of each entity were affected by changes in government subsidies, manufacturer incentives, and various costs [41]. Delavar et al. took LED panel manufacturers as an example and established a two-level closed-loop supply chain system dynamics model considering information sharing to explore the effects of the order preparation time, fixed order cost, and information sharing on the bullwhip effect of closed-loop supply chain systems [42]. Although the above research has achieved certain research objectives, the constructed model does not simultaneously simulate the complexity of the closed-loop supply chain and the interactivity of the behavior choice of the main firm.

In addition to the above research, there are also scholars who have studied closed-loop supply chain systems by combining evolutionary games and system dynamics. For example, Li et al. constructed an evolutionary game model with government participation under the concept of cloud manufacturing, and simulated the evolutionary game process through system dynamics in order to analyze the information sharing enthusiasm and influencing factors of remanufacturers and recyclers in the closed-loop supply chain. At the same time, they also verified the effectiveness of information sharing by constructing a closed-loop supply chain system dynamics model in a cloud environment [43]. Zhang et al. analyzed the interaction mechanism between government choice of intervention strategy and manufacturer’s choice of green technology strategy in the context of a carbon emission cap. The simulation results of the system dynamics show that the model cannot achieve a stable evolutionary equilibrium strategy under the static carbon trading price, and has a stable evolutionary equilibrium strategy under the dynamic carbon trading price, and the dynamic carbon trading price can promote the realization of carbon emission reduction [44].

Through the use of evolutionary game and system dynamics in literature [43,44] to carry out related research, the authors mainly guide the system dynamics modeling by replication dynamic equations, and then simulate the game results through the system dynamics model. They do not analyze the impact of the established evolutionary stability strategy on the operation of the closed-loop supply chain, and do not consider whether the operation of the closed-loop supply chain can re-correct this strategy combination when implementing this strategy combination. Therefore, unlike the model constructed in the existing research, this paper first simulates the complex structure of the closed-loop supply chain in terms of literature [43,44] material, capital, and information through system dynamics, and then explores the interactive influence of the decision-making of the members of the closed-loop supply chain through an evolutionary game. Finally, the results of the evolutionary game are applied to the operation of the closed-loop supply chain to correct the results of the evolutionary game.

2.3. Contribution of This Paper

The existing research on closed-loop supply chain systems has made some achievements, but there are still some gaps and deficiencies in the research content and research methods. This paper explores how changes in the external factors affect the operation of, and decision-making of each participant within, the closed-loop supply chain through a new perspective (rising prices of new parts), which enriches the relevant research perspective of closed-loop supply chain systems. We internalize the closed-loop supply chain theory, evolutionary game theory, and system dynamics theory, and construct a complex system model to try to break through the external coupling problem when the original three theories are combined. At the same time, the model introduces the government’s dynamic reward and punishment mechanism and considers the flow of material, capital, and information in the closed-loop supply chain, and realizes the introduction of the mixed strategy selection probability of the game subject into the operation of the closed-loop supply chain, so that the simulation of the closed-loop supply chain is closer to reality. Finally, this paper provides a reference for the government’s dynamic reward and punishment strategy optimization and enterprise strategy selection through scenario simulation.

3. Model Construction

Evolutionary game theory takes into account the limited nature of the human cognitive ability and believes that people cannot always be accurate when acquiring and using information to make decisions. It highlights the dynamic evolution process of decision-making and is suitable for exploring the impact of rising prices of new parts on the strategic choices of participants in closed-loop supply chains. System dynamics refers to understanding and predicting nonlinear changes in some complex systems over time by showing the interaction, mutual influence, and interdependence of the system with the help of computer modeling technology, which is suitable for simulating the product circulation and enterprise profit of a closed-loop supply chain. Therefore, this chapter gradually builds a simulation model through evolutionary game theory and system dynamics theory.

3.1. Establishment of Evolutionary Game Model

3.1.1. Model Assumptions

In order to enable the established evolutionary game model to reasonably and accurately simulate the strategic interaction behaviors of member enterprises in the closed-loop supply chain, the following assumptions and explanations are made for the game players, behavioral strategies, and related parameters.

Game subject assumption: As the closed-loop supply chain involves a large number of participants, in order to accurately reflect the operating state of the closed-loop supply chain system, this paper selects three main participants of the closed-loop supply chain, namely: remanufacturer (M), retailer (R), and recycler (T). The parties in the game cannot be completely rational due to their own understanding and environment. Therefore, all three parties participate in the game under limited rationality.

Remanufacturer behavior strategy assumption: Combined with the reality, this paper believes that the government supports the high-green production of remanufacturers and the public prefers high-green products. When remanufacturers choose high-green production, the closed-loop supply chain will have a better policy and market environment, thus enhancing the operational stability of the closed-loop supply chain. Therefore, it is assumed that the remanufacturer has two behavioral strategies: (1) High-green production. Remanufacturers not only use recycled parts for remanufacturing, but also optimize the manufacturing process (invest in energy saving research and development or purchase new equipment to reduce energy consumption, gas waste, and wastewater emissions, etc.). (2) Low-green production. The remanufacturer only uses recycled parts in the production process and does not make additional investments in manufacturing process optimization. The probability that the remanufacturer chooses high-green production and low-green production is α and (1 − α), respectively.

