Mathematical Problems in Engineering

Mathematical Problems in Engineering / 2019 / Article

Research Article | Open Access

Volume 2019 |Article ID 1621395 | https://doi.org/10.1155/2019/1621395

Liang Wang, Tingjia Xu, Longhao Qin, "A Study on Supply Chain Emission Reduction Level Based on Carbon Tax and Consumers’ Low-Carbon Preferences under Stochastic Demand", Mathematical Problems in Engineering, vol. 2019, Article ID 1621395, 20 pages, 2019. https://doi.org/10.1155/2019/1621395

A Study on Supply Chain Emission Reduction Level Based on Carbon Tax and Consumers’ Low-Carbon Preferences under Stochastic Demand

Academic Editor: Sitek Paweł
Received25 Dec 2018
Revised04 Apr 2019
Accepted16 Apr 2019
Published20 May 2019

Abstract

This article focuses on the level of supply chain emission reduction, taking into account consumers’ low-carbon preferences, stochastic market demand, and carbon tax policy. By introducing the emission reduction penalty mechanism and adopting reverse derivation method, it derives the revenue model of the retailer and the manufacturer in decentralized and centralized supply chain when the supply chain reduces emissions or is not under stochastic market demand. The research results are as follows. (i) The optimal retailer’s revenue is strictly monotonous increasing with respect to the consumers’ low-carbon preferences in the decentralized supply chain. However, in the centralized supply chain, the optimal revenue of the retailer and the manufacturer are strictly monotonously decreasing of the consumers’ low-carbon preferences respectively. (ii) The retailer’s revenue is a concave function of the order quantity, and there exists a unique order quantity that can maximize retailer’s revenue. The manufacturer’s revenue is a concave function of the wholesale price, and there exists a unique wholesale price that can maximize manufacturer’s revenue. (iii) When consumers’ low-carbon preferences are given, there is an optimal emission reduction level that maximizes the overall revenue of the supply chain. Furthermore, as the carbon tax increases, the optimal emission reduction level gradually rises. (iv) As the level of emission reduction in the supply chain increases, the range of the revenue sharing coefficient becomes larger, and it is easier for supply chain members to reach a revenue sharing contract. However, when consumers’ low-carbon preferences and carbon tax increase, the opposite is true.

1. Introduction

In recent years, the trend of global warming has further intensified and has gradually become one of the hot spots of international concern. The development of a low-carbon economy has become one of the major strategic initiatives to promote the sustainable development of the global economy. Many countries have successively introduced a series of policies such as carbon tax, cap-and-trade, and low carbon subsidy policy. In such a global green revolution, consumers are increasingly aware of low-carbon consumption. In order to cope with the pressure from the government and the market, manufacturing companies have proposed carbon emission reduction strategies in the supply chain: first, to increase the transformation and upgrading of the company’s carbon emission reduction technology; second, to put forward emission reduction requirements for upstream and downstream suppliers. However, the high level of carbon emission reduction target will undoubtedly increase the operating cost of the entire supply chain, thereby reducing the operational performance of the supply chain. Therefore, it is necessary to study the emission reduction level of supply chain.

The carbon tax policy has been continuously valued by countries all over the world for its simplicity and operability. Carbon tax can limit the total amount of corporate emissions to a given range, which will facilitate companies to change their supply chain management modes, thereby reducing carbon emissions across the supply chain [1]. However, as a means of taxation, carbon tax will inevitably bring additional costs to manufacturing companies, which will have a negative impact on the supply chain operation process [2, 3] and also restrict the emission reduction level of companies.

With the deepening of the global green revolution, consumers’ low-carbon preferences are increasing. In order to gain more market share, manufacturing companies will continue to meet market demand through technological innovation of emission reduction. However, technological innovation driven by excessive emission reduction level will increase the production cost of unit product, thus reducing the market competitiveness of products. At the same time, due to uncertainties such as emission reduction technology update, replacement product introduction, and changes in consumer demand, the market demand for products under corporate emission reduction conditions shows higher randomness, and the operational risk of emission reduction companies will be further aggravated when the cost per unit product increases. However, there are certain interaction effects between carbon tax, consumer’s low-carbon preferences, and carbon emission reduction: on the one hand, carbon tax will increase the operating cost of companies, while consumers’ low-carbon preferences will increase the market demand of low-carbon products and further affect the retail price; on the other hand, to some extent, carbon emission reduction will reduce the cost of carbon tax in supply chain and increase consumers’ preference for products, but it will generate emission reduction costs [4, 5]. In summary, this paper argues that the following issues need to be determined.

