Operational Decisions of Construction and Demolition Waste Recycling Supply Chain Members under Altruistic Preferences

Abstract

How to efficiently and greenly dismantle abandoned buildings and reuse them is a dilemma facing the building material industry’s low-carbon objective. However, relevant studies ignore the influence mechanism of altruistic preferences of enterprises on green dismantling technology in supply chains. Driven by filling this theoretical gap, this paper firstly integrates reciprocal altruism theory and the Stackalberg game method and constructs a construction and demolition waste (CDW) recycling supply chain system consisting of a recycler and a remanufacturer, in which enterprises’ altruistic preferences are considered. The main theoretical outcomes of this paper are as follows. (1) In the case of unilateral altruism, enterprises’ altruistic preference behaviors help in increasing the green dismantling technological level and the amount of CDW recycling. Under the mutual altruism model, the influence of the recycler’s altruistic preference degree on the amount of CDW recycled hinges on the remanufacturer’s altruistic preference degree. (2) The utility of the enterprises and the green dismantling technological level are optimized under the mutual altruism model. (3) In a system of unequal power, unilateral “goodwill” by the follower will have a negative effect on their own interests; the leader plays a crucial role in facilitating equal cooperation and realizing win–win situations. This paper enriches the reciprocal altruism theory in waste management. It also helps in providing guidance for the recycler and remanufacturer in making operational decisions.

1. Introduction

According to statistics to date, the quantity of construction and demolition waste (CDW) of the whole world has reached 10 billion tons, with the United States and China generating 600 million tons and 2500 million tons of CDW, respectively [1]. In addition, CDW in the European Union has reached 35 per cent of the total waste volume [2]. The presence of CDW hinders the transition of society towards resource efficiency and circular development [3,4,5]. Academics are engaged in a heated debate on how to effectively manage CDW and reduce its negative impact on the environment. Many scholars agree that the recycling and resourcing of CDW is an important opportunity to achieve resource recycling, reduce solid-waste pollution and promote low-carbon transition in the construction industry [6,7,8,9,10]. The main workflows of CDW recycling and resourcing can be simplified to dismantling and use for remanufacturing. The first task facing recyclers is dismantling abandoned buildings. Using specific demolition technology, abandoned buildings are dismantled into small fragments and resold to remanufacturers as raw materials for the production of recycled building materials. Traditional building dismantling technologies mainly comprise deconstructive, mechanical and mixed technologies [11], which are often criticized for producing a large amount of debris mixed in with CDW in the dismantling process, leading to increased backfill rates, disposal costs and associated carbon emissions [12]. As a result, experts in the field of engineering are focusing on more environmentally friendly building dismantling technologies (i.e., green dismantling technology), such as selective dismantling technology [12], “self-propelled modular transporter (SPMT) technology + large segment cutting” technology which is applicable to bridge dismantling [13], and soundless chemical demolition and induction heating demolition technology [14]. Researchers have found that selective dismantling technology can increase the recycling rate of CDW by up to 90 per cent and reduce carbon emissions by up to 55 per cent compared to traditional demolition techniques [12]. “SPMT technology + large segment cutting” ensures structural safety while reducing noise and environmental pollution during building dismantling [13]. These suggest that increasing investment in the research and development of green dismantling technology is of key significance in promoting the low-carbon development of the building materials industry.

However, the existing research only focuses on the process innovation of green dismantling technology and lacks in-depth analyses of the mechanisms by which firms’ social preferences influence green dismantling technology. Enterprises are the main body of green innovation, and their social preferences often have a non-negligible impact on their innovation decisions. Reciprocal altruism theory states that even if one is not a relative, altruistic behavior is possible under certain conditions of reciprocity [15,16]. On the one hand, recyclers increase their investment in green dismantling technology to enable the sorting and placement of CDW, which reduces disposal costs and carbon emissions in the production of recycled building materials by remanufacturers. Nowadays, governments around the world are beginning to adopt carbon tax policies to limit the carbon emissions of the building material industry and to realize a zero-carbon goal as quickly as possible. Therefore, remanufacturers will show an altruistic preference for recyclers out of a motivation to increase their investment in the research and development of green dismantling technologies, thus reducing the cost of carbon emissions. On the other hand, the remanufacturer can offset the cost of recyclers’ investments in demolition technology by increasing the resale price per unit of CDW paid to recyclers. This incentivizes recyclers to be altruistic towards remanufacturers. In terms of the natural properties of individuals, they tend to provide help to individuals related or close to them [17]. In a CDW recycling supply chain, remanufacturers receive CDW from recyclers to remanufacture building materials, reflecting the frequent exchange of materials and information between supply-chain enterprises. The social preferences of supply-chain members can significantly affect final operational decisions and are a key topic of scholarly interest [18,19]. Therefore, there is a need to explore the influence mechanisms of altruistic preference behavior of recyclers and remanufacturers in the CDW recycling supply chain.

In summary, this paper addresses two main scientific questions: (1) How does the level of altruistic preference of CDW recycling supply chain members affect operational decisions as well as individuals’ utility? Answering the first question leads us to a new insight relative to an impact factor influencing operational decisions in a CDW recycling supply chain. (2) Which model (remanufacturer altruistic model, recycler altruistic model, or mutual altruistic model) optimizes the utility of supply chain members and the green dismantling technological level? Investigating the second question provides guidance on how enterprises show altruistic preference for better CDW recycling and resourcing.

Distinguishing this paper from other studies, there are three main innovations. Namely, (1) most of the existing studies on reciprocal altruism theory focus only on motivational explanations of altruistic behavior, e.g., “indirect reciprocity aims to obtain benefits from observers” [20] and “competitive altruism is motivated by the desire to help select partners” [21]. However, there is a lack of relevant literature clarifying the role of power structure in influencing the mechanism of altruistic behavior. This paper firstly introduces altruistic preferences into the CDW recycling supply chain to investigate reciprocal altruistic preference behaviors of the recycler as a follower and the remanufacturer as a leader under the master–slave structural model, broadening the field of research on reciprocal altruism theory and providing new perspectives from the building materials industry and the supply chain to supplement the theory. (2) Previous literature has examined the influence of altruistic preference on carbon reduction technologies [22] and greenness of products [23], but no scholars have yet investigated how enterprises’ altruistic preferences influence the green dismantling technological level in the context of the CDW recycling supply chain. Addressing this research gap, this paper enriches scholars’ understanding of the mechanisms and motivations of altruistic preferences. (3) Contemporary theoretical research on waste management has been quite rich in the direction of operational decisions. Key factors affecting operational decisions of CDW recycling supply chain enterprises are revealed both externally and internally to the organization, including information sharing among enterprises [24] and innovation spillovers [25]. However, study on impact of social preferences of enterprises on operational decisions related to CDW management still lacks. This paper combines the reciprocal altruism theory and Stackelberg game method to construct a mathematical model to solve this scientific problem. At the same time, based on the model analysis and numerical simulation, this paper verifies that the reciprocal altruistic preference behavior of enterprises can effectively promote the cooperation of the members of the CDW recycling supply chain and the efficiency of waste management, which enriches the content of the waste management theory.

