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 81–100 of 229 papers
Huajian Jiang
This paper presents ESG-DocQA, a 300-sample benchmark for evidence-grounded question answering over corporate ESG reports. It includes verification, comparison, and inference questions. Three-annotator validation achieved substantial inter-…
G. Silambarasan, Gautam Shivaraj
AI holds promise for climate action but involves significant environmental costs from energy, water, and e-waste. This chapter critiques AI's dual role and proposes an integrated evaluation framework using life-cycle assessment and rebound …
M. B. Danbatta
This study experimentally investigates CO2 absorption using MEA in a countercurrent rotating packed bed (RPB), achieving up to 93% removal efficiency. It also explores GenAI frameworks to extrapolate and simulate RPB performance across vari…
Lira Luz Benites Lázaro, Rosie Day
This review maps 2,505 publications on gender in energy justice and just transitions using bibliometrics, BERTopic, and qualitative analysis. It reveals a focus on justice, energy communities, and acceptance, while critical areas like miner…
(著者不明)
This paper focuses on data quality issues in Scope 3 emissions and proposes machine learning prediction methods. It aims to improve accuracy in estimating supply chain-wide emissions.
(著者不明)
This paper proposes a machine learning approach to predict ESG ratings. It builds models that estimate ESG scores from publicly available corporate data and compares performance with traditional methods. The research contributes to automati…
(著者不明)
This study proposes and validates a machine learning-based approach to analyze corporate climate disclosures, highlighting the use of AI for assessing disclosure quality and compliance.
Zhang Y.
This paper proposes methods to apply artificial intelligence to optimize the operation of thermal power plants, thereby reducing carbon dioxide emissions. By utilizing AI, it is possible to improve combustion efficiency and reduce downtime …
Moreno C.S.
This paper proposes using Bayesian optimization to search for low-energy metal-organic frameworks (MOFs) for carbon capture, leveraging a manifold subspace of geometric descriptors for efficient exploration.
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…
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…
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