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 661–680 of 986 papers
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…
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…
Haylee Massaro
This memo argues that expanding generative AI in schools without environmental accountability risks climate injustice for children. It proposes that governments embed climate disclosure, sustainable procurement standards, and child-centered…
Qi Cheng, Licheng Liu, Yao Zhang +3
This work introduces AgroFlux, a first-of-its-kind spatiotemporal benchmark dataset for carbon and nitrogen flux prediction in agricultural ecosystems, integrating simulations from Ecosys and DayCent with observations from flux towers. It e…
Wenwen Liu
This paper proposes the Hierarchical Coupling Carbon Emission Estimation Model (HCCEEM) for datacenter carbon footprint, integrating GNN and physical modeling across four levels from chip to campus. Validated on a 14-month dataset, it achie…
F. Lin, Chin-Chiu Lee, Te-Nien Chien
This study develops an ML-based framework to classify environmental disclosure characteristics of high vs. low ESG performers in Taiwan (2022-2024). Ensemble models (CatBoost) outperform traditional ones, with carbon emissions, energy effic…
Roberto Spacey Martín, Nicola Ranger, T. Schimanski +1
This paper uses large language models (LLMs) to assess adaptation and resilience (A&R) disclosure in S&P 500 sustainability reports. It develops an A&R framework and finds significant gaps, especially regarding risks, metrics, and targets. …
Stefano Dell’Atti, Matteo Foglia, Grazia Onorato
This paper examines how climate risk disclosures affect firm value for large European nonfinancial firms. It develops a firm-level Climate Risk Exposure (CRE) index using NLP to assess narrative disclosures across transition risk, physical …
Meng Yuan, Ma Zhong
This study develops an industry-specific carbon information disclosure quality (CIDQ) framework for Chinese listed petroleum and petrochemical firms, using LLM (DeepSeek-V3.2) to score disclosure texts. The framework comprises 7 primary, 15…
Ning Wang, Fengjuan Wang, Yitian Li +1
This study examines climate risk's impact on organizational resilience of high-carbon Chinese firms using a text-based climate risk index and panel data from 2008-2023. It finds a dual-path financial lock-in effect through financing constra…
Browse by Topic