To address the scientific questions this paper proposes, several major tasks were completed. Firstly, a Stackelberg game model consisting of a recycler and a remanufacturer considering the altruistic preference behaviors was constructed to reveal the mechanism of reciprocal altruistic preference of CDW recycling supply chain members on operational decisions. Further, this paper explores and compares the effects of the altruistic preference behaviors of the recycler and remanufacturer on operational decisions and the utility of supply chain members under different models (remanufacturer altruistic model, recycler altruistic model, and mutual altruistic model).

2. Literature Review

A literature review on green dismantling technology, altruistic preferences, development of reciprocal altruism theory, and operational decisions in the CDW recycling supply chain is provided in this section.

2.1. Studies on Green Demolition Technology

With the frequent occurrence of urban renewal activities, the original high-carbon, inefficient, time-consuming and disorganized dismantling technologies can no longer meet the needs of the green development of the construction industry. Thanks to advances in research at the intersection of lasers, chemistry and mechanics, new building dismantling technologies are emerging and becoming more sophisticated. Scholars focusing on green dismantling technology are dedicated to the field of technological development that can be directly applied to the demolition process as well as analytical techniques that enable better organization of dismantling. Literature combing revealed that mainstream dismantling technologies mainly include blasting, soundless chemical demolition and induction heating demolition, selective dismantling and “water jet demolition”. Of these, blasting is often applied to wall-slab high-rise buildings [26]; silent chemistry and induction heating dismantling is conducive to reducing carbon emissions during blasting or layer-by-layer dismantling [14,27]; selective dismantling is based on the systematic dismantling of buildings, effectively increasing the recycling rate of CDW [12]; “water jet demolition” focuses on separating the cement mortar from the aggregate to increase the recycling rate [28]. Some scholars have turned their attention to analytical technologies used for assessment and control prior to dismantling to reduce the risks and costs and increase the efficiency of dismantling. For example, image-maximizing laser scanning can be used to pre-assess buildings before they are deconstructed, which helps in improving dismantling safety and waste recycling rates [29]. Furthermore, “computerized 4D simulation” enables technical simulation of the dismantling process to help recyclers develop and select the optimal dismantling solution, thus increasing the efficiency of dismantling [30].

Despite the continuous iteration and updating of green dismantling technology to adapt to the concept of recycling and low-carbon development in cities, there is still a gap in the research on organizational behavioral decision-making around green dismantling technological level by recyclers. Existing research on the dismantling of abandoned buildings mostly focuses on its application value, and no scholars have yet explored the influence mechanism of stakeholders on green dismantling technological level by recyclers. In addition, research on interpretations of recyclers’ motivation to green innovation behavior as well as the theoretical guidance to incentivize the development of green dismantling technology by recyclers is sorely lacking. Therefore, this paper takes the lead in proposing a Stackelberg game model that considers recyclers’ green dismantling technologies and discusses the impact of altruistic preference behaviors of members within the CDW recycling supply chain system on green dismantling technological level based on the reciprocal altruism theory.

2.2. Studies on Altruistic Preferences

Altruistic preferences are a relatively common attribute of humans in their daily trading activities [31], which is frequently introduced by scholars. Most of the literature focuses on the effect of altruistic preferences of enterprises or how to address negative effects of altruistic preferences. First, supply chain members’ altruistic preference behavior affects their operational decisions and has certain positive or negative effects on the whole supply chain. Some merchants reward consumers for recommending their products for promotional purposes. Altruistic preferences of consumers should be considered when determining optimal reward structure [32]. In addition, under the psychology of altruistic preferences, platforms tend to subsidize merchants affected by adverse condition shocks. The relationship between subsidies and platforms’ altruistic preferences is: compared to disadvantaged platforms, dominant platforms subsidize retailers if and only if their altruistic preference level is high; no matter which platform, subsidies increase as the platform’s altruistic preference increases [33]. In terms of positive effects of supply chain members’ altruistic preferences, studies have suggested that it helps in increasing each other’s profits [31,34] and supply chain members’ utility [35], dampening the shock effect of tariff surges on international supply chains [34] and promoting the goals of even profit distribution and green supply-chain development [18]. Although altruistic preferences have some positive effects on supply chains, their negative effects need to be paid more attention to and addressed by scholars. Scholars believe that if enterprises hold asymmetric social preferences (self-interested or altruistic), it negatively influences green supply chains and reduces the utility of supply chain members [36]. In addition, altruistic behavior may also lead to free-riding behavior [34]. Another study on altruistic preferences focuses on how to address the negative effects of enterprises’ altruistic behavior. For example, a reasonable contract is usually effective in resolving conflicts of interest between businesses [37]. Some scholars have used quantitative analysis to infer that supply chain profits can be optimized when firms have equal altruistic preferences [35].

From the literature review above, the research on altruistic preference is relatively abundant. However, no scholars in the construction industry have yet introduced altruistic preferences into the research context of CDW resourcing. Particularly, research on examining the impact of altruistic preferences on green dismantling technology of recyclers is lacking. As a matter of fact, the construction industry, as a huge industrial system, has more frequent interactions and exchanges among enterprises in the supply chain. Therefore, altruistic preferences between upstream and downstream enterprises cannot be ignored.

2.3. Studies on the Reciprocal Altruism Theory

Intentions of reciprocal altruistic behavior can be explained through social exchange theory [38]. That is, reciprocity is based on the fact that altruistic behavior can be profitable [21]. Specifically, there are two types of reciprocal behavior, direct and indirect. Direct reciprocal behavior emphasizes direct cooperation between two individuals in a mutually beneficial relationship. While indirect reciprocal behavior tends to obtain benefits from observers [20]. Indirect reciprocity potentially leads to the perpetuation of malicious behavior, which in turn is detrimental to cooperation between individuals [20]. In recent years, people’s cooperative exchanges have gradually deepened with new problems emerging in practice. Traditional reciprocal altruism theory can no longer explain some social phenomena well. As scholars’ understanding to cooperation becomes more and more profound, some new theories have been extended to form a good supplement to traditional reciprocal altruism theory. Roberts argues that there is also a special case of altruism that is not rewarded in kind or in any other form, e.g., competitive altruism [21]. Competitive altruism refers to the conscious altruistic tendencies displayed by individuals to optimize strategic alliances. When individual interests conflict with the collective interests of social problems, the use of competitive altruism to solve such problems is more effective than indirect reciprocity [39]. This suggests that publicizing the contributions of individuals within a collective can help foster cooperation in a context where people tend to choose altruistic partners. Similar to competitive altruism’s emphasis on reputation, scholars have thought of using signaling gains to explain unconditional altruistic behavior. This is when altruism is viewed as an individual quality signal and used as a quality indicator [15]. Some scholars have also attributed the phenomenon of unconditional altruism to the emergence of strong reciprocity (the tendency of individuals to cooperate while punishing betrayers at some cost) [40]. Gintis argues that strong reciprocity cannot be perpetuated unless cooperation is completely unconditional [40]. Deng et al. provide a strong critique of this assertion and build on Gintis’ model to modify and argue that strong reciprocity can evolve [41]. Some scholars have studied the altruistic behavior of individuals based on whether or not they have been previously benefited by the other person and have called it contingent reciprocity [42]. Other researchers have made the argument that social and cultural systems have some influence on altruistic behavior [43].

