In recent years, the global shift toward sustainable development and carbon neutrality goals has positioned the electric car industry as a pivotal sector for industrial upgrading. In China, the “China EV” market has experienced rapid growth, driven by national strategies such as the “New Energy Vehicle Industry Development Plan (2021–2035).” This plan emphasizes the deep integration of the industry chain and innovation chain to overcome key technological bottlenecks. However, the “China EV” sector still faces challenges, including immature core technologies and low efficiency in industrial chain coordination. The fusion network formed by the industry chain and innovation chain involves multiple heterogeneous nodes, where any disturbance can significantly impact the system, making it a typical complex network. Understanding how to achieve technological breakthroughs and industrial efficiency through “dual-chain” synergy has become a central issue for academia and industry.
Existing empirical studies on dual-chain integration primarily focus on spatial spillover effects, synergy efficiency, key node identification, and policy drivers. For instance, some researchers have used spatial econometric models to verify the spatial spillover effects of dual-chain integration on green innovation, finding that policy support can enhance cross-regional technology diffusion efficiency by 15%–20%. Others have applied social network analysis to construct collaborative industry chain innovation networks, demonstrating the acceleration effect of upstream-downstream connections on innovation implementation. In Shaanxi Province, under policy guidance, it has been confirmed that dual-chain integration requires breaking resource barriers between the innovation chain and industry chain through technology-sharing platforms. Studies on emerging industries have shown that regional venture capital optimizes the input end of the innovation chain, achieving synergistic growth in patent numbers and market value. Multi-case analyses indicate that policies integrating innovation resources in the industry chain can overcome bottlenecks such as insufficient corporate influence and shortages of high-end talent, improving collaborative innovation efficiency by 28%. Additionally, digital economy policies enhance the “technology push” of the innovation chain, indirectly driving the integration of the industry and innovation chains. However, most empirical research remains limited to statistical descriptions of cooperative relationships, lacking systematic modeling. The impact of heterogeneous node capabilities—such as research institutions with strong innovation but weak industrialization capabilities, and manufacturing firms with strong industrialization but weak innovation capabilities—on network evolution has not been quantitatively analyzed.
Based on complex network theory, scholars have explored dual-chain integration from multiple perspectives. For example, exponential random graph models have been used to quantify the connection preferences among enterprises, universities, and research institutes, revealing that differences in entity types between the industry and innovation chains enhance the stability of cooperative relationships. Hierarchical supply chain network dynamic evolution models have uncovered the “lifecycle effect” of node exits during the transformation of the electric car industry chain, showing that node survival time is significantly positively correlated with technological innovation capability. By introducing edge benefit indicators into the electric car industry chain network, it has been found that high-benefit edge aggregation accelerates technology diffusion, but this ignores the empowering effect of innovation chain nodes on industry chain nodes and the impact of dynamic elimination of inefficient nodes on network efficiency. Analyses of the dynamic evolution characteristics of the electric car industry chain have demonstrated the significant influence of policy drivers on network topology, leading to the proposal of dynamic preferential attachment mechanisms. Policy-technology synergy network models under carbon neutrality goals have revealed the guiding role of subsidy and standard policies in the dynamic adaptation of dual chains. Nevertheless, research on the evolutionary modeling of the fusion network between the industry chain and innovation chain for electric cars remains scarce. Some studies have built “network of networks”-type innovation networks for the electric car industry, quantifying the synergy of upstream, midstream, and downstream sub-networks through composite system coordination degree models, and found that dependent edges reduce system disorder, but they did not deeply analyze the synergistic adaptation mechanism of heterogeneous nodes in the fusion network. Others have constructed electric car supply chain networks from a complex network perspective, identifying scale-free and small-world characteristics, but they focused only on the single industry chain without incorporating the technology spillover effects of innovation chain nodes or dynamically simulating real-time node addition and elimination.
