Transcript “carbon price only” policy - Centre International de Recherche

Urbanization and low-carbon growth pathways
Modeling the interactions
between energy and real estate prices
Henri WAISMAN, Jean-Charles HOURCADE
([email protected])
Urban Energy and Carbon Modeling in Rapidly Urbanizing World
IIASA - Vienna, 10/11 March 2011
1
20 years of a surprising absence
in energy-economy modeling
 What was “obvious” in the early nineties
 Large competitive advantage of oil-based motor and fuels over substitutes
(biofuels, electricity, hydrogen)
 Apparent low price elasticity of mobility and energy demand for transportation
 Mobility and transportation are driven by other “signals” than energy prices
 What should have been done
A strong collaboration between energy, transportation and urban economists
(Hourcade ,1993)
 What happened :
A methodological lock-in due to three converging intellectual dynamics:
 The ‘Elephant and rabbit stew metaphor’ legitimates to treat the energy sector
independently from the rest of the economy (Hogan & Manne 1977)
 The TD/BU controversy about the energy efficiency gap focused the debate on
technological efficiency
 Extrapolating electricity optimization models to the entire energy system
 The overwhelming majority of energy-economy models adopt carbon price as the
only driver of decarbonizing economies.
The Impasse of the « carbon price only » frameworks
Together with political vagaries, harsh lobbying and weak economic reasoning,
there may be an economic rationale behind the difficulties in making a deal around
policy architectures built around a “pure” pricing of carbon
 A carbon price at 50$/tCO2
 doubles the cost of cement in India and hurts segments of the steel industry in the EU
 …but hardly affects mobility demand (low price-elasticity)
 Consequences for cost assessment of climate policies
 Underestimated : an often ignored caveat of energy-economy modeling
« Most models use a global least cost approach to mitigation portfolios with universal
emissions trading, assuming transparent markets, no transaction cost, and thus perfect
implementation of mitigation measures throughout the 21st century. » (AR4, WGIII )
 Overestimated : in absence of complementary policies in the transport sector
• very high carbon prices are needed to curve down transport emissions
(low elasticity of mobility demand to energy prices)
• other determinants : non-energy prices and non price signals
(real estate prices, risk-adjusted capital cost, infrastructure policies)
Housing and Energy prices:
Two contrasted dynamics
Housing real sales price
Housing services real price
Gasoline real price
Real prices time series 1960-2006
indexes 100 in year 2000
Real price time series 1960-2006
(index 1=2000 level)
1.8
1.7
1.6
1.5
1.4
1.3
1.2
1.1
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
Housing real sales price
Housing service real price
Gasoline real price
2006
2005
2004
2003
2000
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1990
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1991
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1980
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1980
1979
1978
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1976
1975
1974
1973
1970
1972
1971
1970
1969
1968
1967
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1965
1964
1963
1962
1960
1961
1960
0.0
2006
Intertwined methodological issues to be solved
 Modeling 2nd best economies with
 Imperfect foresight
 Inertia of capital stocks
 Market imperfections (underutilization of production factors)
 Representing structural change driving the decoupling between growth and energy
 Beyond pure energy efficiency, the fundamentals of the material content of the
economy C-T-L (Hourcade 1993):
 Consumption styles (preferences)
 Technical potentials (resource and technology availability, asymptotes)
 Location patterns
 Capturing the interplay between energy prices, land prices and the growth engine
(productivity, demography, savings) in an opened economy
 Endogenizing the urbanization process and location decisions in urban/rural areas
IMACLIM, an attempt to model 2nd best economies
in a General Equilibrium framework
Transport
Transport
Economic signals
(prices, quantities,
Investments)
(reduced forms of BU models)
Oil
under constraints
Dynamic sub-modules
Industry
Agricolture
...
Electricity
Energy
Static Equilibrium (t)
Static Equilibrium (t+1)
under updated constraints
Technical and structural
parameters
(i-o coefficients, population,
productivity)
 Hybrid matrixes in values, energy and « physical » content (Mtoe, pkm)
 Secure the consistency of the engineering based and economic analyses
 Explicit accounting of inertias on equipement stocks
 Technical asymptotes, basic needs
 Solowian growth engine in the long run but transitory disequilibrium
 Unemployment, excess capacities
 Investments under imperfect foresight (informed by sectoral models)
 Trade and capital flows under exogenous assumption about debts
Macroeconomic assessment of
a “carbon price only” policy (w/o international transfers)
700
Typical cost profile for a 450ppmCO2 scenario
Carbon price ($/tCO 2)
600
2010
0%
400
-1%
(in % of BAU GDP)
500
300
200
100
0
2010
2030
2050
2070
2090
-2%
-3%
-4%
-5%
-6%
2030
2050
2070
2090
At the roots of our “bad news”
Significant short-term losses:
• Inertia in installed capital and imperfect foresight limit the pace of
decarbonization, and requires high carbon prices
• Increased production costs transmitted to consumers
• Inertia in changing households equipment reinforces the loss of purchasing
power
• Macroeconomic feedbacks (unemployment, lower wages, lower
consumption…)
Long-term losses:
• Inertia of infrastructures, location choices, urban forms
• Rebound effect of mobility needs requires very high carbon prices in the
second half of the century
Stylised facts in the transportation sector
• Rebound effect due to energy efficiency improvement,
• Demand induction by transportation and urban infrastructures,
• Drivers of demand evolve over different time scales:
• Location of production, consumption and housing (decades and
sometimes one century)
• Infrastructures (decades)
• Private equipments (few years)
• Energy and real estate prices (volatile)
Inertia, ‘lock-in’, risks of maladjustments
Modeling modal choice and mobility demand
Utility maximization:
With
U
i
Ci  bni 


