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:
1474372667
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Huiying Ye (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Governance: 1 paper
- Productivity: 1 paper
Claim outcomes
- Consumer Welfare: 1 paper
- Fiscal And Macroeconomic: 1 paper
- Adoption Rate: 1 paper
- Automation Exposure: 1 paper
- 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 |
|---|---|---|---|
| Modeling finds AI development is net-polluting: under business-as-usual, modest (ICT-like) AI raises 2100 warming by ~0.1°C while an Industrial-Revolution-scale AI adds ~0.8°C, with climate costs offsetting roughly 20–25% of AI’s economic gains; stronger abatement sharply cuts those losses and in the model supports higher optimal AI investment.arxiv | Huiying Ye provider id |
2026-08-25 | 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.