GX & Decarbonization Research

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…

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…

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…

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…

Shan Wang, Yanze Wang, Yue Gao +1

This paper empirically analyzes the effect of 'AI-washing'—corporate overstatement of AI capabilities—on actual energy transition efforts. It proposes methods to detect discrepancies between AI claims and real green actions, assessing the c…

Qi Chen

This paper proposes RADR-MO, a dynamic re-routing framework for supply chain resilience that jointly optimizes cost, resilience, and carbon emissions using graph attention networks and meta-reinforcement learning. It adapts to novel disrupt…

Manoj D. S, Sankari M, Aruneswar Meera S +1

Greenscan-AICO2R is a framework for evaluating and optimizing website carbon emissions. It performs multi-page crawling, rule-based image classification, and NLP-driven tailored recommendations. Simulations show 30-40% emission reduction vi…

Shiju Li, Hongjie Lan, Hanying Jiang +5

This paper proposes a CEEMDAN-SE-Transformer model to improve carbon price forecasting accuracy. Using data from China's Guangdong carbon market (2015-2022) and incorporating factors like oil prices and EU carbon prices, the model outperfor…

Abbas N, Hao L, Ayoubi M

This study proposes a secure control framework using a reliability-gated GRU to detect and correct false data injection (FDI) attacks on floating offshore wind turbines (FOWT). The method evaluates sensor prediction reliability and compensa…

Athira K.

This paper proposes an AI-based method to predict green bond performance, contributing to climate finance and ESG evaluation. Details are limited due to missing abstract, but it represents a key intersection of AI and sustainable finance.

Adelina Zeqiri

This paper proposes a conceptual framework linking AI-driven marketing to sustainable tourism outcomes, mapping four pathways: smart operations, environmental intelligence, demand shaping, and governance. It advances eight testable proposit…

V. Vairagade

This paper presents an AI-assisted, data-driven approach for designing cement-based hybrid materials and forecasting their lifecycle performance to enhance environmental sustainability. While specific findings are not available, the work li…

N. Subhan, Nouman Ali, H. Hanif

This study analyzes contagion effects of green (Cardano, Stellar) and non-green (Bitcoin, Monero) emerging cryptocurrencies on energy markets (WTI, Brent, Natural Gas) using Quantile VAR and quantile connectedness frameworks. Using daily da…