August 20, 2020
Using Models in Energy Policymaking
Computer models use mathematical representations of real
example, they might cover changes within individual
world systems to gain insights into complex processes,
energy facilities, states, or countries.
linkages between elements in a system, and how changes to
a system might affect outcomes. Such models have become
Model resolution is typically “hard-wired” into the
commonplace in many spheres of activity, including energy
mathematical equations that comprise the model and the
policymaking. Models are simplified representations of the
computer code that solves those equations. Increasing
real world. Simplification—along with bias, outdated and
model resolution to provide greater levels of temporal or
inaccurate assumptions, and other factors—can limit a
spatial detail frequently requires rewriting the underlying
model’s accuracy. Accordingly, models are frequently
computer code, a time-intensive process that may also
revised to improve their predictive accuracy. Well-honed
require additional computing resources. Decreasing the
models may provide useful insights for policymakers.
resolution to provide less detail, however, tends to be less
burdensome. Many energy system model outputs are
This analysis provides an overview of energy system
reported in aggregated form (i.e., with less resolution). For
models and considerations for how Members of Congress
example, a model might estimate monthly values but a
might use models to inform their policy positions.
summary report might only provide annual values.
Examples from past policy debates are included.
Models require inputs in the form of numerical data at a
Overview of Energy System Models
resolution that generally matches the model. For example, if
Energy system models estimate energy supply, demand,
a model is built to provide monthly estimates, the input data
prices, and related factors over defined time periods.
should have at least monthly values. Weekly or daily values
Energy system models are not one-size-fits-all
could also serve as inputs, but typically such data would be
decisionmaking tools. Model developers design models to
aggregated first. Likewise, if a model is built to provide
address different questions. Additionally, model design
state-level estimates, national-level input data would often
choices represent a trade-off between complexity, speed,
be insufficient. Inputs typically include historical data about
energy systems and related factors.
The federal government supports some energy system
Models include both exogenous (outside the model) and
models, including the Department of Energy National
endogenous (inside the model) factors. Exogenous variables
Energy Modeling System (NEMS) maintained by the U.S.
are provided as input to a model and may include key
Energy Information Administration (EIA). Data and
drivers of an energy system (e.g., economic activity,
computer code associated with federally-supported models
population), specific energy system developments (e.g.,
are generally available for free to the public. Private
future energy prices), or relationships between variables.
companies, academic researchers, policy advocates, and
Endogenous factors in an energy system are determined by
others also develop and maintain models. Some of these
solving the mathematical equations that comprise the
modelers may provide some data or code to the public, but
they often limit access.
Energy system models vary in the extent to which they rely
Model Design Elements
on exogenous variables. A greater reliance typically allows
Many models currently used in energy policymaking are
for cheaper and faster models with greater transparency.
energy economic models that seek economic optimization.
However, a large reliance on exogenous variables can also
This occurs when the supply of energy goods and services
increase the extent to which model results are biased by
exactly fulfills demand, taking into account the cost of
modelers’ assumptions, potentially reducing the utility 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)
Interpreting Model Results
may be difficult for energy economic models to assess.
The U.S. energy system is large and complex. Thousands of
energy producers interact with millions of consumers in
Models vary in the amount of detail they provide, known as
ways shaped by market forces, policies, and other factors.
their resolution. Models have different time, or temporal,
Models contain mathematical equations that try to capture
resolution. For example, they might cover changes over
the cause-and-effect relationship between parts of the
hours, months, or years. Many models intended to support
energy system. Accordingly, models can help identify the
policymaking examine the energy system over two or three
sometimes counterintuitive effects that changes in one part
decades. Models also have different spatial resolution. For
of an energy system can cause in another.
Using Models in Energy Policymaking
In other words, models can follow causal relationships
hydraulic fracturing and related practices that would occur
throughout an energy system to identify potential outcomes.
in the following few years.
