GX Research Hub · English

GX & Decarbonization Research

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.

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Topic: #AI × ESG (clear)

Showing 601–620 of 943 papers

Peer-reviewed🇪🇺 EuropeJournalRemote Sensing2026#AI × ESGDOI

Remote Sensing and AI-Based Monitoring of Soil Properties for Tier-3 MRV Framework of Complex Mediterranean Agroforestry Systems

Dimitra Palantza, Konstantinos Karyotis, Judit Torres Fernández del Campo +2

This study develops a hybrid machine learning and remote sensing framework for high-resolution soil organic carbon (SOC) mapping in Mediterranean agroforestry systems. Using Sentinel-2 data and environmental covariates, the model achieves R…

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Peer-reviewedJournalEnergy Conversion and Management: X2026#AI × ESGDOI

Algorithmic intelligence for industrial decarbonization: a comparative analysis of meta-heuristic optimization versus commercial solvers in designing resilient hybrid microgrids

Md. Fardous Hasan Bappy

This paper compares meta-heuristic optimization algorithms with commercial solvers for designing resilient hybrid microgrids aimed at industrial decarbonization. It analyzes how AI-driven optimization can enhance energy efficiency and cost …

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Peer-reviewedJournalJournal for Research in Applied Sciences and Biotechnology2026#AI × ESGDOI

Optimizing Self-Compacting Concrete (SCC) Mix for Different Grades Using AI (ANN/RF models) for Low- Carbon Construction

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 …

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Peer-reviewed🇨🇳 ChinaJournalSustainable Energy Technologies and Assessments2026#AI × ESGDOI

Carbon intensity and its associations with labor productivity and income inequality in China’s transition to carbon neutrality: A machine learning analysis of the energy sector

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 …

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Peer-reviewedJournalInternational Journal of Engineering Applied Sciences and Technology2026#AI × ESGDOI

INTELLIGENT SOLAR-POWERED DIRECT AIR CAPTURE AND ELECTROCHEMICAL CARBON UTILIZATION: A MACHINE LEARNINGENHANCED MULTI-OBJECTIVE OPTIMIZATION AND SLIDING MODE CONTROL FRAMEWORK FOR NET-ZERO INDUSTRIAL DECARBONIZATION

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.…

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Peer-reviewed🇨🇳 ChinaJournalWuhan University Journal of Natural Sciences2026#AI × ESGDOI

Analysis of Tripartite Evolutionary Game in Marketization of New Energy Electricity Prices Based on Large Language Models

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…

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