This page provides an English interface to the gxceed GX paper corpus. The corpus aggregates papers from 14 contributing 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 41–60 of 1414 papers
Daoud Y.
This study proposes a crude oil price forecasting approach combining explainable AutoML with ESG-driven market signals. By integrating ESG information as predictive inputs, it aims to capture how sustainability factors influence energy mark…
Bogdan V.
This paper examines how artificial intelligence can enhance integrated reporting (IR) in Romania. IR integrates financial and non-financial disclosure, and AI may improve data integration and reporting quality. As an emerging EU market case…
Huang J.
This paper examines how the tone of ESG disclosure shapes the link between ESG ratings and firm performance. Using textual analysis to quantify ESG tone, it shows that tone moderates the ratings-performance association. It highlights the in…
Sudhanwa Purandare, Sumanth Banakar, Venkatesh Ankarla Sri Ramuloo +1
This paper proposes a cognitive digital twin combined with deep reinforcement learning for carbon-aware and resilient supply chain management. It contributes a framework that balances carbon constraints with disruption resilience in operati…
Dinh H.T.
This paper examines how AI and green human resource management (GHRM) promote green finance and sustainable corporate performance. No abstract is available, but it sits at the AI × ESG intersection, linking AI-enabled practices to sustainab…
Wusu G.
Proposes an AI-assisted environmental data governance architecture for MRV (monitoring, reporting, verification) in sustainable infrastructure. Aims to strengthen data quality, transparency, and verifiability underpinning disclosure systems…
Chao-Kang He, Qinjun Wang, Wen-Yue Xie
This study integrates multi-source geographic and remote-sensing data with four ML algorithms (RF, CB, XGB, LGBM) to build a 0.1° annual gridded inventory of China's anthropogenic CH4 emissions for 2019–2025. LGBM achieves the best accuracy…
Guang-Yu Wan, Pei Jiang, Jing-Fen Hua +1
Using Chinese A-share firms (2013–2023) and an ML-assisted textual measure of carbon-reduction disclosures, this study finds such disclosures are positively associated with firm value, more so for internally oriented operational disclosures…
Fan Jia
This study proposes a dual-dimensional framework assessing ESG rating quality through validity and utility lenses, implemented in China's A-share market targeting local adaptability and entrepreneurial enablement. Using machine learning for…
Si-Yu Shen, Chao Li, Jun Xie +3
This study builds a financial-materiality-oriented ESG disclosure measure, applying a Text Match Pretrained Transformer (TMPT) to Japanese Annual Securities Reports of listed food and beverage firms (2020-2024) and SASB industry material to…
Mingyi Li
This thesis examines blockchain, AI/ML, and LLMs as an integrated system for climate and sustainability reporting. A PRISMA-based review identifies three clusters: ESG reporting challenges, technology applications/limits, and regulatory/ass…
Zinat Ara Noor, Sheikh Mohammed Rabiul Alam, A.B.M. Kamal Pasha +1
A scoping review of AI and multi-sensor remote sensing for salt-marsh blue carbon assessment. It proposes a digital-twin research agenda, charting a path toward scalable, high-accuracy monitoring of coastal carbon stocks.
Adeboye Oyegunle, Olaf Weber
This study uses AI and NLP to examine how well 16 major global climate risk banking regulations align with 83 peer-reviewed articles (2017–2024). It finds only moderate alignment (74.2% cosine similarity), showing regulators partially incor…
Maia Vilela M, Baesso Grimoni JA
Using generative AI and 6,700 origin-destination survey responses, this study calculates commuting-related GHG emissions at USP's Butantan Campus. Commuting can exceed Scope 1 and 2 emissions, underscoring the need to include Scope 3 in hig…
Islam MS
EcoAccel-ITAD is a telemetry-driven, non-destructive diagnostic and carbon allocation framework for second-life heterogeneous AI accelerators at decommissioning. It pulls low-level degradation metrics via vendor APIs (NVML, ROCm SMI, OneAPI…
Zhang Y.
This study uses machine learning and scenario analysis to examine how the synergy between digital finance and green finance affects agricultural carbon emission intensity in China. It suggests that combining digital tools with green capital…
Lim T.
A review mapping the state of the art at the intersection of ESG and artificial intelligence in finance. It surveys AI/ML applications to ESG scoring, disclosure text analysis, and risk assessment, and outlines research takeaways. Useful as…
Alotaibi E.M.
This work proposes an AI-enabled framework for ESG compliance auditing aimed at stakeholders. It leverages AI to verify ESG data and automate audit processes, seeking to strengthen disclosure reliability and accountability. Details are unav…
Sudha Rani P.R.
This work proposes a method to assess corporate greenwashing risk by combining explainable machine learning (XAI) with environmental governance frameworks. It applies ML to ESG disclosure data while making the reasoning interpretable, aimin…
Bo-Xuan Liu
This study builds an LLM-enhanced agent-based simulation to evaluate differentiated allowance benchmarking under China's expanding carbon market. Using a five-dimensional Carbon Disclosure Index, 60 energy-intensive firm agents are classifi…
Browse by Topic