1 Introduction
According to the BP World Energy Statistical Yearbook, China is the world’s highest carbon emitter. China’s carbon emissions increased at an average annual rate of 1.2% from 2016 to 2019, exceeding the global average annual growth rate of 0.8% (Statistical, 2020). China announced, at the 75th session of the United Nations General Assembly, that it aims to peak carbon dioxide emissions before 2030 and achieve carbon neutrality before 2060 (Chen and Chao, 2021). With the rapid development of motor vehicles, carbon emissions in the transportation field account for 15% of the country’s terminal carbon emissions and the average annual growth rate in the transportation field has remained above 5% (Yang et al., 2014). Therefore, reducing the exhaust emissions of motor vehicles is extremely important to achieve carbon peaking and carbon neutrality goals in the transportation sector.
With low-carbon advantages, such as no petroleum fuel consumption and exhaust emissions, new energy vehicles (NEVs) have become the automotive industry’s leading choice in achieving carbon peaking and neutrality (Jenn et al., 2019; Bonsu, 2020; Zhao et al., 2021). However, NEVs are currently unable to achieve mass uptake—the low level of technology is the key factor that restricts the continuous growth in the sales of NEVs. To support NEV companies in increasing technological innovation investment and to promote the development of NEVs, the Ministry of Industry and Information Technology and five other ministries and commissions jointly issued the dual credit policy (DCP) (Ministry of Industry and Information Technology, 2020). Under the DCP, each NEV can receive a certain amount of credits, and manufacturers sell credits in the credit market to receive credit revenue. Moreover, the higher the NEVs’ technical level, the higher the credits. However, the NEVs’ technology improvement depends on technological breakthroughs in parts and components, and therefore, suppliers are required to bear the burden of investment in technological innovation. Facing high technological innovation investment and high risk, suppliers’ willingness to innovate is not strong and their enthusiasm is not high. Therefore, how to effectively motivate suppliers to invest in technological innovation under the DCP, improve the NEVs’ technological level, and reduce carbon emissions in the transportation sector are urgent problems to resolve.
Under the development concept of coordination, openness, and sharing, the formation of alliances has become an essential method of enterprise cooperation. In the NEV supply chain, Mercedes-Benz and CATL have reached a strategic partnership, in which CATL provides module-less battery packs for Mercedes-Benz passenger car products. Module-less battery packs directly integrate battery cells into the battery pack, saving the traditional module link. The integrated advanced battery system gives Mercedes-Benz cars better range, charging speed, safety, and sustainability (CATL, 2020). BMW Group and Envision Power have reached a long-term cooperation agreement. The next-generation lithium-ion battery cells developed by Envision Power can enable BMW Group’s new models to show outstanding performance with high performance, high safety, and zero carbon emissions (BMW Group, 2022). Enterprise alliances and cooperation help secure the resources needed in the global market and help enhance the market competitiveness of enterprises. However, alliance cooperation between enterprises will cause changes in the NEV supply chain structure, which will usually affect the interests of multiple subjects.
Therefore, this paper constructs a technological innovation decision-making model and a bargaining game model based on an alliance strategy under the DCP in order to study the DCP’s impact on technological innovation and carbon emissions. We need to solve three problems: first, whether the DCP can promote the NEVs’ technological innovation and effectively reduce carbon emissions in transportation, and how can the policy be adjusted to prompt this effect? Second, how can suppliers be effectively motivated to invest in technological innovation and reduce carbon emissions in transportation, realizing the optimal incentive effect? Third, what impact does alliance cooperation in the NEV supply chain have on enterprises?
There are three main contributions to this paper. First, we develop a game model of the NEV supply chain and demonstrate the DCP’s positive incentive effect on reducing carbon emissions in the transportation field through rigorous theoretical deduction, which provides a theoretical basis for the government to achieve carbon peaking and carbon neutrality goals in the transportation sector by adjusting the DCP. Second, we demonstrate that the supplier can be effectively incentivized to increase its investment in technological innovation by centralizing decision-making and profit-sharing mechanisms, making the incentive effect of the DCP on technological innovation and reducing carbon emissions in transportation optimal. Third, we demonstrate that an alliance between the manufacturer and supplier is not to increase profits but to enhance their product advantages, and suppliers can gain higher profits by participating in the alliance, providing a theoretical reference for decision-making.
The rest of this paper is as follows: Section 2 reviews the related literature, Section 3 introduces the studied problem and builds the model, Section 4 solves the model and analyzes the optimal solution, Section 5 presents the coordination strategy, Section 6 discusses the numerical example and sensitivity analysis, Section 7 is the discussion, and Section 8 outlines the conclusion.
2 Literature review
This research involves two streams of the literature, namely, on dual credit policy and supply chain coordination.
2.1 Dual credit policy
Given that China’s implementation of the DCP is only recent, research on the DCP is still in its infancy and has inconsistent conclusions.
Some scholars believe that the DCP can positively stimulate the development of NEVs. For example, Dong and Zheng studied the impact of DCP on enterprises’ total factor productivity and believed that the policy could significantly improve the technological productivity of enterprises (Dong and Zheng, 2022). Wang et al. (2020) concluded that the DCP could increase the willingness of enterprises to innovate green technology. Yang et al. (2021) believed that the DCP could positively stimulate NEVs’ development, and a moderate credit price could improve the NEVs’ technical level. Zhou et al. (2019) found that the DCP raises the green technology threshold for NEVs. Li et al. (2020) studied the impact of DCP on manufacturers’ R&D decisions and proved that DCP could effectively replace the subsidy policy to promote NEVs’ development. Ma et al. (2021) found that implementing the DCP could significantly increase the supply chain investment in the NEVs’ R&D and improve the NEVs’ technical level. Zhou and Shen believed that the DCP incentivizes automakers to develop more NEV modes and improve manufacturers’ production capacity (Zhou and Shen, 2020).
Some scholars believe that the DCP cannot achieve the expected incentive effect and needs further revision. For instance, Lou et al. (2020) found that the DCP might not help manufacturers improve NEVs’ technical level. He et al. (2021) believed that the gradual tightening of the DCP would effectively promote electric vehicle R&D intensity. Kong et al. (2022) believed that the DCP could promote the sustainable development of the automobile industry, but the incentive effect on NEVs is limited. Cheng and Fan pointed out that a higher credit price is more conducive to promoting the expansion of NEVs than setting a higher proportion of NEV production (Cheng and Fan, 2021). Zhao et al. (2019) believed that the DCP could promote the technological innovation of NEVs but would lose its role in promoting technological innovation in 2025 or earlier. Meng et al. (2022) found that the DCP promotes the development of the NEV industry, whereas the capital constraints weaken the incentive effect of the DCP. Ma et al. (2023) found that when there is information asymmetry in the supply chain, the incentive effect of the DCP is weakened.
Throughout the literature, scholars have not studied much on the impact of the DCP on the NEVs’ technological innovation, and the research conclusions are inconsistent. Moreover, there is almost no research on the impact of the DCP on carbon emissions in transportation. Therefore, the impact of the DCP on technological innovation and carbon emission reduction needs to be explored further. Considering the background of the DCP, it combines technology innovation with carbon emission reduction in transportation and provides a new research idea for future research.