This paper addresses these gaps by constructing a policy-driven dynamic evolution model for the dual-chain fusion network of electric cars in China. We distinguish between four types of industry nodes (raw materials, core components, manufacturers, and charging/aftermarket services) and three types of innovation nodes (universities, research institutes, and innovation platforms). Node capabilities are characterized by “innovation index” and “industry index” dimensions, simulating the synergistic logic of “technology supply-demand traction” across chains. We introduce “preferential attachment rules” and “competitive elimination mechanisms” to quantify the combined effects of technological competition and policy drivers. Macro and micro policy coefficients are set to reveal the nonlinear driving effects of policies on network scale and integration degree through simulation. The model is validated using multi-regional data, with error rates controlled below 5%. This provides a new theoretical framework for understanding the synergistic evolution of the industry and innovation chains, offering actionable strategies for policymakers and enterprises.
The evolution characteristics of the electric car dual-chain fusion network include policy-driven dynamics, dynamic openness, hierarchical heterogeneity, ecological integration, and connection preferences. Policy drivers are evident as the electric car industry, being a national strategic focus, accelerates technology transformation and industrial upgrading under multi-dimensional policy guidance, forming a “policy-innovation-industry” synergistic闭环. Dynamic openness arises from the coverage of diverse technologies like intelligent networking and cloud computing, leading to dynamic node flow (new entities joining/inefficient entities exiting) driven by technological iterations and market uncertainties, ensuring network adaptability. Hierarchical heterogeneity manifests in the industry chain, where upstream raw material enterprises focus on technological breakthroughs, while downstream manufacturers emphasize规模化 production capacity and market response efficiency; in the innovation chain, universities and research institutes concentrate on basic technology R&D, while innovation platforms偏向 application transformation and industrial adaptation. Both chains exhibit gradients of “strong innovation-weak industrialization” and “strong industrialization-weak innovation,” forming a synergistic adaptation mechanism through capability complementarity. Ecological integration involves innovation as the core link, covering the entire lifecycle from R&D to service, where the innovation chain drives industry chain development through technological breakthroughs, and industry chain demands force innovation chain upgrades. Connection preferences indicate that industry nodes优先连接 high-industrialization-capability nodes to ensure supply chain stability, while innovation nodes倾向连接 high-innovation-capability entities to accelerate technology diffusion. Cross-chain connections show industry端偏好 high-innovation nodes and innovation端偏好 high-industrialization nodes.
Compared to existing network models, our model incorporates node type differentiation, macro and micro policy coefficients, dynamic elimination mechanisms, and integration degree metrics, as summarized in Table 1.
| Comparison Dimension | Existing Representative Models | This Model |
|---|---|---|
| Node Coverage | Only industry chain nodes (e.g., supply chain networks without innovation chain) | 4 types of industry nodes and 3 types of innovation nodes |
| Policy Drive Design | Qualitative analysis of innovation ecosystem evolution dynamics without quantified policy indicators | Introduction of macro policy G and micro policy g coefficients, with node competition elimination rules |
| Dynamic Evolution Mechanism | Static analysis of sub-network characteristics without dynamic node performance | Dynamic preferential attachment based on threshold H, bidirectional connections from innovation to industry and vice versa |
| Fusion Synergy Definition | Analysis of topological features without cross-chain indicators | Integration degree = number of cross-chain connections / total connections |
| Empirical Validation Scope | Single-region data or no empirical validation | Long-term data validation from multiple regions (e.g., Shaanxi, Yangtze River Delta, Pearl River Delta) |
To abstract the nodes in the electric car dual-chain fusion network, we categorize industry nodes into raw material suppliers (e.g., high-energy-density battery providers), core component suppliers (e.g., motor manufacturers), manufacturers (e.g.,整车 assembly), and charging/aftermarket service points (e.g., fast-charging technology providers). Innovation nodes include universities, research institutes, and innovation platforms. Each node is assigned an innovation index $\alpha_i$ and an industry index $\beta_i$, both ranging from (0,1), generated from normal distributions to reflect realistic capability distributions. Other parameters include node importance $\tau_i$ (based on betweenness centrality), node openness $\lambda_i$ (based on degree), and node age $Y_i$. Policy influence is captured by a macro coefficient $G$ (uniformly distributed in (0,1)) and micro coefficients $g^D$, $g^S$, $g^M$, $g^E$, $g^U$ for each node type (normally distributed in (0,1)).
