GX & Decarbonization Research

Yuzhi Wang, Cong Zhang

This study analyzes the causal effect of China's intellectual property pilot policy on low-carbon green energy eco-co-evolution (LCEE) using spatial Durbin DID and double machine learning. The inclusive potential of regional innovation ecos…

Youbi D.

This paper systematically reviews the literature on AI applications in sustainable finance, mapping current research and identifying emerging paths. It analyzes AI's impact on ESG assessment, climate-related disclosure, and investment decis…

Gupta U.

This paper proposes a green procurement policy for AI labs' compute resources, requiring minimum environmental disclosures, energy metering, and emission reduction targets. It addresses the growing energy consumption of AI computing and pro…

Vijeta, Neha Yadav, Meenakshi Rao

This conceptual study proposes integrating forensic accounting into sustainability reporting to enhance credibility and stakeholder trust. It addresses greenwashing and weak assurance by applying forensic techniques and emerging technologie…

Vijeta, Neha Yadav, Meenakshi Rao

This conceptual study proposes a framework integrating forensic accounting into sustainability reporting to enhance credibility and stakeholder trust. It addresses greenwashing and selective disclosure by applying techniques like fraud risk…

Олег Ивченко, Iryna Ivchenko

This paper proposes an observability approach for real-time monitoring of carbon emissions from distributed models. By visualizing energy consumption and carbon footprint, it provides practical insights for reducing the environmental impact…

Vu Thi Hoai Thu, Dang Thi Kieu Oanh, Trieu Anh Ngoc

This study integrated GEDI LiDAR, Sentinel-1/2, SRTM, and machine learning to estimate aboveground blue-carbon stocks in the Cua Dai estuary, Vietnam. Random Forest performed best, yielding mean AGC of 13.659 Mg C/ha and total 109,472 Mg C.…

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…

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…

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…

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…

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. …

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 …

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…

Patrycja Hąbek, Kjartan Sigurðsson, Małgorzata Radomska

This paper examines the impact of AI on sustainability reporting. It discusses how AI can automate ESG data collection, analysis, and disclosure, enhancing efficiency and accuracy. It also addresses challenges and regulatory implications of…

Dajiang Liu, Tao Yu, Huiying Xu

This paper proposes a GAT-LSTM hybrid model for dynamic carbon footprint accounting in prefabricated steel structure buildings, integrating spatio-temporal data to estimate emissions across the construction process. It overcomes limitations…

Tarak Barhoumi, Najoua Mezghani

This paper proposes CarbontoScope IA–SFAX, an augmented intelligence system to visualize urban carbon footprints, applied to Sfax, Tunisia. It identifies hidden emission sources, providing data for urban planning and policy. Integrating AI …

P. Vibin Sri Balaji, Srinivasan R

This study proposes an AI-based framework integrating satellite data and machine learning to identify optimal solar plant sites and forecast 10-year energy output. Using NASA POWER data and Random Forest regression, it achieves high predict…