GX & Decarbonization Research

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…

Huijuan Sun, Ning Bai

This study uses double machine learning to identify the causal impact of energy transition on corporate ESG performance, revealing mechanisms and providing implications for corporate sustainability strategies.

Metin Katlav, Kazim Turk

This paper presents an explainable stacked ensemble learning and multi-objective optimization framework for the intelligent design of green and low-carbon geopolymer concrete. It enables efficient exploration of mix proportions that balance…

Basaula Pululu Jordan, Xinyu Yang, Y Shi +5

This paper proposes an uncertainty-aware BIM-LCA methodology integrating automated quantity takeoff, probabilistic carbon assessment, and explainable machine learning optimization to reduce embodied carbon in building design. Using a case s…

Wanqiao Che, Weimin Zhuang

This paper proposes an AI-enabled pre-design programming method for low-carbon buildings, where spatial form is optimized based on carbon flow analysis. It introduces a novel approach to building design that integrates AI to automate carbon…

Md Qamruzzaman, Abdulrahman Alomair, Mohammed Alomair

This paper examines the interconnections among FinTech adoption, intellectual capital, ESG disclosure, and dividend policy on firm value in financial institutions from an emerging economy (Bangladesh). Using fixed-effects, GMM, CS-ARDL, qua…

Zaidoon Alhatabat, Diala Ershaid, Nashat Ali Almasria +1

This paper reviews current trends in integrated reporting (IR) within the convergence of ESG, digital transformation, and Industry 5.0. It finds that AI, NLP, and blockchain enhance reporting accuracy and timeliness but introduce ethical an…

Marcin Trybulec, Agnieszka Karman, Anna Sedlak +1

This paper presents a dataset of ESG reports, providing a benchmark for natural language processing and machine learning methods applied to ESG evaluation and analysis. It enables applications such as automated extraction of disclosure cont…

Badescu, Luca Arthur

This paper examines AI's potential for assessing urban business sustainability using a literature review and empirical data from S&P Global CSA 2024 (≈13,000 companies). AI adoption significantly improves ESG performance, especially environ…

S. Saranya, K. Chandrasekar

This review systematically examines how AI technologies (ML, predictive analytics, IoT) enhance supply chain sustainability (carbon reduction, waste minimization) and operational efficiency. It synthesizes cases in demand forecasting, inven…

Xiyue Cao, Xujiang Qin, Yanqiu Zuo +2

This review comprehensively synthesizes AI applications for carbon emissions management across monitoring (satellite remote sensing, sensor networks, ML), prediction (deep learning, ensemble learning, statistical learning), and reduction (i…

Dileep G. Menon, Sangeetha Gunasekar, Badri Narayanan Gopalakrishnan

This paper uses text mining to analyze ESG disclosure of Indian hospitality firms and examines its relationship with financial performance. Findings likely indicate a positive correlation between ESG disclosure and financial metrics.