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 986 papers
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…
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.
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…
Mahi Sharanya Sreedhar, Vaidehi V., Ashwini Patil
Proposes a comprehensive framework for measuring and standardizing the environmental footprint of AI systems across their lifecycle. It organizes measurement metrics and policy-compliance requirements, advancing institutionalized environmen…
Rebin Bilal Mohammed, Mohammed Mustafa Ahmad Alzrary
Using SEM on 385 survey responses from medium-to-large Kurdistan firms, this study shows AI adoption significantly improves sustainability accounting and reporting quality. Sustainability accounting partially mediates AI's positive effect o…
shihui miao, Liu Hongxun
This paper applies machine learning to examine the reliability of ESG disclosure and the misallocation of green finance. It quantitatively tests how disclosure reliability affects the proper allocation of green capital, contributing to dete…
Zhang A.Y.
This paper examines how machine learning is reshaping ESG research. It discusses ML applications to ESG evaluation, scoring, and disclosure analysis, highlighting methodological renovation in how sustainability data is processed and assesse…
Joni Prasetiyanto, Mochammad Al Musadieq, Muhammad Faisal Riza +1
This peer review report addresses a systematic literature review on how digital innovation drives corporate sustainability. From 231 Scopus records, 49 quantitative open-access studies (2023-2026) were synthesized. Digital innovation genera…
Sajead Mowafaq Alshdaifat
Using PLS-SEM in SmartPLS on survey data from accounting, finance, sustainability and governance professionals in Jordanian medium and large firms, this chapter shows that Artificial Intelligence Capability (AIC) significantly improves both…
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