In this research, I systematically investigate how government reward-punishment policies and blockchain technology jointly affect the optimization of vehicle traction battery recycling decisions. The study is motivated by the rapid growth of new energy vehicles and the increasingly urgent need to deal with large-scale retirement of vehicle traction batteries. Without effective recycling, the retired vehicle traction battery may cause severe environmental pollution and waste of critical metal resources such as lithium, cobalt, nickel, and manganese. However, the existing recycling system suffers from weak policy execution, fragmented recycling channels, information asymmetry, and lack of trust among supply-chain participants. To address these challenges, I combine evolutionary game theory and Stackelberg game models to compare the effects of static and dynamic government reward-punishment mechanisms, and further evaluate the value of blockchain technology under four mainstream recycling modes. Throughout the paper, the term “vehicle traction battery” is used to emphasize the core object of the closed-loop supply chain under consideration. The findings can help governments design more adaptive policy portfolios and help enterprises choose appropriate recycling modes and blockchain investment strategies.
Background and Research Motivation
The transition toward low-carbon transportation has made new energy vehicles a strategic pillar in mitigating climate change. In China, the production and sales of new energy vehicles have maintained rapid growth for many years, making it the largest market in the world. As a consequence, a large number of vehicle traction batteries will reach their end-of-life stage in the coming decade. According to industry forecasts, the cumulative retired vehicle traction battery volume in China will exceed three million tons by 2030. If these retired batteries are not properly handled, hazardous substances from the electrodes and electrolyte can leak into soil and groundwater, creating persistent ecological damage that may offset part of the carbon-reduction benefits of electric vehicles.
At the same time, vehicle traction batteries contain valuable materials that are indispensable for the energy transition. Efficient recycling of vehicle traction batteries can reduce dependence on imported raw materials, stabilize supply chains, and realize substantial economic value. The recycling industry is therefore expected to grow into a billion-dollar sector. In response, the Chinese government has enacted multiple policies, including extended producer responsibility regulations, echelon utilization guidelines, and more recent joint enforcement actions. Even with these policy efforts, however, the actual recycling rate through formal channels remains low because informal recyclers often offer higher prices, take advantage of low environmental-compliance costs, and make the traceability of retired batteries extremely difficult.
The structural obstacles to efficient vehicle traction battery recycling include information gaps among battery producers, vehicle manufacturers, consumers, and recyclers; insufficient collaboration among supply-chain members; and the high trust cost caused by uncertain battery state and residual value. Therefore, my research aims to answer two central questions. First, how can government reward-punishment mechanisms, especially dynamic ones, improve the strategic willingness of manufacturers and consumers to participate in vehicle traction battery recycling? Second, how does blockchain technology, as a digital trust enabler, affect the pricing, profit, and recycling performance of different recycling modes? By answering these questions, I intend to provide a coherent framework for designing both policy instruments and technology-driven governance mechanisms.
Theoretical Background and Literature Review
The conceptual foundation of this study comes from closed-loop supply chain theory. A closed-loop supply chain integrates the forward flow of products from raw-material extraction, production, and distribution to consumption with the reverse flow of collection, inspection, remanufacturing, recycling, and disposal. For the vehicle traction battery, the forward chain starts with battery manufacturing and vehicle assembly, while the reverse chain starts when a consumer returns a retired vehicle traction battery to a formal recycling channel. The goal of a closed-loop supply chain is not only to recover economic value but also to reduce environmental burdens through resource circulation and life-cycle management.
Game theory provides a rigorous tool to analyze strategic interactions among multiple decision-makers. Evolutionary game theory relaxes the assumption of perfect rationality and focuses on how bounded-rational agents adjust their strategies through learning and imitation. This is particularly suitable for studying policy-driven behavioral evolution in vehicle traction battery recycling because no single participant has complete information or unbounded computational capability. Stackelberg games, on the other hand, capture leader-follower relationships in supply chains. In my model, the vehicle traction battery manufacturer often acts as the Stackelberg leader because it controls the battery production technology and wholesale pricing, while the vehicle manufacturer follows by setting retail prices and collection prices.