It can be said that reciprocal altruism theory is a good theoretical tool to explain cooperation between groups. However, there is a dearth of studies that use reciprocal altruism theory to explain the behavioral mechanisms of the CDW recycling supply chain. This paper aims to analyze how altruistic preference of enterprises influences green dismantling technology using reciprocal altruism theory making up for the relevant research deficiencies. It should be noted that previous studies have considered the reciprocal altruistic behavior between individuals under equal conditions, and have not included the power structure in the research scope of reciprocal altruism theory.

2.4. Studies on Operational Decisions in a CDW Recycling Supply Chain

The operation of a CDW recycling supply chain is composed of decisions made by the various actors involving recyclers and remanufacturers. They are not only influenced by external factors such as the market and the government, but also by the interactions between the stakeholders in the supply chain. The first stream of research on operational decisions focuses on the influence of external factors on operational decisions in a CDW recycling supply chain, including government intervention [44,45], consumer green preferences [46], and consumer risk attitudes [6]. Specifically, from the perspective of government, the rate of illegal landfill of CDW reduces when the government intervenes appropriately [44]. From a consumer perspective, both green preferences and risk attitudes of consumers affect the market selling price of productions [6,46]. In addition, consumers’ risk attitudes also affect the level of green innovation in enterprises [5]. The second stream of research on operational decision-making explores the impact of internal organizational factors on operational decision-making, including information sharing [24], technology spillovers [25], and power structure [47]. Research has shown that the information-sharing behavior of recyclers with more pessimistic market demand forecasts leads to higher levels of environmental responsibility among remanufacturers [24]. Technology spillovers can significantly increase the level of collaborative innovation between enterprises [25].

Table 1. Research topics and content related to this study.

Topic	Content of Relevant Studies	Bibliography
Studies on green dismantling techniques	Technologies directly applied to the dismantling process	[12,14,26,27,28]
	Technologies used for assessment and control prior to dismantling	[29,30]
Studies on altruistic preferences	Effect of altruistic preferences of enterprises	[18,32,33,34,35]
	How to address negative effects of altruistic preferences	[35,37]
Studies on the reciprocal altruism theory	Traditional reciprocal altruism theory	[20,21,38]
	Extension of reciprocal altruism theory	[21,39,40,41,42,43]
Studies on operational decisions in a CDW recycling supply chain	External factors	[5,6,44,45,46]
	Internal factors	[24,25,47]

summarizes and categorizes the studies related to this paper. The above literature summarizes the factors influencing the operational decisions of CDW recycling supply chain members from both external and internal research perspectives, providing theoretical guidance for building materials enterprises to make relevant decisions to improve CDW recycling efficiency. However, existing studies have neglected that how supply chain members’ social preferences influence operational decisions when summarizing internal factor sets. On the basis of this gap, the remanufacturer and recycler’s altruistic preference behaviors are integrated to explore how their altruistic preference behavior affects operational decisions in a CDW recycling supply chain.

3. Problem Description and Relevant Assumptions

Green dismantling technologies have been commonly used in construction and demolition activities. In China’s Xiong’an New Area, for example, green dismantling technologies have been pioneered in the demolition of relocation and resettlement in Xiong’an New Area [48]. High-tech equipment such as hydraulic shears, diamond saws and intelligent automated mobile crushing and screening machines were introduced to participate in the dismantling work, and on-site sorting and transportation of CDW to maximize the resourcing of CDW. These processes can be simplified into a CDW recycling model, as shown in Figure 1.

Figure 1 illustrates the process of CDW recycling. Specifically, this paper considers a CDW recycling supply chain system, including a remanufacturer, a recycler, a CDW production unit, and consumers. By dismantling abandoned buildings and infrastructures, the recycler recovers CDW from CDW production unit at a unit price w₁. The remanufacturer produces recycled building materials with CDW buying from the recycler at a price w₂, and sells them to the building materials market, assuming a unit price of p for the recycled building materials. For the recycler, one of the unavoidable challenges in the process of building dismantling is the occurrence of noise and pollution of the environment (dust, etc.) [49]. More importantly, if the CDW is not effectively separated during the dismantling of the abandoned building, the remanufacturer will need to spend more attention on the treatment of the CDW, resulting in an increase in carbon emissions [12]. This is contrary to the international consensus on carbon reduction. As key stakeholders in the dismantling and recycling phases of abandoned constructions, recyclers need to take more responsibility and invest more in green dismantling technology to help the remanufacturer in upgrading the level of emission reduction [13]. It is assumed that green dismantling technology level of the recycler is g.

Table 2. Explanation of variables and their meanings.

Variable	Meaning	Reference
a	Basic market size for CDW	[5]
q	Quantity of CDW recycling (decision variable)	[50]
g	Green dismantling technological level by the recycler (decision variable)	[5]
p	Sales price of recycled building materials	[25]
w1	Unit recycling price for CDW paid by the recycler to CDW production unit	[24]
w2	Unit recycling price for CDW paid by the remanufacturer to recycler (decision variable)	[24]
η	Influence coefficient of green dismantling technological level on the recycling price	[50,51]
h	Cost coefficient of green dismantling technological level	[52]
s	Tax per unit of carbon emissions	[6]
${\theta }_m$	Altruistic preference coefficient for the remanufacturer, ${\theta }_m\in (0,1)$	[34]
${\theta }_r$	Altruistic preference coefficient for the recycler, ${\theta }_r\in (0,1)$	[34]

lists the variables that appear in this paper and provides a detailed explanation of their meaning.

For modeling purposes, the following assumptions are made:

(1)

In the real market, the remanufacturer decides the price to be paid to the recycler for the recycled CDW, so it tends to have more initiative. Additionally, compared with the recycler, the remanufacturer is closer to the building materials sales market. The remanufacturer can deliver valuable market information to the recycler to support the recycler’s effective decision-making. Therefore, this paper recognizes that the remanufacturer is the leader in the supply chain [24].

(2)

Without loss of generality and to make the model easy to solve, this paper considers that all CDW recycled can be reused [25], neglecting the effect of green dismantling technological level on the proportion of CDW reused.

(3)

As previously emphasized, it has been shown that altruistic preferences have a significant effect on enterprises’ technological innovation [53]. Society is a vast network of complex interactions among countless people, altruistic preferences are prevalent in all fields and have received attention from scholars [53,54,55]. In order to align the model with reality and to explore whether the altruistic preferences of enterprises in the CDW recycling supply chain could increase recyclers’ level of green dismantling technology. This paper assumes that the recycler and remanufacturer’s altruistic preference coefficients are ${\theta }_r$, ${\theta }_m$, respectively. The recycler and remanufacturer’s utility functions are $Um={\pi }_m+{\theta }_m{\pi }_r$ and $Ur={\pi }_r+{\theta }_r{\pi }_m$, respectively [33].