2.2 Supply chain coordination
Coordination between manufacturers and suppliers is a crucial issue in supply chain management, and many scholars have studied supply chain coordination by designing a series of contracts. For example, Li et al. (2019) introduced an incentive compatibility mechanism, studied a low-carbon supply chain’s emission reduction cooperation mechanism, and explored the impact of cooperative decision-making contracts on supply chain performance. He et al. (2019) studied the online shopping supply chain containing a single online retailer and a third-party logistics enterprise and adopted a bilateral effort cost-sharing contract to realize coordination. Wang et al. (2017) considered consumers’ low-carbon preferences and studied the supply chains’ coordination under wholesale price contracts and cost-sharing contracts. Cachon and Lariviere believed that revenue-sharing contracts could coordinate supply chains with a single retailer and distribute supply chain profits arbitrarily (Cachon and Lariviere, 2005). He et al. studied the coordination by various contract types when supply and demand are uncertain, such as the contract of supplier management inventory partnership plus production subsidy, the revenue-sharing contract with advance purchase discount, the contract combining the return policy and wholesale price, and the return policy and sales discount and penalty contract (He et al., 2009; He and Zhao, 2012; He and Zhao, 2016). Dai studied the supply chain coordination contract in the context of retailers’ financial constraints (Dai, 2020).
The bargaining model is most commonly used to analyze cooperation between firms (Nash, 1953). For example, Guo and Iyer analyzed the multilateral bargaining problem between manufacturers and retailers, and believed that when the difference in retailer’s sales price is insignificant, the manufacturer will choose to negotiate the price simultaneously. When the price difference is significant, the manufacturer will negotiate the price sequentially (Guo and Iyer, 2013). Feng and Lu analyzed outsourcing decisions and contract choices in the supply chain based on a bargaining model (Feng and Lu, 2013a; Feng and Lu, 2013b). Basak (2017) compared and analyzed the competitive model and competitive enterprises that can be used to obtain optimal profits, applying the bargaining model (Basak, 2017). Zhang et al. (2017) demonstrated the impact of enterprises’ bargaining power and centralized procurement efficiency on enterprises’ willingness to participate in centralized procurement efficiency on enterprises’ willingness to participate in centralized procurement in China’s pharmaceutical market. Escapa and Gutierrez quantitatively studied the distribution of the potential benefits between countries based on bargaining models (Escapa and Maria, 1997).
Scholars have studied the supply chain’s coordination mechanism from different perspectives, providing a solid theoretical and methodological reference for analyzing the coordination of the NEV supply chain. However, existing research mainly focuses on enterprises’ one-on-one cooperation and competition, lacking consideration of reality. In the NEV supply chain, it is common for a manufacturer to cooperate with multiple suppliers, in which case, how the manufacturer incentivizes multiple suppliers to cooperate in technological innovation, realizing supply chain coordination? This paper designs centralized decision-making and profit-sharing mechanisms, which effectively motivate suppliers to increase investment in NEVs’ technological innovation, which provides a theoretical frame for future research.
3 Problem description and model
3.1 Problem description
Most of the carbon emissions in the transportation sector come from the exhaust emissions caused by fossil energy such as gasoline and diesel consumed during the operation and use phase of automobiles. In contrast, NEVs do not rely on fossil fuels, significantly reduce vehicle exhaust emissions, and alleviate environmental pollution; therefore, developing NEVs is an effective way to achieve carbon peaking and carbon neutrality goals in transportation. According to statistics, the market share of NEVs will increase by 1% and carbon emissions will reduce by 1.29 million tons. A 1% reduction in the electricity consumption coefficient of pure electric vehicles will reduce carbon emissions by 510,000 tons; however, whether the NEVs’ market share increases or the power consumption coefficient of electric vehicles is reduced depends on improvement in NEVs’ technical level and investment in technological innovation.
A NEV supply chain consists of one manufacturer and multiple parts suppliers. Suppliers are responsible for investment in the technological innovation of parts, and the technological innovation level determines the technical level of NEVs. The manufacturer procures parts from suppliers and sells the NEVs to customers after the manufacturer completes production.
Under the DCP, each NEV will receive a specific amount of credit and manufacturers sell NEV credits in the credit market to obtain credit revenue. At the same time, the number of credits is related to NEV’s technical level, and the higher the technical level, the higher the NEV credits per vehicle. Thus, the DCP will significantly affect the technological innovation investment decisions of the NEV supply chain.
The NEV supply chain companies can choose decentralized and centralized decision-making modes to decide on technological innovation investment. When the NEV supply chain chooses to make decentralized decisions, the manufacturer and supplier determine technological innovation investments and prices through the Stackelberg game to maximize their profits. When the NEV supply chain chooses to make centralized decisions, supply chain companies decide NEVs’ technological innovation investments and sales prices together to maximize supply chain profits.
Furthermore, the manufacturer may form alliances with suppliers to participate in profit distribution negotiations. The decision-making process is as follows: the manufacturer decides to align with the supplier. Manufacturers and suppliers then make decisions to maximize total profit, including decisions on technological innovation, sales prices, and negotiation sequence. If the manufacturer does not align with the supplier, the manufacturer bargains with the supplier sequentially, determines the supplier’s profit, and signs a contract. If the manufacturer and a supplier ally, the alliance first negotiates with the supplier sequentially, then the alliance will determine the parties’ profits and a contract will be signed according to the agreed profit distribution rules. Finally, the NEV supply chain will conduct technological innovation, procurement, production, and sales based on the cooperation contract to achieve their respective profits.
3.2 Model
The NEV market demand function is q=a−p+θ∑i=1nTi q=a−p+θ∑i=1nTi (Ma et al., 2018; Wang and Huang, 2021), where a a is the potential demand, p p is the NEV sales price, θ θ is the sensitivity coefficient of market demand, and the greater θ θ is, the more significant the impact of the supplier’s technical level on market demand. Ti(i=1,2,⋯,n) Tii=1,2,⋯,n is the technological innovation of supplier i i .
Under the DCP, the profits of NEV manufacturers are affected by the revenue from NEV credit trading. Each NEV at the current level of technology T0 T0 can earn NEV credit ε ε . Furthermore, considering the supplier’s investment in technological innovation to raise the technical level of NEV to ∑i=1nTi ∑i=1nTi , the NEV credits that can increase λ(∑i=1nTi−T0) λ∑i=1nTi−T0 , where λ λ is the NEV technology innovation credit coefficient and λ>0 λ>0 . Each vehicle can earn NEV credits ε+λ(∑i=1nTi−T0) ε+λ∑i=1nTi−T0 (Liu et al., 2022). For simplicity and generality, let T0=0 T0=0 and ε=0 ε=0 .
Suppose the credit price is pe pe ; then, the manufacturer’s credit revenue for the production of NEVs is peqλ(∑i=1nTi) peqλ∑i=1nTi .
Considering that the technological innovation investment of the component supplier is Ii=12kiT2i Ii=12kiTi2 (Gurnani and Erkoc, 2008; Zheng et al., 2019), ki>0(i=1,2,⋯,n) ki>0i=1,2,⋯,n , ki ki is the technological innovation cost coefficient. Both the manufacturer and the supplier’s production costs are zero.
The NEVs’ carbon emission reduction comes from two aspects: the replacement of fuel vehicles by NEVs and the reduction of carbon emissions brought about by the technological innovation of NEVs, such as the lower electric energy consumption. Considering each fuel vehicle’s carbon emissions is G0 G0 , assuming that each NEV can replace fuel vehicles μ μ and can reduce carbon emissions μG0 μG0 (Cheng and Fan, 2021), and μ>0 μ>0 , μ μ is the replacement rate of NEVs. Considering the technological innovation of NEVs is ∑i=1nTi ∑i=1nTi and carbon emission reduction is δ∑i=1nTi δ∑i=1nTi (Cao and Wu, 2020; Hao and Li, 2020), and δ>0 δ>0 , δ δ is the technological innovation emission reduction coefficient. Therefore, producing q q NEVs can reduce carbon emissions (μG0+δ∑i=1nTi)q μG0+δ∑i=1nTiq .
Thus, the profit function of the supplier i i is
πi=wiq−12kiT2i.(1) πi=wiq−12kiTi2.(1)
The manufacturer’s profit consists of NEV sales revenue and credit revenue, and the profit function is
πm=(p−∑i=1nwi)q+peλq(∑i=1nTi),(2) πm=p−∑i=1nwiq+peλq∑i=1nTi,(2)
where p=ω+∑i=1nwi p=ω+∑i=1nwi and wi(i=1,2,⋯,n) wii=1,2,⋯,n are the parts price of supplier i i , and ω ω is the manufacturer’s markup.
