1 Introduction
Nowadays, the electric vehicle (EV) industry is entering a new stage of accelerated development, which not only injects strong new momentum into the economic growth of countries, but also helps to cope with the challenges of climate change and improve the global ecological environment
[1]. At the end of 2022, there were more than 26 million EVs on the road worldwide. The performance of China's EV market is particularly outstanding
[2]. In 2022, the sales of EVs in China reached 5 million, accounting for about half of the global total sales. As the power system of EV, electric vehicle batteries (EVBs) need to be replaced when their capacity falls to 70%~80% of the original capacity
[3]. With the increasing production and sales of EVs, massive of EVBs are being retired. Statistics show that the scale of retired EVBs in China will reach 7 million tons by 2030
[4].
Retired EVBs have significant economic and environmental value. In general, batteries retired from EVs can be reused in cascade utilization first and then materials recovery utilization, which will yield economic benefits
[5]. This recycling route is illustrated in
Figure 1. In order to meet the service conditions of EVs, the performance requirements of EVBs are higher than those of ordinary batteries. Therefore, even if an EVB needs to be retired due to its capacity loss, there is still enough remaining capacity for cascading utilization scenarios such as energy storage and low-speed electric vehicles. When the residual capacity of an EVB drops to less than 30%, it will be directly disassembled to obtain internal valuable metal materials, which is an important economic benefits source for battery recycling
[6]. The recycling of retired EVBs can not only bring economic benefits, but also produce far-reaching environmental benefits. Although EVs have excellent carbon emission reduction effect during driving, the manufacturing process of EVBs is far from low-carbon production
[7]. The whole process of battery production can be divided into upstream raw material acquisition and battery manufacturing. Data show that the carbon emission of battery production process is estimated at 61~106 kg/kWh. The cascade utilization of retired EVBs means that less manufacturing new batteries, which can achieve the effect of reducing carbon emissions
[8]. Meanwhile, metal materials recovery from retired battery is conducive to less mining of mineral resources, thus reducing carbon emissions. In particular, retired EVBs contain a large volume of toxic and harmful substances, which can become a long-term environmental hazard if not handled properly. At present, the recycling situation of retired EVBs in China is not optimistic. Less than 30% of retired batteries are recycled through qualified formal enterprises. Such a large number of retired EVBs introduce a serious risk of serious environmental contamination and resource waste without effective collection and utilization. Therefore, the recycling of retired EVBs is of practical necessity and urgency.
Figure 1 Schematic diagram of retired EVBs recycling |
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In recent years, China has issued a series of relevant management policies to promote the recycling of retired EVBs. The extended producer responsibility system for the recycling of retired EVBs is implemented. As a result, EV manufacturers bear the primary responsibility for battery recycling, and relevant enterprises should take on corresponding responsibilities to ensure effective recycling. By the end of 2022, 10, 235 recycling service outlets had been set up across the country by EV manufacturers and battery recycling enterprises. In addition, rising raw material prices have led many upstream manufacturers of the electric vehicle industry chain to pay attention to the recycling of retired EVBs
[9]. For example, Build Your Dreams (BYD), the leader of the EV manufacturers in China, builds the closed-loop industrial chain to realize the recycling of lithium, nickel, cobalt and other metal materials.
However, battery recycling in China is still costly, which is mainly due to the high buyback price, high recovery and cascade utilization cost caused by immature technology, and so forth. Instead of recycling retired EVBs from users, the way for EV manufacturers to make profits is still to sell the EVs to consumers. In this situation, EV manufacturers lack willingness for recycling and reluctant to invest resources in building the recycling network. The imperfect value chain and technology chain hinder the sustainable development of retired EVB recycling industry
[10]. As an important part of circular economy, the development of EVB recycling industry needs policy and financial support. The Chinese government has implemented a number of incentive policies to encourage and support the recycling of retired EVBs. These incentive policies can be mainly divided into two categories: Direct policy instrument and indirect policy instrument. The direct policy instrument mainly refers to financial support, such as subsidies and tax incentives, which can directly and effectively promote battery recycling. Some cities where the EV industry has developed rapidly have already introduced subsidy policies to support the recycling of retired EVBs. For example, the Shenzhen government introduced a financial subsidy policy in 2018, providing a subsidy of 20 CNY per kWh according to the battery capacity. The Hefei government issued an incentive policy in 2022 to provide recycling subsidy of no more than 20 CNY per kWh for EV manufacturers. The indirect policy instrument mainly contains the technology policy and environmental policy. In order to develop circular economy and improve resource utilization efficiency, the National Development and Reform Commission (NDRC) issued the "The 14th Five-Year Plan for Circular Economy Development", which listed the recycling of retired EVBs as an important national project and emphasized to increase financial funding to support technological innovation. Meanwhile, China has issued several policies, including setting targets for metal recovery rate in batteries and investing in related science and technology. Moreover, the carbon trading scheme (CTS), which promotes energy efficiency and emission reduction, is widely accepted worldwide as a reaction to global warming caused by CO
2 emissions
[10]. In July 2021, China's carbon trading market was officially launched. Taking into account the emission reduction and energy savings effect of EVs, the state is accelerating the integration of the EVs industry into the carbon trading market.
