In the absence of comprehensive legislation to curb greenhouse gas (GHG) emissions in the United States, policymakers have been pursuing climate change mitigation through sector or technology-specific regulatory measures. Comprehensive climate policies would cover most or all sources of GHG emissions and incentivize reductions at least cost through a market mechanism--such as a carbon tax, cap-and trade system, or hybrid instrument--by achieving an equalization of marginal abatement costs across participants (Metcalf, 2009). Regulatory measures, by contrast, require that GHG emissions reductions be achieved through compliance with sector-specific technology or efficiency targets. Examples of such regulatory measures include new source performance standards for power plant pollutant emissions, vehicle fuel economy standards, renewable or low carbon fuel standards, and renewable or clean electricity standards.
This paper examines the efficiency and distributional implications of federal regulation in the U.S. electric power and transportation sectors by employing a numerical simulation model with a unique treatment of regional, technology, and household income heterogeneity. The goal is to closely approximate current proposals implemented or under consideration in the U.S.
We investigate the impact on economy-wide costs and emissions reductions of introducing a clean energy standard (CES) or renewable portfolio standard (RPS), which would mandate the introduction of renewable generation (as well as other cleaner fuel sources in a CES), and a vehicle fuel economy standard modeled after the U.S. Corporate Average Fuel Economy (CAFE) Program, which mandates increases in on-road fuel economy of new vehicles sold in each vehicle model year. We explore how the costs are distributed across households in different regions and income categories. We compare the cost effectiveness and the distribution of impacts of policies alone and in combination, and investigate the welfare impact of such policies relative to an efficient instrument (in this case, a cap-and-trade system that creates a market for emissions permits). (1)
This paper proceeds as follows. Section 2 describes the numerical model used for quantitative policy assessments. Section 3 describes and interprets the model results. Section 4 performs a structural sensitivity analysis by investigating the impact of electricity policies in a coupled modeling framework that introduces a more detailed representation of technology. Section 5 concludes.
THE U.S. REGIONAL ENERGY POLICY (USREP) MODEL
This study makes use of a comprehensive energy-economic dataset that features a consistent representation of energy markets in physical units as well as detailed economic accounts of regional production, bilateral trade, and energy resources for the year 2006. The data set merges detailed state-level data for the U.S. with national economic and energy data. Social accounting matrices (SAM) in our hybrid dataset are based on data from the IMPLAN (IMpact analysis for PLANning) data (IMPLAN, 2008) and U.S. state-level accounts of energy balances and prices from the Energy Information Administration (EIA, 2009). Table 1 provides an overview of the data sources used.
The IMPLAN data provide consistent regional accounts of production, consumption, and bilateral trade for the 50 U.S. states (and the District of Columbia). The dataset includes input-output tables for each state that identify 509 commodities and existing taxes. Bilateral state-to-state trade data in the IMPLAN database are derived using a gravity approach (Lindall et al., 2006). (2) The base year for the IMPLAN accounts in the version we use here is 2006. To improve the characterization of energy markets in the IMPLAN data, we use constrained least-squares optimization techniques to merge IMPLAN data with data on physical energy quantities and energy prices from the Energy Information Administration's State Energy Data System for 2006 (EIA, 2009). (3)
For this study, we aggregate the dataset to 12 U.S. regions, 10 commodity groups, and 9 households grouped by annual income classes (see Table 2). States identified in the model include California, Texas, Florida, and New York, along with several other multi-state regional composites. Mapping of states to aggregated regions is shown in Figure 1. This structure separately identifies larger states, allows representation of separate electricity interconnects, and captures some of the diversity among states in use and production of energy. Our commodity aggregation identifies five energy sectors and five non-energy composites. Energy commodities include coal (COL), natural gas (GAS), crude oil (CRU), refined oil (OIL), and electricity (ELE), which distinguishes energy goods and specify substitutability between fuels in energy demand. Elsewhere, we distinguish energy-intensive products (EIS), other manufacturing (OTH), agriculture (AGR), commercial transportation (TRN), household vehicle transportation (HVT), and services (SRV). Primary factors in the dataset include labor, capital, land, as well as fossil fuels and natural resources.