Policy instruments have been studied extensively. Prior studies have evaluated subsidies, carbon taxes, deposit-refund systems, reward-punishment schemes, and their combinations. However, most previous models treat government reward and punishment as fixed constants. In reality, governments may adjust the intensity of rewards and penalties based on the observed behavior of the public and the actual level of regulatory effort. A static policy cannot easily adapt to changing compliance rates or market conditions. Thus, I extend existing research by explicitly comparing static and dynamic reward-punishment mechanisms in a tripartite evolutionary game.
The notion of blockchain technology has also drawn recent attention in vehicle traction battery recycling. Blockchain provides decentralized, tamper-proof, and traceable records. It can support lifecycle data sharing, prevent false declarations, and increase consumer confidence. Yet many studies analyze blockchain only in a single recycling mode or use oversimplified trust assumptions. In my research, I treat consumer trust in blockchain as a quantifiable parameter and introduce it into a demand and recycling function. More importantly, I compare four recycling modes under both blockchain-free and blockchain-enabled conditions in a unified analytical framework. This integration clarifies when and why blockchain creates different performance gains across modes.
Tripartite Evolutionary Game under Static and Dynamic Reward-Punishment Mechanisms
Model Setting
In the first part of my research, I consider three strategic groups: the government, the vehicle traction battery manufacturer, and the consumer. The manufacturer chooses between active recycling and passive recycling. The consumer chooses between formal recycling channels and informal recycling channels. The government chooses between active regulation and passive regulation. The equilibrium outcome is affected by the net benefits of recycling, the government’s regulatory cost, the level of environmental awareness, and the design of reward-punishment mechanisms.
The main parameters in this evolutionary game are summarized in the following table.
| Symbol | Definition |
|---|---|
| \(C_m\) | Manufacturer’s total cost of active recycling |
| \(I_m\) | Manufacturer’s hidden benefit from active recycling, such as brand improvement |
| \(\alpha_1\) | Recycling technology level under active recycling |
| \(\alpha_2\) | Recycling technology level under passive recycling |
| \(I_{mc}\) | Maximum resource-recycling benefit from a retired vehicle traction battery |
| \(I_{c1}\) | Consumer’s revenue from trading via a formal recycling channel |
| \(I_{c2}\) | Consumer’s revenue from trading via an informal channel |
| \(C_{c1}\) | Consumer’s transaction cost in the formal channel |
| \(C_{c2}\) | Consumer’s transaction cost in the informal channel |
| \(\mu\) | Consumer environmental-preference coefficient |
| \(\beta\) | Decline ratio of informal-channel profit when the government strictly regulates |
| \(R_m\) | Government reward for active manufacturer recycling |
| \(F_m\) | Government fine for passive manufacturer recycling |
| \(C_g\) | Government’s regulatory cost |
The proportions of manufacturer active recycling, consumer formal-channel participation, and government active regulation are denoted by \(x, y, z\), respectively. Based on the assumptions, I derive the expected payoffs for each strategy and obtain the following replicator dynamic equations:
\[
\frac{dx}{dt}=x(1-x)\left[I_m-C_m + y(\alpha_1-\alpha_2)I_{mc} + z(F_m+R_m) – y z R_m\right],
\]
\[
\frac{dy}{dt}=y(1-y)\left[C_{c2}-C_{c1}+I_{c1}-(1-\beta z)I_{c2} + x z R_m + \mu x L\right],
\]
\[
\frac{dz}{dt}=z(1-z)\left[x(F_m+R_m)-F_m-C_g\right].
\]
To evaluate stability, I calculate the Jacobian matrix of the dynamic system. An evolutionarily stable strategy must satisfy the condition that all eigenvalues of the Jacobian matrix at an equilibrium are negative. I perform the standard stability analysis for the eight pure-strategy equilibria and find that the system can evolve to three possible stable states in a static reward-punishment scenario, depending on the following conditions:
- If \(C_g > F_m\), the government obtains less benefit from fining passive manufacturers than the regulatory cost, so the system tends to converge to the non-cooperative state \((0,0,0)\).
- If \(C_g < F_m\) and the consumer’s net benefit from informal trade is still greater than that from formal trade even under strong punishment of informal recyclers, the system can converge to \((0,0,1)\).
