GX & Decarbonization Research

Benjamin Josef Schüßler, Jakob Prange

This study extends a sentence-level dataset of German ESG reports with crowdsourced readability annotations and evaluates various readability scoring methods. It finds that while LLM prompting can distinguish clear from hard-to-read sentenc…

R. M. Ellahi, J. Qureshi, M. H. Farooqui +2

This study proposes two pathways for generative AI to enhance supply chain sustainability: automated reporting and predictive analytics. PLS-SEM analysis shows predictive analytics is the strongest predictor, with regulatory pressure and su…

Kerstin Forster, Lucas Keil, Victor Wagner +3

This paper develops a machine learning framework to extract ESG indicators from corporate reports, applied to 600 large European firms (2014-2023). It reveals a transparency gap between high and low ESG-rated firms, narrowing over time, and…

Dongxin He, Linhan Jiao, Jingyi Yang +1

This paper examines the impact of carbon information disclosure quality on corporate risk-taking in China using a five-dimensional vectorized text index from ESG, CSR, and sustainability reports of A-share listed firms (2007-2023). Higher-q…

Fahad Ullah Khan, Asad Amin

This review comprehensively surveys FinTech applications (AI, ML, big data, blockchain) in ESG scoring, climate risk assessment, and green bonds. It highlights how digital tools improve efficiency and data coverage while addressing challeng…

Tamás Zelles, Bernadett Domokos, Sándor Remsei

This paper examines how combining narrative techniques with machine learning can enhance decision-making, particularly in accounting and sustainability reporting. It finds that narrative-driven reporting with expert interpretation improves …

Thierry Warin, Christophe Bisson

This paper longitudinally compares scenario knowledge representation across IPCC synthesis reports from AR1 to AR6 using a RAG pipeline. Early reports relied on qualitative conditional reasoning; later reports increasingly used quantitative…

Chong Zhang

This paper proposes a digital management platform for corporate carbon emissions using a cloud-edge-end distributed architecture, integrating IoT and AI for automatic aggregation, precise calculation, and intelligent analysis. It establishe…

Huipeng Yang, Yihang Yu, Yuhan Wu +2

This study constructs a climate risk index from annual report text mining of Chinese A-share firms (2016-2024) and examines its impact on the cost of debt financing. Using regression and mediation analysis, it identifies two channels: reduc…

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…

Sini Guo, Mengzi Yin, Wai-Ki Ching

This paper proposes a network-based method for online portfolio selection that incorporates ESG scores. It leverages machine learning to integrate ESG criteria into investment decisions, offering a dynamic approach to sustainable portfolio …

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…