mlsanityData to 14 Oct 2026

GreenIQ: A Deep Search Platform for Comprehensive Carbon Market Analysis and Automated Report Generation

arXiv 2503.16041 ↗First posted 20 Mar 2025cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

This study introduces GreenIQ, an AI-powered deep search platform designed to revolutionise carbon market intelligence through autonomous analysis and automated report generation. Carbon markets operate across diverse regulatory landscapes, generating vast amounts of heterogeneous data from policy documents, industry reports, academic literature, and real-time trading platforms. Traditional research approaches remain labour-intensive, slow, and difficult to scale. GreenIQ addresses these limitations through a multi-agent architecture powered by Large Language Models (LLMs), integrating five specialised AI agents: a Main Researcher Agent for intelligent information retrieval, a Report Writing Agent for structured synthesis, a Final Reviewer Agent for accuracy verification, a Data Visualisation Agent for enhanced interpretability, and a Translator Agent for multilingual adaptation. The system achieves seamless integration of structured and unstructured information with AI-driven citation verification, ensuring high transparency and reliability. GreenIQ delivers a 99.2% reduction in processing time and a 99.7% cost reduction compared to traditional research methodologies. A novel AI persona-based evaluation framework involving 16 domain-specific AI personas highlights its superior cross-jurisdictional analytical capabilities and regulatory insight generation. GreenIQ sets new standards in AI-driven research synthesis, policy analysis, and sustainability finance by streamlining carbon market research. It offers an efficient and scalable framework for environmental and financial intelligence, enabling more accurate, timely, and cost-effective decision-making in complex regulatory landscapes

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 50 references that are in the map. First posted on arXiv 20 Mar 2025.

Builds on

13 of 50 references.

References · 13

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  4. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
  5. 2023A Survey of Large Language Modelscited by 2,576
  6. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  7. 2022Competition-Level Code Generation with AlphaCodecited by 1,462
  8. 2019Tackling Climate Change with Machine Learningcited by 265
  9. 2024From Persona to Personalization: A Survey on Role-Playing Language Agentscited by 212
  10. 2023Writer-Defined AI Personas for On-Demand Feedback Generationcited by 36
  11. 2023Synergistic Interplay between Search and Large Language Models for Information Retrievalcited by 11
  12. 2024Enhancing Legal Compliance and Regulation Analysis with Large Language Modelscited by 10
1 more
  1. 2023Leveraging AI-derived Data for Carbon Accounting: Information Extraction from Alternative Sourcescited by 1

Built on by

Not cited yet.