GX & Decarbonization Research

Dan Qiu, Bangjun Wang

Using TOE-P framework with NCA and dynamic fsQCA on Chinese listed mining firms (2015-2025), this study identifies multiple configurational paths to high green and low-carbon performance. No single condition is necessary; synergy among tech…

Jun-Cheol Lee

This study experimentally validates low-carbon mortar mixtures generated by GPT-5.5. With 60% GGBFS replacement, it achieved 60% cement reduction and ~56% embodied carbon reduction while meeting 28-day strength of 40 MPa. GPT-5.5 is useful …

Anonymous Authors for Review

This dataset and supplementary materials support a study that uses large language models (LLMs) to quantify materiality misalignment risk in ESG reporting. It offers a methodology for assessing alignment between corporate disclosures and ma…

negar shabanpour, Sehl Mellouli, Stéphane Roche

Household consumption accounts for ~72% of global emissions, yet traditional surveys are costly and biased. This PRISMA-ScR scoping review maps NLP methods for analyzing individual climate behaviors, screening 2,580 records and including 10…

Toqeer Ali Syed, Ali Akarma, Muhammad Tayyab Naqash +3

This PRISMA-guided review distinguishes agentic AI from conventional ML for SDG 11 and 13, analyzing 60 eligible studies (14 fully agentic). It proposes a reference architecture linking an agentic layer with urban digital twins. Real-data t…

Irfan M.

This paper proposes a method for constructing sustainable investment portfolios using AI, aiming to bridge green finance and technological innovation. It leverages AI to analyze ESG factors and climate-related risks, enhancing investment de…

Hu P.

This paper proposes a method to detect corporate greenwashing by analyzing the readability of ESG disclosures. Low readability is associated with information concealment, serving as a useful signal for investors and regulators. It is an emp…

Jamil J. Jaber, A. A. Alkhawaldeh, Qusay Ayman Sulayman Mazahreh +3

This study predicts credit risk in commercial banks using machine learning (XGBoost) and a fuzzy rule-based model (SLAVE) on panel data from 40 banks across seven Middle Eastern countries (2014-2023). Regression results show profitability a…

Samrana Afzal, Rimsha Kanwal, Habibullah +1

This study introduces 'algorithmic power' to assess AI's implications for Pakistan's climate diplomacy. Proposing a framework of data, analytical, and diplomatic capacities, it uses composite indices (ARI, DLR, AIS) to estimate Pakistan's l…

Dara Ghahremani

Replication materials for a doctoral study on how institutional, state, corporate, and foreign ownership affect ESG performance and annual-report ESG disclosure among European listed banks. Includes Python/Stata code for panel analysis, tex…

Krishna Rao, Andrew Dumit, Shaena Ulissi +7

PCFBench is the first benchmark that decomposes product carbon footprint (PCF) estimation into six independently evaluable tasks, with 614 expert-labeled items probing decomposition, retrieval, ontology matching, and numerical extraction. T…

Junjie Cai, Xijian Li, Shoukun Chen

This study proposes a method combining COMSOL multi-physics simulation and machine learning to accurately predict coal rock gas extraction volume, targeting the Qinglong Coal Mine in Guizhou. The XGBoost-LSTM hybrid model achieved the best …

Bing Zou, Xiang Yang, Yuhan Jin +1

This study proposes a multicollinearity-reduced conditional variational autoencoder (MCLRCVAE) to generate joint wind-solar scenarios for low-carbon dispatch of industrial integrated energy systems. Representative scenarios are selected via…

Karli Eka Setiawan, Marvel Martawidjaja, Hayyun Lisdiana

This study proposes a CNN-based image classification approach to automatically detect faults in solar panels. Using a public Kaggle dataset with six classes, Inception-V3 achieved the highest accuracy of 91% and best F1-scores in four categ…

Sahrin, Alfin, Utami, Erna, Shoffiana, Nur +1

This study develops LSTM-based models to predict power output of polycrystalline solar panels using environmental data. Comparing pure LSTM, CNN-LSTM, LSTM-AE, and GWO-LSTM, the GWO-LSTM achieves the highest accuracy (R²=0.98, MAPE=4.3%), d…

Ghasemzadeh, Nima, Javaherian, Amirreza, Yari, Mortaza +3

This study proposes a medium-scale biogas-fueled gas turbine poly-generation system supplying electricity, heating, cooling, and water. Multi-objective optimization using machine learning and Grey Wolf algorithms improves efficiency, cost, …

Meng X.

This paper proposes a methodology using Fuzzy AHP and Fuzzy WASPAS to evaluate ESG criteria and formulate green finance investment strategies. It provides a quantitative framework for investors to incorporate ESG factors into investment dec…

Nguyen Q.

This paper analyzes how data quality of Scope 3 emissions affects the prediction accuracy of machine learning models. It evaluates the impact of data variability on model performance, highlighting the potential and limitations of ML for cor…

R.Kaladevi, V.Umarani, V. N +1

This study builds an ML and explainable AI system to assess green loan eligibility. Using synthetic data, multiple models were evaluated, with Gradient Boosting achieving high accuracy. SHAP analysis identified credit score and energy effic…

Yukun Sang, Kannan Loganathan, Lu Lin

Using China's AI Pilot Zone policy as a quasi-natural experiment, this study employs a multi-period DID approach on listed firms (2014-2024) to show the policy significantly improves corporate sustainable development performance (SDP). Dyna…