- If consumers have sufficiently high environmental preference and manufacturers obtain a larger resource-recycling benefit from active recycling, the ideal stable equilibrium \((0,1,1)\) can be reached, where manufacturers actively recycle, consumers select formal channels, and the government can relax its regulation.
Comparison with Dynamic Reward-Punishment Mechanisms
A static reward-punishment mechanism applies fixed reward and fine values throughout the whole evolution process. In contrast, a dynamic mechanism adjusts the policy intensity according to the state of the system. In my design, the government’s punishment is assumed to be inversely related to the regulatory probability: \(F_m^d = v / z\), where \(v\) is a positive dynamic coefficient. The reward is assumed to be positively proportional to the manufacturer’s recycling probability: \(R_m^d = R_m x\). This design reflects the realistic idea that when government monitoring is weak, violators should face greater penalties to maintain deterrence; when manufacturers actively recycle, they should receive greater rewards as an incentive.
After substituting the dynamic reward-punishment functions into the replicator equations, I solve the eigenvalues of the new Jacobian matrix. The dynamic mechanism can eliminate some undesired equilibria and enlarge the basin of attraction of the desired equilibrium. Below I list the representative equilibrium points and their eigenvalue conditions.
| Equilibrium | Conditions for local stability |
|---|---|
| \((0,0,1)\) | \(F_m^d < I_m – C_m\), \(C_{c2}-C_{c1}+I_{c1}-I_{c2} > \mu L\) |
| (1,0,0) | \(C_g < F_m^d\), \((1-\beta)I_{c2}-C_{c2}>I_{c1}-C_{c1}\) |
| (0,1,1) | \(C_g < F_m^d\), \(\mu L + I_{c1}-C_{c1}>I_{c2}-C_{c2}\) |
| (1,1,0) | \(C_g < F_m^d\), \(I_{c1}-C_{c1}>(1-\beta)I_{c2}-C_{c2}\) |
Numerical Simulation Results
I use numerical simulations to visualize the evolution paths. In the static case, when the government fine \(F_m\) is below a threshold, the manufacturing strategy eventually goes to passive recycling. When the fine exceeds the threshold, the system reaches the desired state. The critical value of \(F_m\) lies between two and three in my benchmark scenario. On the other hand, increasing the government reward \(R_m\) does not alter the final equilibrium; it only affects the convergence speed. Interestingly, if the reward is too large while the penalty is also large, the convergence to the desired equilibrium becomes slower because of marginal diminishing returns and strategic inertia.
The recycling technology level \(\alpha_1\) has a significant effect. When it is below a threshold, the evolution path may exhibit cycles and no stable state within a finite horizon. When it exceeds the threshold, both the manufacturer and consumers move to the cooperative strategy. Consumer environmental preference \(\mu\) also determines the direction of consumer behavior. At \(\mu=0.1\), consumers prefer informal channels; at \(\mu=0.2\), consumers quickly shift to formal channels and the system stabilizes at the ideal state. This indicates that raising environmental awareness is an important complementary policy target.
In the dynamic mechanism, I set the dynamic coefficient \(v=2.0\). The simulation result shows that the convergence speed in the dynamic mechanism is much faster than in the static mechanism. The system reaches the stable state after roughly five time units under the dynamic reward-punishment design, while it needs more than twelve time units in the static scheme. Furthermore, the dynamic path is smoother and contains fewer oscillations. This clearly verifies that dynamic reward-punishment mechanisms outperform static ones in guiding a vehicle traction battery recycling ecosystem.
Recycling Mode Decision Optimization without Blockchain
Model and Four Recycling Modes
In the second part of my research, I construct a three-tier closed-loop supply chain consisting of a vehicle traction battery manufacturer, a new-energy vehicle manufacturer, and consumers. The forward flow sells batteries from the battery manufacturer to the vehicle manufacturer, who then sells the finished vehicle to consumers. The reverse flow handles retired vehicle traction batteries. I examine four recycling modes:
- Battery manufacturer recycling (NB): the vehicle traction battery manufacturer directly collects retired batteries from consumers and performs recycling and reuse.
- Vehicle manufacturer recycling (NV): the new-energy vehicle manufacturer uses its own sales network to collect retired vehicle traction batteries and transfers them to the battery manufacturer.
