This page provides an English interface to the gxceed GX paper corpus. The corpus aggregates papers from 13 open scholarly metadata sources and uses AI-assisted classification to identify signals related to measurement, policy narratives, outcomes, implementation, industrial adoption, and verification.
The goal is not only to discover papers, but to observe how GX research is distributed across research substance, implementation narratives, external expectations, implementation substance, and judgment formation.
Summaries are AI-assisted. Always refer to the original paper for authoritative conclusions.
Showing 1–5 of 5 papers
Yilei Zhao, Wentao Zhang, Lei Xiao +3
This paper introduces ESGAgent, a hierarchical multi-agent system with specialized tools for in-depth ESG analysis. It also presents a three-level benchmark from 310 corporate sustainability reports. Empirical results show 84.15% accuracy o…
Junkai Chen
This study analyzes AI information disclosure in ESG reports of listed companies in the US, Europe, and China. It finds that ESG reports are the primary channel, with significant disparities across AI domains (development, application, manu…
Lars Wilhelmi, Christian Bruns, Matthias Schumann
This study investigates the use of Vision Language Models (VLMs) to extract ESG metrics, particularly GHG emission-reduction targets, from corporate sustainability reports. Using Design Science Research Methodology, we developed an extracti…
Kunj Modh, Varun Patel, Vivek Devani
This paper presents an AI-powered platform for automated analysis of ESG reports. It uses a Retrieval-Augmented Generation (RAG) approach to extract ESG metrics from text and tables, integrating FinBERT-ESG for classification, ChromaDB for …
Kunj Modh, Varun Patel, Vivek Devani
This paper presents ESG Analytics, an AI platform for automated analysis of ESG reports. It uses a Retrieval-Augmented Generation (RAG) approach to extract ESG metrics from text and tables, integrating FinBERT-ESG for classification, Chroma…
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