GX & Decarbonization Research

Nawal Louzi, Mohammad Q. Al-Jamal, Mahmoud AlJamal +2

This study proposes a framework integrating AnyLogic simulation and graph-based multi-agent deep reinforcement learning (Graph-MAPPO) to optimize pavement maintenance for carbon reduction and renewable energy use. It manages pavement condit…

Aso Abdullah, Abdullah Omar Yassen, Mohammad Talha

Using panel data from 5,378 firms across Asia, Europe, and North America (2015-2024), this study examines how board governance structures affect climate governance effectiveness and transition performance. Independent and gender-diverse boa…

Dr. Ayalapogu Ratna Raju, T. Akila, Dr.K Umapathy +3

This study proposes a data-centric framework using a Multilayer Perceptron (MLP) to predict carbon emission reduction potential across the lifecycle of residential buildings. Analyzing 1,640 projects, the model classifies reduction levels w…

Guillermo Llopis

AI inference services fall under Scope 3 Category 1 under CSRD, yet lack standardized accounting. This paper proposes a four-tier framework from token-based physical estimation to spend-based EEIO, correcting 10-40x overestimates from gener…

Fatima Zahra El-Yazidi, H. Mounir

This paper proposes an AI-based predictive energy management system for a hybrid solar-wind-battery system producing green hydrogen. It combines machine learning for day-ahead solar and wind generation forecasting with a reinforcement learn…

Meziouni, Reda

This thesis proposes a sovereign AI-driven compliance framework for supply chain due diligence under the German LkSG. It extends the WBCSD PACT protocol with a standardized JSON schema, a deterministic rule engine, an offline Mistral 7B nar…

Vincenzo Barrile, Emanuela Genovese, Clemente Maesano +2

This study integrates machine learning, remote sensing, and ecosystem modeling to assess carbon dynamics in a semi-arid region of Tunisia. It maps land use/land cover, estimates carbon stocks and emissions using InVEST, and simulates future…

Cong Doanh Duong

This study demonstrates that AI-based reverse logistics improves net-zero green performance through circular economy practices, but tariff policy uncertainty weakens these effects. Based on a three-wave survey of 209 ISO 14001-certified hig…

Jiayuan Guo, Kai Quan Zhang, Yanlai Wang +1

This study integrates XGBoost with SHAP interpretability to identify carbon emission drivers of agricultural exports using panel data from Central China (1993-2019). The optimized model achieves high predictive accuracy (R²=0.935), revealin…

Musa Lefagana, H. Suleiman

This study proposes a conceptual model where cloud accounting moderates the relationship between audit quality and sustainability disclosure. It identifies five audit quality dimensions (anomaly detection, cost efficiency, risk assessment, …

Sonia Sayari, Nidhal Mgadmi, Majed A Helmi, Hanen Louati, Abdulaziz M Basahel, Hamed Yousef Alshinkity, Sumayya Albalawi

Using US data from 1990-2024, this study examines how green economy, FinTech, and governance affect environmental sustainability. Applying cointegration, error-correction, QVAR, and heatmap diagnostics, it finds that renewable energy, fores…

J. Chinna

This study analyzes the impact of AI on ESG reporting from 2019 to 2025, showing adoption surged from 15% to 63%. AI evolved from basic data collection to machine learning, NLP, and generative AI, improving transparency, accuracy, and timel…

Maria Aluchna, Leszek Bohdanowicz, Bogumił Kamiński

This study examines how female board representation affects the scope of climate-related disclosure (energy use and GHG emissions) after mandatory NFRD reporting. Using a difference-in-differences design on 72 Warsaw Stock Exchange firms, i…

Xiaoyu Guo, Weisen Wang, Zhanghua Xu +6

This study develops a UAV-LiDAR and machine learning framework for organ-level aboveground carbon (AGC) estimation in bamboo forests. XGBoost and Random Forest achieved highest accuracy (stem R2=0.82, leaf R2=0.73), offering a cost-effectiv…

Bochen Zhang, H. Huang, Zhili Han +1

This paper empirically analyzes the impact of digital-real integration (DRI) on green total factor productivity (GTFP) using panel data from Chinese industrial enterprises. Fixed-effects and mediation analyses show that DRI significantly im…

Yuxuan Guo, Linxin Yin, Ruihan Xiong +2

This study addresses the severe risk of capital chain rupture in China's construction industry, focusing on vulnerable subcontractors. It proposes an AI-assisted risk control platform and innovative insurance products, using gradient boosti…

Zahra Yaghoubi, Mahyar Mansouri, Hosein Alimardani +2

This study applies explainable machine learning to optimize methane tri-reforming (TRM) for low-emission hydrogen production and CO2 utilization. Using 46,464 equilibrium data points, a neural network achieved the best predictive performanc…

Jiyoung Ko, Yong-Woon Kim, Y. Byun

This study proposes a deep learning framework for accurate greenhouse detection and segmentation from high-resolution aerial images. By integrating class-weighted loss and class-aware sampling into YOLOv8, it achieved mAP@0.5:0.95 of 0.566.…

Laura Elena Jiménez-Casillas, Román Rodríguez-Aguilar, Marisol Velázquez-Salazar +1

This study measures the individual impact of CSR and sustainable operations on financial performance for Mexican listed firms using Random Forest and explainable ML (ICE, PDP, SHAP). Environmental scores show the most consistent contributio…

Sajjad Ahmed

This systematic review analyzes how digitalizing integrated management systems (IMS) impacts operational efficiency, sustainability, and strategic value, drawing on 69 studies and corporate reports. AI, IoT, and cloud technologies enable re…