- Mixed recycling (NBV): both the battery manufacturer and the vehicle manufacturer independently collect vehicle traction batteries and compete on recycling prices.
- Alliance recycling (N(B+V)): the battery manufacturer and the vehicle manufacturer establish a joint recycling platform, share resources, and cooperate to maximize the total profit of the alliance.
To keep the model tractable, I assume that both manufacturers are risk-neutral, the information is symmetric, and the consumer demand for new-energy vehicles is driven by the retail price and the recycling-related demand gain \(g\). The market demand is defined by \(D=(1+g)\phi – \theta p\), where \(\phi\) is the potential market size and \(\theta\) is the price-sensitivity coefficient. Because consumers consider the availability and transparency of vehicle traction battery recycling as an added value, the demand gain parameter \(g\) connects the reverse chain to the forward market.
The total recycling quantity of retired vehicle traction batteries in mode \(i\) is modeled by
\[
Q_{r}^{i}=a + b r_{j}^{i} – \delta r_{l}^{i},
\]
where \(a\) is the voluntary return quantity, \(b\) is the consumer sensitivity to the recycling price, \(r_j^i\) is the recycling price offered by recycler \(j\), and \(\delta\) denotes the channel competition intensity. In the absence of blockchain, the information about battery state is opaque, so consumers rely heavily on the offered price when deciding where to return their retired vehicle traction battery.
Equilibrium Solutions
I solve each mode by backward induction. The vehicle traction battery manufacturer acts as the Stackelberg leader and first decides the wholesale price \(w\) and possibly the collection price. The vehicle manufacturer then decides the retail price \(p\) and possibly its own collection price. The main equilibrium results for the four blockchain-free modes are summarized below.
| Mode | Wholesale price \(w^i\) | Retail price \(p^i\) | Recycling price from battery manufacturer \(r_B^i\) | Recycling price from vehicle manufacturer \(r_V^i\) |
|---|---|---|---|---|
| NB | \(\frac{c\theta+(1+g)\phi}{2\theta}\) | \(\frac{3c\theta+(1+g)\phi}{4\theta}\) | Derived from first-order condition | — |
| NV | \(\frac{c\theta+(1+g)\phi}{2\theta}\) | \(\frac{3c\theta+(1+g)\phi}{4\theta}\) | Transfer price derived | Derived from first-order condition |
| NBV | \(\frac{c\theta+(1+g)\phi}{2\theta}\) | \(\frac{3c\theta+(1+g)\phi}{4\theta}\) | Derived jointly | Derived jointly |
| N(B+V) | Coordinated internally | \(\frac{c\theta+(1+g)\phi}{2\theta}\)? | Unified \(r_{BV}\) | Unified \(r_{BV}\) |
I further derive the total supply-chain profit and recycling rate for each mode under the benchmark parameter setting. The chosen numerical values are based on typical market data in China. For example, the potential market size is 300,000 units, the retail price sensitivity is 1.6, the carbon trading price is set at 60 CNY per ton, and the average net recycling benefit per retired vehicle traction battery is 29,349 CNY. The competition intensity coefficients for battery-manufacturer and vehicle-manufacturer channels are set to 0.45 and 0.5, respectively. The following table reports the main performance indicators.
| Mode | Supply Chain Profit | Sales Volume | Recycling Quantity | Recycling Rate (%) |
|---|---|---|---|---|
| NB | 4,933,474,747 | 49,700 | 18,228 | 36.68 |
| NV | 4,859,473,348 | 49,700 | 9,150 | 18.41 |
| NBV | 5,000,859,104 | 49,700 | 19,225 | 38.68 |
| N(B+V) | 6,540,679,996 | 99,400 | 20,050 | 20.17 |
Several important conclusions emerge from this comparison. First, the wholesale prices in different modes are identical, because without blockchain the upstream production and wholesale decisions do not depend on which reverse-channel structure is used. Second, the retail price in the alliance recycling mode is lower than in the other three modes, because the alliance can eliminate double marginalization and adjust the retail price jointly. Third, the mixed recycling mode achieves the highest recycling rate since two independent channels compete for the same retired vehicle traction battery resource and thereby stimulate more consumers to return their batteries. Fourth, the alliance recycling mode provides the highest total supply-chain profit because it reduces channel conflict, avoids duplicated investment, and realizes economies of scale. These findings establish a clear benchmark for evaluating whether blockchain technology can further improve the economic and environmental performance of each mode.
