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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…

The Guardian · Climate

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

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…

Climate

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

Evidence-backed gain

TCS HyperVault to invest ₹70,000 crore in 1GW AI data centre campus in Hyderabad

TCS HyperVault to invest ₹70,000 crore in a 1GW AI data centre campus on 264 acres in Hyderabad’s Bharat Future City, creating 7,000 jobs with green design.

Climate

Evidence-backed gain

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…

Climate

Evidence-backed gain

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…

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Both readings

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…

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…

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…

Climate

Is the environmental impact of datacentres finally cutting through?

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…

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…

Climate

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

Impact of allometric reference selection on management scale aboveground biomass estimation via Sentinel-2 and machine learning

Accurate estimation of aboveground biomass (AGB) is essential for sustainable forest management, carbon accounting, and climate change mitigation. In remote sensing-based biomass mapping, field-derived AGB values are commonly used as reference data; however, these values are strongly influenced by the selected allometric equation. This study evaluates how alternative allometric reference datasets affect Sentinel-2-based AGB estimation at the forest management scale in Pinus brutia stands. Reference AGB values we…

Climate

Impact of allometric reference selection on management scale aboveground biomass estimation via Sentinel-2 and machine learning

Productivity-based sampling for field-scale soil organic carbon mapping: from regional models to carbon farming needs

This study evaluates an innovative field-scale targeted sampling strategy within a regional hybrid spatial prediction model that combines machine learning and geostatistics. The framework is designed so that newly collected field observations are incorporated only through the local residual kriging step, while the regional Random Forest trend model remains unchanged, allowing field-scale predictions to be refined without full model refitting. The proposed sampling approach integrates a Normalized Difference Vege…

Climate

Productivity-based sampling for field-scale soil organic carbon mapping: from regional models to carbon farming needs

A carbon aware job scheduling framework for data center sustainability using deep learning training

Abstract Deep learning workloads have experienced rapid growth which has resulted in higher energy consumption and increased carbon emissions for contemporary data centres. The existing solutions of carbon tracking and static scheduling systems provide insufficient capacity to implement carbon awareness in actual machine learning operational processes. In this paper, we present EcoSchedAI (Eco-...

Climate

A carbon aware job scheduling framework for data center sustainability using deep learning training

Beyond concentration-based analysis: explainable AI identifies environmental settings influencing urban PM 10 variability

This study applies an explainable artificial intelligence framework to investigate PM10 variability using routine regulatory air-quality data from a single monitoring station, targeting data-limited conditions. A four-year dataset (2020-2023) of PM10, PM2.5, NO2, SO2, O3, and meteorological predictors was analyzed using ensemble machine-learning models with metaheuristic hyperparameter optimization. The best-performing model achieved high predictive performance (R2 = 0.913), supporting model-based interpretation…

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