We forecast both C[O.sub.2] and non-C[O.sub.2] greenhouse gases. Non-C[O.sub.2] greenhouse gases are based on U.S. EPA inventory data (EPA, 2009), and are included following the approach in Paltsev et al. (2005) with endogenous costing of abatement measures (Hyman et al., 2002). Energy supply is regionalized by incorporating data for regional crude oil and natural gas reserves (DOE, 2009), coal reserves estimated by the U.S. Geological Survey (USGS, 2009), and shale oil (Dyni, 2006). Our approach to characterize wind resource and incorporate electricity generation from wind in the model is described in detail in Section 4.1. We derive regional supply curves for biomass from data from Oakridge National Laboratories (2009) that describes quantity and price pairs for biomass supply for each state.
Our data set permits calculation of existing taxes rates comprised of sector and region-specific ad valorem output taxes, payroll taxes and capital income taxes. The IMPLAN data has been augmented by incorporating regional tax data from the NBER TAXSIM model (Feenberg & Coutts, 1993) to represent marginal personal income tax rates by region and income class.
2.2 Model Overview
Our modeling framework draws on a multi-commodity, multi-region, multi-household numerical general equilibrium model of the U.S. economy. The key features of the model are briefly outlined below and described in detail in Rausch et al. (2010a, 2010b). (4) The model assumes a recursive-dynamic approach implying that economic agents have myopic expectations and base their decisions on current period information.
In each industry gross output is produced using inputs of labor, capital, and natural resources including coal, natural gas, crude oil, and land, and produced intermediate inputs. We employ constant-elasticity-of-substitution (CES) functions to characterize how production technologies respond to changes in energy and other input prices; the IMPLAN data describe the initial production systems. All industries are characterized by constant returns to scale (except for fossil fuels and agriculture, which are produced subject to decreasing returns to scale) and are traded in perfectly competitive markets.
Advanced energy supply options are specified as "backstop" technologies that enter endogenously if and when they become economically competitive with existing technologies. Competitiveness of advanced technologies depends on their initial cost disadvantage compared to conventional technologies, in addition to the endogenously determined input prices. The advanced technology options are summarized in Table 3.
Three technologies produce perfect substitutes for conventional fossil fuels (natural gas from coal, a crude oil product from shale oil, and refined oil from biomass). The remaining nine are electricity generation technologies (biomass, wind without backup, wind with gas backup, wind with biomass backup, natural gas combined cycle with and without carbon capture and sequestration, integrated coal gasification combined cycle with and without carbon capture and sequestration, and advanced nuclear). We adopt a top-down approach of representing technologies following Paltsev et al. (2005, pp. 31-42) where each technology can be described through a nested CES function. The logic behind our approach to represent electricity generated from intermittent wind resources is explained in detail in Section 4.1.
Consumption, labor supply, and savings result from the decisions of representative households in each region maximizing utility subject to a budget constraint that requires that full consumption equals income in a given period. Lacking specific data on capital ownership, households are assumed to own a pool of U.S. capital--that is they do not disproportionately own capital assets within the region in which they reside. (5) Given input prices gross of taxes, firms maximize profits subject to technology constraints.
Firms operate in perfectly competitive markets and maximize their profit by selling their products at a price equal to marginal costs. In each region, a single government entity approximates government activities at all levels--federal, state, and local.
We adopt a putty-clay approach where a fraction of previously installed capital becomes non-malleable and frozen into the prevailing techniques of production. Vintaged production in a given industry that uses non-malleable capital is subject to a fixed-coefficient transformation process in which the quantity shares of capital, labor, intermediate inputs and energy by fuel type are set to be identical to those that prevailed in the period when the capital was installed. Each of the sector-specific vintages is tracked through time as a separate capital stock. This formulation means...
Markets versus Regulation: The Efficiency and Distributional Impacts of U.S. Climate Policy Proposals.
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