Retailer behavior strategy assumption: Remanufactured product sales are the premise of closed-loop supply chain operation. On this basis, whether the retailer is actively selling can reflect the operation status of the closed-loop supply chain to a certain extent. Therefore, this paper assumes the retailer’s behavior strategy based on the research topic: (1) Actively sell. The enthusiasm is mainly manifested in investing more expenses in the sales promotion of remanufactured products to promote sales growth. (2) Passive selling. Retailers do not make much effort to sell the products and do not carry out active publicity and promotion. The probability that retailers choose to sell positively and negatively is β and (1 − β), respectively.

Recycler behavior strategy assumption: On the basis of providing recycled parts, the recycler providing low-quality recycled parts may have a negative impact on the closed-loop supply chain operation. Therefore, this paper assumes the behavior strategy of recyclers: (1) Provide high-quality recycled parts. Recyclers make additional investments to improve the quality of their machinery and disassembly processes to provide remanufacturers with high-quality levels of recycled parts. (2) Provide low-quality recycled parts. Recyclers do not make extra efforts to improve the quality level of their recycled parts compared to providing high-quality recycled parts. The probabilities of recyclers choosing to provide high-quality recycled parts and low-quality recycled parts are γ and (1 − γ), respectively.

Parameter assumption: The symbol representation of the parameters and their meanings are shown in

Table 1. Parameter comparison table.

Subject	Parameters	Descriptions
Remanufacturer	$p_M$	M Remanufacturer supply unit price
	$q_M$	M Delivery rate
	$e_M$	R’s publicity and promotion will enhance M’s corporate image
	$c_{NM}$	Cost of new parts
	$c_{RM}$	Recycling parts cost
	$c_{TM}$	Recycling parts quality inspection costs
	$l$	M Government fines for low-green production
	$c_{UM}$	Additional inputs when the M conducts high-green production
	$w_1$	M’s subsidy for R’s publicity and promotion
	$f_M$	M Fixed expenditure
	$g_M$	Government subsidy factor M
Retailer	$p_R$	Sales unit price
	$q_R$	R Sales rate
	$c_{AR}$	R Publicity and promotion investment
	$f_R$	R Fixed expenditure
	$j$	R’s image loss during negative sales
	$g_R$	Government subsidy factor R
Recycler	$p_T$	Unit price of recycled products
	$q_T$	T Recovery rate
	$c_{HT}$	T Extra investment in high-quality parts recovery
	$w_2$	Relief of extra investment from M and R to T
	$v$	Fine imposed by M on T when T provides low-quality recycled parts
	$f_T$	T Fixed expenditure
	$g_T$	Government subsidy factor T

.

3.1.2. Payment Matrix

According to the different strategy choices of the remanufacturers, retailers and recyclers, the payment matrix under the combination of eight strategies is obtained, as shown in

Table 2. Tripartite payment matrix when remanufacturer (M) adopts high-green production strategy (α).

Retailer (R)	Recycler (T)
	Provide High-Quality Recycled Parts (γ)	Provide Low-Quality Recycled Parts (1 − γ)
Active sales (β)	$p_M(1+g_M)q_M+e_M-c_{NM}-c_{RM}-c_{UM}-w_1-f_M$	$p_M(1+g_M)q_M+e_M+v-c_{NM}-c_{RM}-c_{TM}-c_{UM}-w_1-f_M$
	$p_R(1+g_R)q_R+w_1-p_M{q}_M-c_{AR}-f_R$	$p_R(1+g_R)q_R+w_1-p_M{q}_M-c_{AR}-f_R$
	$c_{RM}(1+g_T)-p_T{q}_T-c_{HT}+w_2-f_T$	$c_{RM}-p_T{q}_T-v-f_T$
Negative sales (1 − β)	$p_M(1+g_M)q_M-c_{NM}-c_{RM}-c_{UM}-f_M$	$p_M(1+g_M)q_M+v-c_{NM}-c_{RM}-c_{TM}-c_{UM}-f_M$
	$p_R{q}_R-p_M{q}_M-j-f_R$	$p_R{q}_R-p_M{q}_M-j-f_R$
	$c_{RM}(1+g_T)-p_T{q}_T-c_{HT}-f_T$	$c_{RM}-p_T{q}_T-v-f_T$

and

Table 3. Tripartite payment matrix when remanufacturer (M) adopts low-green production strategy (1 − α).

