In recent years, the rapid growth of the electric vehicle (EV) market in China has led to a significant increase in the number of retired power batteries. As early-generation electric vehicles reach the end of their lifecycle, managing these batteries has become a critical issue. The echelon utilization of power batteries—where retired batteries are inspected, classified, and repurposed for secondary applications—offers a sustainable solution. However, consumer concerns about the safety and quality of these repurposed batteries hinder widespread adoption. Blockchain technology, with its traceability and transparency features, can address these concerns by providing a verifiable record of the battery’s lifecycle. In this paper, we investigate the impact of blockchain technology on pricing strategies within a supply chain involving battery manufacturers and authorized processors for echelon utilization in the China EV market. We develop a two-stage game-theoretic model to analyze optimal decisions under different scenarios, incorporating consumer behavior, competition between battery types, and cost structures. Our findings provide insights into when blockchain adoption is beneficial and how it affects supply chain members, consumer surplus, and social welfare.
The electric vehicle industry in China is experiencing exponential growth, with millions of units sold annually. This surge has resulted in a large volume of retired power batteries, which, if not managed properly, could pose environmental risks. Echelon utilization involves repurposing these batteries for less demanding applications, such as energy storage, thereby extending their lifespan and reducing waste. However, the success of this approach depends on consumer trust in the quality and safety of repurposed batteries. Blockchain technology can enhance transparency by recording the entire lifecycle of a battery, from manufacture to retirement and repurposing. This traceability reduces information asymmetry and increases consumer confidence, potentially driving demand for echelon products. In our study, we focus on a supply chain consisting of a battery manufacturer (e.g., a company like BYD) and an authorized processor (e.g., a recycling firm). The manufacturer retrieves retired batteries from the market, categorizes them into high-performance (H-type) and low-performance (L-type) based on remaining capacity, and sells them to the processor at wholesale prices. The processor then refurbishes and sells these products to consumers. We model this as a Stackelberg game, where the manufacturer acts as the leader and the processor as the follower, to derive optimal pricing strategies under both blockchain-enabled and non-blockchain scenarios.
To formalize our model, we define key parameters and variables. Let $c_H$ and $c_L$ represent the recovery costs for H-type and L-type batteries, respectively, with $c_H > c_L$. The wholesale prices are denoted as $w_H^j$ and $w_L^j$, and retail prices as $p_H^j$ and $p_L^j$, where $j \in \{N, B\}$ indicates the scenario (N for no blockchain, B for blockchain). Consumer willingness-to-pay for H-type products is $\theta$, uniformly distributed in [0,1], and for L-type products, it is $\beta\theta$, where $\beta \in [0,1)$ measures the substitutability between the two types. When blockchain is adopted, consumer perception improves, scaling willingness-to-pay by a factor $k \geq 1$. Thus, the utility functions are $u_H = k\theta – p_H^j$ and $u_L = k\beta\theta – p_L^j$ for blockchain scenarios, and $k=1$ otherwise. The demand functions are derived from consumer choice behavior:
$$q_H^j = \frac{k – k\beta + p_L^j – p_H^j}{k(1-\beta)}$$
$$q_L^j = \frac{p_H^j – p_L^j}{k\beta(1-\beta)}$$
The profit functions for the battery manufacturer and authorized processor are as follows. For the manufacturer:
$$\pi_M^j = (w_L^j – c_L)q_L^j + (w_H^j – c_H)q_H^j + \lambda \pi_P^j – F$$
where $\lambda$ is the profit-sharing ratio allocated to the manufacturer, and $F$ is the fixed cost of blockchain adoption. For the processor:
$$\pi_P^j = (1-\lambda)\left[(p_H^j – w_H^j)q_H^j + (p_L^j – w_L^j)q_L^j\right]$$
We solve this model using backward induction to obtain equilibrium solutions. The following table summarizes the key notations used in our analysis:
| Parameter | Meaning |
|---|---|
| $q_H^j$, $q_L^j$ | Demand for H-type and L-type products |
| $c_H$, $c_L$ | Recovery costs for H-type and L-type batteries |
| $F$ | Blockchain adoption cost |
| $\theta$ | Consumer willingness-to-pay for H-type |
| $\beta$ | Substitutability of L-type for H-type |
| $\lambda$ | Profit-sharing ratio |
| $k$ | Blockchain-induced utility multiplier |
| $\pi_M^j$, $\pi_P^j$ | Profits of manufacturer and processor |
| $w_H^j$, $w_L^j$ | Wholesale prices |
| $p_H^j$, $p_L^j$ | Retail prices |
In the blockchain scenario (B), we derive the equilibrium outcomes as shown in the table below. The results indicate that blockchain adoption increases wholesale and retail prices for both product types due to enhanced consumer trust. However, it also alters demand patterns, with H-type products experiencing higher demand and L-type products lower demand, as consumers prefer higher-quality options when information is transparent.
