Research Article
A Blockchain-based Transparent Data Ecosystem for Cross-regional Economic Collaboration: Architecture and Scalability Analysis
@ARTICLE{10.4108/eetsis.15396, author={Xuefeng Yang and Weizu Lu}, title={A Blockchain-based Transparent Data Ecosystem for Cross-regional Economic Collaboration: Architecture and Scalability Analysis}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={13}, number={5}, publisher={EAI}, journal_a={SIS}, year={2026}, month={10}, keywords={Blockchain, Cross-regional Collaboration, Scalable Consensus, Graph Attention Network, Suppy Chain Analytics, Data Ecosystem}, doi={10.4108/eetsis.15396} }- Xuefeng Yang
Weizu Lu
Year: 2026
A Blockchain-based Transparent Data Ecosystem for Cross-regional Economic Collaboration: Architecture and Scalability Analysis
SIS
EAI
DOI: 10.4108/eetsis.15396
Abstract
INTRODUCTION: The rapid development of the digital economy has intensified the demand for efficient and trustworthy cross-regional data sharing, yet current systems suffer from data silos, trust deficits, and scalability limitations, constraining the circulation of data elements across regional boundaries. OBJECTIVES: This study proposes a blockchain-based transparent data ecosystem architecture that addresses trust establishment and intelligent analytics requirements, providing technical infrastructure for data-driven cross-regional economic decision-making in the digital economy context. METHODS: The architecture comprised four layers: a blockchain layer employing hierarchical grouped Practical Byzantine Fault Tolerance (PBFT) consensus to reduce communication complexity from O(n^2) to O(n√n), a smart contract layer implementing risk-aware access control policies, a data storage layer combining on-chain hash indexing with off-chain distributed storage, and a data analytics layer utilizing Graph Attention Networks (GAT) for supply chain prediction. Experimental evaluation was conducted on Hyperledger Fabric for scalability testing, with the SupplyGraph benchmark dataset for analytics validation. RESULTS: The proposed consensus optimization achieves 453 TPS at 100 nodes, a 206% improvement over standard PBFT, with communication overhead reduced by 90%. GAT reduces prediction error by 36.95% compared to baseline methods, with attention weight analysis confirming meaningful association patterns aligned with supply chain domain knowledge. CONCLUSION: The proposed architecture integrates trustworthy data sharing with intelligent analytics within a unified framework. The hierarchical consensus design aligns with governance structures inherent in regional economic integration, while embedding analytics within the blockchain environment enables auditable analysis processes. The results demonstrate that the architecture meets the performance requirements of large-scale cross-regional transaction systems in the digital economy environment, facilitating efficient data element circulation across administrative boundaries.
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