Publication2024
The transformative potential of artificial intelligence (AI) in tackling climate change: From theory to practice
The record
- Type
- Publication
- Date
- 2 December 2024
- Level
- International — held abroad
- Journal
- Community Resilience and Climate Change Challenges Pursuit of Sustainable Development Goals Sdgs
- Pages
- 431-448
- Published
- 2 December 2024
- DOI
- 10.4018/979-8-3693-6522-9.ch023
- Scopus ID
- 86000256461
- Authors
- 3
- Open access
- No
- Citations
- 0
- International co-authorship
- Yes
- Co-author countries
- IN, MY
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
13 Take urgent action to combat climate change and its impacts
AI analysis of climatic datasets, satellite imagery of Antarctic icebergs, and meltwater monitoring enables proactive climate change mitigation strategies
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
AI methodology development for climate prediction represents research capacity in environmental science
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
17.6 Enhance North-South, South-South and triangular cooperation on science, technology and innovation, and knowledge sharing
Structural signal: co-authorship outside India.
- Rule-derived
- Emitted by a deterministic rule from structural signals rather than judged from the record’s text. The rule carries a version, so rule-derived and judged labels stay separable.
- Produced by
- sdg17-structural-v1, under kortex-publication-v1
Viksit Bharat @2047
vb-2.5 Green economy, energy transition and climate resilience
AI-driven climate change monitoring and mitigation strategies advance energy transition and climate resilience
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
Evidence
Kortex hosts no documents. Evidence is an outbound link to the authority that holds it.
No outbound evidence link is recorded for this activity. The record below states which source it came from and when it was last read.
Provenance
Which source system this record came from, how many source rows were resolved into it, and when each was last read.
- Source rows resolved into this record
- 1
Each record carries the date Kortex last read it from its source system. That is a freshness date and not a verification: Kortex records what its sources say and when it read them, and it does not check those statements against any external authority. No claim on this site is described as verified, because none is.
Citing this record
This URL is stable. A record’s permalink is frozen when it is published and is not regenerated if its title is later corrected.
KIIT Deemed to be University. “The transformative potential of artificial intelligence (AI) in tackling climate change: From theory to practice”. Kortex.