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 101–120 of 271 papers
Zhou Y.
This paper proposes a sustainability framework integrating AI and green finance for cleaner energy production, ESG integration, and climate action. It leverages AI for ESG evaluation and links with green finance to accelerate corporate deca…
Nicolodi A.
This paper proposes a method to detect potential greenwashing in green bond disclosures by analyzing the omission of SDG references and the use of overly optimistic environmental tone. Using natural language processing, it provides investor…
Binsaddig R.
This paper examines how artificial intelligence-driven fintech contributes to green finance and environmental sustainability. It explores AI's role in risk assessment and investment optimization to support environmentally conscious capital …
Gera S.
This paper surveys natural language processing (NLP) techniques applied to financial narratives, including earnings calls, ESG disclosures, and 10-K filings. It highlights how NLP can extract material ESG information, detect sentiment, and …
Siddique M.A.
This study investigates the predictive power of ESG disclosure for corporate carbon risk using machine and deep learning models. By comparing model performance, it suggests that ESG disclosures can serve as effective predictors of carbon ri…
Petri M.
This paper comparatively analyzes the effectiveness of AI tools in mitigating greenwashing in ESG reporting, examining different AI methods to enhance disclosure credibility.
Peng C.
This paper proposes a novel Spatio-Temporal Dual-Channel Attention Gated Mamba model for monitoring greenhouse gas emissions from municipal wastewater treatment processes. The model leverages attention mechanisms and state space models to c…
Luo X.
This paper proposes a CF-EEMD-LSSVR machine learning model to evaluate and predict greenhouse gas emissions from lead smelting systems. The model captures complex emission patterns, achieving high prediction accuracy. It contributes to emis…
Teri Grimmer, Wookyoung Kim, Harim Jung +1
This study examines how AI-enabled analytics enhances ESG reporting in resource-constrained museums. Using resource-based view and dynamic capabilities theory, it conceptualizes AI-enabled ESG reporting as a strategic organizational capabil…
Joseph Agossa
This paper introduces KenyaESG, a sentence-level ESG disclosure classification dataset built from annual, integrated, and sustainability reports of firms listed on the Nairobi Securities Exchange (2010-2024). For each of the three pillars (…
Warit Attharat, Kritsada Puangsuwan, Supattra Puttinaovarat
This paper presents a web-based platform that uses deep learning (CNN) and GIS to detect lighting status in rooms, enabling real-time monitoring of electricity usage and carbon emissions. High classification accuracy is achieved, supporting…
Zhiyong Wu, Yun Qiao, Youren Ma
This paper proposes a standardized, AI-driven framework for assessing energy conservation in retrofitted and operational buildings across ASEAN. It integrates EUI, operational patterns, equipment loads, and local climate data to create adap…
Yu Xing, Siyuan Zou, Guohua Liu
This paper proposes a machine learning ensemble framework for carbon price prediction across China's seven regional pilot carbon markets. It integrates price, volume, inter-market, and macroeconomic variables using XGBoost, LightGBM, and Ra…
Zesen Wang, Lijuan Lan, Yonggang Li +1
This paper proposes TriHead-GAN, a Transformer-based GAN with a triple-head discriminator that jointly supervises distributional authenticity, cross-variable dependencies, and temporal smoothness for carbon emission time series generation. …
Xin-song Ma, Ren-wei Zhang
This study proposes a multi-scale adaptive graph convolution model for carbon price prediction, capturing complex price dynamics in carbon markets using deep learning.
H. Liao, Chang-Yi Kao, K. Ang
This paper introduces a zero-trust orchestration framework for Smart Fabs to comply with the EU's SSbD, CSDDD, and CBAM. Using Professional Proxies and industrial data spaces, it automates compliance verification via federated machine learn…
Na Du, Baoyi Liu, Ao Gong +4
This paper proposes a deep reinforcement learning approach for optimal dispatch of regional integrated energy systems with carbon capture and trading. The method adapts to dynamic carbon prices, balancing economic and environmental objectiv…
Huan Liu, Shaojun Bai
This study applies multidimensional text analysis to corporate ESG reports to examine whether high ESG performers exhibit distinct linguistic patterns. By leveraging NLP methods, it sheds light on the relationship between disclosure quality…
Zhaoshuai Dang
This dataset comprises carbon price data from four Chinese regional markets (Hubei, Guangdong, Beijing, Shanghai). It is used for multivariate and univariate carbon price forecasting experiments with the AMST-Pyraformer model, an adaptive m…
Ahmed A. A. Abdel-Wareth, Ahmed A. Ahmed, Mohamed O. Taqi +2
A comprehensive review of AI applications (machine learning, sensors, data analytics) in livestock farming for climate resilience and sustainability. Covers GHG mitigation, feed efficiency, disease prevention, and animal welfare, highlighti…
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