In recent years, the global shift toward sustainable transportation has positioned electric vehicles (EVs) at the forefront of technological and policy discussions. As a leading market in this transition, China has implemented various subsidy policies to accelerate the adoption and advancement of electric vehicles. Among these, the tiered subsidy adjustment multiplier design (TSAMD) stands out as a key mechanism aimed at incentivizing technological progress in the electric vehicle sector. This study explores how TSAMD influences product technology selection among manufacturers in the China EV market, focusing on battery energy density as a critical technological indicator. By developing a profit-maximization framework and employing counterfactual simulations, we assess the effectiveness of this policy in driving technological upgrades. Our analysis reveals that while TSAMD encourages the adoption of higher-energy-density batteries, its impact on average energy density improvements is modest compared to alternative subsidy designs. This research not only provides an empirical evaluation of tiered subsidies but also offers insights for optimizing policies to foster long-term growth in the electric vehicle industry.
The proliferation of electric vehicles represents a pivotal step in reducing carbon emissions and dependence on fossil fuels. In China, the government has played an active role in promoting the electric vehicle market through financial incentives, such as purchase subsidies. These subsidies are often structured in a tiered manner, where higher subsidies are allocated to vehicles with superior performance metrics, like longer driving ranges or higher battery energy densities. The rationale behind this approach is to encourage manufacturers to invest in advanced technologies, thereby enhancing the overall quality and appeal of electric vehicles. However, the effectiveness of such policies in actually influencing technological choices remains underexplored. This study addresses this gap by examining the impact of TSAMD on battery technology selection in the China EV industry, using a structural model based on firm-level decision-making.
Attribute-based subsidies, such as TSAMD, are widely used in various industries to promote products with positive externalities. In the context of electric vehicles, these subsidies aim to bridge the cost gap between conventional and advanced technologies, making EVs more accessible to consumers. Previous research has primarily focused on the demand-side effects of such policies, such as how subsidies influence consumer purchasing behavior. For instance, studies have shown that subsidies can increase the adoption of electric vehicles by reducing upfront costs, but their effectiveness may vary based on factors like consumer income levels and regional policies. On the supply side, however, there is limited empirical evidence on how tiered subsidies affect manufacturers’ technology choices. This study contributes to the literature by providing a micro-level analysis of how TSALD shapes product technology selection in the China EV market, using a novel analytical framework that incorporates firm profit maximization.
The Chinese government introduced TSAMD as part of its broader strategy to support the electric vehicle industry. Starting in 2017, subsidies were adjusted based on battery energy density, with higher multipliers applied to vehicles equipped with batteries exceeding specific thresholds. For example, in 2018, electric vehicles with battery energy densities between 120 and 140 Wh/kg received a standard subsidy, while those with densities above 160 Wh/kg qualified for a 1.2x multiplier. This policy design was intended to incentivize manufacturers to adopt more advanced battery technologies, such as ternary lithium or phosphate iron lithium batteries, which offer higher energy densities and longer driving ranges. Over time, the subsidy thresholds were updated to reflect technological advancements, such as raising the minimum energy density requirement from 90 Wh/kg in 2017 to 125 Wh/kg in 2019. Despite these adjustments, the actual impact on technological progress has been debated, prompting this investigation into the efficacy of TSAMD.
