GX & Decarbonization Research

Alistair Vance, Sora Tanaka, Mateo Benitez

This paper proposes an auditable governance framework for sustainable and fair e-commerce using AI. It integrates packaging waste, last-mile emissions, return rates, accessibility, and ESG disclosure into four layers: material circularity, …

Alistair Vance, Sora Tanaka, Mateo Benitez

This paper proposes a four-layer governance framework for sustainable e-commerce, using AI as an auditable decision-support layer for packaging waste, last-mile emissions, high return rates, accessibility, and ESG disclosure. The layers cov…

Alistair Vance, Sora Tanaka, Mateo Benítez +1

This paper proposes a governance framework for sustainable and fair e-commerce, integrating packaging, circular logistics, accessibility, and ESG disclosure with auditable intelligence (AI).

İsmail Öğütçen, Ümit Yılmaz

This study examines whether ESG rating disagreement predicts corporate greenwashing and how digital transformation (DTI) moderates this via disclosure and performance channels. Using 8,111 firm-year observations of Chinese A-share companies…

Hieu Thanh Nguyen, Hoa Minh Pham, Anh Thao Nguyen +1

This study examines the relationship between ESG disclosure and corporate tax avoidance among Vietnamese non-financial listed firms (2020-2024). Using panel data regression and machine learning models, it finds that ESG disclosure reduces t…

Thirunavukarasu Suryalakshmi, Gurumoorthy Yohanandhan Rajaa Vikhram

This paper proposes a method combining IIoT and digital twin technologies with machine learning (k-means clustering and anomaly detection) to improve industrial energy efficiency. Applied to air leak detection, it achieves 6.22 kWh/day ener…

*Pramod Prabhakar1 and Rajesh Kumar2 *Pramod Prabhakar1 and Rajesh Kumar2

This review comprehensively summarizes environmental impacts of livestock systems (GHG emissions, water pollution, biodiversity loss) and mitigation technologies such as precision feeding, anaerobic digestion, AI, and IoT. It highlights the…

Elena A. FEDOROVA, Anzhelika A. KOVALEVA, Lyudmila I. CHERNIKOVA

This paper applies LDA topic modeling to Russian companies' non-financial reports to construct an industry-standardized social (S) index. It identifies industry differences in social disclosure and the influence of specific topics on market…

Qianxing Chen

This paper traces the evolution of AI models for ESG assessment, from topic models to domain-adaptive Transformers and retrieval-augmented language models. It focuses on multi-label phrase annotation from news, earnings calls, and reports, …

(著者不明)

This study applies cosine similarity to analyze the time series of CEO messages from major real estate developers, capturing shifts in management discourse.

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%…