According to this background, in order to promote the industrialization of retired EVBs recycling, therefore, it is necessary to analyze the mechanism and effect of different incentive policies. To this end, a series of research questions arise: 1) What are the effects of different incentive policies on the recycling of retired EVBs? 2) Which incentive policy is more effective to promote the recycling of retired EVBs? 3) How to implement combination policies to achieve optimal policy effectiveness? To address these questions, we built a system dynamics (SD) model to reveal the complex feedback relationship between incentive policies and the development of retired EVBs recycling. Several policy scenarios are designed to simulation analyze the impact of recycling subsidy, technological advancement, and carbon trading on major indicators such as return on investment (ROI), collection ratio, and then explore the carbon reduction benefits and economic benefits of recycling. Our work shows that both recycling subsidy and technological advancement can significantly promote the recycling of retired EVBs, but the impact mechanisms are different. Recycling subsidy can improve ROI, thereby increasing the willingness of EV manufacturers to recycle retired EVBs. Technological advancement can reduce the recovery and cascade utilization cost, and thus improving economic benefits. In addition, we find that the CTS is beneficial for promoting the recycling of retired EVBs, but lower carbon trading price limit the positive impact of CTS. The findings are helpful to comprehensively understand the EVBs recycling system and provide reference for the government to formulate scientific incentive policies.
The remainder of this paper is organized as follows: Section 2 reviews the related literature. Section 3 builds the system dynamics model. Section 4 tests the validity of the SD model. Section 5 simulates and discusses policy scenarios. Section 6 summarizes and discusses policy enlightenment.
2 Literature Review
We review the related literature in three areas in this section: Recycling of retired EVBs, policy analysis on EVBs recycling, and the application of SD.
2.1 Recycling of Retired EVBs
In recent years, with the number of retired EVBs increases, studies on the efficient recycling of retired EVBs have become a hotspot in academia. The academic research about recycling of retired EVBs mainly focuses on the collection management, economic and environmental benefits, and utilization technology.
Currently, the majority of retired EVBs in China flow into informal collection channels, thus, scholars have conducted research around collection management. Dong and Ge
[11] found that due to the limited collection outlets and low collection compensation, consumers are unwilling to sell retired EVBs to formal collection channels. Government regulation and incentive mechanism are two important policy tools to promote the collection of retired EVBs
[12, 13]. The main processing route of retired EVBs recycling is first cascade utilization and then material recovery utilization
[14]. Whether a retired EVB is cascade utilization mainly depends on the residual capacity. When the residual capacity is between 30% and 80%, it is suitable for cascade utilization first. When the residual capacity is lower than 30%, the EVB will have to be disassembled directly to recovery metal materials. Recovery utilization is mainly to recover metal materials such as lithium, cobalt, and nickel after dismantling retired EVBs
[15, 16]. Both cascade utilization and recovery utilization have great potential in gaining economic and environmental benefits. For the economic benefits, Haram, et al.
[17] calculated the economic benefits of cascade utilization from the perspective of benefit-cost ratio, and the results show that when the cost of cascade utilization is lower than 80% of new EVB, cascade utilization battery is more profitable than new EVB. Jiang, et al.
[18] analyzed the cost-benefit of EVBs recycling in China and found that cascade utilization for energy storage will generate higher economic advantages than recovery utilization alone. From the environmental benefits point of view, the main benefit of EVBs recycling is the reduction of carbon emissions. First of all, recovery utilization is conducive to reducing the mining of metal mineral resources, thus achieving the effect of carbon reduction. Chen, et al.
[19] investigated the carbon footprint of EVBs production by using a life-cycle assessment method, and found that battery remanufacturing through recycled materials emits 51.8% less carbon emission than battery production using raw materials. Secondly, cascade utilization can reduce the production of new batteries, thus reducing carbon emissions. Kurland
[20] assessment found that manufacturing a 1 kWh of battery capacity will produce 55 kg of carbon dioxide emission.
In addition, although retired EVBs recycling has huge economic and environmental benefits, it faces the challenge of the immaturity of utilization technology. Material recovery utilization is faced with problems such as low extraction rate of important metal materials and leakage risk of harmful materials. Currently, the main metal extraction techniques are pyro-metallurgical or hydro-metallurgical uses
[21]. Under the current technical condition, the recovery rate of nickel, cobalt and metal can reach more than 90%, but the recovery rate of lithium metal is low
[22]. The challenges of cascade utilization include three main aspects: Assessing the health and residual value of retired EVBs, safety hazards management of cascade utilization, and efficient testing and regrouping
[23]. Advanced and effective cascade utilization and material recovery technology can minimize the cost of recycling
[24]. Hence, technological advancement is the key factor that affects the sustainable development of retired EVBs recycling industry.
The above research provides sufficient theoretical basis for the work of this paper. In addition, most current studies on the utilization of retired EVBs focus on the benefit aspect, without considering the input-output effect comprehensively from both cost and benefit.
2.2 Policies for Retired EVB Recycling
Due to the high recycling cost and the imperfect recycling organization system, reasonable profitability has not been achieved to support the sustainable development of retired EVBs recycling. Therefore, considering the important strategic significance of carbon emission reduction, it is necessary to support the development of EVBs recycling industry through incentive policies.
The study of the effects of incentive policies on retired EVBs recycling industrial development is relatively important. Some scholars analyze the impact of incentive policies using analytical modeling methods. Gu, et al.
[25] built a two-period battery utilization closed-loop supply chain model composed of battery manufacturers, secondary users and government. They found that the government will consider a subsidy only when the residual capacity of EVBs is relatively high or remanufacturing rate is relatively low. Chen, et al.
[26] constructed a game model with an EV manufacturer and an EVB manufacturer to compare three subsidy policies and provide optimal subsidy strategies and the corresponding thresholds for the recycling of retired EVBs. Zhang, et al.
[27] developed a tripartite evolutionary game model to simulate batteries-to-reutilization under the subsidy scenario, and discovered that formulate a reasonable subsidy reduction path is beneficial to promoting the long-term development of retired EVBs recycling. Zhu, et al.
