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 201–220 of 271 papers
Xiaoling Yang, Mohd Rahimie Abd Karim, B. Abdullah
Using panel data from China's A-share listed companies from 2014 to 2023, this study empirically shows that AI significantly enhances corporate green competitiveness across strategy, innovation, and practices. The mechanisms include improve…
Roberto Moro-Visconti
This paper shows that AI adoption in corporate governance improves ESG performance by 8-15% through reducing information frictions and expanding the basin of attraction for high-ESG outcomes. Using 450 firms over 2014-2024, the author finds…
M. Eraña-Díaz, Juana Enríquez-Urbano, B. Martínez-Bahena +3
This study proposes a two-phase computational framework combining K-Means clustering and a binary ANN classifier to capture heterogeneity in environmental performance across manufacturing units in remanufacturing. Using 1000 synthetic recor…
Jayesh Rane
This paper assesses green AI adoption in industrial systems via SWOT analysis using mixed methods. Findings show organizational willingness, technological readiness, and regulatory standards positively influence adoption (73.4% variance). O…
Fadi Alkaraan, Mahmoud Elmarzouky, V. G. Venkatesh +3
This study uses large-scale UK data (32,273 firms from the UK Innovation Survey, FTSE All-Share data, and annual reports) to show that AI-powered Industry 4.0 technologies enable circular economy strategy practices, boosting ESG and ecosyst…
Dr. Syed Shameel Ahmed Quadri, Dad Ansari, Zonaira Akbar +1
This study empirically examines how AI-driven ESG signal processing enhances green finance intelligence, leading to better sustainable capital allocation and capital market efficiency. Using survey data from 312 financial professionals and …
Z. Baharom
This critical review examines machine learning (ML) in corporate financial sustainability (CFS), highlighting risks of automated greenwashing and reinforced inequalities in ESG scoring and predictive analytics. It proposes a four-pillar fra…
Yasser Elouargui, Abdellatif Sassioui, M. Chergui +4
This paper introduces ESG-Graph, a lightweight and interpretable graph-based framework for ESG text classification. It leverages a taxonomy based on the European Sustainability Reporting Standards (ESRS) and uses a multi-layer Graph Attenti…
Siqi Sun, Ben Wu, Mali Jin +3
This paper introduces ESG-Bench, a benchmark dataset for evaluating LLMs on ESG report understanding and hallucination mitigation. The dataset includes human-annotated QA pairs with factual support labels. Chain-of-Thought prompting and fin…
Fatma Bennaceur, Ali Bendob, Anwar Hasan Abdullah Othman
This study uses AI, ML, and NLP to assess ESG disclosure quality among Islamic and conventional banks in the UAE (2021-2024). Conventional banks score higher on climate risk and compliance, while Islamic banks show consistency and emphasis …
Andreja Primec, Jernej Belak, Matic Čufar
This paper examines the extent to which major sustainability reporting standards (CSRD/ESRS, GRI, ISSB, SASB) address AI-related risks. Through content analysis of 20 corporate reports and doctrinal legal analysis, it finds that AI risk dis…
Fiona Magee
This paper develops a web application using LLMs to automatically summarize, analyze, and compare ESG reports. It enables stakeholders to reduce manual review effort, identify trends, and benchmark against peers. The prototype demonstrates …
Amani Ebnaoof
This study examines the relationship between AI adoption and ESG disclosure among Saudi-listed non-financial firms from 2020-2024. Using fixed-effects regression on 130 firms, it finds that AI adoption significantly improves ESG disclosure …
C. Kim, H. Na
This study develops an AI-based screening framework using ESG ratings to predict corporate carbon emissions among Korean KOSPI-listed firms. Comparing ML and DL models, a hybrid ensemble of CatBoost, GAN, and Transformer outperforms individ…
W. Okere, Cosmas Ambe, Sanele Phumlani Vilakazi
This systematic review synthesizes empirical evidence (2015-2025) on AI methods in green finance and ESG reporting in emerging markets. Machine learning and NLP dominate, applied to carbon disclosure, ESG scoring, and green bond verificatio…
Claire Monteleoni, Lawrence A. Palinkas, Michalle Mor Barak +6
This paper explores how artificial intelligence intersects with equity issues in climate-induced migration. It analyzes the impact of AI on decision-making and resource allocation in climate migration, considering ethical and social challen…
Jinguo Li
This study, based on 313 workers in Chinese furniture companies, finds that AI-enabled dynamic capabilities, green servant leadership, and Industry 4.0 adoption foster green servitization, which fully mediates their impact on net-zero produ…
Zaheda Daruwala
This paper introduces ESGI (ESG with Intelligence), a paradigm integrating AI into ESG investing. Through systematic literature review, it proposes a dynamic, data-driven ecosystem using NLP, XAI, and other AI methods to enhance transparenc…
Raghuveeran, Preethi
This paper introduces CAIR, a real-time carbon-aware inference routing framework for LLMs. It routes each request to the smallest model meeting accuracy and latency constraints, using carbon intensity and a budget. Preliminary analysis show…
Sandeep Garg, Aadil Naved Khan, Amit Sharma
This paper discusses how AI-powered green investments can balance environmental goals and social justice. It highlights AI's role in climate risk pricing, renewable portfolio optimization, and monitoring impacts on workers to ensure a just …
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