(1) Under stochastic market demand, how do we set the emission reduction level of the supply chain when considering the interaction effect of carbon tax and consumers’ low-carbon preferences?

(2) What effect does carbon tax and consumers’ preferences have on revenue sharing contracts between supply chain members?

In view of this, this paper first constructs a price function of retail price on consumers’ low-carbon preferences and emission reduction level. Based on the penalty mechanism and reverse derivation method, it determines the revenue models of the retailer and the manufacturer when the supply chain reduces emissions or not under stochastic demand and conducts a comparative study from the perspective of decentralized and centralized decision-making. Furthermore, the optimal emission reduction level of supply chain based on carbon tax and consumers’ low-carbon preference under stochastic demand is determined by mathematical modeling, and the relationship between parameters is simulated and analyzed by MATLAB. The rest of the paper is organized as follows. Section 2 proposes research questions on the basis of literature review. Section 3 derives the supply chain members’ revenue models for decentralized and centralized supply chain under the condition of supply chain emission reduction and nonemission reduction. In Section 4, we determine the optimal emission reduction level based on carbon tax and consumers’ low-carbon preferences under stochastic demand through mathematical modeling. In Section 5, we explore the dynamic relationship among decision parameters through simulation analysis. Furthermore, we conclude our research findings in Section 6 with closing remarks.

2. Literature Review

The literature reviewed here mainly involves three research directions: (i) carbon tax policy and carbon emission reduction; (ii) supply chain emission reduction decision-making, operational mechanism and cooperation; (iii) consumers’ low carbon preferences and supply chain emission reduction.

Carbon tax, as a kind of emission reduction measures with extremely high market efficiency, has been widely used in developed countries and regions. Marron and Toder [6] believed that an effective carbon tax policy can reduce the risk of climate change, minimize the cost of emission reduction, promote the innovation of low-carbon technology, and increase the public revenue of society. Metcalf [7] emphasized that a key factor in using carbon taxes to reduce emissions is the reasonable pricing of greenhouse gas emissions. Chen and Hao [8] found that when a company incurs a higher carbon tax, it will receive a higher percentage of carbon emission reduction, and the carbon tax policy has a greater impact on the carbon reduction of low-efficiency company. Xiang and Lawley [9] found that the carbon tax in British Columbia (BC) significantly reduced the natural gas consumption of local residents by approximately 7%. Ding, Zhang and Song [10] argued that the increase in carbon taxes could accelerate the spread of energy technologies and significantly reduce carbon discharges peak value. For the economic effect of the carbon tax, Gros et al. [2] thought that the carbon tax will increase the cost of transformation for energy-intensive industries, which in turn will reduce GDP. Liu and Lu [11] believed that the government could reduce the carbon tax collection cost in a disguised manner by reducing production tax and consumption tax, which would be beneficial to the adjustment of the economic structure and the improvement of continuous emission reduction. Xie et al. [3] found that the implementation of carbon tax had a slight negative impact on the economic growth of Chongqing in China. Due to the different geographical location, industrial structure, energy structure, and other characteristics, the impacts of carbon tax on the level of economic activity in different regions are different. Zhao et al. [12] found that when the carbon tax reaches 30 yuan/ton or the free quota of carbon dioxide is less than 50%, Chinese investors prefer wind power generation to coal-fired power generation.

