GX & Decarbonization Research

Krasteva-Hristova R

This exploratory study analyzes the 2024 UK transparency reports of Deloitte, EY, KPMG, and PwC to assess governance disclosures for AI-assisted professional judgment in audit and sustainability assurance. It develops a seven-dimension AI-J…

Kodela, Kalyan Chakravarthy, Roy, Stabak, Ghorbanzadeh, Shaghayegh +2

This paper proposes the Spatio-Temporal Graph Multi-Agent Carbon Flux Model (STG-MACFM), a learning-driven framework within a GeoAI-enabled digital twin. It uses hierarchical agents, reinforcement learning, and graph attention networks to d…

Poorvika N.

This paper proposes a framework integrating AI-powered digital twins and zero-trust security for predictive maintenance of net-zero assets, aiming to enhance grid reliability and optimize renewable energy asset operations.

Yifan Duan, Si Zheng, Lihuan Li +2

GHGbench provides an open dataset and benchmark for company- and building-level greenhouse-gas prediction. The company track covers 12,000+ firms with Scope 1+2 and Scope 3 disclosures; the building track harmonizes 491,591 records from 13 …

Yanming Guo, Charles Guan, Jin Ma

ExioML is an open-source dataset of environmentally extended multi-regional input-output tables for emission factors, covering 49 regions and 28 years (1995-2022). It includes GPU-accelerated computational tools and two aggregation schemes …

Triana Arias Abelaira, María Jesús Guillén Palomino, Lázaro Rodríguez Ariza +1

This study uses a bibliometric approach to analyze 221 articles on climate risk in finance, focusing on the integration of AI techniques. It finds that NLP and digital transformation are key themes, linking AI innovation policies to greenwa…

Anuj Pal

This paper uses Large Language Models (LLMs) to evaluate corporate ESG disclosure quality by distinguishing symbolic from substantive sustainability communication. It introduces the Operational ESG Disclosure Coding Framework (OEDCF) and co…

Radosveta Krasteva-Hristova, Vanya Georgieva

This conceptual article examines how AI can support sustainability assurance under CSRD, ESRS, ISSA 5000, and the EU AI Act. It explores applications such as disclosure identification, ESRS mapping, anomaly detection, greenwashing risk scre…

Weixiang Xu, Yi-Fan Shi, Jinhui Zheng

This study uses panel data from cities in the Yangtze River Economic Belt (2011-2021) to empirically analyze AI's impact on urban green and low-carbon development. Applying dual machine learning and spatial Durbin models, it finds that AI p…

Lin K.

This review comprehensively examines the application of machine learning (ML) to CO2 geological storage. It covers predictive modeling, site selection, monitoring, and risk assessment, highlighting how ML enhances storage efficiency and saf…

Šribar, Jernej, Mohorcic, Mihael, Čampa, Andrej

This paper proposes CoMAD V2G, a multi-agent deep reinforcement learning framework for coordinated management of V2G-enabled EVs and shared energy storage in Positive Energy Districts. Validated with real-world datasets, it reduces grid rel…

Sayyed Sadaqat Hussain Shah, Arshad Javed, Muhammad Mahboob Khan +2

This study examines how AI adoption influences green bond issuance and corporate ESG scores using panel data from 54 economies (2019-2024) and multiple models (fixed-effects, quantile regression, TVP-VAR-SV). It finds that a one-standard-de…

Meng-Sian Chen, Jing Wang, Mengfei Peng

This paper proposes a bi-objective optimization model for virtual power plants (VPPs) integrating wind, PV, storage, and gas units, using the NSGA-II algorithm to minimize total cost and carbon emissions. A case study reveals a trade-off be…

Yun-Kae Kiang, King H. Lam

This paper proposes ReGMS, a retrieval-grounded multi-agent architecture for climate scenario analysis to support IFRS S2/TCFD reporting. Specialized LLM agents coordinate to build and verify transition and physical-risk scenarios. Using NG…

Dauda Bola Abdulsalam, Duminda Kuruppuarachchi, Helen Roberts

This study applies NLP to ESG/sustainability reports of S&P 500 firms (2018-2023) to measure TNFD framework adherence. Findings show that TNFD alignment, clear nature-related risk/opportunity identification, and comprehensive reporting are …

Adrian Lim, Putri Rahayu

This review synthesizes AI applications across financial time-series forecasting, portfolio construction, and firm-level sustainability analysis, with emphasis on AI-driven ESG rating prediction and disclosure signal extraction. It argues f…

Christie Azour

This paper demonstrates AI's effectiveness in ESG investing. AI processed ESG documents 1,200 times faster than humans, reduced rating divergence by 34%, detected greenwashing with 83% accuracy, identified climate risks 8-14 months earlier,…

Cristina Caro-González, Daniel Jato-Espino, Yudith Cardinale

This systematic review of 64 studies on integrating ESG factors into financial risk prediction finds that traditional econometrics still dominate (48%), but ML (39%), NLP (8%) are growing. ML models, especially ensemble methods and neural n…

Xiao-Xing Cai, Wang Yang, Xiyu Ren +3

This paper proposes a human-AI collaborative framework to enhance the trustworthiness and comparability of sustainability (ESG) ratings. The framework consists of STRIDE (principled criteria and scoring) and SR-Delta (discrepancy analysis),…

D. Anjaneyalu, M. V. Subramanyam, A. K. N. Rani +3

This paper combines econometric panel-data modeling with machine learning (gradient boosting, explainable AI) to estimate the causal impact of ESG-bond issuance on corporate credit ratings and cost of capital. Findings show ESG-bond issuers…