To analyze the effects of TSAMD, we develop a structural model based on the profit-maximization behavior of electric vehicle manufacturers. The model assumes that firms choose battery technologies for their vehicles by weighing the costs and benefits, including subsidies and consumer preferences. The profit function for a vehicle model i equipped with battery j at time t is defined as:
$$ \pi_{ijt} = X_{ijt} \gamma + \alpha (S_{ijt} – MC_{ijt}) $$
where \( X_{ijt} \) represents vehicle characteristics influencing price premiums, \( S_{ijt} \) is the subsidy amount, and \( MC_{ijt} \) is the marginal cost. The parameter \( \alpha \) captures the impact of the net subsidy on profits. The revenue function includes a random shock term:
$$ R_{ijt} = \pi_{ijt} + \varepsilon_{ijt} $$
Assuming that \( \varepsilon_{ijt} \) follows an independent and identically distributed extreme value distribution, the probability of choosing battery j from the available set \( L_t \) is given by the multinomial logit form:
$$ Pb_{ijt} = \frac{\exp(\pi_{ijt})}{\sum_{k \in L_t} \exp(\pi_{ikt})} $$
This probability is used to derive the market share of battery technologies within specific driving range categories. The empirical model is specified as:
$$ \ln s_{rjt} – \ln s_{r0t} = \alpha (S_{rjt} – S_{r0t}) + \beta (k_{rjt} – k_{r0t}) + (b_{jz(t)} – b_{0z(t)}) + \lambda_r + \lambda_t + u_{rjt} $$
where \( s_{rjt} \) is the share of vehicles in driving range category r using battery j at time t, \( k_{rjt} \) represents vehicle characteristics like driving range, and \( b_{jz(t)} \) captures battery-specific effects over time. The parameters are estimated using fixed effects for driving range and time, with standard errors clustered at the driving range level to account for potential correlations.
Data for this study are sourced from China’s “New Energy Vehicle Promotion Recommendation Catalog” (推荐目录), which includes detailed parameters for all pure electric passenger vehicles eligible for subsidies from 2017 to 2021. This dataset provides information on battery energy density, driving range, battery type, and other vehicle characteristics. After cleaning and validation, the sample consists of 2,571 unique electric vehicle models. Battery technologies are categorized based on energy density (e.g., below 105 Wh/kg, 105–120 Wh/kg, up to 180 Wh/kg and above) and type (ternary lithium, phosphate iron lithium, and others). Driving ranges are grouped into categories aligned with subsidy thresholds, such as 100–150 km, 150–200 km, and so on, up to 400 km and above. Subsidy amounts are calculated as the product of the base subsidy for the driving range category and the energy density adjustment multiplier.
Descriptive statistics highlight the evolution of the China EV market over the study period. The average battery energy density increased from 117.45 Wh/kg in 2017 to 150.10 Wh/kg in 2021, reflecting technological advancements. Similarly, the average driving range rose from 223 km to 415 km, while subsidy amounts decreased due to policy adjustments. The following table summarizes key variables:
| Variable | Mean | Std. Dev. | Min | Max |
|---|---|---|---|---|
| Energy Density (Wh/kg) | 143.22 | 20.39 | 91.1 | 206 |
| Subsidy (10,000 CNY) | 1.69 | 1.32 | 0 | 6 |
| Driving Range (km) | 348.43 | 117.58 | 100 | 1008 |
| Battery Energy (kWh) | 46.26 | 18.70 | 9 | 144.4 |
| Manufacturer Price (10,000 CNY) | 15.85 | 9.90 | 2.68 | 75.43 |
The distribution of battery types also shifted over time. Ternary lithium batteries dominated the market in earlier years, but phosphate iron lithium gained share later due to cost advantages and energy density improvements. The table below shows the proportion of vehicle models using different battery types by year:
| Year | Ternary Lithium (%) | Phosphate Iron Lithium (%) | Other (%) |
|---|---|---|---|
| 2017 | 72.23 | 9.78 | 18.99 |
| 2018 | 75.58 | 4.46 | 19.96 |
| 2019 | 82.05 | 10.38 | 7.57 |
| 2020 | 71.66 | 24.31 | 4.02 |
| 2021 | 53.53 | 43.57 | 2.90 |
Empirical results from the baseline regression model indicate that subsidies have a statistically significant positive effect on manufacturer profits. The coefficient for the subsidy variable is estimated at 0.888, meaning that a 1-unit increase in subsidy (10,000 CNY) raises profits by approximately 0.888 units, all else equal. The driving range variable also shows a positive coefficient, suggesting that longer ranges contribute to higher profits, possibly due to consumer preferences. The regression output is presented below:
| Variable | Coefficient | Std. Error | Significance |
|---|---|---|---|
| Subsidy | 0.888 | 0.136 | *** |
| ln(Driving Range) | 1.484 | 0.413 | ** |
Robustness checks, including alternative baseline groups and sample restrictions, confirm the stability of these results. For instance, when using a different baseline battery technology or excluding outliers, the subsidy coefficient remains positive and significant, supporting the validity of our model.