goods i
S j  bn j 


energy services j
j
.
S Mobility  CES  pkmair , pkm public , pkmcars , pkmnon motorized 
Under two constraints:
4 modes
Income   pi  Ci  p public  pkmpublic  pair  pkmair 
i
pkm j
Tdisp 
 
Modes j
0
 j  u  du

Energies Ei
pEi   pkmcars   Eicars 
Infrastructure investments and congestion effects
Utility maximization:
With
U
i
Ci  bni 


goods i
S j  bn j 


energy services j
j
.
S Mobility  CES  pkmair , pkm public , pkmcars , pkmnon motorized 
0,25
Under two constraints:
(h/km).
PKTpkm
Time
Timeperper
4 Modes
0,2
Income   pi  Ci  p public  pkmpublic 0,15
pair  pkmair 
i
pkm j
Tdisp 
 
Modes j
0
 j  u  du

pEi   pkmcars   Eicars 
Energies Ei
0,1
0,05
0
PKT
pkm
Capacity
Infrastructure investments
Transport infrastructure and cost of climate policies
 Redirection of investments towards low-carbon transport infrastructure
 Relocation of production/distribution towards less transport-dependent processes
700
Carbon price ($/tCO2)
Typical cost profile for a 450ppmCO 2 scenario
600
2010
0%
500
-1%
(in % of BAU GDP)
400
300
200
100
0
2010
2030
2050
2070
-2%
-3%
-4%
-5%
2090
-6%
2030
2050
2070
2090
Urban organizations and constrained mobility
Utility maximization: U
With

i
Ci  bni 


goods i
S j  bn j 


energy services j
j
.
S Mobility  CES  pkmair , pkm public , pkmcars , pkmnon motorized 
Under two constraints:
4 modes
Income   pi  Ci  p public  pkmpublic  pair  pkmair 
i
pkm j
Tdisp 
 
Modes j
0
 j  u  du

Energies Ei
Constrained mobility
(commuting)
pEi   pkmcars   Eicars 
IMACLIM, a tool to investigate the interplay between
Systems of Cities in Interaction and growth patterns
3.
Capture the feedbacks on growth patterns
Aggregate
Economic
variables:
Price, Wage,
Profit,
Production
Spatial disaggregation
into a system of cities in interaction
2
3
Migration of firms
and population
Re-aggregation
of technical coefficients
Static Equilibrium t+1
Static Equilibrium t
1
Oil
Represent the spatial dynamics among a number of urban agglomerations
sport
T ran
sport
T ran
2.
E l ectricity
E n ergy
Disaggregate the national economy into a System of Cities in Interaction
In dustry
A g ricolture
...
1.
Transport basic needs, productivity,
investment costs
The system of cities in interaction

