GX & Decarbonization Research

Yantong Li, Taoyu Chen, Yajie Guo +3

This study uses NLP and XGBoost-SHAP on Sina Weibo data to analyze urban travel behavior changes under extreme heat and oil price shocks. Key findings: heat leads to trip reduction (52.4%) and motorized travel (24.6%), with a transition int…

Siyi Wang

This paper proposes a low-carbon intelligent path planning method using genetic algorithms for machining carbon fiber reinforced polymer aerospace components. Experimental results show a reduction in path length, machining time, energy cons…

Yaping Luo, Jianxian Wu

This paper explores the potential of AI technologies to reduce carbon emissions in the transport sector, focusing on optimization, automation, and data-driven decision-making. It likely examines case studies or models where AI steers transp…

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…

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 …

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,…

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…

(著者不明)

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.

(著者不明)

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 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.

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…

O. Issah, Mutala Zubeiru, Samuel Anaba

This study uses machine learning (Random Forest, XGBoost) on a global sample of 5,000 firms across 50 countries to predict ESG reporting quality. XGBoost achieves R² of 0.78 vs 0.62 for panel regression. SHAP analysis identifies firm size, …

Xiaonan Dong, sungjin son

This study examines how AI adoption affects corporate carbon neutrality performance using Resource Orchestration Theory. Analyzing panel data of Chinese A-share listed manufacturing firms (2018-2023), it finds that AI significantly enhances…

A. Sunitha, K. Srinivas, T.Radhika, B.Chandrakala Naik, P. Sandya Rani

This study examines how AI, big data analytics, and FinTech innovations affect ESG disclosure quality and corporate transparency, using survey data from 312 professionals in India, UAE, and UK, analyzed via PLS-SEM. Results show all three d…

A. Sunitha, K. Srinivas, T.Radhika, B.Chandrakala Naik, P. Sandya Rani

This paper empirically examines the combined impact of AI, big data, and fintech on ESG disclosure quality and corporate transparency using PLS-SEM on a sample of 312 professionals from India, UAE, and UK. Results show all three digital con…

Abdullah Albizri, Ahmad Jumah

This study develops a large language model (LLM) approach to measure firms' conformity with the TCFD framework from unstructured sustainability reports. Analyzing a sample of U.S.-listed firms, it finds that higher TCFD conformity is associ…