Selection of Production Strategies for New Energy Vehicles: An Analysis of the Impact of Government Intervention Policies

Ge, Yingxi; Chen, Kehong

doi:10.3390/systems13010061

Open AccessArticle

Selection of Production Strategies for New Energy Vehicles: An Analysis of the Impact of Government Intervention Policies

by

Yingxi Ge

¹ and

Kehong Chen

^1,2,*

¹

School of Management, University of Science and Technology of China, Hefei 230026, China

²

Anhui Province Key Laboratory of Digital Intelligence Supply Chain, International Institute of Finance, Hefei 230026, China

^*

Author to whom correspondence should be addressed.

Systems 2025, 13(1), 61; https://doi.org/10.3390/systems13010061

Submission received: 27 December 2024 / Revised: 14 January 2025 / Accepted: 17 January 2025 / Published: 19 January 2025

(This article belongs to the Section Supply Chain Management)

Download

Browse Figures

Versions Notes

Abstract

:

The aim of this study was to analyze the strategic choices and profit variations of a monopolistic automobile manufacturer capable of producing both traditional fuel vehicles and new energy vehicles, with a particular focus on government interventions. Using a theoretical model, the research examined firm-level production decisions by incorporating consumer preferences and market competition under three policy scenarios: no government intervention, government subsidies, and tax policies. The key findings are as follows: (1) In the absence of government intervention, the firm’s production strategy is influenced by consumer preferences for new energy vehicles. Specifically, the firm prioritizes the production of new energy vehicles when consumer preference is high, fuel vehicles when preference is low, and both types when preference is moderate. (2) Government subsidies substantially reduce the production of fuel vehicles while promoting the production of new energy vehicles. However, excessively high subsidies may lead the firm to revert to fuel vehicle production. (3) Tax policies influence production strategies in a manner similar to subsidy policies. (4) When government intervention is weak and competition between fuel vehicles and new energy vehicles is intense, subsidy policies are more effective; however, when competition is less intense, tax policies may be more beneficial. Under strong government intervention, subsidy policies are found to be more effective. This research contributes to the literature by providing a theoretical foundation for government policymaking in the new energy vehicle sector, offering insights into firm-level production decisions under various policy environments. The originality of this study lies in its comparison of the effectiveness of subsidy and tax policies in promoting new energy vehicle production, which helps guide policymakers in designing optimal policy interventions.

Keywords:

new energy vehicles; subsidy policy; tax policy; production strategy

1. Introduction

1.1. Research Background

The escalating global challenges of oil scarcity, environmental pollution, and climate change necessitate a decisive shift away from traditional fossil-fuel-powered vehicles. New energy vehicles (NEVs), characterized by their low carbon emissions and high energy efficiency, have emerged as a pivotal solution to mitigate these pressing concerns [1]. While the rapid growth of the NEV market presents significant opportunities for sustainable transportation, its successful expansion hinges on effective government intervention.

Government subsidies and policy support have been instrumental in promoting the adoption of NEVs. By directly providing financial incentives to consumers who purchase NEVs, government subsidies reduce the upfront costs, thereby stimulating demand. Existing research has demonstrated that government subsidies can significantly enhance consumer acceptance of NEVs [2,3]. China, a frontrunner in the NEV sector, has implemented a series of government subsidy policies to improve managerial efficiency, boost consumer demand for electric vehicles, and encourage manufacturers to innovate and expand market reach [4]. For instance, the Chinese government has set a target for 20% of government vehicles to be electric [5]. Additionally, initiatives such as Shougang Group’s and ZTE Corporation’s construction of a stereo garage with wireless charging capabilities in 2016 have further supported the development of charging infrastructure [6]. The development of charging stations is also backed by national policies, as evidenced by the “moderate forward construction” principle outlined in the EV Charging Infrastructure Development Guidelines (2015–2020) [7].

Tax policies have also played a significant role in promoting NEV adoption. By exempting NEVs from purchase taxes, governments can substantially reduce the overall cost of ownership, thereby stimulating sales. Furthermore, in addition to providing tax incentives for NEV manufacturers, governments can increase taxes on traditional fuel vehicles, leveraging the higher costs of conventional vehicles to accelerate the transition to NEVs [8,9]. The effectiveness of tax policies has been demonstrated in countries such as Norway, where the exemption of import duties and value-added taxes for pure electric and fuel cell electric vehicles has significantly incentivized consumer purchases [10]. Indonesia also offers tax incentives to manufacturers developing electric vehicle ecosystems [11].

While both tax policies and subsidies are essential tools for promoting NEV adoption, they operate through distinct mechanisms and have different impacts on market actors and policy outcomes. Tax policies primarily influence market behavior by adjusting tax rates. By reducing taxes on NEVs or increasing taxes on traditional fuel vehicles, governments can shift consumer preferences towards NEVs. This approach leverages market mechanisms, relying on price signals to guide consumer and producer behavior. This mechanism is analogous to the control and scheduling of electric vehicle charging. There are two primary types of control and scheduling for electric vehicle charging: direct control and indirect control. Direct control involves centralized management, where a utility or aggregator directly determines the charging behavior of EVs [12]. In contrast, indirect control employs price signals or incentives to influence EV owners’ charging decisions [13]. In contrast, subsidies involve direct government intervention, with funds being transferred to consumers or producers to reduce costs. Subsidies exert a more immediate impact on market demand by directly reducing the purchase price of NEVs. For consumers, tax policies reduce the overall cost of ownership for NEVs, making them more attractive compared to traditional vehicles. By increasing the cost of traditional fuel vehicles, tax policies further incentivize consumers to switch to NEVs. Subsidies, on the other hand, directly reduce the upfront costs for consumers, stimulating immediate demand. However, the withdrawal of subsidies may lead to a decline in consumer demand for NEVs.

Given the distinct mechanisms of tax policies and subsidies in promoting the adoption of new energy vehicles, it is crucial to conduct in-depth research into their differential impacts on the production strategies of automotive manufacturers. Existing studies primarily focus on macro-level policy evaluations, employing econometric models to assess the overall effects of policies (e.g., Wang and Li [14] and Sun et al. [15]). However, these studies have yet to delve deeply into the differentiated impacts of various policy instruments and the influence of policies on firms’ micro-level behaviors. This study aims to fill this research gap by examining the impact of government intervention measures, particularly subsidies and tax policies, on NEV production from the perspective of firm-level production decisions.

This study employs equilibrium analysis to investigate how a manufacturer capable of producing both conventional and electric vehicles makes optimal production decisions under subsidy and tax policies, thereby analyzing the impact of different government intervention strategies on promoting electric vehicle production. Specifically, this study first characterizes firm demand by examining consumer preferences under various policy interventions. Then, equilibrium results for firms are derived through equilibrium analysis under different policy scenarios. Finally, by comparing firms’ equilibrium decisions in the absence of government intervention, under subsidy policies, and under tax policies, this study aims to answer the following three questions: (1) How does a firm formulate its production strategy based on market demand and consumer preferences in the absence of policy intervention? (2) How do optimal production strategies and profits vary under different policy interventions, including subsidies and tax policies? (3) What policy recommendations can be derived from the above analysis?

1.2. Results

This study presents several key findings regarding the production decisions of firms in the electric vehicle (EV) market under various government interventions. The results provide insights into the effects of subsidies and tax policies on promoting EV production, as well as how these factors interact with consumer preferences and market conditions.

In the absence of government intervention, the study finds that firms’ production decisions are primarily influenced by consumer preferences for electric versus conventional vehicles. Specifically, when consumer preference for EVs is high, firms tend to prioritize the production of EVs. Conversely, when consumer preferences lean more towards conventional vehicles, firms are more likely to focus on producing fuel-powered cars. When consumer preference is moderate, firms opt for a mixed production strategy, manufacturing both types of vehicles to meet varying demands.

The introduction of subsidy policies leads to a noticeable shift in production towards electric vehicles. As subsidies for EVs increase, the production of conventional vehicles is suppressed, and the production of EVs is promoted. However, this study also reveals that excessive subsidies may have unintended consequences. When the subsidy rate surpasses a certain threshold, firms may adjust their production strategies by returning to the production of conventional vehicles. This suggests that while subsidies can stimulate the adoption of EVs within a certain range, overly generous subsidies may distort the market and lead to suboptimal outcomes.