The dynamic evolution of the fusion network follows these steps: Initialization at time $T=0$ with 15 nodes (2 manufacturers, 3 core components, 4 raw materials, 4 aftermarket services, 2 innovation nodes). At each time step, a new node is generated based on an innovation node $U_i$, with its type determined by a threshold $H$ calculated as:
$$H = \alpha_{U_i} \cdot \beta_{U_i} \cdot \lambda_{U_i} \cdot \tau_{U_i} \cdot (1 – Y_{U_i})$$
where $H$ partitions define node types: $H < H_1$ for innovation nodes, $H_1 \leq H < H_2$ for raw material nodes, $H_2 \leq H < H_3$ for aftermarket service nodes, $H_3 \leq H < H_4$ for core component nodes, and $H \geq H_4$ for manufacturer nodes. Based on empirical data from Shaanxi Province, the thresholds are set as $H_1=0.38$, $H_2=0.46$, $H_3=0.57$, $H_4=0.64$. Concurrently, industry nodes generate new nodes with probabilities influenced by policy coefficients and node attributes. For example, the probability of generating an innovation node from an industry node $I_i$ is:
$$P_{i,c}^* = \frac{1}{1 + \exp(-\theta \cdot G \cdot g \cdot Y_i \cdot \lambda_i \cdot \alpha_i)}$$
where $\theta$ is a decay coefficient. New nodes are attached to the network via preferential attachment. For an innovation node $U_i$, the connection probability to an industry node $I_i$ is:
$$P_{U_i,I_i} = G \cdot g^U \cdot \tau_{I_i} \cdot \lambda_{I_i} \cdot \beta_{I_i} \cdot \alpha_{I_i}^{-1}$$
For an industry node $I_i$, the connection probability to other nodes $J_j$ is:
$$P_{I_i,J_j} = G \cdot g^I \cdot \tau_{J_j} \cdot \lambda_{J_j} \cdot \beta_{J_j}$$
Cross-chain connection probability is defined as the maximum of the above probabilities. Finally, a competitive elimination mechanism removes the node with the lowest competition strength $Q_i$ in its category at each time step, where $Q_i$ is calculated as:
$$Q_i = \lambda_i \cdot \beta_i \cdot \tau_i \cdot \alpha_i \cdot Y_i^{-1}$$
This ensures that nodes with low innovation, industry capability, or age are淘汰, optimizing the network toward high innovation and industrialization.
We designed 12 simulation schemes to analyze the impact of different parameters, categorized by macro policy coefficient $G$ ranges: (0.0,0.4), (0.4,0.7), and (0.7,1.0). Within each range, four schemes with varying node innovation indices $\alpha_i$, industry indices $\beta_i$, and micro policy coefficients $g$ were tested. For example, in the $G \in (0.0,0.4)$ range, Scheme 1 had the lowest $\alpha_i$ and $\beta_i$ values, while Scheme 4 had the highest. Simulations were run for $T=500$ time steps, with 10 independent runs averaged for analysis.
The simulation results show that the network degree distribution follows a power-law characteristic, with power-law exponents $\alpha$ ranging from 1.426 to 1.478, confirming scale-free network properties. The betweenness probability distribution indicates that most nodes have low betweenness values (concentrated in [0,0.05]), suggesting that only a few nodes act as hubs. The average clustering coefficient $R_j$ increases with higher innovation and industry indices, from 0.011 in Scheme 1 to 0.019 in Scheme 4, indicating a denser network. Global efficiency $R_e$, defined as the average inverse shortest path length, improves over time, with Scheme 1 showing a 19% increase from $T=100$ to $T=500$, and higher schemes demonstrating greater efficiency. The average shortest path length $R_l$ decreases over time, with Scheme 1 reducing by 8% and Scheme 4 by 4%, indicating enhanced connectivity. Integration degree $R_n$, the ratio of cross-chain connections to total connections, increases significantly, from 31% in Scheme 1 to 46% in Scheme 4, highlighting improved synergy between chains.
