Download What to Do? Does Science have a Role?

What to Do?
Does Science have a Role?
Klaus Hasselmann
Max-Planck-Institut for Meteorology, Hamburg,
European Climate Forum
Heraeus Seminar Energy and Climate
A Physics Perspective on
Energy Supply and Climate Change
Prediction, Mitigation and Adaptation
26 - 29 May 2008, Physikzentrum Bad Honnef,
Discussion topics
Interconnections:
Climate change
adaptation
policy
mitigation
What to Do?
Does Science have a Role?
Hasselmann and Barker,
Change, in press:
Climatic
• IPCC Working Group 3 (in contrast to WG1)
has had very little political influence
• The influential Stern Report, for example,
developed its political recommendations
independently
• Needed is a new UN “Climate Policy Panel”
that interacts continuously with policymakers
• But to be effective a Climate Policy Panel will
need to develop a new suite of Integrated
Assessment (coupled climate-socioeconomic) models
No longer disputed:
• Climate change is real
• The costs of unregulated climate change greatly
exceed the costs of mitigation
• There exist various technologies that, in
combination, could limit global warming to
acceptable levels (< 20C above pre-industrial)
• The estimated costs (-1% to 4% of GDP), although
appearing high today (a few trillion $), are quite
affordable in the long term (a delay in long-term
global growth, if at all, of a few months to a year)
Strongly disputed:
• How best transform our present
unsustainable global economic system based
on fossil fuels into a sustainable carbon-free
system?
• Do scientists have the right tools to provide
useful signals that will be heard in the noisy
debate over conflicting stakeholder interests,
divergent national goals and the stresses of
globalization?
Answers:
Traditional (main stream) economists:
Yes, the available standard generalequilibrium macro-economic models are fine.
Physics-based economists:
No, we need a new generation of dynamic
multi-agent dynamic models.
Overview
• Available technologies for closing the wedge
between the BAU (Business as Usual) emissions
trajectory and the sustainable emissions goal
• Proposed strategies for closing the wedge
• Traditional versus multi-agent economic models
- with three examples of the latter:
1) Ginti’s model of the “invisible hand”
2) A Multi-Actor Dynamic Integrated Assessment
Model (MADIAM) of climate policies
3) A climate-policy evolution model
• Conclusions
Overview
• Available technologies for closing the wedge
between the BAU (Business as Usual) emissions
trajectory and the sustainable emissions goal
• Proposed strategies for closing the wedge
• Traditional versus multi-agent economic models
- with three examples of the latter:
1) Ginti’s model of the “invisible hand”
2) A Multi-Actor Dynamic Integrated Assessment
Model (MADIAM) of climate policies
3) A climate-policy evolution model
• Conclusions
Filling the wedge between projected BAU emissions
and a sustainable emissions path (T< 20C)
Business as Usual
Energy
efficiency:
zero mean
cost
Low fruits
renewables
sustainability path
High fruits
(solar, and
unproven or
controversial
options: CCS,
nuclear, fusion,
…)
Overview
• Available technologies for closing the wedge
between the BAU (Business as Usual) emissions
trajectory and the sustainable emissions goal
• Proposed strategies for closing the wedge
• Traditional versus multi-agent economic models
- with three examples of the latter:
1) Ginti’s model of the “invisible hand”
2) A Multi-Actor Dynamic Integrated Assessment
Model (MADIAM) of climate policies
3) A climate-policy evolution model
• Conclusions
Basic climate policy instruments:
1. Whip: internalization of external costs (carbon
price)
2. Carrot: subsidies (societal investments that are
unprofitable for individual investors – bridging
the difference between low discount rates
appropriate for public investments and high
discount rates demanded by private investors)
3. Regulatory framework: for sectors that are not
amenable or sufficiently responsive to marketbased instruments (automobile emissions,
building insulation, etc.)
