GX & Decarbonization Research

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…

K. Zhou, Y. H. Cao, H. Yang

Using data from Shanghai and Shenzhen A-share listed companies (2010-2023), this study employs a two-way fixed-effects model to analyze the impact of venture capital (VC) on corporate carbon performance. VC improves carbon performance by al…

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.…

Qiusheng Gao, Meichen Yao, Liang Duan

This paper proposes a new paradigm for low-carbon retrofitting of existing wastewater treatment plants by coupling AI fine-tuned operation with dynamic in-situ retrofitting. AI optimizes operations to reduce energy consumption and greenhous…

Fahrettin Pala, Emine Kaya, Esra Nur Akpınar +3

Using panel data from 50 countries (2002-2019), this study examines how AI, renewable energy technology innovation (RETI), and regulatory quality (RQ) affect low-carbon energy transition. AI promotes transition across all income groups, whi…

Xiaojie Lin, Jian Li, Rui Jing +4

This paper reviews the role of AI in transforming energy systems for carbon neutrality. It highlights how data-driven prediction, physics-informed learning, reinforcement learning, digital twins, and generative models enhance renewable fore…

Jiachen Shen, Jian Shi, Hui Zhong +3

This paper applies mechanism-guided graph learning to evaluate carbon border policies (e.g., CBAM) in interconnected European electricity markets, integrating AI methods with climate policy assessment to quantify cross-border policy spillov…

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. …