Supply chain emission reduction has become a hot issue in the theoretical and practical fields. In terms of the carbon emission reduction decision-making, Du et al. [13] found that market risk affects carbon emission reduction, while Tao et al. [14] argued that different supply chain forms, such as closed-loop supply chains, also have an impact on carbon emission reduction. Luo et al. [15] found that, under the cap-and-trade policy, coopetition will bring more profits and less total carbon emissions to the two manufacturers. Rao et al. [16] suggested that changes in carbon tax will have an impact on the optimal choice of emission reductions, and, with the increase of carbon tax, carbon emission reductions will present a nonlinear emission reduction trend. Cao et al. [17] studied the impacts of cap-and-trade policy (CTP) and low carbon subsidy policy (LCSP) on manufacturer’s production and carbon emission reduction level and believed that the carbon emission reduction level increases with the increase in carbon trading price and has nothing to do with the unit low carbon subsidy. Madani and Rasti-Barzoki [18] established a game model for the government providing emission reduction subsidies and carbon tax and analyzed the impact of government fiscal and taxation policies on the optimal emission reduction decisions in the supply chain. For the carbon reduction mechanisms, Drake et al. [19] argue that when a company has a mix of clean and nonclean technologies, investment and subsidies increase the expected emissions to some extent, but do not affect a company’s optimal capacity. Fahimnia et al. [20] proposed the way to price carbon for maximum environmental revenue per dollar increase in supply chain cost, which makes it easy for the company to make trade-offs from emission costs. Chen et al. [21] studied the optimal pricing and unit carbon emissions decisions of two manufacturers under the balanced and imbalanced power structures. It was found that, under the balanced power structure, the higher the efficiency of carbon emission reduction, the greater the manufacturer’s output and the more investment in green technology. For supply chain cooperation in carbon reduction, Benjaafar et al. [22] argued that it is possible to effectively reduce carbon emissions through operational adjustments and collaboration with supply chain members without significantly increasing costs. Ghosh and Shah [23] found that when supply chain members use cost-sharing contracts for cooperation, the emission reduction level and benefits will be affected by the cost of abatement and consumers’ low-carbon preferences. Yu and Han [24] adopted two types of contracts, i.e., the modified wholesale price (MW) and the modified cost-sharing contract (MS), and achieved supply chain coordination, which will promote the supply chain efficiency, but will not bring additional benefits to the manufacturer.

To a certain extent, consumers’ low-carbon preferences will promote companies to reduce emissions and improve emission reduction technologies. For the role of consumers’ low-carbon preferences in reducing emissions, Wang, Zhao & He [5] argued that higher consumer’s low-carbon preferences will improve the retailer’s position in the supply chain revenue sharing contract negotiations, which in turn will enhance the overall emission reduction level of the supply chain. Shewmake et al. [25] found that if consumers are more sensitive to goods with carbon labels, then the value of their individual carbon footprint will be higher, and the simulation results showed that consumers’ low-carbon preferences have a more significant effect on carbon emission reduction of alcohol and meat. Xia et al. [26] found that the improvement of consumers’ low-carbon awareness encourages supply chain members to invest in emission reduction, which is beneficial to their profits and utilities. For the impact of low-carbon preferences on corporate earnings, Liuabc [27] thought that the increase of consumers’ low-carbon preferences will have an impact on the competitive results of supply chain members, but if the cost of abatement does not have an advantage, the profitability of eco-friendly companies will tend to decline. Shuai et al. [28] considered that the education level and monthly income of consumers are the main factors for their purchase of low-carbon products, so consumer choice is the key to determining the benefits of low-carbon products of the manufacturer. Du et al. [13] constructed an emission-sensitive demand function considering consumers’ low-carbon preferences and analyzed the impact of low-carbon preferences on market demand and supply chain members’ returns.

For the issue of supply chain emission reduction, the previous literatures did not consider the effects of market stochastic demand, carbon tax, and consumers’ low-carbon preferences simultaneously. Moreover, when constructing the revenue model of supply chain, the retail price is usually regarded as a constant, ignoring the dynamic effects of carbon emissions and other factors on the retail price, which is inconsistent with the reality [13, 18, 22]. In view of this, the main innovations of this paper are as follows. Firstly, it constructs a function of retail price on consumers’ low-carbon preferences and emission reduction level. Secondly, introducing the emission reduction penalty mechanism and adopting the reverse derivation method, it derives revenue models of the retailer and the manufacturer in decentralized and centralized supply chain when the supply chain reduces emissions or not under stochastic market demand. Thirdly, it investigates the optimal emission reduction level by mathematical modeling.

3. Basic Assumptions and Model Building

This paper considers a two-stage supply chain system consisting of one manufacturer and one retailer . The information between members is completely symmetrical. In the process of revenue decision-making, the manufacturer as the leader first selects the wholesale price, and the retailer acts as the follower and then determines the order quantity.

3.1. Basic Assumptions

Assumption 1. In order to strengthen the emission reduction, it is assumed that the government will impose penalties on the manufacturer that does not reduce emissions. The fine is (<0.5) times the wholesale price of the unit product, and the government imposes a carbon tax of $ t on the carbon emissions per unit.

Assumption 2. It is assumed that the carbon emission of the manufacturer’s unit product is , which also represents the production technology level of the manufacturer. The smaller the , the higher the production technology level of the manufacturer. Consumers have low-carbon preferences, and we assume that the purchase price that they are willing to pay depends on the manufacturer’s carbon emissions, where . v is a constant, k denotes the consumer’s sensitivity to the carbon footprint, and the carbon reduction level of the supply chain is a continuous variable.

