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 42 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…
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…
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…
Ivan Savin (5189054), Mateo López Carel, Eva Schlindwein
Using computational linguistics on 1,477 ESG reports from STOXX Europe 600, this study identifies 34 main topics (six environmental). It finds that topics like sustainable value chains and renewable energy correlate with improved environmen…
Mengxi Xie
This dissertation combines machine learning (NLP) with econometrics to empirically analyze environmental policies. The first three studies examine the EU's Carbon Border Adjustment Mechanism (CBAM) via meta-analysis, US equity market reacti…
Ahmed G. Abo-Khalil
This paper proposes a unified framework integrating AI-driven renewable forecasting, grid economics, and lifecycle assessment. Using deep learning, it reduces prediction errors by 50% and operational costs by 18.7%. The study includes AI en…
Chris Meniw
This whitepaper analyzes the dual role of AI: optimizing power grids and integrating renewables while increasing energy and carbon footprint from compute infrastructure. It examines deployments in smart grids, industrial optimization, and r…
Kowalik R.
This paper reviews AI applications in photovoltaic-integrated buildings, covering energy forecasting, intelligent control, and net-zero performance. It demonstrates the effectiveness of machine learning and optimization methods. The study e…
Zhang H.
This paper proposes a machine learning-based method to assess the risk level and analyze the impact range of CO2 pipeline leakage in CCUS, contributing to the safety of decarbonization technologies.
Obaid Ullah, BenYan Tan, Ali Zeb +1
This paper applies machine learning to examine how artificial intelligence, carbon pricing, and green finance affect renewable energy affordability in advanced economies. It quantifies the impact of policy interventions and identifies drive…
Ninglin Ou, Mohammad A. Razzaque, Iftekher Islam Shovon +5
This paper proposes an emission-aware Reinforcement Learning (SAC-based) strategy for EV charging scheduling, incorporating real-time carbon intensity and renewable energy availability. Tested on EV2Gym with EirGrid data, it achieves up to …
Nicolodi A.
This paper proposes a method to detect potential greenwashing in green bond disclosures by analyzing the omission of SDG references and the use of overly optimistic environmental tone. Using natural language processing, it provides investor…
H. Liao, Chang-Yi Kao, K. Ang
This paper introduces a zero-trust orchestration framework for Smart Fabs to comply with the EU's SSbD, CSDDD, and CBAM. Using Professional Proxies and industrial data spaces, it automates compliance verification via federated machine learn…
Han-Teng Liao, Chang-Yi Kao
As the EU CBAM approaches, this paper proposes a RegTech reference architecture using the International Data Spaces (IDSA) framework to reconcile regulatory transparency and data sovereignty in global semiconductor-petrochemical value chain…
Qina Zhu, Shanshan Jiang, A. Du
Using data from 2630 Chinese A-share listed companies (2010-2022), this study demonstrates that AI enhances corporate ESG performance. Based on information processing theory, it identifies three mechanisms: improving green innovation rate, …
Wenze Wu, Mei Sun, Mark Goh
This study constructs firm-level indices of supply chain digitalization (SCD) and corporate green transition (CGT) via textual analysis of Chinese A-share manufacturing firms' annual reports. It finds that SCD significantly promotes CGT, me…
Somasundaram, Deepa, Krishnamoorthy, N., Anand, J. Vijay +3
This paper proposes an AI-driven framework for EV charging station placement integrating LSTM-based spatiotemporal demand forecasting, GA-PSO multi-objective optimization, and deep reinforcement learning for adaptive resilience. Evaluation …
Francisco Javier Rodríguez-Ruiz, Ana María García-Berbaneu, Alexsander Luiz Telpisow-Scheid
This study proposes an automated methodology using NLP to extract, classify, and assess ESG content from corporate sustainability reports. It uses a taxonomy aligned with European reporting standards for listed SMEs, enabling scalable and r…
Mrs. Awantika Deshpande, Mr. Kiran More
This paper proposes an AI-driven education system to enhance ESG reporting knowledge and consistency. It integrates NLP, machine learning, and knowledge graphs for framework analysis, adaptive learning, and real-time reporting assistance. E…
Stefan Vieweg, Christoph Klein
This review synthesizes 2022-2025 evidence on AI in green finance and ESG, proposing the RESP-ESG framework for responsible adoption. It maps eight archetypes, showing NLP and ML enhance ESG signal extraction and climate analytics, while hi…
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