(4)

Improved green dismantling technology by recyclers not only help to reduce noise and resource wastage, but also represent a significant opportunity to reduce the cost of carbon emissions for remanufacturers. This is because recyclers can reduce the disposal process for remanufacturers by adopting green dismantling technology that enable the separation of CDW during the demolition of buildings [12]. To realize a low-carbon and circular economic development model as early as possible, governments around the world have established incentives and penalties to limit carbon emissions from the construction industry. In this paper, it is assumed that a penalty s is required to be paid per unit of carbon emissions. For ease of calculation, every unit of recycled building material generates 1 unit of carbon emissions. According to a similar study [56], the cost of the remanufacturers due to carbon emissions is $\left[\left(1-g\right)q\right]s$.

(5)

The basic market size of CDW is constant a, and the supply is q. Due to financial constraints, if recyclers increase their investment in R&D of green dismantling technology, the recycling price paid to CDW production unit will inevitably decrease. On the other hand, the green demolition behavior of CDW recyclers can improve their green image [51], thus promote the willingness of CDW production unit to supply CDW and offsetting part of the price effect. It is assumed that the influence coefficient of the green dismantling technological level on the recycling price paid by recyclers to CDW production unit is η. Drawing on Zheng and Jin [50], this paper constructs the inverse supply function for CDW as $w_1=a+q-\eta g$.

(6)

To incentivize recyclers to develop green dismantling technologies, the penalty per unit of carbon emissions should be sufficiently high. At the same time, to ensure the decision variables are positive, this paper assumes that $2\eta <s<p-a$.

(7)

Different altruistic models have optimal solutions within the full set of altruistic coefficients when $2h>{\left(s+\eta \right)}^2$. To ensure that the model fits a realistic scenario, the profits of the remanufacturers and recyclers are positive, deducing that $0<{\theta }_m<{\displaystyle \frac{3}{4}}$, $0<{\theta }_r<{\displaystyle \frac{1}{3}}$.

4. Model Developed and Solved

4.1. Optimal Solutions under Non-Altruistic Model

The members of the CDW recycling supply chain all have their own profit maximization as their decision objective when they do not have altruistic preference behavior. The remanufacturer firstly decides the price per unit of CDW to be paid to the recycler $w_2$. The recycler decides the green dismantling technological level $g$ and quantity of CDW recycling $q$ based on $w_2$. Therefore, the remanufacturer and recycler’s decision-making objective functions are shown in Equations (1) and (2):

$${\pi }_m=\left(p-w_2-s+gs\right)q$$

(1)

$${\pi }_r=\left(w_2-\left(q+a-\eta g\right)\right)q-{\displaystyle \frac{1}{2}}h{g}^2$$

(2)

The Hessian matrix of recycler’s objective function is:

$$H_r=\left(\begin{array}{cc}-2& \eta \\ \eta & -h\end{array}\right)$$

(3)

When $2h-{\eta }^2>0$, the objective function of the recycler’s decision is strictly concave. The optimal decisions of the recycler are $q={\displaystyle \frac{h\left(-a+w_2\right)}{2h-{\eta }^2}}$ and $g={\displaystyle \frac{\eta \left(a-w_2\right)}{-2h+{\eta }^2}}$, respectively. Substituting into the decision objective function for the remanufacturer, deducing that the objective function of the remanufacturer is also concave when $h>{\displaystyle \frac{\eta \left(s+\eta \right)}{2}}$. The optimal solutions are shown in Equations (4)–(8):

$$w_2={\displaystyle \frac{1}{2}}\left(a+p-s+{\displaystyle \frac{s\left(a-p+s\right)\eta }{-2h+\eta \left(s+\eta \right)}}\right)$$

(4)

$$q=-{\displaystyle \frac{h\left(a-p+s\right)}{4h-2\eta \left(s+\eta \right)}}$$

(5)

$$g=-{\displaystyle \frac{\left(a-p+s\right)\eta }{4h-2\eta \left(s+\eta \right)}}$$

(6)

$${\pi }_m={\displaystyle \frac{h{\left(a-p+s\right)}^2}{8h-4\eta \left(s+\eta \right)}}$$

(7)

$${\pi }_r={\displaystyle \frac{h{\left(a-p+s\right)}^2\left(2h-{\eta }^2\right)}{8{\left(-2h+\eta \left(s+\eta \right)\right)}^2}}$$

(8)

4.2. Optimal Solutions under Recycler Altruistic Model (R)

Under recycler altruistic model, the recycler prefers to increase the profit level of the remanufacturer in addition to focusing on its own profit. Assuming that ${\theta }_r$ is the recycler’s altruistic preference degree. The remanufacturer and recycler’s utility functions are shown in Equations (9) and (10), respectively.

$$U\left({\prod }_m\right)={\pi }_m=\left(p-w_2-s+gs\right)q$$

(9)

$$U({\prod }_r)=\left(w_2-\left(q+a-\eta g\right)\right)q-{\displaystyle \frac{1}{2}}h{g}^2+{\theta }_r\left(\left(p-w_2-s+gs\right)q\right)$$

(10)

At this point, the Hessian matrix of $U({\prod }_r)$ is:

$$H=\left(\begin{array}{cc}-2& \eta +s{\theta }_r\\ \eta +s{\theta }_r& -h\end{array}\right)$$

(11)

When $2h-{\left(\eta +s{\theta }_r\right)}^2>0$, $U({\prod }_r)$ is strictly concave. The optimal solution for q, g is found and substituted into the objective function for the remanufacturer, deducing that the optimal decisions exist when $2h-\left(s+\eta \right)\left({\theta }_{r}s+\eta \right)>0$. The optimal solutions are shown in Equations (12)–(16):

$${w_2}^R={\displaystyle \frac{a\left(2h+\left(\left(-2+{\theta }_r\right)s-\eta \right)\left({\theta }_{r}s+\eta \right)\right)+\left(p-s\right)\left(h\left(2-4{\theta }_r\right)+\left({\theta }_{r}s+\eta \right)\left({\theta }_{r}s-\eta +2{\theta }_r\eta \right)\right)}{2\left(-1+{\theta }_r\right)\left(-2h+\left(s+\eta \right)\left({\theta }_{r}s+\eta \right)\right)}}$$

(12)

$$q^R=-{\displaystyle \frac{h\left(a-p+s\right)}{4h-2\left(s+\eta \right)\left({\theta }_{r}s+\eta \right)}}$$

(13)

$$g^R=-{\displaystyle \frac{\left(a-p+s\right)\left({\theta }_{r}s+\eta \right)}{4h-2\left(s+\eta \right)\left({\theta }_{r}s+\eta \right)}}$$

(14)

$$U_m^R={\displaystyle \frac{h{\left(a-p+s\right)}^2}{4\left(-1+{\theta }_r\right)\left(-2h+\left(s+\eta \right)\left({\theta }_{r}s+\eta \right)\right)}}$$