Retailer (R)	Recycle (T)
	Provide High-Quality Recycled Parts (γ)	Provide Low-Quality Recycled Parts (1 − γ)
Active sales (β)	$p_M{q}_M+e_M-c_{NM}-c_{RM}-l-f_M$	$p_M{q}_M+e_M-c_{NM}-c_{RM}-c_{TM}-l-f_M$
	$p_R(1+g_R)q_R-p_M{q}_M-c_{AR}-f_R$	$p_R(1+g_R)q_R-p_M{q}_M-c_{AR}-f_R$
	$c_{RM}(1+g_T)-p_T{q}_T-c_{HT}+w_2-f_T$	$c_{RM}-p_T{q}_T-f_T$
Negative sales (1 − β)	$p_M{q}_M-c_{NM}-c_{RM}-l-f_M$	$p_M{q}_M-c_{NM}-c_{RM}-c_{TM}-l-f_M$
	$p_R{q}_R-p_M{q}_M-j-f_R$	$p_R{q}_R-p_M{q}_M-j-f_R$
	$c_{RM}(1+g_T)-p_T{q}_T-c_{HT}-f_T$	$c_{RM}-p_T{q}_T-f_T$

.

3.1.3. Replication Dynamic Equation

According to the tripartite payment matrix, the expected revenue of high-green production and low-green production of the remanufacturer can be obtained, expressed by $U_{M1}$ and $U_{M2},$ respectively, and the average expected return of the remanufacturer is ${\overline{U}}_M$.

The expected revenue of the remanufacturer in high-green production $U_{M1}$:

$$\begin{array}{ll}{U}_{M1}& =\beta \gamma (p_M(1+g_M)q_M+e_M-c_{NM}-c_{RM}-c_{UM}-w_1-f_M)+\beta (1-\gamma )(p_M(1+g_M)q_M+e_M\\ & +v-c_{NM}-c_{RM}-c_{TM}-c_{UM}-w_1-f_M)+(1-\beta )\gamma (p_M(1+g_M)q_M-c_{NM}-c_{RM}-c_{UM}-f_M)\\ & +(1-\beta )(1-\gamma )(p_M(1+g_M)q_M+v-c_{NM}-c_{RM}-c_{TM}-c_{UM}-f_M)\end{array}$$

(1)

The expected revenue of the remanufacturer in low-green production $U_{M2}$:

$$\begin{array}{ll}{U}_{M2}& =\beta \gamma (p_M{q}_M+e_M-c_{NM}-c_{RM}-l-f_M)+\beta (1-\gamma )(p_M{q}_M+e_M-c_{NM}-c_{RM}-c_{TM}-l-f_M)\\ & +(1-\beta )\gamma (p_M{q}_M-c_{NM}-c_{RM}-l-f_M)+(1-\beta )(1-\gamma )(p_M{q}_M-c_{NM}-c_{RM}-c_{TM}-l-f_M)\end{array}$$

(2)

The remanufacturer’s average expected revenue ${\overline{U}}_M$:

$${\overline{U}}_M=\alpha U_{M1}+(1-\alpha )U_{M2}$$

(3)

The replication dynamic equation can be interpreted as follows: if the fitness or payout of a strategy is higher than the average fitness of the population, the strategy will be diffused in the population, which shows that the growth rate of the proportion of individuals using a strategy in the population is greater than zero. Replication dynamic equations are essentially dynamic differential equations that describe the frequency of a particular strategy being adopted in a population.Therefore, according to Equations (1) and (3) above, the replication dynamic equation of the remanufacturer is constructed as (4) [45,46].

$$F(\alpha )=\frac{d\alpha }{dt}=\alpha (U_{M1}-{\overline{U}}_M)=\alpha (1-\alpha )(g_M{p}_M{q}_M-c_{UM}+l-\beta w_1+v-\gamma v)$$

(4)

Similarly, the replication dynamic equations of the retailer and recycler are (5) and (6), respectively.

$$F(\beta )=\frac{d\beta }{dt}=\beta (U_{R1}-{\overline{U}}_R)=\beta (1-\beta )(g_R{p}_R{q}_R-c_{AR}+j+\alpha w_1)$$

(5)

$$F(\gamma )=\frac{d\gamma }{dt}=\gamma (U_{T1}-{\overline{U}}_T)=\gamma (1-\gamma )(g_T{c}_{RM}-c_{HT}+\alpha v+\beta w_2)$$

(6)

According to the replication dynamic Equations (4)–(6) of the remanufacturer, retailer, and recycler, the replication dynamic equation group (7) is obtained.

$$\{\begin{array}{l}F(\alpha )=\frac{d\alpha }{dt}=\alpha (U_{M1}-{\overline{U}}_M)=\alpha (1-\alpha )(g_M{p}_M{q}_M-c_{UM}+l-\beta w_1+v-\gamma v)\\ F(\beta )=\frac{d\beta }{dt}=\beta (U_{R1}-{\overline{U}}_R)=\beta (1-\beta )(g_R{p}_R{q}_R-c_{AR}+j+\alpha w_1)\\ F(\gamma )=\frac{d\gamma }{dt}=\gamma (U_{T1}-{\overline{U}}_T)=\gamma (1-\gamma )(g_T{c}_{RM}-c_{HT}+\alpha v+\beta w_2)\end{array}$$

(7)

3.2. Establishment of System Dynamics Model

3.2.1. Model Assumptions

In order for the established system dynamics model reasonably and accurately simulate the real situation of the closed-loop supply chain, this paper analyzes the main activities of the closed-loop supply chain system and makes relevant assumptions.

