June 2022

In a blow to the fight against climate change, the Supreme Court on Thursday limited how the nation’s main anti-air pollution law can be used to reduce carbon dioxide emissions from power plants.

By a 6-3 vote, with conservatives in the majority, the court said that the Clean Air Act does not give the Environmental Protection Agency broad authority to regulate greenhouse gas emissions from power plants that contribute to global warming.

The court’s ruling could complicate the administration’s plans to combat climate change. Its proposal to regulate power plant emissions is expected by the end of the year.

President Joe Biden aims to cut the nation’s greenhouse gas emissions in half by the end of the decade and to have an emissions-free power sector by 2035. Power plants account for roughly 30% of carbon dioxide output.

The justices heard arguments in the case on the same day that a United Nations panel’s report warned that the effects of climate change are about to get much worse, likely making the world sicker, hungrier, poorer and more dangerous in the coming years.

The power plant case has a long and complicated history that begins with the Obama administration’s Clean Power Plan. That plan would have required states to reduce emissions from the generation of electricity, mainly by shifting away from coal-fired plants.

But that plan never took effect. Acting in a lawsuit filed by West Virginia and others, the Supreme Court blocked it in 2016 by a 5-4 vote, with conservatives in the majority.

With the plan on hold, the legal fight over it continued. But after President Donald Trump took office, the EPA repealed the Obama-era plan. The agency argued that its authority to reduce carbon emissions was limited and it devised a new plan that sharply reduced the federal government’s role in the issue.

New York, 21 other mainly Democratic states, the District of Columbia and some of the nation’s largest cities sued over the Trump plan. The federal appeals court in Washington ruled against both the repeal and the new plan, and its decision left nothing in effect while the new administration drafted a new policy.

Adding to the unusual nature of the high court’s involvement, the reductions sought in the Obama plan by 2030 already have been achieved through the market-driven closure of hundreds of coal plants.

Power plant operators serving 40 million people called on the court to preserve the companies’ flexibility to reduce emissions while maintaining reliable service. Prominent businesses that include Apple, Amazon, Google, Microsoft and Tesla also backed the administration.

Nineteen mostly Republican-led states and coal companies led the fight at the Supreme Court against broad EPA authority to regulate carbon output.
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As Russia pounds Ukrainian cities to rubble, water and sewer systems have broken down in some places. The British Defense Ministry says Mariupol is at risk of a major cholera outbreak. Just how big the threat is, though, is not clear. Scientists disagree over where the strains of cholera that can cause a major outbreak come from, and whether they are present in Ukraine currently. Producer:  Steve Baragona
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Future waves of COVID-19 might be predicted using internet search data, according to a study published in the journal Scientific Reports.

In the study, researchers watched the number of COVID-related Google searches made across the country and used that information, together with conventional COVID-19 metrics such as confirmed cases, to predict hospital admission rates weeks in advance.

Using the search data provided by Google Trends, scientists were able to build a computational model to forecast COVID-19 hospitalizations. Google Trends is an online portal that provides data on Google search volumes in real time.

“If you have a bunch of people searching for ‘COVID testing sites near me’ … you’re going to still feel the effects of that downstream at the hospital level in terms of admissions,” said data scientist Philip Turk of the University of Mississippi Medical Center, who was not involved in the study. “That gives health care administrators and leaders advance warning to prepare for surges — to stock up on personal protective equipment and staffing and to anticipate a surge coming at them.”

For predictions one or two weeks in advance, the new computer model stacks up well against existing ones. It beats the U.S. Centers for Disease Control and Prevention’s “national ensemble” forecast, which combines models made by many research teams — though there are some single models that outperform it.

Different perspective

According to study co-author Shihao Yang, a data scientist at the Georgia Institute of Technology, the new model’s value is its unique perspective — a data source that is independent of conventional metrics. Yang is working to add the new model to the CDC’s COVID-19 forecasting hub.

Watching trends in how often people Google certain terms, like “cough” or “COVID-19 vaccine,” could help fill in the gaps in places with sparse testing or weak health care systems.

Yang also thinks that his model will be especially useful when new variants pop up. It did a good job of predicting spikes in hospitalizations thought to be associated with new variants such as omicron, without the time delays typical of many other models.

“It’s like an earthquake,” Yang said. “Google search will tell me a few hours ahead that a tsunami is hitting. … A few hours is enough for me to get prepared, allocate resources and inform my staff. I think that’s the information that we are providing here. It’s that window from the earthquake to when the tsunami hit the shore where my model really shines.”

The model considers Google search volumes for 256 COVID-19-specific terms, such as “loss of taste,” “COVID-19 vaccine” and “cough,” together with core statistics like case counts and vaccination rates. It also has temporal and spatial components — terms representing the delay between today’s data and the future hospitalizations it predicts, and how closely connected different states are.

Every week, the model retrains itself using the past 56 days’ worth of data. This keeps the model from being weighed down by older data that don’t reflect how the virus acts now.

Turk previously developed a different model to predict COVID-19 hospitalizations on a local level for the Charlotte, North Carolina, metropolitan area. The new model developed by Yang and his colleagues uses a different method and is the first to make state- and national-level predictions using search data.

Turk was surprised by “just how harmonious” the result was with his earlier work.

“I mean, they’re basically looking at two different models, two different paths,” he said. “It’s a great example of science coming together.”

Using Google search data to make public health forecasts has downsides. For one, Google could stop allowing researchers to use the data at any time, something Yang admits is concerning to his colleagues.

‘Noise’ in searches

Additionally, search data are messy, with lots of random behavior that researchers call “noise,” and the quality varies regionally, so the information needs to be smoothed out during analysis using statistical methods.

Local linguistic quirks can introduce problems because people from different regions sometimes use different words to describe the same thing, as can media coverage when it either raises or calms pandemic fears, Yang said. Privacy protections also introduce complications — user data are aggregated and injected with extra noise before publishing, a protection that makes it impossible to fish out individual users’ information from the public dataset.

Running the model with search data alone didn’t work as well as the model with search data and conventional metrics. Taking out search data and using only conventional COVID-19 metrics to make predictions also hurt the new model’s performance. This indicates that, for this model, the magic is in the mix — both conventional COVID-19 metrics and Google Trends data contain information that is useful for predicting hospitalizations.

“The fact that the data is valuable, and [the] data [is] difficult to process are two independent questions. There [is] information in there,” Yang said. “I can talk to my mom about this. It’s very simple, just intuitive. … If we are able to capture that intuition, I think that’s what makes things work.”
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