In addition to subsidies, tax policies also affect production decisions, albeit in a more tempered manner. Tax policies, by increasing the cost of manufacturing conventional vehicles, indirectly encourage the production of EVs. However, the study finds that tax policies do not have as strong an effect as subsidies, particularly in markets where subsidies are already prevalent. This indicates that while tax policies can complement subsidies, they are more effective in environments where subsidies are not as aggressive.

Finally, the study also compares the effectiveness of these policies under different market conditions. When competition between conventional and electric vehicles is high, subsidy policies are found to be more effective in promoting EV production. In contrast, when market competition is lower, tax policies appear to be more beneficial. Under strong government intervention, particularly in the form of high subsidy levels, subsidy policies prove to be the most effective strategy for encouraging the production of electric vehicles.

1.3. Literature Review

This study is situated at the intersection of two streams of literature: subsidy policy and tax policy.

1.3.1. Research on Subsidy Policies

Research on subsidy policies is extensive, with studies across different regions highlighting their impact on the adoption of new energy vehicles (NEVs).

In developed countries, subsidies have been a major tool to stimulate the adoption of NEVs. For instance, in Norway, Holtsmark and Skonhoft [16] argue that the relatively low range of most electric vehicles (EVs) means that, under subsidy policies, driving an EV results in very low marginal costs for the owners. This encourages Norwegian households to purchase a second car, which stimulates private car use instead of public transportation or cycling, thereby increasing greenhouse gas emissions. This unintended consequence illustrates the complexities of subsidy policies, especially in developed countries where private car ownership is prevalent. In Japan, Zhang et al. [17] conducted an empirical analysis of photovoltaic systems in all 47 Japanese prefectures between 1996 and 2006. They concluded that regional government subsidies significantly contributed to the adoption of photovoltaic systems. This study further reinforces the role of government subsidies in promoting renewable energy adoption, particularly in developing regions that may face different challenges compared to developed countries. Further, Li et al. [18] employed a system dynamics model to capture the dynamic interactions among the government, manufacturers, and consumers, analyzing the effects of various subsidy schemes—acquisition subsidies, R&D subsidies, and dynamic versus static subsidies—on the EV industry. Their empirical study based on data from China indicates that both acquisition and R&D subsidies stimulate EV demand and reduce CO₂ emissions. While acquisition subsidies yield a more substantial short-term impact, they are also more expensive; in contrast, R&D subsidies offer more sustainable, long-term benefits. Meanwhile, Shao et al. [19] proposed a game-theoretic model to compare two types of EV policies: subsidies for electric vehicles and subsidies for charging infrastructure under different market structures. Their findings reveal that higher levels of charging infrastructure and greater social welfare are achieved when a monopolistic EV manufacturer operates its own charging stations or when competing manufacturers jointly manage charging infrastructure. Additionally, subsidies for charging infrastructure may generate greater social welfare if the infrastructure provider is a third-party firm in a monopoly or duopoly EV market or one of the manufacturers in a duopoly market.

In developing countries, the effectiveness of subsidies varies significantly depending on the economic and institutional context. In China, Lu et al. [20] highlight that EV purchase subsidies have greatly stimulated price-sensitive consumer preferences and increased EV penetration. However, these subsidies have also placed a substantial fiscal burden on the government. As these subsidies are gradually phased out, China has explored alternative policies such as mileage subsidies, congestion fee discounts, parking fee reductions, and access to dedicated bus lanes. In India, Srinivasan [21] investigated the effectiveness of an interest subsidy offered by the Indian federal Ministry of New and Renewable Energy for solar thermal systems. Unlike the capital subsidy for solar photovoltaic systems, which is distributed through government-controlled channels, the interest subsidy is routed through mainstream banking channels and proves more effective in both intent and outcome. By encouraging innovation, improving service delivery, and expanding product choices for consumers, this subsidy contributes to more inclusive development. Kalish and Lilien [22] developed a model to analytically examine the effects of price subsidies over time on market diffusion. They discovered that the optimal level of subsidy does not necessarily increase over time, which has important implications for the design of subsidy policies in both developed and developing economies. Zhao et al. [23] examine the impact of government subsidies on the transition of Chinese automakers towards electric vehicle production. Using panel data from 128 listed companies, the research finds that excessively high subsidies can hinder the transformation process. Instead, a balanced approach is needed, combining stricter technical requirements with lower subsidy intensity. The study recommends that policymakers should target specific areas of the industry, promote demand-side growth, and integrate market mechanisms to foster a sustainable electric vehicle industry. Shang et al. [24] investigate the impact of government subsidies on EV sales in 224 Chinese cities using a quasi-natural experiment approach. The findings from a panel data analysis show that a 1% increase in purchase subsidies leads to a 1.36% increase in EV sales and a 2.31% increase in market share. The study also demonstrates the significant role of non-financial incentives, such as parking benefits and purchase restrictions, in promoting EV adoption. Furthermore, the effectiveness of subsidies varies across cities and can be enhanced by complementary non-financial policies. Shen et al. [25] reviewed studies related to renewable energy subsidies.

1.3.2. Research on Tax Policies

Tax policies, especially carbon taxes and other environmental taxes, are also widely studied in the context of developed countries. In Europe, Yan [26] studied 10 pairs of BEVs and their internal combustion engine vehicle (ICEV) counterparts across 28 European countries from 2012 to 2014. The results show that, under incentive schemes, the cost reductions achieved by switching from large BEVs to their ICEV counterparts are larger than those from switching to small BEVs. A 10% increase in total tax incentives leads to a 3% average increase in the share of BEV sales. However, the study also finds that tax incentives are still costly when used to reduce CO₂ emissions and other environmental externalities, despite recent improvements in green electricity generation and lower battery costs. Arnold et al. [27] examined tax policies designed to accelerate economic recovery while promoting long-term growth. They suggested that while short-term tax relief can stimulate demand, it can also undermine long-term growth if not carefully designed, as such tax cuts may be hard to reverse. Based on cases from some European countries such as the UK, the Netherlands, Sweden, Finland, and Norway, Wang et al. [28] focused on carbon emissions and used a three-stage Stackelberg game model for decentralized supply chains and a two-stage Stackelberg game model for centralized supply chains to test government carbon tax policies. Chang and Woo [29] investigated South Korean consumers’ willingness to pay an EV charging tax using the contingent valuation method. Income was found to be the most significant factor influencing acceptance. The study forecasts potential tax revenue losses due to declining fuel tax revenue as EV penetration increases. The results indicate that a charging tax of 22 KRW/km (0.02 USD/km) is acceptable to consumers. Implementing this tax could mitigate revenue losses and ensure long-term fiscal sustainability in the transition to electric vehicles. Pan and Shittu [30] utilized the GCAM-USA model to investigate the impact of state-level electric vehicle (EV) purchase incentives and carbon taxes on US decarbonization. The results show that the effectiveness of these policies varies significantly across states, particularly in the Midwest. The study highlights the need for interstate collaboration in electricity generation to achieve national decarbonization goals, emphasizing the importance of tailored state-level strategies that consider the interplay between EV adoption and carbon tax policies.

Research on carbon taxes in developing countries has also gained traction. For instance, in China, Liu and Hu [31] analyzed carbon tax policies and the interaction between carbon taxes and green supply chain cooperation. Their findings suggest that the carbon tax rate and the power disparity between manufacturers and retailers play key roles in determining the effectiveness of green supply chain coordination. Their results indicate that higher carbon tax rates encourage manufacturers to invest more in R&D, while low development costs maximize tax revenue but reduce green performance. Based on the Chinese context, Liu et al. [32] constructed an evolutionary game model to analyze the impact of government emission taxes and subsidies on manufacturers’ decisions. They found that, despite numerous government incentives, the development of China’s EV industry has not met the expected goals. Their study also suggests that when the government implements dynamic tax or dynamic subsidy strategies, evolutionary game outcomes show stability. Bansal and Gangopadhyay [33] examined environmental policies in the presence of eco-conscious consumers, finding that uniform subsidy policies improve environmental quality, while uniform tax policies worsen it. However, discriminatory subsidy policies reduce pollution and increase welfare, while discriminatory tax policies may increase pollution and reduce welfare. Breaking away from national and regional concepts, Deng et al. [34] took an industry perspective to examine consumer preferences for green products and the associated risks of research and development (R&D) failure. They focused on the impact of different carbon tax policies on emissions reduction and profits within a green supply chain consisting of manufacturers and retailers. Their findings suggest that an increase in the unit carbon tax encourages manufacturers to invest more in R&D, while wholesale and retail prices follow a pattern of initially rising and then falling as the carbon tax increases. Yadav et al. [35] analyzed the demand for gasoline, diesel, and electric vehicles (EVs) in India using non-linear cointegration techniques. The findings reveal asymmetric demand patterns and price inelasticity for gasoline and diesel, rendering taxation ineffective in reducing their consumption. However, lower electricity prices and carbon taxes on conventional fuels can significantly boost EV demand. These findings suggest that a combination of price incentives and carbon taxation could accelerate the transition to electric mobility and contribute to India’s net-zero emissions target.

