GX & Decarbonization Research

Je Hyun BAE, JaeHo Lee, Eun-Chong Kim +1

This study uses Gemini 3 Flash to measure disclosure quality in Korean voluntary sustainability reports. It finds that voluntary reporters have higher ESG ratings, and textual quality and reporting continuity are associated with subsequent …

Yanbing Zhang, Changzheng Zhang, Chengyu Li

This paper investigates the emergence and dissolution of green innovation bubbles among Chinese power industry chain enterprises from 2016 to 2023. Using the AMO framework and double machine learning (DDML), it identifies causal drivers, an…

Bartholomew Paudyn

This paper critically examines ESMA's Strategy 2023-28, focusing on how AI and machine learning in ESG ratings may hinder sustainable finance and technological innovation. It argues that algorithmic decision-making exacerbates greenwashing …

(著者不明)

This book provides a comprehensive exploration of how finance, technology, governance, and social behavior are being transformed to address climate change. It focuses on how AI, blockchain, and IoT are redefining climate accountability and …

Abhinav Mahajan

This paper frames corporate climate risk as a multi-domain decision problem and proposes the Climate AI Decision System (CADS) architecture that integrates AI for evidence-based management. It synthesizes literature on climate finance, ML f…

Shikder S.H.

This paper proposes an AI-driven framework for green finance and environmental monitoring in Bangladesh, addressing data deficits. It conceptualizes how AI can integrate financial and environmental data to support green investments, though …

Li Y.

This study empirically tests whether LLMs can replace human reading in analyzing sustainability reports. It provides evidence on the accuracy and efficiency of LLMs for information extraction and evaluation, highlighting the potential for A…

Kongthon A.

This paper proposes a method to analyze climate risk disclosures in the banking sector using transformer models and generative AI. It applies NLP techniques to extract climate-risk-related information from disclosure documents and assess th…

Martillano D.A.

This paper presents a borehole and near-surface GHG emission monitoring system using a self-calibrating algorithm and zone-based clustering analysis, enabling accurate and localized GHG measurements.

Zhenchen Sun, Min Xiao, D Zhang +3

This paper proposes CaStNet, a causality-guided decomposition and cell-state-driven attention framework for carbon price forecasting. It uses Granger causality for residual decomposition and cell-state differential velocity for adaptive att…

Bangtong Huang, Dongfang Niu, Qi Xu +1

This paper proposes a deep learning approach to optimize resource and capacity management in low-carbon additive manufacturing (3D printing). By leveraging AI, it aims to reduce energy and material usage, lowering carbon footprint. It demon…

Yiming Peng, Minfei Liang, Cise Unluer

This review synthesizes machine learning (ML) applications for low-carbon concrete, covering cement production, mix design, supplementary materials, and recycled aggregate. ML enhances process control and multi-objective optimization but fa…

Seon Joo Kim, Hyo Jin Park

This study uses text mining to extract Social (S) content from sustainability reports of four Korean port authorities, analyzing disclosure patterns via frequency analysis, N-gram, topic modeling, and network analysis. Results show that dis…

Jonas Rieger, Felipe Muñoz, Lars Grönberg +11

This paper applies RollingLDA, a dynamic topic model, to 1,996 news articles to analyze spatio-temporal narratives of socio-environmental conflicts in Chile's energy transition. It identifies twelve topics, revealing the evolution of confli…

Ram Chandra Chaurasia Ram Chandra Chaurasia, Rajshekhar Singh Rajshekhar Singh

This study proposes an AI-IoT framework for real-time methane monitoring, prediction, and carbon footprint reduction in underground coal mines. Using LSTM, random forest, and reinforcement learning, it achieves over 93% prediction accuracy …

Lei Wang, Zheng Wang, Maolin Fang +4

This paper constructs a green performance evaluation system for distribution network suppliers from economic, technical, and green dimensions, defining a carbon footprint accounting method. It proposes an improved AHP-SVR model, validated o…

Abdul Rauf Agbor Seidu

This paper applies sentiment analysis to model public perceptions of energy insecurity and renewable adoption in Ghana's green energy transition. Using machine learning, it extracts sentiment from social media and survey data, offering insi…

Zhouyi

This paper presents a protocol for a PRISMA-ScR scoping review that audits validation and reporting practices in LLM-assisted ESG disclosure coding against emerging general-purpose standards (GUIDE-LLM, TRIPOD-LLM). It also synthesizes ESG-…

Güven Korkut, Murat Emeç, Muzaffer Ertürk

This study develops an AI-driven framework integrating K-Means, PCA, and Random Forest applied to the IFCMA Climate Policy Database. It compares policy portfolios of nine emerging and thirty-two developed economies, revealing structural und…

Saleh Mohammed Baqader

This paper proposes ESG-C, a bilingual (English-Arabic) NLP framework to measure ESG disclosure quality in Saudi Tadawul annual reports. It uses a weighted index of verifiability, sector specificity, quantitative clarity, and standards alig…