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Equilibrium Assumptions and Efficiency
Agreements: Macroeconomic Effects of Climate
Policies for UK Road Transport, 2000-2010
Comparing North American and European Approaches to Climate Change
Viessmann European Research Centre, Wilfrid Laurier University, Canada
September 28, 2007
Jonathan Rubin, University of Maine
Terry Barker, University of Cambridge
Outline
•
•
Results
Intro: macroeconomic models of energy
efficiency improvements
– CGE v. Sectoral models
– Integration of top-down, bottom-up
modelling
•
•
•
Modelling the macroeconomic rebound
effect using MDM-E3
Application to transport sector
Findings and conclusions
Results
• Voluntary efficiency agreement
– Positive macroeconomic impacts
• GDP, employment, inflation
– Reductions in energy demand & CO2
• Equivalent fuel taxes (in terms of CO2)
– Without revenue recycling
• Lower GDP & employment
– With revenue recycling that reduces income taxes
• Eliminates negative macroeconomic impacts
• Equity/distributional issues remain
• Inflation neutral scenario, VA & fuel duties
UK Energy Efficiency Policies
• Significant part of 2000 UK Climate Change
Programme and 2003 Energy White Paper
– review of UK CCP launched in Sept 04 - reported
March 2006
• Updated DTI projections for CO2 emissions
(including CCL, 9% renewables, excluding EU
ETS):
1990
Projection for 2010
Target for 2010
‘Carbon gap’
161 MtC
144 - 145 MtC (10% reduction)
129 MtC (20% reduction)
15 – 16 MtC
Effect of Climate Change
Programme Measures
Source: DTI, UK energy and CO2 emissions projections, Feb 2006
Energy Efficiency Policies Included
in Projections
Target sector
Policy/measure
Domestic
Building Regulations
1.3
Energy Efficiency Commitment
1.4
Warm Front
0.4
Appliance Standards and Labelling
0.2
UK ETS
0.3
CCL package
1.1
CCAs
2.9
Building Regulations
0.5
Public Sector
Public Sector measures
0.3
Transport
VA Package
2.3
10 Year Transport Plan
0.8
Business
Total
CO2 savings in 2010
(MtC)
11.5
Global GHG mitigation in context:
energy in world GDP
1970
services
71%
2000
manuf.
21%
energy
4%
agriculture
4%
2100
servic
es
77%
energy
agricul 3%
ture
2%
services
82%
agriculture
2%
energy
manuf.
2%
14%
manuf.
18%
Source:
E3MG2.0
Global GHG mitigation in context:
energy in world GDP
1970
services
71%
2000
manuf.
21%
energy
4%
agriculture
4%
2100
servic
es
77%
energy
agricul 3%
ture
2%
energy
industries
represent
<4% GDP
services
82%
agriculture
2%
energy
manuf.
2%
14%
manuf.
18%
Source:
E3MG2.0
Global GHG mitigation in context:
energy in world GDP
1970
services
71%
2000
manuf.
21%
energy
4%
agriculture
4%
energy
industries
represent
<4% GDP
2100
servic
es
77%
manuf.
18%
energy
agricul 3%
ture
2%
future energy use
is about growth &
investment
services
82%
agriculture
2%
energy
manuf.
2%
14%
Source:
E3MG2.0
Macroeconomic Modeling of
Energy
• CGE models
– treatment of energy as factor of production in
a production function with labor and capital
• Econometric models
– traditional approach with energy demand
equations
– Literature on income and price elasticities
Macroeconomic Costs of Mitigation
• Costs not directly observable from market prices
– outcome of complex energy-environment-economy (E3) system
– involve changes in environment that have no market valuations
– hypothetical: comparison of 2 states of the E3 system over future
years
• Macroeconomic costs usually measured in terms of
future loss of GDP, comparing one hypothetical state
of the world with another
• Debate: are such costs to be offset by ancillary
benefits and benefits from use of tax or emission
permit revenues?
– taxes/auctioned permits may incur high “political” costs
– free allocation of emission permits (as in phase I EU emissions
trading scheme (ETS)) yields no revenues to recycle
Treatment of Technological
Change in Cost Modelling
• Usual assumption in IPCC literature is of
autonomous growth in energy efficiency, constant
across all economies: therefore no effect on
efficiency from stabilisation policies
• However there is good evidence that
– higher real prices of energy increase efficiencies (e.g. Popp,
2002; Jaffe, Newell and Stavins, 2003)
– costs of renewable power fall as markets develop (e.g.
McDonald and Schrattenholzer, 2001)
• New research:
– modelling of endogenous technological change (bottom-up and
top-down) and implications for policy action
– low-carbon paths as low-cost, even beneficial, global options
Induced Technological Change in
Global Climate Models
• Method: introduce R&D and/or learning-by-doing into
costs of energy technologies, so that higher real
carbon prices induce change
• CGE models face special problems
– whole-economy increasing returns are incompatible with a
general solution
– increased substitution possibilities (e.g. to renewable power or
carbon-capture) are typically introduced in the only one sector
(energy)
– economic growth remains largely given by assumption, with
general technological change unaffected by the energy sector
technologies
• An open question: can increased technological
change lead to higher economic growth?