[28] estimate that a combination of higher tax rates and the environmental tax policy proves most effective in promoting increased recycling rates of retired EVBs. Wang, et al.
[29] built an EVB recycling model that takes carbon emissions into account, and analysis show that carbon tax is a significant factor influencing the design for recycling system. Yi, et al.
[30] established a Stackelberg game model and introduced carbon trading policy and power battery cost coefficients. The results show that carbon trading policy can increase the recovery of retired EVBs. Lin, et al.
[31] studied the regulatory mechanism of retired EV batteries' recycling, and they believed that carbon trading price will affect the regulatory effect. Jiao, et al.
[32] constructed a CLSC game model to study the effect of carbon trading policy and technological advancement on the decision-making, and found that technological advancement is more effective than carbon trading policy to promote the recycling of retired EVBs.
The above research literature mainly analyzes the impact of single policies, such as subsidy mechanism and carbon trading, on the recycling of retired EVBs through the game mathematical model method, but lacks the comparison of the advantages of different policy effects and the analysis of policy combination effects. Additionally, the recycling of retired EVBs involves many factors like policy, technology, cost, benefit and their complex interrelationships, but existing research overlook the complex feedbacks relationship between related factors.
2.3 Application of System Dynamics
SD approach is the main modeling and analytic tool in this research. SD, founded by Professor Forrester of MIT, is a discipline that studies information feedback complex systems
[33]. SD has been widely applied to the analysis of industrial, social, and economic systems
[34, 35]. SD models can be used for both qualitative and quantitative analysis. When the values of the model parameters are uncertain, such as when using causal loop diagrams to describe the system's complex feedback relationships, a qualitative model may be more appropriate
[36]. While a quantitative SD model is simulated through variable assignment and function relation construction, which can forecast the long-term development trend of the system
[37].
Many researchers use SD approach to investigate the effects of policies on complex systems. In particular, the method has been widely applied in waste products management. To illustrate, Wang, et al.
[38] employed SD methodology to analyze the impact of subsidy and preferential tax policies on China's mobile phone recycling industries. Zhang, et al.
[39] analyzed the impact of subsidy policy on the economic feasibility of waste photovoltaic modules recycling using SD model. Song, et al.
[40] developed a SD model to analyze the effect of subsidy on the diffusion of electric vehicles in China. Ma, et al.
[41] developed a system dynamics model to assess impacts of five policies on the supply and demand of recycled aggregate. Gao and Chen
[42] studied the influence of reward and punishment mechanism on e-waste recycling using evolutionary games and system dynamics.
This paper focuses on the impact of government incentive policies on the long-term development of battery recycling. Meanwhile, retired EVBs recycling is a complex social and economic system affected by many factors. In this case, using SD modeling to conduct the incentive policy scenarios stimulation analysis is feasible as SD is good at studying complex system problems with long-term.
2.4 Research Contributions
In summary, while China has the largest EV market share, it is still in the early stages of retired EVBs recycling industry and has not yet established a relatively complete recycling system. Therefore, we established a causal loop diagram to analyze the complex feedback relationship between the retired EVBs recycling and its influencing factors, and a stock-flow diagram model to simulate the cost and benefits of recycling. We further set five scenarios based on various incentive policies and ran simulations to see how the collection ratio, ROI, carbon emission reduction, and corresponding government spending changed over time. The main contributions of this paper are as follows: 1) The retired EVBs recycling incurs high costs while achieving economic and environmental benefits. However, most existing research on the recycling benefits often overlooks these costs. Different from the existing literature, from the perspective of cost-benefit, we analyze the performance indicators of recycling, such as ROI, collection ratio and carbon emission reduction, and uses these as the basis for policy selection in this paper. 2) Different from the existing literature that focuses on the impact of a single incentive policy, this paper analyzes and compares various incentive policies, such as recycling subsidy, technological advancement, carbon trading, and combination policy, to provide reference for the government to formulate more rational and scientific incentive policies. 3) Retired EVBs recycling is a complex dynamic system, and its cost-economic benefits are affected by many factors such as policy, technology and market. We use SD approach to build a policy-simulation model to investigate the complex feedback relationship between the incentive policies and the development of battery recycling.
3 System Dynamics Model
This paper establishes a feedback relationship model and a stock-flow model from qualitative and quantitative perspectives to analyze the cost-benefit and industry development trends of the EVBs recycling industry under different incentive policy scenarios.
3.1 System Feedback Structure
Based on the actual situation in China, this paper takes EV manufacturers as the main body of recycling, and constructs a system dynamics model from the perspective of the overall industry chain. The system dynamics model of EVBs recycling in this paper contains four subsystems: A recycling subsystem, a carbon reduction subsystem, a cost subsystem and a benefit subsystem. The simulation software VENSIM PLE was used to create the SD model in this paper. The feedback results between subsystems are shown in Figure 2.
Figure 2 Feedback loop diagram for retired EVBs recycling |
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It contains six important feedback loops. B1, B2 and B3 are negative feedback loops. R1, R2 and R3 are positive feedback loops. B1, B2 and B3 show that the more retired EVBs for recycling, the more recycling cost. The increase in recycling cost leads to a decrease in return on investment (ROI), thereby reducing the recycling willingness of EV manufacturers. R1, R2 and R3 indicate that the more retired EVBs for recycling, the more recycling benefits. The increase in recycling benefit leads to higher ROI, which increases the recycling willingness of EV manufacturers, and increases the collection ratio. The ROI is directly determined by the costs and benefits. ROI is a critical driving factor for the sustainable growth of the EVBs recycling industry, as well as the main basis for EV manufacturers' participation in recycling decisions. However, due to the limitations of recycling scale and technology, the cost of collection and utilization is still relatively high in the early stages, and it is often difficult to achieve the expected ROI. At this moment, the government is attempting to improve the ROI by implementing incentive policies to motivate EV manufacturers to invest in recycling. With the advancement of recycling technology, the economic benefits are gradually improving. Therefore, the retired EVBs recycling system has formed a sustainable balancing feedback loop.
