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1 1 (August 20, 2020)

handle is hein.crs/govdbme0001 and id is 1 raw text is: Using Models in Energy Policymaking August 20, 2020 Computer models use mathematical representations of real world systems to gain insights into complex processes, linkages between elements in a system, and how changes to a system might affect outcomes. Such models have become commonplace in many spheres of activity, including energy policymaking. Models are simplified representations of the real world. Simplification-along with bias, outdated and inaccurate assumptions, and other factors-can limit a model's accuracy. Accordingly, models are frequently revised to improve their predictive accuracy. Well-honed models may provide useful insights for policymakers. This analysis provides an overview of energy system models and considerations for how Members of Congress might use models to inform their policy positions. Examples from past policy debates are included. Overview of Energy System Models Energy system models estimate energy supply, demand, prices, and related factors over defined time periods. Energy system models are not one-size-fits-all decisionmaking tools. Model developers design models to address different questions. Additionally, model design choices represent a trade-off between complexity, speed, and cost. The federal government supports some energy system models, including the Department of Energy National Energy Modeling System (NEMS) maintained by the U.S. Energy Information Administration (EIA). Data and computer code associated with federally-supported models are generally available for free to the public. Private companies, academic researchers, policy advocates, and others also develop and maintain models. Some of these modelers may provide some data or code to the public, but they often limit access. Mode Design Elements Many models currently used in energy policymaking are energy economic models that seek economic optimization. This occurs when the supply of energy goods and services exactly fulfills demand, taking into account the cost of producing energy goods and services and what consumers are willing to pay for them. Factors that are difficult to assign a dollar value to (e.g., health impacts of pollution) may be difficult for energy economic models to assess. Models vary in the amount of detail they provide, known as their resolution. Models have different time, or temporal, resolution. For example, they might cover changes over hours, months, or years. Many models intended to support policymaking examine the energy system over two or three decades. Models also have different spatial resolution. For example, they might cover changes within individual energy facilities, states, or countries. Model resolution is typically hard-wired into the mathematical equations that comprise the model and the computer code that solves those equations. Increasing model resolution to provide greater levels of temporal or spatial detail frequently requires rewriting the underlying computer code, a time-intensive process that may also require additional computing resources. Decreasing the resolution to provide less detail, however, tends to be less burdensome. Many energy system model outputs are reported in aggregated form (i.e., with less resolution). For example, a model might estimate monthly values but a summary report might only provide annual values. Models require inputs in the form of numerical data at a resolution that generally matches the model. For example, if a model is built to provide monthly estimates, the input data should have at least monthly values. Weekly or daily values could also serve as inputs, but typically such data would be aggregated first. Likewise, if a model is built to provide state-level estimates, national-level input data would often be insufficient. Inputs typically include historical data about energy systems and related factors. Models include both exogenous (outside the model) and endogenous (inside the model) factors. Exogenous variables are provided as input to a model and may include key drivers of an energy system (e.g., economic activity, population), specific energy system developments (e.g., future energy prices), or relationships between variables. Endogenous factors in an energy system are determined by solving the mathematical equations that comprise the model. Energy system models vary in the extent to which they rely on exogenous variables. A greater reliance typically allows for cheaper and faster models with greater transparency. However, a large reliance on exogenous variables can also increase the extent to which model results are biased by modelers' assumptions, potentially reducing the utility of the model. The U.S. energy system is large and complex. Thousands of energy producers interact with millions of consumers in ways shaped by market forces, policies, and other factors. Models contain mathematical equations that try to capture the cause-and-effect relationship between parts of the energy system. Accordingly, models can help identify the sometimes counterintuitive effects that changes in one part of an energy system can cause in another. p\w -- , gn'a', goo mppm qq\ a , q 'S I 11LIANJILiN,

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