Abstract
As a key strategy for carbon emission reduction, electric energy substitution (EES) has gained significant attention in the global energy transition. This study investigates how policy intensity differences impact the promotion of electric vehicles (EVs), a typical EES technology in transportation. Using system dynamics theory, we constructed a flow diagram model to simulate EV promotion under varying policy scenarios—natural promotion and government-led promotion. The results highlight that high-intensity carbon tax and dual-credit policies accelerate EV adoption more effectively, with government-led promotion demonstrating greater policy potential. Additionally, combined policy tools outperform single policies in promoting EVs. This paper concludes with recommendations to enhance product usability, foster policy coordination, and design tailored rural EV promotion policies.
1. Introduction
In the context of global climate change and sustainable development, electric energy substitution has emerged as a pivotal approach to optimize energy consumption structures and reduce carbon emissions. As the core of EES technology, electric vehicles (EVs) play a critical role in transforming the transportation sector and achieving carbon neutrality goals. However, the widespread adoption of EVs is influenced by multiple factors, with policy intensity standing out as a key driver of its promotion process .
My research team and I aim to address the gap in understanding how policy intensity differences affect EV promotion under distinct scenarios—natural diffusion and government intervention. By quantifying the impact of policy tools (e.g., carbon tax and dual-credit policies), we seek to provide theoretical insights for policymakers to design more effective promotion strategies.
2. Literature Review
2.1 Policy Types and EV Promotion
Previous studies have shown that different policy types exert varying influences on EES adoption. For instance, Li et al. (2019) empirically validated the effectiveness of urban policies in promoting EES technologies in China. Chen et al. (2021) emphasized the need to distinguish product attributes when formulating promotion policies. Notably, Xia and Liu (2023) found that charging infrastructure policies significantly alleviate user inconvenience, thereby boosting EV adoption .
2.2 Policy Stability and Its Impact
While domestic research on policy stability is limited, international studies highlight its critical role. Sajid et al. (2023) revealed that stable long-term policies enhance market confidence and investment in EES, whereas unstable policies deter consumer and producer participation. Srivastava et al. (2022) echoed this, noting that policy uncertainty disrupts market stability and hinders technology adoption .
2.3 Policy Intensity and Promotion Dynamics
Research on policy intensity indicates that higher-intensity policies more effectively drive EV sales and market share. Tang et al. (2022) found that strong industrial policies accelerate EES adoption, while Guo et al. (2019) showed low-intensity policies fail to change user preferences significantly. Cheng and Li (2018) observed regional disparities in EV industry clusters due to uneven policy intensity, and Ghadge et al. (2022) emphasized the synergistic effects of combined policies in enhancing promotion efficiency .
3. Theoretical Analysis of EV Promotion under Policy Intensity Differences
3.1 Mechanisms of Policy Influence on EES Promotion
Policies shape EV promotion through two primary mechanisms: resource allocation and product usability. High-intensity incentive policies (e.g., subsidies) enhance market penetration by redirecting resources toward EV production and consumption. For example, Guo and Wang (2024) found that market penetration increases with the intensity of promotional policies. Additionally, infrastructure policies (e.g., charging network expansion) improve product usability, as shown by Bai et al. (2024), who linked grid infrastructure quality to higher user acceptance .
Restrictive policies also play a role. For instance, tightening emission standards or implementing fuel vehicle sales bans (as in Macau, China) significantly boosts EV adoption, as demonstrated by Huang et al. (2024). Similarly, strict traffic restrictions (e.g., odd-even license plate policies) in cities accelerate EV sales compared to lenient policies .
3.2 EV Promotion Policies in Practice
China has implemented several key policies to advance EV adoption. The Guidance on Promoting Electric Energy Substitution (2016) and Guidance on Vigorously Implementing Renewable Energy Substitution Actions (2024) outline policy frameworks for EES. By 2024, China’s new energy vehicle (NEV) penetration exceeded 40%, with cities like Shanghai and Shenzhen achieving 53.2% penetration through intensive fiscal incentives and preferential road access .
The charging infrastructure has also expanded rapidly, with 11.88 million chargers nationwide (49.4% YoY growth), including 3.39 million public chargers (34.3% YoY) and 8.49 million private chargers (56.4% YoY growth under subsidy policies) . These developments highlight the critical role of policy intensity in driving EV ecosystem development.
4. Model Construction: System Dynamics Approach for EV Promotion
4.1 Scenario Presets for EV Promotion
- Government-led Promotion Scenario:
This scenario assumes minimal organic promotion, with the government actively engaging potential users. The promotion process is modeled using an unrestricted growth model (population explosion model), where the target is set at 1000‰ (100%) penetration . - Natural Promotion Scenario:
Here, EVs diffuse organically through social networks, with the government playing a supporting role. Using an information diffusion (virus spread) model, the target is set at 800‰ (80%) penetration, as the model approaches but never reaches 100% .
4.2 Flow Diagram Model and Variables
The system dynamics model includes two stock variables: EV promotion progress and patent applications, and two rate variables: EV growth and patent increase. Key variables and their assignments are summarized in Table 1 and Table 2.
