Many of the Midwest states, including Ohio, face significant state budget shortfalls- Ohio faces a projected $8 billion dollar hole in its next budget. With the shortfalls, is very unlikely additional revenue will be available to support existing programs.
The state budget crisis occurs at the same time U.S. EPA has been very active in revising federal air quality standards (National Ambient Air Quality Standards- NAAQS). As a result of changes to federal standards, states face a massive workload in the next few years on air quality issues.
Below is a chart showing all of the revised federal air quality standards. In response to each new standards, the states must develop plans for reducing emissions to show compliance with the revised standards (State Implementation Plans- SIPs). In the next four years, States will be required to develop at least five new SIPs.
Preparation of SIPs is important work that can have wide ranging impacts on the economy. If additional regulations to reduce air pollution are necessary, these new regulations increase compliance costs for businesses.
In determining whether additional regulatory programs are needed, states and U.S. EPA rely upon air quality modeling. Using air qualify modeling to evaluate alternatives is complex work and sometimes modeling can be inaccurate.
When Ohio EPA evaluated options for Cleveland to attain the 1997 ozone standard (85 ppt), modeling predicted no combination of controls could bring the area into compliance. After an intensive effort by multiple parties (locals, Ohio EPA and U.S. EPA) it was determined Cleveland did not need to adopt aggressive controls to comply because the modeling was either:
- Underestimating the benefits of some existing pollution reduction programs; or
- Data regarding emissions from existing sources in the modeling was outdated.
With states facing budget shortfalls and unprecedented amounts of air quality work, one has to question whether a similar effort could be undertaken in the next couple of years. If that is not the case, decisions on costly new controls could be based on inaccurate or incomplete data.