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 361–380 of 1487 papers
Kollia M
This Perspective proposes the ZERO framework to shift AI data-centre sustainability evaluation upstream, before resource consumption. It combines hard admissibility constraints, workload characterisation, local resource-state assessment, fe…
Zhang Q, Sheng S, Liu T
A two-stage lexicographic framework for geo-distributed AI inference that preserves service while optimizing carbon and water. Trace-driven analysis of 26,392 requests reveals a 21-point carbon-water frontier, quantifying trade-offs. Provid…
C D, VISMAYA, V P, ANAMIKA, JURIYA, FATHIMA +2
Proposes an AI-based framework integrating IoT, cloud, and Random Forest Regression to monitor data center operations, predict heat generation, and recommend optimal heat recovery applications. Aims to improve energy efficiency, reduce cool…
Yao, Huaying
This replication package audits corporate climate-target claims against mandatory verified emissions from the EU Transaction Log and US EPA GHGRP. Using only public data, it parses 1,779 claims into six adjudication tiers and documents sens…
Dariusz Sala, Alla Polyanska, Vladyslaw Psyuk
This paper uses bibliometric analysis of 146 publications to show the evolution of energy transition research from technical aspects to climate change, renewables, and policy. It proposes a Digital-Twin-Oriented Techno-Economic Decision-Sup…
Chethan K Murthy, Prateek Verma, Subhash Mondal
This paper introduces an agentic AI ESG consultant using a privacy-preserving, localized RAG system to automate ESG compliance gap analysis for regulations like CSRD and BRSR. It bridges corporate disclosures and legal statutes via iterativ…
Yutong Shen, Wang Yang, Yue Shen
CogDeBias is a multilingual framework integrating LLMs and ML to automatically detect and mitigate six cognitive biases (e.g., confirmation bias, sunk cost fallacy) in corporate annual reports. A bilingual corpus of 1,200 reports (600 Engli…
Maria Luiza Xavier de Holanda Cavalcanti, Lúcio Câmara e Silva, Luciano Costa +3
This systematic review analyzes 400 experimental articles (2015-2026) on AI applications in wind energy, highlighting a paradigm shift from RNN/LSTM to attention-based Transformers. It identifies emerging trends like foundation models for t…
Antans Sauhats, Diāna Žalostība, Roman Petrichenko +4
This paper proposes an AI-based surrogate modeling framework using artificial neural networks (ANNs) to accelerate long-term power system transition planning. By combining stochastic scenario generation with detailed simulations and ANN sur…
Sejan, Sajid Hassan, Apu, Arman Hossain
This dataset provides monthly embodied GHG emissions for 616 NAICS-6 commodities from US federal procurement (FY2015-FY2024), covering $2.77 trillion and 486.9 MtCO2e. It exactly joins contract obligations with EPA emission factors, validat…
Ma Y.
This study uses interpretable machine learning to identify greenwashing in heavily polluting enterprises and uncover its multidimensional driving mechanisms. By analyzing corporate ESG disclosures and behavioral data, it builds a model that…
Usama Aslam, Vikram Kumar, Muhammad Ahsan Niazi +1
This paper proposes Graph-X, a unified graph-structured deep learning and stochastic optimization framework for day-ahead electricity price forecasting and risk-aware VPP bidding. It models market-clearing behavior by converting bids into p…
Pengyu Zhang
This study empirically examines how ESG rating fluctuations affect investor psychology (anxiety, stress) and subsequent market reactions, using panel data from 1,236 Chinese A-share firms (2018-2023) and machine learning/big data analysis. …
Yuhao Gu, Han Lai
This paper explores how FinTech (blockchain, AI, big data) enhances green finance efficiency through environmental transparency, risk mitigation, and green innovation. Empirical results show blockchain improves disclosure authenticity, AI r…
K. Logožar
This paper proposes the VAF framework, arguing that smart ports can serve as governance infrastructures that make maritime flows visible and accountable. It integrates digital technologies like AI, digital twins, and blockchain with governa…
V. Vignesh, G. V. Narendran, R. Senthil Kumar +1
A hybrid framework integrating Fuzzy C-Means clustering with temporal deep learning (NARX, RNN, LSTM, GRU) is proposed for multi-parameter forecasting in blast furnaces. Evaluated on 43,396 industrial DCS samples, FCM-GRU achieved the best …
Andreas Poyias, Panayiotis Mourtopallas, Diamanto Platanou +3
This study develops a digital framework for optimizing green hydrogen integration in food industry steam production, which accounts for up to 57% of energy use. LightGBM models trained on PV and weather data achieve high forecasting accurac…
Firat Bolat, Hande Demi̇rel
This study integrates Sentinel-5P TROPOMI satellite observations with AIS data to monitor ship emissions in the Istanbul Strait, estimating annual CO2 of 213,678 t, NOx of 5,970 t, and SOx of 686 t. A correlation of 0.76 between AIS-derived…
Kejun Xie
This study examines the relationship between board environmental expertise and carbon disclosure quality (CDQ) using 28 ASX-listed firms. Analyzing 140 firm-year observations from 2019-2023 with Tobit regression, it finds a significant posi…
Kjartan Sigurðsson
This chapter analyzes AI's transformative role in sustainability reporting, particularly ESG, arguing it represents institutional change rather than mere technological improvement. It examines how blockchain, generative AI, and real-time mo…
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