GX & Decarbonization Research

Kokubu, Hiroyuki

A quarterly snapshot of GX disclosure metrics machine-extracted from integrated reports and sustainability reports of TSE Prime-listed companies. Covers Scope 1/2/3 emissions, SBT status, TCFD disclosure, CDP score, renewable ratio, interna…

Sara Sghiouri, Mohamed Bezza, H. Sabir +2

This review systematically organizes AI applications in wind turbine predictive maintenance, highlighting their potential to improve reliability and reduce costs in the transition to low-emission energy. AI-based fault prediction enhances w…

Meng-Ying Ma, Palida Tuersun, Gulijiang Kuerban +2

This paper proposes a method for carbon price forecasting that integrates multi-source carbon market data using a structured cross-market attention mechanism. The step-sensitive design aims to capture abrupt price movements. By applying AI …

Avuzwa Lerotholi, Ibidun Christiana Obagbuwa, Olaperi Okuboyejo

This paper proposes a machine learning and explainable AI (XAI) approach to predict ESG risk scores, enhancing transparency in ESG assessments. It enables investors and companies to understand the drivers of ESG ratings. Originating from So…

Ruosong Hou, Wei Guo, Ziheng Zhao +2

This study proposes a data-model hybrid method for multi-timescale low-carbon economic dispatch of manufacturing-park virtual power plants. It integrates empirical uncertainty scenarios with a production-constrained stochastic program, achi…

Turgay Battal

This repository provides code, trained model weights, and calculation workbook to reproduce the results of a paper in Transportation Research Part D (TRD-D-26-01334). It applies distributional reinforcement learning to robust low-carbon shi…

Suliman Khan, Safat Al-Deen, Chi King Lee

This paper presents an experimental dataset of ultra low-carbon concrete designed for performance prediction and machine learning applications. It supports decarbonization in construction by enabling efficient material design and CO2 reduct…

Suliman Khan, Safat Al-Deen, Chi King Lee

This paper provides an experimental dataset of ultra low-carbon concrete for performance prediction and machine learning applications. The dataset supports decarbonization in the construction sector, enabling performance evaluation and opti…

Shuangyang Zhai, Zishan Zhang, Haoyu Hou +2

This study examines whether China's carbon emissions trading (CET) pilot policy affects auditor industry specialization using a staggered difference-in-differences design on A-share firms from 2010 to 2022. Results show that CET exposure in…

Tamara Menichini, Stefania Roberta Miccoli, Nicoletta Maria Strollo

This paper systematically reviews AI's role in ESG reporting from a knowledge management perspective, identifying nine positive impact categories on SAR. Using the SECI model, it conceptualizes AI as an enabling infrastructure that enhances…

Yelmen B.

This study models energy use and GHG emissions in orange production in Turkey using artificial neural networks (ANN). It demonstrates AI application in agriculture for emission prediction and energy efficiency improvement, offering insights…

Wang H.

This paper proposes an AI-forecasting-based approach to manage cloud computing resources in a carbon-neutral manner, optimizing energy efficiency and reducing carbon emissions to support net-zero targets. It exemplifies the application of A…

Imad Ud Din Durrani, Hassan Raza

This study integrates disclosure tone (positivity, negativity, uncertainty) into machine learning models to enhance financial performance prediction for non-financial firms in Pakistan. Textual data from annual reports were analyzed using a…

Ritwick Boyra, Rajesh Bose, A. Khan +5

This paper proposes CrossChain-VaxTrace, an integrated architecture combining cross-chain blockchain, federated graph neural networks, and edge AI to address vaccine supply chain challenges. It achieves 67% energy savings via PoA consensus …

Marisol Coba-Martínez, Jarniel García-Morales, G. Guerrero-Ramírez +4

Proposes an intelligent control strategy for alkaline water electrolysis to produce green hydrogen on-demand, matching the stoichiometric requirements of methanol synthesis. ANN models with classical and conformable activation functions are…

Nicolas van der Nest

AI-assisted analysis of 30 Singapore Exchange-listed firms' reports reveals a gap between disclosure and implementation in supplier sustainability governance. While Scope 3 disclosure is universal, enforceable supplier controls remain limit…

Shunhao Mai, Zenglu Zhang, Jie Zhu +2

This paper introduces the Disclosure-Emission Gap (DEG) Index, a novel metric to detect corporate carbon washing by quantifying the divergence between disclosed environmental commitments and verified emission performance. Using CarbonBERT-C…

Jinpeng Dai, Fanghui Lu, Riccardo Maddalena +2

This study uses CPO-XGBoost machine learning to predict compressive strength of limestone calcined clay cement (LC3) and optimize low-carbon mix designs. LC3 reduces CO2 emissions in cement production, contributing to construction decarboni…

Zeqi Zhang, Yingjie Li, Danhui Lai +2

This paper proposes a low-carbon operational decision-making method for computing-energy parks using large language models (LLMs) and multi-agent collaboration. A dynamic carbon emission factor (DCEF) captures time-varying carbon signals, a…

Tong Qian, Yang Liu, Yunlin Huang +2

This review examines how AI enables electricity–computing synergy to address data center energy demands and renewable energy variability. It highlights deep learning for renewable forecasting and deep reinforcement learning for flexible wor…