GX & Decarbonization Research

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…

Chandan Sharma, Priya Rani

This study applies NLP and clustering to analyze ESG reporting evolution in India from 2017-2024. It finds ownership structure significantly influences reporting, with private firms more transparent. Firm size affects only environmental dim…

Tao Li, Yi Wu, Mingyang Li

This study proposes a stacking ensemble model with SHAP interpretability to map forest carbon density and net primary productivity. Using forest data from China, it provides insights for management zoning. The work fits the AI×ESG intersect…

An Thi Binh Duong, Linh Tran Khanh Do, Scott McDonald +4

This study develops and empirically tests a strategic framework using evolutionary machine learning (EML) to drive sustainable performance in construction firms. Analyzing 213 Vietnamese construction companies, it finds that aligning EML in…

Yufei Xi, Jiangzhang Zhu, Peng Wang +1

Proposes EvoGame-CAKNet, a hybrid framework for carbon price forecasting integrating evolutionary game theory to model multi-agent strategies, multi-head contextual attention for long-range dependencies, and Kolmogorov-Arnold networks for n…

Gaopeng Jiang, Huihui An, Yaling Tian +2

This study analyzes carbon emission reduction drivers and decoupling effects in the transport industry of the Yangtze River Delta region. Using the extended STIRPAT-Ridge model and Tapio decoupling model, it finds that year-end resident pop…

Ramirez, Dely, Muñoz Tabora, Jonathan, Melgar Dominguez, Ozy Daniel

This deposit provides the dataset and R scripts for a study analyzing the decoupling of GDP per capita from CO2 emissions in Honduras (1990-2023). It includes machine learning methods (k-means, Random Forest) and econometric tests (EKC, Tap…

Zhao C.

This review comprehensively covers multiphase metering and intelligent error correction techniques for CO2 transport in CCUS systems. It highlights the effectiveness of AI/ML-based error correction in improving measurement accuracy, and dis…

Yang J.

This study proposes a machine learning approach to predict ESG disclosure quality using board secretaries' characteristics. It analyzes how secretaries' attributes affect disclosure quality, demonstrating the effectiveness of AI-based predi…

O. Zhytkevych

This paper proposes a conceptual hybrid economic-machine learning framework for decarbonization analysis, integrating Pigouvian externality theory with self-organizing maps (SOM) to cluster countries into homogeneous decarbonization systems…

M. Uddin, Md. Samsuzzaman Sobuz, Mohamed Ghalla +5

This study developed a hybrid machine learning model (XGB-HistGB) to predict compressive strength, cost, and CO2 emissions of biochar-incorporated concrete. Using a dataset of nine input parameters, the model achieved high accuracy (R2=0.95…