(15)

$$U_r^R=-{\displaystyle \frac{h{\left(a-p+s\right)}^2\left(-2h+{\left({\theta }_{r}s+\eta \right)}^2\right)}{8{\left(-2h+\left(s+\eta \right)\left({\theta }_{r}s+\eta \right)\right)}^2}}$$

(16)

4.3. Optimal Solutions under Remanufacturer Altruistic Model (M)

Under the remanufacturer altruistic model, the remanufacturer considers both its own profit and the recycler’s profit. Assuming that ${\theta }_m$ is the remanufacturer’s altruistic preference degree, the remanufacturer and recycler’s utility functions are shown in Equations (17) and (18).

$$U({\prod }_m)=\left(p-w_2-s+gs\right)q+{\theta }_m\left(\left(w_2-\left(q+a-\eta g\right)\right)q-{\displaystyle \frac{1}{2}}h{g}^2\right)$$

(17)

$$U({\prod }_r)={\pi }_r=\left(w_2-\left(q+a-\eta g\right)\right)q-{\displaystyle \frac{1}{2}}h{g}^2$$

(18)

At this point, the recycler objective function Hessian matrix is:

$$H_r=\left(\begin{array}{cc}-2& \eta \\ \eta & -h\end{array}\right)$$

(19)

When $2h-{\eta }^2>0$, the decision objective function of the recycler is strictly concave. Find the optimal solution for q, g and substitute it into the objective function for the remanufacturer, deducing that the optimal decisions exist when $h>\frac{1}{2}\eta \left(-\frac{2s}{-2+{\theta }_m}+\eta \right)$. The optimal solution is shown in Equations (20)–(24):

$${w_2}^M={\displaystyle \frac{2h\left(a\left(-1+{\theta }_m\right)-p+s\right)+2as\eta +\left(a-a{\theta }_m+p-s\right){\eta }^2}{2\left(-2+{\theta }_m\right)h-{\theta }_m{\eta }^2+2\eta \left(s+\eta \right)}}$$

(20)

$$q^M={\displaystyle \frac{h\left(a-p+s\right)}{2\left(-2+{\theta }_m\right)h-{\theta }_m{\eta }^2+2\eta \left(s+\eta \right)}}$$

(21)

$$g^M={\displaystyle \frac{\left(a-p+s\right)\eta }{2\left(-2+{\theta }_m\right)h-{\theta }_m{\eta }^2+2\eta \left(s+\eta \right)}}$$

(22)

$$U_m^M=-{\displaystyle \frac{h{\left(a-p+s\right)}^2}{4\left(-2+{\theta }_m\right)h-2{\theta }_m{\eta }^2+4\eta \left(s+\eta \right)}}$$

(23)

$$U_r^M=\frac{h{\left(a-p+s\right)}^2\left(2h-{\eta }^2\right)}{2{\left(2\left(-2+{\theta }_m\right)h-{\theta }_m{\eta }^2+2\eta \left(s+\eta \right)\right)}^2}$$

(24)

4.4. Optimal Solutions under Mutual Altruistic Model (MR)

When both the remanufacturer and recycler have altruistic preference behaviors, they consider the profit level of the other party. The objective functions of both parties are shown in Equations (25) and (26):

$$U({\prod }_m)=\left(p-w_2-s+gs\right)q+{\theta }_m\left(\left(w_2-\left(q+a-\eta g\right)\right)q-{\displaystyle \frac{1}{2}}h{g}^2\right)$$

(25)

$$U({\prod }_r)=\left(w_2-\left(q+a-\eta g\right)\right)q-{\displaystyle \frac{1}{2}}h{g}^2+{\theta }_r\left(\left(p-w_2-s+gs\right)q\right)$$

(26)

At this point, the Hessian matrix of recycler’s objective function is:

$$H=\left(\begin{array}{cc}-2& \eta +s{\theta }_r\\ \eta +s{\theta }_r& -h\end{array}\right)$$

(27)

when $2h-{\left(\eta +s{\theta }_r\right)}^2>0$, $U({\prod }_r)$ is strictly concave. In addition, $U({\prod }_m)$ is strictly concave when $h>-\frac{\left({\theta }_{r}s+\eta \right)\left(\left(2+{\theta }_m\left(-3+{\theta }_r\right){\theta }_r\right)s-\left(-2+{\theta }_m+{\theta }_m{\theta }_r\right)\eta \right)}{2\left(-2+{\theta }_m+{\theta }_m{\theta }_r\right)}$. The optimal solutions are as in Equations (28)–(32):

$${w_2}^{MR}={\displaystyle \frac{\left(a\left(-2\left(-1+{\theta }_m\right)h-\left({\theta }_{r}s+\eta \right)\left(\left(2+{\theta }_r\left(-1-3{\theta }_m+2{\theta }_m{\theta }_r\right)\right)s+\eta -{\theta }_m\eta \right)\right)+\left(p-s\right)\left(2h\left(1+{\theta }_r\left(-2+{\theta }_m{\theta }_r\right)\right)+\left({\theta }_{r}s+\eta \right)\left(-\eta +{\theta }_r\left(s+{\theta }_m\left(-2+{\theta }_r\right){\theta }_{r}s+\left(2-{\theta }_m{\theta }_r\right)\eta \right)\right)\right)\right)}{\left(\left(-1+{\theta }_r\right)\left(2h\left(-2+{\theta }_m+{\theta }_m{\theta }_r\right)+\left({\theta }_{r}s+\eta \right)\left(\left(2+{\theta }_m\left(-3+{\theta }_r\right){\theta }_r\right)s-\left(-2+{\theta }_m+{\theta }_m{\theta }_r\right)\eta \right)\right)\right)}}$$

(28)

$$q^{MR}=-{\displaystyle \frac{h\left(-1+{\theta }_m{\theta }_r\right)\left(a-p+s\right)}{2h\left(-2+{\theta }_m+{\theta }_m{\theta }_r\right)+\left({\theta }_{r}s+\eta \right)\left(\left(2+{\theta }_m\left(-3+{\theta }_r\right){\theta }_r\right)s-\left(-2+{\theta }_m+{\theta }_m{\theta }_r\right)\eta \right)}}$$

(29)

$$g^{MR}=-{\displaystyle \frac{\left(-1+{\theta }_m{\theta }_r\right)\left(a-p+s\right)\left({\theta }_{r}s+\eta \right)}{2h\left(-2+{\theta }_m+{\theta }_m{\theta }_r\right)+\left({\theta }_{r}s+\eta \right)\left(\left(2+{\theta }_m\left(-3+{\theta }_r\right){\theta }_r\right)s-\left(-2+{\theta }_m+{\theta }_m{\theta }_r\right)\eta \right)}}$$