4 Model solving and analysis
4.1 Optimal strategy for the NEV supply chain under the DCP
4.1.1 Optimal solution for decentralized decision-making
Under the DCP, when the NEV supply chain makes decentralized decisions (labeled D D ), the manufacturer and parts supplier use the Stackelberg game to determine technological innovation investment and NEV sales prices, aiming to maximize their profits.
We adopt the reverse induction method in this section to solve the optimal solution.
First, suppliers decide the technological innovation investment and parts price to maximize their profits.
Derive Eq. 1 from wi wi and Ti Ti . Let ∂πi∂wi=0 ∂πi∂wi=0 and ∂πi∂Ti=0 ∂πi∂Ti=0 , and by solving it jointly, we obtain wD∗i=N(a−ω)(n+1)N−θ2M wiD∗=Na−ωn+1N−θ2M and TD∗i=N(a−ω)θki((n+1)N−θ2M) TiD∗=Na−ωθkin+1N−θ2M , where M=∑δ=1n∏i=1nkikδ M=∑δ=1n∏i=1nkikδ and N=∏i=1nki N=∏i=1nki .
Then, the manufacturer decides on NEV sales prices intending to maximize its profits. Substituting wD∗i wiD∗ and TD∗i TiD∗ into πm πm , and deriving πm πm to ω ω , let ∂πm∂ω=0 ∂πm∂ω=0 ; the solution yields ωD∗=a((n+1)N−θ(θ+2λ)M)2N(n+1)−2θ(θ+λ)M ωD∗=an+1N−θθ+2λM2Nn+1−2θθ+λM and pD∗=a((2n+1)N−θ(θ+2peλ)M)2N(n+1)−2θ(θ+peλ)M pD∗=a2n+1N−θθ+2peλM2Nn+1−2θθ+peλM .
Furthermore, we obtain the optimal strategy under decentralized decision-making: TD∗i=aθNki(2(n+1)N−2θ(θ+peλ)M) TiD∗=aθNki2n+1N−2θθ+peλM , πD∗m=a2N4((n+1)N−θ(θ+peλ)M) πmD∗=a2N4n+1N−θθ+peλM , qD∗=aN2(n+1)N−2θ(θ+peλ)M qD∗=aN2n+1N−2θθ+peλM , wD∗iaN2(n+1)N−2θ(θ+peλ)M wiD∗aN2n+1N−2θθ+peλM , πD∗i=a2N2(2ki−θ2)8ki((n+1)N−θ(θ+peλ)M)2 πiD∗=a2N22ki−θ28kin+1N−θθ+peλM2 , πD∗=a2N((4n+2)N−θ(3θ+2λ)M)8((n+1)N−θ(θ+peλ)M)2 πD∗=a2N4n+2N−θ3θ+2λM8n+1N−θθ+peλM2 , and ID∗i=(aθM)22ki(2(n+1)N−2θ(θ+peλ)M)2 IiD∗=aθM22ki2n+1N−2θθ+peλM2 . The reduction of carbon emissions is GD∗=(μG0+δaθNki(2(n+1)N−2θ(θ+peλ)M))aN2(n+1)N−2θ(θ+peλ)M GD∗=μG0+δaθNki2n+1N−2θθ+peλMaN2n+1N−2θθ+peλM .
4.1.2 Optimal solution for centralized decision-making
Under the DCP, the NEV supply chain makes centralized decisions (marked C C ). NEV companies aim to maximize profits in the supply chain and jointly decide on technological innovation investments and sales prices for NEVs.
Therefore, the profit of the NEV supply chain is
πC=pq+peλq(∑i=1nTi)−12∑i=1nkiT2i.(3) πC=pq+peλq∑i=1nTi−12∑i=1nkiTi2.(3)
Equation 3 is derived from Ti Ti and p p ; let ∂πC∂Ti=0 ∂πC∂Ti=0 and ∂πC∂p=0 ∂πC∂p=0 ; then, we solve them synchronously and obtain TC*i=a(θ+peλ)Nki(2N−(θ+peλ)2M) TiC*=aθ+peλNki2N−θ+peλ2M and pC*=aN−λ(θ+peλ)2N−M(θ+peλ)2 pC*=aN−λθ+peλ2N−Mθ+peλ2 .
Furthermore, we obtain the optimal strategy under centralized decision-making πC*=a2N2(2N−(θ+peλ)2M) πC*=a2N22N−θ+peλ2M , qC*=aN2N−(θ+peλ)2M qC*=aN2N−θ+peλ2M , and IC*i=(a(θ+peλ)N)22ki(2N−(θ+peλ)2M)2 IiC*=aθ+peλN22ki2N−θ+peλ2M2 . The reduction of carbon emissions is GC*=(μG0+δa(θ+peλ)Nki(2N−(θ+peλ)2M))aN2N−(θ+peλ)2M GC*=μG0+δaθ+peλNki2N−θ+peλ2MaN2N−θ+peλ2M .
4.2 Optimal decision-making without the DCP
4.2.1 Optimal solution for decentralized decision-making
Without the DCP, peλ=0 peλ=0 .
The profit function of the supplier i i is
πi=wiq−12kiT2i.(4) πi=wiq−12kiTi2.(4)
The profit function of the manufacturer is
πm=(p−∑i=1nwi)q.(5) πm=p−∑i=1nwiq.(5)
If the NEV supply chain makes decentralized decisions (labeled ND ND ), it aims to maximize its profits and determine technological innovation and price through the Stackelberg game. The decision-making process contains two stages. First, the manufacturer decides the sales price based on the parts price and technological innovation. Then, suppliers decide on their technological innovation investments and component prices.
By solving via reverse induction, we can obtain the optimal strategy TND∗i=aθNki(2(n+1)N−2Mθ2) TiND∗=aθNki2n+1N−2Mθ2 , wND∗iaN2(n+1)N−2θ2M wiND∗aN2n+1N−2θ2M , pND∗=a((2n+1)N−θ2M)2N(n+1)−2θ2M pND∗=a2n+1N−θ2M2Nn+1−2θ2M , qND∗=aN2(n+1)N−2θ2M qND∗=aN2n+1N−2θ2M , IND∗i=(aθM)22ki(2(n+1)N−2θ2M)2 IiND∗=aθM22ki2n+1N−2θ2M2 , πND∗m=a2N4((n+1)N−θ2M) πmND∗=a2N4n+1N−θ2M , πND∗i=a2N2(2ki−θ2)8ki((n+1)N−θ2M)2 πiND∗=a2N22ki−θ28kin+1N−θ2M2 , and πND∗=a2N((4n+2)N−3θ2M)8((n+1)N−θ2M)2 πND∗=a2N4n+2N−3θ2M8n+1N−θ2M2 . Furthermore, the reduction in carbon emissions is defined as GND∗=(μG0+δaθNki(2(n+1)N−2Mθ2))aN2(n+1)N−2θ2M GND∗=μG0+δaθNki2n+1N−2Mθ2aN2n+1N−2θ2M .
4.2.2 Optimal solution for centralized decision-making
Without the DCP, peλ=0 peλ=0 . The profit function of the NEV supply chain is
πC=pq−12∑i=1nkiT2i.(6) πC=pq−12∑i=1nkiTi2.(6)
If the NEV supply chain makes centralized decisions (labeled NC NC ), they jointly decide on technological innovation and prices for NEVs to maximize total profits.
