GX & Decarbonization Research

José Alejandro Fernández Fernández, Renata Kubus, Inés Martín de Santos

Using 2,612 US firm-year observations (Refinitiv, 2019–2022) across five sectors, this study applies explainable ML (random forests, SHAP, partial dependence) to link financial health and ESG scores. Firm size is the most consistent predict…

Andy Dwiki Iranda, M. Mujiya Ulkhaq

This study analyzes 62 sustainability reports from Indonesian listed companies (fiscal 2023) using word frequency analysis via Orange Data Mining. Terms were categorized into four CSR dimensions: environmental (54.6%), social-labor (28.7%),…

Masradin Masradin, Syamsuddin Syamsuddin, Neks Triani +2

This systematic literature review (PRISMA 2020) examines Big Data and AI in capital-market-based accounting, synthesizing 29 studies into four aspects: data infrastructure, analytics, disclosure/reporting, and governance/assurance, integrat…

Rinaldi Sinaga, Enny Susilowati Mardjono, Nila Tristiarini

Using NLP on 198 palm oil plantation firms across eight producing countries, this study builds a Greenwashing Severity Index (GSI) measuring divergence between corporate ESG self-disclosure and external media/NGO narratives. It integrates E…

Dong-Xue Qiao

As EV competition shifts to data-driven agility, traditional rear-view marketing causes forecast distortion, inventory imbalance, and higher lifecycle emissions. Based on BYD, Tesla, and Geely, this paper builds a sustainable marketing fram…

J. de Lara, Esther Guerra, Jacinto Corral +1

This paper proposes an intelligent, model-based approach to automating sustainability reporting. It appears to use model-driven methods to structure disclosure items and streamline the reporting process. Though no abstract is available, it …

Pouyan Mobaseri Abed

This paper presents a comprehensive study applying AI methods—predictive analytics, machine learning, and process optimization—to reduce industrial waste and carbon emissions in manufacturing. Results across diverse scenarios show significa…

Fabian Dablander, Simon Wimmer, Jonas M B Haslbeck

First comprehensive LLM-based assessment of The Guardian's climate coverage (N=18,785 articles, 2010–2025). Coverage rose sharply after 2018 and stayed elevated; causes and mitigation are dominated by fossil fuels and renewables, while agri…

Kaiwei Jia, Wenhua Wang

An empirical study using double machine learning (DML) to causally test whether AI adoption mitigates or exacerbates supply chain carbon footprints. It offers causal evidence on the ambiguous environmental effects of AI, relevant to Scope 3…

Fabian Dablander, Simon Wimmer, Jonas M B Haslbeck

First comprehensive LLM-based analysis of 18,785 Guardian climate articles (2010–2025). Coverage rose sharply after 2018 and stayed elevated; causes and mitigation focus on fossil fuels and renewables, while agriculture, overconsumption, ca…

Maciej M. Sokołowski

This chapter examines AI's role in climate action and energy policy, focusing on energy transition, grid modernization, efficiency, renewables, and emissions reduction. It compares how the EU and Japan integrate AI into climate, energy, and…

Shen S.

This paper applies LSTM and Transformer deep-learning models to forecast the development trend of the green finance market. By combining long-term temporal dependency with attention mechanisms, it aims to predict future movements of green f…

Chen W.

This paper proposes federated learning analytics to verify and assure the authenticity of ESG data within digital apparel ecosystems. From a platform governance perspective, it addresses how to secure trust in distributed supply-chain ESG i…

Anis Omri, Fadhila Hamza

Using panel data from 23 emerging economies (2015–2023), this study shows AI advances the renewable energy transition partly through green technological innovation. Cybersecurity strengthens AI's effect on both innovation and renewables, wh…

Aisu Mustafaevna Bodur, Victor Sergeevich Tokarev, Alexander Sergeevich Nevzorov

This paper reviews digital technologies for reducing agriculture's carbon footprint and supporting sustainable development. It surveys global and Russian GHG emissions and examines smart greenhouses, smart farming, IoT, AI, and big data. Th…

Jinglong Chen, Qinyi Gu, Wencheng Lin +1

This study applies a large language model to conduct multitopic analysis of how China's low-carbon policies diffuse across regions and time. It demonstrates an AI-driven method for mapping policy theme propagation from policy text. As an ea…

S. Nawazish, Serish Umer, T. Jabeen

This review maps the intersection of AI, fintech, and climate-risk assessment across 2022–2026 literature, regulation, and practice. It builds a taxonomy of AI applications—physical and transition risk modelling, scenario analysis, greenwas…

Anonymous 2 Anonymous2

A zero-shot multi-scope LCA framework estimates ML workload footprints without executing models, using DAG traversal and topological depth extraction. It unifies operational carbon, Scope 3 embodied hardware carbon, network transmission car…

Funny Chibwe, Tendai James Mapanga

This study examines expert perspectives on AI-enabled ESG carbon accounting and reporting in Zimbabwe. It highlights AI's potential to automate carbon measurement and disclosure while identifying data infrastructure and institutional gaps a…

Danqing Lin, Dan He, Yu Zhao

This study introduces a multi-agent LLM framework that extracts ESG and financial indicators directly from banks' annual reports, using category-specific prompts to handle synonyms, scales and unit inconsistencies. Applied to hundreds of re…