GX & Decarbonization Research

Mohammad Ridwan, Jeremy Ko, C. Leung +1

This study examines how AI innovation, energy consumption, economic growth, industrialization, and population affect the US ecological footprint from 1996 to 2024 using ARDL and robustness tests. Results show AI innovation reduces ecologica…

Gao Aixiong, Hong He, Quan Zhang

This study examines the causal impact of industrial artificial intelligence (AI) on urban carbon emissions using panel data from 260 Chinese cities (2005-2019). A novel city-level industrial AI index is constructed. Results show that indust…

Jess B. Kozman, Jack Bashian

This paper emphasizes that AI/ML workflows for low-carbon subsurface energy projects (geothermal, CCS) require high-quality, well-curated geotechnical data adhering to FAIR principles. It highlights that many datasets lack integration and g…

Guangfan Sun, Song Yue, Jianqiang Xiao +1

This study empirically examines how AI enables green innovation in Chinese aviation manufacturing firms. It identifies three channels: technological empowerment, labor structure optimization, and improved resource access. The positive effec…

Aditya Tripathi, M. Machado, L. Spierdijk +1

This systematic literature review synthesizes insights from circular business models, industrial symbiosis, eco-industrial parks, and closed-loop supply chains to inform the development of Hubs for Circularity (H4Cs). It highlights the role…

Xiaoling Yang, Mohd Rahimie Abd Karim, B. Abdullah

Using panel data from China's A-share listed companies from 2014 to 2023, this study empirically shows that AI significantly enhances corporate green competitiveness across strategy, innovation, and practices. The mechanisms include improve…

Roberto Moro-Visconti

This paper shows that AI adoption in corporate governance improves ESG performance by 8-15% through reducing information frictions and expanding the basin of attraction for high-ESG outcomes. Using 450 firms over 2014-2024, the author finds…

M. Eraña-Díaz, Juana Enríquez-Urbano, B. Martínez-Bahena +3

This study proposes a two-phase computational framework combining K-Means clustering and a binary ANN classifier to capture heterogeneity in environmental performance across manufacturing units in remanufacturing. Using 1000 synthetic recor…

Jayesh Rane

This paper assesses green AI adoption in industrial systems via SWOT analysis using mixed methods. Findings show organizational willingness, technological readiness, and regulatory standards positively influence adoption (73.4% variance). O…

Fadi Alkaraan, Mahmoud Elmarzouky, V. G. Venkatesh +3

This study uses large-scale UK data (32,273 firms from the UK Innovation Survey, FTSE All-Share data, and annual reports) to show that AI-powered Industry 4.0 technologies enable circular economy strategy practices, boosting ESG and ecosyst…

Dr. Syed Shameel Ahmed Quadri, Dad Ansari, Zonaira Akbar +1

This study empirically examines how AI-driven ESG signal processing enhances green finance intelligence, leading to better sustainable capital allocation and capital market efficiency. Using survey data from 312 financial professionals and …

Z. Baharom

This critical review examines machine learning (ML) in corporate financial sustainability (CFS), highlighting risks of automated greenwashing and reinforced inequalities in ESG scoring and predictive analytics. It proposes a four-pillar fra…

Yasser Elouargui, Abdellatif Sassioui, M. Chergui +4

This paper introduces ESG-Graph, a lightweight and interpretable graph-based framework for ESG text classification. It leverages a taxonomy based on the European Sustainability Reporting Standards (ESRS) and uses a multi-layer Graph Attenti…

Siqi Sun, Ben Wu, Mali Jin +3

This paper introduces ESG-Bench, a benchmark dataset for evaluating LLMs on ESG report understanding and hallucination mitigation. The dataset includes human-annotated QA pairs with factual support labels. Chain-of-Thought prompting and fin…

Fatma Bennaceur, Ali Bendob, Anwar Hasan Abdullah Othman

This study uses AI, ML, and NLP to assess ESG disclosure quality among Islamic and conventional banks in the UAE (2021-2024). Conventional banks score higher on climate risk and compliance, while Islamic banks show consistency and emphasis …

Andreja Primec, Jernej Belak, Matic Čufar

This paper examines the extent to which major sustainability reporting standards (CSRD/ESRS, GRI, ISSB, SASB) address AI-related risks. Through content analysis of 20 corporate reports and doctrinal legal analysis, it finds that AI risk dis…

Fiona Magee

This paper develops a web application using LLMs to automatically summarize, analyze, and compare ESG reports. It enables stakeholders to reduce manual review effort, identify trends, and benchmark against peers. The prototype demonstrates …

Amani Ebnaoof

This study examines the relationship between AI adoption and ESG disclosure among Saudi-listed non-financial firms from 2020-2024. Using fixed-effects regression on 130 firms, it finds that AI adoption significantly improves ESG disclosure …

C. Kim, H. Na

This study develops an AI-based screening framework using ESG ratings to predict corporate carbon emissions among Korean KOSPI-listed firms. Comparing ML and DL models, a hybrid ensemble of CatBoost, GAN, and Transformer outperforms individ…

W. Okere, Cosmas Ambe, Sanele Phumlani Vilakazi

This systematic review synthesizes empirical evidence (2015-2025) on AI methods in green finance and ESG reporting in emerging markets. Machine learning and NLP dominate, applied to carbon disclosure, ESG scoring, and green bond verificatio…