Legal analysis reveals severe treaty conflicts in China's mandatory data-sharing framework, suggesting balanced reforms to protect foreign investments while preserving domestic data governance.
The booming development of open science and the digital economy has rendered scientific data a core productive factor and a legally protected investment asset under international investment agreements (IIAs). While China has established a nationwide scientific data-sharing system to advance data circulation and technological innovation, the mandatory data disclosure mechanisms, frequent policy adjustments, and imperfect data quality control embedded in this framework generate inherent tensions with IIA investment protection obligations. This paper defines its core research focus on normative conflicts between China’s scientific data-sharing framework and IIAs, relevant investment arbitration risks, and balanced solutions that reconcile international treaty compliance with China’s legitimate data regulatory authority. Drawing on doctrinal legal analysis, normative comparison, and arbitral case review, it systematically examines legal frictions between domestic data-sharing rules and IIA provisions. It first clarifies scientific data’s dual attributes as valuable economic assets and protected investments, then identifies three key dispute risks: data IP infringement and asset depreciation from mandatory sharing, rising compliance costs and frustrated investor expectations due to abrupt regulatory changes, and investment losses caused by uncorrected erroneous shared data. Further, it analyzes legal restrictions imposed by core IIA clauses that substantially curtail China’s domestic data governance autonomy. To balance regulatory sovereignty and international treaty compliance, this study proposes targeted optimization paths: clarifying the boundary of mandatory data sharing to protect data intellectual property rights, standardizing framework adjustment procedures to stabilize investor expectations, and constructing a full-process data quality control system.
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Liu et al. (2026) studied this question.
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