GX & Decarbonization Research

Dauda Bola Abdulsalam, Duminda Kuruppuarachchi, Helen Roberts

This study applies NLP to ESG/sustainability reports of S&P 500 firms (2018-2023) to measure TNFD framework adherence. Findings show that TNFD alignment, clear nature-related risk/opportunity identification, and comprehensive reporting are …

Adrian Lim, Putri Rahayu

This review synthesizes AI applications across financial time-series forecasting, portfolio construction, and firm-level sustainability analysis, with emphasis on AI-driven ESG rating prediction and disclosure signal extraction. It argues f…

Christie Azour

This paper demonstrates AI's effectiveness in ESG investing. AI processed ESG documents 1,200 times faster than humans, reduced rating divergence by 34%, detected greenwashing with 83% accuracy, identified climate risks 8-14 months earlier,…

Cristina Caro-González, Daniel Jato-Espino, Yudith Cardinale

This systematic review of 64 studies on integrating ESG factors into financial risk prediction finds that traditional econometrics still dominate (48%), but ML (39%), NLP (8%) are growing. ML models, especially ensemble methods and neural n…

Xiao-Xing Cai, Wang Yang, Xiyu Ren +3

This paper proposes a human-AI collaborative framework to enhance the trustworthiness and comparability of sustainability (ESG) ratings. The framework consists of STRIDE (principled criteria and scoring) and SR-Delta (discrepancy analysis),…

D. Anjaneyalu, M. V. Subramanyam, A. K. N. Rani +3

This paper combines econometric panel-data modeling with machine learning (gradient boosting, explainable AI) to estimate the causal impact of ESG-bond issuance on corporate credit ratings and cost of capital. Findings show ESG-bond issuers…

R. M. Ellahi, J. Qureshi, M. H. Farooqui +2

This study proposes two pathways for generative AI to enhance supply chain sustainability: automated reporting and predictive analytics. PLS-SEM analysis shows predictive analytics is the strongest predictor, with regulatory pressure and su…

Kerstin Forster, Lucas Keil, Victor Wagner +3

This paper develops a machine learning framework to extract ESG indicators from corporate reports, applied to 600 large European firms (2014-2023). It reveals a transparency gap between high and low ESG-rated firms, narrowing over time, and…

Dongxin He, Linhan Jiao, Jingyi Yang +1

This paper examines the impact of carbon information disclosure quality on corporate risk-taking in China using a five-dimensional vectorized text index from ESG, CSR, and sustainability reports of A-share listed firms (2007-2023). Higher-q…

Fahad Ullah Khan, Asad Amin

This review comprehensively surveys FinTech applications (AI, ML, big data, blockchain) in ESG scoring, climate risk assessment, and green bonds. It highlights how digital tools improve efficiency and data coverage while addressing challeng…

Thierry Warin, Christophe Bisson

This paper longitudinally compares scenario knowledge representation across IPCC synthesis reports from AR1 to AR6 using a RAG pipeline. Early reports relied on qualitative conditional reasoning; later reports increasingly used quantitative…

Chong Zhang

This paper proposes a digital management platform for corporate carbon emissions using a cloud-edge-end distributed architecture, integrating IoT and AI for automatic aggregation, precise calculation, and intelligent analysis. It establishe…

Sini Guo, Mengzi Yin, Wai-Ki Ching

This paper proposes a network-based method for online portfolio selection that incorporates ESG scores. It leverages machine learning to integrate ESG criteria into investment decisions, offering a dynamic approach to sustainable portfolio …

Shukang Zheng, Genhua Ma, H Yang +2

This paper addresses decarbonization of urban last-mile delivery by proposing a demonstration-regularized Lagrangian deep RL algorithm for multimodal systems (road vehicles, off-peak metro freight, electric drones). The approach models the …

*Pramod Prabhakar1 and Rajesh Kumar2 *Pramod Prabhakar1 and Rajesh Kumar2

Livestock systems have significant environmental impacts including GHG emissions and water pollution. This review summarizes mitigation approaches such as AI and IoT monitoring, precision feeding, biogas recovery, and genetic improvements. …

Yufei Li, Tianhao Chen, Wei Ke +1

This paper presents a reproducible benchmark for reusing sentence-level ESG labels from the SASB-Aligned ESG Sentences corpus. It evaluates label stability through supervised classifiers, GPT-4o, and Claude annotation, finding that coarser …

Khushi Jatolia

This paper proposes Smart Carbon Tracker, an AI-driven web application for tracking, analyzing, and predicting carbon emissions using machine learning. It combines a React frontend, Node.js backend, MongoDB, and a Python AI module to help u…

Shilin Ou, Yifan Xu, Luyao Zhang

This paper introduces SolarChain-Eval, a physics-constrained benchmark for evaluating trustworthy economic agents in decentralized energy markets. It integrates an LLM-based Planner/Auditor layer to supervise RL agents, revealing a utility-…

Salinas JVR, Calderon AD, Macayan JV +1

This review comprehensively synthesizes AI techniques (ML, DL, RL, fuzzy logic) applied to solar, wind, and hydropower systems. Deep learning achieves up to 50% error reduction in forecasting, while RL and hybrid AI-physics models enable ad…

Nivetha Martin, Rajkumar S, Said Broumi

This study proposes an integrated neutrosophic MCDM framework with AI (random forest, SHAP) for selecting Li-ion batteries for BEVs. Unlike simple weighted sum methods, it applies multiple MCDM techniques to determine criterion weights and …