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 661–680 of 999 papers
Jesús Pérez Expolio
This paper proposes a combination of machine learning and quantum variational sampling to optimize carbon capture systems under uncertainty. It applies AI/ML techniques to enhance CCUS process efficiency and cost reduction, promising indust…
Deepti Singh, Rakesh Kumar, Pooja
This study applies AI (ANN, RF, and GA) to optimize self-compacting concrete (SCC) mix designs, achieving 21–29% carbon emission reductions across grades M20–M60 while maintaining strength and workability. Using a dataset of 120 mixes, ANN …
Mohaddeseh Azimi, Zhengfu Bian, Narges Salehi Shahrabi
This study uses machine learning to analyze the associations between carbon intensity, labor productivity, and income inequality in China's energy sector. It provides quantitative insights into how the decarbonization transition may affect …
Alessandro Sura, EMANUELE DI VENTURA
This study compares stakeholder engagement in EFRAG and ISSB sustainability standard-setting using NLP. Sentiment and topic analysis of comment letters reveals that EFRAG shows balanced sentiment and broader thematic focus, while ISSB empha…
Murali Krishna Pasupuleti
This book proposes a framework for 'Green Intelligence Digital Twins' - computational models that integrate uncertainty quantification, causal inference, trustworthy machine learning, and lifecycle engineering to support climate-resilient, …
Muazzam Mirjalolova
This paper provides a policy-oriented analysis of AI opportunities and challenges for climate mitigation and sustainable development, covering energy efficiency, environmental monitoring, and resource management. It emphasizes that effectiv…
Yu Nie, Ting Mao, Yupeng Liu +3
This study uses machine learning on satellite imagery to reconstruct Xiamen, China's 30-year urban metabolism: building stock growth, material flows, embodied emissions, and cement carbonation offsetting 6.6% of lifecycle emissions. It achi…
Yassine Charabi
This data descriptor presents the Carbon Valley dataset, a probabilistic carbon-accounting framework for AI infrastructure. It includes 1.92M annual records across four deployment pathways (Lift-Off, Base, Headwinds, High Efficiency) spanni…
Adel Elgammal
This study proposes an intelligent control framework integrating machine learning-based multi-objective optimization and robust sliding mode control for a solar-powered direct air capture (DAC) and electrochemical carbon utilization system.…
Rakesh Dondapati
This paper develops a Greenwashing Intelligence System (GIS) using multimodal AI (transformer-based NLP, satellite data, emissions data, controversy records, financial disclosures, supply-chain signals) to construct a Narrative Ambition Sco…
Xu Cheng
This paper proposes a transient multiphysics hybrid energy storage framework integrating physics-based battery, thermal storage, and supercapacitors with a hybrid physics–deep learning surrogate strategy using PINN and Transformer models. I…
Xue Jiang, Xiaoli Ji
This paper applies machine learning (automated preprocessing, GNN, reinforcement learning, multi-objective optimization) to optimize path selection for green/low-carbon industrial clustering. Under the context of renewable energy integratio…
Waqas Ahmed
This study examines the impact of Big Data Analytics (BDA) and ESG disclosure on corporate budgeting forecast accuracy using a sample of US and EU firms (2015-2024) with a hybrid econometric and machine learning approach. It finds that firm…
A. Gomaa
This systematic review examines how AI transforms ESG assessment, disclosure, and monitoring. It identifies barriers including data fragmentation, algorithmic bias, regulatory inconsistency, and lack of explainability, and proposes a future…
Hui Mao, Benyan Tan, Ribesh Khanal +2
This paper uses large language models (LLMs) to analyze the strategic choices of photovoltaic generators, grid enterprises, and government in the marketization of new energy electricity prices. It combines an evolutionary game model with LL…
Yuanshang Ji, Bin He, Sheng Yu +1
This paper proposes a method to predict carbon footprint at the manufacturing stage during product design driven by large language models (LLMs). Using a vibration isolation platform as a case study, it demonstrates the feasibility of quant…
Sonali R. Kanthe, Shruti A. Nikalje, Mahesh S. Hol +3
This paper proposes an AI-powered carbon footprint tracker that helps individuals measure and reduce their environmental impact. The system integrates modules for carbon calculation, an AI-driven green chatbot, AR visualization, and report …
Xiang-Bo Tang, You-Wei Huang, Han Su
This study constructs a machine learning-based prediction model for net carbon sink in Chinese agriculture and analyzes driving factors. Results show that effective irrigated area is the most significant factor, affecting crop carbon absorp…
Ming Li, Jun Wang, Xihang Li +3
This paper proposes a Hybrid Whale Optimization Algorithm (HWOA) for flexible process planning in part manufacturing under low carbon constraints. It aims to reduce carbon emissions while maintaining production efficiency, showcasing AI app…
Sun J, Wenxiang Peng
This study uses double machine learning to evaluate the impact of digital trade (cross-border e-commerce pilot zones) on pollution control and carbon mitigation (PCCM) in 280 Chinese cities (2011-2023). Digital trade significantly improves …
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