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Climate · 47 stories · page 1 of 4

South Park has ​​AI datacentres​, sentient penises ​and a billionaire problem – in more ways than one
ClimateEvidence-backed problem

South Park has ​​AI datacentres​, sentient penises ​and a billionaire problem – in more ways than one

A little over a year ago, South Park creators Trey Parker and Matt Stone renewed their deal with Paramount after a contentious negotiation that held up production of the 2025 season. That $1.5bn agreement raised questions about whether Paramount, newly owned by David Ellison, would really offer the creators creative freedom – or was the billionaire class solidifying its hold over a comedy series as cover to meddle in other divisions, like CBS News? The deal also made Parker and Stone likely billionaires themselv…

The Guardian
Carbon dioxide hydrogenation on copper and nickel catalysts <i>via</i> a conformal sampling approach
Evidence-backed gain

Carbon dioxide hydrogenation on copper and nickel catalysts <i>via</i> a conformal sampling approach

We present a computational workflow, the Conformal Sampling of Catalytic Processes (CSCP) approach, and its application to the case of heterogeneous hydrogenation/reduction of carbon dioxide (CO 2 ) on copper and nickel catalysts. CO 2 activation is of critical importance for a sustainable global future and one of the major reactions for which sustainable routes must be found urgently. We use the fcc(100) facet of Cu as a worked-out case, and exhaustively derive at the DFT level its reaction mechanisms. We then…

Ecological risk zoning of soil heavy metal pollution using biomarker-enhanced indexing and machine learning
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Ecological risk zoning of soil heavy metal pollution using biomarker-enhanced indexing and machine learning

Traditional soil heavy metal risk assessments suffer from fragmented indices and poor logical integration. Compared to single-chemical indicators, biomarkers more sensitively reflect biological effects and early ecological risks. To address these issues, a method for constructing a comprehensive index integrating biological and non-biological multi-indexes was proposed in this study. First, the Criteria Importance Through Intercriteria Correlation (CRITIC)-Fuzzy Biomarker Response Index (CFBRI) is developed by i…

‘Tidal wave’ of Pfas being launched to satisfy AI industry, campaigners warn
Evidence-backed problem

‘Tidal wave’ of Pfas being launched to satisfy AI industry, campaigners warn

Pfas companies are launching a “tidal wave” of forever chemicals production to meet demand from the AI industry, a campaign organisation has warned. Despite alarm over the chemicals, which have been linked to cancer, a survey of the 10 biggest manufacturers found most had plans to increase production. Many frame their investments around the “AI revolution”, because of the role Pfas play in semiconductor production and next-generation datacentre cooling systems. But opponents warn that the chemicals, which never…

Physics-informed machine learning for universal ash fusion temperatures prediction: A novel categorical chain differential framework
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Physics-informed machine learning for universal ash fusion temperatures prediction: A novel categorical chain differential framework

Accurate prediction of ash fusion temperatures (AFTs) is crucial for ensuring the operational efficiency and safety of solid-fuel boilers and gasifiers. However, conventional machine learning methods typically treat each characteristic temperature as an independent prediction target, resulting in temperature inversions that violate the required physical ordering of AFTs. This study aimed to develop a Categorical Chain Differential framework for the coupled and physically consistent prediction of the four AFTs ac…

Climate

Reinforcement Learning for Electric Vehicle Charging Management: Theory and Applications

The growing complexity of electric vehicle charging station (EVCS) operations—driven by grid constraints, renewable integration, user variability, and dynamic pricing—has positioned reinforcement learning (RL) as a promising approach for intelligent, scalable, and adaptive control. After outlining the core theoretical foundations, including RL algorithms, agent architectures, and EVCS classifications, this review presents a structured survey of influential research, highlighting how RL has been applied across va…

Reinforcement Learning for Electric Vehicle Charging Management: Theory and Applications
Climate

Explainable AI-Supported Cyber-Physical Collaboration for Sustainable Manufacturing in Industry 5.0

In this study, we present an innovative approach to the sustainable manufacturing of industrial parts using an explainable artificial intelligence (XAI)- based cyber-physical collaboration system for Industry 5.0. Current cyber-physical human systems (CPHSs) have been found to integrate AI only to a limited extent and often lack explainability. Consequently, there is a need to improve their scalability and flexibility, in keeping with the tenets of Industry 5.0: resilience, long-term viability, and human-centric…

Explainable AI-Supported Cyber-Physical Collaboration for Sustainable Manufacturing in Industry 5.0
Climate

Machine learning-assisted nitrogen-doped carbon dots for Fe<sup>3+</sup> detection in aqueous environments

The concentration of iron ions is a crucial indicator for assessing water quality. In this study, nitrogen-doped carbon dots (NCDs) were synthesized using a microwave-assisted method with citric acid and urea as precursors, thereby establishing a fluorescence sensing platform for the detection of alkaline pH and Fe 3+ . During Fe 3+ detection, the fluorescence intensity of NCDs was specifically quenched as the concentration of Fe 3+ increased, demonstrating good linearity across the ranges of 1-10 µM and 10-100…

Machine learning-assisted nitrogen-doped carbon dots for Fe<sup>3+</sup> detection in aqueous environments
Climate

Machine learning for monitoring and assessment of potentially toxic elements in soils: a synthesis of spatial validation, explainability, and uncertainty

Potentially toxic elements (PTEs) in soils pose persistent risks to ecosystems, groundwater, and food systems, creating a need for reliable spatial assessment tools. Machine learning (ML) is increasingly used to map PTE concentrations from environmental covariates, but many studies still rely on spatially naive validation, limited interpretation, and incomplete uncertainty reporting. This review synthesizes recent advances (2020-2025) in ML-based PTE mapping with emphasis on four requirements for monitoring-grad…

Machine learning for monitoring and assessment of potentially toxic elements in soils: a synthesis of spatial validation, explainability, and uncertainty
Climate

Quantitative decoupling of source-pathway-receptor driving mechanisms for integrated soil risk via interpretable machine learning

Most existing concentration-based risk assessments of potentially toxic elements (PTEs) in farmland soils tend to overlook source-specific transport pathways and receptor-related risks. In this study, an integrated risk index-machine learning framework based on the source-pathway-receptor concept is developed to comprehensively evaluate PTE risks in a typical mining city. The framework integrates the improved Nemerow index (INI), potential ecological risk index, Monte Carlo simulation-based health risk assessmen…

Quantitative decoupling of source-pathway-receptor driving mechanisms for integrated soil risk via interpretable machine learning
Climate

Is the environmental impact of datacentres finally cutting through?

Anti-datacentre sentiment is growing from across the political spectrum in the US. More than a dozen states have considered moratoria on datacentres. New York became the first US state to enact a temporary ban last month. Progressive stalwarts Senator Bernie Sanders and Representative Alexandria Ocasio-Cortez have proposed a national moratorium. And even Greg Abbott, the far-right governor of Texas, called for a ban on datacentre development in rural swaths of his state. Fuelling alarm about datacentres are envi…

Is the environmental impact of datacentres finally cutting through?