3.2 Stock-flow Model
Establishing a stock and flow model is an important step to fully comprehend the system's feedback and evolution trends in the SD model. In view of this, we built stock-flow model by extending the feedback loops diagram above. Figure 3 illustrates the stock-flow model for retired EVBs recycling.
Figure 3 The stock-flow diagram model of retired EVBs recycling |
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The model is constructed using building blocks categorized as stocks, flows, constants and connectors. Stock variables (symbolized by rectangles) are the state variables, flow variables (symbolized by valves) are the rates of change in stock variables and they represent those activities, which fill in or drain the stock variables. In our model there exist six stock variables and eight flow variables. The connectors, represented by arrows, are the information links representing the causal relationship in the model. The model framework is based on the current situation of China's retired EVBs recycling industry and incentive policies. It mainly covers three processes of collection, cascade utilization and recovery utilization of the EVBs recycling industrial chain. Several key performance indicators such as cost, benefit, carbon emission reduction, and collection ratio are evaluated.
3.3 Mathematical Formulation
3.3.1 Recycling Subsystem
In recycling subsystem, we mainly calculate the scale and collection of ratio retired EVBs. There are four key variables: retired EVB scale (RES) collection ratio (CR), amount of cascade utilization (ACU) and amount of recovery utilization (ARU).
1) Retired EVBs scale
We use the EVs market share projection data from "New Energy Vehicle Industry Development Plan (2021–2035)" (Office of the State Council, 2019) to predict the EV sales in China. According to the plan, by 2025, the sales of EVs will account for 20% of the total sales of vehicles, and by 2030 and 2035, the proportion will reach 40% and 60%, respectively. We forecast annual EV sales for 2022–2035 based on historical and planned sales. Figure 4 shows the annual sales of EVs.
Figure 4 The annual sales of EVs in China |
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The scale of retired EVBs are estimated based on the annual sales volume and battery life of EVs, and the scale of EVBs is estimated in tons.
The weight of EVBs is related to their energy density (Wh/kg) and capacity (kWh)
[18]. At present, the energy density of EVBs is about 100~150 Wh/kg. China's target in 2030 is 300 Wh/kg. Battery capacity indicates the amount of energy that can be extracted from the battery, which determines the EV range. There is a linear relationship between battery capacity and EV range, and thus the future battery capacity is estimated based on the future EV range. This study uses forecasts for EV range that start at 300 km in 2020 and increase by 100 km every five years before and after 2035.
is amount of using EVBs scale;
represents the unit battery weight.
indicates the EV power battery life;
represents the amount of using EVBs’ initial value;
represents annual EVBs sales, which is equal to annual EV sales.
2) Collection ratio
The ROI will affect the willingness of EV manufacturers to participate in the recycling of retired EVBs, and the willingness will further affect the collection ratio. In practice, when the actual ROI is lower than the expected ROI, the EV manufacturers are unwilling to recycle. However, with the development of the scale and technology of retired EVBs recycling, the collection rate can only slowly increase at a minimum speed
[39]. When the actual ROI is higher than the expected ROI, EV manufacturers are more willing to participate in participate in retired EVB recycling. According to the requirements of the NDRC, by the end of 2025, China's batteries collection ratio should reach 70%. At the same time, considering the natural loss of EVB and referring to the recycling regulations of waste electronic products, we set the final collection ratio target (CRT) of retired EVBs at 90%.
CR is collection ratio increment,
indicates the collection ratio's initial value,
represents the minimum value of collection ratio increment,
represents recycling willingness,
represents expected ROI.
3) Amount of cascade utilization and recovery utilization
After retired EVBs are collected, the batteries with higher residual capacity will be used in cascade utilization first, and the batteries with less than 30% capacity will be directly recovery utilization. However, due to the immaturity of recovery and cascade utilization technology and inconsistent battery standards in China, the cost of recovery and cascade utilization is high. indicates the amount of cascade utilization's initial value; is cascade utilization increment; represents the amount of cascade utilization completed; indicates cascade utilization lifespan. CUR indicates the cascade utilization ratio.
The amount of recovery utilization consists of two parts: Low-capacity batteries after collection and batteries after cascade utilization completed.
3.3.2 Carbon Emission Subsystem
The recycling of retired EVBs can reduce carbon emissions. In comparison to the carbon emissions caused by the acquisition and processing of raw materials for the manufacture of new batteries, the cascade utilization 1 kWh of battery capacity can reduce 59 kg carbon emissions
[43]. Meanwhile, remanufacturing battery can reduce carbon emissions 38% compared with manufacturing with virgin materials, that is, recovery utilization one ton of retired EVBs can reduce carbon emissions by 4, 200 kg
[44]. Therefore, unit cascade utilization emission reduction
is 5.3 ton, and unit recovery utilization emission reduction
is 4.2 ton. Annual carbon emission reduction
can be expressed by
3.3.3 Cost Subsystem
In this subsystem, we primarily calculate the total recycling cost of retired EVBs. The recycling cost includes three parts: Collection cost , recovery utilization cost , cascade utilization cost and fixed assets depreciation . Total recycling cost can be expressed by
Collection cost includes buyback price and transportation cost . is set to 11000 CNY/ton based on the buyback price of current recycling market of retired EVBs in China. The value of transportation cost is 500 CNY/ton. According to China's subsidy policy, in order to motivate EV manufacturers to recycle retired EVBs, the government will give subsidy S based on the amount of recycling. The following equation can be used to describe the collection cost:
Recovery utilization cost can be expressed by Eq. (13), where are the unit recovery utilization cost and it includes cost of waste disposal, labor cost, raw material cost, and so on.
