In recent years, the dual credit policy has emerged as a pivotal driver in the transition toward sustainable transportation, with EV cars becoming increasingly mainstream. However, challenges such as limited driving range and insufficient charging infrastructure persist, necessitating continuous technological innovation to meet consumer demands. This review synthesizes existing research on the dual credit policy and its interplay with technological innovation in the EV car supply chain. We analyze key findings, identify gaps, and propose future directions to aid researchers, policymakers, and industry practitioners in advancing this field. By integrating theoretical models, empirical evidence, and dynamic perspectives, this article aims to provide a comprehensive overview that underscores the critical role of innovation in enhancing the competitiveness and sustainability of EV cars.
The dual credit policy, implemented in 2018, shifted the automotive industry from policy-driven to market-driven mechanisms by allowing manufacturers to trade credits based on their production of EV cars and fuel-efficient vehicles. This policy has accelerated the adoption of EV cars, but issues like range anxiety and charging accessibility remain significant barriers. Technological innovation is essential to address these challenges, and this review explores how the dual credit policy influences innovation dynamics within the supply chain. We begin by examining studies on the dual credit policy, followed by an analysis of technological innovation research, and conclude with limitations and future prospects.
Research on the Dual Credit Policy
The dual credit policy has been extensively studied for its impact on the automotive market, particularly in promoting EV cars. Theoretical models demonstrate that the policy increases the price of traditional vehicles, reduces their demand, and lowers profits for conventional manufacturers, while boosting demand and profits for EV cars. For instance, the policy encourages carbon reduction in fuel vehicles and supports the growth of the EV car sector. Key parameters, such as the credit ratio for EV cars and the accounting coefficients, play crucial roles; a higher credit ratio negatively affects fuel vehicle manufacturers, whereas a higher accounting coefficient positively influences EV car producers.
Government subsidies, while initially effective in incentivizing R&D, have led to fiscal burdens and fraudulent claims. In contrast, the dual credit policy mitigates these issues by reducing government reliance and preventing free-riding by technologically lagging firms. Empirical studies show that the policy significantly enhances R&D investments in EV car companies. The price of credits, which is determined by market transactions, exhibits a non-monotonic effect on production volumes. Under subsidy phase-out scenarios, lower credit prices can stimulate demand for EV cars by making them more affordable. The tradable credit mechanism allows EV car manufacturers to monetize their innovations through credit sales, further driving technological advancements.
To summarize these findings, Table 1 provides an overview of key studies on the dual credit policy’s effects:
| Aspect | Key Findings | Model/Approach |
|---|---|---|
| Credit Price Effects | Non-monotonic impact on EV car production; lower prices boost demand under subsidy withdrawal. | Game theory models |
| R&D Investment | Policy increases R&D in EV car firms; reduces free-riding. | Empirical analysis |
| Market Dynamics | Raises traditional vehicle prices; shifts demand to EV cars. | Equilibrium models |
| Carbon Reduction | Promotes emissions reduction in fuel vehicles. | Theoretical simulations |
Mathematically, the impact of credit price on manufacturer decisions can be represented using a profit function. Let $P_c$ denote the credit price, $Q_{ev}$ the quantity of EV cars produced, and $Q_f$ the quantity of fuel vehicles. The profit $\pi$ for a manufacturer is given by:
$$\pi = R_{ev}(Q_{ev}) + R_f(Q_f) – C_{ev}(Q_{ev}) – C_f(Q_f) + P_c \cdot (C_{ev} – C_f)$$
where $R_{ev}$ and $R_f$ are revenues from EV cars and fuel vehicles, respectively, $C_{ev}$ and $C_f$ are costs, and the credit term reflects the net credits earned. This model highlights how credit trading influences production strategies, with higher $P_c$ incentivizing more EV car output.
Research on Technological Innovation
Technological innovation in the EV car supply chain involves high costs, risks, and uncertainties, but it is crucial for improving range, charging efficiency, and overall performance. Studies often employ models like stochastic stopping to analyze R&D decisions, where a higher success rate of innovation positively affects investment levels before achieving breakthroughs. For example, the probability of successful innovation $\theta$ influences the optimal R&D expenditure $I$ according to:
$$I^* = \arg \max \left[ \theta \cdot V – (1-\theta) \cdot C \right]$$
where $V$ is the value of innovation and $C$ is the cost. This underscores the importance of managing risks in EV car development.
Government interventions, such as R&D subsidies, have been shown to reduce innovation costs and encourage supply chain actors to invest in technology. Research indicates that subsidy policies effectively promote collaborative R&D, with coefficients representing government incentives affecting firms’ willingness to adopt carbon reduction technologies. For instance, a subsidy rate $s$ can be incorporated into a cost function, lowering the effective R&D cost to $(1-s) \cdot C$. Comparative studies reveal that subsidizing manufacturers leads to better green quality outcomes than subsidizing retailers, as it directly targets production innovations for EV cars.
