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 181–200 of 923 papers
Aynur İNCEKIRIK
This study analyzes the price dynamics of blockchain-based carbon credit tokens (BCT, MCO2, KLIMA) using deep learning methods (LSTM, GRU, transfer learning) with daily data from Oct 2021 to Nov 2025. Findings show strong internal correlati…
Jingyi Xu, Minghui Cheng, Anchen Sun
This paper introduces BeDA, a multimodal large-language-model instrument, to audit corporate building-decarbonization disclosure. Applied to a global panel of 2,246 firms (2003-2023), it finds that only about one in five firm-reports disclo…
Zhonghao He, Haihong Jiang, Jing He +7
This paper proposes a process-aware deep learning approach for low-cost greenhouse gas sensing. Using composting as a case study, it incorporates process information to improve sensing accuracy. The method is scalable to other anthropogenic…
Peixoto E.
This paper proposes an architecture for monitoring and reporting ESG indicators in AI-driven value chains. It automates data collection and analysis across the supply chain, facilitating stakeholder reporting. By leveraging AI, it enables m…
Augustine Okeke, Ifeanyi Ugbebor
This study introduces the SDG-Purpose Alignment Index (SPAI), a computational construct quantifying how CEO letters align thematically, tonally, and stylistically with specific SDG targets. Analyzing 740 CEO letters from 148 listed firms ac…
Iqra Mubeen, Samina Rauf
This paper investigates whether ESG sentiment generated by LLMs can predict portfolio risk. It finds that positive ESG sentiment correlates with lower volatility and downside risk, while negative sentiment increases risk exposure. LLMs outp…
Jiaxiang Wang, T X Chen
This study uses MGWR-SHAP analysis to evaluate the scale-dependent and non-linear effects of land-cover configuration on carbon performance in Tianjin, China. It reveals that different land cover types have varying impacts at different spat…
Олег Ивченко, Iryna Ivchenko
This paper proposes reporting standards for energy consumption and carbon footprint of open-source AI training. It presents specific metrics and methodologies to enhance transparency, contributing to decarbonization in the AI sector.
T Zhang, Ting Jin, Kang Wu +1
This paper proposes a Temporal Dendritic Neural Model (TDNM) with a Dendritic Adaptive Learning algorithm for multivariate carbon emission forecasting. Using panel data from 54 Chinese cities (1999-2023) with 13 driving factors, it outperfo…
Shuai Li, Pei Yu, Z J Yang +1
This supplement accompanies research applying machine learning (XGBoost, Bayesian optimization) to low-carbon concrete mix design. It includes material-stage emission factors, optimized hyperparameters, minimum-embodied-carbon candidate mix…
Q Zhang, 胜生 余, Tongna Liu
This paper provides a reproducibility artifact (v2.0.0) for an environmental decision model evaluating carbon-water trade-offs in geo-distributed AI workload allocation. It includes portable Python code and test data, supporting only scenar…
Priyanka Aggarwal, Archana U Singh, Deepali Malhotra
This study analyzes sustainability reports of 269 NIFTY 500 firms from FY2015-2023 using text mining. It identifies three phases of ESG reporting evolution: initial establishment (2015-17), governance consolidation (2018-20), and balanced i…
Kyung Bae Jang, Tae Ho Woo
This study uses a system dynamics approach to analyze how generative AI optimizes nuclear energy's contribution and mitigates vulnerabilities of carbon-intensive sources. Test 1 (low initial value) outperformed others, with the highest sens…
Ummesara ., Chaitanya Kumar Sahu, R Ranjith +5
This review integrates trends in AI-enabled regenerative agriculture (RA) to promote climate resilience. AI tools (ML, remote sensing, IoT) enable dynamic monitoring of soil/crop health, carbon accounting, and adaptive farm management, acce…
Pieter de Jong, Inga Timmerman, Alona Bilokha
This study introduces the Claim-Reality Divergence (CRD) index, combining satellite methane data (Sentinel-5P TROPOMI) with NLP analysis of SEC filings and earnings calls for 233 US heavy emitters (2018-2023). A one standard deviation incre…
Nguyen V.
This paper proposes a method to learn domain-specific embeddings for ESG question answering. It uses a retrieval-augmented generation framework to adapt embeddings to the ESG domain, enabling accurate extraction of answers from ESG document…
D. R, Santhosh Kumar A G
This secondary data analysis (2020-2025 sources including Bloomberg, MSCI, World Bank) examines AI's role in ESG integration and sustainable finance. It finds that NLP and ML improve ESG data coverage by up to 40% and enhance climate risk p…
Murali Krishna Pasupuleti
This paper proposes the Generative Climate-Aware Deep Hedging (G-CADH) framework, which combines deep hedging with generative market models to manage portfolio risk and financial resilience under climate change. It integrates a conditional …
Anuj Pal
This study evaluates the ability of large language models (LLMs) to distinguish between symbolic and substantive sustainability communication in ESG disclosures. It constructs a dataset from annual and sustainability reports of publicly lis…
Mohammad Khaled al-Bashiti, M.Z. Naser
This paper presents an autonomous ML approach using Bayesian optimization and NSGA-II/III to optimize green concrete mixtures. Analyzing over 2,300 real mixes, it achieves an average 40% reduction in CO2 emissions and 30% reduction in mater…
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