GX & Decarbonization Research

Marcus Becker, Timo Zimmermann, Judith Maria Beermann

This study applies a GRI-oriented RAG workflow to eight years of Borussia Dortmund's sustainability reports, finding moderate and fluctuating evidence quality rather than steady improvement. It demonstrates RAG's utility in structuring disc…

Nanning Guo, Ming Li, Arjun Rachana Harish +2

This paper provides a systematic review of authenticity in ESG disclosure, outlining current challenges and future research directions. It emphasizes the role of AI and technology in enhancing disclosure quality and credibility, offering in…

Prima Ayu Novita Junianto, Dedy Iswanto

This systematic literature review (PRISMA, 19 studies) examines AI competency, sustainability assurance quality, and future-ready auditors in emerging markets. It finds that AI, blockchain, and big data transform assurance via continuous au…

Jin Yan, Yang Zhao, Xue Gao +3

Combining machine learning with causal inference, this study detects low-carbon technology innovation take-offs across 18 countries (2001-2022) and evaluates over 1,600 policy interventions. It finds that market-based instruments (especiall…

Rui Lu

Proposes request-level carbon accounting for AI inference, considering service boundaries, routes, and electricity sources. Public-data implementation reduces median absolute percentage error by 56.3% vs EcoLogits.

Filiz Mızrak, Umut Elbir

This study develops a reliability-aware Bayesian framework to assess readiness for digital-twin-enabled smart-energy systems across 20 major international airports. Using 15 capability indicators in four domains plus an auxiliary transparen…

Avant Muhammad Armadan, Fitriaty, M. H. Saputra

This study examines ESG disclosure in sustainability reports of IDX ESG Leaders index companies, evaluating patterns that may signal greenwashing. Using a mixed-methods approach with an ESG Disclosure Index based on GRI standards and NVivo …

Yaodan Chi, Xinyu Ding, Qingcai Zhou +2

This paper proposes a novel multi-objective artificial lemming algorithm for optimal operation of a microgrid integrating carbon capture and power-to-methanol. The method balances operational cost and CO2 emissions, deriving efficient Paret…

Mohammed Aldawsari, Saeed Tiari, Sultan Aldossary +1

This paper proposes AI-based prediction and optimization of energy and exergy efficiency in thermal systems. Improving energy efficiency directly contributes to decarbonization and emission reduction in industrial sectors. The application o…

Kaisheng Yang, Ximan An

This study constructs a climate risk attention indicator for Chinese funds using Word2Vec text analysis of annual reports. Applying the Brinson model, it finds that climate risk attention boosts excess returns, with a 1% increase in acute p…

Baptiste Rigaux, Mark Vergouwen

DETeCT proposes a data-efficient method for classifying low-carbon technologies into multiple categories. It achieves high classification performance with limited data, aiming to automate technology classification. This can support corporat…

Daichen Guo, Sanglin Zhao

This study empirically examines the impact of corporate digital transformation (DX) on carbon emissions and the mediating role of ESG performance, using Chinese A-share listed companies from 2010 to 2023. DX reduces carbon emissions by impr…

Yuanyuan Ma, Siyu Peng, Yun Yu

This study develops a hybrid forecasting framework integrating CEEMDAN, sample entropy reconstruction, and coefficient of variation ensemble to capture multi-scale nonlinear carbon price dynamics. SHAP and TVP-SV-VAR reveal scale-specific a…

Sohail M.

This paper empirically examines how AI adoption affects ESG performance in the German banking sector, focusing on the mediating role of green finance. It suggests that AI can enhance banks' ESG assessments and promote green lending.

Xuelong Zhang, Xiaoling He, Mei Li +3

Using panel data of Chinese listed energy firms (2010-2023), this study shows AI adoption significantly reduces carbon emission intensity. Executive digital background strengthens the effect, and low-carbon city pilots provide complementary…

Alfa Yohannis, Alexander Waworuntu

This paper compares the carbon footprint of document-oriented, relational, and hybrid object-relational databases for image-based time-series workloads in Green IoT, proposing a carbon-aware storage selection method. It leverages AI/ML tech…

Saad Khan, Ahmed A. Aboushady, Firdous Nazir +1

This study proposes a privacy-preserving framework to detect, classify, and localize unregistered behind-the-meter low-carbon technologies (PV, EVs, heat pumps) in low-voltage networks. Using GDPR-compliant measurements, it combines LSTM au…

Aurélien Saussay, Misato Sato, Francesco Vona

Using U.S. online job vacancy data, this paper develops a skill-based NLP method to identify low-carbon jobs within occupations. Low-carbon job creation is prevalent in low-skilled occupations but requires more complex skills, yielding a mo…

Issam Benhayoun

This study analyzes factors determining ISSB adoption intention among 335 Moroccan accounting professionals. It demonstrates that institutional pressures influence adoption intention only through perceived usefulness and ease of use. Using …

Kishore Kumar, Susela Devi Suppiah, Preeti Bhaskar +1

This paper examines policy architectures for governing AI to facilitate circular economy transitions, focusing on challenges and opportunities in emerging economies. It discusses how AI technologies can support circular economy principles a…