GX & Decarbonization Research

Can Hankendi, Ayse K. Coskun, Benjamin K. Sovacool

This paper goes beyond AI's carbon emissions to analyze the whole-system energy and environmental sustainability of datacenters in Denmark, Germany, and Norway. It evaluates how renewable energy availability and cooling technologies affect …

Paul Foulquier, Ryma Haddad, Ali Assem Mahmoud +4

This paper presents a machine learning approach to accelerate the design of complex and high entropy alloys for protective coatings in low-carbon energy applications (nuclear, high-temperature electrolysis). It introduces the French DIADEM …

Adrian Friday, Abdessalam Elhabbash, Ignatius Ezeani +3

This volume contains the proceedings of the 2nd International Workshop on Low Carbon Computing (LOCO 2026), held at Lancaster University, UK. It covers topics such as carbon measurement and reporting, sustainable software engineering, energ…

Yao Lv, Jincheng Mu, Kanglei Du +2

This study proposes a machine learning-driven approach to optimize the mix design of recycled aggregate concrete, maximizing its carbon sequestration potential. It aims to reconcile low-carbon design with carbon fixation, contributing to de…

Ye Duan, Minghan Yang, Zhaowei Hou +3

This study analyzes spatiotemporal carbon emission patterns across 19 Chinese urban agglomerations (2006-2023) using spatial statistics and machine learning (random forest, SHAP). It identifies industrial structure and economic development …

Bora DK

This review focuses on large-scale PEM electrolyzer plant management for green hydrogen production, highlighting the limitations of digital twin technology. Passive digital twins cannot autonomously close the control loop, causing predictio…

ABIR MAH, Paul B

This paper proposes an AI-accelerated MDO framework for hydrogen-electric aircraft that integrates aerodynamics, propulsion, thermal management, and structural mass. Physics-informed neural network surrogates replace high-fidelity solvers w…

Ziya07

This dataset contains 5-minute interval operational data from a synthetic Hybrid Energy Storage System (HESS), including solar/wind generation, grid power, battery/supercapacitor states, hydrogen production, load demand, supplied power, pow…

ALPSALAZ, Feyyaz, Aslan, Emrah, Özüpak, Yıldırım +1

This repository provides code and dataset for an explainable multi-task LSTM-GRU framework predicting power loss and assessing energy efficiency in hybrid renewable energy storage systems. It includes preprocessing, sliding-window generatio…

Kokubu, Hiroyuki

This dataset release analyzes the CDP corporate questionnaire (Module 7) as an institutional computation device, modeling 2,958 datapoints across 2024-2026 cycles. Each datapoint is classified on three axes (Substance, Narrative, Enforceabi…

Nik Abdullah Bin Rozali, Amli Omar Bin Ismail, Sherry Ameera Binti Mustaffa @ Sulaiman +1

This study proposes 'Green by Design', a socio-technical framework integrating carbon accounting into enterprise architecture to address Scope 3 emissions from digital transformation. Using design science research and thematic analysis, it …

Enmin Zhang, Jianmin Wang, Juanjuan Chen +1

Using a panel of Chinese A-share firms, this study finds an inverted U-shaped relationship between controlling shareholders' equity pledge and corporate green transformation. Initially, equity pledge eases liquidity constraints and promotes…

Marco Cucculelli, Noemi Giampaoli, Matteo Renghini

This paper operationalizes the EU's Net-Zero Industry Act (NZIA) technology framework at the firm level, using large-scale web scraping to extract unstructured data from corporate websites and map Italian firms' adoption and development of …

Dharni Patel, Urvi Deore, Y. A. Vishwa Priya +3

VayuCredit is a machine learning framework for carbon emission monitoring and credit quantification, tailored to India's upcoming Carbon Credit Trading Scheme (CCTS). It uses three models—trend prediction, anomaly detection, and regression—…

Yan Luo

This study proposes an AI-driven ESG scoring framework using Slime Mould Algorithm (SMA) for feature selection and Random Forest Regressor (RFR) for prediction. With rigorous preprocessing, it achieves RMSE 0.3649, MAE 0.2538, and R² 0.9912…

Nyo Me Htun, Toshiaki Owari, Satoshi Suzuki +11

This study develops a multi-sensor machine learning framework integrating UAV multispectral imagery and LiDAR data to estimate living biomass carbon stocks in managed forests in Hokkaido, Japan. XGBoost achieved the highest accuracy (R2=0.8…

Chenyan Wang, Yingying Jiang

This study proposes a data-driven, AI-assisted design framework for community low-carbon recycling terminals, combining K-means clustering and Kano-AHP to quantify user needs and prioritize design features. The developed terminal and platfo…

Kuanhou Tian, Le Sun

Using Chinese A-share listed firms from 2012-2023, this study finds institutional ownership is positively associated with absolute carbon emissions but negatively with emission intensity, with site visits strengthening monitoring. It highli…

Zixin Ma, Daliang Xu, Bobo Yang +6

Applies machine learning to synergistically optimize carbon emissions and contaminant removal in wastewater treatment plants. Proposes a control method that balances process efficiency and GHG reduction, contributing to carbon neutrality.

Yang Wang, Wenhao Chen, Mengyi Di +3

Proposes a blockchain-enabled game-theoretic carbon trading mechanism for shipping enterprises, combining a Stackelberg game between regulator and firms with a bidding game solved via no-regret learning. Simulations show reduced emissions a…