LONDON – Long before the real-world effects of climate change became so abundantly clear, data already painted a bleak picture of the scale of the problem.

For decades, carefully collected data on weather patterns and sea temperatures were fed into models that analyzed, predicted, and explained the effects of human activities on climate. Now the alarming answer has emerged, and one of the biggest questions of the next few decades will be how data-driven approaches can overcome the climate crisis.

Data and technologies like artificial intelligence (AI) are expected to serve a critical function. Yet that will only happen if major changes occur in data management. A move away from the commercial proprietary models that currently predominate in large, developed economies will have to be made. While the digital world might seem a climate-friendly one - better to Zoom to work than to drive - digital and Internet activity already accounts for around 3.7 percent of total greenhouse-gas (GHG) emissions, which is about the same as air travel. In the United States, data centers devour about 2 percent of total electricity use.

The figures for AI are much worse. Training a machine learning (ML) algorithm emits 284,000 kilograms of carbon dioxide - five times the lifetime fuel use of the average car, and 60 times more than a transatlantic flight, per one estimate. The rapid growth of AI means these emissions are set to skyrocket. Blockchain, the technology behind Bitcoin, is perhaps the worst offender. On its own, Bitcoin mining - the computing process used to verify transactions - leaves a carbon footprint roughly equal to New Zealand’s.

Fortunately, ways that AI use can cut carbon dioxide (CO2) emissions are legion, with the biggest opportunities in buildings, electricity, transport, and farming. The electricity sector, blamed for around one-third of GHG emissions, has ad