I position this study at the intersection of environmental governance, closed-loop supply chain management, and digital trust infrastructure. The accelerating retirement wave of electric vehicle battery packs has created an urgent need for decision frameworks that can simultaneously address behavioral incentives and informational credibility. In what follows, I develop an integrated analytical framework that combines evolutionary game theory and Stackelberg game models to examine how dynamic reward-punishment mechanisms and blockchain technology jointly shape the recycling of electric vehicle battery packs. The core argument I advance is that neither policy intervention nor technological empowerment alone can achieve optimal recycling outcomes; rather, their coordinated deployment creates a dual-driver governance pathway that aligns the interests of governments, manufacturers, and consumers.
1. Research Background and Problem Statement
The global transition toward low-carbon transportation has positioned the electric vehicle industry as a central engine of industrial transformation. China, in particular, has emerged as the world’s largest market for new energy vehicles, with annual production and sales exceeding 16 million units. This explosive growth has inevitably produced a corresponding surge in retired electric vehicle battery packs. Industry projections indicate that by 2030, cumulative retired electric vehicle battery packs in China will exceed three million tons, transforming what was once a latent concern into an immediate operational challenge.
The environmental and resource implications of this retirement wave are profound. An electric vehicle battery pack contains complex chemical systems including electrolytes and heavy metals such as cobalt, nickel, manganese, and lithium. When these electric vehicle battery packs enter informal recycling channels characterized by crude dismantling and open disposal, hazardous substances leach into soil and groundwater, causing persistent ecological damage. Simultaneously, these same materials represent critical strategic resources whose recovery can significantly reduce dependence on imported raw materials and enhance national resource security. The economic potential of the recycling industry is substantial, with market projections reaching hundreds of billions of yuan by 2030.
Despite clear policy intent and growing market demand, the current recycling system for electric vehicle battery packs faces structural obstacles that suppress efficiency. Informal recyclers operating with minimal environmental compliance costs can offer higher acquisition prices to consumers, systematically crowding out formal channels. The absence of a unified information infrastructure means that data on electric vehicle battery pack health status, usage history, and residual value remain fragmented across multiple stakeholders, creating severe information asymmetry. Without transparent and trustworthy traceability mechanisms, consumers cannot verify the fairness of recycling valuations, and manufacturers cannot coordinate effectively across the reverse supply chain. These interconnected challenges point to a central research imperative: designing institutional and technological solutions that can simultaneously incentivize responsible behavior and establish credible information flows for electric vehicle battery pack recycling.
2. Theoretical Foundations and Literature Positioning
I ground this study in four theoretical pillars. Closed-loop supply chain theory provides the systemic perspective for integrating forward and reverse flows of electric vehicle battery packs. Game theory—specifically evolutionary game theory and Stackelberg games—offers rigorous tools for modeling strategic interactions among heterogeneous stakeholders. Government reward-punishment policy theory illuminates how external interventions can correct market failures in the recycling of electric vehicle battery packs. Blockchain technology theory provides the conceptual basis for understanding how distributed ledgers can replace fragile trust with cryptographic verification.
2.1 Closed-Loop Supply Chain Framework
The closed-loop supply chain for electric vehicle battery packs encompasses the full cycle from raw material extraction through battery production, vehicle assembly, consumer use, retirement, collection, and resource recovery. I conceptualize this system as comprising three primary tiers. Power battery manufacturers occupy the upstream position, producing electric vehicle battery packs and possessing deep technical knowledge of cell chemistry and degradation mechanisms. New energy vehicle manufacturers integrate these electric vehicle battery packs into complete vehicles and maintain direct relationships with consumers through sales and service networks. Consumers represent both the demand side for vehicles and the supply side for retired electric vehicle battery packs.
Within this framework, I identify four mainstream recycling modes. In the power battery manufacturer recycling mode, the battery producer directly collects retired electric vehicle battery packs from consumers. In the new energy vehicle manufacturer recycling mode, the vehicle producer leverages its existing service network to collect used electric vehicle battery packs and transfers them to battery manufacturers. In the hybrid recycling mode, both parties independently establish collection channels and compete for retired electric vehicle battery packs. In the alliance recycling mode, the two manufacturers integrate resources and coordinate decisions to maximize joint profits.
2.2 Evolutionary Game Theory and Stackelberg Games
Evolutionary game theory relaxes the assumption of full rationality and examines how boundedly rational agents adjust strategies through learning and imitation over time. The concept of evolutionarily stable strategy captures the long-run convergence of population behavior. I employ this framework to model the tripartite interaction among government, manufacturers, and consumers in the recycling of electric vehicle battery packs. Stackelberg game theory, in contrast, models sequential decision-making where a leader anticipates the follower’s best response. I use this approach to derive optimal pricing and recycling decisions under different channel structures.