1.3.3. Novelty and Key Contributions

This study makes several key contributions to the literature on government policy interventions in the electric vehicle (EV) market. First, it compares the effectiveness of subsidy and tax policies in promoting EV adoption, providing a nuanced view of their impacts on consumer behavior and firm production strategies. Second, unlike most studies that focus on individual policy types, our work integrates the interaction between government subsidies and tax policies, analyzing how these policies influence the equilibrium production strategies of monopolistic firms. Third, this research expands on the existing literature by considering consumer preferences and market competition between traditional and new energy vehicles, offering practical insights for policymakers in choosing the optimal policy intervention.

In contrast to the studies mentioned above on subsidy and tax policies, our research focuses on a comparative analysis of how these policies influence the production strategies of monopolistic firms. Taking into account consumer preferences and the competition between traditional and new energy vehicles, we explore how the equilibrium production strategies of monopolistic firms change. By comparing subsidy policies with tax policies, we provide insights from the policymaker’s perspective on how to select the optimal intervention policy based on market and firm characteristics.

2. Model

2.1. Problem Description and Demand Structure

We consider a profit-maximizing monopolistic automobile manufacturer capable of producing both traditional fuel vehicles (FVs) and new energy vehicles (EVs). For instance, the Volkswagen Group can produce fuel vehicles (e.g., Passat and Golf) as well as the ID series of pure electric vehicles. Accordingly, the manufacturer’s production strategies can be classified into three types: producing only fuel vehicles (F strategy), producing only new energy vehicles (E strategy), and producing both fuel and new energy vehicles (H strategy). These three strategies are denoted by superscripts

F, E, a n d H

, respectively.

It is assumed that consumer preferences for FVs and EVs are represented by

β

and

1 - β

, respectively. The demand for FV

D_{f}^{i, j}

and the demand for EV

D_{e}^{i, j}

are given by

D_{f}^{i, j} = β a - p_{f}^{i, j} + γ (p_{e}^{i, j} - p_{f}^{i, j})

D_{e}^{i, j} = (1 - β) a - p_{e}^{i, j} - γ (p_{e}^{i, j} - p_{f}^{i, j})

where the superscript

i = o, s, t

denotes no policy intervention, subsidy policy, and tax policy, respectively, and

j = F, E, H

represents the F, E, and H strategies, respectively. Here,

a

denotes the total potential market demand,

p_{f}

and

p_{e}

represent the prices of FVs and EVs, respectively, and subscripts

f

and

e

are used to indicate FVs and EVs, respectively. The parameter

γ

represents the intensity of competition between the two types of vehicles when both are present in the market. Specifically, under the F or E strategy,

γ = 0

, indicating an absence of competition in the market. The relevant notations and parameters are listed in Table 1.

2.2. Case Without Government Policy Intervention

In this scenario, the government does not intervene in automobile production. The firm needs to determine its production strategy based on market demand and consumer preferences. The three strategies available to the firm are as follows:

Under the F strategy, the market demand is

D_{f}^{o, F} = β a - p_{f}^{o, F}

and the corresponding profit for the firm is

π^{o, F} = p_{f}^{o, F} D_{f}^{o, F}

Under the E strategy, the market demand is

D_{e}^{o, E} = (1 - β) a - p_{e}^{o, E}

and the corresponding profit for the firm is

π^{o, E} = p_{e}^{o, E} D_{e}^{o, E}

Under the H strategy, the market demand for FVs is

D_{f}^{o, H} = β a - p_{f}^{o, H} + γ (p_{e}^{o, H} - p_{f}^{o, H})

and the demand for EVs is

D_{e}^{o, H} = (1 - β) a - p_{e}^{o, H} - γ (p_{e}^{o, H} - p_{f}^{o, H})

The corresponding profit for the firm is

π^{o, H} = π_{e}^{o, H} + π_{f}^{o, H} = p_{e}^{o, H} D_{e}^{o, H} + p_{f}^{o, H} D_{f}^{o, H}

By solving the firm’s profit functions under each strategy, Lemma 1 can be derived.

Lemma 1.

The equilibrium outcomes for the firm under different strategies under the scenario without policy intervention are shown in Table 2.

Proof can be found in the Appendix A.

From Lemma 1, we observe that the monopoly price (under the F and E strategies) is not always higher than the competitive price (under the H strategy). Specifically, when

0 < β < \frac{1}{2}

, we have

p_{f}^{o, H} > p_{f}^{o, F}

, indicating that producing both types of competing vehicles allows the firm to sell FVs at a higher price. At this point, consumer preference for FVs is relatively low, and under a monopoly strategy (either single FV or EV production), the firm might need to lower the price of traditional fuel vehicles to boost sales. However, under the H strategy, the firm can adjust the price difference between the two vehicle types, leveraging their substitutability to increase FV sales, thus providing greater pricing flexibility. Similarly, when consumer preference for EVs is low, that is,

\frac{1}{2} < β < 1

, the price of EVs under the H strategy is higher than under the monopoly strategy, i.e.,

p_{e}^{o, H} > p_{e}^{o, E}

.

Lemma 2.

Without policy intervention, when

0 < β < \frac{1}{2}

,

\frac{\partial p_{f}^{o, H}}{\partial γ} > 0

and

\frac{\partial p_{e}^{o, H}}{\partial γ} < 0;

when

\frac{1}{2} < β < 1

,

\frac{\partial p_{f}^{o, H}}{\partial γ} < 0

and

\frac{\partial p_{e}^{o, H}}{\partial γ} > 0

.

In the absence of policy intervention, when the firm chooses to produce both fuel vehicles and new energy vehicles, the impact of competition on the prices of fuel vehicles and new energy vehicles is opposite and strictly dependent on consumer preferences. Specifically, when consumer preference for new energy vehicles is high (

β < \frac{1}{2}

), the price of fuel vehicles

p_{f}^{o, H}

increases with intensified competition, while the price of new energy vehicles

p_{e}^{o, H}

decreases. This is because, with a strong consumer preference for new energy vehicles, lowering the price of fuel vehicles does not significantly increase their sales volume, and raising the price has little negative effect on sales. Thus, adopting a price increase strategy for fuel vehicles is more beneficial to the firm’s profit. Conversely, lowering the price of new energy vehicles can stimulate substantial demand from potential consumers, making a price reduction strategy for new energy vehicles advantageous for maximizing profit. Similarly, when consumer preference for fuel vehicles is high, the firm is likely to implement a price reduction strategy for fuel vehicles and a price increase strategy for new energy vehicles.

From Lemma 2, we can also observe that, when consumer preference for a specific type of vehicle is relatively low, the profit from producing only that type of vehicle is lower than the profit from a mixed strategy. Specifically, when

0 < β < β_{1} ≝ \frac{\sqrt{γ (1 + 2 γ)} - 1}{2 γ - 1}

, we have

π^{o, F} < π^{o, H}

; when

β_{2} ≝ \frac{2 γ - \sqrt{γ (1 + 2 γ)}}{2 γ - 1} < β < 1

, we have

π^{o, E} < π^{o, H}

. From this, we can easily identify the optimal region for the firm to choose the H strategy. Correspondingly, we can determine the optimal regions for choosing the E and F strategies. The firm’s optimal choices are summarized in Proposition 1.

Proposition 1.

When

β < β_{1}

, the firm chooses the E strategy; when

β_{1} < β < β_{2}

, the firm chooses the H strategy; and when

β > β_{2}

, the firm chooses the F strategy.

As illustrated in Figure 1, the optimal production strategy for a firm is closely linked to consumer preferences. When consumer preferences for either fuel-powered vehicles (or electric vehicles) significantly exceed the other, firms should align with market demand and focus on producing the corresponding type of vehicle in order to maximize market share and profit. However, when consumer preferences are neutral towards both vehicle types, firms should adopt a diversified production strategy, producing both fuel-powered and electric vehicles to cater to a broader consumer base, thereby maximizing potential profits.

