Abstract
Cultivating and building world-class advanced manufacturing clusters is a crucial strategic planning for countries in the new era. China’s power battery industry has captured a significant share of the global market, and it has emerged as one of the most promising national strategic emerging industries with the potential to become a world-class cluster. However, most regions with power battery production capacities face issues such as weak and unsustainable innovation capabilities, hindering their competitiveness and aspirations to become world-class clusters. This dissertation examines the construction and operation of an innovation ecosystem within the power battery industry cluster from a management perspective, drawing upon concepts of open and collaborative innovation from innovation management theory. It employs social network analysis and system dynamics to explore the essential components and operational pathways within the ecosystem. The findings provide valuable insights for regions to enhance their power battery industry cluster innovation ecosystems and boost innovation momentum.
1. Introduction
1.1 Research Background
The global power battery market has expanded rapidly in recent years, with China accounting for a substantial portion of this growth. The total installed capacity of power batteries worldwide reached 517.9 GWh in 2022, with China holding a 60.4% share of the market. This. This remarkable achievement stems from the swift development of power battery clusters in cities like Changzhou and Yibin, which have enhanced their production capacity and technological sophistication. Consequently, China’s power battery industry stands poised to evolve into a world-class cluster.
National strategies, such as those outlined in China’s “19th and 20th National Congress Reports,” emphasize fostering world-class advanced manufacturing clusters and integrating strategic emerging industries, including new energy, to drive economic growth. The Ministry of Industry and Information Technology of China has also issued guidelines aimed at accelerating the development of a new. The Ministry of Industry and Information Technology of China has also issued guidelines aimed at accelerating the development of a new energy vehicle industry, further underscoring the importance of power batteries.
1.2 Research Purpose and Significance
1..
1.2 Research Purpose and Significance
1.2.1 Research Purpose
This dissertation aims to:
- Analyze the innovation ecosystem within Changzhou’s power battery industry cluster and depict its innovation cooperation network using social network analysis.
- Based on the analysis, construct a comprehensive framework for the power battery industry cluster’s innovation ecosystem.
- Explore the operational pathways within the ecosystem using system dynamics and verify the rationality of the framework.
- Provide policy recommendations to enhance the innovation ecosystem and boost regional economic development.
1.2.2 Research Significance
Theoretical Significance:
- Expanding Innovation Ecosystem Research: This study contributes to the limited existing literature on the innovation ecosystem of power battery industry clusters. By analyzing the ecosystem’s components and operational pathways, it fills gaps in the understanding of collaborative innovation within industry clusters.
Practical Significance:
- Guiding Policy Formulation: The findings offer practical insights for policymakers in devising strategies to bolster innovation within power battery industry clusters, thereby promoting regional economic growth and sustainability.
- Boosting Competitiveness: By enhancing the innovation ecosystem, this research aims to strengthen the competitive position of China’s power battery industry on the global stage.
2. Literature Review and Theoretical Framework
2.1 Definition of Power Battery Industry Cluster
The power battery industry encompasses the development, production, and application of rechargeable lithium-ion batteries primarily used in electric vehicles. Based on Michael Porter’s definition of industrial clusters and the Ministry of and the Ministry of Industry and Information Technology’s guidelines on power batteries, a power battery industry cluster can be defined as a geographically concentrated, a power battery industry cluster can be defined as a geographically concentrated group of core enterprises, supporting enterprises, government agencies, research institutions, and other stakeholders involved in the power battery value chain.
2.2 Innovation Ecosystem Theory
The concept of an innovation ecosystem originates from natural ecosystems, where diverse species interact and coexist. Analogously, an innovation. Analogously, an innovation ecosystem comprises various actors, such as enterprises, research institutions, governments, and financial institutions, that collaborate to drive innovation. This ecosystem emphasizes. This ecosystem emphasizes open and collaborative innovation, fostering knowledge sharing and technology transfer.
2.3 System Dynamics Theory
System dynamics is a methodology that simulates.
2.3 System Dynamics Theory
System dynamics is a methodology that simulates the behavior of complex systems over time, allowing for the identification of causal relationships and feedback loops. By modeling the system dynamics, researchers can. By modeling the system dynamics, researchers can better understand its operational pathways and predict future trends. This approach is particularly useful in analyzing innovation ecosystems, which are inherently complex and dynamic.. This approach is particularly useful in analyzing innovation ecosystems, which are inherently complex and dynamic.
3. Analysis of Power Battery Industry Cluster Innovation Ecosystem
3.1 Overview of Changzhou’s Power Battery Industry Cluster
Changzhou, located in Jiangsu Province, China, has emerged as a leading power battery industry cluster. It boasts over 100 power battery and related enterprises, with a total industrial output exceeding RMB 100 billion in 2022. Key enterprises such as CAT. Key enterprises such as CATL, ZEEV, and Svolt have established significant presences in Changzhou, contributing to its position as a global leader in power battery production capacity and installed capacity.
3.2 Social Network Analysis of the Innovation Cooperation Network
Using Gephi software, the innovation cooperation network within Changzhou’s power battery industry cluster was depicted based on patent co-application data and collaboration agreements. The network comprises 65 nodes. The network comprises 65 nodes (representing enterprises, universities, and research institutions) and 216 edges (representing collaboration ties). Key network metrics are summarized in Table 1.