| Variable | Equilibrium Expression |
|---|---|
| $w_H^B$ | $\frac{k – c_H – k\lambda}{2 – \lambda}$ |
| $w_L^B$ | $\frac{k\beta – k\beta\lambda – c_L}{2 – \lambda}$ |
| $p_H^B$ | $\frac{k(2-3\lambda) – c_H}{2(2-\lambda)}$ |
| $p_L^B$ | $\frac{k\beta(2-3\lambda) – c_L}{2(2-\lambda)}$ |
| $q_H^B$ | $\frac{k(1-\beta) – (c_H – c_L)}{2k(1-\beta)(2-\lambda)}$ |
| $q_L^B$ | $\frac{\beta(c_H – c_L) – c_L(1-\beta)}{2k\beta(1-\beta)(2-\lambda)}$ |
| $\pi_M^B$ | $\frac{(1-\lambda)[k^2(1-\beta)^2 – (c_H – c_L)^2]}{4k(1-\beta)(2-\lambda)^2} – F$ |
| $\pi_P^B$ | $\frac{(1-\lambda)^2[k^2(1-\beta)^2 – (c_H – c_L)^2]}{4k(1-\beta)(2-\lambda)^2}$ |
For the non-blockchain scenario (N), we set $k=1$ and $F=0$ in the above expressions. Comparing the two scenarios, we find that blockchain adoption is only beneficial when the substitutability parameter $\beta$ is moderate, i.e., $\beta \in [c_L/c_H, (k(c_H – c_L))/k]$. This ensures both product types remain in the market. Additionally, there exists a threshold for blockchain cost $F$; if $F \leq \bar{F}$, where $\bar{F} = \frac{(k-1)[\beta^2(c_H – c_L) + (1-\beta)c_L]}{4k(2-\lambda)(1-\beta)}$, the manufacturer has an incentive to adopt blockchain. In this case, consumer surplus and social welfare improve due to increased transparency and trust in the China EV market.
We further analyze the impact of key parameters on equilibrium outcomes. For instance, as $\beta$ increases, the profits of both supply chain members rise, indicating that stronger competition between product types can be beneficial under blockchain. This is because blockchain amplifies the perceived value of higher-quality products, leading to better margins. The following proposition summarizes a key finding:
Proposition 1: In the blockchain scenario, the manufacturer’s profit $\pi_M^B$ increases with $\beta$ if $\beta$ is within the moderate range, and blockchain adoption is optimal only if $F \leq \bar{F}$.
Proof: By differentiating $\pi_M^B$ with respect to $\beta$, we obtain $\frac{\partial \pi_M^B}{\partial \beta} = \frac{(1-\lambda)[k^2(1-\beta) + (c_H – c_L)^2]}{4k(2-\lambda)^2} > 0$ for $\beta$ in the specified range. The threshold $\bar{F}$ is derived by setting $\pi_M^B \geq \pi_M^N$ and solving for $F$.