Question: Full Employment of
Labor and “Efficient” Economy?
• Economy-wide studies that assume full
efficiency report that the regulatory policies
incur costs
– Parry and Williams (1999) - CGE compare 8 policy instruments
to reduce CO2
• Find: High costs of the energy efficiency regulations,
exacerbated by tax interaction effects
– Smulders and Nooij (2003) - CGE analyse energy conservation
on technology and economic growth
• Find: Policies that reduce the level of energy use
unambiguously depress output levels
– Pizer et al. (2006) - CGE calibrated to sectoral models of the US
• Find: CAFE standards to be significantly more
expensive than broad carbon taxes.
Alternative Approach
• A main alternative approach: detailed
sectoral studies that feed into a CGE
macroeconomic model
– Roland-Holst (2006) uses a CGE model for
California to assess energy efficiency policies
for CO2 reductions
• Find: CO2-efficiency policies can reduce
transportation CO2 emissions by nearly 6%
and increase Gross State Output by over 2%
The Approach of MDM-E3
(Multisectoral Dynamic Model – w/ EnergyEnvironment-Economy system)
• MDM is a multisectoral regional econometric model of the
UK economy developed in the 1990s
• Equilibrium & constant returns to scale are not assumed
• The solutions are dynamic, integrated and consistent
across the model and submodels
• Energy demand is derived from demand for heat & power
from demand for final products
– No explicit production function
– 2-level hierarchy: aggregate energy demand equations and fuel
share equations
– Aggregate demand affected by industrial output of user industry,
household spending in total, relative prices, temperature,
technical progress indicator, trends, efficiency policies
MDM-E3 Theory and Data
• Econometric, dynamic, structural, post-Keynesian
– based on time series and cross-section data
– cointegration techniques identify long-run trends in 22 sets of equations
– Structural: 50 industries, 13 energy users, 11 energy carriers, 51 HH
categories
• Assumptions
– Social groups (not representative agents) i.e. parameters vary across
sectors and regions
– Variable returns to scale and degrees of competition across sectors
– Path dependency and emphasis on “history” rather than “equilibrium”
– Short-term and long-run solutions
• With induced technological change
– Technological Progress Indicators (TPI) (incl. R&D) in many
equations e.g. in energy-use, export, import, price, employment
equations
Energy Users
1
2
3
4
5
6
7
8
9
10
11
12
13
Energy Submodel
Power Generation
Other Transformation
Energy industries own use
Iron and Steel
Energy-Intensive Industries
Mineral Products
Chemicals
Other Industry
Rail Transport
Road Transport
Water Transport
Air Transport
Domestic Use (Households)
Other Final Demand (including commerce,
government, agriculture and construction)
}
MDM-E3 & Transport
• Top-down macroeconomic model
• Bottom-up transport system efficiency
feedback to macro economy
– Efficiency improvements estimated offline
• Feedback from macro economy not
incorporated in detailed transport sector
The UK Energy System in MDM-E3
top-down driver: demand for energy
Energy
intensive
industry CHP
Other industry
equipment
demand for gas
& electricity
low carbon
process
fuel cells
coal, gas &
electricity
demand for
electricity
Solar
energy
demand for
coal & gas
Electricity
CHP
trade
investment &
regulation
Other energy
supply
CHP
demand for
gasoline
Micro CHP
Energy
demand
Commercial
buildings
CHP
UK &
imported coal
Transport
vehicles onboard
demand for gas
& electricity
prices &
availability
Human
energy
(walking,
cycling)
Household
appliances &
dwellings Micro
CHP
Solar
energy
UK & imported
oil & gas
investment &
trade
investment in
new technologies
Energy from
Wind, wave,
waste, landfill
tidal energy
mines
bottom-up driver: supplies of solar & human energy and of technologies
MDM-E3 : Aggregate EnergyDemand Equations
• Autoregressive distributed lag (ARDL) model
– energy consumption (Et) depends on
– energy price (Pt), output (Yt), temperature (TEt) & lagged values:
Et=a0+a1Pt+ a2Yt + a3TEt + a4Et-1 + a5Yt-1 + a6Pt-1 + a7TEt-1+εt
• Re-parameterisation give error-correction mechanism (ECM)
model:
ΔEt=b0+b1Δ Pt+ b2ΔYt + b3ΔTEt + b4(Et-1 – b5Pt-1 – b6Yt-1- b7TEt-1) +
εt
• Augmented by time trends and/or accumulated investment to
represent energy efficiency improvements
• ECM model distinguishes between long-term and adjustment
parameters
UK Transport Efficiency Policies
• Voluntary Agreements on vehicle CO2 emissions
reductions
– European Commission and the European, Japanese and Korean
Automobile Manufacturers Association to reduce average CO2
emissions from their new cars to 140 g/km by 2008 -2009
– Targets are expected to be met via fuel saving technologies
• Company Car Tax
– Company cars are taxed on a percentage of their list price
according to one of 21 CO2 emissions bands.