2.3 Government Reward-Punishment Mechanisms
Government intervention in electric vehicle battery pack recycling takes two primary forms. Rewards compensate manufacturers and consumers for the additional costs of environmentally responsible behavior. Punishments increase the economic burden of non-compliant actions. The design challenge lies in calibrating these instruments to achieve desired behavioral outcomes at minimum social cost. I distinguish between static mechanisms, where reward and punishment levels remain fixed, and dynamic mechanisms, where they adjust based on observed compliance rates. This distinction forms a core analytical contribution of my study.
2.4 Blockchain Technology as Trust Infrastructure
Blockchain technology offers a decentralized, tamper-resistant, and fully traceable record of transactions and product histories. For electric vehicle battery pack recycling, blockchain can create a digital twin of each battery’s lifecycle, recording initial capacity, cycling history, degradation trajectory, and safety status. This transparency addresses the root cause of information asymmetry that currently plagues the recycling of electric vehicle battery packs. I model blockchain adoption as an endogenous decision variable, where the level of investment and consumer trust jointly determine economic outcomes.
3. Evolutionary Game Analysis under Static and Dynamic Reward-Punishment Mechanisms
I construct a tripartite evolutionary game model involving government, new energy vehicle manufacturers, and consumers. Each player has two strategic options. Manufacturers choose between active recycling and passive recycling of electric vehicle battery packs. Consumers choose between formal channels and informal channels for returning their retired electric vehicle battery packs. The government chooses between active regulation and passive regulation.
3.1 Model Assumptions and Payoff Matrix
I assume that all players are boundedly rational and seek to maximize expected utility. Let \(x\) denote the probability that manufacturers choose active recycling, \(y\) the probability that consumers choose formal channels, and \(z\) the probability that government chooses active regulation. The payoff matrix is constructed based on the following parameters and relationships.
| Parameter | Definition |
|---|---|
| \(C_m\) | Cost for manufacturers to actively recycle electric vehicle battery packs |
| \(I_m\) | Implicit benefits from active recycling (brand image, consumer trust) |
| \(I_{mc}\) | Maximum revenue from full utilization of recycled electric vehicle battery packs |
| \(\alpha_1\) | Technology level under active recycling |
| \(\alpha_2\) | Technology level under passive recycling |
| \(I_{c1}\) | Consumer revenue from formal channel transactions |
| \(I_{c2}\) | Consumer revenue from informal channel transactions |
| \(C_{c1}\) | Consumer cost for formal channel transactions |
| \(C_{c2}\) | Consumer cost for informal channel transactions |
| \(\mu\) | Consumer environmental preference coefficient |
| \(L\) | Maximum environmental benefit from formal channel transactions |
| \(C_g\) | Government cost of active regulation |
| \(R_m\) | Government reward for active recycling |
| \(F_m\) | Government penalty for passive recycling |
| \(\beta\) | Reduction in informal channel revenue due to government enforcement |
| \(\theta\) | Proportion of government reward passed to consumers |
| \(R_e\) | Environmental benefit to government from successful recycling |
| \(C_{e1}\) | Environmental governance cost when consumers use formal channels but manufacturers recycle passively |
The expected payoff for manufacturers choosing active recycling is:
$$U_{11} = yz\left[I_{mc}\alpha_1 + I_m – C_m + R_m\theta + R_m(1-\theta)\right] + y(1-z)\left[I_{mc}\alpha_1 + I_m – C_m\right] + (1-y)z\left[R_m – C_m\right] + (1-y)(1-z)\left[-C_m\right]$$
The expected payoff for passive recycling is:
$$U_{12} = yz\left[I_{mc}\alpha_2 – F_m\right] + y(1-z)\left[I_{mc}\alpha_2\right] + (1-y)z\left[-F_m\right] + (1-y)(1-z)\cdot 0$$
The replicator dynamic equation for manufacturers is:
$$F(x) = \frac{dx}{dt} = x(1-x)\left[(\alpha_1 – \alpha_2)I_{mc} + I_m – C_m + z(F_m + R_m) – y\theta R_m\right]$$
3.2 Stability Analysis
Setting \(F(x)=0\), \(F(y)=0\), and \(F(z)=0\) yields equilibrium points. I identify eight pure-strategy equilibria and analyze their stability using the Jacobian matrix. The key findings are summarized below.
