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View corpus contextStronger provincial cross-border data rules in China are linked to more resilient digital-economy supply chains, with the biggest gains in regions already advanced in digital development; policymakers can reach resilience through several combinations of regulation, infrastructure, innovation and digital-trade policies.
Advancing Sustainable Digital Industrial Security: Cross-Border Data Flow Regulation and Resilience of Digital Economy Core Industrial Chains in China
openalex
quasi_experimental
medium evidence
8/10 relevance
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Using a 2015–2025 panel of 31 Chinese provinces and a novel Data Flow Regulation Index, the paper finds that stricter cross-border data-flow rules are associated with higher resilience of core digital-economy industrial chains, particularly in provinces with higher digital-economy development, and that resilience can be achieved via multiple regulatory-plus-investment policy combinations.
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View corpus contextAgainst the backdrop of deepening global digital economic integration and intensifying geopolitical competition, cross-border data flow regulation has become a core institutional arrangement for coordinating development and security. Based on panel data from 31 Chinese provinces from 2015 to 2025, this study proposes an adapted provincial DFRI and a comprehensive resilience index, which together provide an operational foundation for examining the relationship between data regulation and industrial chain resilience across Chinese regions. It empirically examines the effects, transmission mechanisms, and configurational pathways through which regulation influences industrial chain resilience. The results show that cross-border data flow regulation significantly enhances the resilience of the core industrial chains of the digital economy. The balance between efficiency and security plays a key mediating role in the process through which regulation affects industrial chain resilience. Moreover, the regulatory effect exhibits significant heterogeneity in the level of digital economic development. The coefficient of REG is higher in provinces whose digital economy development index exceeds the sample mean than in provinces at or below the sample mean. Further, fuzzy-set qualitative comparative analysis (fsQCA) identifies four equivalent pathways through which regulatory improvement, infrastructure development, innovation investment, digital trade, and industrial compatibility jointly drive high resilience. The findings provide empirical evidence and policy implications for constructing regionally differentiated data governance systems and enhancing the resilience of China’s core digital economy industrial chains.
Summary
Main Finding
Cross-border data flow regulation significantly improves the resilience of core digital-economy industrial chains across Chinese provinces (31 provinces, 2015–2025). The effect operates mainly by balancing efficiency and security, is stronger in provinces with above-average digital-economy development, and can be achieved via multiple combinatory pathways of regulation, infrastructure, innovation, digital trade, and industrial compatibility.
Key Points
- Dataset and scope: provincial panel (31 Chinese provinces), 2015–2025.
- Measurement innovations: an adapted provincial Data Flow Regulation Index (DFRI) and a comprehensive industrial-chain resilience index were constructed to operationalize regulation and resilience.
- Main empirical result: stricter/more advanced cross-border data flow regulation (higher DFRI/REG) is associated with higher resilience of core digital economy industrial chains.
- Mediation: the balance between efficiency and security is a key transmission mechanism—regulation strengthens resilience by improving this balance.
- Heterogeneity: regulatory effects vary with digital-economy development. Provinces with a digital-economy index above the sample mean show a larger positive coefficient for REG than provinces at or below the mean.
- Configurational analysis: fuzzy-set QCA uncovers four equivalent pathways (combinations of regulatory improvement, infrastructure development, innovation investment, digital trade, and industrial compatibility) that lead to high industrial-chain resilience.
- Policy relevance: evidence supports regionally differentiated data-governance strategies that combine regulation with targeted investments.
Data & Methods
- Data: panel data covering 31 Chinese provinces over 2015–2025.
- Main variables:
- REG / adapted provincial DFRI: index capturing cross-border data-flow regulation strength/quality at the provincial level.
- Comprehensive resilience index: multi-dimensional measure of industrial-chain resilience for core digital economy sectors.
- Digital-economy development index (used for heterogeneity analysis).
- Econometric approaches:
- Panel regression analysis (controls and fixed effects implied by panel setup) to estimate the effect of regulation on resilience.
- Mediation analysis to test whether the efficiency–security balance explains the effect.
- Heterogeneity analysis by splitting sample by digital-economy development (above vs. at/below mean).
- Fuzzy-set qualitative comparative analysis (fsQCA) to identify multiple, equivalent causal configurations (pathways) combining regulatory improvement, infrastructure, innovation, digital trade, and industrial compatibility that yield high resilience.
- Outcome: consistent evidence across methods that regulation supports resilience, with mechanisms and context dependence clarified.
Implications for AI Economics
- Data governance strengthens AI supply-chain resilience: Cross-border data flow rules that are well-designed can improve the robustness of AI model training, data exchange, and platform operations in regional industrial ecosystems.
- Balance efficiency and security for productive AI ecosystems: Regulations that simultaneously facilitate lawful, efficient data exchange and ensure security/privacy create better conditions for resilient AI development and deployment.
- Regional tailoring matters: The same regulatory approach yields stronger resilience in regions with higher digital-economy readiness. Policy for AI should be calibrated to local infrastructure and capabilities rather than one-size-fits-all.
