AI estimates 85% of the population is affected by climate change A team of researchers in Germany used machine learning to crunch at least 60,000 studies related to climate change.

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A team of researchers in Germany used machine learning to curate at least 60,000 studies related to climate change.

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Climate change is here. And it will get much worse if we don’t take action soon. that was loud a Historical Report of the Intergovernmental Panel on Climate Change (IPCC), released in August, warned that rising temperatures would affect every region of the planet.

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A new paper published Monday in Nature Climate Change adds some specificity to that claim. Using machine learning techniques to analyze more than 60,000 climate change-related studies, researchers in Germany estimate that 85% of the population is affected by human-induced climate change. The study was led by Max Callaghan of Berlin’s Mercator Research Institute on Global Commons and Climate Change.

“There is overwhelming evidence that the effects of climate change are already being observed in human and natural systems,” the paper reads. “We estimate that responsible anthropogenic impacts may occur in up to 80% of the world’s land area, where 85% of the population resides.”

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study goes on COP26The United Nations Climate Change Conference in Glasgow, which runs from 31 October to 12 November. COP26 will bring together world leaders, including Joe Biden and Boris Johnson, but not China’s Xi Jinping in particular, as new commitments to reducing carbon emissions are expected to be reached. The Paris Agreements were reached at COP21 in 2015, and observers expect more ambitious commitments to carbon neutrality can be agreed upon in Glasgow.

Machine learning is a type of artificial intelligence that gets smarter as more information: Think speech-to-text software, which gets more accurate and is able to hear more bells and whistles. Callaghan and team aim to uncover not only the planet’s plight as the effects of climate change become more known, but also to use machine learning to reveal gaps in scientific research.

The researchers fed machine learning software called BERT (or Bidirectional Encoder Representation from Transformers) 2,373 abstracts on climate change-related papers. Once information on climate change was obtained, the algorithm was able to identify studies that could show the effects of climate change, even if those studies did not attribute climate change findings. The paper referenced one such study on the relationship between the timing of snowfall and the population growth of mammals.

The paper reads, “We aimed to map all potentially relevant studies on climate-related changes, rather than a list of studies, where the relationship between an observed climate trend and specific effects has been demonstrated with high confidence.” “While conventional assessments may offer relatively accurate but incomplete pictures of the evidence, our machine-learning-assisted approach produces a detailed preliminary but quantitatively uncertain map.”

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