Extending Macroeconomic Impacts Forecasting for NEMS.

• Author: Court, Christa D.
• Position: National Energy Modeling System

1. INTRODUCTION

Decision makers involved in energy policy are facing unprecedented challenges arising from globalization, decarbonization, and the advent of new energy technologies. Since the early 1970s energy-economic forecasting models have been used to assess the potential consequences of meeting these types of energy market challenges (Hoffman and Wood 1976; Nakata 2004; Bhattacharyya and Timilsina 2010). Forecasts generated by these models help to inform public energy system planning decisions and private energy sector investments. Available forecasts (e.g., the International Energy Association [IEA]'s World Energy Outlook [WEO] or the United States [U.S.] Energy Information Administration [EIA]'s Annual Energy Outlook [AEO]), however, do not include detailed estimates of the economic impacts associated with changes in long-term energy economic conditions at the industry-level.

Recognizing the need for a model that could generate comprehensive estimates of the macroeconomic impacts of changes in the U.S. energy system, the U.S. Department of Energy (DOE)'s National Energy Technology Laboratory (NETL) partnered with researchers at West Virginia University (WVU)'s Regional Research Institute (RRI) to develop the WVU/NETL econometric input-output (ECIO) model. The NETL/WVU ECIO model (hereafter, the ECIO model) is an impacts forecasting model capable of generating industry-level estimates of changes in employment, labor income, and total output resulting from proposed shocks to the U.S. energy economy.1

This paper provides an overview of the ECIO model as of late 2019, including the model's design, assumptions, and standard model outputs. The ECIO model exists as a series of interrelated equations that characterize the interdependencies of industries, value added sectors, and components of final demand for the U.S. economy. Equations are connected across three primary modules and several sub-modules. Following a traditional input-output (IO) accounting framework, interactions between industries are represented by the sales and purchases of goods and services.

While not the only model available to estimate the employment and labor income impacts of a changing energy landscape,2 the ECIO model makes at least three important contributions to the literature on energy-economic modeling. First, similar to the National Renewable Energy Laboratory (NREL)'s Jobs and Economic Development Impact (JEDI) models, the ECIO model can be used to estimate the economic impacts of constructing and operating new energy facilities (e.g., a power plant) (Goldberg 2004). The ECIO model, however, can also be used to estimate the economic impacts of research, design, and development (RD&D) spending, the deployment of new energy technologies, and/or unplanned capacity retirements. Second, the ECIO model is able to respond to changes in energy sector prices, which influence production costs both directly and indirectly across industries. The effects of changes in energy prices are incorporated in the ECIO model using a variant of the IO price model described by Bazzazan and Batey (2003).3 Third, unlike other static IO modeling frameworks (e.g., IMPLAN) the ECIO model is dynamic in nature to be consistent with both the U.S. EIAs National Energy Modeling System (NEMS) and the U.S. Environmental Protection Agency's U.S. Nine-Region (USEPA9r) version of the Market Allocation (MARKAL) model, and to allow for the estimation of impacts forecasts over decadal time frames.

While not without skepticism (Pindyck 2013; Reed et al. 2019), both NEMS and the EPAUS9r version of MARKAL are integrated assessment models (IAMS) used regularly to forecast the effects of shocks on energy-economic conditions for the U.S.4 Model results are delivered in the form of alternate outcome scenarios.5,6 Scenarios include information on updates to the aggregated electricity generation, transmission, and distribution sector such as changes in the technology and/or resource mix used to meet demand. Annual use tables within the ECIO model's framework can be modified to reflect such changes.

The remainder of this paper is organized as follows. Section 2 discusses how the ECIO model fits within the continuum of impacts forecasting frameworks and provides a more in-depth overview of the ECIO model's design and general assumptions. Section 3 describes the standard results presentation using an example application of the ECIO model. Section 4 concludes and provides a brief discussion of future model extensions.