4. Technical and financial transfer from rich to poor
countries
fossil energy
low-fuits
renewables
energy costs
climate damage
costs
high-fruits
renewables
(solar energy)
fossil energy
1. whip
low-fruits
renewables
(Kyoto, carbon price:
internalize external costs)
energy costs
climate damage
costs
high-fruits
renewables
(solar energy)
fossil energy
1. whip
low-fruits
renewables
high-fuits
renewables
(solar energy)
(Kyoto, carbon price:
internalize external costs)
low-cost renewables
become competetive
energy costs
climate damage
costs
remain noncompetitive
fossil energy
1. whip
low-fruits
renewables
high-fruits
renewables
(solar energy)
(Kyoto, carbon price:
internalize external costs)
low-cost renewables
become competetive
remain noncompetitive
energy costs
climate damage
costs
problem: limited
abatement capacity!
fossil energy
1. whip
low-fruits
renewables
(Kyoto, carbon price:
internalize external costs)
high-fruits
renewables
(solar energy)
2. carrot
(Post-Kyoto: subsidies)
price reduction
low-cost renewables
become competetive
energy costs
climate damage
costs
both become
competitive!
Basic climate policy instruments:
1. Whip: internalization of external costs (carbon
price)
2. Carrot: subsidies (societal investments that are
unprofitable for individual investors – bridging
the difference between low discount rates
appropriate for public investments and high
discount rates demanded by private investors)
3. Regulatory framework: for sectors that are not
amenable or responsive to market-based
instruments (automobile emissions, building
insulation, etc.)
4. Technical and financial transfer from rich to poor
countries
8
TC/yr
BAU per capita emissions (speculative)
USA
6
USA
EU+Japan
4
EU+
Japan
China
World
2
India
World
China
India
2000
Sustainability
GOAL
2050
2100
8
TC/yr
6
Convergence and contraction paths
Achievable only with significant
N-S transfer of investments and
technology
USA
USA
EU+Japan
4
USA
EU+
Japan
China
2
World
China
India
2000
World
EU+Japan
India
China
Sustainability
GOAL
Indien
2050
2100
Challenge for global climate policy: arrive at an
equitable international agreement structured on
a combination on the four basic instruments:
1. carbon price
2. subsidies
3. regulatory framework
4. technical and financial transfer from developed
to emerging and developing countries
Task for science:
Which type of coupled climate-socio-economic
(IA) models should one apply to assess the
impact of alternative climate policies?
Overview
• Available technologies for closing the wedge
between the BAU (Business as Usual) emissions
trajectory and the sustainable emissions goal
• Proposed strategies for closing the wedge
• Traditional versus multi-agent economic models
- with three examples of the latter:
1) Ginti’s model of the “invisible hand”
2) A Multi-Actor Dynamic Integrated Assessment
Model (MADIAM) of climate policies
3) A climate-policy evolution model
• Conclusions
Traditional coupled climate-economic
(integrated assessment-IA) model
climate
policy
regulatory
instruments
scenario
predictions
climate
system
ghg emissions
economic
system
impacts on
production,welfare,…
Single-actor “invisible hand“ establishes market equilibrium
Shortcomings of economic equilibrium models:
• exclusion of important dynamical processes
(technological change, structural unemployment,
rich-poor inequalities, business cycles, financial
instabilities, globalization adjustments, ……)
• inadequate representation of divergent interests
between different actors (“tragedy of the commons”
conflict between individual goals and societal
responsibilities – in particular: climate , actordependent discount factors, business-labor
relations, trade agreements,,....)
• inadequate treatment of equity issues (burden
sharing, rich-poor inequalities, conflict potential,
terrorism, …)
Multi-actor integrated assessment model
climate
policy
scenario
predictions
climate
system
Actors:
governments, voting
public, media, CEOs,
consumers, firms,
workers, …
ghg emissions
regulatory
instruments
economic
system
impacts on
production,welfare,…
Multi-actor dynamic evolution, market response actor dependent
Historical interjection:
Four stages in the development of economic
theory (a physicist’s view).