Assumption 3. It is assumed that the manufacturer’s emission reduction cost is , is the cost coefficient, and h is the retailer’s order quantity; when the manufacturer does not reduce emissions, . In the centralized supply chain, the manufacturer shares revenue with the retailer through the revenue sharing contract, and the revenue sharing coefficient is . That is, the ratio of the sales revenue of the retailer is , and the remaining is transferred to the manufacturer.

Assumption 4. It is assumed that the consumer demand is stochastic, the density function and distribution function of remain unchanged before and after the manufacturer adopts low-carbon technology, and the distribution function is the increasing failure rate (IFR) function.

Assumption 5. It is assumed that the direct production cost is for the manufacturer’s yielding unit goods, is the publicity expense of the unit product, and is the storage and transportation cost of the unit product (the above parameters are all greater than zero).

In order to intuitively demonstrate the meaning of each parameter and variable in the above assumptions, we present the symbols and definitions of the parameters in Table 1.


NotationDescriptions

Fine multiple of the wholesale price of the unit product., 0<<0.5
tTax per unit of carbon emissions.
The carbon emissions of the manufacturer’s unit product, it also represents the manufacturer’s production technology level.
pThe purchase price that consumers are willing to pay, .
vConstant.
kConsumer’s sensitivity to the carbon footprint.
Carbon emission reduction level of the supply chain.
Manufacturer’s emission reduction cost.
Emission reduction cost coefficient
hRetailer’s order quantity
Revenue sharing coefficient, that is, the ratio of the sales revenue of the retailer.
The ratio of the sales revenue of the manufacturer.
xConsumer demand
cDirect production cost of the manufacturer’s yielding unit goods.
Publicity expense of the unit product.
Storage and transportation cost of the unit product.

3.2. Model Construction

This paper first divides the supply chain into two types according to whether or not reducing emissions. Next, it further discusses the revenue model, wholesale price, order quantity of the supply chain from decentralized, and centralized decision-making.

3.2.1. Revenue Model with Non-Emission Reduction under Stochastic Demand in the Supply Chain

When the supply chain does not reduce emissions, the level of carbon emission reduction is , then the retail price of the product is , and the expectation of consumers with stochastic demand is . The manufacturer first gives the wholesale price u, and the retailer determines the order quantity based on the wholesale price, market demand and retail price. Let and denote the expected revenue of the retailer and the manufacturer when they do not reduce emissions, respectively, and we have

Proposition 6. When the supply chain does not reduce emissions, the retailer’s revenue is a concave function of the order quantity, and there is a unique order quantity that maximizes the retailer’s revenue.

Proof. The first-order partial derivative of (1) on order quantity isAnd the second-order partial derivative of Eq. (1) on isBecause the retail price , the consumers’ sensitivity to the carbon footprint , the level of carbon emissions , and the revenue sharing coefficient ; hence, there is . Therefore, the retailer’s revenue is a concave function of the order quantity h, and there is a unique order quantity that maximizes the retailer’s revenue.

When (3) equals zero, the order quantity and the wholesale price satisfies the relationship of

(1) Revenue of the Retailer and the Manufacturer in the Decentralized Supply Chain. Let , , respectively, indicate the manufacturer’s wholesale price and the retailer’s order quantity in the decentralized supply chain and , denote the revenue of the retailer and manufacturer in the decentralized supply chain with no reducing emissions, and the revenue sharing coefficient . In order to maximize his own profits, the retailer will make and satisfy the functional relationship in (5), and the wholesale price will be further obtained:

Proposition 7. In the decentralized supply chain with no reducing emissions, the manufacturer’s revenue is a concave function of the wholesale price, and there is a unique wholesale price that can maximize the manufacturer’s revenue.

