Distributional Implications of Proposed US Greenhouse Gas Control - - PowerPoint PPT Presentation

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 state-

level 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 high- resolution 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) California (CA) Florida (FL) New York (NY) New England (NENGL) South East (SEAST) North East (NEAST) South Central (SCENT) North Central (NCENT) Mountain (MOUNT) Pacific (PACIF) Non-Energy Agriculture (AGRIC) Services (SERV) Energy-Intensive (EINT) Other Industries (OTHR) Transportation (TRAN) Energy Coal (COAL) Conventional Crude Oil (OIL) Oil from Shale (OIL) Refined Oil (ROIL) Natural Gas (GAS) Electric: Fossil (ELEC) Capital Labor Land Crude Oil Shale Oil Natural Gas Coal Nuclear Hydro Wind 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

Income class Annual Income (2006$) Cumulative Population for whole US (in %)a hhl hh10 hh15 hh25 hh30 hh50 hh75 hh100 hh150 Less than $10,000 $10,000 to $15,000 $15,000 to $25,000 $25,000 to $ $30,000 $30,000 to $50,000 $50,000 to $75,000 $75,000 to $100,000 $100,000 to $150,000 $150,000 plus 7.3 11.7 21.2 31.0 45.3 65.2 78.7 91.5 100.0 Table 1. Income Classes Used in the USREP Model and Cumulative Population.

a Based on Consumer Expenditure Survey Data for 2006.

MIT USREP Model: Key Features

• Production and consumption technologies are represented by nested constant-

elasticity-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
• f 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

• ther 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 low- income 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

WM Scenario KB Scenario CANT Scenario

Scenarios

Scenario Description WM_LS Waxman-Markey. A fraction of the allowance value has to be retained to satisfy revenue- neutrality. 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 revenue- neutrality. 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

• ffsets. 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 Tax WM Modeled Here

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)

WM_LS Observed Income and Expenditure Shares Fixed Expenditure Shares Fixed Factor Income Shares

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