The impact of macro policy coefficient $G$ is substantial. For schemes with weak node capabilities, increasing $G$ from (0.0,0.4) to (0.7,1.0) raises the average degree by 17%, global efficiency by 29%, and integration degree by 25%. For schemes with strong node capabilities, similar improvements are observed: average degree increases by 15%, global efficiency by 26%, and integration degree by 22%. Micro policy coefficients $g$ also play a crucial role; for instance, a 0.1 increase in $g$ can lead to a 15% rise in cross-chain connections. These findings underscore the importance of policy drivers in fostering electric car industry growth.
To validate the model, we collected real-world data from 2015 to 2024 for Shaanxi Province, Yangtze River Delta, and Pearl River Delta regions on electric car industry and innovation chains. The data include node counts, edge numbers, average degree, average shortest path length, average clustering coefficient, and integration degree. For example, in Shaanxi Province, node count grew from 50 in 2015 to 900 in 2024, average degree from 2.40 to 5.00, average shortest path length from 5.20 to 3.30, and integration degree from 0.20 to 0.68. Similar trends were observed in the Yangtze River Delta and Pearl River Delta, with the latter showing the highest integration degree (0.80 in 2024). Comparing the 12 simulation schemes to 2024 data, error rates for key metrics (e.g., average degree, integration degree) were below 5% for optimal schemes in each region. For instance, in Shaanxi, Scheme 7 had an average degree error rate of 1.0%, clustering coefficient error rate of 2.8%, and integration degree error rate of 4.5%. In the Yangtze River Delta, Scheme 11 achieved error rates below 5%, and in the Pearl River Delta, Scheme 10 had the lowest errors. This validation confirms the model’s effectiveness in simulating the long-term evolution of China EV dual-chain fusion networks.
In conclusion, this study develops a policy-driven evolutionary model for the dual-chain integration of electric cars in China, based on complex network theory. The model captures the dynamic synergy between industry and innovation chains, incorporating heterogeneous nodes, policy coefficients, and competitive mechanisms. Simulation results reveal scale-free network characteristics, with power-law exponents between 1.426 and 1.478. Macro policy coefficient $G$ significantly drives network expansion, while node innovation index $\alpha_i$ and industry index $\beta_i$ enhancements reduce average shortest path length by 4%–8% and increase integration degree by 31%–46%. Micro policy coefficients $g$ optimize cross-chain connections, with a 0.1 increase boosting connections by 15%. Empirical validation using multi-regional data shows error rates under 5%, demonstrating the model’s robustness.
Based on these findings, we recommend three policy actions: First, establish a dynamic monitoring platform for electric car dual-chain integration to quantify node capabilities and network metrics, enabling tailored subsidies and resource allocation. For example, in Shaanxi, focus on补齐 innovation短板 in raw materials and core components; in the Yangtze River Delta, enhance cross-regional collaboration; and in the Pearl River Delta, strengthen market-driven innovation. Second, implement node dynamic elimination and replenishment mechanisms based on competition strength $Q_i$, reallocating resources from inefficient nodes to high-potential ones. National coordination offices should facilitate inter-regional policy alignment to reduce development disparities. Third, apply differentiated node empowerment strategies by adjusting micro policy coefficients $g$ for specific node types. In Shaanxi, increase $g^D$ for raw material nodes and $g^S$ for core components to foster innovation-chain-driven industrialization; in the Yangtze River Delta, raise $g^M$ for manufacturers to support smart manufacturing and $g^U$ for innovation nodes to promote technology transfer; in the Pearl River Delta, boost $g^S$ for core components to accelerate pilot testing and $g^E$ for aftermarket services to encourage new business models. These measures will strengthen key technological innovation and industrial chain coordination, providing a reference for policy-making and industrial practice in the China EV sector.