Qualitative Materials on Electric Bicycle Law Enforcement in Guangzhou

Interview scripts, photographs, and legal documents support a study exploring enforcement lag in urban e-bike governance. Materials include an interview outline, coded qualitative data, on-site observation photos, and third-party media images. Author PENG, MINGGANG deposited this collection in the Harvard Dataverse, last updated in April 2026.

Use Cases

• Analyze interview scripts to identify common themes in stakeholder perceptions of e-bike regulations.
• Use coded qualitative data to train NLP models for classifying arguments about governance challenges.
• Compare on-site observation photographs with third-party media images to assess narrative framing of enforcement.
• Cross-reference interview responses with the provided laws and regulations to map perceived regulatory gaps.

Strengths

• Contains multiple qualitative data types: interview scripts, coded data, photographs, and legal texts.
• Focuses on a specific, timely urban governance issue in a major Chinese city (Guangzhou).

Limitations

• Sample size and row counts for textual data are unknown, limiting quantitative analysis.
• Data is exclusively qualitative, requiring manual content analysis or specialized NLP preprocessing.
• Geographic scope is limited to a single city, reducing generalizability.

Provenance

Source

Harvard Dataverse, authored by PENG, MINGGANG.

Collection Method

Collected via qualitative research methods including interviews, on-site observations, and document collection.

Freshness

Last updated in April 2026.

Geography

Guangzhou, China.