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 221–240 of 923 papers
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.
Poorvika N.
This paper proposes a framework integrating AI-powered digital twins and zero-trust security for predictive maintenance of net-zero assets, aiming to enhance grid reliability and optimize renewable energy asset operations.
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…
Yifan Duan, Si Zheng, Lihuan Li +2
GHGbench provides an open dataset and benchmark for company- and building-level greenhouse-gas prediction. The company track covers 12,000+ firms with Scope 1+2 and Scope 3 disclosures; the building track harmonizes 491,591 records from 13 …
Yanming Guo, Charles Guan, Jin Ma
ExioML is an open-source dataset of environmentally extended multi-regional input-output tables for emission factors, covering 49 regions and 28 years (1995-2022). It includes GPU-accelerated computational tools and two aggregation schemes …
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…
Triana Arias Abelaira, María Jesús Guillén Palomino, Lázaro Rodríguez Ariza +1
This study uses a bibliometric approach to analyze 221 articles on climate risk in finance, focusing on the integration of AI techniques. It finds that NLP and digital transformation are key themes, linking AI innovation policies to greenwa…
Anuj Pal
This paper uses Large Language Models (LLMs) to evaluate corporate ESG disclosure quality by distinguishing symbolic from substantive sustainability communication. It introduces the Operational ESG Disclosure Coding Framework (OEDCF) and co…
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…
HEMSデータに基づく特徴量重要度分析を用いた住宅エネルギー性能評価
(著者不明)
This paper proposes a method to evaluate residential energy performance using feature importance analysis based on HEMS (Home Energy Management System) data. By analyzing energy consumption patterns, it identifies effective energy-saving me…
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…
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