Model and Analysis of Coupled Innovation in Green Industry Clusters Based on Network Science

Abstract

In light of rising environmental issues and the need for sustainable development, green sector clusters have emerged as essential hubs for encouraging innovation and accelerating the transition to a more sustainable economy. Using ideas from network science and computer modeling, this paper provides a complete analysis of coupled innovation among green industrial clusters. The study begins by creating a network representation of green industrial clusters, which includes interconnected enterprises, research institutes, government agencies, and other stakeholders. Network analysis tools are then used to understand the structural features and dynamics of these clusters, such as community detection algorithms that find cohesive subgroups and influential individuals in the network. The study then applies agent-based modeling tools to replicate the process of coupled innovation within green industrial clusters. Agents are individual actors within clusters who engage inside the network, participating in collaborative activities such as knowledge sharing, technology transfer, and joint research and development. By replicating these interactions, the project hopes to understand the mechanisms driving innovation inside the clusters and evaluate the influence of various methods and interventions on innovation results. The study's findings provide useful insights into the aspects that influence innovation in green industrial clusters, such as network structure, collaborative dynamics, and policy interventions. Key findings emphasize the importance of network centrality, community structure, and research cooperation intensity in generating innovation outcomes in these clusters. Additionally, the study has practical implications for policymakers, industry stakeholders, and researchers working to promote sustainable innovation methods in green sector clusters.