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 81–100 of 705 papers
Capt Yashodhan Mahajan
This paper explores how AI can transform sustainability and audit reporting from compliance-driven processes to intelligence-driven ones. It argues that AI enables deeper analysis, risk assessment, and higher-quality disclosures, offering s…
Dely Ramírez, Jonathan Muñoz Tabora, Ozy D. Melgar‐Dominguez
This study assesses GDP-CO2 decoupling in Honduras (1990-2023) using k-means, PELT, Tapio index, EKC modeling, Granger causality, and Random Forest. An inverted-U EKC is supported, but renewable share has negligible predictive power, sugges…
Xuanye Cai, Jie Zhang, Jian Yu +2
This dataset provides annual Energy Transition Policy Intensity (ETPI) scores for 31 Chinese provinces from 2000 to 2025. Using DeepSeek-assisted text identification and binary coding of 11,399 policy documents, it measures policy objective…
Schimanski T.
ClimRetrieve is a benchmarking dataset for evaluating information retrieval systems on corporate climate disclosures. It enables assessment of retrieval and question-answering performance on TCFD/ISSB-aligned reports, supporting the develop…
Abdulsamed Güneş, Orhan Yaman, Elif Feyza Sari +1
This study develops an MLP model to predict green hydrogen production potential for 2,535 cities using renewable energy and hydrogen capacity data, with SHAP interpretability and Folium mapping. It accurately identifies high- and low-potent…
Nguyễn Thị Hoa Hồng, Hoang Minh Hieu, Nguyen Tien Dat
This study uses machine learning (Logistic Regression, Random Forest, XGBoost, Deep ANN with SHAP) on Global Entrepreneurship Monitor microdata to predict Sustainable Entrepreneurial Adaptation (SEA) under climate risk. Physical climate ris…
Marwa S. Hassan, Shymaa Khamis, Ahmed Barakat +4
This study develops an interpretable AI-enabled decision-support workflow for drinking-straw substitution, integrating per-use GHG indicators from literature with user evidence from online reviews extracted via NLP. Silicone ranked highest …
Pablo Catret Ruber, David Garcia-Rodriguez, Domingo Jose Iglesias Fuente +3
This paper proposes machine learning models to predict greenhouse gas concentrations at ICOS sites, leveraging ecosystem-specific analyses to improve accuracy. It contributes to climate monitoring and carbon accounting applications.
Kozol E.
This paper discusses the application of artificial intelligence in sustainable finance, focusing on ESG-based financial instruments and decision-making. It examines how AI-driven ESG data analysis and scoring contribute to the development o…
Liu Q.
This paper presents an airport case study using AI-driven digital twin technology to optimize energy consumption and achieve net-zero goals. By combining AI and simulation, it demonstrates potential for operational efficiency and carbon red…
Bingwen Zhao, Luchan Xu, 郑振海 +2
This study employs an SSA-LSTM hybrid model to accurately predict heat and power loads of a district heating system, combined with carbon accounting and three policy scenarios to evaluate carbon peak timing and emission reduction potential.…
Dimitris Bakalbasis, Dimitris Karadimas, Anastasia Vassilakopoulou
This paper presents the design, implementation, and initial validation of an Industrial Metaverse Platform for CCS value chains, developed within the COREu project. It integrates digital twins, AI-driven analytics, IoT-based monitoring, and…
Abdul-Hussein Aziz A, Abbas IT
This study proposes a hybrid HMPCS algorithm combined with ML models (LSTM, LightGBM) to improve solar power forecasting accuracy. Experiments show the HMPCS-optimized LSTM achieves RMSE 0.139 and R² 0.93, reducing error by 23% over baselin…
Louzada RO, Silva RHd, Correia SAC +6
This study demonstrates that ignoring spatial autocorrelation in validation inflates apparent accuracy of UAV-LiDAR and machine-learning carbon stock models. Testing 98 scenario-response combinations in a Brazilian savanna, single-partition…
Suryawinata, Bonny, Darmadi, Herru, Mansuan, Melki
This systematic literature review (47 papers, 2019-2025) examines generative design (GD) for optimizing building energy, daylighting, and thermal comfort. Rhino/Grasshopper with multi-objective genetic algorithms dominate (87.2%), while gen…
Feiyu Cai, Jing Qiu, Yi Yang +4
This paper proposes a proactive spatial-temporal carbon response framework combining deep learning and LLM-based multi-agent systems to accurately forecast day-ahead nodal carbon intensity (NCI). It integrates geographically dispatchable lo…
Zheng Liang, Li Shenshen, Hu Xuefei +2
This dataset provides monthly carbon emission predictions at 0.01 degree resolution for Beijing and surrounding areas from 2019-2024, generated using a weakly supervised neural network (CCFI-Net) that integrates remote sensing, meteorologic…
(著者不明)
This paper proposes a bidirectional temporal convolutional network combined with exogenous variable enhancement for carbon market price forecasting, aiming to improve prediction accuracy. It uniquely merges machine learning techniques with …
Hu B.
This paper empirically investigates the relationships between attitudes toward emission trading schemes (ETS), acceptance of artificial intelligence (AI), climate entrepreneurship, and sustainable performance. Using AI-driven analysis, it r…
Mishra L.
This paper presents a document question-answering (DocQA) system that simplifies ESG accountability. Users can query ESG-related documents in natural language and receive accurate answers, enhancing reporting and compliance efficiency.
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