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 341–360 of 991 papers
D. Samontaray, Randheer Kokku, N. M. Nasir +1
This study examines the relationship between FinTech disclosure and ESG reporting among non-financial firms listed on the Saudi Stock Exchange from 2021-2024 using textual analysis. An ESG Disclosure Index and a FinTech adoption measure wer…
Meng-Sian Chen, Jing Wang, Mengfei Peng
This paper proposes a bi-objective optimization model for virtual power plants (VPPs) integrating wind, PV, storage, and gas units, using the NSGA-II algorithm to minimize total cost and carbon emissions. A case study reveals a trade-off be…
张荪理, Jian Yin
This paper empirically analyzes the impact of intelligent technology penetration on the green transition of urban energy systems in Chinese cities using double/debiased machine learning, suggesting contributions to emission reduction and re…
Ahmet Eser, M. Sadrettin Zeybek, Ayşe Dinçer +2
This study uses machine learning to analyze the rheological properties of alginate/Fe3O4 composite suspensions and simultaneously assesses the carbon footprint of the process. ML models predict rheological parameters such as viscosity and e…
Ziming Xue, Jun Zhou
This paper proposes a Q-learning-based feedback optimization method for low-carbon flexible job shop scheduling. It uses type-2 fuzzy sets to handle uncertainty and optimizes for reduced energy consumption and carbon emissions.
Watheq J. Al‐Mudhafar, Ahmed Alsubaih, Kamy Sepehrnoori
This review systematically examines ML applications in the CCS lifecycle, covering pre-injection evaluation, injection optimization, and post-injection monitoring. It covers methods like Random Forest, SVR, XGBoost, and deep learning for an…
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…
Yun-Kae Kiang, King H. Lam
This paper proposes ReGMS, a retrieval-grounded multi-agent architecture for climate scenario analysis to support IFRS S2/TCFD reporting. Specialized LLM agents coordinate to build and verify transition and physical-risk scenarios. Using NG…
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.
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