Proof. It can be known from (2) that when the revenue sharing coefficient , the revenue function of the manufacturer is . The first-order partial derivative of on isThe second-order partial derivative of on isThe first-order partial derivative of (5) on isFurther, the second-order partial derivative of on isSubstitute (9) and (10) into (8), we can obtainwhereWe rewrite the numerator of and get Based on the previous assumptions, it illustrates the manufacturer’s profit . Furthermore, the penalty coefficient ; otherwise it will cause a loss for the manufacturer. Since the first part and the fourth part in (13) are both greater than zero, we further analyze the characteristic of the second part and the third part in it.
For the second part in (13), we firstly substitute in (6) with and obtainHence, it can be deduced that the second part in (13) is greater than zero.
We further analyze the third part of (13). Since consumer demand distribution function has a characteristic of increasing failure rate (IFR), that is, and , there existthat is,
We further have . Hence, it is multiplied both sides by , and we deduce that the third part in (13) is greater than zero. For in (9), it shows in (8). It can be seen that the manufacturer’s revenue is a concave function of the wholesale price, and there exists a unique wholesale price at which the manufacturer has the greatest benefit when .

According to (5), is expressed as a function of , and we can obtain the first partial derivative of (2) on ; that is,

When (16) equals zero, the optimal wholesale price of the manufacturer in the decentralized supply chain is

After the manufacturer determines the optimal wholesale price, the retailer acts as the follower to match the optimal order quantity. At this time, the optimal expected revenue for the retailer and the manufacturer is

(2) Revenue of the Retailer and the Manufacturer in the Centralized Supply Chain. According to (1) and (2), the expected revenue of the supply chain as a whole is

The first-order partial derivative of (20) on is

When (21) equals 0, the order quantity in the centralized supply chain can be obtained, and the total revenue of the supply chain reaches the maximum . In the centralized supply chain, if the supply chain members reach a revenue sharing contract, the revenue sharing coefficient in (1) and (2) is . According to the characteristics of the revenue sharing contract, the retailer’s optimal order quantity decision, , needs to make the total revenue of the decentralized supply chain equal to that of the centralized supply chain; that is, . From (5) and (21), the following equation set can be obtained:

And it can be deduced that the optimal wholesale price in the centralized supply chain without reducing emissions is

By substituting (23) into (5), we can obtain the optimal order quantity , and it satisfies the following equation:

For the centralized supply chain without reducing emissions, the optimal expected revenue of the retailer and the manufacturer is

It can be seen from (25) and (26) that the retailer’s revenue in the centralized supply chain increases with the rise of the revenue sharing coefficient , while the manufacturer’s revenue is getting smaller with it. Since the participants can care for the overall revenue of the supply chain only under the premise of maximizing their own revenue, therefore, the benefits of the retailer and the manufacturer in centralized supply chain cannot be less than the benefits of decentralized supply chain. As far as this premise is met, the retailer and the manufacturer will reach a revenue sharing contract.

Hence, there exist , , and we can further obtain an inequality about the revenue sharing coefficient :

When the revenue sharing contract is reached, the retailer and the manufacturer can get more profits only if the value of satisfies (27), and the value of is determined not only by the position of the retailer and the manufacturer in the supply chain but also by their own negotiating abilities.

3.2.2. Revenue Model with Emission Reduction under Stochastic Demand in the Supply Chain

When the manufacturer reduces emissions, the expected consumers’ stochastic demand is expressed as . Let and denote the expected revenue of the retailer and the manufacturer with emission reduction, respectively. From (1) and (2), we can get

Let the first-order partial derivative of (28) on equal 0; hence, the retailer’s optimal order quantity can satisfy the following equation:

Proposition 8. When the supply chain reduces emission, the retailer’s revenue is a concave function of the order quantity, and there exists a unique order quantity that can maximize the retailer’s revenue; the manufacturer’s revenue function is a concave function of the wholesale price, and there exists a unique wholesale price that can maximize the manufacturer’s revenue.

The proof process of Proposition 8 is the same as Propositions 6 and 7. Moreover, we will analyze the revenue of the retailer and the manufacturer in the decentralized and centralized supply chain with emission reduction.

(1) Revenue of the Retailer and the Manufacturer in the Decentralized Supply Chain. In the decentralized supply chain, the revenue sharing coefficient and the order quantity satisfying (30) are the optimal order quantity of the retailer. According to (30), it can be seen that the retailer’s optimal order quantity is a function of the manufacturer’s wholesale price . By substituting into (29) and replacing , we further obtain the first-order partial derivative on :

Let the above equation be zero, and we get the manufacturer’s optimal wholesale price

where is the optimal order quantity corresponding to . For decentralized supply chain with emission reductions, the optimal expected revenue for the retailer and the manufacturer is

(2) Revenue of the Retailer and the Manufacturer in the Centralized Supply Chain. In the centralized supply chain, the revenue sharing coefficient , and the expected revenue of the overall supply chain under emission reduction is

