Climate Sustainability & Global Systems

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Vol. 1 No. 1 (2026)
View Vol. 1 No. 1 (2026)

The inaugural issue of Climate Sustainability & Global Systems (CSGS) presents interdisciplinary research addressing climate dynamics, sustainability transitions, environmental data innovation, and global systems governance.

As a quarterly, peer-reviewed open access journal, CSGS is committed to advancing rigorous, transparent, and globally relevant scholarship. This issue features original research articles that contribute to system-level understanding of climate risk, sustainability modeling, and governance frameworks.

All articles published in this issue have undergone double-blind peer review.

Published: 2026-06-15

Review Articles

  • Attention: From Tires to Organisms – NationalTransport, Exposure, and Eco-Health Risks of6PPD/6PPD-Q in Terrestrial-Aquatic Systems

    Yirui Liu, Jundong Zhang, Yaolin Zhang, Yang Hu (Author)
    1-16
    Abstract: Urbanization and global transportation expansion have led to widespread release of tire-derived contaminants N-(1,3-dimethylbutyl)-N’-phenyl-p-phenylenediamine (6PPD) and its quinone derivative (6PPD-Q), posing critical environmental and public healthrisks. As a key tire anti-ozonant, 6PPD rapidly oxidizes to 6PPD-Q, a highly toxic compound linked to aquatic organism mor-tality. This review synthesizes their "tires-to-organisms" pollution continuum across terrestrial-aquatic systems. Focusing onChina (the world’s largest tire producer/consumer), 6PPD-Q emissions surged 97.5% (68.2–134.7... [Read More]

Methods & Data Papers

  • Application of the Pollutants-FCNN Framework:A Multi-Task Neural Network Approach for Con-taminant Toxicity Prediction Based on Tox21Data

    Yaolin Zhang, Menghui Li, Yiming Zou (Author)
    17-24
    Abstract: Contaminant toxicity prediction is a pivotal technology for environmental risk assessment and chemical safety management, but traditional experimental methods are limited by high resource consumption, prolonged test cycles, and ethical controversies related to animal experimentation. This study proposes the Pollutants-FCNN Framework, a multi-task learning-based neural network architecture integrated with a multi-head attention mechanism, dedicated to predicting four major toxicity types (biological toxicity, cell toxicity, neurotoxicity, genotoxicity) using the Tox21 benchmark dataset. The... [Read More]
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