Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
2Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2449457815
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Yuanhong Peng (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Innovation: 1 paper
Claim outcomes
- Governance And Regulation: 1 paper
Papers in the Semantic Scholar view
Latest stored Semantic Scholar author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| China's AI pilot zones are associated with roughly 6% lower urban carbon emissions on average, rising to an estimated 15.6% total abatement once spatial spillovers are included. Gains are concentrated in some coastal manufacturing hubs, while certain inland and resource-dependent regions show weak or marginally higher emissions, pointing to the need for region-specific AI governance.openalex | Yuanhong Peng provider id |
2026-07-10 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 0 cumulative citations. This is a coverage summary, not an author score or h-index.