Lump-sum transfer based on capital income. Technology funding. Allocated to regions based on energy use; within regions allocated to households on a ...
Distributional Implications of Proposed US Greenhouse Gas Control Measures Sebastian Rausch, Gilbert E. Metcalf, John M. Reilly and Sergey Paltsev Paper prepared for the UC-UI-RFF Energy Policy Symposium Distributional Aspects of Energy and Climate Policy January 20-21, 2010
Presentation • • • •
USREP model Policy Scenarios Results Summary
MIT USREP Model: Overview • MIT USREP (US Regional Energy Policy) model is a new multi-region, multi-sector, multi-household computable general equilibrium (CGE) model of the US economy for analyzing US energy and greenhouse gas policies • Recursive dynamic model similar to the MIT EPPA (Emissions Prediction and Policy Analysis) Model (Paltsev et al, 2005)
• Captures heterogeneity across regions and income groups in the United States
MIT USREP Model: Database • Base year calibration based on a new integrated statelevel economic-energy dataset for the US for the year 2006 which merges together : – Economic data from IMPLAN (Social Accounting Matrices for each state) – Physical energy and price data from EIA’s State Energy Data System (SEDS) – Population and household data from US Census Bureau and – GHG inventories data from EPA (for Kyoto gases) – Fossil fuel reserves data from USGS and DOE, and highresolution wind resource data from NREL – Tax data from IMPLAN and the NBER TAXSIM tax simulator
MIT USREP: Sectoral Breakdown and Primary Input Factors Region
Sectors
Primary Input Factors
Alaska (AK)
Non-Energy
Capital
California (CA)
Agriculture (AGRIC)
Labor
Florida (FL)
Services (SERV)
Land
New York (NY)
Energy-Intensive (EINT)
Crude Oil
New England (NENGL)
Other Industries (OTHR)
Shale Oil
South East (SEAST)
Transportation (TRAN)
Natural Gas
North East (NEAST)
Energy
Coal
South Central (SCENT)
Coal (COAL)
Nuclear
North Central (NCENT)
Conventional Crude Oil (OIL)
Hydro
Mountain (MOUNT)
Oil from Shale (OIL)
Wind
Pacific (PACIF)
Refined Oil (ROIL) Natural Gas (GAS) Electric: Fossil (ELEC)
Electric: Nuclear (NUC) Electric: Hydro (HYD) Advanced Technologies
Advanced Technologies • • • • • • • • • •
Coal Gasification Biomass Liquids Biomass Electricity Intermittent Wind Wind With Gas Backup Wind With Biomass Backup Advanced Gas (Natural Gas Combined Cycle) Advanced Gas With Carbon Capture And Sequestration Advanced Coal With Carbon Capture And Sequestration Advanced Nuclear
MIT USREP Model : Regional Aggregation
• State-level dataset allows flexible regional aggregation • In our analysis, we focus on 12 model regions to capture differences in electricity costs and to help focus on how different regions and states differ
MIT USREP Model: Income Classes Table 1. Income Classes Used in the USREP Model and Cumulative Population. Income class
Annual Income (2006$)
Cumulative Population for whole US (in %)a
hhl
Less than $10,000
7.3
hh10
$10,000 to $15,000
11.7
hh15
$15,000 to $25,000
21.2
hh25
$25,000 to $ $30,000
31.0
hh30
$30,000 to $50,000
45.3
hh50
$50,000 to $75,000
65.2
hh75
$75,000 to $100,000
78.7
hh100
$100,000 to $150,000
91.5
hh150
$150,000 plus
100.0
a Based on Consumer Expenditure Survey Data for 2006.
