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 321–340 of 1487 papers
Y. D. Bao, Yiyi Lu, C. Liu +5
This paper proposes an LSTM-based forecasting framework for data center energy consumption, integrated into a dual-carbon data management pipeline. The model, featuring attention pooling and multi-task PUE prediction, reduces RMSE by up to …
Tariq Muhammad Hussein Al-Zoubi, Odai Al-Hailat, A. Alomar +1
AI is transforming ESG data collection and disclosure but raises transparency, verification, and AI-enabled greenwashing concerns. This doctrinal legal study proposes an integrated governance framework combining transparency, human oversigh…
Su W, Wang J, Tu M +1
This study bridges academic theory and corporate practice by analyzing the intersection of carbon emissions, carbon taxation, and supply chain inventory models (SCIM). Using Main Path Analysis on 3,359 papers (1995-2025), it identifies 41 r…
Rosella Castellano, Federico Cini, Annalisa Ferrari +1
This paper proposes a methodological framework to detect overreporting and underreporting in corporate sustainability disclosures by leveraging divergences among ESG rating agencies. It interprets rating gaps as signals, contributing to rel…
Ya-Ting Jhan, Sheng-Feng Hsieh
This paper examines whether ESG narratives are financially informative by using LLM-based summarization and sentiment analysis. It empirically tests the value of ESG disclosure text for investment decisions.
Jiayi Zhu, Lei Yang, Wenhui Zhou
This paper explores integrating AI into remanufacturing supply chains to enhance sustainability and intelligence diffusion. AI-driven demand forecasting and quality control can reduce waste and improve resource efficiency, supporting the tr…
Gatty M.S.
This paper examines the role of FinTech and AI in advancing green finance. It likely discusses applications such as AI-driven ESG assessment, climate disclosure analysis, and green bond selection. Specific methods and findings are unclear, …
Silpa M.
This paper proposes a federated learning approach to analyze ESG disclosure data from NSE sectoral indices. It aims to improve ESG assessment accuracy while preserving data privacy. The methodology offers technical insights applicable to co…
Moojin Park
This preprint asks whether ESG regulatory events mandating independent third-party assurance produce larger asset-price reactions than mere disclosure expansions. It formalizes a signaling game where verification intensity enters the cost f…
Dominic Ayegba Okoliko, Mehita Iqani, Mehita Iqani
This living repository provides supplementary materials for a multi-phase project analyzing the salience of climate change in electoral discourse in South Africa's 2024 national election and Nigeria's 2023 presidential election, using BERTo…
Newton. Tyler James
AquaContam uses over 4 million samples from 95,223 U.S. water systems to show that ML models learn monitoring processes as much as contamination. Apparent lead-model performance is largely a target-leakage artifact, and monitoring is inequi…
Bushra Ansari, Amjid Khan, Raja Muhammad Usman
This study examines how AI contributes to resilient and sustainable supply chains via multiple case studies. Optimization-driven AI cut emissions or resource waste in 5 of 6 global cases, and predictive analytics/digital twins halved disrup…
Meiqin Jiang, Jinxing Che, Yaoxin Tan +1
This paper proposes CarbonTimer, a large time series model to address data scarcity in carbon price forecasting. It enables accurate predictions with limited data, potentially aiding carbon market risk management and policy formulation.
Xiaobin Wang, Yuning Jiang, Zhihua Zhang +4
This paper proposes a contextual information-based distributionally robust optimization method for decarbonizing oil and gas field power systems. It integrates renewable energy while managing uncertainty, aiming to reduce operational costs …
Fan Zhang, Bo Wen, Ditao Niu +4
This study integrates life-cycle carbon accounting, statistical analysis, Monte Carlo simulation, and machine learning to assess embodied carbon in RC buildings. Using data from 124 buildings in Shaanxi Province, it finds material productio…
Murali Krishna Pasupuleti
This monograph presents an integrated research architecture for planetary resilience, combining Earth-system AI, ClimateTech modeling, and sustainable energy intelligence. It covers uncertainty-aware design, physics-guided ML, MLOps, and se…
Ömer Algorabi, Ersin Namlı
This study compares deep learning (GRU) and boosting models for forecasting electricity generation in Türkiye and Germany, then evaluates 2030 BAU, ST, and EC scenarios. GRU performs best for Türkiye, boosting for Germany. Scenario analysis…
Xie M.
This paper empirically analyzes the impact of green finance and financial technology on regional green energy technological innovation using dual machine learning and spatial econometric models. It reveals the effects of financial policy an…
Mejda T.
This paper analyzes the causal impact of regtech adoption on corporate greenwashing using staggered DiD, DDD, and generalized random forests. It explores heterogeneous effects across firms, contributing to the AI×ESG intersection by applyin…
Annu Dhull, D. Abdukhamidov, D. Yusupov +3
This paper proposes a digital twin framework for real-time monitoring and performance optimization of green hydrogen electrolysers. It combines physics-based models with AI prediction to enhance efficiency and stability under fluctuating re…
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