To assess the impact of battery technology on profits, we estimate the relative contribution of different energy density levels. The results show that lower-energy-density batteries were more profitable in earlier years, but this advantage diminished over time as higher-energy-density technologies became more cost-effective. For example, in 2017, batteries with energy densities below 105 Wh/kg had a positive profit contribution relative to the baseline, but by 2021, batteries with densities of 160 Wh/kg and above showed higher relative contributions. This trend aligns with technological advancements and changing consumer demands in the electric vehicle market.
Counterfactual analysis is conducted to evaluate the effects of TSAMD compared to a uniform subsidy design. In the uniform scenario, all vehicles receive the same subsidy multiplier, regardless of energy density. In the TSAMD scenario, subsidies are tiered based on energy density thresholds. The total subsidy expenditure is held constant across both scenarios to isolate the design effect. The results indicate that TSAMD incentivizes manufacturers to adopt higher-energy-density batteries. Specifically, the number of vehicle models choosing batteries with energy densities of 160 Wh/kg and above increases by over 14% under TSAMD compared to the uniform design. However, the impact on average energy density is limited, with only a 1% increase. This suggests that while TSAMD encourages movement toward higher technologies, the threshold-based design may lead to clustering at subsidy boundaries rather than continuous improvement.
The table below summarizes the changes in vehicle model counts by energy density category under TSAMD versus uniform subsidies:
| Energy Density (Wh/kg) | Uniform Subsidy | TSAMD | Policy Effect (%) |
|---|---|---|---|
| <105 | 58 | 51 | -12 |
| 105–120 | 66 | 56 | -16 |
| 120–130 | 266 | 257 | -3 |
| 130–140 | 195 | 174 | -11 |
| 140–150 | 909 | 880 | -3 |
| 150–160 | 155 | 150 | -3 |
| 160–170 | 418 | 477 | 14 |
| 170–180 | 133 | 145 | 9 |
| ≥180 | 97 | 106 | 9 |
Further analysis explores alternative subsidy designs, such as a quasi-linear scheme where the subsidy multiplier increases linearly with energy density. This approach could potentially boost average energy density by up to 10%, as it provides continuous incentives for technological improvement rather than discrete jumps at thresholds. The findings highlight the importance of aligning subsidy structures with technological progress to maximize impact in the electric vehicle industry.
In conclusion, this study demonstrates that tiered subsidies, like TSAMD, can influence technology selection in the China EV market by encouraging the adoption of higher-energy-density batteries. However, the current design’s impact on overall technological advancement is modest, and alternative approaches, such as linear subsidy schemes, may yield greater improvements. Policymakers should consider refining subsidy designs to provide stronger incentives for continuous innovation, ensuring that the electric vehicle sector remains competitive and sustainable. Future research could extend this framework to other aspects of electric vehicle technology, such as charging infrastructure or battery recycling, to provide a comprehensive view of policy effectiveness. As the China EV market continues to evolve, adaptive policies will be crucial for driving long-term growth and environmental benefits.
The implications of this research extend beyond the electric vehicle industry to other sectors where attribute-based subsidies are used. By emphasizing the role of policy design in technological selection, this study offers a replicable framework for evaluating and optimizing subsidies in emerging industries. For instance, similar approaches could be applied to renewable energy technologies, such as solar panels or wind turbines, where tiered incentives might promote efficiency gains. In the context of electric vehicles, ongoing monitoring and adjustment of subsidy policies will be essential to keep pace with rapid technological changes and consumer preferences. As China strives to meet its carbon neutrality goals, effective policy instruments like TSAMD will play a vital role in shaping the future of transportation.
Overall, the integration of economic modeling with empirical data provides a robust basis for understanding how subsidies drive technological choices in the electric vehicle market. This study underscores the need for dynamic policy frameworks that not only stimulate initial adoption but also foster continuous innovation. By leveraging insights from this analysis, stakeholders in the China EV ecosystem—including manufacturers, policymakers, and researchers—can collaborate to design more effective strategies for sustainable mobility. The journey toward a greener transportation system relies on such evidence-based approaches, ensuring that electric vehicles remain at the forefront of global efforts to combat climate change.