GX & Decarbonization Research

Liu X.

This study proposes an adaptive recurrent neural network model to predict risks in carbon emission trading markets. The model aims to improve prediction accuracy for market volatility and price fluctuations.

Vidura perera

This paper develops the Adaptive Green Finance Effectiveness Theory (AGFET), an integrated framework explaining how green finance effectiveness is conditional on policy uncertainty, governance, institutional quality, and technological capab…

Aria Dolatabadian, Khalil Kariman

This review compares classical and machine learning (ML) approaches for soil organic matter and carbon analysis. ML techniques like random forests and neural networks improve prediction accuracy and scalability, but the paper concludes that…

SangYoun Kim, T. Woo, Usama Ali +8

This systematic review of 906 publications (2016-2025) categorizes AI applications into flue gas treatment, air quality management, and CO2 capture. It identifies a shift from traditional monitoring to AI-driven surrogate modeling and gener…

Ritik Raj, Survi Sinha, Atreyi Pramanik +1

This paper reviews how AI enhances efficiency, sustainability, and system integration across the hydrogen value chain. AI modeling and optimization improve environmental impact assessment of hydrogen production routes like waste polymer and…

Taedong Lee, Sinjae Kang, D. Utami +1

This study analyzes 634 sustainability reports (2014-2024) from the top 200 Korean firms using Structural Topic Modeling (STM). It identifies 13 topics and a phased shift: CSR and basic environmental management (2014-2018); workplace safety…

Lakshmi Narasimha Prasad Nagaragere, Dr.S. Prabakar

Proposes a vernacular ESG transformation model rooted in South Asian institutional cultures. Using AI-based environmental impact assessment and ESG reporting tools, the study demonstrates through pre/post case studies in family-owned busine…

Manav Gangar, Nikhil D'Souza, Rishika Kapasi +2

This paper proposes a machine learning framework for detecting greenwashing by combining supervised classification and unsupervised anomaly detection. It introduces a Greenwashing Discrepancy Score (GDS) to quantify the mismatch between fir…

Fuoli M.

This paper uses corpus-assisted analysis to evaluate the substance of corporate net-zero pledges, applying NLP methods to quantify commitment specificity and target rigor, revealing significant variation in pledge quality and potential gree…

Singh S.K.

This paper proposes policy frameworks that leverage AI technologies to accelerate the transition to a net-zero economy. It explores how data-driven decision-making and predictive models can support the design and evaluation of effective dec…

Khara S.

This paper explores the pathways to achieve net-zero energy using artificial intelligence, discussing how AI can optimize energy efficiency and integrate renewable energy sources.

Qian He, Ying Jin, Chen Chen +1

This study examines China from 1990-2023 using machine learning methods (LASSO, causal forest, Gaussian process) to analyze how trade openness, structural transformation, and government effectiveness affect coal usage and energy costs, ulti…

Fei Wang, Lei Jiao, Konglin Zhu +4

This paper addresses scheduling of cloud-edge federated learning under carbon neutrality constraints, incorporating demand response. It proposes methods to optimize the trade-off between learning performance and carbon emissions reduction.

Mykhailo Prazian

This paper explores how digital technologies, including AI and IoT, can be integrated into supply chains to reduce Scope 3 emissions and enhance cyber-physical resilience. It highlights the potential for improved emissions visibility and ri…

Shadrach Kukuchuku, Rachael Dickson, Tamunotonye Sotonye Ibanibo

This paper proposes a Digital Asset Management Framework integrating IoT monitoring, Asset Health Index modelling, ML-based predictive maintenance (Random Forest, ANN, SVM), and lifecycle optimization (Genetic Algorithm, PSO) for renewable …

Kuang Y.

This study uses double machine learning to estimate the causal impact of green finance on urban energy efficiency, providing insights for policy and investment decisions.

Xu P.

This paper evaluates the causal effect of China's emissions trading scheme (ETS) using high-frequency trading data and double machine learning (DML). It estimates the impact of policy intervention on carbon prices and trading volumes, provi…

Jakkaraju V.T.D.

This paper proposes Predictive Green FinOps, a method for jointly optimizing cost, carbon, and reliability in AI-intensive cloud environments. It uses predictive analytics to dynamically adjust resource allocation, reducing carbon footprint…

Singh V.

This paper applies FinBERT, a financial domain-specific BERT model, to conduct sentiment analysis on ESG disclosures of Indian mutual funds using a pillar-based classification approach. The study demonstrates the effectiveness of NLP techni…

Khan T.

This paper proposes a conceptual framework for using AI in Scope 3 GHG emissions measurement, reporting, monitoring, and assurance from a critical systems perspective. It examines AI's potential to improve data quality and automation while …