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Climate · 45 stories · page 1 of 3

Artificial Intelligence and Climate Risk Shocks
ClimateEvidence-backed gain

Artificial Intelligence and Climate Risk Shocks

Artificial intelligence (AI) has been widely applied across various fields and has demonstrated effectiveness to some extent. However, some scholars have raised concerns about its ethical implications and potential rebound effects, particularly in the context of climate issues. To address these debates, we obtained cross-national panel data for 51 countries from 1996 to 2023 through the ISETS Energy Finance Network, WIPO, and World Bank databases. A two-way fixed effects model was used to examine the relationshi…

Risk Analysis
‘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
Evidence-backed gain

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…

Reinforcement Learning for Electric Vehicle Charging Management: Theory and Applications
Evidence-backed gain

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…

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?
Climate

Developing and evaluating automated deep learning and human-in-the-loop vision-language systems for microplastic characterization

Microplastic (MP) pollution poses escalating environmental risks, demanding efficient and reproducible tools for morphological characterization of plastic particles. Traditional manual microscopy is labour-intensive, operator-dependent, and poorly suited to large-scale monitoring. This study presents a comparative evaluation of two distinct artificial intelligence paradigms for the analysis of optical microscope images of microplastics. The first paradigm is a domain-specific, multi-task deep learning (DL) class…

Developing and evaluating automated deep learning and human-in-the-loop vision-language systems for microplastic characterization
Climate

US building twice as much gas-fired capacity as China in AI boom, analysis finds

The US has surged ahead of China in the building of new gas-fired power generation, largely to feed a boom in artificial intelligence (AI) that is adding vast amounts of planet-heating emissions, a new analysis has found. For decades, China’s rapid economic growth has seen it outpace the US in the addition of new gas power generation but a recent “frenzy” in datacenter construction for AI has reversed this, said Global Energy Monitor (Gem) in its new report. The US is now building twice as much gas-fired capacit…

US building twice as much gas-fired capacity as China in AI boom, analysis finds