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 1–20 of 271 papers
SangYoun Kim, T. Woo, Usama Ali +8
This systematic review of 906 publications (2016-2025) categorizes AI applications into flue gas treatment, air quality management, and CO2 capture. It identifies a shift from traditional monitoring to AI-driven surrogate modeling and gener…
Ritik Raj, Survi Sinha, Atreyi Pramanik +1
This paper reviews how AI enhances efficiency, sustainability, and system integration across the hydrogen value chain. AI modeling and optimization improve environmental impact assessment of hydrogen production routes like waste polymer and…
Taedong Lee, Sinjae Kang, D. Utami +1
This study analyzes 634 sustainability reports (2014-2024) from the top 200 Korean firms using Structural Topic Modeling (STM). It identifies 13 topics and a phased shift: CSR and basic environmental management (2014-2018); workplace safety…
Lakshmi Narasimha Prasad Nagaragere, Dr.S. Prabakar
Proposes a vernacular ESG transformation model rooted in South Asian institutional cultures. Using AI-based environmental impact assessment and ESG reporting tools, the study demonstrates through pre/post case studies in family-owned busine…
Manav Gangar, Nikhil D'Souza, Rishika Kapasi +2
This paper proposes a machine learning framework for detecting greenwashing by combining supervised classification and unsupervised anomaly detection. It introduces a Greenwashing Discrepancy Score (GDS) to quantify the mismatch between fir…
Fuoli M.
This paper uses corpus-assisted analysis to evaluate the substance of corporate net-zero pledges, applying NLP methods to quantify commitment specificity and target rigor, revealing significant variation in pledge quality and potential gree…
Singh S.K.
This paper proposes policy frameworks that leverage AI technologies to accelerate the transition to a net-zero economy. It explores how data-driven decision-making and predictive models can support the design and evaluation of effective dec…
Khara S.
This paper explores the pathways to achieve net-zero energy using artificial intelligence, discussing how AI can optimize energy efficiency and integrate renewable energy sources.
Qian He, Ying Jin, Chen Chen +1
This study examines China from 1990-2023 using machine learning methods (LASSO, causal forest, Gaussian process) to analyze how trade openness, structural transformation, and government effectiveness affect coal usage and energy costs, ulti…
Fei Wang, Lei Jiao, Konglin Zhu +4
This paper addresses scheduling of cloud-edge federated learning under carbon neutrality constraints, incorporating demand response. It proposes methods to optimize the trade-off between learning performance and carbon emissions reduction.
Mykhailo Prazian
This paper explores how digital technologies, including AI and IoT, can be integrated into supply chains to reduce Scope 3 emissions and enhance cyber-physical resilience. It highlights the potential for improved emissions visibility and ri…
Shadrach Kukuchuku, Rachael Dickson, Tamunotonye Sotonye Ibanibo
This paper proposes a Digital Asset Management Framework integrating IoT monitoring, Asset Health Index modelling, ML-based predictive maintenance (Random Forest, ANN, SVM), and lifecycle optimization (Genetic Algorithm, PSO) for renewable …
Kuang Y.
This study uses double machine learning to estimate the causal impact of green finance on urban energy efficiency, providing insights for policy and investment decisions.
Xu P.
This paper evaluates the causal effect of China's emissions trading scheme (ETS) using high-frequency trading data and double machine learning (DML). It estimates the impact of policy intervention on carbon prices and trading volumes, provi…
Jakkaraju V.T.D.
This paper proposes Predictive Green FinOps, a method for jointly optimizing cost, carbon, and reliability in AI-intensive cloud environments. It uses predictive analytics to dynamically adjust resource allocation, reducing carbon footprint…
Singh V.
This paper applies FinBERT, a financial domain-specific BERT model, to conduct sentiment analysis on ESG disclosures of Indian mutual funds using a pillar-based classification approach. The study demonstrates the effectiveness of NLP techni…
Khan T.
This paper proposes a conceptual framework for using AI in Scope 3 GHG emissions measurement, reporting, monitoring, and assurance from a critical systems perspective. It examines AI's potential to improve data quality and automation while …
Feiya Luo, Yanyun Nian, Pinqi Rao +2
This study proposes a scalable workflow integrating UAV LiDAR and hyperspectral data for individual tree species classification and carbon stock estimation in urban forests. Using an adaptive feature selection method (ACV-DCC) and RF classi…
Jacob Goldverg, Elvis Rodrigues, Tevfik Kosar
This paper proposes spatiotemporal scheduling methods for data transfers to minimize carbon footprint, leveraging AI/optimization techniques and accounting for temporal and geographical variations in carbon intensity. It contributes to ener…
Ivan Savin (5189054), Mateo López Carel, Eva Schlindwein
This paper uses regression to link topic prevalences extracted from ESG reports to environmental KPIs. It demonstrates how NLP and machine learning can quantitatively assess the relationship between disclosure content and actual environment…
Browse by Topic