Table 1: Network Metrics of Changzhou’s Power Battery Industry Cluster Innovation Cooperation Network
| Metric | Value |
|---|---|
| Number of Nodes | 65 |
| Number of Edges | 216 |
| Network Density | 0.104 |
| Network Diameter | 4 |
| Average Clustering Coefficient | 0.454 |
| Average Path Length | 2.282 |
The network’s low density and short diameter indicate a sparse but efficiently connected system, with most nodes reachable within a few steps. The high average clustering coefficient further underscores the presence of tightly knit collaboration clusters within the network.
3.3 Key Nodes and Collaboration Patterns
Through social network analysis, key collaboration patterns emerged. Core enterprises like CATL and ZEEV exhibit high centrality measures, signifying their pivotal roles in facilitating knowledge transfer and collaboration. Universities and research institutions, such as Changzhou University and the Jiangsu Provincial Key Laboratory of Advanced Power Batteries, also demonstrate strong collaboration ties.
4. Framework Construction of the Innovation Ecosystem
4.1 Identification of Key Components
Based on the social network analysis and a review of relevant literature, the key components of the power battery industry cluster’s innovation ecosystem were identified and categorized into three modules: supply, demand, and support .
4.2 Supply Module
The supply module encompasses enterprises’ research and development (R&D) departments, universities, and research institutions that generate new technologies and patents. Key factors influencing this module include R&D investment, talent pools, and the number of research platforms.
4.3 Demand Module
The demand module comprises power battery enterprises’ production departments and downstream applications such as new energy vehicle manufacturers. This module is driven by market demand for power batteries, which influences product innovation and commercialization.
4.4 Support Module
The support module provides the infrastructure necessary for innovation, including government policies, financial support, public service platforms, and intellectual property protection. Key factors in this module are government incentives, tax benefits, and the availability of testing and certification facilities.
5. Modeling the Innovation Ecosystem using System Dynamics
5.1 System Dynamics Model Construction
A system dynamics model was constructed using Vensim PLE software to analyze the operational pathways and feedback loops within the power battery industry cluster’s innovation ecosystem. The model incorporated key variables and causal relationships identified through literature review and expert interviews.
5.2 Causal Loop Diagram
The causal loop diagram illustrates the interactions among the various components of the ecosystem . It highlights positive and negative feedback loops that influence the overall level of innovation within the cluster.
5.3 Stock and Flow Diagram
The stock and flow diagram provides a more detailed visualization of the dynamic behavior of the system. It includes state variables (stocks), rate variables (flows), and auxiliary variables that govern the system’s evolution over time.
5.4 Model Equations
Model equations were formulated based on empirical data and expert judgments. Examples include:
- R&D Investment (State Variable):
R&D_Investment = INTEG(Annual_Change_in_R&D_Investment, Initial_R&D_Investment) - Number of Patents (State Variable):
Number_of_Patents = INTEG(Annual_Number_of_New_Patents, Initial_Number_of_Patents) - Market Demand (Auxiliary Variable):
Market_Demand = f(GDP_Growth, Consumer_Preferences, Policy_Support)
6. Model Validation and Simulation Results
6.1 Model Validation
The model was validated through historical data comparison and expert review. Historical data on R&D investment, patent filings, and market demand were used to calibrate the model parameters. The results demonstrated a close match between the simulated and actual values, confirming the model’s reliability.
6.2 Simulation Results
The model was simulated over a 10-year horizon (2023-2033) to analyze the ecosystem’s dynamics. Key findings include:
- Increasing R&D Investment: As R&D investment grows, so does the number of patents filed and the level of innovation within the cluster.
- Market Demand Drivers: Market demand for power batteries is influenced by GDP growth, consumer preferences, and government policies.
- Policy Impact: Government incentives and tax benefits significantly boost R&D investment and market demand.
Table 2: Simulation Results of Key Indicators
| Year | R&D Investment (Million CNY) | Number of Patents | Market Demand (GWh) |
|---|---|---|---|
| 2023 | 5,000 | 200 | 100 |
| 2028 | 8,000 | 400 | 180 |
| 2033 | 12,000 | 700 | 300 |
7. Policy Recommendations
7.1 Enhance R&D Investment
Governments should increase financial support for R&D activities within the power battery industry cluster. This includes providing grants, tax incentives, and low-interest loans to encourage enterprises to invest more in innovation.
7.2 Foster Collaboration
Facilitate collaboration among enterprises, universities, and research institutions through joint R&D projects, technology transfer programs, and personnel exchanges. Establish innovation alliances to pool resources and expertise.
7.3 Strengthen Market Demand
Promote the adoption of electric vehicles through subsidies, tax rebates, and charging infrastructure development. Enhance consumer awareness of the benefits of electric vehicles to stimulate market demand.
7.4 Improve Intellectual Property Protection
Strengthen intellectual property laws and enforcement mechanisms to protect innovators’ rights. Establish specialized IP service platforms to provide legal assistance and dispute resolution services.
7.5 Build Public Service Platforms
Develop public service platforms for testing, certification, and standardization to support innovation activities. Offer training programs for entrepreneurs and technicians to upgrade skills and knowledge.
8. Conclusion
This dissertation examined the construction and operation of the innovation ecosystem within China’s power battery industry cluster. Through social network analysis and system dynamics modeling, the study identified key components and operational pathways that drive innovation within the ecosystem. The findings offer valuable insights for policymakers and industry stakeholders seeking to enhance the competitiveness of China’s power battery industry on the global stage.
Future research could explore the impact of international collaboration, environmental regulations, and emerging technologies on the innovation ecosystem. Additionally, longitudinal studies tracking the ecosystem’s evolution over time could provide further insights into its resilience and adaptability.