GreenIQ: A Deep Search Platform for Comprehensive Carbon Market Analysis and Automated Report Generation
- URL: http://arxiv.org/abs/2503.16041v2
- Date: Fri, 21 Mar 2025 17:33:33 GMT
- Title: GreenIQ: A Deep Search Platform for Comprehensive Carbon Market Analysis and Automated Report Generation
- Authors: Oluwole Fagbohun, Sai Yashwanth, Akinyemi Sadeeq Akintola, Ifeoluwa Wurola, Lanre Shittu, Aniema Inyang, Oluwatimilehin Odubola, Udodirim Offia, Said Olanrewaju, Ogidan Toluwaleke, Ilemona Abutu, Taiwo Akinbolaji,
- Abstract summary: GreenIQ is an AI-powered deep search platform designed to revolutionise carbon market intelligence.<n>System achieves seamless integration of structured and unstructured information with AI-driven citation verification.<n>A novel AI persona-based evaluation framework highlights its superior cross-jurisdictional analytical capabilities.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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
Related papers
- From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review [1.4929298667651645]
We present a comparison of benchmarks developed between 2019 and 2025 that evaluate large language models and autonomous AI agents.
We propose a taxonomy of approximately 60 benchmarks that cover knowledge reasoning, mathematical problem-solving, code generation and software engineering, factual grounding and retrieval, domain-specific evaluations, multimodal and embodied tasks, task orchestration, and interactive assessments.
We present real-world applications of autonomous AI agents in materials science, biomedical research, academic ideation, software engineering, synthetic data generation, mathematical problem-solving, geographic information systems, multimedia, healthcare, and finance.
arXiv Detail & Related papers (2025-04-28T11:08:22Z) - LLM Agent Swarm for Hypothesis-Driven Drug Discovery [2.7036595757881323]
PharmaSwarm is a unified multi-agent framework that orchestrates specialized "agents" to propose, validate, and refine hypotheses for novel drug targets and lead compounds.
By acting as an AI copilot, PharmaSwarm can accelerate translational research and deliver high-confidence hypotheses more efficiently than traditional pipelines.
arXiv Detail & Related papers (2025-04-24T22:27:50Z) - Toward Agentic AI: Generative Information Retrieval Inspired Intelligent Communications and Networking [87.82985288731489]
Agentic AI has emerged as a key paradigm for intelligent communications and networking.
This article emphasizes the role of knowledge acquisition, processing, and retrieval in agentic AI for telecom systems.
arXiv Detail & Related papers (2025-02-24T06:02:25Z) - Are Large Language Models Ready for Business Integration? A Study on Generative AI Adoption [0.6144680854063939]
This research examines the readiness of other Large Language Models (LLMs) such as Google Gemini for potential business applications.<n>A dataset of 42,654 reviews from distinct Disneyland branches was employed.<n>Results presented a spectrum of responses, including 75% successful simplifications, 25% errors, and instances of model self-reference.
arXiv Detail & Related papers (2025-01-28T21:01:22Z) - CarbonChat: Large Language Model-Based Corporate Carbon Emission Analysis and Climate Knowledge Q&A System [4.008184902967172]
This paper proposes CarbonChat: Large Language Model-based corporate carbon emission analysis and climate knowledge Q&A system.<n>A diversified index module construction method is proposed to handle the segmentation of rule-based and long-text documents.<n>14 dimensions are established for carbon emission analysis, enabling report summarization, relevance evaluation, and customized responses.
arXiv Detail & Related papers (2025-01-03T08:45:38Z) - FinRobot: AI Agent for Equity Research and Valuation with Large Language Models [6.2474959166074955]
This paper presents FinRobot, the first AI agent framework specifically designed for equity research.
FinRobot employs a multi-agent Chain of Thought (CoT) system, integrating both quantitative and qualitative analyses to emulate the comprehensive reasoning of a human analyst.
Unlike existing automated research tools, such as CapitalCube and Wright Reports, FinRobot delivers insights comparable to those produced by major brokerage firms and fundamental research vendors.
arXiv Detail & Related papers (2024-11-13T17:38:07Z) - Generative AI in Health Economics and Outcomes Research: A Taxonomy of Key Definitions and Emerging Applications, an ISPOR Working Group Report [12.204470166456561]
Generative AI shows significant potential in health economics and outcomes research (HEOR)<n>Generative AI shows significant potential in HEOR, enhancing efficiency, productivity, and offering novel solutions to complex challenges.<n>Foundation models are promising in automating complex tasks, though challenges remain in scientific reliability, bias, interpretability, and workflow integration.
arXiv Detail & Related papers (2024-10-26T15:42:50Z) - ScholarChemQA: Unveiling the Power of Language Models in Chemical Research Question Answering [54.80411755871931]
Question Answering (QA) effectively evaluates language models' reasoning and knowledge depth.
Chemical QA plays a crucial role in both education and research by effectively translating complex chemical information into readily understandable format.
This dataset reflects typical real-world challenges, including an imbalanced data distribution and a substantial amount of unlabeled data that can be potentially useful.
We introduce a QAMatch model, specifically designed to effectively answer chemical questions by fully leveraging our collected data.
arXiv Detail & Related papers (2024-07-24T01:46:55Z) - Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository Exploration [64.19431011897515]
This paper presents Alibaba LingmaAgent, a novel Automated Software Engineering method designed to comprehensively understand and utilize whole software repositories for issue resolution.
Our approach introduces a top-down method to condense critical repository information into a knowledge graph, reducing complexity, and employs a Monte Carlo tree search based strategy.
In production deployment and evaluation at Alibaba Cloud, LingmaAgent automatically resolved 16.9% of in-house issues faced by development engineers, and solved 43.3% of problems after manual intervention.
arXiv Detail & Related papers (2024-06-03T15:20:06Z) - An Autonomous Large Language Model Agent for Chemical Literature Data
Mining [60.85177362167166]
We introduce an end-to-end AI agent framework capable of high-fidelity extraction from extensive chemical literature.
Our framework's efficacy is evaluated using accuracy, recall, and F1 score of reaction condition data.
arXiv Detail & Related papers (2024-02-20T13:21:46Z) - Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis [57.70772230913099]
Chemist-X automates the reaction condition recommendation (RCR) task in chemical synthesis with retrieval-augmented generation (RAG) technology.
Chemist-X interrogates online molecular databases and distills critical data from the latest literature database.
Chemist-X considerably reduces chemists' workload and allows them to focus on more fundamental and creative problems.
arXiv Detail & Related papers (2023-11-16T01:21:33Z) - On the Opportunities of Green Computing: A Survey [80.21955522431168]
Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades.
The needs for high computing power brings higher carbon emission and undermines research fairness.
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
arXiv Detail & Related papers (2023-11-01T11:16:41Z) - Large Language Models for Information Retrieval: A Survey [58.30439850203101]
Information retrieval has evolved from term-based methods to its integration with advanced neural models.
Recent research has sought to leverage large language models (LLMs) to improve IR systems.
We delve into the confluence of LLMs and IR systems, including crucial aspects such as query rewriters, retrievers, rerankers, and readers.
arXiv Detail & Related papers (2023-08-14T12:47:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.