This page provides an English interface to the gxceed GX paper corpus. The corpus aggregates papers from 14 contributing 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 41–60 of 1673 papers
Kotak, Bhavin
CarbonGate defers non-urgent GitOps deployments to low-carbon-intensity forecast windows under a max-delay SLA. Replaying a simulated workload against 12 months of Great Britain NESO grid data, forecast-based deferral with a 4h limit cut av…
Yong-Jing Gao, Jing Tang, Amizawati Mohd Amir +2
Using Chinese A-share firms (2014-2023), this study links corporate AI investment to the specificity of climate risk disclosure, measured as the share of climate statements containing checkable information. AI investment is positively but m…
Yi-Ke Chen, Yu-Heng Cheng, Xi-Yuan Zhou +1
This paper proposes PSA, an LLM-based strategic bidding agent for coupled electricity-carbon markets that operates zero-shot without historical data. A dual-layer reasoning mechanism separates physical cost computation from strategic decisi…
Insyirah Putikadea, Ambar Kusumaningsih, Cantika Sari Siregar +2
This review examines AI data-driven applications in Environmental Management Accounting (EMA) for carbon accounting and managerial decision-making. Using PRISMA and bibliometric/thematic synthesis in Scopus, it identifies five AI applicatio…
Sha-Sha Chang, Yi-Jia Liu
This study applies text-mining methods to climate risk disclosures of Chinese listed firms, examining how disclosure content relates to corporate energy efficiency. It quantitatively links disclosure quality and language to actual decarboni…
Huan Chen
Analyzing annual reports and policy documents from Midea, Gree, and TCL, this study examines how AI converts green compliance costs into competitive advantage for Guangdong manufacturers. AI-driven energy forecasting and process optimizatio…
Yichen Wu, Shanyue Jin
Using panel data from Chinese A-share listed firms (2009–2023), the study builds a firm-level climate risk disclosure (CRD) index via Word2Vec semantic expansion and TF-IDF weighting. CRD is positively associated with ESG performance, with …
Hasnain Ahmad Saddiqi, Nasser Ahmed Al-Azri, Muhammad Abdul Qyyum
This paper applies AI surrogate modeling to quantify greenhouse gas emissions across the global LNG carrier fleet and to identify targeted decarbonization pathways. It links fleet-wide emissions accounting with efficient abatement targeting…
Zeyu Wang
This paper analyzes how Climatiq's AI carbon computing capabilities are translated into thyssenkrupp Rasselstein's transport and procurement processes via the Celonis platform, supporting Scope 3 emission reduction potential identification.…
张蓉
A replication package for a study validating corporate climate commitment credibility. It releases a frozen 51-document, 20-firm corpus, dual-coder CRED codebooks, blind re-adjudication records, and a consolidated analysis script reproducin…
Youbi D.
A bibliometric analysis mapping the AI–sustainable finance intersection using a TCCM (Theory, Context, Characteristics, Methodology) framework. It visualizes research trends and proposes a conceptual framework for how AI is applied to ESG e…
Pelumi Damola Adeyinka, Oluwadayo Mafolasere Olaniyi, Valerie Ojinika Ejiofor +2
Analyzing Form 10-K filings of 488 S&P 500 firms with dictionary-based text measures, LDA and latent profile analysis, the study identifies four AI-governance disclosure profiles differing by sector. Fuzzy-set QCA reveals two equifinal path…
Fu-Yang Deng, Peng Wang, Xin-Yao Liang +1
Using Chinese listed firms and their bonds (2012–2024), this study combines city-level weather data, annual-report text analyzed with MacBERT, and bond trading data to measure the gap between physical climate risk exposure and managerial at…
H. Jin, W. Liu
A physics-informed neural network (PINN) framework reconstructs facility-level carbon flux by coupling upstream fuel metering with downstream concentration gradients, embedding mass conservation, combustion stoichiometry, and advection-diff…
(著者不明)
Using 1,178 firm-year observations from 62 listed Bangladeshi financial institutions (2005-2023), the study tests how intellectual, human, and natural capital disclosure affect sustainability performance. Panel regressions, system-GMM, and …
Young‐Chan Lee, Chuyu Yang
Using 7,278 statutory reports from 808 non-financial KOSPI firms (FY2016–2025), the authors train an LLM-annotated classifier to code every narrative sentence by ESG issue and specificity, then apply SASB industry standards to separate mate…
Thuy Duong Phan, Thi Thanh Hoang
Using OCR and an LLM to classify annual-report paragraphs into E/S/G, the study scores ESG disclosure for 30 Vietnamese listed energy firms (2015–2024). FGLS regressions reveal an inverse U-shaped association between ESG disclosure and ROA,…
Karol Skiba, Aleksandra Mach, Joanna Sagan +1
Machine learning predicts building-material GWP for production (A1–A3) and module D from EPD resource-use and waste indicators. XGBoost reached R2 of 0.963 (A1–A3) and 0.791 (module D). Non-renewable primary energy dominated production pred…
Ceren Kına, Harun Tanyıldızı
This study applies hybrid deep-machine learning models to predict the CO2 footprint of geopolymer concrete. By estimating embodied carbon from mix design and material properties, it enables low-carbon optimization at the design stage. It of…
Roni Yunis, Irpan Adiputra Pardosi
This systematic review examines carbon footprint prediction models from 2018–2024, covering process-based, input-output, hybrid, grey system, and AI-based approaches. It identifies persistent challenges in data quality, model complexity, sc…
Browse by Topic