(30)

$$U_m^{MR}={\displaystyle \frac{h{\left(-1+{\theta }_m{\theta }_r\right)}^2{\left(a-p+s\right)}^2}{2\left(-1+{\theta }_r\right)\left(2h\left(-2+{\theta }_m+{\theta }_m{\theta }_r\right)+\left({\theta }_{r}s+\eta \right)\left(\left(2+{\theta }_m\left(-3+{\theta }_r\right){\theta }_r\right)s-\left(-2+{\theta }_m+{\theta }_m{\theta }_r\right)\eta \right)\right)}}$$

(31)

$$U_r^{MR}={\displaystyle \frac{h{\left(-1+{\theta }_m{\theta }_r\right)}^2{\left(a-p+s\right)}^2\left(2h-{\left({\theta }_{r}s+\eta \right)}^2\right)}{2{\left(2h\left(-2+{\theta }_m+{\theta }_m{\theta }_r\right)+\left({\theta }_{r}s+\eta \right)\left(\left(2+{\theta }_m\left(-3+{\theta }_r\right){\theta }_r\right)s-\left(-2+{\theta }_m+{\theta }_m{\theta }_r\right)\eta \right)\right)}^2}}$$

(32)

5. Analysis of Propositions

According to the modeling assumptions in Section 3, Section 5 sets out 7 propositions to analyze and compare the relationship between enterprises’ utility, operational decisions and altruistic preference degree.

Proposition 1. 

Influence of altruistic preference degree on supply chain operational decisions under unilateral altruistic model:

(1)

${\displaystyle \frac{\partial {w_2}^R}{\partial {\theta }_r}}<0$, ${\displaystyle \frac{\partial q^R}{\partial {\theta }_r}}>0$, ${\displaystyle \frac{\partial g^R}{\partial {\theta }_r}}>0$.

(2)

${\displaystyle \frac{\partial {w_2}^M}{\partial {\theta }_m}}>0$, ${\displaystyle \frac{\partial q^M}{\partial {\theta }_m}}>0$, ${\displaystyle \frac{\partial g^M}{\partial {\theta }_m}}>0$.

Proposition 1 suggests that the recycling price paid by the remanufacturer decreases as recycler’s altruistic preference increases under recycler altruistic model, forcing the recycler to buy more CDW to maintain profit levels. Under the remanufacturer altruism model, the recycling price paid by the remanufacturer increases as the remanufacturer’s altruistic preference increases. It is also worth mentioning that the amount of CDW recycled and green dismantling technological level increases as unilateral altruistic preference level increases, regardless of the remanufacturer or recycler’s altruistic preference. It shows that the enterprises’ altruistic behavior in the unilateral altruism model helps in recycling CDW and promoting green dismantling technological level.

Proposition 2. 

Influence of altruistic preference degree on the recycling price under mutual altruistic model:

(1)

$\frac{\partial w_2^{MR}}{\partial {\theta }_m}>0$

(2)

When ${\displaystyle \frac{s^2}{2}}+s\eta +{\displaystyle \frac{{\eta }^2}{2}}<h<2s^2+s\eta +{\displaystyle \frac{{\eta }^2}{2}}$, if ${\theta }_r>A$, ${\displaystyle \frac{\partial w_2^{MR}}{\partial {\theta }_r}}<0$, if ${\theta }_r<A$, ${\displaystyle \frac{\partial w_2^{MR}}{\partial {\theta }_r}}>0$; When $h>2s^2+s\eta +{\displaystyle \frac{{\eta }^2}{2}}$, $\frac{\partial w_2^{MR}}{\partial {\theta }_r}<0$.

$$\mathrm{A}={\displaystyle \frac{{\theta }_m{s}^2+{\left(-\left(-1+{\theta }_m\right){{\theta }_m}^2{s}^4\left(-2h+{\left(s+\eta \right)}^2\right)\right)}^{1/3}}{{\theta }_m{s}^2}}$$

Proposition 2 suggests that the recycling price increases with the remanufacturer’s altruistic preference degree under mutual altruistic model. In terms of the relationship between recycling prices and the degree of altruistic preference of the recycler an inverted U-shaped relationship exists between the recycling price and the recycler’s altruistic preference degree when the cost coefficient of the green dismantling technology does not exceed a certain value. The recycling price is negatively correlated with the recycler’s degree of altruistic preference in the case of a high cost coefficient of green dismantling technology.

Proposition 3. 

Influence of altruistic preference degree on the quantity of CDW recycling under mutual altruistic model:

(1)

When ${\displaystyle \frac{1}{2}}{\left(s+\eta \right)}^2<h<B$, ${\displaystyle \frac{\partial q^{MR}}{\partial {\theta }_r}}>0$; When $h>B$, ${\displaystyle \frac{\partial q^{MR}}{\partial {\theta }_r}}<0$.

(2)

${\displaystyle \frac{\partial q^{MR}}{\partial {\theta }_m}}>0$.

$$\mathrm{B}={\displaystyle \frac{1}{2}}\left({\displaystyle \frac{\left(-2+{\theta }_m{\theta }_r\left(6-3\left(1+{\theta }_m\right){\theta }_r+2{\theta }_m{{\theta }_r}^2\right)\right)s^2}{\left(-1+{\theta }_m\right){\theta }_m}}+{\displaystyle \frac{2s\eta }{{\theta }_m}}+{\eta }^2\right)$$

Proposition 3 shows that under mutual altruistic model, in the case of a low cost coefficient of green dismantling technology, the recycler’s altruistic behavior promotes CDW recycling. However, under the condition of a high cost coefficient of green dismantling technology, the altruistic behavior of the recycler inhibits waste recycling. The altruistic behavior of the remanufacturer in the mutual altruistic model always promotes CDW recycling.

Proposition 4. 

Influence of altruistic preference degree on green dismantling technological level under mutual altruistic model: ${\displaystyle \frac{\partial g^{MR}}{\partial {\theta }_m}}>0$, ${\displaystyle \frac{\partial g^{MR}}{\partial {\theta }_r}}>0$.

Proposition 4 shows that green dismantling technological level is positively correlated with the altruistic preferences of the remanufacturer and the recycler under the mutual altruistic preference model. From a recycler’s perspective, increasing the level of green dismantling technology reduces the carbon tax payable by remanufacturers, so the recycler with altruistic preferences have an incentive to improve dismantling technology. From the perspective of a remanufacturer, a remanufacturer with an altruistic preference will increase the recycling price per unit of CDW paid to the recycler. This supports the recycler’s green innovative behaviour in disguise.

Proposition 5. 

Comparison of green dismantling technological level under different altruistic models:

(1)

If ${\theta }_r>{\displaystyle \frac{{\theta }_m\eta }{2s-{\theta }_{m}s}}$, $g^{MR}>g^R>g^M>g$.

(2)

If ${\theta }_r<{\displaystyle \frac{{\theta }_m\eta }{2s-{\theta }_{m}s}}$, $g^{MR}>g^M>g^R>g$.

Proposition 5 suggests that the highest green dismantling technological level exists under mutual altruistic model. Under non-altruistic model, green dismantling technological level by the recycler is lowest. In the case that the recycler’s altruistic preference degree is high, the green dismantling technological level is higher under the recycler altruism model than under the remanufacturer altruism model. Under the condition that the recycler’s altruistic preference degree is low, the green dismantling technological level is higher under the remanufacturer altruism model. Additionally, the green dismantling technological level is more likely to be higher under the recycler altruism model.