The closed-loop supply chain system mainly includes government supervision activities, remanufacturing activities, sales activities, recycling activities, and so on. In government regulatory activities, in order to maximize the regulatory efficiency, the government’s regulatory strategy is not fixed, and its regulatory intensity often changes with the specific behaviors of the remanufacturers, retailers, and recyclers. In remanufacturing activities, the remanufacturer will try to obtain the remanufactured product demand information to guide the production of remanufactured products. The production of a qualified remanufactured product requires the participation of many raw materials and parts, among which new parts and recycled parts are important components of the remanufactured product, and their prices will be affected by the market supply and demand conditions and the price of related finished products. In order to ensure that the recycled parts provided by the recycler can reach the qualified quality level, the remanufacturer usually needs to spend a certain amount of effort and cost to monitor the quality of the recycled parts. At the same time, in order to control costs and improve the production efficiency, remanufacturers will use recycled parts and new parts without discrimination. In sales activities, consumers’ demand for remanufactured products is not static, and their purchase frequency and quantity are often affected by subjective preferences—for example, demand is affected by the product’s green level [47] and price [48]. Based on the current social environment and other conditions being the same, this paper believes that consumers are more willing to buy green products. In recycling activities, recyclers obtain recyclable products from the consumer market and disassemble them for processing, and the quality level of the recycled parts they provide will change with their own operations and government regulations. Therefore, through the analysis of the main activities of the closed-loop supply chain, this paper makes the following assumptions.

Assumption 1. 

Under the premise that the government can keep abreast of the changes in the strategic choices of remanufacturers, retailers and recyclers, the intensity of government rewards and punishments is linearly correlated with the probability of the game players’ strategy selection.

Assumption 2. 

Remanufacturers can understand the change of market demand through the seller order rate and market research.

Assumption 3. 

Other parts that make up the remanufactured product can normally meet the production requirements of the remanufactured product.

Assumption 4. 

The price of new and recycled parts is affected by the price of related products in the market.

Assumption 5. 

There are no significant functional differences between the qualified recycled parts and the new parts.

Assumption 6. 

Remanufacturer quality inspection efforts are influenced by the recyclers’ willingness to provide high-quality recycled parts.

Assumption 7. 

The consumer market has a preference for green behaviors in the closed-loop supply chain, and the strategy choice of the game players can affect the demand for remanufactured products in the consumer market.

3.2.2. Construction of Closed-Loop Supply Chain System Dynamic Flow Diagram Module

According to the hypothesis of the system dynamics model and the actual situation, this paper sets up the government dynamic reward and punishment mechanism. In other words, during the operation of the closed-loop supply chain, the government’s rewards and punishments will be adjusted in real time with the different behaviors of the member enterprises in the closed-loop supply chain. Therefore, based on the practice of Liu [49], this paper sets the dynamic reward and punishment functions of M government fines for low-green production, government subsidy factor M, government subsidy factor R, and government subsidy factor T as: $l=\left(1-\alpha \right)\ast 8000$, $g_M=0.3\ast \alpha +0.1$, $g_R=0.2\ast \beta ,$ and $g_T=0.3\ast \gamma +0.2$, respectively. On the basis of the dynamic reward and punishment mechanism of the government and referring to the relevant literature [23], a closed-loop supply chain material flow and capital flow system dynamics flow diagram module is constructed using the Vensim DSS software, as shown in Figure 1. In the flow diagram module, the remanufacturer’s recycled product inventory, the retailer’s recycled product inventory, the recycler’s recycled product inventory, and the remanufacturer’s profit, the retailer’s profit, and the recycler’s profit are set as state variables. M New part procurement rate, M Delivery rate, R Sales rate, T Recovery rate, and The Recycling parts supply rate of T are set as rate variables; 19 variables, such as Initial demand, various smoothing times and delays, some coefficients, and fixed expenses are set as constants, and the remaining variables are auxiliary variables.

3.2.3. Construction of Evolutionary Game System Dynamics Flow Diagram Module

According to the closed-loop supply chain material flow and capital flow system dynamic flow diagram module and the tripartite evolutionary game replication dynamic equation group (7), the evolutionary game system dynamic flow diagram module is constructed, as shown in Figure 2. In the dynamic flow diagram module, M The probability of implementing high-green production, R The probability of actively selling remanufactured products, and Probability that T provides high-quality recycled parts are set as the state variables, and the change rate of the three strategy selections is set as the rate variable. The relevant variables in the closed-loop supply chain material flow and capital flow system dynamics flow diagram module are introduced to calculate the expected revenue of the remanufacturers, retailers, and recyclers under different decisions.