Solving Eq. 6, we get the optimal strategy TNC∗i=aθNki(2N−θ2M) TiNC∗=aθNki2N−θ2M , qNC∗=aN2N−θ2M qNC∗=aN2N−θ2M , INC∗i=(aθN)22ki(2N−θ2M)2 IiNC∗=aθN22ki2N−θ2M2 , pNC∗=aN2N−Mθ2 pNC∗=aN2N−Mθ2 , and πNC∗=a2N2(2N−θ2M) πNC∗=a2N22N−θ2M . Furthermore, the reduction in carbon emissions is defined as GNC∗=(μG0+δaθNki(2N−θ2M))aN2N−θ2M GNC∗=μG0+δaθNki2N−θ2MaN2N−θ2M .
Proposition 1. The DCP can stimulate the NEV supply chain to increase technological innovation investment, raise the technical level and sales of NEVs, and effectively reduce carbon emissions in the transportation field.Proof. The optimal strategy with and without a DCP is compared. First, let us compare the optimal strategy under decentralized decision-making. Substituting ID∗i IiD∗ , IND∗i IiND∗ , qD∗ qD∗ , qND∗ qND∗ , TD∗i TiD∗ , TND∗i TiND∗ , GND∗ GND∗ , and GD∗ GD∗ into ID∗i−IND∗i IiD∗−IiND∗ , qD∗−qND∗ qD∗−qND∗ , TD∗i−TND∗i TiD∗−TiND∗ , and GD∗−GND∗ GD∗−GND∗ yields ID∗i−IND∗i=a2MN2θ3peλ(2(n+1)N−Mθ(2θ+peλ))8ki((n+1)N−θ(θ+peλ)M)2((n+1)N−θ2M)2>0 IiD∗−IiND∗=a2MN2θ3peλ2n+1N−Mθ2θ+peλ8kin+1N−θθ+peλM2n+1N−θ2M2>0 , qD∗−qND∗=aMNθλpe((n+1)N−θ2M)((n+1)N−θ(θ+peλ)M)>0 qD∗−qND∗=aMNθλpen+1N−θ2Mn+1N−θθ+peλM>0 , TD∗i−TND∗i=aNMθ2peλ2ki((n+1)N−Mθ2)((n+1)N−θ(θ+peλ)M)>0 TiD∗−TiND∗=aNMθ2peλ2kin+1N−Mθ2n+1N−θθ+peλM>0 , and GD∗−GND∗=μG0(qD∗−qND∗)+δ(TD∗iqD∗−TND∗iqND∗)>0 GD∗−GND∗=μG0qD∗−qND∗+δTiD∗qD∗−TiND∗qND∗)>0 .Then, we compare the optimal strategy under centralized decision-making. Substituting IC∗ IC∗ , INC∗ INC∗ , qC∗ qC∗ , qNC∗ qNC∗ , TC∗i TiC∗ , TNC∗i TiNC∗ , GNC∗ GNC∗ , and GC∗ GC∗ into IC∗−INC∗ IC∗−INC∗ , qC∗−qNC∗ qC∗−qNC∗ , TC∗i−TNC∗i TiC∗−TiNC∗ , and GC∗−GNC∗ GC∗−GNC∗ yields IC∗−INC∗=a2N2peλ(2θ+peλ)(4N2−(Mθ(θ+peλ))2)2ki(2N−(θ+peλ)2M)2(2N−θ2M)2>0 IC∗−INC∗=a2N2peλ2θ+peλ4N2−Mθθ+peλ22ki2N−θ+peλ2M22N−θ2M2>0 , qC∗−qNC∗=aMNpeλ(2θ+peλ)(2N−(θ+peλ)2M)(2N−θ2M)>0 qC∗−qNC∗=aMNpeλ2θ+peλ2N−θ+peλ2Μ2N−θ2M>0 , TC∗i−TNC∗i=aNpeλ(2N+Mθ(θ+peλ))ki(2N−(θ+peλ)2M)(2N−θ2M)>0 TiC∗−TiNC∗=aNpeλ2N+Mθθ+peλki2N−θ+peλ2M2N−θ2M>0 , and GC∗−GNC∗=μG0(qC∗−qNC∗)+δ(TC∗iqC∗−TNC∗iqNC∗)> GC∗−GNC∗=μG0qC∗−qNC∗+δTiC∗qC∗−TiNC∗qNC∗)> 0
.Q.E.D.
Compared to the scenario without the DCP, implementing the DCP stimulates the NEV supply chain to increase technological innovation investment and enhance NEVs’ technical level. The DCP will promote the transformation of the NEV supply chain in “relying on technology” to obtain core competitiveness.Moreover, the continuous improvement of NEVs’ technical level, the increasing number of NEVs, and the continuous increase in the NEVs’ market share will help the energy upgrading and transformation of the transportation industry, thereby significantly reducing the total carbon emissions from the transportation sector and enabling the carbon peaking and carbon neutrality goals of this sector to be achieved quickly.
Proposition 2. Under the DCP, the higher the technological innovation credit coefficient or credit price, the higher the NEVs’ technical level, the higher the NEV sales, and the more significant the incentive effect of the DCP on reducing carbon emissions in the transportation industry.Proof. Deriving TC∗i TiC∗ , TD∗i TiD∗ , qC∗ qC∗ , qD∗ qD∗ , GD∗ GD∗ , and GC∗ GC∗ to pe pe yields ∂TC∗i∂pe=aNλ(2N+M(θ+peλ)2)ki(2N−M(θ+peλ)2)2>0 ∂TiC∗∂pe=aNλ2N+Mθ+peλ2ki2N−Mθ+peλ22>0 , ∂TD∗i∂pe2aθ2NMλki(2(n+1)N−2θ(θ+peλ)M)2>0 ∂TiD∗∂pe2aθ2NMλki2n+1N−2θθ+peλM2>0 , ∂qC∗∂pe2aNMλ(θ+peλ)(2N−M(θ+peλ)2)2>0 ∂qC∗∂pe2aNMλθ+peλ2N−Mθ+peλ22>0 , ∂qD∗∂pe2aθNMλ(2(n+1)N−2θ(θ+peλ)M)2>0 ∂qD∗∂pe2aθNMλ2n+1N−2θθ+peλM2>0 , ∂GD∗∂pe=aMNθλ(aMδθ+μG0((n+1)N−Mθ(θ+peλ)))2((n+1)N−Mθ(θ+peλ))3>0 ∂GD∗∂pe=aMNθλaMδθ+μG0n+1N−Mθθ+peλ2n+1N−Mθθ+peλ3>0 , and ∂GC∗∂pe=aMNλ(aδ(2N+3M(θ+peλ)2)+2G0(θ+peλ)(2N−M(θ+peλ)2))(2N−M(θ+peλ)2)3>0 ∂GC∗∂pe=aMNλaδ2N+3Mθ+peλ2+2G0θ+peλ2N−Mθ+peλ22N−Mθ+peλ23>0 .Deriving TC∗i TiC∗ , TD∗i TiD∗ , qC∗ qC∗ , qD∗ qD∗ , GD∗ GD∗ , and GC∗ GC∗ to λ λ yields ∂TC∗i∂λ=aNpe(2N+M(θ+peλ)2)ki(2N−M(θ+peλ)2)2>0 ∂TiC∗∂λ=aNpe2N+Mθ+peλ2ki2N−Mθ+peλ22>0 , ∂qC∗∂λ2aNMpe(θ+peλ)(2N−M(θ+peλ)2)2>0 ∂qC∗∂λ2aNMpeθ+peλ2N−Mθ+peλ22>0 , ∂qD∗∂λ2aθNMpe(2(n+1)N−2θ(θ+peλ)M)2>0 ∂qD∗∂λ2aθNMpe2n+1N−2θθ+peλM2>0 , ∂TD∗i∂λ2aθ2NMpeki(2(n+1)N−2θ(θ+peλ)M)2>0 ∂TiD∗∂λ2aθ2NMpeki2n+1N−2θθ+peλM2>0 , ∂GD∗∂λ=aMNθpe(aMδθ+μG0((n+1)N−Mθ(θ+peλ)))2((n+1)N−Mθ(θ+peλ))3>0 ∂GD∗∂λ=aMNθpeaMδθ+μG0n+1N−Mθθ+peλ2n+1N−Mθθ+peλ3>0 , and ∂GC∗∂λ=aMNpe(aδ(2N+3M(θ+peλ)2)+2G0(θ+peλ)(2N−M(θ+peλ)2))(2N−M(θ+peλ)2)3>0 ∂GC∗∂λ=aMNpeaδ2N+3Mθ+peλ2+2G0θ+peλ2N−Mθ+peλ22N−Mθ+peλ23>0 .
