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 441–460 of 1414 papers
Shaohuai Feng
This repository provides supplementary data for the manuscript on the SCOPE framework, a reproducibility-driven variable screening method for carbon price forecasting. It includes raw and processed data, additional analyses, supplementary f…
Anonymous
Replication data for a two-period study of CSRD disclosure quality around the 2026 Omnibus scope reduction, covering 188 EU firms and 376 reports coded with a frozen LLM prompt on a ten-dimension instrument. Includes validation outputs (hum…
H. T. Dinh, Hà Thị Thu Bùi, H. Nguyen +3
Using survey data from 853 hotel employees in Vietnam, this study employs PLS-SEM to examine how AI and CSR influence employee green behavior (EGB), green finance (GF), and green performance (GP). Findings show AI positively impacts GF and …
Mohamed Vajeebu Mohamed Hussain Pareeth
This review proposes an AI-driven optimization framework for green hydrogen production networks in Saudi industrial cities (NEOM, Yanbu, Jubail, etc.). It integrates machine learning, digital twins, stochastic optimization, and explainable …
Liu Na, Wan Nordin Wan‐Hussin, Chin Sok Fun
This study examines whether ESG-linked executive compensation influences carbon disclosure quality in China's A-share listed firms, and whether board climate oversight mediates this relationship. Using panel data from 2021-2023, it finds th…
Luana Serino, Manlio Del Giudice, Francesco Campanella +1
This study empirically examines the impact of AI-related disclosure on ESG outcomes in U.S. firms. It investigates whether AI adoption and disclosure are associated with improved ESG performance, contributing to the emerging literature on A…
Sanjana Kewalramani, Sharvari Nimje, Tanishq Rajpuria +2
This review systematically organizes research applying AI/ML to Kubernetes scheduling for improving energy efficiency and reducing carbon emissions. It shows that ML-based workload placement optimization can effectively reduce data center e…
T. Sindhu, Dr. K. Sivakumar, Dr. D. Jayanthi +2
This review examines geospatial technologies (GIS, remote sensing, UAV, LiDAR) combined with machine learning for carbon stock estimation in mango orchards. Integration of satellite imagery and ML models (Random Forest, SVM, ANN) improves p…
Jian-yun Lu, Qing-Yi Liu, Yu-Qi Wang +5
This study develops a Bayesian multiobjective optimization framework with Gaussian process surrogates and uncertainty-aware search to map trade-offs in pharmaceutical wastewater treatment. Optimized operating windows improve removal efficie…
Qian Ma, Chunxiao Liu, Kui Huang +6
This paper proposes D-PGNet, a deep projected gradient network that accelerates low-carbon economic dispatch with energy storage and carbon trading costs. It embeds physical constraints as differentiable projection layers, achieving optimal…
Le Sun, Jia Liu, Kuanhou Tian
Using Chinese A-share listed firms from 2008-2024, this study finds that low-carbon transition strategy disclosure increases analyst following. The effect is driven by enhanced investor relations and richer information environment, but mana…
Winda Wulandari, Sri Widyastuti, Harnovinsah Harnovinsah +1
This systematic literature review reconceptualizes financial reporting integrity as a socio-technical governance phenomenon shaped by governance accountability, sustainability legitimacy, and technological transparency. Analyzing 28 Scopus …
Yaacob Ibrahim, Khaled Hussainey, Taghred Mokhtar Sayed Moawad
This study examines whether carbon exposure affects corporate borrowing costs in the MENA region using a panel of 771 firm-year observations from 2016 to 2023. Combining fixed effects, IV, GMM, quantile regression, and machine learning, it …
Cristian Valentin Hapenciuc, Daniela Mihaela NEAMTU, Teodora Cajvan +1
This study applies FinBERT to financial news text to analyze the impact of ESG narratives on market volatility. Focusing on tech, energy, and banking sectors in Eastern Europe, it finds signs of a 'regime shift' where ESG transitions from a…
D. Barro, A. Basso, M. Corazza +1
This study uses machine learning to analyze the impact of the energy transition on Eurozone sovereign credit risk. It evaluates how the shift to renewable energy affects government bond yields and CDS spreads, offering insights into the imp…
Simona Epasto, Maria Lúcia, Masiel Melissa Pereira
This paper examines AI integration in agrivoltaic systems, focusing on socio-territorial implications for rural landscapes. Through case studies including the European SYMBIOSYST project and GIS/UAV geospatial monitoring, it analyzes how pr…
Ke W
This paper frames environmental monitoring as collaborative governance infrastructure under ESG and net-zero policies, and explores how 'Quantum Intelligence (QI)'—combining quantum optimization and quantum machine learning—can reshape the …
(著者不明)
This paper demonstrates that integrating IoT and BI platforms improves Scope 3 emissions reporting accuracy by 40% in logistics. It emphasizes data unification from a governance perspective, offering new possibilities for carbon tracking an…
Minyou Qing, Suhaiza Zailani
This review systematically analyzes the application of AI-driven practices in road freight transport (RFT) decarbonization. Following the SPAR-4-SLR protocol, 227 articles were reviewed using bibliometric analysis and science mapping. Publi…
Seyed Ali Hosseini, Francesco Grimaccia, A. Niccolai
This paper introduces fuzzy logic into reward design for deep reinforcement learning agents in carbon emission trading (ETS), aiming to optimize trading strategies. Fuzzy rewards improve decision-making under uncertainty, balancing emission…
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