The first-order partial derivative of (35) on is

When (36) equals 0, the optimal order quantity of the supply chain can be obtained, and the overall benefit of the supply chain reaches the maximum. According to the characteristics of the revenue sharing contract, the retailer’s optimal order quantity decision, , needs to make the total revenue in the decentralized supply chain equal to that of the centralized supply chain. Referring to (30) and (36), the following equation set is obtained:

By solving the above equation set, the optimal wholesale price for centralized supply chain with emission reduction can be obtained; that is,

Substituting (38) into (30), the optimal order quantity corresponding to satisfies the following equation:

For the centralized supply chain with emission reduction, the optimal expected revenue of the retailer and the manufacturer are as follows:

Using the same analysis method as Section 3.2.1 (2), this section considers that the benefits of the retailer and the manufacturer in the centralized supply chain cannot be less than the benefits obtained in the decentralized supply chain; hence, we have and . Furthermore, we get an inequality about the revenue sharing coefficient :

4. Optimal Emission Reduction Level Based on Carbon Tax and Consumers’ Low-Carbon Preferences under Stochastic Demand

The final revenue of the supply chain is determined by sales revenue and production costs. On the one hand, sales revenue is determined by consumer demand and product price; with the consumers’ expected demand , it can be seen that the consumers’ low-carbon preferences will affect the unit price of products. On the other hand, in addition to order quantity and wholesale price, the choice of supply chain emission reduction level will also change production costs, while the supply chain will pay carbon tax for the carbon emissions generated. Since consumer’s low carbon preferences and emission reduction level will affect the final revenue of supply chain from income and cost, respectively, this section will discuss the impact of low-carbon preferences on revenue and the determination of the optimal emission reduction level of the manufacturer.

4.1. Impact of Consumers’ Low-Carbon Preferences on Revenue in Supply Chain Emission Reduction

As shown in (28), the consumers’ low-carbon preferences in the supply chain reduction are reflected in the product retail price and the sensitivity to carbon footprint. When the manufacturer emits more carbon dioxide per unit product, consumers who are more sensitive to the carbon footprint (the larger the value) are willing to pay less for the unit product, which ultimately has an impact on the optimal revenue of the retailer and the manufacturer. The relationship between low-carbon preferences and revenue is discussed below.

Proposition 9. In the case of emission reduction, the optimal revenue of members in the centralized supply chain is strictly monotonously decreasing with respect to consumers’ low-carbon preferences. In the decentralized supply chain, only when Condition (I) is satisfied can the retailer’s optimal revenue be strictly monotonously increasing of the consumers’ low-carbon preferences. Only when Condition (II) is met can the manufacturer’s optimal revenue strictly monotonously decrease with respect to consumers’ low-carbon preferences.

Condition ().  It is when , and

Condition ().   It is when

Proof. For the centralized supply chain, the first-order partial derivative of (39) on isSubstituting (45) into (40), we get the first-order partial derivative on k:Similarly, it can be proved thatSince (46) and (47) are greater than zero, the manufacturer’s optimal revenue is strictly monotonously increasing with respect to consumers’ low-carbon preferences. Similarly, for decentralized supply chain, we haveWhen the parameters satisfy  ,we have >0 and >0. Hence, the retailer’s optimal revenue is strictly monotonous increasing with respect to consumers’ low-carbon preferences. Furthermore, we can getWhen the parameters satisfy  ,we have and . Hence, the manufacturer’s optimal revenue is strictly monotonous decreasing with respect to consumers’ low-carbon preferences.

4.2. Optimal Emission Reduction Level in the Supply Chain

Since the choice of emission reduction level in the supply chain will have an impact on the revenue of the retailer and the manufacturer, we study the optimal emission reduction level of the manufacturer as below.

Proposition 10. When the supply chain reduces emission, the optimal revenue of the retailer in the centralized (or decentralized) supply chain is strictly monotonously increasing with respect to the emission reduction level.

Proof. In the centralized supply chain, the optimal order quantity satisfies the following equation:And the first-order partial derivative of (52) on isIt can be seen that the optimal order quantity is strictly monotonously increasing with respect to the emission reduction level.
The first-order partial derivative of (40) on isIt shows that the retailer’s optimal revenue increases monotonously with respect to the optimal order quantity. Therefore, the retailer’s optimal revenue is also strictly monotonously increasing with respect to the emission reduction level.
Similarly, in the decentralized supply chain, it can be deduced thatWhen the parameters satisfy   and