3.2.4. Main Variable Equation of System Dynamics Model

According to the interaction of the internal factors of the system dynamics model, the equation relationship between the variables of the model is constructed. The main equations are as follows:

M Remanufactured products inventory = INTEG ((M New part procurement rate + The Recycling parts supply rate of T)/2 − M Delivery rate); Initial Value = M Base remanufacturing quantity

R Remanufactured products inventory = INTEG (M Delivery rate-R Sales rate); Initial Value = 0

T Recycled products inventory = INTEG (T Recovery rate-The Recycling parts supply rate of T; Initial Value = 1000

M Profit = INTEG (M Revenue − M Cost); Initial Value = 0

R Profit = INTEG (R Revenue − R Cost); Initial Value = 0

T Profit = INTEG (T Revenue − T Cost); Initial Value = 0

M The probability of implementing high-green production = INTEG (IF THEN ELSE ((M The probability of implementing high-green production + M Change rate of strategy selection) > 1, 1 − M The probability of implementing high-green production, IF THEN ELSE ((M The probability of implementing high-green production + M Change rate of strategy selection) < 0, − M The probability of implementing high-green production, M Change rate of strategy selection))); Initial Value = 0.4

R The probability of actively selling remanufactured products = INTEG (F THEN ELSE((R The probability of actively selling remanufactured products + R Change rate of strategy selection) > 1, 1 − R The probability of actively selling remanufactured products, IF THEN ELSE((R The probability of actively selling remanufactured products + R Change rate of strategy selection) < 0, − R The probability of actively selling remanufactured products, R Change rate of strategy selection))); Initial Value = 0.5

Probability that T provides high-quality recycled parts = INTEG (IF THEN ELSE ((Probability that T provides high-quality recycled parts + T Change rate of strategy selection) > 1, 1 − Probability that T provides high-quality recycled parts, IF THEN ELSE ((Probability that T provides high-quality recycled parts + T Change rate of strategy selection) < 0, - Probability that T provides high-quality recycled parts, T Change rate of strategy selection))); Initial Value = 0.4

3.3. Model Test

In order to test whether the model operation is real and effective, a model test is conducted on the system dynamics model, including a visual check, operation check, and robustness test [50].

First, the model intuitively is checked. By observing whether the variable setting, feedback relationship, and function setting conform to the actual situation of the component remanufacturing closed loop supply chain, the rationality of the model construction is determined. When setting the variables, we combined with the real system and always followed the “What will it drive? “and” What drives it?” to determine the system variables. When determining the feedback relationship, we aimed to fit the causal relationship in the real closed-loop supply chain. When the function is set, the function relation of the variables can simulate the real closed-loop supply chain operation. Therefore, through the visual inspection of the model, no technical errors appear in the construction of the model.

Secondly, the model is tested. Run the software to check whether the model can run smoothly and attain the actual running results. The system dynamics model in this paper has been adjusted and tested many times, and no simulation results that are inconsistent with the reality (numerical fluctuation anomaly, numerical overflow, etc.) are found. The model can simulate the component remanufacturing closed loop supply chain to a large extent.

Finally, the robustness of the model is tested. The sensitivity analysis of some key variables is carried out to explore whether the output results of the model will change dramatically due to small perturbations. Specifically, the values of variables $c_{UM}$, $l$, $c_{HT}$, $v$, $c_{AR}$, $j$, etc., were changed up or down by 10% to observe the changes in the system state variables. If the sensitivity is less than 1, it indicates that the model is relatively robust [51]. The calculation formula of sensitivity is shown in Equation (8).

$$S_Y=\frac{1}{n}{\displaystyle \sum _{i=1}^n\left|\frac{\Delta Y_i}{Y}•\frac{X}{\Delta X_i}\right|}$$

(8)

In Equation (8), $S_Y$ represents the state variable sensitivity. Y is the state variable (including M Remanufactured products inventory, M Profit, R Remanufactured products inventory, R Profit, T Recycled products inventory, T Profit). X is the key variable selected (including $c_{UM}$, $l$, $c_{HT}$, $v$, $c_{AR}$, $j$). $\Delta Y_i$ is the change of state variable Y when the key variable fluctuates by 10% in the ith order. $\Delta X_i$ is the fluctuation difference of the ith key variable. Number of sensitivity test points n = 2.

The sensitivity test values of each key variable were calculated according to the sensitivity calculation in Equation (8). It can be seen from

Table 4. Sensitivity analysis table.

Variable	M Remanufactured Products Inventory	M Profit	R Remanufactured Products Inventory	R Profit	T Recycled Products Inventory	T Profit
$c_{UM}$	0.2420	0.6696	0.6388	0.0705	0.4249	0.1533
$l$	0.0871	0.1060	0.1169	0.0218	0.6398	0.2354
$c_{HT}$	0.0802	0.0389	0.0781	0.0010	0.8962	0.3071
$v$	0.0018	0.0044	0.0070	0.0012	0.0010	0.0002
$c_{AR}$	0.0026	0.0064	0.0052	0.0815	0.0000	0.0000
$j$	0.0008	0.0019	0.0007	0.0073	0.0000	0.0000

that this model has good robustness.