3. Analysis of Subsidy Policy

To promote the adoption of new energy vehicles, governments often implement subsidy policies to incentivize manufacturers or consumers. To enhance consumer willingness to purchase new energy vehicles, the government commits to providing financial support to consumers who buy new energy vehicles, specifically offering a subsidy proportional to the purchase price.

Under the F strategy, the manufacturer’s profit,

π_{f}^{s, F}

, remains the same as in the case without government intervention. Under the E strategy, the government provides a subsidy to consumers purchasing new energy vehicles, equal to a proportion

θ

of the vehicle’s price. As a result, the market demand for new energy vehicles is

D_{e}^{s, E} = (1 - β) a - (1 - θ) p_{e}^{s, E}

with a corresponding profit of

π_{e}^{s, E} = p_{e}^{s, E} D_{e}^{s, E}

Under the H strategy, the demand for new energy vehicles is

D_{e}^{s, H} = (1 - β) a - (1 - θ) p_{e}^{s, H} - γ ((1 - θ) p_{e}^{s, H} - p_{f}^{s, H})

and the demand for fuel vehicles is

D_{f}^{s, H} = β a - p_{f}^{s, H} + γ ((1 - θ) p_{e}^{s, H} - p_{f}^{s, H})

The corresponding total profit is

π^{s, H} = π_{e}^{s, H} + π_{f}^{s, H}

By solving the firm’s profit functions under each strategy, Lemma 3 can be derived.

Lemma 3.

The equilibrium outcomes for the firm under different strategies under the subsidy policy intervention are shown in Table 3.

Intuitively, when the government provides subsidies for purchasing new energy vehicles, one might expect an increase in the price of new energy vehicles, as firms could raise prices to capture additional value transferred from the government through the subsidy without affecting consumers’ purchasing expenditure. Meanwhile, the price of fuel vehicles would be expected to decrease to maintain competitiveness against subsidized new energy vehicles. However, as shown in Lemma 3, this is not necessarily the case. We summarize this counterintuitive result in Proposition 2.

Proposition 2.

When the government provides subsidies,

\frac{\partial p_{e}^{s, E}}{\partial θ} > 0, \frac{\partial p_{f}^{s, H}}{\partial θ} > 0

, and

\frac{\partial p_{e}^{s, H}}{\partial θ} > 0

.

From Proposition 2, we observe that, under both the E and H strategies, as government subsidies increase (i.e.,

θ

increases), the firm will correspondingly raise the price of new energy vehicles to achieve higher returns, implying that

\frac{\partial p_{e}^{s, E}}{\partial θ} > 0

and

\frac{\partial p_{f}^{s, H}}{\partial θ} > 0

. Interestingly, we also find that under the H strategy, the price of fuel vehicles rises with increasing subsidies for new energy vehicles, i.e.,

\frac{\partial p_{e}^{s, H}}{\partial θ} > 0

. This is because the firm’s goal is to maximize profits, and when the profitability of the two types of vehicles diverges, the firm will aim to de-prioritize the lower-margin product. Therefore, with increased subsidies for new energy vehicles, the firm prefers to boost the sales and price of new energy vehicles to capture higher profits. In this process, aside from the “discount” effect provided by the subsidy to consumers, the firm will also raise the price of the alternative fuel vehicles, further discouraging consumers from purchasing fuel vehicles and incentivizing more consumers to choose new energy vehicles. Thus, the price of fuel vehicles also rises as government subsidies increase.

To characterize the firm’s equilibrium strategy under government subsidies, we define the following thresholds:

β_{1}^{s u b} = \frac{8 {(- 1 + θ)}^{2} ψ + γ^{2} θ^{2} (1 - 2 ψ + 2 θ ψ) + 2 γ (- 1 + θ) (4 - 8 ψ + θ (- 1 + 8 ψ))}{4 {(1 - θ)}^{2} + γ^{2} θ^{2} - 8 γ (1 - θ)}

β_{2}^{s u b} = \frac{2 (2 + γ^{2} θ^{2} ψ - 4 ψ + 4 θ ψ + γ (θ - 8 ψ + 8 θ ψ))}{4 + γ^{2} θ^{2} - 8 γ (1 - θ)}

β_{3}^{s u b} = \frac{1 - \sqrt{1 - θ}}{θ}

β_{4}^{s u b} = \frac{γ (1 + γ) θ}{2 (1 - θ) + γ (4 - 3 θ)}

where

ψ = \sqrt{\frac{γ}{(1 - θ) (4 + 8 γ) - γ^{2} θ^{2}}}

.

Proposition 3.

Under the subsidy policy, the firm chooses the E strategy when

β < \min \{β_{3}^{s u b}, \max \{β_{1}^{s u b}, β_{4}^{s u b}\}\}

; it chooses the H strategy when

\max \{β_{1}^{s u b}, β_{4}^{s u b}\} < β < β_{2}^{s u b}

; and it chooses the F strategy when

β > β_{2}^{s u b}

or

β_{3}^{s u b} < β < β_{4}^{s u b}

.

Under the subsidy policy, the firm’s production strategy is influenced not only by consumer distribution

β

and competition

γ

, but also by the intensity of the government subsidy. Similar to Proposition 1, when consumer preference for new energy vehicles is relatively high (i.e.,

β

is low), the firm still chooses the E strategy. Conversely, when consumer preference for new energy vehicles is relatively low (i.e.,

β

is high), the firm will opt for the F strategy. Otherwise, the H strategy is the optimal choice. However, the subsidy policy alters this trend. As shown in Figure 2, as the intensity of the government subsidy increases (i.e.,

θ

increases), the feasible space for the F strategy gradually shrinks, indicating that government subsidies have a significant effect in terms of discouraging the production of fuel vehicles. However, once the subsidy reaches a certain threshold, the firm’s preference for the F strategy may strengthen again in cases of higher product substitutability. This occurs because, when subsidies are excessively high, firms are incentivized to set prices at extreme levels, which may drive consumers away from the market altogether. In this situation, the production of new energy vehicles will no longer be viable, prompting the firm to revert to the F strategy. From Proposition 3, we can conclude that although subsidy policies can promote the production of new energy vehicles, excessively high subsidies may actually hinder the adoption of new energy vehicles.

We present the impact trends of the subsidy policy through Proposition 4.

Proposition 4.

Under the subsidy policy, there exist threshold values

θ_{1}

and

θ_{2}

such that for any

0 < θ < 1

, the following occurs:

(i): When $0 < θ < θ_{1}$ , $\frac{\partial β_{1}^{s u b}}{\partial θ} > 0$ ; when $θ_{1} < θ < 1$ , $\frac{\partial β_{1}^{s u b}}{\partial θ} < 0$ ;
(ii): $\frac{\partial β_{2}^{s u b}}{\partial θ} > 0$ , $\frac{\partial β_{3}^{s u b}}{\partial θ} > 0$ , and $\frac{\partial β_{4}^{s u b}}{\partial θ} > 0$ ;
(iii): When $0 < θ < θ_{2}$ , $β_{3}^{s u b} > β_{4}^{s u b}$ ; when $θ_{2} < θ < 1$ , $β_{3}^{s u b} < β_{4}^{s u b}$ ;
(iv): $β_{2}^{s u b} > β_{3}^{s u b} > β_{1}^{s u b}$ .

Proposition 4 indicates that as subsidies increase, the firm increasingly prefers the E and H strategies, and the feasible space for the F strategy correspondingly shrinks (see Figure 3). However, when the subsidy exceeds a certain threshold,

θ_{2}

, the space for the F strategy unexpectedly expands as the subsidy increases, achieved by encroaching on the space for the H strategy. Furthermore, this encroachment effect becomes more pronounced when competition intensifies.

4. Analysis of Tax Policy

This section examines the impact of government intervention on the new energy vehicle industry from the perspective of restricting fuel vehicles. Specifically, in this case, the government imposes a value-added tax on firms that produce fuel vehicles, with the tax calculated based on the firm’s sales revenue at a rate

t

.

It should be noted that the tax policy does not alter the demand functions, so the demand functions under the tax policy remain the same as in the scenario without policy intervention.

Under the F strategy, the automobile manufacturer’s profit is

π^{t, F} = (1 - t) p_{f}^{t, F} D_{f}^{t, F}

Under the E strategy, the manufacturer’s profit

π^{t, E}

is the same as in the case without policy intervention. Under the H strategy, the profit function is

π^{t, H} = π_{e}^{t, H} + (1 - t) π_{f}^{t, H} = π_{e}^{o, H} + (1 - t) π_{f}^{o, H}

By solving the profit functions for each strategy, Lemma 4 can be derived.

