GX & Decarbonization Research

(著者不明)

A modular decision system fuses heterogeneous supply-chain data, applies two-stage emission estimation with time-series residual correction, and solves constrained multi-objective optimization for carbon strategy. On 21,000+ enterprise even…

Eleni F. Tsantsani, Kosmas G. Kosmidis, Leonidas L. Fragidis +1

This paper proposes an Artificial Neural Network (ANN) model using the Levenberg–Marquardt algorithm to predict ESG scores from financial data, evaluated on S&P 500 firms with two rating agency datasets, outperforming prior work. It also in…

Aarush Agarwal, Deepti Mehrotra, Renuka Nagpal +1

This paper applies the best–worst method (BWM), a multi-criteria decision-making technique, to data-driven ESG scoring for sustainable portfolio management. It offers a quantitative framework for weighting ESG criteria and integrating them …

The PLOS One Editors

This editorial note explores using AI to analyze CEO strategy narratives in sustainable management reports and incorporate them into corporate performance prediction. It bridges non-financial sustainability disclosure text with quantitative…

Shixiong Guo, Xinzhi Wang

This paper proposes Auto-LCA, a system architecture for continuous product-level carbon estimation combining automated supply-chain data acquisition, low-latency multilingual NLP for extracting life-cycle attributes from unstructured docume…

Naveen Virmani, Srikant Gupta, Koppiahraj Karuppiah +1

This paper examines federated learning as a means to enhance the scalability and credibility of ESG reporting, identifying and prioritizing adoption challenges. Through literature review and expert panels, 18 validated challenges were analy…

Xunzhang Pan, Tianming Shao, Jianxiao Wang +5

This paper uses LLM agents to generate and evaluate global mitigation pathways for the 1.5°C target, integrating Sustainable Development Goals (SDGs). It sits at the intersection of AI and climate policy, offering a novel approach that comb…

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…