GX & Decarbonization Research

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 …

Ellen Kulset, Charlotte Haugland Sundkvist, Tonny Stenheim

This study uses dictionary-based tone analysis to examine climate disclosures under the mandatory CSRD/ESRS framework. Analyzing firms listed on the Oslo Stock Exchange, it finds that disclosures are generally not neutral: firms use more po…

Isaiah Oluwasegun Owolabi

This paper presents an ESG-as-Code framework that translates ESG regulatory texts (CSRD, SDR, SEC) into machine-executable compliance logic. It demonstrates how containerized AI workflows on Kubernetes-OpenStack enable automated, auditable,…

Abhinav Mahajan

This paper frames corporate climate risk as a multi-domain decision problem and proposes the Climate AI Decision System (CADS) architecture that integrates AI for evidence-based management. It synthesizes literature on climate finance, ML f…

Basaula Pululu Jordan, Xinyu Yang, Y Shi +5

This paper proposes an uncertainty-aware BIM-LCA methodology integrating automated quantity takeoff, probabilistic carbon assessment, and explainable machine learning optimization to reduce embodied carbon in building design. Using a case s…

Zaidoon Alhatabat, Diala Ershaid, Nashat Ali Almasria +1

This paper reviews current trends in integrated reporting (IR) within the convergence of ESG, digital transformation, and Industry 5.0. It finds that AI, NLP, and blockchain enhance reporting accuracy and timeliness but introduce ethical an…