Lemma 4.

The equilibrium outcomes for the firm under different strategies with the tax policy are shown in Table 4.

Proposition 5.

When the government imposes a tax on fuel vehicle production, (1)

\frac{\partial p_{f}^{t, F}}{\partial t} = 0, \frac{\partial p_{f}^{t, H}}{\partial t} > 0

; (2) when

β < \frac{2 t γ (2 - t) (1 + γ)}{4 + 8 γ - 4 t (2 + 3 γ) + t^{2} (4 + 6 γ + γ^{2})}

,

\frac{\partial p_{e}^{t, H}}{\partial t} > 0

; and when

β > \frac{2 t γ (2 - t) (1 + γ)}{4 + 8 γ - 4 t (2 + 3 γ) + t^{2} (4 + 6 γ + γ^{2})}

,

\frac{\partial p_{e}^{t, H}}{\partial t} < 0

.

When the government imposes a tax on fuel vehicle production, it reduces the profitability of fuel vehicles, thereby forcing the firm to adjust its pricing. Interestingly, we find that under the F strategy (producing only fuel vehicles), the firm does not change the price of fuel vehicles. In contrast, under the H strategy (producing both fuel and new energy vehicles), the firm adjusts the prices of both fuel and new energy vehicles.

Compared to the scenario without policy intervention, the fact that the firm does not change the fuel vehicle price under the F strategy may be due to the focus on value-added tax without considering production costs. Unlike the subsidy policy, which reduces the purchase cost, the tax policy indirectly increases the purchase cost. Nevertheless, the firm still chooses to raise the price of fuel vehicles under the H strategy. This implies that whether or not the government lowers consumers’ purchasing costs, the firm will opt to increase the fuel vehicle price under the H strategy. However, unlike the subsidy policy, which leads the firm to raise the price of new energy vehicles, under the tax policy, the firm only raises the price of new energy vehicles when the preference for them is high (i.e.,

β

is low). Conversely, when consumer preference for new energy vehicles is low (i.e.,

β

is high), the firm chooses to lower the price of new energy vehicles. This is because the tax policy reduces the profitability of fuel vehicles, prompting the firm to seek more profit from the sale of new energy vehicles. Therefore, when consumer preference for new energy vehicles is high, the firm raises the price to gain higher profits, while when preference is low, the firm lowers the price to boost sales of new energy vehicles.

Similarly, to characterize the firm’s equilibrium strategy under the tax policy, we define the following thresholds:

β_{1}^{t a x} = \frac{(- 1 + t) (8 γ - 2 t γ - t^{2} γ^{2}) + (- 1 + t) (8 (- 1 + t) + 16 (- 1 + t) γ + 2 t^{2} γ^{2}) Ω}{4 + 8 t (- 1 + γ) - 8 γ + t^{2} (4 + γ^{2})}

β_{2}^{t a x} = \frac{2 (2 + t γ + (- 4 - 8 γ + t^{2} γ^{2} + t (4 + 8 γ)) Ω)}{4 + 8 (- 1 + t) γ + t^{2} γ^{2}}

β_{3}^{t a x} = \frac{1 - \sqrt{1 - t}}{t}

β_{4}^{t a x} = \frac{γ (1 + γ) t}{2 (1 - t) + γ (4 - 3 t)}

where

Ω = \sqrt{\frac{γ}{(1 - t) (4 + 8 γ) - t^{2} γ^{2}}}

.

Based on these thresholds, we can derive the firm’s equilibrium production strategy under the tax policy. Given its similarity to the equilibrium strategy under the subsidy policy (Proposition 3), we do not elaborate further here, but we illustrate the firm’s equilibrium strategy in Figure 4.

5. Comparison of Intervention Policies and Recommendations

We consider an identical tax rate and subsidy rate, i.e.,

t = θ

. Consistent with national environmental policies, we assume that the E strategy, where only new energy vehicles are produced, is the optimal choice. The H strategy, producing both new energy and fuel vehicles, is the second-best choice, while the F strategy, producing only fuel vehicles, is the least preferred due to its environmental impact. Based on this, we derive the following conclusions.

Proposition 6.

Given

t = θ ≝ δ

, there exist threshold values

δ_{1}

,

δ_{2}

, and

δ_{3}

, such that the following occurs:

(i): When $δ < δ_{1}$ or $δ > δ_{2}$ , $β_{1}^{s u b} > β_{1}^{t a x}$ ; conversely, when $δ_{1} < δ < δ_{2}$ , $β_{1}^{s u b} < β_{1}^{t a x}$ ;
(ii): When $δ < δ_{3}$ , $β_{2}^{s u b} > β_{2}^{t a x}$ ; conversely, when $δ > δ_{3}$ , $β_{2}^{s u b} < β_{2}^{t a x}$ ;
(iii): $β_{3}^{s u b} = β_{3}^{t a x}$ and $β_{4}^{s u b} = β_{4}^{t a x}$ .

As shown in Figure 5, Proposition 6 reveals that when government intervention is relatively weak (i.e., low subsidy or tax rates) and competition between fuel vehicles and new energy vehicles is intense, we have

β_{1}^{s u b} > β_{1}^{t a x}

and

β_{2}^{s u b} > β_{2}^{t a x}

. In this case, a subsidy policy is more effective. However, when competition between fuel and new energy vehicles is moderate, the opposite results are observed, with

β_{1}^{s u b} < β_{1}^{t a x}

and

β_{2}^{s u b} > β_{2}^{t a x}

. Here, whether the government adopts a subsidy or tax policy, the F strategy (producing only fuel vehicles) is discouraged. The difference lies in that a subsidy policy more strongly supports the H strategy (producing both fuel and new energy vehicles), while a tax policy incentivizes firms to abandon the H strategy in favor of the E strategy (producing only new energy vehicles). Thus, a tax policy might be the preferable choice in this scenario.

When government intervention is strong (i.e., high subsidy or tax rates),

β_{1}^{s u b} > β_{1}^{t a x}

and

β_{2}^{s u b} < β_{2}^{t a x}

always hold. Here, a tax policy is more effective in discouraging fuel vehicle production (i.e., the F strategy), yet it is less successful in encouraging firms to choose the E strategy. In contrast, a subsidy policy achieves a different result with the same logic, prompting firms to favor the E strategy over the H strategy. Considering that the H strategy still involves the production of environmentally polluting fuel vehicles, a subsidy policy may be the better choice when the government opts for a strong intervention approach.

6. Conclusions and Discussion

6.1. Conclusions

With a large population and increasing pressure on resources and the environment, accelerating the development of the green industry of new energy vehicles is urgent for China. To promote the transformation and sustainable development of the automobile industry, the Chinese government has introduced several supportive policies for the new energy vehicle sector, such as financial subsidies for purchasing new energy vehicles and value-added tax exemptions for manufacturers. The effectiveness of these measures warrants attention. Based on this context, in this study, a theoretical model is constructed of a monopolistic automobile manufacturer capable of producing both traditional fuel vehicles and new energy vehicles. The firm’s production strategies and profit variations are analyzed under scenarios with no policy intervention and various policy interventions. The main conclusions are as follows.

Strategy choices without policy intervention: In the absence of government intervention, the firm determines its production strategy primarily based on market demand and consumer preferences. When consumer preference for new energy vehicles is high (low

β

), the firm is inclined to produce new energy vehicles (E strategy). Conversely, when consumer preference for new energy vehicles is low (high

β

), the firm opts to produce fuel vehicles (F strategy). When consumer preference is moderate (intermediate

β

), the firm chooses to produce both types of vehicles (H strategy).

Impact of subsidy policy: Under government subsidy intervention, the firm’s production strategy is influenced not only by consumer preferences and market competition, but also by the intensity of the government subsidy. Compared to no policy intervention, subsidies significantly discourage the production of fuel vehicles and promote the production of new energy vehicles. However, when the subsidy intensity exceeds a certain threshold, the firm may set extreme prices, driving the market away and eventually reverting to the F strategy of producing fuel vehicles. This result indicates that, while subsidies can promote the adoption of new energy vehicles within a certain range, excessive subsidy levels may be counterproductive and hinder sustainable development.