Beyond government support, supply chain collaboration is vital. Cooperative R&D between upstream and downstream partners, such as cost-sharing or profit-sharing agreements, enhances innovation outcomes. For example, in a two-echelon supply chain involving EV car manufacturers and charging infrastructure providers, collaboration can lead to higher range improvements compared to non-cooperative scenarios. Models like differential games are used to analyze dynamic interactions, where cooperation parameters $\alpha$ (cost-sharing ratio) and $\beta$ (profit-sharing ratio) optimize joint outcomes. The total innovation effort $E$ in a cooperative setting can be expressed as:
$$E = \alpha \cdot E_m + \beta \cdot E_r$$
where $E_m$ and $E_r$ represent efforts by manufacturers and retailers, respectively.
Table 2 summarizes key technological innovation mechanisms and their effects on EV car supply chains:
| Mechanism | Description | Impact on EV Cars |
|---|---|---|
| Government Subsidies | Direct financial support for R&D reduces innovation costs. | Increases investment in EV car technology; improves range and charging solutions. |
| Cost-Sharing Contracts | Retailers分担制造商’s R&D costs; enhances collaboration. | Boosts carbon reduction levels and green technology adoption for EV cars. |
| Profit-Sharing Contracts | Revenue sharing post-innovation; aligns incentives. | Superior to cost-sharing in promoting green innovation for EV cars under certain conditions. |
| Differential Games | Dynamic models of supply chain interactions over time. | Optimizes long-term innovation efforts and product greenness for EV cars. |
Innovation also exhibits delay effects, where R&D investments take time to materialize into standards or marketable technologies. Error correction models confirm that technological advancements drive standardization with a time lag $\tau$, which can hinder innovation if not managed properly. In differential game frameworks, the delay effect is incorporated as:
$$\frac{dT}{dt} = \gamma E(t-\tau) – \delta T(t)$$
where $T$ is the technology level, $E$ is the innovation effort, $\gamma$ is the efficiency parameter, and $\delta$ is the decay rate. If $\tau$ exceeds a threshold, firms may cease investing in EV car technologies, highlighting the need for timely innovation cycles.
Limitations of Existing Research
Despite extensive studies, several limitations persist in the current literature on EV car supply chains under the dual credit policy. First, many models focus on single entities, such as individual manufacturers, neglecting the interconnected nature of supply chains involving multiple stakeholders like charging infrastructure providers. This oversimplification fails to capture real-world dynamics where collaboration is essential for scaling EV car adoption. Second, while credit price effects are well-documented, the direct impact of technological innovation on supply chain decisions is often overlooked. For instance, how innovations in battery technology influence credit trading strategies remains underexplored.
Additionally, most research prioritizes government subsidies as the primary incentive, underestimating the role of private sector collaborations in分担 risks and costs. The delay effects of innovation are also inadequately addressed; only a minority of studies incorporate time lags into models, leading to incomplete assessments of long-term outcomes. These gaps limit the applicability of findings to practical scenarios involving EV cars, where rapid technological shifts and supply chain integrations are critical.
Future Research Directions
To address these limitations, future research should expand its scope to encompass entire supply chains, particularly the dyadic relationship between EV car manufacturers and charging infrastructure suppliers. Investigating how credit prices and innovation incentives affect this two-echelon system could yield insights into optimal coordination mechanisms. For example, dynamic models like differential games can simulate interactions over time, accounting for variables such as credit fluctuations and technological progress in EV cars.
As government subsidies phase out, strategic partnerships within supply chains will become increasingly important. Studies should explore various cooperation mechanisms, including cost-sharing, profit-sharing, and joint ventures, to identify the most effective approaches for fostering innovation in EV cars. Comparative analyses of these mechanisms under different policy scenarios could inform best practices for industry practitioners.
Furthermore, incorporating delay effects into innovation models is crucial for realism. Future work should integrate time delays into博弈 frameworks to assess how滞后 influences R&D investments and supply chain performance for EV cars. For instance, extending differential game models to include stochastic delay parameters could provide a more nuanced understanding of innovation cycles.
Finally, adopting dynamic perspectives is essential. While static models offer snapshot insights, they fall short in capturing the evolving nature of EV car markets. Differential games and other dynamic optimization techniques can elucidate long-term trends, such as how incremental innovations accumulate to enhance the range and affordability of EV cars. By addressing these areas, researchers can contribute to a more resilient and innovative EV car supply chain, ultimately supporting global sustainability goals.
In conclusion, the dual credit policy has catalyzed the growth of EV cars, but sustained innovation is imperative to overcome existing challenges. This review highlights the synergies between policy mechanisms and technological advancements, urging a holistic approach that embraces supply chain collaboration, dynamic modeling, and delay-aware analyses. As the EV car industry evolves, interdisciplinary research will play a pivotal role in shaping a future where electric mobility is accessible, efficient, and environmentally friendly.