GX & Decarbonization Research

G Li, Hongxin Guo, Jian Kang +5

This corrigendum refers to a framework that uses machine learning to optimize multi-scale spatial forms within urban blocks for low-carbon performance. It analyzes the relationship between building morphology and energy consumption, aiding …

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 …

Sejal Jain, Pranav Singh, Sachin kushwaha +1

This paper presents CarbonLens, an AI-powered carbon footprint tracking and sustainability analytics platform for personal emissions. It uses React.js, Node.js, Express.js, MongoDB, and Ollama-based LLMs to automatically estimate emissions …

Jiapeng Dai

This study examines how forward integration into global value chains affects energy poverty, moderated by governance. Using panel data from 57 economies (2005-2022) and estimators including double machine learning, it finds that forward int…

Hasitha Priyashantha, Imasha S. Jayathissa, Janak K. Vidanarachchi +3

This policy paper synthesizes evidence on climate adaptation and mitigation for livestock systems in South Asia, emphasizing climate-smart interventions including precision livestock farming with AI and sensors, traditional knowledge, and r…

Petteri Siitonen, Samuli Honkapuro, Salla Annala +1

This study uses BERT-based sentiment analysis on 50,000 Finnish social media posts to examine changes in consumer attitudes towards energy technologies and behaviors before, during, and after the 2022 European energy crisis. Results show in…

Erokhin, D.

This study analyzes 553 YouTube videos and 5060 comments on ESG reporting. Videos mostly endorse ESG, but comments show skepticism and concerns about greenwashing and politicization. Low correlation between video and viewer sentiment highli…

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,…

Je Hyun BAE, JaeHo Lee, Eun-Chong Kim +1

This study uses Gemini 3 Flash to measure disclosure quality in Korean voluntary sustainability reports. It finds that voluntary reporters have higher ESG ratings, and textual quality and reporting continuity are associated with subsequent …

Yanbing Zhang, Changzheng Zhang, Chengyu Li

This paper investigates the emergence and dissolution of green innovation bubbles among Chinese power industry chain enterprises from 2016 to 2023. Using the AMO framework and double machine learning (DDML), it identifies causal drivers, an…

Bartholomew Paudyn

This paper critically examines ESMA's Strategy 2023-28, focusing on how AI and machine learning in ESG ratings may hinder sustainable finance and technological innovation. It argues that algorithmic decision-making exacerbates greenwashing …

(著者不明)

This book provides a comprehensive exploration of how finance, technology, governance, and social behavior are being transformed to address climate change. It focuses on how AI, blockchain, and IoT are redefining climate accountability and …

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…

Shikder S.H.

This paper proposes an AI-driven framework for green finance and environmental monitoring in Bangladesh, addressing data deficits. It conceptualizes how AI can integrate financial and environmental data to support green investments, though …

Li Y.

This study empirically tests whether LLMs can replace human reading in analyzing sustainability reports. It provides evidence on the accuracy and efficiency of LLMs for information extraction and evaluation, highlighting the potential for A…

Kongthon A.

This paper proposes a method to analyze climate risk disclosures in the banking sector using transformer models and generative AI. It applies NLP techniques to extract climate-risk-related information from disclosure documents and assess th…

Martillano D.A.

This paper presents a borehole and near-surface GHG emission monitoring system using a self-calibrating algorithm and zone-based clustering analysis, enabling accurate and localized GHG measurements.

Zhenchen Sun, Min Xiao, D Zhang +3

This paper proposes CaStNet, a causality-guided decomposition and cell-state-driven attention framework for carbon price forecasting. It uses Granger causality for residual decomposition and cell-state differential velocity for adaptive att…

Bangtong Huang, Dongfang Niu, Qi Xu +1

This paper proposes a deep learning approach to optimize resource and capacity management in low-carbon additive manufacturing (3D printing). By leveraging AI, it aims to reduce energy and material usage, lowering carbon footprint. It demon…