| Equilibrium | Condition | Interpretation |
|---|---|---|
| \(E_1=(0,0,0)\) | \(C_g > F_m\) | Government, manufacturers, and consumers all choose passive strategies |
| \(E_4=(1,0,0)\) | \(F_m > C_g\), \((1-\beta)I_{c2} – C_{c2} > I_{c1} – C_{c1}\) | Government regulates actively but manufacturers and consumers remain passive |
| \(E_5=(0,1,1)\) | \(I_{c1} + \mu L – C_{c1} > I_{c2} – C_{c2}\), \(I_m + (\alpha_1-\alpha_2)I_{mc} > C_m\) | Ideal equilibrium with active recycling, formal channels, and passive regulation |
3.3 Dynamic Reward-Punishment Mechanism
In reality, government penalties are not fixed but adjust inversely with the probability of active regulation. When regulatory intensity is high, the probability of detecting violations increases, allowing the government to reduce penalty levels without compromising deterrence. Conversely, when regulatory intensity is low, penalties must increase to compensate for reduced monitoring. Similarly, rewards adjust positively with manufacturer recycling performance. I specify the dynamic penalty as \(F_m^d = v/z\) and the dynamic reward as \(R_m^d = R_m \cdot x\), where \(v\) represents the dynamic penalty coefficient.
The replicator dynamic equation under dynamic mechanisms becomes:
$$F^d(x) = x(1-x)\left[(\alpha_1 – \alpha_2)I_{mc} + I_m – C_m + zF_m^d – yR_m^d\theta\right]$$
Numerical simulations reveal that the dynamic mechanism accelerates convergence to the ideal equilibrium by approximately sixty percent compared to the static mechanism. The dynamic path is smooth without oscillation, while the static path exhibits significant fluctuations before settling.
| Mechanism | Convergence Time | Path Stability | Evolutionary Efficiency |
|---|---|---|---|
| Static reward-punishment | \(t \approx 12.5\) | Oscillatory | Baseline |
| Dynamic reward-punishment | \(t \approx 5.0\) | Smooth | Improved by 60% |
3.4 Sensitivity Analysis of Key Parameters
I examine how critical parameters affect the evolutionary path. The penalty threshold for manufacturers lies in the interval \([2, 3]\) under my parameterization. Below this threshold, manufacturers converge to passive recycling. Above it, active recycling becomes the stable strategy. Reward levels influence the speed but not the direction of evolution. Manufacturer recycling technology level has a critical threshold in \([0.5, 0.6]\). Consumer environmental preference above 0.2 drives convergence to formal channels. The proportion of consumer revenue reduction in informal channels accelerates but does not determine the final equilibrium.
4. Recycling Mode Decisions without Blockchain Empowerment
I now shift from the evolutionary policy perspective to the operational decision level. I construct a three-tier closed-loop supply chain comprising power battery manufacturers, new energy vehicle manufacturers, and consumers. The demand function for new energy vehicles incorporating electric vehicle battery packs is:
$$D = \phi + (1-\theta)g – p$$
where \(\phi\) represents market potential, \(g\) is the recycling demand gain coefficient, and \(\theta\) is the price sensitivity coefficient. The total recycling quantity of electric vehicle battery packs is:
$$Q_r = a + b r_j – \delta r_k, \quad j,k \in \{B, V\}, j \neq k$$
where \(a\) represents voluntary returns independent of price, \(b\) is consumer sensitivity to recycling price, and \(\delta\) is the channel competition coefficient.
4.1 Model Formulation for Four Recycling Modes
In the power battery manufacturer recycling mode (NB), the battery manufacturer sets both the wholesale price \(w\) and the recycling price \(r_B\). The vehicle manufacturer then sets the retail price \(p\). The profit functions are:
$$\pi_B^{NB} = (w-c)D + (r + E_r\zeta – c_r)(a + b r_B)$$
$$\pi_V^{NB} = (p-w)D$$
Solving via backward induction yields optimal prices:
$$w^{NB} = \frac{\phi + (1-\theta)g + c}{2}$$
$$r_B^{NB} = \frac{a + b(r – E_r\zeta + c_r)}{2b}$$
$$p^{NB} = \frac{3(\phi + (1-\theta)g) + c}{4}$$
In the new energy vehicle manufacturer recycling mode (NV), the battery manufacturer sets the transfer price \(r_B\) and wholesale price \(w\). The vehicle manufacturer sets the consumer recycling price \(r_V\) and retail price \(p\). The optimal prices are:
$$r_V^{NV} = \frac{3(a + b r) – b(3c_r + E_r\zeta – r_B)}{4b}$$
In the hybrid recycling mode (NBV), both manufacturers independently collect electric vehicle battery packs. The competition between channels affects recycling quantities and prices. In the alliance recycling mode (N(B+V)), the two manufacturers coordinate to maximize joint profit, eliminating double marginalization.