Spatial structure of cities
 Monocentric and axisymmetrical
 Firms are clustered into the adimensionnal centre
 Spatial distribution of households : tradeoff on housing/commuting costs
 j  p j q j  R j ( x) j ( x)  2 j .w j x
income
consumption
housing costs
Households/ Workers
Firms
commutingcosts
x
0

Multi-level interactions
 Inter-city trade (iceberg structure)
 Monopolistic competition & imperfect substitution among varieties
 Agglomeration effect on production
 Spatial dynamics
 Differentiated attractiveness of cities (investment profitability)
 Migration of investments towards the most attractive cities
 Migration of firms and associated labor force
dj
Calibration at base year 2001:« Revealed » parameters
Calibration on 20 largest US cities (52% of sectoral GDP)
 « Empirical data » : Population, Density, Production, Wage
Labor productivity in 2001
(production units per worker)
0,16
0,14
Attractiveness index in 2001
0,12
0,1
0,9
0,08
0,8
0,06
0,04
0,7
0,02
0,6
0
NY
LA
CH
SF
PHI BOS DET DAL WSH MIA
Unitary commuting cost in 2001
(km-1 )
0,5
0,4
0,3
0,005
0,2
0,004
0,1
0,003
0
0,002
NY
0,001
0
NY LA
CH
SF PHI BOS DET DAL WSH MIA
LA
CH
SF
PHI BOS DET DAL WSH MIA
A consistent view of possible urban dynamics
Share of urban production
among the 10 largest agglomérations
Population migration between 2001 and 2050
(thousands)
30%
9000
25%
8000
7000
20%
6000
15%
5000
10%
4000
3000
5%
2000
0%
NY
LA
CH
SF
PHI
2001
BOS
DET
DAL WSH MIA
1000
0
NY
2050
LA
CH
SF
PHI
Spatial extension
(km)
30,0
25,0
20,0
15,0
2001
2050
10,0
5,0
0,0
NY
LA
CH
SF
PHI BOS DET DAL WSH MIA
BOS
DET
DAL WSH MIA
Urban densification policy and CO2 emissions
Spatial extension in 2050
Spatial policy to limit urban sprawl
 reduction of constrained mobility
(20 cities, only commuting)
30
25
20
15
10
5
0
NY
LA
CH
SF
BAU
0,0%
-0,5%
2010
2020
2030
2040
BOS
DET
DAL
WSH
densification policy
2050
2000
2010
2020
2030
2040
1,0%
0,5%
0,0%
-1,0%
-0,5%
-1,0%
-1,5%
-1,5%
-2,0%
-2,0%
MIA
Kaya identity for automobile CO2 emissions
Variation of automobile CO2 emissions
under densification policy
2000
PHI
-2,5%
-3,0%
-2,5%
passenger-km
-3,0%
unitary energy consumption
carbon intensity
2050
Urban densification policy and costs of climate policies
 Carbon price: 380$/tCO2  360$/ tCO2 (-5%)
 Average land price within urban areas: +1.5%
Economic activity : GDP losses discounted at 3%
Carbon price
only
Carbon price
+
densification policy
450ppm CO2
-0.16%
-0.15%
410ppm CO2
-0.84%
-0.67%
Conclusion
IMACLIM, a methodological tool for consistency checks between expertises
 material content of economic growth
 transport, infrastructure policies and mitigation
 endogenizing urban systems in a global energy-economy model
Quantification of the impact of urban policies on carbon and real estate prices
 important complement to carbon pricing for ambitious mitigation objectives
 not only for carbon mitigation : political implementation, social dimensions
(welfare effects, distributional issues)
On-going research:
 real estate markets and scarcity rents
 interplay between transport infrastructure, modal choice and the dynamics of
real estate at the local level
linkages between labor productivity and agglomeration effects