Impact of tax policy: The government restricts fuel vehicle production by imposing a value-added tax on fuel vehicle manufacturers. The study finds that the effects of the tax policy on the firm’s production strategy are similar to those of the subsidy policy.

Policy comparison: When government intervention is weak (low subsidy or tax rates) and competition between fuel and new energy vehicles is intense, a subsidy policy is more effective. However, when competition between fuel and new energy vehicles is moderate, a tax policy may be the better choice. When the government adopts strong intervention measures, a subsidy policy might be the more favorable option.

6.2. Value of the Work and Findings

Our findings are consistent with the literature on government policies influencing electric vehicle (EV) production, but offer new insights, especially in understanding the differentiated effects of subsidy and tax policies under various market conditions.

First, our results are consistent with the findings of Wang and Li [14], who also found that changes in consumer preferences directly influence the production strategies of firms. We further analyze the impact of moderate consumer preferences on production decisions, which has not been extensively discussed in previous studies. Our study provides new insights into the effects of moderate consumer preferences on firm-level decisions. Regarding subsidy policies, our findings confirm the results of Holtsmark and Skonhoft [16], who argued that excessively high subsidies can distort the market and hinder technological progress. We find that high subsidy levels may lead firms to revert to producing conventional vehicles, reinforcing the notion of diminishing marginal effects of subsidies. This highlights the need for policymakers to be cautious when designing subsidy programs. In terms of tax policies, Liu et al. [32] also found that tax policies have a more moderate effect, especially when the tax rate is low. In our study, tax policies promote the production of EVs by increasing the costs of conventional vehicles, but their effect is less pronounced in markets where subsidy policies are strong. We believe that the impact of tax policies is more significant under lower levels of government intervention, especially in less competitive markets. Finally, Shao et al. [19] explored the comparative effectiveness of subsidy and tax policies in competitive environments and concluded that subsidies are more effective in competitive markets. This is consistent with our study, where we find that subsidy policies are more effective in markets with intense competition between conventional and electric vehicles.

Our study offers new insights into the interaction between subsidy and tax policies. We find that the effectiveness of these policies varies across different market environments. In highly competitive markets, subsidies are more effective at promoting EV production, while in less competitive markets, tax policies may be more effective. This finding provides practical guidance for policymakers in tailoring policies to different market structures.

6.3. Limitations of the Work and Future Research

While this study provides valuable insights, it has several limitations. First, our model assumes a monopolistic market structure, but in real-world markets, multiple manufacturers may influence the effectiveness of policy interventions. Future research could explore the effects of these policies in competitive market environments. Moreover, although this study focuses on subsidies and tax policies, it does not consider other external factors, such as fluctuations in raw material prices or changes in global market demand, which may significantly impact production decisions. Future studies could incorporate these factors into the model to further enrich the analysis of policy effects.

Author Contributions

Conceptualization, Y.G. and K.C.; methodology, Y.G. and K.C.; software, Y.G. and K.C.; validation, Y.G. and K.C.; formal analysis, Y.G. and K.C.; investigation, Y.G. and K.C.; resources, Y.G. and K.C.; data curation, Y.G. and K.C.; writing—original draft preparation, Y.G. and K.C.; writing—review and editing, Y.G. and K.C.; visualization, Y.G. and K.C.; supervision, Y.G. and K.C.; project administration, Y.G. and K.C.; funding acquisition, K.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was made possible as a result of the National Natural Science Foundation of China (72472145, 72101245); the Fundamental Research Funds for Central Universities (WK2040000110 and WK204000036); the Anhui Postdoctoral Scientific Research Program Foundation (BJ20240004-002); and the General Project of the New Humanities Fund of the University of Science and Technology of China (FSSF-A-230106).

Data Availability Statement

No data was used for the research described in the article.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Proof of Lemma 1.

We will prove the case for the H strategy, with other cases being derivable in a similar manner. Under the H strategy, the firm’s profit function is as follows:

π^{o, H} = p_{e}^{o, H} ((1 - β) a - p_{e}^{o, H} - γ (p_{e}^{o, H} - p_{f}^{o, H})) + p_{f}^{o, H} (β a - p_{f}^{o, H} + γ (p_{e}^{o, H} - p_{f}^{o, H}))

The Hessian matrix with respect to

{p_{e}^{o, H}, p_{f}^{o, H}}

is given by

(\begin{matrix} - 2 (1 + γ) & 2 γ \\ 2 γ & - 2 (1 + γ) \end{matrix})

The first-order leading principal minor is negative, and the second-order leading principal minor is positive. Thus,

π^{o, H}

has a maximum with respect to

{p_{e}^{o, H}, p_{f}^{o, H}}

. By simultaneously solving

\frac{\partial π^{o, H}}{\partial p_{e}^{o, H}} = 0

and

\frac{\partial π^{o, H}}{\partial p_{f}^{o, H}} = 0

, we obtain the results presented in Table 2. □

Proof of Lemma 2.

It is straightforward to obtain the following:

\frac{\partial p_{f}^{o, H}}{\partial γ} = \frac{a (1 - 2 β)}{2 {(1 + 2 γ)}^{2}}

\frac{\partial p_{e}^{o, H}}{\partial γ} = - \frac{a (1 - 2 β)}{2 {(1 + 2 γ)}^{2}}

It is evident that the signs of these partial derivatives depend only on the sign of

1 - 2 β

. Thus, the results of Lemma 2 follow. □

Proof of Proposition 1.

When the firm chooses the E strategy, it must satisfy both

\frac{1}{4} a^{2} {(1 - β)}^{2} > \frac{a^{2} β^{2}}{4}

and

\frac{1}{4} a^{2} {(1 - β)}^{2} > \frac{a^{2} (1 - 2 β + 2 β^{2} + γ)}{4 + 8 γ}

. Solving this system of inequalities yields

β < β_{1} = \frac{\sqrt{γ (1 + 2 γ)} - 1}{2 γ - 1}

. The remaining conclusions can be derived similarly. □

Proof of Lemma 3.

This lemma can be proven in a manner similar to Lemma 1. We omit the repetitive mathematical steps. □

Proof of Proposition 2.

We provide the proof for

\frac{\partial p_{e}^{s, H}}{\partial θ} > 0

; the other cases can be derived similarly. From Table 3, we have

\frac{\partial p_{e}^{s, H}}{\partial θ} = \frac{- a γ (- 4 - 8 γ + γ^{2} (- 4 + θ) θ + β (4 + γ^{2} θ^{2} + 2 γ (4 - 2 θ + θ^{2})))}{{(4 (- 1 + θ) + 8 γ (- 1 + θ) + γ^{2} θ^{2})}^{2}}

Since the denominator is squared, we only need to focus on the numerator. Let

T = - a γ (- 4 - 8 γ + γ^{2} (- 4 + θ) θ + β (4 + γ^{2} θ^{2} + 2 γ (4 - 2 θ + θ^{2})))

Since

\frac{\partial T}{\partial β} = - a γ (4 + γ^{2} θ^{2} + 2 γ (4 - 2 θ + θ^{2})) < 0

T

is decreasing with respect to

β

. Thus, we have

T \geq T (β = 1) = 2 a γ^{2} (1 + γ) (2 - θ) θ > 0

Therefore,

T > 0

always holds, which implies

\frac{\partial p_{e}^{s, H}}{\partial θ} > 0

. □

Proof of Proposition 3.

Similar to the proof of Proposition 1, we first derive the system of inequalities that must be satisfied for the firm to choose the E strategy as follows:

\frac{a^{2} {(1 - β)}^{2}}{4 (1 - θ)} > \frac{a^{2} β^{2}}{4}

\frac{a^{2} {(1 - β)}^{2}}{4 (1 - θ)} > \frac{a^{2} (β (2 + γ θ) - (1 + γ) - β^{2} (2 - θ))}{γ^{2} θ^{2} - (4 + 8 γ) (1 - θ)}

\frac{a^{2} (β (2 + γ θ) - (1 + γ) - β^{2} (2 - θ))}{γ^{2} θ^{2} - (4 + 8 γ) (1 - θ)} > 0

Additionally, in the region where the H strategy is infeasible, the E strategy is preferred. This can be represented by the following system of inequalities:

\frac{a^{2} {(1 - β)}^{2}}{4 (1 - θ)} > \frac{a^{2} β^{2}}{4}

\frac{a^{2} (β (2 + γ θ) - (1 + γ) - β^{2} (2 - θ))}{γ^{2} θ^{2} - (4 + 8 γ) (1 - θ)} < 0

By combining these two systems of inequalities, we obtain the conditions under which the firm chooses the E strategy. Similarly, the conditions for the firm to choose the F and H strategies can be derived. □

Proof of Proposition 4.