The initial unit production cost of industrial products is typically high when compared to the large-scale application stage. With the expansion of the scale of recovery utilization and technological advancement, unit recovery utilization cost will gradually decrease
[45]. Unit recovery utilization cost can be expressed by
where
denotes the initial unit recovery utilization cost; LC is the technology learning rate;
indicates the initial recovery utilization amount of retired EVBs. We set the initial technology learning rate at 10% since it is still in the initial stages of small-scale
[46].
Cascade utilization cost can be expressed by Eq. (15), where are the unit cascade utilization cost and it includes technical processing cost and operation and maintenance cost.
Retired EVBs recycling requires investment in fixed assets, such as equipment, workshops, etc. Fixed asset investment (FAI) generally depends on the recycling capacity and economies of scale
[47]. Fixed assets depreciation (FAD) is calculated according to the 20-year depreciation life, and the residual value at the end of the period is 10% of the initial investment.
3.3.4 Benefit Subsystem
There are three benefits of retired EVBs recycling: Recovery utilization benefit , cascade utilization benefit and carbon emission benefit . Total recycling benefit can be calculated according to
Recovery utilization benefit mainly derived from the metal materials. Currently, EVBs in use are mostly lithium-ion batteries. And almost half of the mass of lithium-ion batteries are composed of metal elements, including nickel, lithium, cobalt, and manganese
[48].
Material prices are obtained from Shanghai Metals Market (SMM, 2022). Table 1 provides the mainly metal material contents in each ton of retired EVBs and the unit prices of materials. Recovery utilization benefit can be calculated according to
Table 1 Recyclable material contents in retired EVBs |
| Material type | Quantity Mi | Material recovery rate Yi | Unit price Vi |
| Lithium | 19 | 80% | 245.2 |
| Nickel | 121 | 90% | 204.9 |
| Manganese | 70 | 90% | 15.5 |
| Cobalt | 23 | 90% | 48.2 |
where refers to the mass of material , represents material recycling rate, denotes materials' unit prices.
The economic benefits of cascade utilization are estimated based on the most widely used energy storage projects
[49]. Energy storage service means charging electricity at a low price during the off-peak period and discharging during the peak period. In particular, the battery capacity determines the cascade utilization benefits, and here the battery weight is converted into battery capacity according to the battery energy density. While the price difference between peak and off-peak hours of power in China is thought to be 0.7 CNY per kWh
[50]. The economic benefits from the energy storage can be calculated according to
where is the amount of cascade utilization; represents the battery energy density; is remaining capacity ratio of cascade utilization batteries; is the price difference of peak-valley electricity; is the efficiency of the battery energy storage system, which averaged 75%. Here, is assumed to be 0.7 CNY/kWh; is assumed to 60%.
Enterprises can earn benefit from carbon trading. represents the carbon trading price. Carbon emission benefit can be calculated according to
equals total recycling benefit minus total recycling cost. It is commonly accepted that recycling businesses require a of at least 10% to be operational. Value-added tax are the most common types of taxes, with rates of 13%. Meanwhile, to promote the recycling and utilization of resources, the Chinese government gives tax rebates to enterprises engaged in resource utilization activities.
where denotes the tax cost; represents the value-added tax rate; denotes the tax refund ratio. The notations, used in the model, are summarized and explained as in Table 2.
Table 2 Notations used in the model |
| Notations | Descriptions | | Notations | Descriptions |
| RES | Retired EVB scale | | Pb | Buyback price |
| Bw | Unit battery weight | Ct | Transportation cost |
| Lb | EV battery life | S | Subsidy |
| CR | Collection ratio | RUC | Recovery utilization cost |
| W | Recycling willingness | Curu | Unit recovery utilization cost |
| ROI | Return on investment | CUC | Cascade utilization cost |
| ROIex | Expected return on investment | FAI | Fixed asset investment |
| ACU | Amount of cascade utilization | RC | Recycling capacity |
| ACUC | Amount of cascade utilization completed | FAD | Fixed assets depreciation |
| CUR | Cascade utilization ratio | B | Total recycling benefit |
| Lc | Cascade utilization lifespan | Bru | Recovery utilization benefit |
| ARU | Amount of recovery utilization | Bcu | Cascade utilization benefit |
| Qce | Annual carbon emission reduction | Bce | Carbon emission benefit |
| Ucu | Unit cascade utilization emission reduction | Db | Battery energy density |
| Uru | Unit recovery utilization emission reduction | C | Total recycling cost |
| Cc | Collection cost | P | Carbon trading price |
| Cru | Recovery utilization cost | Pt | Total recycling profit |
| Ccu | Cascade utilization cost | Ctax | Tax cost |
| Cf | Fixed assets depreciation | itax | Value-added tax rate |
| E | Energy storage system efficiency | rtax | Tax refund ratio |
| K | Minimum collection ratio increment | LC | Technology learning rate |
4 Data Sources and Validation of the SD Model
4.1 Data Sources of the SD Model
To guarantee the authority and validity of the model, we set parameter initial values by referring to relevant literature, data published by national institutions, and market survey. Table 3 lists the initial values of the main parameters and their data sources.