4. Scenario Analysis of Rising Cost of New Parts

4.1. Model Operation and Baseline Scenario Analysis

The initial running conditions of the model are set. The initial time is 0, the end time is 50, the unit is weeks, and the running step is 0.25. By referring to the relevant literature [23,52] and combining with the operation law of the closed-loop supply chain, the initial value of the constant is set as follows: Demand smoothing time = 3; Mean life = 60; Value factor of recycled products = 0.35; New parts value factor = 0.12; Value factor of recovery parts = 0.1; M Base remanufacturing factor = 0.2; M Delay in delivery = 2; M New parts procurement delay = 0.3; The recycled parts supply delay of T = 1; M Fixed expenditure = 3000; R Basic sales factor = 0.3; R Order delay time = 3; R Fixed expenditure = 1000; T Basic recovery factor = 0.5; T Recovery parts supply rate smoothing time = 0.5; T Recovery delay = 0.2; T Fixed expenditure = 2000. Considering the different risk preferences of closed-loop supply chain enterprises, it is considered that remanufacturers and recyclers are risk-averse enterprises, and their initial strategy selection probability is set to 0.4—that is, $\alpha =0.4$, $\gamma =0.4$. As a risk-neutral enterprise, the retailer’s initial strategy selection probability is set as 0.5—that is $\beta =0.5$. The following simulation results are obtained by running the model.

Figure 3 shows the product circulation in the closed-loop supply chain. It can be found that the retailer faces the consumer market and can respond to changes in market demand more quickly. The production speed of the remanufactured products is affected by the retailer’s order and its own market research, and the response to market demand changes is relatively lagging. The recycling behavior of the recyclers is mainly driven by the remanufacturing production activities and has nothing to do with the total amount of discarded products in the market. Therefore, influenced by the demand for remanufactured products during the initial operation of the model, the inventory of the retailer, the remanufacturer, and the recycler in the early stage decreased successively. With the formal operation of the closed-loop supply chain, the inventory of the remanufacturer and the recycler leveled off, while the inventory of the retailer showed an upward trend. This is due to the subsidy support from the government and remanufacturers, which leads to the active ordering of remanufactured products by retailers, and thus the increase in inventory.

Figure 4 shows the profit simulation of the three parties in the closed-loop supply chain. It can be found that among the tripartite enterprises, the profit of the retailer is the largest, that of the remanufacturer is the second, and that of the recycler is the least. By analyzing the key points of profitability, we can find that the remanufacturer needs to rely on batch manufacturing to share the costs to achieve profitability, and the key point of profitability lies in batch production. While the retailer’s products are sold to consumers, the key point of profit is the profit of a single product. The recycler often has weak independent profitability due to the small business scale, weak technical ability, and poor information acquisition ability. The key point of its profitability lies in government policy support and supply chain-related enterprise support. Therefore, under the condition of the same product flow, the profit situation of the three enterprises is quite different. In addition, from the perspective of capital, the recycler has the least profit, which may be the weak link in the three-level closed-loop supply chain, which can easily affect the stability of the closed-loop supply chain.

Figure 5 shows the strategy selection of the game players. It can be seen that despite their different risk preferences, both the recycler and the retailer quickly reached the evolutionary stability strategy within 5 weeks. When the remanufacturer chooses high-green production, it is constrained by the extra investment $c_{UM}$ and the government’s incentive and punishment measure $l$, which leads to the wavering of its strategy choice in the first 5 weeks. According to Figure 6, with the increase in the remanufacturer’s output, the marginal production cost per unit of product gradually decreases. From 5 to 10 weeks, when the remanufacturer chooses to carry out high-green production, the expected income rapidly becomes dominant, which makes the remanufacturer gradually inclined to carry out high-green production. Therefore, the early government reward and punishment strategy plays a crucial role in encouraging the remanufacturer to carry out high-green production.

4.2. Rising Scenario Setting and Simulation Analysis

With the advance of supply chain globalization, production enterprises are paying increasing attention to the smooth, safe, and stable supply chain. However, force majeure events such as trade sanctions and COVID-19 will not only affect the market demand of products, but also have a serious negative impact on the supply side of the manufacturers, such as price rises and material supply shortages. Therefore, in order to simulate the situation when the price of new parts rises due to force majeure events or changes in the market supply and demand conditions, this paper sets two rising scenarios of low and high rising ranges, as shown in

Table 5. Scenario setting of new parts increase.

	Initial Value of New Parts Value Factor	Range of Increase	The Adjusted Value of the New Parts Value Factor
Low-rise scenario	0.12	25%	0.15
High-rise scenario	0.12	50%	0.18

.

4.2.1. Low-Rise Scenario Analysis

According to the scenario setting, the value factor of new parts in the system dynamics model was increased by 25% and set as 0.15. The simulation results and analysis are as follows.