Table 1: Variable Assignments for Promotion Scenarios
| Variable Name | Assignment | Scenario Description |
|---|---|---|
| Promotion Scenario | 0 or 1 | 0 = government-led; 1 = natural |
| Scenario Promotion Coefficient | IF THEN ELSE(Scenario=1, 0.7, 1.5) | Varies by scenario |
| Owner Communication Coefficient | IF THEN ELSE(Scenario=1, 0.2, 0.1) | Varies by scenario |
Table 2: Stock and Rate Variable Equations
| Variable Name | Equation |
|---|---|
| EV Promotion Progress | Progress(T0) + EV Growth(T1) |
| Patent Applications | Applications(T0) + Patent Increase(T1) |
| EV Growth | IF THEN ELSE(Scenario=1, α × Purchase Tendency × β × (1000 – Progress), α × Purchase Tendency × (Progress + 10) × β) where α = Scenario Coefficient, β = Owner Coefficient |
| Patent Increase | IF THEN ELSE(Time > 60, 0.08, 0.13) |
The EV growth rate equation reflects the core model logic: in natural promotion, growth depends on remaining market potential (1000 – Progress), while in government-led promotion, it depends on current progress and a base growth rate (Progress + 10) .
4.3 Auxiliary Variables and Theoretical Foundations
The model incorporates 22 auxiliary variables and 11 constant variables, drawing from the Technology Acceptance Model (TAM) for constructs like Purchase Tendency and Product Confidence Index. Economic theories underpin variables such as Cost Comparison and Consumer Price Perception . Key auxiliary variable equations include:
- EV Purchase Tendency = Product Confidence Index + Cost Comparison + Private EV Base Growth Rate
- Cost Comparison = (Internal Combustion Vehicle (ICV) Purchase Cost – Consumer Price Perception) × 0.5
- Product Confidence Index = (Product Usability + Charging Piles × Charging Service Perception Coefficient + EV Mobility Convenience) / 3
5. Simulation Results and Analysis
5.1 Scenario Design and Policy Intensity Settings
We designed six scenarios to test the impact of carbon tax and dual-credit policy intensities, as shown in Table 3 and Table 4.
Table 3: Scenario Types and Adjustment Strategies
| Scenario | Promotion Scenario | Policy Tool |
|---|---|---|
| 1 | Government-led | Carbon tax |
| 2 | Natural | Carbon tax |
| 3 | Government-led | Dual-credit |
| 4 | Natural | Dual-credit |
| 5 | Government-led | Combined |
| 6 | Natural | Combined |
Table 4: Policy Intensity Levels (High, Medium, Low)
| Scenario | Policy Variable | High | Medium (Baseline) | Low |
|---|---|---|---|---|
| 1, 2 | Carbon tax | 0.8 | 0.5 | 0.2 |
| 3, 4 | Dual-credit | 0.2 | 0.085 | 0.02 |
5.2 Impact of Carbon Tax Intensity
- Government-led Scenario: High carbon tax (0.8) significantly accelerates EV promotion, with growth rates 50% higher than low carbon tax (0.2). The promotion process completes faster due to enhanced cost incentives for EVs over ICVs .
- Natural Scenario: Carbon tax impact is more pronounced in the mid-term. High intensity boosts promotion rates by 15% compared to low intensity, as organic market signals gradually reflect policy effects .
5.3 Impact of Dual-Credit Policy Intensity
- Government-led Scenario: High dual-credit intensity (0.2) drives EV purchase tendency, resulting in promotion rates 60% higher than low intensity (0.02). Low and baseline intensities fail to meet the promotion target, underscoring the need for strong policies .
- Natural Scenario: Similar to carbon tax, high dual-credit intensity increases diffusion rates by 20% versus low intensity, though the effect is less dramatic than in government-led promotion .
5.4 Combined Policy Effects
- Government-led Scenario: The high carbon tax-high dual-credit combination is the only scenario achieving the 1000‰ target. Other combinations show mixed results, with some lagging due to policy trade-offs .
- Natural Scenario: The high-high combination outperforms single policies by 50%, highlighting synergistic effects. Conversely, low-low combinations severely delay promotion .
Table 5: Summary of Promotion Rate Differences by Policy Intensity
| Policy Type | Scenario | High vs. Low Intensity Rate Difference |
|---|---|---|
| Carbon tax | Government-led | 50% faster |
| Natural | 15% faster | |
| Dual-credit | Government-led | 60% faster |
| Natural | 20% faster | |
| Combined (High-High) | Natural | 50% faster than single high policies |
6. Conclusions and Recommendations
6.1 Key Findings
- Policy Intensity Impacts: Higher carbon tax and dual-credit intensities consistently accelerate EV promotion, with government-led scenarios showing more pronounced effects (50-60% rate differences) versus natural scenarios (15-20%) .
- Policy Synergy: Combined policy tools outperform single policies, especially in government-led promotion, where the high-high combination achieves full penetration .
- Scenario Differences: Policy effects vary between natural and government-led promotion, with the latter better at unleashing policy potential .
6.2 Practical Recommendations
- Enhance Product Usability for Natural Promotion:
- Improve EV technology (e.g., range, charging speed) and user experience through smart charging networks.
- Invest in lightweight materials and advanced driving systems to boost market competitiveness .
- Foster Policy Coordination:
- Integrate fiscal incentives, infrastructure support, and regulatory measures to create synergistic effects.
- Implement dynamic monitoring to adjust policies based on market feedback and technological progress .
- Tailor Rural EV Promotion Policies:
- Design targeted subsidies and after-sales services for rural markets.
- Prioritize charging infrastructure development in counties and villages to address accessibility gaps .
7. Future Research Directions
This study provides a foundation for exploring policy interactions in broader EES contexts. Future work could:
- Investigate regional policy disparities across different economies.
- Incorporate emerging technologies (e.g., vehicle-to-grid) into system dynamics models.
- Analyze long-term policy effects on EV supply chains and circular economy practices.