Q.E.D.
Proposition 2 shows that the DCP always affects the optimal strategy. With the increase of the credit price or technological innovation credit coefficient, the higher the NEVs’ technical level, the higher the sales. Moreover, by improving their technical level and sales of NEVs, carbon emissions in the transportation field can be reduced more effectively. Therefore, the DCP can not only encourage NEV companies to continuously improve the technological innovation of NEVs and enhance the industry’s rapid development but also promote carbon emission reduction in the transportation sector, allowing this sector to achieve its carbon peaking and carbon neutrality goals faster.The government can promote healthy development of the NEV industry by adjusting the technological innovation credit coefficient or credit price, and reduce carbon emissions in the transportation field by adjusting the DCP.
Proposition 3. The higher the carbon emissions of fuel vehicles, the higher the replacement rate of fuel vehicles by NEVs and the more significant the incentive effect of the DCP on reducing carbon emissions. The higher the emission reduction coefficient of NEV technology innovation, the more significant the incentive effect of DCP on reducing carbon emissions.Proof. Deriving GC∗ GC∗ and GD∗ GD∗ to μ μ , G0 G0 , and δ δ yields ∂GC∗∂μ=aNG02N−(θ+peλ)2M>0 ∂GC∗∂μ=aNG02N−θ+peλ2M>0 , ∂GC∗∂G0=aNμ2N−(θ+peλ)2M>0 ∂GC∗∂G0=aNμ2N−θ+peλ2M>0 , ∂GC∗∂δ=a2MN(θ+peλ)2N−(θ+peλ)2M>0 ∂GC∗∂δ=a2MNθ+peλ2N−θ+peλ2M>0 , ∂GD∗∂μ=aNG02(n+1)N−2θ(θ+peλ)M>0 ∂GD∗∂μ=aNG02n+1N−2θθ+peλM>0 , ∂GD∗∂G0=aNμ2(n+1)N−2θ(θ+peλ)M>0 ∂GD∗∂G0=aNμ2n+1N−2θθ+peλM>0 , and ∂GD∗∂δ=a2MNθ(2(n+1)N−2θ(θ+peλ)M)2>0 ∂GD∗∂δ=a2MNθ2n+1N−2θθ+peλM2>0 .
Q.E.D.
Proposition 3 shows that the DCP can motivate NEV companies to increase investment in technological innovation, improve the technical level of NEVs, and increase consumers’ intention to purchase them, thereby increasing the replacement rate of fuel vehicles by NEVs and effectively reducing carbon emissions in the transportation field. Moreover, the higher the replacement rate of fuel vehicles by NEVs, the more significant the emission reduction effect.Meanwhile, the higher the carbon emissions of fuel vehicles, the more significant the incentive effect of the NEVs’ development on carbon emission reduction in transportation. Moreover, the higher the emission reduction coefficient of technological innovation of NEVs, the stronger the effect of the DCP on reducing carbon emissions in the transportation field.
Proposition 4. Decentralized decision-making harms the DCP’s incentive effect and weakens the DCP’s incentive effect on emission reduction in the transportation field.
Proof. We compare and analyze the optimal solution for decentralized and centralized decision-making. Substituting TC∗i TiC∗ , TD∗i TiD∗ , GC∗ GC∗ , and GD∗ GD∗ yields TD∗i−TC∗i=−aN(2Npeλ+2nN(θ+peλ)−Mθ(θ+peλ)2)2ki(N(1+n)−Mθ(θ+peλ))(2N−θ(θ+peλ)2)<0,GD∗−GC∗=μG0(qD∗−qC∗)+δ(TD∗iqD∗−TC∗iqC∗)<0 TiD∗−TiC∗=−aN2Npeλ+2nNθ+peλ−Μθθ+peλ22kiN1+n−Μθθ+peλ2N−θθ+peλ2<0,GD∗−GC∗=μG0qD∗−qC∗+δTiD∗qD∗−TiC∗qC∗<0 .
Q.E.D.
Compared to centralized decision-making, under decentralized decision-making, the investment in technological innovation of NEVs is reduced, NEVs’ technical level decreases, and the impact of the NEVs’ development on carbon emission reduction weakens. Decentralized decision-making harms the DCP’s incentive effect, and decentralized decision-making weakens the DCP’s incentive effect on emission reduction in the transportation field.Therefore, it is essential to implement a coordinated strategy to enable independent companies to achieve the optimal strategy under centralized decision-making, encourage the NEV supply chain to increase investment in technological innovation, improve the NEVs’ technical level, strengthen the DCP’s incentive effect on emission reduction in the transportation field, significantly reduce pollutant emissions in the transportation field, and achieve carbon peaking and carbon neutrality goals in the transportation field faster.
5 The NEV supply chain coordination strategy
Compared to decentralized decision-making, the profit increase is Δπ=a2N(4nN(nN−θ2 M+(peλ)2 M)+M(2N(peλ)2+Mθ(θ−2peλ)(θ+peλ)2))8(N(1+n)−θM(θ+peλ))2(2N−M(θ+peλ)2) ∆π=a2N4nNnN−θ2Μ+peλ2Μ+M2Npeλ2+Mθθ−2peλθ+peλ28N1+n−θΜθ+peλ22N−Μθ+peλ2 . As long as the NEV enterprises get reasonable profit distribution, the smooth implementation of supply chain cooperation can be effectively guaranteed.
Therefore, the next step will be to discuss distributing the profit increase Δπ ∆π after centralized decision-making through bargaining negotiations.
Considering that manufacturers and suppliers negotiate the allocation of the increased profit Δπ ∆π , the profits of manufacturers and component suppliers are Δπm ∆πm , Δπi(i=1,2,⋯,n) ∆πii=1,2,⋯,n , and Δπm+∑i=1nΔπi=Δπ ∆πm+∑i=1n∆πi=∆π .
There are n n pairs of negotiations between the manufacturer and the supplier, and each bargaining negotiation is
Max(Δπm−dm)ζi(Δπi−di)ηi,i=1,2,⋯,n,(7) Max∆πm−dmζi∆πi−diηi,i=1,2,⋯,n,(7)
s.t.(Δπm,Δπi)≥(dm,di), s.t.∆πm,∆πi≥dm,di,
Δπm+Δπi≤Πj,(j=1,2,⋯,n), ∆πm+∆πi≤Πj,j=1,2,⋯,n,
where Πj(j=1,2,⋯,n) Πjj=1,2,⋯,n is the profit available for distribution in the jth jth round of negotiations, Π1=Δπ Π1=∆π and Πj+1=Πj−πj Πj+1=Πj−πj . (dm,di) dm,di is the point of agreement at which negotiations break down, i.e., when the benefits to be obtained by the negotiating parties are less than (dm,di) dm,di . To simplify the analysis, consider dm=0 dm=0 and di=0 di=0 . ζi(0<ζi<1) ζi0<ζi<1 is the manufacturer’s negotiating power, and ηi(0<ηi<1) ηi0<ηi<1 is the bargaining power of the supplier i i .
