Data and AI are unlocking powerful tools to fight the climate crisis—helping organisations use energy smarter, grow food more sustainably, and cut emissions. But, with rising environmental costs and risks of deepening inequality, responsible innovation is essential. Read on to explore how these technologies are being harnessed for real, lasting impact.
From record-breaking droughts to rising sea temperatures, the climate crisis is accelerating – exposing how unsustainable many parts of modern life have become. Nearly 40% of the world’s glaciers are already set to melt, even if we stopped burning fossil fuels today. In Scotland, we’ve seen the driest start to the year since 1964, while across the UK, spring 2025 was the hottest on record. Meanwhile, the buildings where we eat, sleep, and work contribute to a third of global carbon emissions and a third of global waste, and food systems produce a quarter of all greenhouse gases while consuming over 70% of the world’s freshwater.
The need for action is urgent, and data and AI can help drive it, by giving us tools to act faster, smarter, and more efficiently. From improving energy use and managing natural resources to building more resilient food systems, these technologies offer huge potential. But they also come with environmental costs of their own. The challenge is to innovate responsibly, harnessing data and AI to drive real-world impact.
Where Data and AI are Making a Difference
Data and AI are already driving real-world sustainability progress across a range of sectors, turning powerful insights into practical solutions. Take energy, for example: nonprofit tech start-up WattTime uses AI to predict electricity supply and demand across regions, helping reduce carbon emissions on the grid – and this same data can help people at home to shift their electricity use to cheaper, cleaner times. Similarly, Electricity Maps leverages AI to forecast weather patterns, improving planning for energy generated from wind and solar power. Buzz Solutions applies AI to scan power infrastructure, spotting damage or risks like overgrown trees before they cause outages or wildfires.
Closer to home, at The Data Lab, we teamed up with Robert Gordon University and IRT to develop AI that automates cropping of thermal images. This technology makes detecting heat loss in homes faster and easier, allowing for targeted energy efficiency improvements that lower carbon emissions and help Scotland meet its net-zero targets.
Data and AI are also making waves across other sectors. In transport, UK operator First Bus uses AI to improve the reliability and efficiency of public services – key to encouraging more people to choose low-carbon transit. In Glasgow, where rising congestion, or “bus bunching” has disrupted services, AI-powered software now automatically updates bus timetables based on real-time traffic. The result? An 8.3% improvement in punctuality across 30+ routes, making public transport a more attractive alternative to cars and helping reduce emissions in one of Scotland’s busiest cities.
Agriculture is also seeing transformation. Across the UK, around 60% of farmers are using precision tools like GPS-guided tractors, drones, and smart sensors to make more informed decisions about when and where to use water, fertiliser, and pesticides – boosting yields while cutting waste and environmental impact. Scotland-based agri-tech company Trade in Space is using satellite data and machine learning to bring new levels of intelligence to agricultural trading. With support from The Data Lab and researchers at the University of Edinburgh, the company developed tools to predict crop yields under different climate scenarios and map soil variability to understand the impact of environmental issues, helping producers and buyers make more informed, sustainable decisions when trading.
The Challenges We Need to Talk About
While data and AI offer transformative potential for sustainability, they also introduce significant environmental costs. In the UK, data centres already consume 2.5% of the nation’s electricity – a figure expected to rise sharply as AI systems become more widespread. Recent analysis has revealed that by the end of the year, AI alone could account for nearly half of datacentre power consumption. This trend is being accelerated by the rapid growth of generative AI technologies like ChatGPT, which require vast computing power to both train and run, with experts at the University of California estimating that using ChatGPT for between 10 to 50 queries consumes about 2 litres of water. Even major tech companies such as Google and Microsoft have admitted that their AI drives are endangering their ability to meet environmental targets.
There’s also a growing risk that AI-driven sustainability tools could unintentionally reinforce existing inequalities. AI systems are only as good as the data they are trained on – and if that data lacks diversity or fails to represent certain communities, the resulting solutions may leave the most vulnerable behind. For example, smart energy systems that optimise electricity use based on digital feedback might work better in well-connected urban areas, while rural or lower-income regions with limited infrastructure may miss out on the benefits. Similarly, agricultural AI models trained on data from large-scale farms may not apply well to smallholders or traditional growing practices, widening the digital divide in food production.
A real-world example of this can be seen in the ongoing challenge of phasing out the UK’s ageing Radio Teleswitching System (RTS) meters. More than 300,000 households still rely on these legacy devices to control their heating and hot water, often in rural areas or among vulnerable groups where infrastructure upgrades lag behind. As RTS technology is phased out, many customers risk losing heating control if smart meters cannot be installed in time. Limited engineer availability, connectivity issues in remote areas, and distrust of smart meter technology have all slowed the transition, creating the risk of prolonged disruption for vulnerable households. The situation highlights a key challenge in the digital energy transition: without inclusive planning and equitable access, sustainability innovations can unintentionally deepen social divides.
To ensure a just transition, AI for sustainability must be developed inclusively, with diverse data, transparent design, and community engagement at its core.
Looking Ahead: Harnessing AI for Sustainable Impact
While the environmental challenges linked to data and AI are real, it doesn’t mean that we can’t harness these technologies to make a positive difference. The key is using these tools wisely, focusing on energy efficiency, clear and honest tracking of impact, and solutions that reduce emissions and waste. By developing and deploying AI thoughtfully and responsibly, we can maximise its potential to support a more sustainable future. This responsibility extends beyond the big picture and into how we use AI every day. Simple steps – like using tools such as ChatGPT for tasks that add real value, such as brainstorming or summarising, rather than relying on it as a catch-all search engine or to write routine emails – can help reduce unnecessary energy consumption.
Sustainability Month at The Data Lab Community
The Data & AI Meetup is coming back to Aberdeen where we’ll be discussing the role of data & AI in sustainability and achieving net zero.
Data & AI powered tech has huge potential to drive sustainability and accelerate the journey towards a net-zero future – optimising energy usage and enabling smart decisions across industries. However, the tech also comes with its own carbon footprint. Training AI models and managing and storing vast datasets is a hugely energy-intensive process. To truly serve the planet, we need to make sure the benefit of these technologies outweighs their environmental impact.
Join our thriving community of over 6,000 data & AI professionals, students, and enthusiasts and register for these events, here.