To validate our model, we conduct a case study using real-world data from the BYD “Tang” electric vehicle, a popular model in China. We collect parameters such as recovery costs $c_H = 0.62$ and $c_L = 0.38$ (normalized), blockchain utility multiplier $k=1.1$, substitutability $\beta=0.7$, profit-sharing ratio $\lambda=0.5$, and blockchain cost $F=0.01$. Substituting these into our equilibrium equations, we compute optimal prices and profits, as shown in the table below. Sensitivity analysis on $\beta$ and $\lambda$ confirms the robustness of our model, with results aligning with theoretical predictions.
| $\beta$ | $w_H^B$ | $w_L^B$ | $p_H^B$ | $p_L^B$ | $q_H^B$ | $q_L^B$ | $\pi_P^B$ | $\pi_M^B$ |
|---|---|---|---|---|---|---|---|---|
| 0.62 | 0.780 | 0.481 | 0.940 | 0.581 | 0.142 | 0.006 | 0.012 | 0.025 |
| 0.70 | 0.780 | 0.510 | 0.940 | 0.640 | 0.091 | 0.078 | 0.012 | 0.027 |
| 0.78 | 0.780 | 0.539 | 0.940 | 0.699 | 0.003 | 0.183 | 0.015 | 0.034 |
We extend our model to consider alternative scenarios. First, if the authorized processor bears the blockchain cost (denoted as scenario BP), the equilibrium prices and quantities remain unchanged, but the profit distribution shifts. The processor will adopt blockchain only if $F \leq \tilde{F}$, where $\tilde{F} = \frac{(1-\lambda)[k^2(1-\beta)^2 – (c_H – c_L)^2]}{4k(1-\beta)(2-\lambda)^2}$. However, since $\tilde{F} < \bar{F}$, the processor has a higher adoption threshold, making manufacturer-led blockchain more favorable. Second, we examine a unit blockchain cost scenario (BC), where the cost $c$ is incurred per product. Here, wholesale and retail prices increase further, and L-type demand decreases, while H-type demand remains stable. The profit functions become:
$$\pi_M^{BC} = (w_L^{BC} – c_L – c)q_L^{BC} + (w_H^{BC} – c_H – c)q_H^{BC} + \lambda \pi_P^{BC}$$
$$\pi_P^{BC} = (1-\lambda)\left[(p_H^{BC} – w_H^{BC})q_H^{BC} + (p_L^{BC} – w_L^{BC})q_L^{BC}\right]$$
Equilibrium solutions show that $w_i^{BC} > w_i^B$ and $p_i^{BC} > p_i^B$ for $i \in \{H, L\}$, and $q_L^{BC} < q_L^B$, highlighting the cost burden on lower-tier products. Finally, we incorporate a Nash bargaining model to endogenize the profit-sharing ratio $\lambda$. The bargaining problem is:
$$\max_{\lambda} [\pi_M^B(\lambda)]^a [\pi_P^B(\lambda)]^b$$
where $a$ and $b$ represent the bargaining power of the manufacturer and processor, respectively, with $a + b = 1$. The solution yields $\lambda^* = \frac{2(1-a)F + \sqrt{4(1-a)^2 F^2 + 4a(1-a)F h}}{2(1-a)F}$, where $h$ is a function of model parameters. If $a > 0.5$, then $\lambda^* > 0.5$, indicating that a stronger manufacturer commands a higher profit share.
In conclusion, our study demonstrates that blockchain technology can significantly enhance the echelon utilization of electric vehicle power batteries in China by improving consumer trust and optimizing pricing strategies. For supply chain members, blockchain adoption is viable only under specific conditions, such as moderate product competition and manageable costs. This adoption not only boosts profits but also benefits consumers and society through increased transparency. Managers in the China EV industry should consider blockchain as a strategic tool, especially when product differentiation is clear and costs are controlled. Future research could explore dynamic pricing, multi-channel competition, and consumer privacy concerns related to blockchain transparency.