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 541–560 of 943 papers
C. Bhargava, Bhavya Tiwari, P. Bhatnagar +10
This paper develops an XGBoost-based framework to predict CO2 adsorption capacity and classify adsorbent materials using process and material parameters. A comprehensive dataset including activated carbon, zeolites, MOFs, and others was use…
Ke Ren, Weiyu Zhang, Haoxiang Chang +3
This paper proposes a method for carbon price forecasting that models lead-lag relationships among market participants using dynamic graphs and provides uncertainty intervals, potentially improving prediction accuracy and interpretability.
Xin Cheng, Peisi Xu, Sylvie Van Damme
This study develops a framework integrating text mining (Jieba segmentation, dictionary matching) and image classification of social media data to evaluate cultural ecosystem services (CES) from nature-based solutions (NBS) in urban renewal…
ylm0216
This repository provides a reinforcement learning tool for learning optimal trading strategies in carbon credit markets. v1.0.0 is the initial release, including the foundational environment and agent models.
Arda Aktaş, Miguel Poblete-Cazenave
This study uses causal forests to estimate heterogeneous effects of residential energy technologies on health and economic outcomes in India. Combining climate policies with a full transition to clean fuels yields larger reductions in sickn…
Hoeser, Thorsten, Bachofer, Felix, Kuenzer, Claudia
DeepOWT is a globally accessible dataset of offshore wind turbine locations derived from Sentinel-1 SAR imagery using deep learning object detection. Version 4.25.1 enriches dense time series with deep learning event predictions (e.g., depl…
D'Césares, Isaac
GreenRisk scores corporate climate-disclosure paragraphs for greenwashing risk on a 0–100 scale using pinned ClimateBERT signal classifiers and a Mamdani fuzzy rule base. Each score ships with an auditable rule-activation trace and a W3C PR…
Tufail Muhammad, Rubab Hafeez, Waqas Bin Khidmat +5
This paper proposes a hypothesis-driven responsible AI framework for interpretable ESG forecasting using RuleFit, enhancing transparency in ESG scoring.
Yulong Geng, Qi Li, Aobo Guo +3
This study proposes a BBE-weighted positive-unlabeled learning framework to assess and predict the photovoltaic development potential and deployment suitability of open-pit mines in China. It contributes to repurposing mine lands for renewa…
Bo Zhang, Xiao‐Min Zou, Xin Wen +4
This review comprehensively synthesizes machine learning (ML) applications for predicting the cycle life of lithium-ion and sodium-ion batteries. It compares supervised, unsupervised, semi-supervised, and deep learning algorithms, highlight…
KUROSAWA, ISAO
This paper presents ARGUS, an end-to-end deep learning pipeline that jointly estimates hypocenter location, centroid moment tensor, and peak ground acceleration from as few as four to eight stations in 17 ms, targeting induced seismicity mo…
R. F. Ghanim, rd Murad, Ali Ahmad Al-Zaqeba
This study systematically reviews prior literature on how artificial intelligence contributes to corporate sustainability, focusing on accounting and ESG perspectives. It finds that AI enhances transparency, accounting reliability, and fina…
L L Sumbu, F G Chong, D B Enggong +8
The Heart2Miss study demonstrates that an AI-powered decentralized triage pathway using handheld POCUS and telehealth significantly reduces patient travel distance and carbon emissions compared to conventional tertiary center diagnostics. A…
Riddhi Mazumdar, Debashish Sakunia, Prof. Biswajita Parida
This paper introduces EcoPersona AI, a segmentation method classifying consumers by Carbon-Footprint Sensitivity (CFS) using ISO 14067-based product carbon footprints and behavioral signals. It generates adaptive personas grounded in VBN th…
Bhatia, Tanvi
This study compares synthetic survey responses generated by LLMs with human responses in the context of renewable energy acceptance research. Using structural equation modeling, it examines structural coherence, contextual uncertainty, and …
Felix Kubler
This paper applies Bayesian optimization to compute optimal carbon taxes in a dynamic heterogeneous-agent economy with climate change. It shows that when the equilibrium manifold has a low-dimensional Negishi-weight parameterization, Bayesi…
Hongzhi Lu, Hongxue Lu
This scoping review systematically examines the dual environmental impacts of AI in ecosystem management: advantages like real-time monitoring and optimized life-cycle assessments versus costs such as high computational energy and e-waste. …
Sampada G. Kulkarni, S. Harshitha, Asish Kumar Yeleti +4
This paper introduces GreenScore, an AI-based sustainability intelligence system for quick-commerce. It uses an ISO 14040/44 compliant LCA scoring engine with five weighted measures (carbon, water, packaging, transport, seasonal sourcing). …
Brett Duane, Paul Ashley, James Larkin
This study evaluates the feasibility of using ChatGPT to develop a user-friendly carbon footprint calculator for dental practices. Three prompting strategies were tested, showing that structured prompts with emission factors significantly i…
Quang Minh Nguyen, Xinglin Li
This study examines the impact of biodiversity risk exposure on firm performance using a panel of U.S. listed firms from 2001 to 2023. It uses negative biodiversity scores derived from textual analysis of 10-K filings as a proxy for physica…
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