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 221–240 of 274 papers
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 …
Yun Zhu, Qinghan Chen, Ma Zhong
This study proposes a framework for evaluating environmental information disclosure quality (EIDQ) of chemical firms, using a generative AI (DeepSeek-V3.2) scoring system. Analyzing 190 ESG reports from 38 Chinese chemical firms (2020-2024)…
Elham Jahanbakhsh
This study develops a deployment-oriented AI and IoT disclosure index (AIDI) from U.S. annual and sustainability reports (2018-2024) and tests its association with EPA GHG emissions intensity and workplace injury rates. Using fixed-effects …
Pratiti Nayak, Bhavya Tondon, Raushan Tara Jaswal
This paper discusses how AI integration into sustainable finance can facilitate incorporating ESG measurements into decision-making and achieve SDGs, while also addressing challenges such as data quality, compliance, and ethical dilemmas.
Santosh Kumar, Rashi Gupta
This paper examines the role of AI and automation in green finance, covering machine learning for ESG scoring, greenwashing detection, and dynamic portfolio optimization toward green bonds and impact funds aligned with SDGs.
(著者不明)
This paper examines the intersection of AI governance, law, policy, and ethics with green finance and ESG. It discusses regulatory frameworks and ethical considerations for AI applications in sustainable finance.
Nur Muhammad Fadilah, Binastya Anggara Sekti
This chapter comprehensively explores the application of AI in ESG analytics, including machine learning, NLP, and automation for predictive modeling, objective scoring, and continuous monitoring. It covers applications in banking, fintech,…
Hardini Ariningrum, Elsa Fitriandini, Choirunnisa Ramadhani +1
This literature review analyzes the integration of AI and ESG in modern accounting systems. Reviewing 20 peer-reviewed articles from 2021-2025, it finds that AI enhances efficiency, accuracy, and transparency of ESG reporting through automa…
Yamin Qiao, Xiaoyu Min, Sijia Ge +2
This paper examines the dual role of artificial intelligence in achieving carbon neutrality, focusing on emissions forecasting, energy consumption optimization, and sustainable strategy development. It suggests AI can be both a tool and a d…
Kun Luo, Jiale Xue, Puspavathy Rassiah +1
This paper explores the use of AI to align ESG disclosure with actual corporate practices. It proposes methods using NLP and machine learning to enhance the reliability of disclosed information.
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