GX & Decarbonization Research

Hafiz Muhammad Naveed, Huaping Sun, Rabia Akram +2

Using an interpretable MNN-DeepSHAP framework on data from 13 countries (2001-2025), this study reveals structural drivers of CO2 intensity and ecological footprint. It finds that improving renewable energy efficiency (lower levelized cost …

Tanzina Hossain, Mahfuja Malik, K. S. M. Tozammel Hossain +1

This study predicts ESG scores for U.S. manufacturing and service sectors using ten machine learning algorithms. Results show ESG scores are more predictable in manufacturing than services, with XGBoost best for manufacturing and Random For…

Hussam Musa, Zdenka Musová, Frederik Rech +1

This study applies XGBoost and SHAP analysis to predict ESG ratings of 974 Slovak manufacturing firms using multi-year financial data. Leverage, liquidity, debt-servicing capacity, firm age, and tax-related indicators emerged as important p…

Edward I. Altman, Francesco Baldi, Claudia D'Ippolito +1

This study develops a novel ESG rating model called Z-ESG, applying Altman's Z-score logic to environmental, social, and governance indicators. Using discriminant analysis and logistic regression on 325 European listed firms, it produces ES…

M T H Nguyen, Anbinh Vuong, Vanha Tran +1

This paper proposes a method to automate the assessment of sustainability reports aligned with GRI standards using large language models (LLMs) and knowledge graph-based retrieval-augmented generation (RAG). A case study of Vietnamese banks…

Petr Procházka, Емил Велинов, Paul Lacourbe

This paper reviews AI tools that enable sustainability governance in multi-tier supply chains. It categorizes existing tools, identifies gaps, and helps low-resource users (SMEs, lower-tier suppliers) access solutions. The evaluation covers…

Berra Öz, Ali Hakan Işık

This paper analyzes the use of large language models (LLMs) in sustainability reporting, focusing on the performance comparison of retrieval-augmented generation (RAG) and low-rank adaptation (LoRA) techniques. It evaluates their effectiven…

Anonymous Author(s)

This replication study validates a method for refining prompts by learning from their own mistakes in analyzing sustainability reports. It demonstrates how iterative improvement based on incorrect outputs enhances prompt quality for ESG tex…

Anonymous Author(s)

This paper proposes and validates a method to refine prompts for analyzing sustainability reports, where the language model learns from its own mistakes. A case study demonstrates the effectiveness of this approach.

Md. Qamruzzaman, Abdulrahman Alomair, Mohammed Alomair

This paper examines how FinTech adoption, intellectual capital, ESG disclosure, and dividend policy affect firm value in financial institutions in an emerging economy (Bangladesh). Using econometric methods including deep neural networks, i…

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.