The simulation results are analyzed under the low-rise scenario. As Figure 7 shows, compared to the base scenario, the 25% increase in the price of new parts delayed inventory stabilization for the remanufacturers and retailers, but had less of an impact on the recyclers’ inventories. As Figure 8 shows, compared to the base scenario, the 25% increase in the price of new parts reduces the remanufacturers and retailer’s profits, but has little effect on the recycler’s profits. As Figure 9 shows, compared to the base scenario, the 25% increase in the price of new parts delays the time for the remanufacturers to reach an evolutionary stable state, but has less impact on the retailers and recyclers.

4.2.2. High-Rise Scenario Analysis

According to the scenario setting, the value factor of new parts in the system dynamics model was increased by 50% and the value was set as 0.18. The simulation results and analysis are as follows.

The simulation results are analyzed under the high-rise scenario. As shown in Figure 10, compared to the base scenario, the 50% increase in the price of new parts caused the inventory of the remanufacturers and retailers to continue to decrease from the benchmark inventory level to zero after 30 weeks, and the inventory of the recyclers dropped to an extremely low level and then remained unchanged. The main reason for this result is that when other conditions remain unchanged, the rising cost caused by the rising price of new parts renders the remanufacturer unable to continue to purchase parts for subsequent production, resulting in supply chain disruption, as shown in Figure 11. As Figure 12 shows, compared to the base scenario, the 50% increase in the price of new parts leads to a significant decline in profits for the remanufacturer, retailer, and recycler. Among them, the profit of the retailer continues to decline after the remanufacturer stops the production of remanufactured products, and the profits of the remanufacturer and the recycler remain at a very low level after the decline. This is due to the 50% increase in the price of new parts, which makes it impossible for remanufacturers to maintain continuous production, resulting in the interruption of product circulation in the closed-loop supply chain, which, in turn, significantly reduces the profits of the remanufacturers, sellers, and recyclers. As Figure 13 shows, compared with the base scenario, the 50% increase in the price of new parts causes the strategy evolution path of the remanufacturers, retailers, and recyclers to change significantly. The willingness of remanufacturers to carry out high-green production fluctuates and cannot reach an evolutionary equilibrium state, while the willingness of retailers and recyclers to actively sell and provide high-quality recycled parts is very strong in the early stage and then fluctuates in the later stage. The main reason for this is that the remanufacturer’s willingness to carry out high-green production is not high due to the dual impact of rising costs and government reward and punishment strategies, and it has always been unable to achieve an evolutionary equilibrium. When the remanufacturer is able to supply the remanufactured products, the retailer is willing to actively sell, and the recycler is willing to supply the remanufacturer with high-quality recycled parts.

4.3. Summary of Simulation Results

By comparing and analyzing the simulation results of the baseline scenario, low-rise scenario, and high-rise scenario, the following conclusions are made: (1) The closed-loop supply chain stability reflected in the product circulation, corporate profit, and strategy selection has a certain tolerance for the price rise of new parts, and the 25% increase in the price of new parts will not cause substantial damage to its stability; (2) When the price of new parts increases by 50%, the closed-loop supply chain will be broken at the point of the remanufacturer, which will have an impact on the retailer and the recycler, and the stability of the closed-loop supply chain will be seriously damaged; (3) The government’s dynamic reward and punishment strategy fails to regulate and guide the member enterprises of the closed-loop supply chain when the price of new parts rises by 50%.

5. Optimization of Reward and Punishment Strategies

When the price of new parts increases by 50%, the closed-loop supply chain will be broken from point of the remanufacturer, and the regulating and guiding effect of the government’s dynamic reward and punishment strategy will be reduced, or even rendered ineffective. Therefore, this paper seeks to adjust and optimize the government’s dynamic reward and punishment strategy for remanufacturers to make the closed-loop supply chain operate again and to restore stability.

5.1. Optimization Scheme of Reward and Punishment Strategy

According to the simulation analysis in Section 4, it can be found that the increase in remanufacturing cost caused by the 50% increase in the price of new parts is the main reason that the remanufacturer cannot continue production. Therefore, the optimization idea of the government dynamic reward and punishment strategy for the remanufacturer is to relieve the financial pressure of the remanufacturer and encourage the remanufacturer to resume production under the premise of not excessively increasing the financial pressure of the government. The optimized government dynamic reward and punishment strategy will further increase the tolerance of the whole closed-loop supply chain to the price increase in new parts. Adjust the function M government fines for low-green production and government subsidy factor M and set it as $l=\left(1-\alpha \right)\ast a_1+b_1$ and $g_M=a_2\ast \alpha +b_2$, where $a_1$, $b_1$, $a_2,$ and $b_2$ are design parameters. $a_1=7200$, $b_1=$0, $a_2=0.24,$ and $b_2=0.17$ are determined through multiple simulation and tuning tests on the model. M government fines for low-green production and government subsidy factor M vary with the probability (α) of high-green production of the remanufacturer, as shown in Figure 14. According to Figure 14, it can be found that the government fine for low-green production of M before and after optimization is 0 when α = 1, because as long as the remanufacturer resolutely carries out high-green production, the government will not fine it. Compared with the original government dynamic reward and punishment strategy, the optimized government dynamic reward and punishment strategy focuses on stimulating the willingness of the remanufacturer to carry out high-green production by increasing government subsidies and reducing government fines when the probability of high-green production of the remanufacturer is low.

