GX & Decarbonization Research

Hao Xu, Xixuan Peng, Xiaodan Jin +2

This study evaluates co-benefits of reducing CO2 and air pollutants from transport. A random forest model predicts PM2.5 concentrations and health benefits are estimated using GEMM. In the best scenario, CO2 emissions peak in 2032 with a 51…

Benneth Oyinna, Zubairu Usman, Aisha Abisoye +2

This study presents a machine learning framework for monitoring and forecasting Nigeria's industrial CO2 emissions to support the 2060 net-zero target. Comparing four models (MLR, Prophet, SVR, Random Forest), MLR performed best (R²=0.978).…

Ibrahim M.

This review paper discusses recent advances in CO2 capture, utilization, and storage (CCUS), with a particular focus on the application of machine learning techniques to optimize processes and the integration of circular economy principles.…

Kai-Robin Lange, José Cassola, Marcelo Lufin +11

This paper applies dynamic topic modeling (RollingLDA) to 1,996 validated news articles from 2011-2025 to analyze public narratives around socio-environmental conflicts related to Chile's energy transition. It identifies twelve topics, reve…

Gyu Jin Pyo, Mi Jin Noh, Yang Sok Kim

This paper applies DirectLiNGAM, a causal structure learning algorithm, to analyze vehicle energy consumption in the EV-ICEV transition market. It identifies causal relationships among factors affecting energy consumption, contributing to e…

Alireza Vafaei Sadr, Seyyed Sina Hejazian, Ajith Vemuri +4

This study estimates CO2 equivalent emissions from AI-assisted stroke imaging in the US. The Ideal AI scenario emits ~16,375 metric tons CO2eq/year, and relocating computation to cleaner-energy states reduces emissions by 54.75%, highlighti…

Xiaodan Qi, Yuxin Chen, Hongyan Zhao +1

This study uses explainable machine learning (XGBoost with SHAP) to predict sustainable purchase intention for green prepared dishes based on 805 survey responses from Jilin Province, China. Predictors are organized into environmental cogni…

Abdulkarim Hamdan J. Alhazmi, Sardar M. N. Islam, M. Prokofieva

Proposes the CMA Agentic AI Platform to address three governance challenges in Saudi Arabia's audit market. Segment 1 uses autonomous drone swarms for asset verification and ESG compliance monitoring via deep learning and thermal imaging. S…

Delia Deliu

This study explores how digital transformation and sustainability transition (Twin Transition) reshape auditing. It finds that Agentic AI, blockchain, and big data enable continuous auditing and ESG traceability but raise issues of algorith…

Mohamed Sheta, Bassam A. Ibrahim, M. Osman +2

This study uses machine learning to examine how internal audit competency and maturity affect ESG assurance engagement. Findings show that competency is the most influential factor, outweighing structural maturity. The model explains 50.97%…

Ahyahudin Sodri, G. B. Imasuly, Nuraeni Nuraeni +1

This study develops an explainable machine learning framework using XGBoost and SHAP to assess urban flood vulnerability across Indonesia. Integrating satellite and geospatial data, it computes a Flood Vulnerability Index (FVI) for 514 dist…

Behzad Satari

This study proposes a comprehensive framework integrating life cycle assessment (LCA), techno-economic analysis (TEA), and machine learning (ML) for sustainability assessment of hydrothermal carbonization (HTC) of food and agro-industrial w…

Ahmed Seffah

This paper argues that genuine sustainability requires organizational transformation over mere technology adoption, proposing a human-centric AI framework. It emphasizes leadership and strategic use of AI for sustainability goals.

Chung-Hao Hsu, Asif Khan

This chapter proposes a Data–Blockchain–Algorithm–Governance Assurance Framework for auditing smart and sustainable supply chains. It extends traditional audit reasoning to AI-driven decision systems and sustainability reporting, addressing…

Yongzhi Huang

This paper develops Fiscal Geometry as an evidence-routing framework for sustainability disclosure credibility in AI-enabled assurance. It argues that before AI can assure sustainability claims, claims must be routed through a structured ev…

Rola Shawat, Abanoub Wassef, Yaacob Ibrahim +3

This study uses a 2×2 experiment with Egyptian MBA/DBA students to examine how assurance level (limited vs reasonable) and assurer type (audit vs non-audit firm) affect non-professional investors' reactions to sustainability disclosures. Re…

Evha Rozario, Shuchita Shahnaz, Foysal Mahmud +4

This paper proposes an integrative predictive analytics framework for data-driven sustainability in urban infrastructure, focusing on reducing energy consumption and carbon footprint. The framework consists of six interdependent layers incl…

Weihua Qu, Z D Guo

This paper examines the impact of carbon risk on corporate financial distress in China, using the Paris Agreement as an exogenous shock and combining difference-in-differences with double machine learning. It finds that high-carbon firms ar…

Marco Fiorentini, Matteo Francioni, Stefano Zenobi +9

This study develops a machine learning framework using remote sensing and multiple data sources (satellite, climate, soil, crop) to estimate soil organic carbon (SOC). Adding an artificial 'zone management' covariate improves prediction acc…

Ikechukwu Okwuosa

This study optimizes low-carbon hydrogen production from biogas steam reforming using an integrated ANN-GA framework. An ANN model (4-12-1 architecture) trained on Aspen HYSYS simulation data achieved R=0.99. GA optimization identified opti…