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Power plants play a huge role in emitting pollutants that make up the ozone. This pollution browns and blackens our horizons. We call it smog. Smog has been linked to premature deaths, thousands of emergency room visits, and tens of thousands of asthma attacks each year. Pollution in the ozone is particularly dangerous to small children and the elderly, who are often warned to stay indoors on days with poor air quality due to pollutants.
Not only are the pollutants spewed out by power plants bad for our health, but they contain greenhouse gases that have been linked with climate change; thus they are killing the world as we know it as well.
Despite the danger to both humankind and the earth, the Bush administration created rules that allowed power plants to emit uncontrolled pollution into the air in cities that already have severely polluted air. Under rules created by the Environmental Protection Agency (EPA), plants could buy rights to pollute – sometimes from plants hundreds of miles away – instead of installing modern emission controls.
But the U.S. Court of Appeals for the D.C. Circuit found that the EPA rules were illegal. “This decision will mean cleaner air and stronger air quality protections across the country,” said John Walke, Clean Air Director for the Natural Resources Defense Council. “With summer smog blanketing our communities, this decision is a welcome relief and promise of stronger health safeguards.”
“The EPA rule let power plants pump uncontrolled air pollution into regions that already had dangerous smog levels. We said that violated the law, and the court agreed,” said David Baron an attorney for Earthjustice, who filed the suit.
The EPA rule, which was overturned, was part of an air pollution trading program aimed at reducing pollution that travels between states. The rule, however, allowed power plants in heavily polluted areas to buy pollution credits from other plants, which could be hundreds of miles away. Swapping credits led to power plants in highly polluted areas getting away without curbing emissions, creating an even darker cloud on the horizon.
The challenged Bush rules also allowed new plants to claim offset credits for historical pollution reductions from plants that closed down decades ago, allowing such credits even in cities that lacked programs to assure that they would still meet health standards on time if the old credits were used. But the ruling changed that. The court held that the credits could not be allowed in cities that lacked approved plans.
The ruling also rejected weakening Clean Air Act limits on new and expanded factories in polluted communities. The law requires new plants to more than offset their increased emissions, for example by arranging for greater pollution reductions from other facilities in the area.
While this ruling to overturn illegal policy is a breath of fresh air, it is only a small step in the right direction. More than policy change, a change in mindset is required. A shift from coal to other forms of energy is required. Efficiency is required.
Photo Credit: Picture_taking_fool via flickr under Creative Commons License
There has been atmospheric cooling the last 8 years, and no new high global annual temperatures in the last 11 years. Anthropogenic (or man caused) global warming is not proved. None of the computer models replicate this fact.
The global warming adherents base their argument of proof on more than 20 different computer models called general circulation models (also known as global climate models or GCMs). Each computer model is composed of dozens of mathematical equations representing known scientific laws, theories, and hypotheses. Each equation has one or more constants. The constants associated with known laws are very well defined. The constants associated with known theories are generally accepted but probably some of them may be off by a factor of 2 or more, maybe even an order of magnitude. The equations representing hypotheses, well, sometimes the hypotheses are just plain wrong. Then each of these equations has to be weighted against each other for use in the computer models, so that adds an additional variable (basically an educated guess) for each law, theory, and hypothesis. This is where the models are tweaked to mimic past climate measurements.
The SCIENTIFIC METHOD is: (1) Following years of academic study of the known physical laws and accepted theories, and after reviewing some data, come up with a hypothesis to explain the data. (2) Develop a plan to obtain and analyze new data. (3) Collect and analyze the data, this may even require new technology not previously available. (4) Determine if the hypothesis is correct, needs refinement, or is wrong. Either way, new data is available for other researchers. (5) Submit results, including data, for peer review and publication.
The output of the computer models run out nearly 90 years forward is considered to be data, but it is not a measurement of a physical phenomenon. Also, there is no way to analyze this so called data to determine if any or which of the hypotheses in the models are correct, need refinement, or are wrong. Also, this method cannot indicate if other new hypotheses need to be generated and incorporated into the models. IT JUST IS NOT THE SCIENTIFIC METHOD.
The worst flaw in the AGW argument is the treatment of GCM computer generated outputs as data. They then use it in follow on hypotheses. For example, if temperature rises by X degrees in 50 years, then Y will be effected in such-and-such a way resulting in Z. Then the next person comes along and says, well, if Z happens, the effect on W will be a catastrophe. “I need (and deserve) more money to study the effects on W.” Hypotheses, stacked on hypotheses, stacked on more hypotheses, all based on computer outputs that are not data, using a process that does not lend to proof using the SCIENTIFIC METHOD. Look at their results, IF, MIGHT, and COULD are used throughout their news making results. And when one of the underlying hypotheses is proven incorrect, well, the public only remembers the doomsday results 2 or three iterations down the hypotheses train. The hypotheses downstream are not automatically thrown out and can even be used for more follow on hypotheses.