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 221–240 of 1487 papers
Radosveta Krasteva-Hristova, Vanya Georgieva
This conceptual paper systematically examines AI's role in sustainability assurance, identifying five value-adding domains: evidence extraction, criteria mapping, anomaly/greenwashing screening, external-data triangulation, and documentatio…
Rajeswari Kannan, Raj Firke, Shreya Bengle +1
This paper presents GreenBench, a benchmarking framework to evaluate energy efficiency and carbon footprint of open-source LLM inference on Apple Silicon. Measurements show 30-40x better energy efficiency per token than datacenter GPUs for …
Sheetal Thapa, Asha Rani N R
This chapter reviews AI's role in reducing carbon footprints, covering real-time emissions monitoring, predictive modeling, and sustainable decision-making. It presents case studies (Google data centers, IBM Green Horizon, Climate TRACE) an…
Rui Zhao, WenXuan Sun, Xiao Zhou +3
This study constructs a standardized, traceable global carbon emission factor database for road construction. It leverages large language models to extract key data from unstructured documents, ensuring reliability through manual verificati…
Dan Qiu, Bangjun Wang
Using TOE-P framework with NCA and dynamic fsQCA on Chinese listed mining firms (2015-2025), this study identifies multiple configurational paths to high green and low-carbon performance. No single condition is necessary; synergy among tech…
Jun-Cheol Lee
This study experimentally validates low-carbon mortar mixtures generated by GPT-5.5. With 60% GGBFS replacement, it achieved 60% cement reduction and ~56% embodied carbon reduction while meeting 28-day strength of 40 MPa. GPT-5.5 is useful …
Anonymous Authors for Review
This dataset and supplementary materials support a study that uses large language models (LLMs) to quantify materiality misalignment risk in ESG reporting. It offers a methodology for assessing alignment between corporate disclosures and ma…
negar shabanpour, Sehl Mellouli, Stéphane Roche
Household consumption accounts for ~72% of global emissions, yet traditional surveys are costly and biased. This PRISMA-ScR scoping review maps NLP methods for analyzing individual climate behaviors, screening 2,580 records and including 10…
ANJALI CHAUDHARY, Hebah Shalhoob, Kholoud Y. Bajunaied +5
This study provides a comprehensive analysis integrating AI optimization for biofuel production on degraded lands with climate mitigation and green finance mobilization. Based on 152 studies and international datasets, it shows AI technique…
Toqeer Ali Syed, Ali Akarma, Muhammad Tayyab Naqash +3
This PRISMA-guided review distinguishes agentic AI from conventional ML for SDG 11 and 13, analyzing 60 eligible studies (14 fully agentic). It proposes a reference architecture linking an agentic layer with urban digital twins. Real-data t…
Irfan M.
This paper proposes a method for constructing sustainable investment portfolios using AI, aiming to bridge green finance and technological innovation. It leverages AI to analyze ESG factors and climate-related risks, enhancing investment de…
Hu P.
This paper proposes a method to detect corporate greenwashing by analyzing the readability of ESG disclosures. Low readability is associated with information concealment, serving as a useful signal for investors and regulators. It is an emp…
Jamil J. Jaber, A. A. Alkhawaldeh, Qusay Ayman Sulayman Mazahreh +3
This study predicts credit risk in commercial banks using machine learning (XGBoost) and a fuzzy rule-based model (SLAVE) on panel data from 40 banks across seven Middle Eastern countries (2014-2023). Regression results show profitability a…
Amel Mahmood Ali Alobaidi
This study examines the impact of voluntary climate anxiety disclosure on investor behavior using mixed methods: textual analysis, archival data, and experiments. Disclosure significantly predicts investor behavior, moderated by firm size a…
Sinhika Deshmukh, S. Karve
This study proposes and evaluates a blockchain-enabled traceability framework for Tata Steel's supply chain. Scenario modeling suggests traceability coverage could rise from 42% to 98%, scrap reuse from 60% to 88%, enabling an estimated 3.0…
Wang-Chen Yang, H. Arian, Cui-Cui Luo
This paper proposes a constrained residual learning framework for portfolio optimization in China and Europe's carbon markets. It applies machine learning to capture carbon price dynamics and optimize risk-return trade-offs, offering improv…
Samrana Afzal, Rimsha Kanwal, Habibullah +1
This study introduces 'algorithmic power' to assess AI's implications for Pakistan's climate diplomacy. Proposing a framework of data, analytical, and diplomatic capacities, it uses composite indices (ARI, DLR, AIS) to estimate Pakistan's l…
Hannes Cordes
This systematic review maps NLP tasks used to analyze sustainability reports, categorizing 160 studies hierarchically. It finds keyword search, content analysis, and topic modeling are most common, and highlights limitations such as linguis…
Rongyu Ji
This qualitative case study of Microsoft examines how ESG information disclosure affects capital market performance in AI enterprises. It finds that completeness, openness, and AI-specific governance of ESG disclosure are high, with respons…
Dara Ghahremani
Replication materials for a doctoral study on how institutional, state, corporate, and foreign ownership affect ESG performance and annual-report ESG disclosure among European listed banks. Includes Python/Stata code for panel analysis, tex…
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