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.

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.

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.

The NEV market demand function is q = a − p + θ ∑ i = 1 n T i (Ma et al., 2018; Wang and Huang, 2021), where a is the potential demand, p is the NEV sales price, θ is the sensitivity coefficient of market demand, and the greater θ is, the more significant the impact of the supplier’s technical level on market demand. T i i = 1,2 , ⋯ , n is the technological innovation of supplier 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 T 0 can earn NEV credit ε . Furthermore, considering the supplier’s investment in technological innovation to raise the technical level of NEV to ∑ i = 1 n T i , the NEV credits that can increase λ ∑ i = 1 n T i − T 0 , where λ is the NEV technology innovation credit coefficient and λ > 0 . Each vehicle can earn NEV credits ε + λ ∑ i = 1 n T i − T 0 (Liu et al., 2022). For simplicity and generality, let T 0 = 0 and ε = 0 .

Suppose the credit price is p e ; then, the manufacturer’s credit revenue for the production of NEVs is p e q λ ∑ i = 1 n T i .

Considering that the technological innovation investment of the component supplier is I i = 1 2 k i T i 2 (Gurnani and Erkoc, 2008; Zheng et al., 2019), k i > 0 i = 1,2 , ⋯ , n , k i 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 G 0 , assuming that each NEV can replace fuel vehicles μ and can reduce carbon emissions μ G 0 (Cheng and Fan, 2021), and μ > 0 , μ is the replacement rate of NEVs. Considering the technological innovation of NEVs is ∑ i = 1 n T i and carbon emission reduction is δ ∑ i = 1 n T i (Cao and Wu, 2020; Hao and Li, 2020), and δ > 0 , δ is the technological innovation emission reduction coefficient. Therefore, producing q NEVs can reduce carbon emissions μ G 0 + δ ∑ i = 1 n T i q .

Thus, the profit function of the supplier i is π i = w i q − 1 2 k i T i 2 . (1)

The manufacturer’s profit consists of NEV sales revenue and credit revenue, and the profit function is π m = p − ∑ i = 1 n w i q + p e λ q ∑ i = 1 n T i , (2) where p = ω + ∑ i = 1 n w i and w i i = 1,2 , ⋯ , n are the parts price of supplier i , and ω is the manufacturer’s markup.

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 and component suppliers have the same bargaining power β 0 < β < 1 , and α + β = 1 .

When the supplier participates in the j t h round of negotiation, the profits made by the manufacturer and supplier i are ζ i ζ i + η i Π j and η i ζ i + η i Π j , and after further substituting Π 1 = ∆ π , the supplier’s profit is β α j − 1 Δ π and the manufacturer’s profit is α j Δ π .

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 j t h round negotiations, the profit is α j Δ π , 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 profit is π i * = β α n − 1 Δ π + π i D * .The manufacturer’s profit is π m * = 1 − n β α n − 1 Δ π + π m D * .

Proof. Suppose the supplier participates in the j − ε t h round of negotiations j = 2,3 , ⋯ , n ; ε ∈ 1,2 , ⋯ , j − 1 , its allocated profit is β Π j − ε ; if the supplier participates in the j t h round negotiation, its allocated profit is β Π j . Comparing the profits of suppliers participating in rounds j − ε t h and participating in j t h round negotiations, we obtain ∆ π j = β Π j − ε − Π j = β α j − ε − 1 1 − α ε > 0 . Therefore, the supplier’s profit from participating in the j − ε t h round negotiations is higher than the profit from participating in the j t h 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 S T j = P 1 , P 2 , ⋯ , P j . j = 1,2 , ⋯ , n , where P j is the profit that the supplier should give to the automaker to get j t h round negotiation. After giving profit to the automaker, the supplier’s actual profit is β α j − 1 Δ π − P j . If the supplier bargains in the last round, the supplier’s profit is β α n − 1 Δ π , which is the lowest supplier’s profit by bargaining, where P n = 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 ( α i − 1 − α n − 1 ) Δ π . Substituting P j * into β α j − 1 Δ π − P j , the profit after the supplier’s concession is β α n − 1 Δ π , 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 − n β α n − 1 Δ π , 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 β α n − 1 Δ π and the manufacturer’s profits 1 − n β α n − 1 Δ π ; 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.

Consider that in an alliance between a manufacturer and a supplier, s * indicates the supplier participating in the alliance, then the supplier s * allies with the manufacturer B 1 = s * , m , and the negotiation power of the alliance B 1 is the total of the manufacturer and supplier’s negotiating power s * , that is, α B 1 = α + β = 1 , while the negotiation power of suppliers who do not participate in cooperation remains unchanged and remains β .

When the supplier s * allies with the manufacturer, causing a change in the supply chain structure, the number of suppliers participating in the negotiation decreases from n to n − 1 , and the alliance B 1 needs to conduct n − 1 rounds of one-by-one negotiations with suppliers. When the alliance B 1 negotiates with suppliers who are not involved in cooperation in the j t h round, the profits of the alliance B 1 and suppliers that do not participate in cooperation are π B 1 = 1 1 + β j Δ π and β 1 + β j Δ π .

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 − 1 Δ π + π i D ∗ . The profit of the alliance B 1 is 1 − n − 1 β 1 + β n − 1 Δ π + π i D ∗ + π m D ∗ . The profit of participating alliance suppliers s * is π s * * = β 1 − n − 1 β 1 + β n − 1 Δ π + π i D ∗ . The profit of the manufacturer is π m * * = α 1 − n − 1 β 1 + β n − 1 Δ π + π m D ∗ .

Proof. It can be seen from the proof of Proposition 5 that when the supplier 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 * to strive for an earlier negotiation order. Similar to Proposition 5, the maximum limit of the supplier concession is β 1 1 + β j − 1 1 + β n − 1 Δ π , 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 1 1 + β j − 1 1 + β n − 1 Δ π . 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 − 1 Δ π , 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 B 1 is 1 − n − 1 β 1 + β n − 1 Δ π , which is the sum of the alliance’s negotiation profit and supplier concessions; the alliance B 1 obtains excess profits due to the right to decide the negotiation sequence. Furthermore, assuming that within the alliance B 1 , the manufacturer and supplier distribute alliance profits through bargaining negotiations; then, by substituting the bargaining power of manufacturers and suppliers α and β , the profits of the manufacturer and supplier s * are α 1 − n − 1 β 1 + β n − 1 Δ π and β 1 − n − 1 β 1 + β n − 1 Δ π , 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 ∗ ∗ and π i ∗ ∗ into π s ∗ ∗ − π i ∗ ∗ , we obtain π s ∗ ∗ − π i ∗ ∗ = β 1 − n − 1 β 1 1 + β n − 1 Δ π − β 1 + β n − 1 Δ π = β 1 + β n − 1 1 + β n − 1 − n − 1 β − 1 Δ π . Because 1 + β n − 1 = 1 + n − 1 β + n 2 β 2 + ο β 2 > n − 1 β + 1 , we get π i ∗ ∗ − π i ∗ = β 1 1 + β n − 1 Δ π − β α n − 1 Δ π = β Δ π 1 + β n − 1 1 − 1 − β 2 n − 1 > 0 . Therefore, we get π s ∗ ∗ > π i ∗ ∗ > π i ∗ .

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.

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: k 1 = 10 , k 2 = 40 , k 3 = 30 , k 4 = 50 , θ = 2 , λ = 0.5 , a = 2000 , α = 0.6 , β = 0.4 , p e = 100 , a n d   λ = 0.005 .

We can obtain the optimal strategy by substituting parameters, as shown in 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.

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.

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.

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.

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.

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.