Proposition 6. 

Comparisons of the utility of the remanufacturer under different altruistic models:

(1)

When${\theta }_r>C$, there is$U_m^{MR}>U_m^R>U_m^M>U_m$.

(2)

When${\theta }_r<C$, there is$U_m^{MR}>U_m^M>U_m^R>U_m$.

$$\mathrm{C}={\displaystyle \frac{2h+s^2-{\eta }^2-\frac{1}{2}\sqrt{4{\left(2h+s^2-{\eta }^2\right)}^2+8{\theta }_{m}s\left(s+\eta \right)\left(-2h+{\eta }^2\right)}}{2s\left(s+\eta \right)}}$$

Proposition 6 shows that the utility of the remanufacturer reaches its maximum value under the mutual altruistic model and its minimum value under the non-altruistic model. Comparison of the utility values of the remanufacturer under unilateral altruism model requires consideration of the magnitude of altruistic preference degree. When the recycler altruism coefficient exceeds the threshold condition, the utility of remanufacturers in the recycler altruism mode is higher. Otherwise, the opposite conclusion is obtained.

Proposition 7. 

Comparison of the utility of the recycler under different altruistic model: $U_r^{MR}>U_r^R>U_r$, $U_r^M>U_r$.

Proposition 7 shows that the recycler’s utility is minimized under the no-altruism model. In addition, the utility of the recycler is higher under the mutual altruistic model than that under the recycler altruistic model. Regarding the comparison of the recycler’s utility under the remanufacturer altruistic mode with the mutual altruistic model and the recycler altruistic model, due to the complexity of the computational equation, we will discuss it using a numerical simulation method, drawing on the approach of other researchers [53].

Please see the Appendix A for the proof of the propositional part.

6. Numerical Simulation

This paper explores how supply chain members’ altruistic preferences degree impacts green dismantling technological level, quantity of CDW recycling, pricing decisions and utilities of supply chain members under different altruistic models. In this section, the propositional part is verified by numerical simulation. Relevant literature is reviewed to determine the parameter values: $a=10$ [57], $\mathrm{h}=12$ [58], $p=31.48$ [59], $s=1.78$ [60], $\eta =0.5$ [61].

6.1. Influence of Altruistic Preference Degree on Supply Chain Members’ Operational Decisions under Unilateral Altruistic Model

Figure 2 shows how the remanufacturer and recycler’s altruistic preference degree affects green dismantling technological level, quantity of CDW recycling, and the recycling price under a unilateral altruistic model, respectively.

Figure 2 shows that the level of green dismantling technology and the amount of CDW recycled increases with the level of altruistic preference in both the remanufacturer altruism model and the recycler altruism model. In terms of the recycling price, it increases with altruistic preference degree under remanufacturer altruistic model and decreases with altruistic preference degree under recycler altruistic model. For the remanufacturer, an increase in the recycling price gives the recycler more money to spend on research into green dismantling technology and on purchasing CDW from a CDW production unit. This reduces their own carbon costs while promoting the recycling of CDW for recycled building materials. Based on the analysis of the altruistic motives of the remanufacturer, the main point of the reciprocal altruism theory that emphasizes that the altruistic motives of one party towards the beneficiary are driven by profit is again confirmed [62]. Unexpectedly, despite increased green dismantling technological level to reduce the remanufacturer’s carbon costs, the remanufacturer has not responded favorably and instead lowered the recycling price. This has forced the recycler to increase the quantity of CDW to compensate for diminishing marginal revenues and thus maintain profitability. That means, in the actual management process, when the recycler actively promotes cooperation, the remanufacturer should not expand its own profit by lowering the price of waste recycling, which is not conducive to maintaining the cooperation between the two parties. Recyclers should take into account the actual external situation and their own interests, and appropriately reduce the degree of altruism, so as to ensure their own healthy development.

Wang et al. asserted that the relationship between the recycler’s altruistic preference behavior and recycling price depends on the quality cost of remanufacturing [63]. This paper argues that the relationship between recycling price and recyclers’ altruistic preference coefficient is independent of the cost coefficient. This stems from the fact that this paper assumes the recycler bears cost of green dismantling technology, while Wang et al. are given the fact that the remanufacturer bears the cost of product quality.

6.2. Influence of Altruistic Preference Degree on Supply Chain Members’ Operational Decisions under Mutual Altruistic Model

Figure 3 represents how altruistic preference degree affects supply chain members’ operational decisions under mutual altruistic model.

Figure 3a,b shows that the remanufacturer and recycler’s altruistic preference degree increase green dismantling technological level under mutual altruistic model. As the dominant player, the remanufacturer supports the recycler to increase the amount of CDW recycling by increasing the recycling price. However, the remanufacturer’s altruistic preference degree should be considered when exploring how recyclers’ altruistic behavior influences the amount of CDW recycling. Figure 3c shows that in the case of a low remanufacturer’s altruistic preference degree, the amount of CDW recycled increases with the recycler’s altruistic preference degree increasing. Under the condition of a high remanufacturer’s altruistic preference degree, the amount of CDW recycling increases and then decreases with respect to the recycler’s altruistic preference degree. That means, thinking from a practical point of view, recyclers should specify the favorable contractual terms they can offer based on a full assessment of the sincerity of the cooperation of the remanufacturer. With the environmental objective of maximizing the amount of recycled CDW, recyclers should be more willing to cooperate with remanufacturers if they are not sufficiently sincere; on the contrary, a moderate willingness to cooperate with remanufacturers if they are more sincere is more conducive to promoting the process of recycling CDW. The remanufacturer reduces the recycling price of CDW due to the altruistic behavior of the recycler. This stems from that the recycler is in a weaker position compared with the remanufacturer as the leader. Their altruistic behavior is not favored by the remanufacturer. Therefore, this paper argues that in a supply chain with an imbalanced power structure, unless the leader takes the initiative to show goodwill, the followers may bring negative effects when they unilaterally show goodwill. Based on a game-theoretic approach, this paper is the first to incorporate power structure into the scope of reciprocal altruism theory, which enriches the understanding of reciprocal altruism theory in existing studies.

In addition, Zhu et al. suggest that altruistic preference cannot always promote carbon emission reduction in supply chains [64], while this paper argues that altruistic preference always promotes carbon emission reduction in supply chains. The reason for this discrepancy is that Zhu et al. argue that upstream e-commerce platforms do not adopt altruistic preference strategies when downstream manufacturing sectors are inefficient in reducing carbon emissions.

6.3. Comparison of Green Dismantling Technological Level under Different Altruistic Models

Figure 4 represents a comparison of green dismantling technological level under different altruistic models.