1. Verbalisation (story telling)_
Adam Smith (1723-1790), David Ricardo (1772-1823)
Karl Marx (1818-1883), John Maynard Keynes (1883-1946)
Joseph Schumpeter (1883-1950), Milton Friedman (1912-2006),...
2. Optimization (marginalization)
Leon Walras(1834-1910), Kenneth Arrow (1921- ) ,
Gerard Debreu (1921-2004), Lionell McKenzie (1919-),...
3. Game theory (interactions between a few players)
John von Neumann (1903-1958), John F. Nash (1928-),...
4. Simulation (continuous dynamics, multi-agent)
Meadows et al, Limits to Growth (1972); Epstein and Axtell,
Growing Artifical Societies (1996) (Sugarscape); John Sterman,
Business Dynamics (2000); Eric Beinhocker, The Origin of Wealth
(2006)....
Historical interjection:
Four stages in the development of economic
theory (a physicist’s view).
1. Verbalisation (story telling)_
Adam Smith (1723-1790), David Ricardo (1772-1823)
Karl Marx (1818-1883), John Maynard Keynes (1883-1946)
Joseph Schumpeter (1883-1950), Milton Friedman (1912-2006),...
2. Optimization (marginalization)
Leon Walras(1834-1910), Kenneth Arrow (1921- ) ,
Gerard Debreu (1921-2004), Lionell McKenzie (1919-),...
3. Game theory (interactions between a few players)
John von Neumann (1903-1958), John F. Nash (1928-),...
quantification
4. Simulation (continuous dynamics, multi-agent)
Meadows et al, Limits to Growth (1972); Epstein and Axtell,
Growing Artifical Societies (1996) (Sugarscape); John Sterman,
Business Dynamics (2000); Eric Beinhocker, The Origin of Wealth
(2006)....
Economic theory is in the process of a radical
paradigm shift from
“traditional economics”
based on a combination of verbalized descriptive
concepts, economic equilibrium analyses and
basic game theoretical elements to
“complexity economics”
based on computer simulations of multi-agent
interactions in a dynamically evolving system.
This raises two questions:
1) The emergence problem;
How do macro-economic structures emerge from
the complex micro-economic interactions of
many agents pursuing different goals?
2) The parametrization problem:
How can one represent the dynamics of macroeconomic systems in terms of the interactions
between a small set of aggregated agents?
And in the present context: can one apply
such models to assess the impacts of climate
policies on the global socio-economic system?
This raises two questions:
1) The emergence problem;
How do macro-economic structures emerge from
the complex micro-economic interactions of a
multitude of agents pursuing different goals?
2) The parametrization problem:
How can one represent the dynamics of macroeconomic systems in terms of the interactions
between a small set of aggregated agents?
And in the present context: can one apply
such models to assess the impacts of climate
policies on the global socio-economic system?
Overview
• Available technologies for closing the wedge
between the BAU (Business as Usual) emissions
trajectory and the sustainable emissions goal
• Proposed strategies for closing the wedge
• Traditional versus multi-agent economic models
- with three examples of the latter:
1) Ginti’s model of the “invisible hand”
2) A Multi-Actor Dynamic Integrated Assessment
Model (MADIAM) of climate policies
3) A climate-policy evolution model
• Conclusions
Herbert Gintis, The dynamics of general equilibrium,
The Economics Journal, 117, 1280-1309 (Oct. 2007)
(Simulations kindly provided by Steffen Fuerst, PIK)
Question: How does the “invisible hand” of Adam Smith
lead to an equilibrium price of goods in which supply and
demand are exactly balanced? (The basic credo of
economic equilibrium theory)
Embarrassing counter-example, Scarf (1960): three
agents offering and demanding three different goods result
in a periodic non-equilibrium price attractor.
Gintis’ approach: a large number of producers and
consumers interacting individually with each other.
