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 6661–6680 of 27862 papers
Rahmi Elsa Diana, Eduwin Eko Franjaya, Septi Maulidyah +5
This study compares three urban farming governance models (private, governmental, community-based) in Tangerang Regency, Indonesia, to assess their performance as Nature-Based Solutions (NbS). It finds that governance structures shape envir…
Petra B. Holden, Gerald Ngoma, Martine Visser +20
Based on 530 interviews in Sub-Saharan Africa, this study analyzes the overlap between current use and desired needs for climate services, finding 39-53% overlap. It highlights priorities for strengthening existing services and developing n…
Rohat Gültekin, Tuğba Yeter, Ceren Görgişen +2
This study tested arbuscular mycorrhizal fungi (AMF) as a nature-based solution to mitigate drought stress in maize under greenhouse conditions. AMF inoculation improved plant height, fresh weight, stem diameter, and root dry weight, especi…
Stephanie Efua Yamoah, Hanson Nyantakyi‐Frimpong
Using qualitative data from Ghana, this study examines gendered participation in nature-based climate solutions (NbCS). It finds that participation is shaped by intersecting power dynamics (land tenure, wealth, leadership) and that organiza…
Shuai Liu, Ting Hua, Shurong Zhang +2
Quantifies the impacts of direct and indirect urban expansion on soil conservation services and carbon stock in China from 2000 to 2020. Indirect effects (cropland expansion under the Farmland Requisition-Compensation Balance policy) caused…
Xingyuan Lei, Xuelin Chen, Yanyan Hu +7
This study synthesizes lignin-derived carbon nanosheets via one-step co-pyrolysis, achieving high CO2 uptake (8.85 mmol/g at 273K) and good CO2/N2 selectivity. The material enables solar-driven regeneration, offering a low-energy carbon cap…
Aditya Singh, Satyabrata Sahoo
This study investigates the kinetics and charge-discharge performance of activated carbon derived from industrial waste for physisorption-based CO2 capture, aiming to combine waste valorization with carbon capture.
FatmaElzahraa S. Seleem, Farah M. Husseini, Maryam G. Elmahgary
This review compares five Direct Air Capture (DAC) methods and provides the first comparative analysis of DACCS and CCUS deployment in Egypt and the UAE. It examines storage sites such as BED&SITRA and Obaiyed in Egypt, and ESI, Al Reyadah,…
Fei Yu, Zhiqi Yang, Xiaochen Zhang +3
This paper reviews recent advances in carbon-based adsorbents for uranium capture, focusing on active site design and interfacial regulation. Applications in nuclear fuel cycle and environmental remediation are expected, but direct relevanc…
Yuzhi Wang, Cong Zhang
This study analyzes the causal effect of China's intellectual property pilot policy on low-carbon green energy eco-co-evolution (LCEE) using spatial Durbin DID and double machine learning. The inclusive potential of regional innovation ecos…
Wei‐Hsin Chen, Y.C. Chen, Jo‐Shu Chang +3
This study applies surrogate-driven multi-objective optimization to sorption-enhanced steam methane reforming (SE-SMR), targeting cost-effective and low-carbon production of high-purity hydrogen. It quantitatively evaluates trade-offs betwe…
Saurabh Tiwari, Hyoju Ahn, Jongwon Lee +1
This study investigates whether metallurgy-informed feature engineering improves machine learning prediction of mechanical properties in low-carbon steel under small-data conditions. Using 300 samples and five physically meaningful descript…
Tomasz Trzepieciński, Marwan T. Mezher, Marek Kowalik +4
This study applies SHAP and neural networks to analyze friction parameters in sheet metal forming. Rape-seed oil showed best lubrication among vegetable oils. Surface roughness was the most important factor (35%). ML models can predict COF,…
Yuhan Wang, Lili Yang, Bing Liu +4
This review focuses on biological treatment of food oily wastewater (FOW), summarizing substrate characteristics, biodegradation pathways, and process performance, while exploring low-carbon perspectives. It highlights potential for energy …
Yong-Qing Chen, Ziyu Liu, Yu-Xiang Gan +2
This study develops sintering-free geopolymer composites using iron ore tailings waste, achieving high-performance EMI shielding with a low-carbon process. It contributes to waste valorization and carbon emission reduction in materials prod…
О.А. Локтионов, Д.М. Комаров
This paper analyzes low-carbon development technologies in the energy sector within a framework of indicators for CO2 emission reduction potential. Although specific methods and findings are not available, it likely offers insights for poli…
Marcelo Carlos Ribeiro, Vanderson Eney de Matos, Stephânia da Consolação Silva Nogueira +2
This protocol documents the plan for a systematic review and meta-analysis of auxiliary and alternative reductant injection in blast furnace ironmaking. It will assess operational, energetic, thermal, metallurgical, and environmental effect…
Canada Foundation for Innovation | Fondation canadienne pour l'innovation
This paper explores pathways for adopting low-carbon and ultra-high-performance concretes in Canada's built environment, contributing to decarbonizing the construction sector. It outlines technical and policy measures to advance sustainable…
Alex Kihehere Mukiga
This qualitative case study of the Ulwazi Educare Centre in Cape Town examines how community-led construction using discarded tires and local materials can create climate-responsive infrastructure. It finds perceived benefits in thermal com…
Hounaïda Moalla, Chrifa Souf, Raja Zmemla
This study proposes a data-driven approach using machine learning to optimize low-carbon concrete mix design. It contributes to reducing CO2 emissions in the construction industry by improving material selection and mix efficiency.
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