• Graduated Vehicle Excise Duty
– GVED - the annual vehicle tax charge – new cars placed in one
of four VED rate bands according to their CO2 emissions
• Projected to reduce transport energy use by 3.1 mtoe
and lower GHG’s by 2.3 MtC
Aside: Canadian Voluntary
MOU
• Commits the Canadian automotive
industry to 5.3Mt reduction in GHG
emissions (CO2e) from the light duty
vehicle sector by 2010
• Reference case GHGs for the light duty
vehicle sector in 2010 are 90.51 Mt of
CO2e.
Rebound Effects
Direct, indirect, economy-wide
• Three direct rebound effects
–
–
–
–
More mileage driven
More comfort taking (air-conditioning)
Shift to larger vehicles
These offset 25% of the estimated gross energy
savings from the policies
• Indirect and economy-wide (result of model)
– Indirect and economy-wide rebound 7% beyond
direct
• Total: 32%
Impacts of VAs on Key
Macroeconomic Variables
Sector
2000
2005
2010
-0.29
-1.86
-2.89
-0.18
-1.20
-1.81
-0.26
-1.61
-2.42
-0.17
-1.11
-1.80
0.05
0.43
0.48
-0.05
-0.77
-1.28
0.00
0.19
0.28
0.00
0.15
0.22
Final Energy Demand (mtoe, level)
Final Energy Demand (% level)
CO2 Emissions (mtC, level)
CO2 Emissions (%, level)
GDP (%, level)
GDP Deflator (%, level)
Employment (%, level)
Public Sector Borrowing (%GDP, level)
Efficiency v. Fiscal Policies to
Reduce Transport CO2 Emissions
Impact
VAs
Additional fuel duties (%/ year 2000-10)
0
Change in standard rate of Income Tax (%)
0
Final Energy Demand (%)
CO2 Emissions (%)
GDP (%)
GDP Deflator (%)
Employment (%)
-1.81
-1.80
0.48
-1.28
0.28
Efficiency v. Fiscal Policies to
Reduce Transport CO2 Emissions
Impact
VAs
Fuel Duties
Additional fuel duties (%/ year 2000-10)
0
4.85
0
0
-1.81
-2.25
-1.80
-1.81
0.48
-0.84
-1.28
3.16
0.28
-0.52
Change in standard rate of Income Tax (%)
Final Energy Demand (%)
CO2 Emissions (%)
GDP (%)
GDP Deflator (%)
Employment (%)
Efficiency v. Fiscal Policies to
Reduce Transport CO2 Emissions
Impact
VAs
Fuel Duties
Fuel Duties Revenue
Recycling
Additional fuel duties (%/ year 2000-10)
0
4.85
3.850
0
0
-3.540
-1.81
-2.25
-1.86
-1.80
-1.81
-1.82
0.48
-0.84
-0.43
-1.28
3.16
-1.29
0.28
-0.52
0.00
Change in standard rate of Income Tax (%)
Final Energy Demand (%)
CO2 Emissions (%)
GDP (%)
GDP Deflator (%)
Employment (%)
Efficiency v. Fiscal Policies to
Reduce Transport CO2 Emissions
Impact
VAs
Fuel Duties
Inflation-neutral: VAs &
Fuel Duties
Additional fuel duties (%/ year 2000-10)
0
4.85
2.025
0
0
0
-1.81
-2.25
-2.37
-1.80
-1.81
-2.42
0.48
-0.84
0.24
-1.28
3.16
0.00
0.28
-0.52
0.04
Change in standard rate of Income Tax (%)
Final Energy Demand (%)
CO2 Emissions (%)
GDP (%)
GDP Deflator (%)
Employment (%)
Impacts of VA’s: Sensitivity
Analysis (2010)
Sector
Base
Higher Oil &
Gas
Higher EU
ETS
-0.181
-1.92
-1.88
-0.180
-1.94
-1.97
0.48
0.46
0.48
-1.28
-1.35
-1.31
0.28
0.28
0.28
0.22
0.23
0.22
Final Energy Demand (% level)
CO2 Emissions (%, level)
GDP (%, level)
GDP Deflator (%, level)
Employment (%, level)
Public Sector Borrowing (%GDP, level)
Discussion & Summary
• VAs 1.8% CO2 Reduction
• VAs v. fuel duties
– Achieving the same CO2 reduction, no recycling of revenues, no
monetary responses
– Energy use in the transport sector goes down more with fuel
duties than with the VAs, but at a cost of loss in GDP of 0.84%
instead of a gain of 0.48%.
– Rate of duty on road fuels has to rise by 4.85% a year (real)
– Effects on inflation and growth is very marked
• Fuel duties increasing prices
• Employment is reduced by 0.5%
• VA’s & smaller fuel duties for inflation-neutrality
– More effective in reducing energy and emissions
Discussion & Summary
• Our approach enables a partial integration of top-down
macroeconomic aspects and bottom-up energy systems
– We do not assume that resources are used at full economic
efficiency
• Limitations
– Bottom-up energy savings and direct rebound effects had to be
imposed on the model
– These are below the level of disaggregation currently in the
model
– No feedbacks incorporated from the wider macroeconomic
effects to the bottom-up energy savings
• Currently working to develop greater sectoral detail for
better integration with the macro model