Energy

EPA Head Admits Clean Power Plan Wouldn’t Impact Global Warming [VIDEO]

Environmental Protection Agency (EPA) Administrator Gina McCarthy said to Congress Wednesday that there’s “absolutely no reason” to measure the impact of the Clean Power Plan by looking at global temperature reductions.

McCarthy admitted during questions by Republican Sen. [crscore]Steve Daines[/crscore] of Montana that her agency can’t measure the impact of its proposed Clean Power Plan on global temperatures, because it would likely be incredibly small. McCarthy specified that the plan’s influence on the environment cannot be quantified.

The Clean Power Plan would eliminate most cheap coal and natural gas power with expensive sources like solar and wind, costing America an expected $41 billion annually. Yet, the plan likely won’t have a large impact on global warming. According to analysis by the libertarian Cato Institute using models created by the EPA, the Clean Power Plan will only advert 0.019° Celsius of warming by the year 2100, an amount so small it can’t be detected.

“I’ve stood and looked at the families that are going to lose their jobs because of this. For what? For one quantitative analysis for 0.02 degrees of warming by the year 2100?” Daines said during the hearing. “If the impact on temperature is virtually negligable, what impact are we trying to drive here?”

McCarthy’s justification for the plan is that cutting carbon dioxide (CO2) emissions from power plants would theoretically encourage other countries to also reduce emissions.

Sen. Daines quoted estimates from the University of Montana, which stated the EPA’s plans would cost his state 7,000 jobs and $1.5 billion in lost economic output by 2025. The estimates stated that the EPA’s plan would be the largest negative economic impact to hit the state in 30 years.

The EPA actually omitted the amount of warming the Clean Power Plan will prevent from regulatory impact analysis. EPA admits it assesses the plan’s benefits “qualitatively because we do not have sufficient confidence in available data or methods.”

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