Table 3 Parameters initial value and data source |
| Abbreviations | Initial value | Data source |
| EVB | 10 million | Li, et al.[51] |
| Lb | 6 years | Jiang, et al.[18] |
| Lc | 5 years | Wu, et al.[50] |
| Ucu | 5.3 ton | Dai, et al.[43] |
| Uru | 4.2 ton | Yu, et al.[44] |
| Pb | 11000 CNY/ton | Zhang, et al.[7] |
| Ct | 500 CNY/ton | Zhang, et al.[7] |
| Curu | 7000 CNY/ton | Zhang, et al.[7] |
| Cucu | 10000 CNY/ton | Zhang, et al.[7] |
| LC | 10% | Shen, et al.[46] |
| Pe | 0.7 CNY/kWh | Wu, et al.[50] |
| Rb | 60% | Gu, et al.[52] |
| K | 0.02 | Zhang, et al.[39] |
| E | 75% | Wu, et al.[50] |
| itax | 13% | State Taxation Administration |
| α | 0.6 | Vlachos, et al.[47] |
4.2 Model Validation
The validity test of SD model has its unique methods, including structure test, software running test, and behavior test. In order to test the validity of the EVBs recycling SD model, certain procedures and tests are used. Firstly, we compare variable, causal relationship, and model equation in the model with the real system of the EVBs recycling and the relevant literature, which ensures that the model meets the structural testing requirements. Secondly, the SD model passed the Vensim software test, which suggests that have no dimensional consistency errors. Finally, we conduct behavior sensitivity test, which confirms that the model behavior exhibits meaningful sensitivity to parameters technology learning rate, subsidy, and carbon trading price, which suggests these parameters are “leverage point” in the EVB recycling system, that means, those parameters are key factor that could be changed to improve the system pattern of behavior. The behavior test provides excellent guidelines for the design of simulation scenarios, as well as the simulation of the effects of incentive policies on the EVBs recycling.
Behavior reproduction test compares the simulation results with real data. Since, simulated values of variables are expected to be compatible with the historical data, if the model works appropriately
[47]. The large-scale retirement of EVBs in China began in 2018, so historical data from 2018 to 2022 was used for behavioral reproduction testing. Historical data sourced from industry authority GGII. As depicted in
Figure 5, the key variables of the model behave according to the expectations. Therefore, the model works appropriately.
Figure 5 Comparison of historical data with simulation data |
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5 Policy Scenarios Simulation Results and Discussion
5.1 Policy Scenarios Setting
To investigate the effects of different intervention policies on the growth of the EVBs recycling industry, we designed five policy scenarios, which are baseline scenario, recycling subsidies scenario, technological advancement scenario, carbon trading scenario, and combination policy scenario, as shown in Table 4.
Table 4 Scenarios and variables setting |
| Scenario | Subsidy | Technology learning rate | Carbon trading price |
| Baseline | 0 | 0.1 | 0 |
| S1 | 20 | 0.1 | 0 |
| S2 | Decrease by 8% annually | 0.1 | 0 |
| T1 | 0 | 0.15 | 0 |
| T2 | 0 | 0.2 | 0 |
| CT | 0 | 0.1 | 50 |
| STC | Decrease by 8% annually | 0.2 | 50 |
5.2 Baseline Scenario
In the baseline scenario, we assume that the recycling technology remains at the original level, EV manufacturers are unable to participate in carbon trading, and governments will not provide subsidy to EV manufacturers. The simulation results are displayed in Figure 6.
Figure 6 Simulation results for baseline scenario |
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As shown in Figure 6(a), the ROI of retired EVBs recycling shows an increasing trend and the growth rate is gradually slow down. However, it is very low or even negative before 2025. This is because the scale of retired EVBs is small and recycling technology is limited at the beginning stage, while the cost of recycling is high. As the retired EVBs scale expands and recycling technology improves, the cost of recycling gradually falls, and industrial economies of scale will begin to emerge. After 2030, ROI is more than 10% and reaches 14% by 2035. The ROI has a significant impact on the collection ratio. When the ROI is low, the willingness of EV manufacturers to participate in recycling is not high, resulting in slow growth of the collection ratio. This will give informal recyclers the opportunity and lead to illegal battery disposal and environmental pollution risks. Figure 6(b) shows that, China's annual retired EVBs scale will be close to 6.4 million tons by 2035. Meanwhile, the cascade utilization and recovery utilization of battery will reduce nearly 20 million tons of carbon emissions by 2035. According to the baseline scenario analysis, the recycling of retired EVBs has significant economic and environmental benefits. However, due to the small scale of retired EVBs and high recycling costs in the early stage, the industrial development was relatively slow without policy support. Therefore, implementing incentive policies is an effective way to boost market vitality and promote the healthy development of the retired EVBs recycling industry.
5.3 Recycling Subsidy Scenario
At present, some local governments in China have implemented recycling subsidy policy to encourage EV manufacturers to recycle retired EVBs. Local governments in China mainly provide subsidies of 10~20 CNY per kWh based on battery capacity. The subsidy scheme can be static or dynamic. Static subsidies refer to subsidy standards do not change over time, while dynamic subsidies standards are adjusted over time. In order to compare the effect of two subsidy schemes on the recycling of retired EVBs, this paper uses 20 CNY/kwh as the static subsidy standard. The dynamic subsidy is set to decrease by 8% annually from 2023 to 2035. Figures 7 and 8 show the simulation results.
Figure 7 Effect of recycling subsidy scenario |
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Figure 8 Subsidy expenditure and efficiency analysis |
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As we can see from
Figure 7, compared with the baseline scenario, both the collection ratio and the ROI improve significantly under two subsidy scenarios.
