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 561–580 of 716 papers
(著者不明)
This paper systematically reviews the literature at the intersection of emerging technologies (AI, blockchain, etc.) and ESG, analyzing research trends across countries like China, USA, and Italy. It suggests that regulatory pressures and i…
Ibrokhimjon U. Tursunaliev, Abrorbek X. Kozimjonov, Jaloliddin U. Suyunov +2
This study explores how AI can enhance environmental auditing and green accounting to improve transparency and reliability of sustainability reporting. Using a mixed-methods approach, it finds that ESG reporting is widespread and AI adoptio…
Akey Sungheetha, Kayapati Rajagopal
This paper proposes an Energy-Aware Explainable AI (EA-XAI) framework for sustainable cloud computing that optimizes energy consumption and carbon footprint. It achieves 34.7% energy reduction, 2.1 tons/month CO2 reduction, and 41.6% cost s…
Hao Wang
This study uses data from 2,334 Chinese A-share listed manufacturing firms (2015-2024) and a double machine learning model to show that AI affordance (AIA) significantly promotes digital-green synergistic transformation (DGST). Technologica…
Ghaprial E.A.A.
This paper explores the integration of AI and digital tools to enhance ESG reporting and sustainable business practices. It covers automation of data collection, analysis, and disclosure, aiming to improve accuracy, reduce greenwashing, and…
Avtar Singh, Dalwinder Kaur Dhillon
This chapter explores the role of artificial intelligence (AI) in environmental governance. It discusses applications such as deforestation monitoring, greenhouse gas tracking, renewable energy optimization, precision agriculture, and extre…
Huajian Jiang
This dataset contains the complete submission package for the ESG-DocQA benchmark paper, including 300 validated samples, annotation guidelines, experimental results, and supplementary materials. It focuses on evidence-grounded question ans…
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…
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