This proposition can be proven similarly to Proposition 2 and is therefore omitted. □

Proof of Lemma 4.

This lemma can be proven in a manner similar to Lemma 1. We omit the repetitive mathematical steps. □

Proof of Proposition 5.

This proposition can be proven similarly to Proposition 2 and is therefore omitted. □

Proof of Proposition 6.

Let the parameter

θ = δ

under the subsidy policy and the parameter

t = δ

under the tax policy. This allows for a direct comparison of the equilibrium strategies across different policies. The comparison process follows the approach used in the proof of Proposition 2 and is therefore omitted. □

References

Ivanovski, K.; Hailemariam, A.; Smyth, R. The effect of renewable and non-renewable energy consumption on economic growth: Non-parametric evidence. J. Clean. Prod. 2021, 286, 124956. [Google Scholar] [CrossRef]
Guo, Q.; Liu, Y.; Cai, L. An experimental study on the potential purchase behavior of Chinese consumers of new energy hybrid electric vehicles. Front. Environ. Sci. 2023, 11, 1159846. [Google Scholar] [CrossRef]
Tang, B.; Liu, J. Development Prospect of China’s New Energy Automobile Industry. J. Beijing Univ. Technol. (Soc. Sci. Ed.) 2015, 17, 1–6. [Google Scholar]
Song, Z.; Liu, Y.; Gao, H.; Li, S. The underlying reasons behind the development of public electric buses in China: The Beijing case. Sustainability 2020, 12, 688. [Google Scholar] [CrossRef]
Haan, P.; Santonja, A.; Zaklan, A. Effectiveness and heterogeneous effects of purchase grants for electric vehicles. Environ. Resour. Econ. 2024, 88, 185–223. [Google Scholar] [CrossRef]
Yang, T.; Long, R.; Li, W.; Rehman, S.U. Innovative application of the public–private partnership model to the electric vehicle charging infrastructure in China. Sustainability 2016, 8, 738. [Google Scholar] [CrossRef]
Zheng, C. The Energy Transition in China: Mid-to Long-Term National Strategies and Prospects. In Fondazione Eni Enrico Mattei Working Papers; Paper 1284; 2019; Available online: https://services.bepress.com/feem/paper1284 (accessed on 16 January 2025). [CrossRef]
Zhang, L.; Qin, Q. China’s new energy vehicle policies: Evolution, comparison and recommendation. Transp. Res. Part A Policy Pract. 2018, 110, 57–72. [Google Scholar] [CrossRef]
Wang, X.; Huang, L.; Daim, T.; Li, X.; Li, Z. Evaluation of China’s new energy vehicle policy texts with quantitative and qualitative analysis. Technol. Soc. 2021, 67, 101770. [Google Scholar] [CrossRef]
Ashok, B.; Kannan, C.; Usman, K.M.; Vignesh, R.; Deepak, C.; Ramesh, R.; Narendhra, T.M.; Kavitha, C. Transition to electric mobility in India: Barriers exploration and pathways to powertrain shift through MCDM approach. J. Inst. Eng. (India) Ser. C 2022, 103, 1251–1277. [Google Scholar] [CrossRef]
Sekretariat Kabinet Republik Indonesia. Disaksikan Presiden Jokowi dan Presiden Xi Jinping, RI–RRT Tandatangani 8; Cabinet Secretariat of the Republic of Indonesia: Jakarta, Indonesia, 2019.
Dayem, K.; Mercier, C.; May-Ostendorp, P. Electric Vehicle Charging Control Strategies; Tech. Rep.; National Rural Electric Cooperative Association’s Business and Technology Strategies Department: Arlington, VA, USA, 2019. [Google Scholar]
Bordin, C.; Tomasgard, A. Behavioural change in green transportation: Micro-economics perspectives and optimization strategies. Energies 2021, 14, 3728. [Google Scholar] [CrossRef]
Wang, D.; Li, Y. Measuring the policy effectiveness of China’s new-energy vehicle industry and its differential impact on supply and demand markets. Sustainability 2022, 14, 8215. [Google Scholar] [CrossRef]
Sun, H.; Wan, Y.; Lv, H. System dynamics model for the evolutionary behaviour of government enterprises and consumers in China’s new energy vehicle market. Sustainability 2020, 12, 1578. [Google Scholar] [CrossRef]
Holtsmark, B.; Skonhoft, A. The Norwegian support and subsidy policy of electric cars. Should it be adopted by other countries? Environ. Sci. Policy 2014, 42, 160–168. [Google Scholar] [CrossRef]
Zhang, Y.; Song, J.; Hamori, S. Impact of subsidy policies on diffusion of photovoltaic power generation. Energy Policy 2011, 39, 1958–1964. [Google Scholar] [CrossRef]
Li, Y.; Liang, C.; Ye, F.; Zhao, X. Designing government subsidy schemes to promote the electric vehicle industry: A system dynamics model perspective. Transp. Res. Part A Policy Pract. 2023, 167, 103558. [Google Scholar] [CrossRef]
Shao, J.; Jiang, C.; Cui, Y.; Tang, Y. A game-theoretic model to compare charging infrastructure subsidy and electric vehicle subsidy policies. Transp. Res. Part A Policy Pract. 2023, 176, 103799. [Google Scholar] [CrossRef]
Lu, T.; Yao, E.; Jin, F.; Yang, Y. Analysis of incentive policies for electric vehicle adoptions after the abolishment of purchase subsidy policy. Energy 2022, 239, 122136. [Google Scholar] [CrossRef]
Srinivasan, S. Subsidy policy and the enlargement of choice. Renew. Sustain. Energy Rev. 2009, 13, 2728–2733. [Google Scholar] [CrossRef]
Kalish, S.; Lilien, G.L. Optimal Price Subsidy Policy for Accelerating the Diffusion Of Innovation. Mark. Sci. 1983, 2, 407–420. [Google Scholar] [CrossRef]
Zhao, Y.; Jian, Z.; Du, Y. How can China’s subsidy promote the transition to electric vehicles? Renew. Sustain. Energy Rev. 2024, 189, 114010. [Google Scholar] [CrossRef]
Shang, W.-L.; Zhang, J.; Wang, K.; Yang, H.; Ochieng, W. Can financial subsidy increase electric vehicle (EV) penetration—Evidence from a quasi-natural experiment. Renew. Sustain. Energy Rev. 2024, 190, 114021. [Google Scholar] [CrossRef]
Shen, N.; Deng, R.; Liao, H.; Shevchuk, O. Mapping renewable energy subsidy policy research published from 1997 to 2018: A scientometric review. Util. Policy 2020, 64, 101055. [Google Scholar] [CrossRef]
Yan, S. The economic and environmental impacts of tax incentives for battery electric vehicles in Europe. Energy Policy 2018, 123, 53–63. [Google Scholar] [CrossRef]
Arnold, J.M.; Brys, B.; Heady, C.; Johansson, Å.; Schwellnus, C.; Vartia, L. Tax Policy for Economic Recovery and Growth. Econ. J. 2011, 121, F59–F80. [Google Scholar] [CrossRef]
Wang, C.; Wang, W.; Huang, R. Supply chain enterprise operations and government carbon tax decisions considering carbon emissions. J. Clean. Prod. 2017, 152, 271–280. [Google Scholar] [CrossRef]
Chang, S.-E.; Woo, J. Are electric vehicle users willing to pay tax for charging electric vehicles? A case study of South Korea. Energy Econ. 2024, 129, 107243. [Google Scholar] [CrossRef]
Pan, W.; Shittu, E. Assessment of mobility decarbonization with carbon tax policies and electric vehicle incentives in the U.S. Appl. Energy 2025, 379, 124838. [Google Scholar] [CrossRef]
Liu, J.; Hu, C. Study on Green Supply Chain Cooperation and Carbon Tax Policy considering Consumer’s Behavior. Math. Probl. Eng. 2020, 2020, 4131936. [Google Scholar] [CrossRef]
Liu, C.; Huang, W.; Yang, C. The evolutionary dynamics of China’s electric vehicle industry—Taxes vs. subsidies. Comput. Ind. Eng. 2017, 113, 103–122. [Google Scholar] [CrossRef]
Bansal, S.; Gangopadhyay, S. Tax/subsidy policies in the presence of environmentally aware consumers. J. Environ. Econ. Manag. 2003, 45 (Suppl. S2), 333–355. [Google Scholar] [CrossRef]
Deng, L.; Tan, J.; Dai, J. Analysis of Decision-Making in a Green Supply Chain under Different Carbon Tax Policies. Mathematics 2023, 11, 4631. [Google Scholar] [CrossRef]
Yadav, P.; Kanjilal, K.; Dutta, A.; Ghosh, S. Fuel demand, carbon tax and electric vehicle adoption in India’s road transport. Transp. Res. Part D Transp. Environ. 2024, 127, 104010. [Google Scholar] [CrossRef]

Figure 1. Firm’s equilibrium strategy without policy intervention.

