GX & Decarbonization Research

Amal Alharthi, Ahmad Alomari, Fawwaz Alrwabdah +3

This paper examines how green digital technologies (ERP, cloud, IoT, AI, big data analytics) improve ESG disclosure quality for industrial firms listed on the Amman Stock Exchange. Using panel data from 30 firms (2020-2024) and institutiona…

Mohsin Khan, Wendy Ashurst

This paper examines the role of AI in enhancing ESG reporting quality, proposing a sector-specific hybrid governance framework. It finds environmental metrics are more amenable to AI verification, while social and governance disclosures req…

Qiao Wang, Bin Li, Shaojie Kong +1

This study uses double machine learning on panel data from 266 Chinese cities to investigate how digital governance policy upgrades (from IBP to IGS) dynamically propel cities toward carbon neutrality. Findings show that the policy upgrade …

Hieu Thanh Nguyen, Hoa Minh Pham, Anh Thao Nguyen +3

This study examines the impact of ESG disclosure on tax avoidance of 118 Vietnamese non-financial listed firms (2020-2024). Using random effects models, it finds that ESG performance (individual E, S, G pillars and a composite index) is neg…

Galina Lisa

This paper examines innovative approaches to sustainability reporting by integrating ESG principles, digital technologies, and transparency mechanisms. Using comparative analysis of GRI, SASB, CSRD/ESRS and case studies from EU and emerging…

Francisco Javier Rodríguez-Ruiz, Ana María García-Berbaneu, Alexsander Luiz Telpisow-Scheid

This study proposes an automated methodology using NLP to extract, classify, and assess ESG content from corporate sustainability reports. It uses a taxonomy aligned with European reporting standards for listed SMEs, enabling scalable and r…

Amir Amel-Zadeh, Tami Dinh, Andreas Seebeck +1

This paper uses deep learning to analyze visuals and text in 3,923 European sustainability reports (2013-2021), documenting a functional separation between graphics and photographs. Firms with stronger ESG performance use more graphics but …

Mrs. Awantika Deshpande, Mr. Kiran More

This paper proposes an AI-driven education system to enhance ESG reporting knowledge and consistency. It integrates NLP, machine learning, and knowledge graphs for framework analysis, adaptive learning, and real-time reporting assistance. E…

Iulia Florentina Voicila Voicila, Elena UrquiaGrande

Using 87,225 private firms from Spain and the UK, this study shows ESG indicators improve financial distress prediction only when disclosure quality is high (UK). In Spain, fragmented ESG data yields no improvement. Machine learning models,…

Stefan Vieweg, Christoph Klein

This review synthesizes 2022-2025 evidence on AI in green finance and ESG, proposing the RESP-ESG framework for responsible adoption. It maps eight archetypes, showing NLP and ML enhance ESG signal extraction and climate analytics, while hi…

Ambika Zutshi, Diane Zandee, Andrew Creed

This paper positions AI as a critical enabler for the transition to a circular economy, using the 10R framework to explore AI applications across circularity levels. It highlights how AI can identify opportunities for material reduction, pr…

Parchami J, Darroudi A, Ali A +2

This paper proposes a hybrid framework combining Variational Mode Decomposition (VMD) with an Adaptive Learning Rate Enhanced Gated Recurrent Unit (ALRE-GRU) optimized by the Enhanced Dynamic Grasshopper Optimization Algorithm (EDGOA) for p…

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This study explores the application of large language models (LLMs) in finance, particularly for stock prediction and ESG evaluation. It compares human judgment with LLM-based analysis, examining the impact on investment performance.

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This paper proposes AI-driven methods in corporate finance to enhance governance and sustainability practices. It demonstrates how AI can automate ESG evaluation and disclosure, strengthening corporate sustainability efforts.

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This paper proposes a human-AI collaboration framework using LLMs for creating benchmark datasets for ESG rating agencies. It achieves efficient and high-quality dataset construction, contributing to standardization in ESG evaluation.

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This industrial survey examines how financial institutions use NLP to extract unstructured ESG data from financial reports, news, and filings. It provides an overview of data extraction methods, value creation potential, and practical chall…

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This study systematically assesses corporate ESG performance using large language models (LLMs) and examines its relationship with stock prices. It demonstrates the applicability of LLMs in ESG analysis.

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This paper proposes a methodology for automating the analysis of ESG disclosures in the oil and gas sector. It demonstrates scalability and adaptability for extracting insights from large-scale disclosure documents, highlighting potential f…

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This paper proposes a method to detect greenwashing by leveraging LLMs to analyze the gap between ESG disclosure and performance scores. Using large language models for text analysis of corporate sustainability reports, it identifies false …

Peng Gao, Kunpeng Zhang, Zongchuan Liu

This paper uses double machine learning to measure urban energy rebound effects (ERE) in China and evaluates the impact of dual-pilot policies (National Big Data Comprehensive Experimental Zones and New Energy Demonstration Cities). Results…