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 201–220 of 986 papers
P. Vibin Sri Balaji, Srinivasan R
This study integrates satellite data and machine learning to identify optimal solar installation sites and forecast 10-year energy output. Using NASA POWER data and physical PV modeling, Random Forest achieves high predictive accuracy (R²≈0…
Kaithari D.K.
This review surveys AI systems for real-time monitoring and optimization of ocean-based carbon capture, utilization, and storage (CCUS) networks. It synthesizes how AI enhances efficiency and risk management in carbon capture and storage, a…
Pranav Nerurkar
This paper empirically examines the 'green halo effect,' revealing real-world inconsistencies between ESG scores and retail investor sentiment. It analyzes how ESG ratings influence investor perception and behavior, questioning the reliabil…
B.K. Tripathy, Riya, Hemant Kumar Saini
This chapter explores energy-saving solutions to reduce the carbon footprint of UAVs across their life cycle. It covers AI-based optimization (ML for energy-aware routing, RL for navigation, GANs for flight energy prediction), blockchain fo…
(著者不明)
This paper explores AI-driven circular economy strategies to achieve carbon neutrality in emerging economies. It discusses how AI optimizes resource efficiency and waste reduction, contributing to emission cuts, with implications for policy…
Isaiah Oluwasegun Owolabi
This paper introduces ESG-as-Code, a deterministic rule-based framework that converts ESG regulatory obligations (CSRD, SFDR, SDR, SEC rules) into machine-executable rule logic. It separates probabilistic interpretation from deterministic c…
Yang S.
This paper proposes a hybrid BiLSTM-XGBR model to predict corrosion depth and compressive strength of cement stone in CCUS wells. It applies AI to assess well integrity, contributing to safe and durable CO2 storage.
K. Strang, N. Vajjhala
This single-case study analyzes ESG compliance measurement in financial software engineering projects as a socio-technical system. Applying an open-source ML workflow to 207 archival records, it finds near-unity correlations between social/…
Xinyi Wang
This conceptual paper critically examines generative AI in ESG reporting. It argues that while generative AI enhances efficiency by reducing cost and time in processing text-intensive data and drafting disclosures, it introduces challenges …
Erica Mascarenhas, Oluseyi A Fayanju, Anna Arroyo +1
The US healthcare sector leads globally in per capita GHG emissions, and federal deregulation heightens the need for voluntary action. Decarbonization across infrastructure, supply chains, and operations is feasible via renewable energy, wa…
W. Wardhiah, Maimoona Majid, Said Musnadi +1
Using panel data from 30 Indonesian firms (2020-2025) linked to the LQ45 Low Carbon Leaders Index, this study examines whether digital transformation (DX) helps convert energy-transition strategies into financial value. A 30-item DX index a…
I. Kravchuk, N. Valinkevych, O. Prysiazhniuk
This paper presents theoretical and applied foundations for investment management in agribusiness distribution networks, integrating portfolio diversification, real options, behavioral finance, ESG metrics, and DeFi. It proposes a hierarchi…
Faridah, Sitti Mujahida Baharuddin, Herminawaty +3
This study analyzes the effects of digital transformation, ESG disclosure, and corporate governance on financial distress, mediated by operational risk and financing constraints, using panel data from Indonesian non-financial firms (2018-20…
Song Dongze
This paper proposes HiCon-ESG, a novel learning method that improves fine-grained classification of ESG disclosure documents. By incorporating confidence-aware hierarchy regularization, it enhances classification accuracy and interpretabili…
Rogelio O. Corona‐Núñez
This dataset provides high-resolution (100m) spatial models of household carbon footprints for the Mexico City metropolitan area, using Random Forest on survey data from 992 dwellings. It maps diet, housing, and transport emissions with sta…
(著者不明)
This paper proposes and evaluates NLP-based methods to automatically detect corporate greenwashing. By analyzing texts such as ESG reports and press releases, it identifies discrepancies between environmental claims and actual performance. …
Lei Lin, Zhi Zhong, Chenglong Li +5
This study proposes SS-UNETR, a semi-supervised foundation model based on a 3D Swin Transformer, for simultaneous identification of multiple geological features (faults, channels, caves) in seismic volumes. Pre-trained on 75,651 field image…
M. Sri, Mrs. A. V. Harshika, L. Nasreen +2
This paper proposes a carbon trading platform leveraging AI and machine learning for real-time pricing, risk assessment, and emission reduction forecasting. It uses blockchain to ensure secure and tamper-proof transactions, offering a scala…
Rongchen Zou
This paper examines ESG rating frameworks embedded in FinTech tools such as AI-driven investment systems and credit-assessment algorithms. While ESG adoption improves transparency and long-term risk oversight, inconsistencies among rating p…
Kalpana K, Backiyavathy M R, Chitdeshwari T +2
This review introduces Engineered Soil Carbon Systems (ESCS), integrating enhanced weathering, engineered biology, nanomaterials, and digital monitoring. It highlights IoT and machine learning for improved MRV, positioning ESCS as a verifia…
Browse by Topic