This page provides an English interface to the gxceed GX paper corpus. The corpus aggregates papers from 13 open scholarly metadata sources and uses AI-assisted classification to identify signals related to measurement, policy narratives, outcomes, implementation, industrial adoption, and verification.
The goal is not only to discover papers, but to observe how GX research is distributed across research substance, implementation narratives, external expectations, implementation substance, and judgment formation.
Summaries are AI-assisted. Always refer to the original paper for authoritative conclusions.
Showing 321–340 of 716 papers
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…
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 …
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…
Yiming Peng, Minfei Liang, Cise Unluer
This review synthesizes machine learning (ML) applications for low-carbon concrete, covering cement production, mix design, supplementary materials, and recycled aggregate. ML enhances process control and multi-objective optimization but fa…
Seon Joo Kim, Hyo Jin Park
This study uses text mining to extract Social (S) content from sustainability reports of four Korean port authorities, analyzing disclosure patterns via frequency analysis, N-gram, topic modeling, and network analysis. Results show that dis…
Jonas Rieger, Felipe Muñoz, Lars Grönberg +11
This paper applies RollingLDA, a dynamic topic model, to 1,996 news articles to analyze spatio-temporal narratives of socio-environmental conflicts in Chile's energy transition. It identifies twelve topics, revealing the evolution of confli…
Ram Chandra Chaurasia Ram Chandra Chaurasia, Rajshekhar Singh Rajshekhar Singh
This study proposes an AI-IoT framework for real-time methane monitoring, prediction, and carbon footprint reduction in underground coal mines. Using LSTM, random forest, and reinforcement learning, it achieves over 93% prediction accuracy …
Lei Wang, Zheng Wang, Maolin Fang +4
This paper constructs a green performance evaluation system for distribution network suppliers from economic, technical, and green dimensions, defining a carbon footprint accounting method. It proposes an improved AHP-SVR model, validated o…
Abdul Rauf Agbor Seidu
This paper applies sentiment analysis to model public perceptions of energy insecurity and renewable adoption in Ghana's green energy transition. Using machine learning, it extracts sentiment from social media and survey data, offering insi…
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