Climate technology (climate tech) and artificial intelligence (AI) are converging to accelerate solutions for one of the most urgent challenges of our time climate change. Climate tech encompasses a broad range of innovations aimed at reducing greenhouse gas emissions, enhancing environmental monitoring, and supporting climate adaptation strategies. AI plays a pivotal role by enabling high-resolution climate modeling, optimizing renewable energy systems, improving early warning systems for natural disasters, and enhancing carbon capture and smart grid technologies. Machine learning algorithms can analyze vast datasets from satellites, sensors, and climate models to detect patterns, predict outcomes, and guide policy and investment decisions. Additionally, AI-driven precision agriculture, sustainable transportation, and circular economy solutions are transforming sectors traditionally associated with high emissions. As climate risks intensify, the integration of AI into climate tech offers scalable, data-driven tools for governments, industries, and communities to act decisively toward a more resilient and sustainable future.
| 2-5 Days | Initial Quality & Plagiarism Check |
| 25-35 Days |
Peer Review Feedback |
| 45-60 Days | Total article processing time |
| English | Publication Language |
| Single-Blind | Peer-Review Model |
| 17% | Acceptance Rate |
| <18% | Similarity Screening Guideline |
| Open Access | Access Model |
All manuscripts undergo editorial assessment and originality screening as part of the journal's evaluation process. The acceptance rate shown is based on journal-level editorial data and may change over time. Similarity reports are assessed editorially and are not interpreted solely on the basis of a numerical similarity score.