(2.) ECIO MODEL DESIGN

Several organizations have developed economic impacts forecasting models, including Terry Barker at Cambridge Econometrics (CE) who has used these models in wide-ranging policy analysis contexts; Dick Conway who has used these models productively for decades in Washington and Hawaii among other areas; Geoffrey Hewings with models of Chicago, St. Louis, and the U.S. Midwest; RRI's Randall Jackson with models of Ohio and the U.S.; Jose Manuel Rueda-Cantuche and Kurt Kratena for the EU-27; Sergio Rey for various California regions; Clopper Almon, Douglas Meade, and others at the University of Maryland with the IN-FORUM model of the U.S. and many other countries; and Guy West, who has applied interindustry econometric models to policy issues in Australia and its regions using this type of model.7,8

Similar to these models, the ECIO model can be calibrated and parameterized to represent the existing structure of an economy, and to forecast, incorporate, and respond to changes in that structure. In the process, temporal changes in prices, interest rates, wage rates, output, employment, income and the like are determined, carrying clear implications for socioeconomic impacts across different groups in the economy. Depending on model design, some of these variables are specified exogenously, and most can be manipulated to reflect scenario specific changes.

As of late 2019, the ECIO model could serve as an extension to both U.S. EIAs NEMS and the U.S. Environmental Protection Agency's USEPA9r version of the MARKAL model. Both models are data-driven energy-economic optimization models used to project energy market conditions, subject to a set of market and technology constraints.9 Results from NEMS and the USEPA9r version of MARKAL are presented as a series of alternate future scenarios that provide a range of alternative possible futures for the U.S. energy-economy (Nakata 2004). At minimum, two competing scenarios are generated: 1) a reference scenario (i.e., base case) and 2) a counterfactual scenario. The reference scenario depicts the current and future state of the energy economy with current laws and regulations in place. The counterfactual scenario depicts an alternative future state of the energy economy, where an underlying goal of a proposed policy or program has been met. The ECIO model is used to generate estimates of the economic impacts associated with departures from the reference scenario (i.e., base case).

The ECIO model consists of three modules and several sub-modules, connected through a series of inter-related equations that represent the interconnections between 32 major industrial sectors of the U.S. economy. The three primary modules include the U.S. macroeconometric module, the industrial output module, and the employment and income module. A configuration of the ECIO's primary modules is shown in Figure 1. Although the code for the macroeconometric module is embedded in the ECIO model's algorithm, it can be considered to be exogenous, in effect, and is shaded to reflect this. Likewise, inputs from NEMS or the USEPA9r version of MARKAL that describe scenario-specific data on new energy technologies, energy sector prices, and related variables are exogenous to the ECIO model. Integration of the three primary modules provides a fully integrated approach to estimating economic activity at the national (and regional) level.

The U.S. macroeconometric module is an adaptation of the Fair model, a public domain macroeconomic econometric model developed by Ray Fair (Fair 2009). The role of this module is to generate forecasts of the components of final demand, which are used as inputs for the ECIO's industrial output module. The industrial output module provides projections of sectoral output for 32 sectors of the U.S. economy (see Table 1 in Appendix B for a complete description of the industrial aggregation scheme used within the ECIO's industrial output module).1011 The employment and income module uses the projections of industrial output from the industrial output module to compute employment for the 32 economic sectors. Altogether, there are five energy and 27 non-energy sectors within the industry classification scheme.12

There are two primary mechanisms that integrate the interindustry and the econometric subsystems. The first is the reliance of the 10 production requirements solution on econometric final demand estimates. Final demand totals by major component are transformed using data from the national 10 accounts. Commodity final demand distributions are then transformed into industry space to determine production levels that meet direct and indirect demands. The second integrating mechanism centers on income estimation. Because income is the primary source of consumption expenditures and because consumption expenditures are the dominant driving force and determinant of overall economic activity (i.e., GDP), income provides a powerful variable for integrating the two model systems.

Econometric time series equations provide the basis for forecasting labor and non-labor income estimates, and production-based output estimates coupled with productivities and wage rates provide a second source for labor income estimates. The model uses a variable weighting parameter in which full or partial weight can be accorded to either the 10 or econometric labor income estimate. By weighting the two equally, the econometric...