MIT USREP Model: Key Features • • •
• • • •
Production and consumption technologies are represented by nested constantelasticity-of-substitution (CES) functions with identical structure as in EPPA model Perfect competition in product and factor markets Vintage capital structure is similar to EPPA model and distinguishes between malleable and non-malleable capital - Malleable capital is fully mobile across industries and regions - Vintaged capital is region and industry specific Labor supply is determined by the household choice between leisure and labor and we assume that labor is fully mobile across industries in a region but it is immobile across US regions Integrated market for fossil fuel resources with regional ownership of fossil fuel resources distributed across regions in proportion to capital income Armington (1969) assumption of product heterogeneity for imports and exports among states and regions and with foreign goods Regional energy supply - Fossil fuels: based on reserves data from US Geological Service and the DOE, and resource depletion model as in EPPA model - Regional supply curves for wind based on high-resolution wind resource data from NREL and a levelized cost approach - Biomass: supply curves from Oakridge National Laboratories
Background & Objectives • From viewpoint of policymakers, distributional effects of policies are often more important than efficiency considerations (“rectangles trump triangles”) • Many climate policy provisions are designed to blunt the impact of the legislation on lower and middle income households, and to balance regional effects • To date, much of the distributional analysis has been done as a side calculation or in an Input-Output framework thereby neglecting behavioral responses to relative price changes and income effects • USREP analysis is general equilibrium in nature and incorporates heterogeneity
Policy Overview • Three cap and trade proposals analyzed: – Waxman-Markey (WM) – Kerry-Boxer (KB) – Cantwell-Collins (CANT)
• Focus on cap and trade allowance allocation • Other aspects of these proposals not modeled
Policy Modeling Details • All three seek an overall reduction of GHG emissions in the US to ~80% below 2005 levels by 2050 with intervening targets. • Cap and trade components of the bills cover most of the economy’s emissions but not necessarily all of them, with other measures directed toward uncapped sectors. • Assume the national goals are met with a cap and trade system that covers all US emissions except for land use CO2 sources (or sinks). • All of these proposals including banking and limited borrowing provisions. • WM and KB allow offsets; CANT does not. We allow offsets in CANT to focus on distributional differences across proposals
Allowance Allocation in Bills
Waxman Markey Allowance Allocation
Allowance Allocation Incidence Allowance Allocation
Treatment in Model
LDC allowance distribution
Lump-sum transfer based on specific energy consumption
Protection for lowincome households
Lump-sum transfer to households with income less than $30,000
Allocation to affected industries
Lump-sum transfer based on capital income
Technology funding
Allocated to regions based on energy use; within regions allocated to households on a lump-sum basis
See Table 3 in paper for more allocation detail
Allowance Allocation in Bills II KB Scenario
WM CANT Scenario Scenario
Scenarios Scenario
Description
WM_LS
Waxman-Markey. A fraction of the allowance value has to be retained to satisfy revenueneutrality.
KB_LS
Kerry-Boxer. A fraction of the allowance value has to be retained to satisfy revenue-neutrality.
CANT_LS
Cantwell-Collins. A fraction of the allowance value has to be retained to satisfy revenueneutrality.
WM_TAX
Waxman-Markey. Full allowance value is allocated. Revenue-neutrality is achieved by increasing marginal personal income taxes for each region and income class by an equal amount (in percentage points).
CANT_TAX
Cantwell. Full allowance value is allocated. Revenue-neutrality is achieved by increasing marginal personal income taxes for each region and income class by an equal amount (in percentage points).
KB_TAX
Kerry-Boxer. Full allowance value is allocated. Revenue-neutrality is achieved by increasing marginal personal income taxes for each region and income class by an equal amount (in percentage points).
Note: All scenarios assume the medium offset case from the Appendix C of Paltsev, et al. (2009) with identical assumptions about supply and costs of offsets. This corresponds to a 203 bmt cumulative emissions target for 2012-2050.
Emissions
Aggregate Emissions: Reference: 298 bmt Policy: 203 bmt
Aggregate Impacts
Distributional Impacts Over Income WM_LS
CANT_LS
Treatment of Revenue Neutrality Lump-Sum
WM Modeled Here
Tax
Welfare Impacts by Region
WM_LS
Welfare Impacts by Region
CANT_LS
Incidence Drivers • Burden a combination of spending side impacts and income side impacts • Counterfactual analysis to decompose different burden forces – assume identical consumption shares across income groups (isolates income side impacts) – assume identical labor and capital income shares across income groups (isolates spending side impacts)
Fixed Expenditure Shares
Observed Income and Expenditure Shares
Fixed Factor Income Shares WM_LS
Summary: Efficiency • CANT less costly than KB or WM – CANT distributes less to lower income households than WM or KB – Lump-sum distributions to low-income households increase energy demand (income effect) – Lump-sum distributions to higher income households are disproportionately saved – Classic equity-efficiency trade-off – Note: this occurs in the lump-sum scenario
Summary: Distribution • WM and KB appear to overcompensate in early years – low-income households – South Central, Texas, Florida
• CANT more distributionally neutral across income groups and regions • Differences dissipate over time • All three policies modestly progressive in short and long-run • Distributional impacts driven more by variation in income sources than variation in spending patterns