4.2 Comparative Results without Blockchain
| 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 |
I observe that the alliance mode achieves the highest supply chain profit due to resource integration and elimination of channel conflict. The hybrid mode achieves the highest recycling rate due to competitive pressure between collection channels. The single-channel modes exhibit lower performance on both dimensions. The retail price in the alliance mode is lower than in other modes, which stimulates market demand for vehicles containing electric vehicle battery packs.
5. Recycling Mode Decisions with Blockchain Empowerment
I extend the model by introducing blockchain technology as an endogenous decision variable. The demand function becomes:
$$D = \phi + (1-\theta)g – p + k\lambda$$
where \(\lambda\) represents the level of blockchain investment and \(k\) represents consumer trust in blockchain technology. The recycling quantity becomes:
$$Q_r = a + b r_j – \delta r_k + k\lambda$$
Blockchain investment cost is a quadratic function of the investment level:
$$C_b = \frac{1}{2}A\lambda^2$$
where \(A\) is the cost coefficient. The battery manufacturer bears a proportion \(t\) of this cost, while the vehicle manufacturer bears \(1-t\).
5.1 Optimal Decisions under Blockchain
For the power battery manufacturer recycling mode (YB), the optimal wholesale price, retail price, recycling price, and blockchain investment level are derived. The key expressions are:
$$w^{YB} = \frac{\phi + (1-\theta)g + c}{2} + \frac{k\lambda}{2}$$
$$p^{YB} = \frac{3(\phi + (1-\theta)g) + c}{4} + \frac{3k\lambda}{4}$$
$$r_B^{YB} = \frac{a + b(r – E_r\zeta + c_r)}{2b} – \frac{\lambda}{2}$$
The blockchain investment level satisfies:
$$\lambda^{YB} = \frac{k(\phi + (1-\theta)g – c) + 2b(r – E_r\zeta + c_r) – 2ab}{2bA – k^2}$$
Similar derivations apply to the other three modes. The key insight is that blockchain investment increases retail prices and decreases recycling prices, while simultaneously increasing recycling quantities and supply chain profits.
5.2 Comparative Results with Blockchain
| Mode | Supply Chain Profit | Sales Volume | Recycling Quantity | Recycling Rate (%) |
|---|---|---|---|---|
| YB | 4,998,974,780 | 50,260 | 19,367 | 38.53 |
| YV | 4,916,297,388 | 50,158 | 9,608 | 19.15 |
| YBV | 5,108,024,171 | 50,456 | 21,906 | 43.42 |
| Y(B+V) | 6,684,186,668 | 100,068 | 23,037 | 23.02 |
Blockchain empowerment improves both supply chain profits and recycling rates across all four modes. The alliance mode continues to achieve the highest profit, while the hybrid mode achieves the highest recycling rate. The improvement magnitude varies across modes, reflecting the different mechanisms through which blockchain addresses information asymmetry in each structural configuration.
5.3 Differential Mechanisms of Blockchain Empowerment
I identify three distinct pathways through which blockchain creates value for electric vehicle battery pack recycling. In single-channel modes, blockchain primarily enhances consumer trust, but the absence of channel competition limits the recycling rate improvement. In the hybrid mode, blockchain eliminates information advantages that could be exploited through misrepresentation of electric vehicle battery pack residual values, thereby reducing destructive competition and increasing recycling efficiency. In the alliance mode, blockchain reduces internal coordination costs and verification frictions that persist even under contractual cooperation.
6. Sensitivity Analysis and Managerial Insights
I conduct extensive sensitivity analysis to identify how key parameters moderate the economic outcomes of blockchain-enabled electric vehicle battery pack recycling.
6.1 Effect of Blockchain Cost Sharing
Regardless of the cost-sharing ratio between battery manufacturers and vehicle manufacturers, both parties achieve higher profits with blockchain than without it. The cost-sharing ratio affects only the distribution of gains, not the overall superiority of blockchain adoption. This finding suggests that blockchain investment possesses self-enforcing properties: both parties have rational incentives to participate without external coercion.