5.1 Non-alignment between the supplier and manufacturer
Considering that component suppliers provide complementary parts to manufacturers, the importance of these parts is roughly the same and the negotiating power of component suppliers does not differ much. Without losing generality, we further assume that the manufacturer has the same negotiating power for suppliers α(0<α<1) α0<α<1 and component suppliers have the same bargaining power β(0<β<1) β0<β<1 , and α+β=1 α+β=1 .
When the supplier participates in the jth jth round of negotiation, the profits made by the manufacturer and supplier i i are ζiζi+ηiΠj ζiζi+ηiΠj and ηiζi+ηiΠj ηiζi+ηiΠj , and after further substituting Π1=Δπ Π1=∆π , the supplier’s profit is βαj−1Δπ βαj−1Δπ and the manufacturer’s profit is αjΔπ αjΔπ .
Therefore, when suppliers and manufacturers negotiate sequentially, component suppliers’ profits are affected by both negotiation power and the negotiation sequence. Negotiating earlier allows the supplier to obtain more profits. Similarly, delays in the sequence of negotiations can also result in a loss of supplier profits. However, when manufacturers participate in the jth jth round negotiations, the profit is αjΔπ αjΔπ , and the profits obtained are only related to its bargaining power and have nothing to do with the negotiation sequence.
Proposition 5. When the manufacturer does not align with the supplier, the supplier i i profit is π∗i=βαn−1Δπ+πD*i πi*=βαn−1Δπ+πiD* .The manufacturer’s profit is π∗m=(1−nβαn−1)Δπ+πD*m πm*=1−nβαn−1Δπ+πmD* .
Proof. Suppose the supplier participates in the (j−ε)th j−εth round of negotiations (j=2,3,⋯,n;ε∈{1,2,⋯,j−1}) j=2,3,⋯,n;ε∈1,2,⋯,j−1 , its allocated profit is βΠj−ε βΠj−ε ; if the supplier participates in the jth jth round negotiation, its allocated profit is βΠj βΠj . Comparing the profits of suppliers participating in rounds (j−ε)th j−εth and participating in jth jth round negotiations, we obtain Δπj=β(Πj−ε−Πj)=βαj−ε−1(1−αε)>0 ∆πj=βΠj−ε−Πj=βαj−ε−11−αε>0 . Therefore, the supplier’s profit from participating in the (j−ε)th j−εth round negotiations is higher than the profit from participating in the jth jth round negotiations, and the supplier will give the manufacturer a concession to obtain a higher negotiation order. Assume that the supplier’s concession strategy is STj=(P1,P2,⋯,Pj).(j=1,2,⋯,n) STj=P1,P2,⋯,Pj.j=1,2,⋯,n , where Pj Pj is the profit that the supplier should give to the automaker to get jth jth round negotiation. After giving profit to the automaker, the supplier’s actual profit is βαj−1Δπ−Pj βαj−1Δπ−Pj . If the supplier bargains in the last round, the supplier’s profit is βαn−1Δπ βαn−1Δπ , which is the lowest supplier’s profit by bargaining, where Pn=0 Pn=0 . The upper bound on the supplier’s transfer profit is the entire profit obtained by bringing the bargaining sequence forward, so the supplier’s transfer profit is P∗j=β(αi−1−αn−1)Δπ Pj*=β(αi−1−αn−1)Δπ . Substituting P∗j Pj* into βαj−1Δπ−Pj βαj−1Δπ−Pj , the profit after the supplier’s concession is βαn−1Δπ βαn−1Δπ , which means that no matter which round of negotiations the supplier participates in, it can only receive the profit from participating in the final round of negotiations, while the final profit of the automaker is (1−nβαn−1)Δπ 1−nβαn−1Δπ , and the manufacturer’s profit is the total of its negotiation profit and the concession of all suppliers.
Q.E.D.
After the supplier gives the manufacturer a concession, regardless of which negotiations the supplier participates in, it can only obtain the profit from participating in the final round of negotiations, which is the minimum profit the supplier can obtain by participating in the negotiations. The manufacturer’s profit is the total of its negotiated profit and the concession of all suppliers, and the manufacturer obtains a higher profit because of their right to decide the negotiation sequence.However, compared to decentralized decision-making, a profit distribution strategy can increase the profits of suppliers βαn−1Δπ βαn−1Δπ and the manufacturer’s profits (1−nβαn−1)Δπ 1−nβαn−1Δπ ; each company’s profits can be improved and the NEV supply chain coordination can be achieved. Moreover, independent NEV supply chain member companies have achieved the optimal technical level and carbon emission reduction through the coordination mechanism under centralized decision-making. The DCP’s incentive effect on carbon emission reduction in the transportation field can be optimized, which is conducive to achieving carbon peaking and carbon neutrality goals in the transportation field.
5.2 Alliances between the manufacturer and supplier
Consider that in an alliance between a manufacturer and a supplier, s∗ s* indicates the supplier participating in the alliance, then the supplier s∗ s* allies with the manufacturer B1={s∗,m} B1=s*,m , and the negotiation power of the alliance B1 B1 is the total of the manufacturer and supplier’s negotiating power s∗ s* , that is, αB1=α+β=1 αB1=α+β=1 , while the negotiation power of suppliers who do not participate in cooperation remains unchanged and remains β β .
When the supplier s∗ s* allies with the manufacturer, causing a change in the supply chain structure, the number of suppliers participating in the negotiation decreases from n n to n−1 n−1 , and the alliance B1 B1 needs to conduct n−1 n−1 rounds of one-by-one negotiations with suppliers. When the alliance B1 B1 negotiates with suppliers who are not involved in cooperation in the jth jth round, the profits of the alliance B1 B1 and suppliers that do not participate in cooperation are πB1=1(1+β)jΔπ πB1=11+βjΔπ and β(1+β)jΔπ β1+βjΔπ .
Proposition 6. When the manufacturer participates in negotiations after allying with the supplier and reaches equilibrium, the profit of the supplier who does not participate in the alliance is π∗∗i=β(1+β)n−1Δπ+πD∗i πi∗∗=β1+βn−1Δπ+πiD∗ . The profit of the alliance B1 B1 is (1−(n−1)β(1+β)n−1)Δπ+πD∗i+πD∗m 1−n−1β1+βn−1Δπ+πiD∗+πmD∗ . The profit of participating alliance suppliers s∗ s* is π∗s∗=β(1−(n−1)β(1+β)n−1)Δπ+πD∗i πs**=β1−n−1β1+βn−1Δπ+πiD∗ . The profit of the manufacturer is π**m=α(1−(n−1)β(1+β)n−1)Δπ+πD∗m πm**=α1−n−1β1+βn−1Δπ+πmD∗ .
Proof. It can be seen from the proof of Proposition 5 that when the supplier s∗ s* allies with the manufacturer for negotiations, the negotiation sequence still affects the supplier’s profits. Moreover, the supplier with the earlier negotiation sequence can obtain more profit distribution. Suppliers in the supply chain will still give the alliance a concession P∗j Pj* to strive for an earlier negotiation order. Similar to Proposition 5, the maximum limit of the supplier concession is β((11+β)j−(11+β)n−1)Δπ β11+βj−11+βn−1Δπ , which is all the profits obtained in advance due to the negotiation sequence; therefore, the supplier’s concession to the alliance is P**j=β((11+β)j−(11+β)n−1)Δπ Pj**=β11+βj−11+βn−1Δπ . After the supplier makes a concession, the supplier who does not participate in the cooperation can only receive the final round of negotiations’ profit β(1+β)n−1Δπ β1+βn−1Δπ , which is the minimum profit a non-cooperative supplier can obtain by participating in the negotiation if the manufacturer allies with a supplier. The profit of the alliance B1 B1 is (1−(n−1)β(1+β)n−1)Δπ 1−n−1β1+βn−1Δπ , which is the sum of the alliance’s negotiation profit and supplier concessions; the alliance B1 B1 obtains excess profits due to the right to decide the negotiation sequence. Furthermore, assuming that within the alliance B1 B1 , the manufacturer and supplier distribute alliance profits through bargaining negotiations; then, by substituting the bargaining power of manufacturers and suppliers α α and β β , the profits of the manufacturer and supplier s∗ s* are α(1−(n−1)β(1+β)n−1)Δπ α1−n−1β1+βn−1Δπ and β(1−(n−1)β(1+β)n−1)Δπ β1−n−1β1+βn−1Δπ , respectively.
