Regional Standard Innovation Networks in China from 2001 to 2023

Granger causality tests and network analysis reveal spatial correlations in standard innovation across Chinese provinces. Shuo Wang's study constructs total, positive, and negative correlation networks using data from 2001 to 2023. The analysis identifies regional disparities and key influencing factors like economic development and industrial structure.

Use Cases

• Analyze centrality metrics to identify core provinces like Beijing and Guangdong within the standard cooperation network.
• Model the impact of variables like marketization level and industrial structure on spatial correlation strength using QAP regression.
• Classify regional blocks (eastern, central, western) based on their patterns of positive and negative correlation links.
• Forecast interregional influences on standard innovation using Granger causality test results from the 22-year time series.

Strengths

• Time series covers a 22-year period from 2001 to 2023.
• Analysis constructs three distinct network types: total, positive, and negative correlation.

Limitations

• The 5.9 MB file size suggests a small dataset, potentially limiting statistical power for complex models.
• Specific row counts and column details for the underlying data are unknown.
• Findings are geographically focused on China, limiting generalizability to other regions.

Provenance

Source

figshare, authored by Shuo Wang.

Collection Method

Constructed using Granger causality tests and network analysis (QAP correlation/regression) on regional standard innovation data.

Time Range

2001 to 2023

Freshness

Data is current through 2023, with the study last updated in April 2026.

Geography

China, analyzed at the provincial level.