Other forms of analysis sometimes attribute effects to the
wrong causes. This often occurs when events or trends are
As part of the Consolidated Appropriations Act, 2016 (P.L.
merely correlated (i.e., they occur at the same time or are
114-113), Congress repealed a prohibition on most crude
driven by the same factors) but are incorrectly believed to
oil exports that had stood since 1975. A focus of the debate
have a causal relationship.
was the effect that removing the ban would have on prices
for consumer goods such as gasoline. Modeling efforts
Models can also provide insight into potential policy
helped address some of these questions. Many models
outcomes by allowing for “experimentation” which may not
determined that repealing the ban would likely have little or
be possible or desirable in the real world. For example, by
no effect on gasoline prices.
varying input parameters, models can help inform
decisionmaking by modeling outcomes resulting from
Considerations for Congressional Use of
Models are increasingly common, but they are just one of
While models attempt to predict the future, their results are
many decisionmaking tools available to Members of
inherently uncertain. Experience has shown that models can
Congress. Other tools, such as stakeholder engagement,
be, and frequently are, wrong due to changing
might be better suited to identify some policy outcomes of
developments in energy markets and the broader economy,
congressional interest. For example, a model might identify
as well as other factors. To some extent, modelers can
an energy sector that is likely to experience a large growth
improve the predictive accuracy of models (i.e., reduce the
or decline, but stakeholder engagement might identify the
difference between model forecasts and actual outcomes)
possible impacts of that trend within a congressional
by increasing their complexity. Many sources of
district. Members of Congress might choose to what extent
uncertainty, though, are hard to eliminate. In addition,
they wish to base policy choices on model results, and how
model results can be very sensitive to their underlying data
models might complement other decisionmaking tools.
and assumptions. Inaccuracies or biases in model input
data, as well as inconsistencies or mistakes in the computer
The U.S. energy system is changing. In some cases, data
code, can negatively affect model results.
collection may lag industry developments, such as new
ways to produce and transport fuels or new forms of
The complexity and data-intensive nature of energy system
electricity generation and storage. Models require relevant
models can limit their transparency to policymakers and the
data to accurately represent such industry changes.
public, especially regarding input data and assumptions. A
key point for policymakers is that understanding model
Several organizations conduct model analysis by
assumptions is often critical to interpreting the results.
congressional request. EIA is one of these. Past examples of
congressional requests for EIA model analysis include
An oft-cited guidance for using models for policymaking is
repealing the crude oil export ban, federal regulation of
“modeling is for insights, not numbers.” This saying
greenhouse gas emissions, and extending a production tax
summarizes the idea that there is inherent uncertainty in
credit for wind generators. EIA’s models do not address all
modeling, and that models are often more useful in
potential areas of interest in energy policy. EIA’s models
identifying trends than for making specific predictions.
are designed to provide estimates of the price and quantity
of energy goods and services. EIA’s models do not estimate
changes in employment associated with policy proposals or
As noted above, models can be useful to policymakers by
estimate all environmental impacts.
identifying trends and by estimating policy outcomes before
policies are implemented. The following examples
In addition, other types of models (i.e., other than energy
demonstrate how models can inform policy debate.
system models) might be relevant to congressional debate
on energy policies. For example, integrated assessment
As part of the Energy Policy Act of 2005 (P.L. 109-58),
models examine the combined economic and environmental
Congress reauthorized a research program in methane
outcomes associated with different policies. Such models
hydrates, a potential source of natural gas. Congress
are often used in debates about greenhouse gas emissions
included in the law a finding that “a shortfall in natural gas
and climate change impacts. Macroeconometric models,
supply from conventional and unconventional sources is
such as those used by the Congressional Budget Office,
expected to occur in or about 2020.” (U.S.C. 30 §2001) In
examine macroeconomic factors like employment, wages,
this case, models identified dual trends of increasing U.S.
and inflation rates. Such models are often used in debates
natural gas consumption and decreasing U.S. natural gas
about energy tax policy and policies to promote energy
production. The expectation of a natural gas shortage, based
on model projections, contributed to Congress’s policy
decision to support research into a new source of natural
Ashley J. Lawson, Analyst in Energy Policy
gas. In this case, the projection was unable to predict a
systemic shift in U.S. natural gas supply due to advances in
Using Models in Energy Policymaking
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