Q.E.D.
When the manufacturer allies with a supplier, the alliance receives excess profits due to the right to decide the negotiation sequence. The manufacturer and supplier share the alliance profits through bargaining negotiations, and suppliers receive higher profits by participating in the alliance. Suppliers who do not participate in the alliance cooperation can only receive the final round of negotiations’ profit and the minimum profit suppliers can obtain by participating in negotiations.
Proposition 7. When the manufacturer allies with a supplier, the profit of all suppliers increases. The supplier who participates in the alliance receives more profit than the supplier who does not. However, the profit of the manufacturer decreases due to the alliance.
Proof. Substituting π∗s∗ πs∗∗ and π∗∗i πi∗∗ into π∗s∗−π∗∗i πs∗∗−πi∗∗ , we obtain π∗s∗−π∗∗i=β(1−(n−1)β(11+β)n−1)Δπ−β(1+β)n−1Δπ=β(1+β)n−1((1+β)n−1−(n−1)β−1)Δπ πs∗∗−πi∗∗=β1−n−1β11+βn−1Δπ−β1+βn−1Δπ=β1+βn−11+βn−1−n−1β−1Δπ . Because (1+β)n−1=1+(n−1)β+(n2)β2+ο(β2)>(n−1)β+1 1+βn−1=1+n−1β+n2β2+οβ2>n−1β+1 , we get π∗∗i−π∗i=β1(1+β)n−1Δπ−βαn−1Δπ=βΔπ(1+β)n−1(1−(1−β2)n−1)>0 πi∗∗−πi∗=β11+βn−1Δπ−βαn−1Δπ=βΔπ1+βn−11−1−β2n−1>0 . Therefore, we get π∗s∗>π∗∗i>π∗i πs∗∗>πi∗∗>πi∗ .
Q.E.D.
When the manufacturer and supplier form alliances, the profits of all suppliers are improved, suppliers who participate in cooperation can obtain more profits than those who do not participate in cooperation, suppliers will actively cooperate with manufacturers, and manufacturers have the initiative to choose partners.However, the manufacturer has aligned themselves with the supplier, resulting in lower profits. Therefore, for manufacturers, alliances do not increase profits, although they enhance their products’ competitive advantage, and it is necessary to choose partners carefully. The research also explains why the manufacturer partners with component suppliers with advanced technology advantages.
6 Numerical analysis and sensitivity analysis
6.1 Numerical analysis
The following analysis is carried out through an example to visually demonstrate the influence of different parameter values on optimal strategies. For example, we use four suppliers of parts to perform numerical analysis. The parameters are as follows: k1=10,k2=40,k3=30,k4=50,θ=2,λ=0.5,a=2000,α=0.6,β=0.4,pe=100,and λ=0.005 k1=10,k2=40,k3=30,k4=50,θ=2,λ=0.5,a=2000,α=0.6,β=0.4,pe=100,andλ=0.005 .
We can obtain the optimal strategy by substituting parameters, as shown in Table 1.
TABLE 1
After implementing the DCP, the technical level and sales of NEVs have increased and the carbon emissions in the transportation field have significantly reduced. The DCP can promote the NEV industry’s development and reduce carbon emissions.
However, we found that the incentive effect of the DCP on NEV technology innovation, sales, and carbon emission reduction is much lower than that under centralized decision-making. Decentralized decision-making weakens the incentive effect of the DCP. Only by making centralized decisions by NEV supply chain companies can the DCP’s incentive effect be optimized.
Therefore, it is essential to design an incentive mechanism to coordinate the NEV supply chain so that independent and decentralized companies can achieve centralized decision-making.
As seen from Table 2, if the supply chain makes centralized decisions, the profits are significantly improved compared to those under the decentralized decision-making. For NEV companies that seek to maximize profits, it is easy to achieve cooperation as long as this can improve profits. By bargaining for the distribution of incremental profits, both suppliers and manufacturers gain more profits than under decentralized decisions. Therefore, the incentive system based on the bargaining game achieves coordination of the NEV supply chain and promotes centralized decision-making by NEV companies.
TABLE 2
As seen from Table 2, when suppliers individually negotiate on a one-on-one basis with manufacturers in the order of negotiation, the profits of all suppliers are improved, and all suppliers’ profits increase by the same amount. When a given supplier and the manufacturer are aligned to negotiate, all suppliers’ profits increase and the given supplier’s profits increase by much more than those of other suppliers. Therefore, forming alliances with manufacturers is the best choice for suppliers to obtain more profits. However, manufacturers’ profits decline after the formation of such an alliance. Therefore, the purpose of the alliance of manufacturers is different from that for suppliers, i.e., not to increase profits but to cooperate with suppliers with technological advantages to enhance product competitiveness.
6.2 Sensitivity analysis
6.2.1 Influence of the DCP
① The impact of the technological innovation credit coefficient
As seen in Figures 1A, B, NEV technology has improved, regardless of the decision-making mode after implementing the DCP compared to without the DCP. Moreover, the higher the technological innovation credit coefficient, the higher the NEVs’ technology level. As seen from Figures 1C, D, implementing the DCP has increased NEV sales under decentralized and centralized decision-making. Moreover, the higher the technological innovation credit coefficient, the higher the NEV sales. As seen from Figures 1E, F, after the implementation of the DCP, regardless of the decision-making mode, the supplier’s technological innovation investment increases, and the higher the technological innovation credit coefficient, the higher the supplier’s technological innovation investment. As seen from Figures 1G, H, after implementing the DCP, carbon emissions in the transportation field are reduced under decentralized and centralized decision-making. With the improved credit coefficient of technological innovation, the impact of the DCP on carbon emission reduction in the transportation field is more significant.
FIGURE 1
Enlarge this image.FIGURE 1. Impact of the technological innovation credit coefficient on the optimal strategy.
② The impact of credit prices
As seen from Figures 2A, B, after the DCP’s implementation, the NEVs’ technology level improves, regardless of the decision-making mode. Moreover, the higher the credit price, the higher the NEVs’ technology level. As seen from Figures 2C, D, NEV sales increased after implementing the DCP. Moreover, the higher the credit price, the higher the NEV sales. According to Figures 2E, F, after the implementation of the DCP, regardless of the decision-making mode, the supplier’s technological innovation investment increases, and with the increase in the credit price, the higher the technological innovation investment. As seen from Figures 2G, H, carbon emissions in the transportation sector reduce after the implementation of the DCP. With the increase in credit prices, the impact of the DCP on carbon emission reduction in the transportation field is more significant.
FIGURE 2
6.2.2 Impact of decision-making methods
As seen from Figures 3A, C, F, G, compared to the decentralized decision-making, the investment in NEV technology innovation increases, the technical level of NEVs increases, NEV sales increase, and carbon emission reduction increases under the centralized decision-making. Moreover, the higher the credit coefficient of technological innovation, the more significant the advantages of centralized decision-making.
FIGURE 3
As seen from Figures 3B, D, E, H, compared to the decentralized decision-making, the investment in NEV technology innovation increases, NEVs’ technical level increases, NEV sales increase, and carbon emission reduction increases under the centralized decision-making. Moreover, the higher the credit price, the more significant the advantages of centralized decision-making.
6.2.3 Impact of alliance negotiations
As seen from Figures 4A, B, the manufacturer and supplier are more profitable under the centralized decision-making than under the decentralized decision-making and obtain incremental profits based on bargaining negotiations. Therefore, the coordination strategy is the optimal strategy for centralized decision-making, improving the technological innovation investment and technical level of NEVs, reducing carbon emissions in the transportation field, and increasing the profits of all companies, thus representing a win–-win situation.
