Supported Intelligence developed the Sectoral Business Decision Model (SBDM) to help interested parties analyze the real impacts of different Clean Power Plan compliance options in individual states and over multiple years. These compliance options can involve billions of dollars of additional costs to be passed along to ratepayers and taxpayers in the state, with significant effects on employment and business decisions, and therefore on demand for electric power and resulting emissions in the future.
The SBDM first projects paths for state-level economic, emissions, and power generation variables under a baseline scenario. The baseline scenario does not assume any changes to a state’s current policies in response to the CPP. It then models how these important factors change under various regulatory scenarios, such as a tax on CO2 emissions and various versions of a cap-and-trade based emissions allowance system. The SBDM also incorporates various tax options, including changes to the state sales or income tax rates.
The Sectoral Business Decision Model reflects significant improvements over traditional models used to analyze electricity dispatch, as well as short-term input-output economic models. In particular:
- For each scenario, additional costs, regulatory impositions, and any related tax changes are natively incorporated into projections for future economic growth.
- The level of economic activity strongly affects demand for electricity, and therefore electricity generation and related emissions. The SBDM natively takes this feedback loop into account.
- Business decisions that involve prices and availability of electricity are modeled for the major sectors of the economy, with different scenarios having different sensitivities to price changes.
- Each state’s economy is modeled using different assumptions about power generation, economic sectors, prices, and potential regulatory scenarios.
- Business decisions related to prices are modeled using sector-specific response functions that are informed by recursive decision models, which were pioneered by Supported Intelligence. These response functions are not constrained to simple linear or log-linear elasticity relationships as in many traditional input-output and other models. They can also identify industrial sectors that are at risk under certain regulatory regimes.
The SBDM is not intended to replace traditional utility dispatch models for modeling electricity dispatch prices within a short period and with known generation capacity. Such models can be used to complement the multi-year projections of economic, emissions, and energy usage possible within the SBDM.