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 201–220 of 716 papers
Kanungo R.P.
This paper critically examines the integration of sustainable finance, AI, and responsible investing. However, no abstract is available, so detailed findings are unknown.
Rine Dewi Mustikasari, Maria Ulpah
Using XGBoost and SHAP on 1,362 global green bonds (2014-2023), this study finds that structural and macroeconomic factors dominate yield formation, while ESG attributes—especially the social pillar—matter after controlling for macro-financ…
Md. Abul Kalam Azad, A. Masum, M. Rahman +3
EcoStack-Pro is a federated learning framework using a stacked ensemble of LightGBM, XGBoost, and Gradient Boosting with a Fed-GenAdaptive algorithm, achieving high-precision ESG auditing across heterogeneous industrial sectors while preser…
Hye-Kyoung An, Moon-Koo Kim, Byoung-Chang Choi +1
This study classifies 5,097 ESG publications into environmental (ENV) and non-environmental (non-ENV) domains, comparing their knowledge structures using bibliometric analysis, international collaboration network analysis, and BERTopic mode…
Wenjun Wang, N. Jamil, Jun Zeng
This paper proposes a dynamic framework enhanced by text mining to analyze the impact of green finance policy on corporate sustainability. It uses literature-calibrated simulation and regularized XGBoost for validation, providing quantitati…
Aryaman Ahuja, Aryan Dutta
This paper presents an AI-enhanced digital twin approach for carbon capture and storage (CCS) under extreme climate conditions, using Bahrain as a case study. It explores how digital twin technology combined with AI can optimize CCS operati…
Zahra Yaghoubi, Mahyar Mansouri, Hosein Alimardani +2
This study applies machine learning to optimize methane tri-reforming (TRM) for low-emission hydrogen production. Using 46,464 operating points, neural networks (NN), random forest, XGBoost, and others were trained, with NN achieving best p…
Chengji Liang, Yantao Li, Jianquan Guo
This paper analyzes the effect of green technology innovation on low-carbon transformation in the logistics industry using panel data and machine learning. Findings indicate that specific technologies significantly contribute to emissions r…
Mohsin Khan¹, Wendy Ashurst²
This paper proposes an AI-enhanced governance framework to improve ESG reporting integrity. It balances algorithmic detection with human judgment in a sector-specific manner, aiming to reduce greenwashing and enhance disclosure quality.
Clément Bamogo, Mustapha Oudani, Amine Belhadi +2
This paper proposes a two-stage stochastic optimization model for fertilizer planning that supports the transition to low-emission fertilizers under cap-and-trade regulation. A reinforcement Q-learning-enhanced variable neighborhood search …
Constantinos Varsos, Adamantia Stamou, George Stamoulis +1
This presentation introduces an incentive-based framework for improving energy efficiency in ICT services, from the EXIGENCE project. It explores gamified incentives for sustainable video streaming, renewable-aware federated learning, and f…
Neetal Vyas, Manmohan Vyas, Komal Singh
This paper examines how fintech startups leverage blockchain, AI, and machine learning to drive sustainable finance, including ESG analysis, green payments, and carbon credits. They enhance transparency, reduce greenwashing, and fill gaps l…
Ameni Boumaiza
This paper proposes an AI-driven demand-side management approach for sustainable power systems in Qatar, integrating reliability and carbon pricing to achieve decarbonization and grid stability.
Ngone Mirimi, H. Manuere
AI-enabled climate decision-support systems are de facto governance infrastructures with political and accountability consequences, yet they are treated as neutral technical tools. The paper reframes transparency as an enforceable instituti…
Dr. Thanakit Ouanhlee
This study examines the relationship between AI integration in compensation systems and human capital accounting disclosure (HCAD) quality among manufacturing organizations in Thailand. It finds that while AI integration is moderate, disclo…
Hasan Talaş, E. Gök, Özen Akçakanat +4
This study uses tree-based machine learning on 428 Turkish non-financial firms to show that ESG and governance signals provide statistically significant additional information for predicting ROE beyond traditional financial ratios. Random F…
Chen Xu, Batkhuyag Ganbaatar
This study examines the impact of intelligent manufacturing on ESG performance for Chinese A-share listed companies (2015-2023) using two-way fixed-effects panel regression, PSM-DID, and System GMM. It finds that intelligent manufacturing s…
Jianfeng Zhang, Tiantian Qi
This study proposes an interpretable predictive model for ESG greenwashing in listed companies using XGBoost and SHAP, based on Chinese listed companies data from 2009-2022 with 16 input variables. The one-period-lagged model achieves 86.82…
Xiaoqian Liu, Gazi Md. Nurul Islam, Xuelian Bai
This paper develops a credit evaluation system for new energy enterprises integrating big data (807 wind/solar plants, over 84 million photovoltaic and 570 million wind power records) with AI time-series modeling, game-theoretic weighting, …
Javier Nieves Almanza, Roberto Yoan Castillo Dieguez
This study analyzes the relationship between CSR (measured by ESG scores) and financial performance of Mexican listed firms from 2014-2024 using ANCOVA, random forest, and probit models. Results show a positive association with ROA and ROE,…
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