5.2. Simulation Results and Analysis

According to the function relation of the optimization scheme of the reward and punishment strategy, the function equation of M government fines for low-green production and government subsidy factor M in the system dynamics model is reset. The system dynamics model optimized after the government dynamic reward and punishment strategies of the remanufacturer was simulated, and the simulation results and analysis are as follows.

The simulation results of the government dynamic reward and punishment strategy optimization scheme are analyzed and summarized. After optimizing the government’s dynamic reward and punishment strategy for remanufacturers, the remanufacturers gradually resume production, the inventory levels of the remanufacturers, retailers, and recyclers increased to a certain extent, and the circulation of the closed-loop supply chain products gradually recovered, as shown in Figure 15. With the gradual recovery of product circulation in the closed-loop supply chain, the profits of the remanufacturers, retailers, and recyclers recovered to a certain extent, as shown in Figure 16. The optimization of government dynamic reward and punishment strategies for remanufacturers can effectively improve the willingness of remanufacturers, retailers, and recyclers to carry out high-green production, active sales, and supply high-quality recycled parts, respectively, as shown in Figure 17. In conclusion, although the optimization of the government’s dynamic reward and punishment strategy for remanufacturers has not restored the operation status of the closed-loop supply chain to the level of the baseline scenario, the optimization of the government’s dynamic reward and punishment strategy for remanufacturers can restore the operation of the closed-loop supply chain and restore the stability of the closed-loop supply chain to a certain extent.

6. Conclusions

In this paper, a complex system model is constructed through internalizing the closed-loop supply chain, system dynamics, and evolutionary game theory in order to analyze the impact on the stability of the closed-loop supply chain, as reflected in the product circulation, corporate profits, and strategic choices when the price of new parts rises due to force majeure events or changes in market supply and demand conditions. Through comparing and analyzing the baseline scenario, the low-rise scenario, the high-rise scenario, and the high-rise scenario after the optimization of the reward and punishment strategy, the following conclusions are drawn:

(1)

The closed-loop supply chain has a certain tolerance for the price rise of new parts, but when the price of new parts rises excessively, the closed-loop supply chain will be broken from the remanufacturer. Specifically, when the price of new parts increases by 25%, it will delay the time for remanufacturers and retailers to reach the stable state of product circulation, reduce the profits of remanufacturers and retailers, and inhibit the willingness of remanufacturers to carry out high-green production in the early stage. The 50% increase in the price of new parts breaks the closed-loop supply chain from the remanufacturer. At this time, the remanufacturer gradually stopped producing remanufactured products, the product circulation of remanufacturers, retailers, and recyclers stagnated, the profits of enterprises decreased, the willingness to choose strategies wavered, and the stability of the closed-loop supply chain was seriously damaged.

(2)

There is a boundary between the regulating and guiding effects of the government dynamic reward and punishment strategies. Specifically, when the price of new parts rises too high, the manufacturer gradually becomes unable to maintain continuous production, leading to the interruption of product circulation until the closed-loop supply chain breaks. At this time, the original government dynamic reward and punishment strategy cannot regulate and guide the closed-loop supply chain stability recovery, and the original government dynamic reward and punishment strategy is invalid.

(3)

Increasing government subsidies for the remanufacturer when the probability of high-green production is low and reducing government fines for the remanufacturer when the probability of high-green production is low can promote the recovery of the closed-loop supply chain in the high-rise scenario. In other words, by increasing the government support of remanufacturers with a low probability of high-green production, the stability of the closed-loop supply chain under the high-rise scenario can be restored, and the tolerance of the closed-loop supply chain as a whole to the rising price of new parts under the high-rise scenario can be improved.

Compared with the existing literature, the main contributions of this paper are the construction of a complex system model and its attempt to break through the external coupling problem when the three theories of closed-loop supply chain, evolutionary game, and system dynamics are combined, as well as realizing the introduction of the mixed strategy selection probability of the game subject into the operation of the closed-loop supply chain. That is, the evolutionary game decision-making results reversibly act on the closed-loop supply chain operation. In terms of practicality, it can guide the government and closed-loop supply chain enterprises to implement some preventive measures in advance in the face of rising prices of new parts. For example, when the price of new parts rises by more than 25%, the remanufacturer can reduce administrative costs and improve production efficiency in advance to prevent an excessive increase in the total production costs. The government should focus on the operation of the remanufacturer, optimize the government dynamic reward and punishment strategy of the remanufacturer in a timely manner, and improve the tolerance of the whole closed-loop supply chain to the increase in new parts. However, this paper also has some limitations. The paper considers the government dynamic reward and punishment measures as the external environment of the closed-loop supply chain, and believes that the government always plays the role of supervisor and leader, without considering the game behavior between the government and the closed-loop supply chain enterprises. In addition, the optimized government dynamic reward and punishment strategy will increase the financial pressure of the government to a certain extent.