FIGURE 4
As seen from Figure 4C, when a manufacturer engages in alliances with a supplier, all suppliers are profitable, all suppliers benefit from alliances, and the suppliers who participate in cooperation earn more margins than suppliers who do not. As seen from Figure 4D, the manufacturer’s profits decrease, proving that the manufacturer is not aligned to improve profits.
7 Discussion
The Chinese government issued the DCP intending to promote the development of NEVs through market incentives. Some researchers believe that the DCP could achieve China’s goals in this regard. The DCP can stimulate manufacturers and suppliers to increase investment in NEVs’ technological innovation, improve NEVs’ technology level, and promote NEVs’ large-scale promotion (Zhou and Shen, 2020; Ma et al., 2021; Yang et al., 2021). Our research confirms this view.
Next, we discuss the essential parameters involved in the proposed models.
Under the DCP, as the technological innovation credit coefficient or credit price increases, the DCP’s incentive effect on the development of the NEV industry and the reduction of carbon emissions in transportation become more significant. Therefore, the government can set and adjust the technological innovation credit coefficient to prompt the NEV industry’s healthy and sustainable development and achieve the transportation sector’s carbon peak and carbon neutrality goals.
The decentralized decision-making weakens the incentive effect of the DCP on technological innovation investment, carbon emission reduction, technology level, and NEV production. With the increase of the technological innovation credit coefficient or credit price, the weakening of the incentive effect of the DCP is more significant. However, the NEV supply chain can achieve one-to-many cooperation through the centralized decision-making and profit-sharing mechanism and could effectively motivate suppliers to increase NEVs’ technological innovation investment and optimize the incentive effect of the DCP.
We found that the impact of alliance negotiations on the profits of manufacturers and suppliers differs. Through bargaining negotiations, both manufacturers and suppliers have obtained more profits than under decentralized decision-making. Moreover, as the manufacturer’s bargaining power increases, the manufacturer’s profit and supplier’s profit decreases. Nevertheless, compared to no alliance, when the manufacturer and a supplier form an alliance, the manufacturer’s profits decrease and the suppliers’ profits increase. The results indicate that the manufacturer’s primary purpose in allying is to enhance their products’ competitiveness rather than to increase their profits, while suppliers could gain more profits by allying.
8 Conclusion
8.1 Main findings
This paper considers the NEV supply chain consisting of one manufacturer and multiple suppliers, constructs a technological innovation decision-making model under the DCP, and studies the optimal technological innovation strategy and the impact of the DCP on carbon emissions. Furthermore, this paper constructs a bargaining game model based on an alliance strategy to study the coordination of the NEV supply chain. Finally, it verifies the DCP’s incentive effect on NEV technology innovation and carbon emission reduction in the transportation field through a numerical example analysis. The main findings of the research are as follows:
① Implementing the DCP can stimulate the NEV supply chain to increase technological innovation investment, improve the technological level and sales of NEVs, and effectively reduce carbon emissions in the transportation sector. Moreover, with the increase of the technological innovation credit coefficient or credit price, the DCP has a significant incentive effect on the technological innovation of NEVs and the reduction of carbon emissions in the transportation sector.
② The decentralized decision-making weakens the incentive effect of the DCP on reducing carbon emissions, and the bargaining game based on alliance negotiation can enable independent enterprises to achieve the optimal strategy for centralized decision-making so that the incentive effect of the DCP on reducing carbon emissions can be optimized.
③ The purpose of an alliance between manufacturers and suppliers is different. For manufacturers, it is not to increase profits but to enhance their product advantages. However, suppliers can profit more by participating in the alliance, providing a theoretical reference for decision-making.
8.2 Theoretical implications
This study bridges the concepts of the DCP, carbon emission reduction, and NEVs’ technological innovation cooperation. The contributions to the existing literature are as follows:
① This paper theoretically further enriched the research content in policy incentive (especially DCP), carbon emission reduction, and technological innovation cooperation of the NEV supply chain, considering the background of the DCP, and combines NEV technological innovation with carbon emission reduction, which provides a new research idea for future research.
② This paper constructs a technological innovation game model of the NEV supply chain under the dual credit policy and a bargaining model considering the alliance. The results show that the NEV supply chain can achieve one-to-many cooperation through centralized decision-making and profit-sharing mechanisms, and could effectively motivate suppliers to increase NEVs’ technological innovation investment and optimize the incentive effect of the DCP, which provides a theoretical reference for future research.
8.3 Practical implications
Based on the main findings, we put forward policy implications as follows:
① The DCP can encourage enterprises to increase investment in NEV technological innovation and significantly reduce carbon emissions in transportation. Moreover, the higher the technology innovation credit coefficient or credit price, the more is invested in technology innovation for NEVs and the more significant the effect on reducing carbon emissions in the transportation sector. Therefore, the government can regulate the NEV industry by setting or adjusting the credit coefficient of technological innovation or credit price, promoting the NEV industry’s healthy and sustainable development, and realizing the carbon peaking and carbon neutrality goals.
② The alliance cooperation between manufacturers and suppliers can improve the suppliers’ profits, enhance the manufacturers’ competitiveness, and achieve a win-win scenario for all parties. The government should encourage alliances among companies to rapidly upgrade NEVs and achieve more quickly the carbon peaking and carbon neutrality goals in transportation.
8.4 Future work
This study has some limitations. First, the demand function is linear and random or other demand function forms deserve further investigation. Second, it is assumed that there is only one supplier for one component. However, there may be different suppliers of the same component; therefore, supplier competition should be considered.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding authors.
Author contributions
BH and YL conceived the original idea for the research and established the model. MM and WM performed the model analyses. MM performed the numerical analyses. All authors drafted and revised the draft.
Funding
This study is supported by the National Social Science Foundation of China (No. 17XGL008), the Fundamental Research Funds for the Central Universities (No. 2020CDJSK02PT12), and the Natural Science Foundation of Chongqing (No. cstc2019jcyj-msxmX0616).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Nomenclature
Abbreviations
NEVs new energy vehicles
DCP dual credit policy
Superscripts
D D decentralized decision
ND ND decentralized decisions without the dual credit policy
Subscripts
m m carmaker
Parameters
a a potential market demand
θ θ consumer preference coefficient for NEV technology
w w wholesale price of the parts
ε ε NEV credits with the technical performance T0 T0
I I supplier’s technology innovation investment
G0 G0 each fuel vehicle’s carbon emissions
δ δ technological innovation emission reduction coefficient
C C centralized decisions
NC NC centralized decisions without dual credit policy
i i supplier
p p NEVs’ price
T T NEVs’ technology innovation
pe pe NEV credit price
λ λ technology innovation credit coefficient
k k supplier’s technology innovation cost coefficient
μ μ replacement rate of NEVs
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Nomenclature Abbreviations NEVs new energy vehicles DCP dual credit policy Superscripts D D decentralized decision ND ND decentralized decisions without the dual credit policy Subscripts m m carmaker Parameters a a potential market demand θ θ consumer preference coefficient for NEV technology w w wholesale price of the parts ε ε NEV credits with the technical performance T0 T0 I I supplier’s technology innovation investment G0 G0 each fuel vehicle’s carbon emissions δ δ technological innovation emission reduction coefficient C C centralized decisions NC NC centralized decisions without dual credit policy i i supplier p p NEVs’ price T T NEVs’ technology innovation pe pe NEV credit price λ λ technology innovation credit coefficient k k supplier’s technology innovation cost coefficient μ μ replacement rate of NEVs* 1College of Economics and Business Administration, Chongqing University, Chongqing, China
* 2College of Computer and Information Science, Chongqing Normal University, Chongqing, China
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