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A Review of Water Quality Forecasting and Classification Using Machine Learning Models and Statistical Analysis

The prediction and management of water quality are critical to ensure sustainable water resources, particularly in regions like Malaysia, where rivers face increasing pollution from industrialisation, agriculture, and urban expansion. This review aims to provide a comprehensive analysis of machine learning (ML) models and statistical methods applied in forecasting and classification of water quality. A particular focus is given to hybrid models that integrate multiple approaches to improve predictive accuracy an…

Water

Both readings

Environmental impact and net-zero pathways for sustainable artificial intelligence servers in the USA

Evidence-backed gain

Advances in machine learning and IoT for water quality monitoring: A comprehensive review

Evidence-backed problem

The carbon and water footprints of data centers and what this could mean for artificial intelligence

Evidence-backed gain

The smart future for sustainable development: Artificial intelligence solutions for sustainable urbanization

Abstract Future tools for supporting collaborations between technology and sustainable development include artificial intelligence (AI) applications in sustainable Urbanization roles. This article highlights the various applications of AI in advancing sustainable urbanization. From urban planning to disaster management, AI technology is revolutionizing the way cities are designed and managed. By leveraging data analytics, machine learning, and predictive modeling, AI is helping city officials make informed decis…

Evidence-backed gain

Ecological footprints, carbon emissions, and energy transitions: the impact of artificial intelligence (AI)

Abstract This study examines the multifaceted impact of artificial intelligence (AI) on environmental sustainability, specifically targeting ecological footprints, carbon emissions, and energy transitions. Utilizing panel data from 67 countries, we employ System Generalized Method of Moments (SYS-GMM) and Dynamic Panel Threshold Models (DPTM) to analyze the complex interactions between AI development and key environmental metrics. The estimated coefficients of the benchmark model show that AI significantly reduc…

Evidence-backed gain

Impact of Artificial Intelligence on the Planning and Operation of Distributed Energy Systems in Smart Grids

This review paper thoroughly explores the impact of artificial intelligence on the planning and operation of distributed energy systems in smart grids. With the rapid advancement of artificial intelligence techniques such as machine learning, optimization, and cognitive computing, new opportunities are emerging to enhance the efficiency and reliability of electrical grids. From demand and generation prediction to energy flow optimization and load management, artificial intelligence is playing a pivotal role in t…

Evidence-backed gain

Artificial intelligence in environmental monitoring: Advancements, challenges, and future directions

• AI-driven pollution detection enhances environmental protection. • Real-time monitoring facilitates prompt interventions for pollution prevention. • Accurate air quality forecasting aids in planning pollution-reducing activities. • AI's role in smart cities fosters sustainable urban development. • AI algorithms integrate diverse data sources for pollution detection. The application of Artificial Intelligence (AI) in environmental monitoring offers accurate disaster forecasts, pollution source detection, and co…

Climate

Datacentres drive up big tech’s carbon emissions to a third of those of France

Microsoft, Amazon and Google’s collective carbon emissions have increased by nearly a fifth in the past year, driven largely by datacentre construction. In the financial year ending March 2026, the three tech companies emitted 119m mTCO₂e (metric tonnes of carbon dioxide equivalent), or about a third of those of France. The previous year, they emitted roughly 101m mTCO₂e, roughly equivalent to the 2024 emissions of Czechia. The US companies’ climate ambitions have been hit in recent years by a boom in demand for…

Climate

Automation and Sustainability—The Impact of AI on Energy Consumption and Other Key Features of Industry 4.0/5.0 Technologies

Automation and sustainability are closely intertwined in the evolution of Industry 4.0 and 5.0, where artificial intelligence (AI) plays a key role in transforming energy consumption and production efficiency. For Industry 4.0, AI-based automation has optimized production, logistics, and resource management, reducing waste and improving throughput through predictive analytics and intelligent control systems. These systems have enabled energy-efficient production lines by automatically adjusting processes to mini…

Climate

Challenges of Artificial Intelligence Development in the Context of Energy Consumption and Impact on Climate Change

With accelerating climate change and rising global energy consumption, the application of artificial intelligence (AI) and machine learning (ML) has emerged as a crucial tool for enhancing energy efficiency and mitigating the impacts of climate change. However, their implementation has a dual character: on one hand, AI facilitates sustainable solutions, including energy optimization, renewable energy integration and carbon reduction; on the other hand, the training and operation of large language models (LLMs) e…

Climate

Digital Technologies and Sustainable Development: Evidence from FinTech, AI, and Blockchain Adoption in G20 Economies

In the wake of rapid digital transformation, emerging technologies like FinTech, AI, and Blockchain are reimagining how countries pursue sustainable development. This study examines how FinTech adoption, Artificial Intelligence (AI) readiness, and Blockchain activity influence sustainable development performance across G20 economies over the period 2015–2023. Drawing on Innovation-Driven Growth Theory, the Technology–Organization–Environment framework, and Institutional Theory, the analysis evaluates both the di…

Climate

Optimizing solar and wind forecasting with iHow optimization algorithm and multi-scale attention networks

Deep learning models often encounter two key challenges in developing intelligent and scalable forecasting frameworks for renewable energy systems: input feature space dimensionality and sensitivity to hyperparameter settings. These limitations increase computational cost and compromise generalization and robustness. This paper presents a hybrid deep learning-optimization framework that leverages cognitively inspired metaheuristics to address these challenges, employing the Binary iHow Optimization Algorithm (bi…

Climate

Knowledge-Based Feature Selection Substantially Enhances Data-Driven Wastewater Treatment Modeling

Data-driven modeling in wastewater treatment is increasingly constrained by the reality of small, high-dimensional data, where the abundant monitoring parameters in small-sized data sets obscure fundamental mechanistic understandings. This study proposes a knowledge-driven feature selection framework that integrates mechanistic insights with statistical correlations to identify the most informative predictive features. Using nitrous oxide (N 2 O) emission prediction at a full-scale plant as a case study, we comp…

Climate

‘There were stoats in kitchen cupboards’: AI deployed to help save Orkney’s birds

At first, the stoat looks like a faint smudge in the distance. But, as it jumps closer, its sleek body is identified by a heat-detecting camera and, with it, an alert goes out to Orkney’s stoat hunters. Aided by an artificial intelligence programme trained to detect a stoat’s sinuous shape and movement, trapping teams are dispatched with the explicit aim of finding and killing it. It is the most sophisticated technology deployed in one of the world’s largest mammal eradication projects, which has the aim of dete…