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 141–160 of 705 papers
M. Kayakuş, Mustafa Terzioğlu, Dilşad Erdoğan +3
This study applies a multi-method NLP framework to analyze green finance discourse on social media. Using TF-IDF, FinBERT sentiment analysis, LDA topic modeling, and keyword co-occurrence networks on 23,452 X posts, it finds that discourse …
M. Raslan, M. Mostafa, Ahmed S. Abdellah +2
This paper proposes a Demand Side Management strategy using Non-Intrusive Load Monitoring (NILM) to optimize green hydrogen microgrid costs. By identifying and shifting deferrable appliances (dishwasher, washing machine) from evening peak t…
Francesco Smaldone, Benedetta Esposito, Daniela Sica +1
This study profiles employability skills needed for the climate-circularity nexus in agri-food using large-scale text mining and LLM-based classification of job ads. Findings highlight the prominence of transversal skills, growing demand fo…
Sohan Kumar Jha, Nirmala Kushwah
This paper critically examines AI's role in sustainable finance and green banking. It develops an integrated conceptual model linking AI capabilities to ESG integration, climate risk analytics, green credit allocation, and portfolio managem…
Gangineni R.
This paper uses a bibliometric review to analyze trends in sustainable marketing and green finance in the age of AI, identifying key research themes and applications of AI in these fields.
Kumar T.
This paper explores the role of AI in sustainable finance, balancing ESG investment strategies, central bank digital currencies, and financial privacy. It likely discusses AI-driven ESG evaluation, optimization of investment decisions, impl…
Twinamatsiko E.
Proposes a machine learning approach to incorporate ESG factors into investment decision-making. Develops a framework for integrating ESG criteria to support responsible and sustainable investments.
Shien T.M.
This paper proposes an AIoT-based waste classification system for solid waste management, contributing to the Sustainable Development Goals (SDGs). It aims to improve efficiency and promote recycling, reducing environmental impact.
Aman Ali, Hritik Verma, Prateek Maurya +1
This paper reviews traditional carbon footprint methods (LCA, emission factors) and their limitations across sectors (manufacturing, construction, logistics, transport, digital infrastructure), including fixed emission factors and high unce…
Boaz Mwubahimana, Dingruibo Miao, Jianguo Yan +7
This paper proposes CarbonFormer, a causal spatiotemporal framework for forest aboveground carbon density (ACD) estimation. It overcomes CNN limitations by using an LSTM-based temporal causal decoder and a cross-causal loss function to enfo…
Wanting Wang
This study uses machine learning to analyze how negative ESG emotions on social media affect the quality of corporate ESG disclosure, testing whether public pressure drives disclosure improvement.
Q Zhang, 胜生 余, Tongna Liu
This paper provides a reproducibility artifact for carbon-aware scheduling in AI data centers, including deadline-aware LP and rolling-MPC code, external-trace-informed scenario builders, and matched-service accounting. It aims to reduce ca…
Sofianos Panagiotis Fotias, Vassilis Gaganis
This paper proposes Bayesian neural network surrogates for optimizing Carbon Capture and Storage (CCS) operations. The AI-driven approach enhances CCS efficiency, contributing to decarbonization cost reduction.
Olena Zhytkevych, Andriy Matviychuk
This paper uses Kohonen self-organizing maps (SOM) to cluster 40 countries by decarbonization potential, identifying six clusters (leaders, followers, resource-intensive economies, etc.). It analyzes dynamic trajectories over time and ident…
Aarya Bhat
This study introduces Reverse Carbon Advisory Framework (RCAF), combining reverse consulting with carbon credit strategy optimization. It identifies three major pitfalls: regulatory misalignment, market mismanagement, and strategic blindnes…
Verma J.
This paper provides a bibliometric overview of artificial intelligence in business management and its impact on sustainable finance. It maps the research landscape from past to present trends and identifies future directions, highlighting t…
Singh J.P.
This paper examines the role of higher education institutions in integrating green finance and artificial intelligence to build a sustainable future. It explores how universities can promote AI-driven green finance, develop educational prog…
Woon Leong Lin, A. Ignasiak-Szulc
This systematic review (2013–2024) examines AI integration into CSR, identifying five empirical clusters and three generative mechanisms: datafication and auditing, algorithmic mediation of responsibility, and stakeholder salience reweighti…
Haoying Niu, Qinzi Xiao, Mingyun Gao
This study uses text from annual reports of Chinese A-share listed green companies (2015-2024) to construct a climate resilience indicator via word frequency and sentiment analysis. It compares four ensemble learning models, finding that XG…
Runxin Hua
This paper proposes an end-to-end LSTM-Copula hybrid model for joint risk modeling in carbon-electricity markets. Using Chinese market data from 2021–2025, the model integrates LSTM-GARCH for marginal forecasts, EVT for tail risk, and time-…
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