Figure 7(a) shows that the time to reach expected ROI of 10% in the static subsidy scenario and the dynamic subsidy scenario is 2024 and 2025, and the ROI will reach 26% and 17% by 2035, respectively. As shown in
Figure 7(b), the collection ratio in the static subsidy scenario and the dynamic subsidy scenario reaches 56% and 52% by 2025, and 90% and 89% by 2030, respectively. Therefore, subsidy policy can encourage EV manufacturers to actively participate in recycling. Previous studies have also highlighted the positive impact of subsidy mechanisms on promoting retired EVBs recycling (Wei, et al.
[5]; Chen, et al.
[26]). This is because the recycling subsidy, as an effective economic incentive policy, which can relieve the financial pressure and drives greater investment in recycling capacity. Further comparative analysis suggests that the static subsidy helps to stimulate the collection ratio and the ROI more than the dynamic subsidy, but at the expense of more subsidy expenditure, as shown in
Figure 8(a). The government can introduce dynamic subsidy or static subsidy to promote the recycling of retired EVBs. Considering the budgetary constraints of government finance, it is very important to evaluate the efficiency of the two subsidy mechanisms from the perspective of environmental protection. Therefore, we use the input-output ratio method in economics to analyze the efficiency of the two subsidy mechanisms. That is, subsidy efficiency is calculated as the ratio of annual carbon emission reduction to annual subsidy expenditure.
Figure 8(b) shows the results of subsidy efficiency analysis. The efficiency of the static subsidy increases slowly, from 21.9 tons per thousand CNY in 2023 to 24.1 tons of carbon emission reduction per thousand CNY in 2035. However, the efficiency of the dynamic subsidy grows exponentially, reaching 72.1 tons carbon emission reduction per thousand CNY in 2035. The results suggest that the dynamic subsidy can achieve more carbon emission reduction for each CNY of subsidy expenditure. In this case, a dynamic subsidy scheme in which the overall subsidy expenditure falls step by step to boost efficiency is a preferable option for the government.
5.4 Technological Advancement Scenario
The technology advancement policy is represented by the R&D investment and industry-university-research cooperation, which can reduce the unit cost by improving the technology maturity. The technology learning rate, which reflects the level of technological advancement, is set at 10% in the baseline scenario. To compare the impact of different level of technology advancement, this paper set two scenarios of T1 and T2, and the technology learning rate is 15% and 20%, respectively. Figure 9 shows the scenario simulation results.
Figure 9 Effect of technological advancement scenario |
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From
Figure 9, it can be seen that the ROI and the collection ratio significantly increases under the technological innovation scenario. According to
Figure 9(a), by 2025, the collection ratio in T1 an T2 reach 33% and 43%, and 87% and 90% by 2030, respectively, which is significantly higher than the baseline scenario. All ROIs in T1 and T2 considerably increase in comparison to the baseline scenario, as shown by
Figure 9(b). According to
Figure 10, technological advancement has a positive impact on the unit recovery and cascade utilization cost, and the higher the technology learning rate, the more significant the impact on the reduction of the utilization cost. Through the above analysis, we can find that technological innovation can improve the economy of recycling mainly by reducing the recovery and cascade utilization cost, thereby stimulating the recycling of retired EVBs. Meanwhile, we can see from
Figure 10 that the cost drops slowly in the early stage, but then accelerates until it reaches a stable value. This is because technological maturity has to accumulate over time, which reflets the lag effect of the technological advancement policy. This finding is supported by the existing literature (Li, et al.
[53]; Zhao, et al.
[54]). Technological research and development activities are risky and uncertain, and it takes time for technological innovations to be translated into commercial applications. Therefore, in the early stages, the impact of technological advancement on the recycling of retired EVBs is not as significant as that of subsidy policy. China is currently in the stage of promoting EVs. The technology innovation of EVBs focuses on improving the cruising range, while the battery recycling technology is neglected, resulting in a low level of recovery and cascade utilization technology
[14]. On one hand, the key technical challenge of cascade utilization is to evaluate the state of health and residual capacity of retired batteries. Because of the variety of battery types and the differences in service life, batteries evaluation and sorting become more difficult and expensive. Therefore, it is very vital to develop intelligent and highly compatible evaluation equipment to improve the economy of cascade utilization. On the other hand, recovery utilization is a critical component of the retired EVBs recycling, which can realize the recycling of valuable materials and reduce the environmental impact of waste treatment. In recent years, extraction technology of metal materials from retired batteries has become a research hotspot. Presently, the most common methods include bioleaching technologies, organic acid leaching method, direct recycling technologies, and low-temperature molten salt technology
[23]. However, these technologies and methods still have some problems, such as high recycling cost, low recycling rate, and complex operation, which are not suitable for large-scale industrial applications. Therefore, it is the future trend to develop the low-cost, high efficiency, and environmental friendliness recycling technology.
Figure 10 Unit utilization under technological advancement scenario |
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5.5 Carbon Trading Scenario
Recycling of retired EVBs contribute significantly to carbon emission reduction. Incorporating the EVs industry into CTS can not only increase the economic benefits of EV manufacturers, but also encourage them to fulfill carbon emission reduction obligations. EV industry is included in CTS, which means EV manufactures can benefit from carbon trading. The current carbon trading price in China is around 50 CNY/ton. In the carbon trading scenario, we set the parameter values of carbon trading price as 50, represented by CT. Figure 11 shows the simulation results of the ROI and the collection ratio under carbon trading scenario.
