GX & Decarbonization Research

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…

Suganthi Pais

This study quantitatively examines the impact of AI-powered FinTech innovations on Circular Economy Transition (CET), with Sustainable Consumption Behavior (SCB) as a mediator. Using SEM on data from 384 respondents, it identifies AI capabi…

Li Zhang, Jinsong Deng

This study integrates geographical detector and machine learning models to analyze carbon emission scenarios in China's thermal power sector. It identifies key regional drivers and provides policy insights for emission reduction, exemplifyi…

Hina Ahmad, Kinza Kamran, Fizzah Muhammad

This study analyzes the 'AI-climate nexus' reshaping international relations. Through document analysis of 127 sources and four case studies, it finds AI enhances emissions verification and grid optimization but introduces vulnerabilities l…

Xinxiu Liu

ESG ratings are crucial in capital markets, but the surge in disclosed data leads to transparency data bias, potentially compromising rating accountability. This interview-based study reveals awareness of this bias, its causes (data quality…

Saeed Askary, Davood Askarany, Hassan Yazdifar +3

This study examines the interplay between AI-related environmental perceptions, ESG reporting timing, and CDP disclosure quality across 18 European countries. Mandatory reporting modestly improves disclosure quality, but effects on carbon i…

Nihad Isgandarov

This paper theorizes and measures systemic economic fragility arising from the simultaneous acceleration of AI adoption and decarbonization. It proposes the Isgandarov Adaptive Transition Theory (IATT) and develops the Isgandarov Transition…

Makhrus Ali, Amin Zaki, Zain Nizam

This paper examines green fintech at the intersection of financial technology and sustainability, analyzing applications such as digital payments, sustainable investment platforms, and climate-risk analytics. It shows that big data, AI, and…

Wang J.

This paper proposes a hybrid CEEMDAN-LSTM model to predict green bond index movements. By applying AI to ESG financial instruments, it offers insights for investment decisions and risk management in the green bond market.

Jason Chen

This study proposes an optimization framework to reduce greenhouse gas emissions in Hong Kong's logistics and transport network. It models 18 administrative districts as a dynamic topological graph and integrates real-time traffic, terrain,…

CINAR U

Proposes a false-alarm-controlled protocol to fairly compare gas indices and deep learning for early detection of coal spontaneous combustion. In simulation and field data from three mines, Graham's index fails to detect fires at false-alar…

Fausania Hibatullah, Rico Dwi Firmansyah, D. Kusrini

This study proposes a portfolio optimization framework integrating FinBERT-based sentiment analysis and DBSCAN clustering for IDX ESG Leaders stocks. Empirical results show that sentiment-informed clustering improves risk-adjusted performan…