Blockchain-Enabled Recycling Decision Optimization
Model Extension
Blockchain technology adds transparency and traceability to the retired vehicle traction battery life cycle. In my blockchain-enabled model, the market demand and recycling quantity both depend on the level of blockchain investment \(\lambda\), and consumer trust in blockchain \(k\). The demand function becomes
\[
D^{Y}=(1+g)\phi – \theta p + k\lambda,
\]
and the recycling quantity becomes
\[
Q_{r}^{Y}=a + b r_{j} – \delta r_{l} + k\lambda.
\]
The parameter \(k\) measures the degree to which consumers trust the traceability records generated by blockchain. A larger \(k\) means that consumers are more willing to pay a premium for vehicles whose vehicle traction batteries have a transparent history, and they are more willing to return retired batteries at a reasonable price.
Blockchain investment incurs a fixed cost that is convex in the degree of investment. I model the total cost as \(C_{\lambda}=\frac{1}{2}A\lambda^{2}\), where \(A\) is the cost coefficient. The cost is shared between the vehicle traction battery manufacturer and the vehicle manufacturer. Let \(t\) be the proportion of blockchain cost borne by the battery manufacturer and \(1-t\) be the proportion borne by the vehicle manufacturer. In the four blockchain-enabled recycling modes, the same Stackelberg decision hierarchy is applied as in the blockchain-free model, except that both parties first agree on the optimal blockchain investment level and cost-sharing arrangement.
Optimal Decisions and Comparisons
I solve the equilibrium for each blockchain-enabled mode, denoted as YB, YV, YBV, and Y(B+V). For instance, under the battery-manufacturer-recycling mode, the battery manufacturer maximizes
\[
\pi_B^{YB}=(w-c)D^{Y}+(\bar{r}+E_r\zeta-r_{cB})Q_r^{Y}-\frac{t}{2}A\lambda^{2},
\]
and the vehicle manufacturer maximizes
\[
\pi_V^{YB}=(p-w)D^{Y}-\frac{1-t}{2}A\lambda^{2}.
\]
The resulting optimal blockchain investment can be derived from the joint first-order conditions. To avoid notational overload, I present the key comparative conclusions instead of listing all equilibrium expressions.
I first compare the blockchain-enabled and blockchain-free cases under the same numerical parameter set, with \(A=500\), \(k=4\), and \(t=0.7\).
| Mode | Supply Chain Profit without Blockchain | Supply Chain Profit with Blockchain | Recycling Rate without Blockchain (%) | Recycling Rate with Blockchain (%) |
|---|---|---|---|---|
| Battery Manufacturer Recycling | 4,933,474,747 | 4,998,974,780 | 36.68 | 38.53 |
| Vehicle Manufacturer Recycling | 4,859,473,348 | 4,916,297,388 | 18.41 | 19.15 |
| Mixed Recycling | 5,000,859,104 | 5,108,024,171 | 38.68 | 43.42 |
| Alliance Recycling | 6,540,679,996 | 6,684,186,668 | 20.17 | 23.02 |
The simulation outputs show that blockchain improves supply-chain profit and recycling rate in all four recycling modes. The most substantial increase in recycling rate occurs in the mixed recycling mode, because blockchain makes the battery-state information publicly verifiable and reduces the ability of either collector to exploit hidden information. The greatest absolute profit gain appears in the alliance recycling mode, because blockchain reduces internal coordination and audit costs inside the alliance.
Proposition 1: Blockchain Reduces Optimal Recycling Price
One interesting finding is that the optimal recycling price after blockchain deployment is lower than the optimal recycling price without blockchain, i.e.,
\[
r_{B}^{Y} < r_{B}^{N}, \quad r_{V}^{Y} < r_{V}^{N}, \quad r_{BV}^{Y} < r_{BV}^{N}.