6.2 Effect of Consumer Price Sensitivity
Higher consumer sensitivity to retail prices reduces total supply chain profit. As consumers become more price-sensitive, the demand for vehicles containing electric vehicle battery packs declines more sharply for any given price increase, compressing manufacturer margins. In contrast, higher consumer sensitivity to recycling prices increases the recycling rate. When consumers are more responsive to recycling price incentives, a given price increase induces a larger increase in returned electric vehicle battery packs.
6.3 Effect of Consumer Trust in Blockchain
| Trust Level \(k\) | Blockchain Investment \(\lambda^{YBV}\) | Retail Price \(p^{YBV}\) | Recycling Rate \(R^{YBV}\) (%) | Supply Chain Profit \(\pi^{YBV}\) |
|---|---|---|---|---|
| 2 | 357.0 | 175,522 | 39.94 | 5,013,479,192 |
| 4 | 756.2 | 176,605 | 43.42 | 5,108,024,171 |
| 6 | 1,253 | 178,711 | 50.19 | 5,263,076,544 |
Consumer trust in blockchain technology is the critical behavioral variable that determines the economic value of technological empowerment. As trust increases, firms invest more aggressively in blockchain infrastructure, retail prices rise due to enhanced willingness to pay, recycling rates improve because consumers trust the fairness of valuations, and supply chain profits increase across all modes. Notably, recycling rates improve even when recycling prices remain unchanged or decline, indicating that trust itself becomes a driver of recycling behavior independent of price incentives.
7. Conclusions and Implications
I draw several integrated conclusions from this study. First, dynamic reward-punishment mechanisms outperform static mechanisms in accelerating convergence to ideal recycling equilibria for electric vehicle battery packs. The penalty threshold is a critical policy parameter that determines whether manufacturers choose active or passive recycling strategies. Reward levels affect evolutionary speed but not the final equilibrium state.
Second, the choice of recycling mode significantly affects supply chain performance. The alliance mode maximizes total supply chain profit through resource integration and elimination of double marginalization. The hybrid mode maximizes recycling rate through competitive pressure. These findings provide clear guidance for firms selecting recycling strategies based on their primary objectives.
Third, blockchain technology creates systematic improvements in both economic and environmental performance. By establishing a credible traceability system for electric vehicle battery packs, blockchain reduces information asymmetry, enhances consumer trust, and enables higher retail prices and lower recycling prices while simultaneously increasing recycling quantities. The economic benefits of blockchain are robust to variations in cost-sharing arrangements.
Fourth, consumer trust in blockchain is the pivotal variable that moderates the effectiveness of technological empowerment. Without sufficient trust, blockchain investment yields limited returns. With high trust, the same investment generates substantially larger improvements in recycling rates and supply chain profits. This finding implies that technology deployment must be accompanied by consumer education and trust-building initiatives.
The managerial implications of this study are twofold. For government policymakers, I recommend implementing dynamic reward-punishment mechanisms that adjust based on observed compliance rates rather than fixed penalty schedules. Policymakers should also support the development of shared blockchain infrastructure for tracking electric vehicle battery packs throughout their lifecycle, reducing the cost burden on individual firms. For corporate decision-makers, I recommend that firms consider alliance or hybrid recycling modes depending on whether profit maximization or recycling rate maximization is the primary objective. Firms should also recognize that blockchain investment is not merely a technological choice but a strategic commitment to transparency that can generate sustainable competitive advantages in the recycling of electric vehicle battery packs.
| Dimension | Policy Implication | Corporate Implication |
|---|---|---|
| Reward-punishment design | Adopt dynamic mechanisms with penalty thresholds | Monitor regulatory probability to anticipate penalty levels |
| Recycling mode selection | Incentivize alliance formation through tax benefits | Choose alliance for profit, hybrid for recycling rate |
| Blockchain investment | Support shared infrastructure and standards | Invest in blockchain as strategic asset with cost-sharing |
| Consumer trust | Launch public education campaigns on traceability | Communicate blockchain verification to build brand trust |
In conclusion, the optimization of electric vehicle battery pack recycling decisions requires a dual-driver approach that combines institutional incentives with technological trust infrastructure. Dynamic reward-punishment mechanisms address the behavioral dimension by making active recycling economically rational for manufacturers and formal channel selection attractive for consumers. Blockchain technology addresses the informational dimension by creating a tamper-resistant record of each electric vehicle battery pack’s lifecycle, enabling fair valuation and efficient coordination. Together, these two instruments create a governance system that is greater than the sum of its parts, capable of driving the recycling of electric vehicle battery packs toward scale, standardization, and resource efficiency.