Figure 2. Firm’s equilibrium production strategy under subsidy policy.

Figure 3. Trend of key thresholds with subsidy.

Figure 4. Firm’s equilibrium production strategy under tax policy.

Figure 5. Comparison of subsidy and tax policies. The horizontal axis (

δ

) represents the same subsidy or tax policy, while the vertical axis (

γ

) denotes the intensity of competition.

β_{1}^{s u b}, β_{1}^{t a x}, β_{2}^{s u b}, β_{2}^{t a x}

represent the thresholds for firms to adopt strategy E under subsidy or tax policies, respectively.

Figure 5. Comparison of subsidy and tax policies. The horizontal axis (

δ

) represents the same subsidy or tax policy, while the vertical axis (

γ

) denotes the intensity of competition.

β_{1}^{s u b}, β_{1}^{t a x}, β_{2}^{s u b}, β_{2}^{t a x}

represent the thresholds for firms to adopt strategy E under subsidy or tax policies, respectively.

Table 1. Notations throughout this paper.

Notations	Description
Parameters
$i = o, s, t$	Superscript, no policy intervention, subsidy policy, and tax policy, respectively
$j = F, E, H$	Superscript, F, E, and H strategies, respectively
$f$	Subscript, FV
$e$	Subscript, EV
$a$	Market size
$β$	Consumer preferences for FV
$1 - β$	Consumer preferences for EV
$γ$	Intensity of competition
$D_{e}^{i, j}, D_{f}^{i, j}$	Demand for FVs and EVs, respectively, under $i$ policy and $j$ strategy
$θ$	Subsidy to consumers purchasing new energy vehicles
$t$	Tax rate
Decision variables
$p_{e}^{i, j}, p_{f}^{i, j}$	Prices of FVs and EVs, respectively, under $i$ policy and $j$ strategy

Table 2. Equilibrium outcomes for the firm under different strategies without policy intervention.

$j$	F Strategy	E Strategy	H Strategy
$p_{f}^{o, j}$	$\frac{a β}{2}$		$\frac{a β + a γ}{2 (1 + 2 γ)}$
$p_{e}^{o, j}$		$\frac{1}{2} a (1 - β)$	$\frac{a (1 - β) + a γ}{2 (1 + 2 γ)}$
$D_{f}^{o, j}$	$\frac{a β}{2}$		$\frac{a β}{2}$
$D_{e}^{o, j}$		$\frac{1}{2} a (1 - β)$	$\frac{1}{2} a (1 - β)$
$π^{o, j}$	$\frac{a^{2} β^{2}}{4}$	$\frac{1}{4} a^{2} {(1 - β)}^{2}$	$\frac{a^{2} (1 - 2 β + 2 β^{2} + γ)}{4 + 8 γ}$

Table 3. Equilibrium outcomes for the firm under different strategies with subsidy policy intervention.

$j$	F Strategy	E Strategy	H Strategy
$p_{f}^{s, j}$	$\frac{a β}{2}$		$\frac{a (γ (- 2 + θ) + β (- 2 + (2 + γ) θ))}{γ^{2} θ^{2} - (4 + 8 γ) (1 - θ)}$
$p_{e}^{s, j}$		$\frac{a (1 - β)}{2 (1 - θ)}$	$\frac{a (2 (1 + γ) - β (2 + γ θ))}{(4 + 8 γ) (1 - θ) - γ^{2} θ^{2}}$
$D_{f}^{s, j}$	$\frac{a β}{2}$		$\frac{a (γ (1 + γ) θ + β (- 2 - 4 γ + 2 θ + 3 γ θ))}{γ^{2} θ^{2} - (4 + 8 γ) (1 - θ)}$
$D_{e}^{s, j}$		$\frac{1}{2} a (1 - β)$	$\frac{a (1 - θ) (2 + 4 γ - β (2 + γ (4 - θ)) + γ^{2} θ)}{(4 + 8 γ) (1 - θ) - γ^{2} θ^{2}}$
$π^{s, j}$	$\frac{a^{2} β^{2}}{4}$	$\frac{a^{2} {(1 - β)}^{2}}{4 (1 - θ)}$	$\frac{a^{2} (β (2 + γ θ) - (1 + γ) - β^{2} (2 - θ))}{γ^{2} θ^{2} - (4 + 8 γ) (1 - θ)}$

Table 4. Equilibrium outcomes for the firm under different strategies with the tax policy.

$j$	F Strategy	E Strategy	H Strategy
$p_{f}^{t, j}$	$\frac{a β}{2}$		$\frac{a ((- 2 + t) γ + β (- 2 + t (2 + γ)))}{- 4 - 8 γ + t^{2} γ^{2} + t (4 + 8 γ)}$
$p_{e}^{t, j}$		$\frac{1}{2} a (1 - β)$	$- \frac{a (- 1 + t) (- 2 (1 + γ) + β (2 + t γ))}{- 4 - 8 γ + t^{2} γ^{2} + t (4 + 8 γ)}$
$D_{f}^{t, j}$	$\frac{a β}{2}$		$\frac{a (t γ (1 + γ) + β (- 2 - 4 γ + t (2 + 3 γ)))}{- 4 - 8 γ + t^{2} γ^{2} + t (4 + 8 γ)}$
$D_{e}^{t, j}$		$\frac{1}{2} a (1 - β)$	$\frac{a (- 1 + t) (2 + 4 γ + t γ^{2} + β (- 2 + (- 4 + t) γ))}{- 4 - 8 γ + t^{2} γ^{2} + t (4 + 8 γ)}$
$π_{t}^{t, j}$	$\frac{1}{4} (1 - t) a^{2} β^{2}$	$\frac{1}{4} a^{2} {(1 - β)}^{2}$	$\frac{a^{2} (1 - t) (- 1 + (- 2 + t) β^{2} - γ + β (2 + t γ))}{- 4 - 8 γ + t^{2} γ^{2} + t (4 + 8 γ)}$

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Share and Cite

MDPI and ACS Style

Ge, Y.; Chen, K. Selection of Production Strategies for New Energy Vehicles: An Analysis of the Impact of Government Intervention Policies. Systems 2025, 13, 61. https://doi.org/10.3390/systems13010061

AMA Style

Ge Y, Chen K. Selection of Production Strategies for New Energy Vehicles: An Analysis of the Impact of Government Intervention Policies. Systems. 2025; 13(1):61. https://doi.org/10.3390/systems13010061

Chicago/Turabian Style

Ge, Yingxi, and Kehong Chen. 2025. "Selection of Production Strategies for New Energy Vehicles: An Analysis of the Impact of Government Intervention Policies" Systems 13, no. 1: 61. https://doi.org/10.3390/systems13010061

APA Style

Ge, Y., & Chen, K. (2025). Selection of Production Strategies for New Energy Vehicles: An Analysis of the Impact of Government Intervention Policies. Systems, 13(1), 61. https://doi.org/10.3390/systems13010061

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

Selection of Production Strategies for New Energy Vehicles: An Analysis of the Impact of Government Intervention Policies

Abstract

1. Introduction

1.1. Research Background

1.2. Results

1.3. Literature Review

1.3.1. Research on Subsidy Policies

1.3.2. Research on Tax Policies

1.3.3. Novelty and Key Contributions

2. Model

2.1. Problem Description and Demand Structure

2.2. Case Without Government Policy Intervention

3. Analysis of Subsidy Policy

4. Analysis of Tax Policy

5. Comparison of Intervention Policies and Recommendations

6. Conclusions and Discussion

6.1. Conclusions

6.2. Value of the Work and Findings

6.3. Limitations of the Work and Future Research

Author Contributions

Funding

Data Availability Statement

Conflicts of Interest

Appendix A

References

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.