- Complement regulation with investments: Regulatory improvements are most effective when combined with digital infrastructure upgrades, innovation funding (R&D), promotion of digital trade, and efforts to align industry standards/compatibility—critical levers for resilient AI value chains.
- Multiple policy pathways: Policymakers can achieve resilient AI-industrial chains through different combinations of interventions (regulation + infrastructure; regulation + innovation + trade; etc.), allowing flexible and context-specific strategies.
- Operational tools for research and policy: The provincial DFRI and the resilience index provide measurable tools for monitoring how data policies affect AI-related industrial resilience and for evaluating trade-offs (e.g., security vs. access) in policy design.
- Recommendations for policymakers:
- Design regionally differentiated cross-border data policies that explicitly weigh efficiency-security trade-offs.
- Invest in digital infrastructure and innovation ecosystems alongside regulatory reform.
- Support digital trade frameworks and industrial compatibility standards to strengthen cross-border AI collaboration and supply-chain robustness.
- Prioritize capacity building in regions with lower digital-economy development to amplify regulatory benefits for AI resilience.
Further research could apply the provincial DFRI and resilience index to study specific AI sub-sectors (model training, edge AI, cloud services), causal identification of regulation effects, and international comparisons.
Assessment
Paper Typequasi_experimental
Evidence Strengthmedium — The panel over 2015–2025 and multiple complementary methods (fixed-effect regressions, mediation, heterogeneity analysis, fsQCA) provide consistent and plausible evidence that stricter cross-border data-flow regulation is associated with higher industrial-chain resilience, but the observational design leaves open endogeneity concerns (reverse causality, omitted time-varying confounders) and the summary does not report quasi-experimental identification (e.g., IV or policy discontinuity) or robustness checks that would support strong causal claims.
Methods Rigormedium — The study uses appropriate panel methods (fixed effects, mediation) and a configurational fsQCA to uncover multiple pathways, which improves construct validity and robustness of inference; however, the supplied description does not document strategies to address key endogeneity threats (instrumental variables, exogenous shocks, lag structure tests, placebo tests), nor does it report diagnostic statistics, standard errors clustering, or sensitivity analyses in the summary, limiting confidence in causal interpretation.
SampleProvincial panel covering 31 Chinese provinces from 2015 to 2025 (approx. 341 province-year observations if balanced); key variables include an adapted provincial Data Flow Regulation Index (DFRI/REG), a multi-dimensional industrial-chain resilience index for core digital-economy sectors, and a provincial digital-economy development index; analyses operate at the province-year level and focus on core digital-economy industrial chains.
Themesgovernance productivity
IdentificationUses provincial panel regression with controls and province and year fixed effects to exploit within-province over-time variation in an adapted Data Flow Regulation Index (DFRI); supports mechanism with mediation analysis (efficiency–security balance), explores effect heterogeneity by splitting the sample on a digital-economy index, and triangulates results with fuzzy-set QCA to identify multiple causal configurations. No explicit natural experiment, instrumental variable, or difference-in-differences design is reported in the supplied text.
GeneralizabilityChina-only provincial data; legal, institutional, and economic contexts may differ substantially in other countries, Province-level aggregation masks firm-level and sector/subsector heterogeneity (AI sub-sectors like model training, edge AI, cloud services may respond differently), Results pertain to 2015–2025 regulatory and technological landscape and may not generalize as technology or international data regimes evolve, DFRI and resilience indices are researcher-constructed; measurement choices and weighting may limit replicability and comparability, Observational design limits causal extrapolation to settings with different confounding structures or policy endowments
Claims (4)
| Claim | Direction | Outcome | Confidence & Evidence | Details |
|---|---|---|---|---|
| Stronger cross-border data-flow regulation is significantly associated with greater resilience of core digital-economy industrial chains across Chinese provinces. Organizational Efficiency | positive | Resilience of core digital-economy industrial chains |
Reading fidelity
high
Study strength
medium
|
n=31
|
| The positive relationship between cross-border data-flow regulation and industrial-chain resilience operates partly through an improved balance between efficiency and security. Organizational Efficiency | positive | Industrial-chain resilience, with the efficiency–security balance as the proposed mediator |
Reading fidelity
high
Study strength
medium
|
n=31
|
| The effect of cross-border data-flow regulation on industrial-chain resilience is stronger in provinces whose digital-economy development index is above the sample mean than in provinces at or below the mean. Organizational Efficiency | positive | Resilience of core digital-economy industrial chains |
Reading fidelity
high
Study strength
medium
|
n=31
|
| Four equivalent configurational pathways combining regulatory improvement, infrastructure development, innovation investment, digital trade, and industrial compatibility lead to high industrial-chain resilience. Organizational Efficiency | positive | High resilience of core digital-economy industrial chains |
Reading fidelity
high
Study strength
medium
|
n=31
four equivalent pathways
|