Figure 4a shows the highest green dismantling technological level under a mutual altruistic model. Therefore, the mutual altruistic preference can promote green dismantling technology, which complements the viewpoint of reciprocal altruism theory in supporting technological innovation and provides theoretical support for co-operative innovation in other fields. Figure 4b shows that green dismantling technological level under recycler altruistic model is higher than that under remanufacture altruistic model, in the case that supply chain members’ altruistic preference degree under the unilateral altruistic model is distributed in the G1 region. The green dismantling technological level under remanufacturer altruistic model is greater than that under recycler altruistic model, if supply chain members’ altruistic preference degree under unilateral altruistic model is distributed in the G2 region.

These conclusions are different from the study of Huang et al. [23], who suggest that product greenness is higher when retailers’ altruistic preferences, whereas this paper argues that the relationship of green dismantling technological level between two unilateral altruistic models is uncertain. This is because the study of Huang et al. comprises a retailer and two manufacturers with co-operative-competitive relationships, which is different from the structure of the composition of the research object of this paper.

The above analyses suggest that the green dismantling technological level of the CDW recycling supply chain is most favorable when all supply chain members exhibit altruistic preferences. That means, in conjunction with practical considerations, members of the CDW recycling supply chain should pay close attention to the carbon emission policies set by the government. When the carbon tax is high, the recycler altruistic preference model is more critical to improve the level of green dismantling technology. At this time, recyclers should actively show willingness to co-operate and provide more favorable conditions to promote green technology innovation. However, when the carbon tax is low, a strong willingness to cooperate on the part of remanufacturers is more conducive to the green development of CDW recycling projects.

6.4. Comparison of Supply Chain Members’ Utility under Different Altruistic Models

Figure 5 represents a comparison of the utility of remanufacturers and recyclers under different altruistic models.

Figure 5a shows that the utility of the remanufacturer is greatest under mutual altruistic model. For the comparison of the remanufacturer’s utility under remanufacturer altruism model and recycler altruism model, the analysis in conjunction with Figure 5a,b reveals that the remanufacturer altruism model is more favorable to the remanufacturer when the region comprising the degree of the remanufacturer and the recycler’s altruistic preferences is located in region $U_{m1}$. Otherwise, the utility of the remanufacturer is higher under recycler altruism model. It is worth mentioning that the remanufacturer altruistic model is always better than the recycler altruistic model for the remanufacturer in the case that the remanufacturer’s altruistic preference degree exceeds the threshold value of 0.718, regardless of the changes in the recycler’s altruistic preference degree. Regarding the comparison of the recycler’s utility of under different altruistic models, similar conclusions can be drawn by combining the analyses in Figure 5c,d.

The above analyses indicate that the supply chain members should carry forward the spirit of mutually beneficial cooperation to continue the innovation of green dismantling technology and increase the utilities in a CDW recycling supply chain. That means, in the actual management process, supply chain members should pay close attention to the altruistic decisions of remanufacturers. As leaders, remanufacturers should cooperate actively, which is more conducive to improving the utility of supply chain members and promoting the positive development of the CDW recycling industry.

6.5. Summary of Comparative Differences in Different Altruistic Models

For both remanufacturers and recyclers, the level of green dismantling technology and supply chain member utility is always maximized in the mutual altruistic model and minimized in the no-altruistic model. The comparison of the two unilateral altruism models depends on the distribution of the degree of altruistic preference. Therefore, we utilize

Table 3. Comparative differences between two unilateral altruism models.

Variable	Distribution of Altruism	Recycler Altruism Model	Remanufacturer Altruism Model
g	G1	H	-
	G2	-	H
U	Um1, Ur1	-	H
	Um2, Ur2	H	-

Note: G1, G2, Um1, Um2, Ur1, Ur2 denote the distribution of altruism in Figure 4 and Figure 5; H indicates that the model is superior.

to represent the comparative differences between unilateral altruism models.

7. Conclusions and Management Insights

7.1. Conclusions

This paper constructs a remanufacturer-dominated CDW recycling supply chain consisting of a remanufacturer and a recycler in view of reciprocity altruism theory. Furthermore, the mechanism of how supply chain members’ altruistic preference degree affects operational decisions under different altruistic models is explored. Main findings are as follows.

(1)

Under the unilateral altruism model, supply chain members’ operational decisions are positively influenced by the remanufacturer’s altruistic preference degree. However, the recycling price to the recycler decreases with altruistic preference of the recycler. In the case that the remanufacturer’s altruistic preference degree is low, the recycler’s altruistic preference leads the amount of CDW recycled to increase; otherwise, inverted U-shaped change in the amount of CDW recycled in relation to the recycler’s altruistic preference degree.

(2)

Under the mutual altruistic model, the green dismantling technological level and the supply chain members’ utility are maximized. Under unilateral altruism model, the results of the comparison of the remanufacturer altruism model and the recycler altruism model are uncertain.

(3)

When the weaker party unilaterally shows altruistic preference characteristics to the stronger party, it will bring negative impacts to itself. Moreover, the stronger party’s altruistic preference behavior is crucial in improving supply chain members’ benefits.

7.2. Management Insights

To fully apply the theoretical results of this paper, three management insights are presented to guide the operational decisions of CDW recycling supply chain enterprises and to facilitate the process of CDW resourcing.

(1)

For remanufacturers, they should actively assume supply chain responsibility and continuously enhance the concept of pursuing win–win co-operation. Specifically, remanufacturers can increase the recovery price per unit of CDW paid to recyclers, in effect supporting recyclers’ innovative green dismantling technology activities.

(2)

For recyclers, it is important not to pander to remanufacturers, which may be counterproductive. They should enhance their negotiating power based on their resource and supplier strengths and force the remanufacturers to compromise. At the same time, recyclers should determine their own level of altruism based on a full assessment of the wholesale prices offered by the remanufacturers in order to increase the recycling rate of CDW.

(3)

For the government, it should continuously optimize the carbon tax policy, coordinate the interests of recyclers and remanufacturers, and alleviate the conflicts in the supply chain. It should fully guide remanufacturers and recyclers to establish win–win cooperation to promote the development of CDW recycling and resource utilization.

Building dismantling and waste recycling is a resource-intensive and technology-intensive social activity that plays a key role in regional development. Future research needs to identify and address the shortcomings of existing research and practice from the social, economic and environmental perspectives in order to remedy theoretical shortcomings and improve green and efficient recycling of CDW. On the social side, how to motivate social organizations or groups to participate in the monitoring system of legally dismantling buildings is a major proposition to further strengthen the self-awareness of recyclers in the development of green dismantling technologies. In addition, how the government, as the market’s gripper, can guide the relevant enterprises to co-operate in all aspects to achieve green recycling of CDW needs to be further explored by scholars. On the economic side, the research and development of green dismantling technology and equipment requires a large amount of capital investment, and it is difficult for recyclers alone to support such a huge innovation cost. Future research can try to solve this important problem from the perspective of coordinating the whole supply chain to cooperate and bear the cost risk. On the environmental side, the specific system of environmental impact assessment of green dismantling technology is still not perfected and unified. How to define the scope of evaluation and construct the link between dismantling technologies and environmental impacts is an urgent issue for scholars to address.