\]
The explanation lies in the trust effect. Let me demonstrate with a simple comparison. Without blockchain, consumers cannot verify the remaining capacity, state of health, or safety of their retired vehicle traction battery. Therefore, they demand a high monetary compensation from the recycler to offset the uncertainty. With blockchain-enabled traceability, consumers can query objective information stored in the distributed ledger. As a result, consumers rely less on inflated prices and instead trust the transparent valuation process. Recyclers are able to lower the price while still attracting returns because consumers perceive the transaction as fair and credible. This is a crucial insight for managers who worry that blockchain is merely an additional fixed cost.
Proposition 2: Consumer Trust Drives Blockchain Investment
My model further reveals that the equilibrium blockchain investment level increases with the consumer trust parameter \(k\). Formally,
\[
\frac{\partial \lambda^{YB}}{\partial k}>0,\quad
\frac{\partial \lambda^{YV}}{\partial k}>0,\quad
\frac{\partial \lambda^{YBV}}{\partial k}>0,\quad
\frac{\partial \lambda^{Y(B+V)}}{\partial k}>0.
\]
This is intuitive: when consumers highly trust the data stored on the blockchain, the technology creates tangible value in both the forward market and the reverse market. In the forward market, higher trust increases consumers’ willingness to pay for electric vehicles equipped with traceable vehicle traction batteries, thereby raising retail prices and profits. In the reverse market, higher trust reduces consumer resistance to fair recycling prices and facilitates the collection of retired vehicle traction batteries. Enterprises, anticipating these gains, are willing to invest more in blockchain infrastructure.
The sensitivity analysis with different values of \(k\) is provided below.
| Trust level \(k\) | Blockchain investment \(\lambda_{YB}\) | Retail price \(p_{YB}\) | Recycling rate \(R_{B}\) (%) | Supply chain profit \(\pi_{YB}\) |
|---|---|---|---|---|
| 2 | 255.9 | 175,444 | 37.13 | 4,947,733,297 |
| 4 | 522.6 | 176,237 | 38.53 | 4,988,277,985 |
| 6 | 812.7 | 177,640 | 40.95 | 5,083,716,123 |
The results show that as \(k\) increases from 2 to 6, the recycling rate and total supply-chain profit increase, even without adjusting the recycling price. This demonstrates that trust itself is a value driver. The managerial implication is that technological deployment should be accompanied by public education, transparent communication, and marketing campaigns to build consumer confidence in blockchain-verified vehicle traction battery information systems.
Impact of Blockchain Cost Sharing Ratio
I next analyze how the cost-sharing ratio \(t\) affects the profits of both manufacturers. Numerical simulation reveals a robust result: regardless of the value of \(t\), both the vehicle traction battery manufacturer and the vehicle manufacturer always earn higher profits in the blockchain-enabled setting than in the blockchain-free setting, so long as the blockchain investment is positive and within an economically feasible range. The cost-sharing ratio only redistributes the profit gain rather than determining whether such a gain exists. This provides a microeconomic foundation for collaborative blockchain adoption in the recycling industry.
At the same time, an excessively high cost share on one participant may reduce the participant’s incentive to cooperate. Therefore, an equitable sharing contract or an alliance-level governance mechanism is needed to sustain long-term cooperation. This insight is especially relevant for the mixed recycling mode, where two independent parties must share the same blockchain infrastructure while still competing on price.
Impact of Consumer Price Sensitivities
I also study the sensitivity of supply-chain profit to the retail-price sensitivity coefficient \(\theta\). As \(\theta\) increases, consumers react more strongly to price increases, so the supply-chain profit decreases. The optimal response is to use blockchain-enabled differentiation to shift consumer attention from price alone to attributes such as traceability, safety, and sustainability. On the recycling side, the consumer sensitivity to the recycling price, denoted by \(b\), has a positive effect on the recycling rate. When \(b\) is high, a small increase in recycling price substantially increases the return quantity. Blockchain amplifies this effect because the returned vehicle traction battery is bundled with trustworthy state information, enabling more precise pricing.
