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 261–274 of 274 papers
Jan-Erik Schirmer
This chapter examines the dual role of AI in climate change: while extensive AI use poses climate risks due to its carbon footprint, selective AI application can enhance corporate climate governance, particularly in reporting and planning u…
Thong Hoang, Mykhailo V. Klymenko, Xiwei Xu +6
This paper introduces an agentic ESG lifecycle framework that integrates multiple AI agents to automate ESG reporting tasks such as information extraction, verification, and report updating. It transforms static ESG reporting into a dynamic…
Muhammad Usman, Yongming Huang, Muhammad Sohail Amjad Makhdum
This study analyzes the impact of AI, green finance, environmental technologies, and renewable energy on GHG emissions in BRICS-T countries. Using second-generation econometric methods, it finds that AI, green finance, and renewable energy …
M. Wheeler, Brandi Everett, Victor R. Prybutok
This paper proposes an Environmental Asset-Cost Framework to systematically evaluate the trade-off between AI's environmental benefits and resource consumption. Through a review of 73 empirical studies, it reveals that AI's environmental im…
R. Santos, María Isabel Casal Reyes
This systematic review examines how digital technologies (AI, analytics, platforms) shape green economic outcomes and how environmental constraints and governance reshape digital diffusion. Four domains are analyzed: labor market restructur…
Md Arafat Rahman, Sazzad Hossain Shaon
This study empirically investigates the role of AI in enhancing ESG disclosures in Bangladesh, using a survey of 434 respondents. It finds that AI integration significantly improves ESG reporting, fostering sustainability culture and climat…
Vibhavari Prasad Mane
This systematic review explores how AI techniques (machine learning, NLP, predictive analytics) enhance ESG measurement, portfolio management, risk analysis, and sustainability disclosure. It also highlights challenges like data quality, al…
Filippo di Pietro, Pilar Giráldez‐Puig, P. Palos-Sanchez +1
This study uses a systematic literature review and bibliometric analysis to examine machine learning applications in climate-related financial challenges. It finds growing AI adoption in emissions forecasting, green investment, and sustaina…
Hind Gatoi, Islam Belhaoua, Kashf Akhtar +3
This study examines whether AI-driven ESG analytics enhance sustainable investment performance. It finds that AI-enhanced high-ESG portfolios achieve higher returns and lower downside risk compared to traditional ESG portfolios. AI-derived …
H. Akula
This paper proposes a novel climate governance framework integrating AI, remote sensing, and trade policy. Using the EU's CBAM as a case, it argues for a universal treaty-based architecture to mitigate adverse impacts on developing countrie…
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 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…
Junying Fan, Daojuan Wang, Yuhua Zheng
This paper proposes FinATG, an AI-driven framework for extracting sustainability-related financial information from English texts to support ESG investment decisions in emerging markets. It achieves entity F1 of 88.5 and REL F1 of 80.2 on s…
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