GX & Decarbonization Research

I. Ferdiyansyah, R. Masman

This study analyzes the relationship between Digital ESG Leadership (DEL) and ESG disclosure quality, strategic integration, and performance in eight Indonesian technology companies (39 firm-years, 2019-2024). Using dictionary-based scoring…

Cheng Qian, Fan Yang, Yu-Zhou Jiang +1

This paper proposes a tri-layer hybrid blockchain architecture for automated carbon auditing in urban transit networks, combining MySQL, IPFS, and Hyperledger Fabric. A smart contract pipeline autonomously verifies hash consistency and emis…

Lei Yu, Zhikai Li, Kai Huang +6

This study develops an LLM-agent-driven optimization framework for water-energy-carbon (WEC) nexus management in irrigation districts. It uses three sequential agents (task analysis, algorithm execution, result analysis) to automate parsing…

Osama Omar

This study systematically reviews the role of AI in climate-resilient green buildings, identifying six key sustainability indicators: energy efficiency, renewable energy integration, carbon footprint reduction, water management, indoor envi…

Sornoza Parrales, Diego, Pincay Pilay, Mercedes Marcela, Castro Chilán, Dayana Michelle +1

This repository provides the analytical corpus and R replication script for a bibliometric review of AI for net-zero construction in the AEC industry. It includes 517 documents from Scopus and Web of Science, with thematic mapping and evolu…

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 …

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…

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…

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…

Chandrani Ray Chowdhury

This chapter reviews generative models for probabilistic forecasting of renewable energy production, addressing the limitations of traditional ML/DL in handling stochastic, nonlinear data. It demonstrates how probabilistic forecasts support…

Jinliang Wang, Xiang Ma, Z. Ding

This paper proposes a method using large language models (LLMs) to automatically extract and analyze ESG disclosure information from corporate sustainability reports. Compared to traditional manual analysis, it enables efficient and compreh…

Y. D. Bao, Yiyi Lu, C. Liu +5

This paper proposes an LSTM-based forecasting framework for data center energy consumption, integrated into a dual-carbon data management pipeline. The model, featuring attention pooling and multi-task PUE prediction, reduces RMSE by up to …

Rosella Castellano, Federico Cini, Annalisa Ferrari +1

This paper proposes a methodological framework to detect overreporting and underreporting in corporate sustainability disclosures by leveraging divergences among ESG rating agencies. It interprets rating gaps as signals, contributing to rel…

Ya-Ting Jhan, Sheng-Feng Hsieh

This paper examines whether ESG narratives are financially informative by using LLM-based summarization and sentiment analysis. It empirically tests the value of ESG disclosure text for investment decisions.

Jiayi Zhu, Lei Yang, Wenhui Zhou

This paper explores integrating AI into remanufacturing supply chains to enhance sustainability and intelligence diffusion. AI-driven demand forecasting and quality control can reduce waste and improve resource efficiency, supporting the tr…