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 181–200 of 1055 papers
Adelina Zeqiri
This paper proposes a conceptual framework linking AI-driven marketing to sustainable tourism outcomes, mapping four pathways: smart operations, environmental intelligence, demand shaping, and governance. It advances eight testable proposit…
V. Vairagade
This paper presents an AI-assisted, data-driven approach for designing cement-based hybrid materials and forecasting their lifecycle performance to enhance environmental sustainability. While specific findings are not available, the work li…
N. Subhan, Nouman Ali, H. Hanif
This study analyzes contagion effects of green (Cardano, Stellar) and non-green (Bitcoin, Monero) emerging cryptocurrencies on energy markets (WTI, Brent, Natural Gas) using Quantile VAR and quantile connectedness frameworks. Using daily da…
Zilin Wang, Meili Feng, M. Johnson +6
Using machine learning models trained on published datasets, this study projects greenhouse gas emissions from hydropower reservoirs under climate change. Under a net-zero scenario, total emissions from installed and planned projects could …
Gautam Shivaraj
This chapter explores the role of AI in enhancing resource efficiency and reducing carbon emissions, covering applications such as energy optimization, waste reduction, and resource management. Through case studies in manufacturing, agricul…
Jiwei Song, Yang Bin Huang, Lianmei Zhu
Using panel data of Chinese A-share listed firms from 2013 to 2024, this paper finds a significant negative association between the disclosure-implementation mismatch in digital transformation and corporate ESG ratings. The mismatch proxy c…
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…
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…
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…
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…
Krishna Rao, Andrew Dumit, Shaena Ulissi +7
PCFBench is the first benchmark that decomposes product carbon footprint (PCF) estimation into six independently evaluable tasks, with 614 expert-labeled items probing decomposition, retrieval, ontology matching, and numerical extraction. T…
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