Figure 11 Effect of carbon trading scenario |
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Figure 11(a) shows that the ROI in carbon trading scenario improved by only about 1% over the baseline scenario.
Figure 11(b) shows that the collection ratio in carbon trading scenario will be reach the 87% in 2035. It can be seen from
Figure 11 that there are little differences in the changes of both the ROI and the collection ratio between carbon trading scenario and the baseline scenario. Carbon emission trading market is an institutional innovation for China to use market mechanism to reduce greenhouse gas emissions and promote low-carbon development. It is also a core policy tool for implementing China's carbon peak and carbon neutrality. However, compared with relatively mature carbon trading markets, such as the European Union, China's carbon trading market has just started and the operational institution of the market is not well organized. The average carbon price is only 50 CNY/ton due to the immature carbon market in China, which is far lower than the international standard carbon trading price of about 40 euros/ton. Hence, the profit incentive from the carbon trading market is weak. This finding is consistent with the analytical results of Jiao, et al.
[32] that now is not the time to introduce CTS into the EVBs recycling industry.
5.6 Combination Policy Scenario
When implementing specific policies, the government usually uses a combination of policies, thus we analyze the policy combination scenario which contains recycling subsidy, technological advancement, and the carbon trading. The values of the three policy parameters in the combination scenario are the same as those in S2, T2 and CT scenarios. We simulate the effect of combination policy, represented by CTS, and analyze the effect difference between combination scenario and single scenario. Figure 12 depicts the simulation results.
Figure 12 Effect of combination policy scenario |
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As can be seen from Figure 12(a), the collection ratio in combination policy scenarios is significantly higher than other single scenarios and reach the 90% collection ratio target first. Meanwhile, Figure 12(b) shows that the cumulative carbon emission reduction effect of combination policy scenarios is optimal. The combination policy shows obvious advantages over the single policy scenario, mainly because the dynamic subsidy policy and the technological advancement policy have complementary effects. The impact of the subsidy on EVBs recycling is immediate, which is a direct financial incentive for recycling enterprise to improve the ROI, as shown in Figure 12(c), in the early stage, the ROI in the S2 scenario is higher than that in the T2 scenario. The impact of technological advancement is time-delay, because the reduction in recycling cost of retired EVBs through technological innovation takes time for accumulation, as shown in Figure 12(d), the reduction rate of unit recovery utilization cost in T2 scenario is significantly faster than that in S2 scenario. In the short term, because the technology is not mature and the recycling cost is high, high subsidy intensity can effectively promote the increase of EVBs recycling capacity. Meanwhile, under the condition of limited government expenditure budget, in order to maximize subsidy efficiency, the government should dynamically reduce subsidy standards according to the level of technological progress. As a result, the effect mechanism of combination policy is primarily driven by subsidy in the early stage and later by technological advancement.
6 Conclusions and Policy Implications
The purpose of this article is to investigate simulated-variation tendency of retired EVBs recycling system under different incentive policies and provide recycling management suggestions. SD, which based on the feedback loops structures and cause-and-effect analysis, has been proposed as a new thought to solve complexity and long-term problems. This paper develops an SD model to analyze the Chinese EVBs recycling system. Furthermore, the SD model's validity is examined, and it can be used to comprehend the long-term behavior of EVBs recycling system under various policy scenarios. Finally, the paper simulates the trend of EVB recycling under various incentive policy scenarios. As previously stated, we can reach the following conclusions and policy implications.
First, with current growth tendency, the annual retired EVBs scale in China will be close to 6.5 million tons by 2035, which has high economic and environmental benefits. Meanwhile, the ROI is very low in the early stage, and the willingness of EV manufacturers to participate in the recycling is not strong, which may lead to more batteries flowing into informal processing channels and bring safety environmental hazards. Therefore, in order to prevent resource waste and environmental damage, it is vital for the government to promote the sustainable development of the industry through intervention measures such as economic incentives and technological innovation support.
Second, the government's incentive policies of recycling subsidy, technological advancement, and carbon trading scheme will promote the battery recycling to varying degrees. Specifically, the subsidy helps to stimulate the recycling of retired EVBs more quickly in the short term, and the dynamic subsidy outperforms the static in terms of subsidy efficiency. Technological advancement, which can reduce the recovery and cascade utilization cost, have positive effect on the EVBs recycling, but the policy effect is time-delay. In addition, due to the imperfect carbon trading market and low carbon trading prices in China, the incentive effect of implementing carbon trading policy is weak. Although currently is not the appropriate time to include the EVB recycling in the carbon trading market, when the carbon trading market is mature, CTS can be implemented to promote the recycling of retired EVBs. Therefore, EV manufacturers should aggressively engage in carbon reduction cooperation throughout the industrial chain to assist the state achieve the dual carbon goal.
Third, compared with a single policy, the combination policy can more effectively promote the recycling of retired EVBs and reduce carbon emissions. The main reason is that dynamic subsidy and technological advancement have complementary effect. Therefore, in the early stages, when the battery recycling volume is small and recycling service outlets and equipment needed to be invested, the government should provide a subsidy to incentive EV manufacturers to expand investment in recycling capacity, thereby promoting retired EVBs recycling. Meanwhile, given the current low level of recovery and cascade utilization technology, the government should guide EV manufacturers to speed up technological innovation from the aspects of R&D investment and industrial chain cooperation. Then, as retired EVBs recycling industry becomes more mature, the government can gradually reduce subsidies. In this way, the EVBs recycling industry can be transformed from subsidy mechanism policy to technology innovation policy, thereby realizing the sustainable development.
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Figure 1 Schematic diagram of retired EVBs recycling
Table 1 Recyclable material contents in retired EVBs