Differences in Blockchain Empowerment across Recycling Modes
The comparison shows that blockchain has heterogeneous effects across the four modes. In the single-channel modes, the room for improvement is limited because channel coverage and consumer reach remain low. In the mixed mode, blockchain primarily reduces information asymmetry between competing collectors and between collectors and consumers; it therefore boosts the recycling rate the most. In the alliance mode, blockchain mainly reduces internal coordination costs, leading to a moderate increase in recycling rate but a significant increase in total profit. This heterogeneity suggests that enterprises should evaluate blockchain investment jointly with their recycling-mode strategy. A vehicle traction battery manufacturer that already operates an alliance recycling network may prioritize blockchain as a tool for internal data integration and audit reduction. If the goal is to maximize collection volume, however, combining blockchain with competitive dual-channel recycling will produce the strongest improvement.
Managerial and Policy Insights
For Government Policymakers
My research supports the transformation from fixed-parameter reward-punishment policies to adaptive, data-driven mechanisms. A government should set an effective penalty level that exceeds the cost advantage of passive recycling. At the same time, it should dynamically adjust the penalty intensity in response to the observed regulatory intensity; otherwise, an unchanged high penalty may become disconnected from actual conditions. Dynamic design not only shortens the convergence time but also improves the stability of the recycling system.
Furthermore, governments should not treat policy incentives and blockchain as independent instruments. Dynamic reward-punishment mechanisms create economic pressure for formal recycling. Blockchain provides the trustworthy information infrastructure that enables formal recycling to be credibly verified. I suggest that governments support the construction of a shared blockchain platform for vehicle traction battery traceability, publish unified data standards, and link regulatory rewards and penalties with on-chain compliance records.
For Enterprises
For a vehicle traction battery manufacturer or an original equipment manufacturer, the decision about recycling mode should be aligned with strategic objectives. If the priority is total profit, the alliance recycling mode is the best choice, especially when blockchain is used to reduce coordination costs. If the priority is market coverage and high recycling rate, the mixed recycling mode may be more appropriate, because the competition between dual collection channels drives consumers to return more retired vehicle traction batteries.
With regard to blockchain, I suggest that enterprises adopt blockchain projects in phases. In the early stage, they should focus on building a minimum viable traceability system covering battery identity, state information, and recycling transactions. As consumer trust in blockchain increases, the benefits will become more visible through higher retail demand, lower price resistance, and more stable collection volumes. Cost-sharing arrangements between the battery manufacturer and the vehicle manufacturer should be carefully negotiated, since both parties benefit from technology adoption.
For Consumers and Society
Consumers play a vital role in the recycling of vehicle traction batteries. My simulation shows that environmental preference is an important factor in steering consumers to formal channels. Therefore, public environmental campaigns and transparent information about the environmental impact of improper disposal are essential. Blockchain can enhance such transparency by letting consumers query the entire life-cycle history of a specific vehicle traction battery and understand the environmental benefit of returning it to a certified recycler.
Conclusion
In this paper, I have constructed a comprehensive optimization framework for vehicle traction battery recycling. The contribution of my research is twofold. First, I extend the evolutionary-game analysis of recycling policy by directly comparing static and dynamic reward-punishment mechanisms. I find that dynamic mechanisms have substantial advantages in guiding the system to the desired state, increasing convergence speed, and improving stability. Policy effectiveness depends critically on the punishment threshold, the recycling technology level of manufacturers, and consumer environmental preference.
Second, I compare four recycling modes under both blockchain-free and blockchain-enabled closed-loop supply chains. The alliance recycling mode maximizes total supply-chain profit, while the mixed recycling mode maximizes recycling rate. Blockchain improves profit and recycling rate in all modes but does so through different channels: in mixed recycling it reduces information asymmetry between competing collectors, whereas in alliance recycling it lowers internal coordination costs. Consumer trust in blockchain is the key behavioral moderator that determines the extent to which blockchain investment creates economic value. As trust increases, firms invest more in blockchain and benefit more in terms of profit and recycling performance.
There are several limitations in my study. I have not incorporated carbon taxes or carbon trading schemes into the current models. The strategic roles of third-party recyclers and cascade-utilization companies also call for deeper exploration. Furthermore, behavioral factors such as fairness concerns and overconfidence may affect actual decision-making. Future research can extend the current framework by embedding these factors into the models and testing the theoretical predictions using richer empirical data. Despite these limitations, my study offers clear evidence that the joint use of dynamic policy regulation and blockchain-based trust empowerment can significantly improve the efficiency, credibility, and sustainability of vehicle traction battery recycling systems.