Dear Colleagues,

Artificial intelligence (AI) and machine learning (ML) techniques have attracted significant interest from researchers in the last 5 years, and huge progress is being made in this area. AI and ML are currently being used to solve a range of real-world problems, from object detection in autonomous vehicles through to machine translation services and code autocompletion in software development. PricewaterhouseCoopers (PwC) estimate that, as well as the obvious productivity benefits AI solutions can bring, harnessing AI to accelerate the move toward low-carbon industries can result in a 4% reduction in greenhouse gases globally when compared to business as usual. These potential gains are particularly prevalent in industries such as energy (where the potential for AI and IoT in smart grids can significantly improve operational efficiency) and transport (where autonomous and semi-autonomous vehicles can deliver improved fuel economy and allow for better traffic optimisation).

As well as direct reduction in greenhouse gases through process improvements, AI can also play a wider role in sustainability, with applications as diverse as monitoring and predicting water quality, analysing deforestation and illegal fishing from satellite images, and providing early warnings of poor air quality due to pollution. Worryingly, large AI models frequently require huge resources to train, with OpenAI’s GPT-3 large language model requiring an estimated 190,000 kWh of electricity. Therefore, best realising the opportunities for sustainability offered by AI models will require responsible research practices and careful decisions that consider long-term environmental and social impact.

This Special Issue focuses on the issues around artificial intelligence and machine learning in sustainability. Topics of interest for publication include but are not limited to the following:

• Artificial intelligence applied to smart grid systems (e.g., expert systems, evolutionary computing, neural networks);
• Artificial intelligence for the analysis and prediction of pollution;
• Intelligent systems/artificial intelligence for enabling low-carbon industries;
• Autonomous systems for sustainability;
• Green artificial intelligence;
• Data issues (such as federated data sharing, de-duplication, data privacy, and ethical concerns).

Dr. Alex Shenfield
Prof. Dr. Marcos Rodrigues
Dr. Bernardi Pranggono
Guest Editors

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• artificial intelligence
• machine learning
• sustainability
• Industry 4.0
• Internet of Things
• green ICT
• smart grid
• data management