Agent type 1: 140 Firms (on average):
- produce 10 different goods,
- set (individual) prices,
- take up credit (from a “central authority“),
- pay taxes,
- imitate successful competitors,
- can go bankrupt (to be replaced by newcomers)
Agent type 2: 5250 workers/consumers/shareholders:
- work or become unemployed (receiving wages or
unemployment benefits)
- consume goods
- buy shares
Agent type 3: one “central authority“:
- imposes and distributes taxes
- creates and lends money/ accepts savings
Results:
• Establishment of a statistical equilibrium, with random
fluctuations about the mean state - although all trades
were based on local information only: a nice confirmation
of the “invisible hand“
• But: Are the actor strategies realistic? No business cycles,
recessions, financial instabilities, technological change,
structural unemployment, social inequalities,....
• Clearly, we have some way to go to construct reasonably
realistic multi-actor economic models!
• Nevertheless: a historical aside on the rate of progress:
- Adam Smith: “The Wealth of Nations“: 1776 ;
- Herbert Gintis: The Economic Journal, October, 2007
This raises two questions:
1) The emergence problem;
How do macro-economic structures emerge from
the complex micro-economic interactions of a
multitude of agents pursuing different goals?
2) The parametrization problem:
How can one represent the dynamics of macroeconomic systems in terms of the interactions
between a small set of aggregated agents?
And in the present context: can one apply
such models to assess the impacts of climate
policies on the global socio-economic system?
Overview
• Available technologies for closing the wedge
between the BAU (Business as Usual) emissions
trajectory and the sustainable emissions goal
• Proposed strategies for closing the wedge
• Traditional versus multi-agent economic models
- with three examples of the latter:
1) Ginti’s model of the “invisible hand”
2) A Multi-Actor Dynamic Integrated Assessment
Model (MADIAM) of climate policies
3) A climate-policy evolution model
• Conclusions
Example 2:
A Multi-Actor Dynamic Integrated Assessment Model
(MADIAM)
Hoos, G, V. Barth and K. Hasselmann, Ecological
Journal , 54, 306-327, 2005
Attempt to capture the emergent structures of a
macro-economic system in terms of the interactions
of a few aggregated actors (firms, households,
governments, banks) pursuing different goals
– and to apply this to climate policy assessment.
MADIAM:
Multi-Actor Dynamic Integrated Assessment Model
Climate
NICCS:
Non-linear Impulse
response coupled
Carbon cycleClimate System
Policy +
CO2 emissions Economics
MADEM:
Multi-Actor
Dynamic
Economic
Model
Climate change :
space-time fields of
temperature, precipitation,
cloud cover, sea level, etc
MADEM (Multi-Actor Dynamic Economic Model)
Actors
Goals
Firms
Workers
Governments
Banks
Maximize profits
Maximize wages
Maximize GDP
Stabilize money supply
All actors strive to achieve individual goals while
jointly committed to avoiding dangerous climate
change (classical “tragedy of the commons”
conflict)
MADEM mathematical structure:
state variables
x = (xi)
control variables
z = (zi) = Ci(x)
( Ci(x) define the actors’ control strategies)
Prognostic equations:
dxi/dt = Fi (x,z) = Gi (x)
10 state variables xi :
physical capital, productivity, employed
workers, wages, household and firm savings,
government budget deficit, energy intensity,
carbon intensity, fossil resources
Control parameters:
Firms:
Investments in physical capital
Investments in productivity
Investments in emissions reduction
Credit uptake/Savings
Consumers/Wage earners:
Wage negotiations
Credit uptake/Savings
Consumer preferences (climate friendly or
climate adverse goods)
Governments:
Emissions tax
Recycled taxes (in consumption or subsidies in
renewables)
Principal driver of economic growth:
Investments in technological change
Firms strive to escape the erosion of profits through
the pressures of competition (increasing wage levels,
diffusion of technological advantages) by continually
investing in technology and know how (human
capital).
Structural unemployment arises when it is more
profitable for firms to invest in productivity
(technology - with associated reduction in
employment) than in physical capital
(Basic idea expressed by classical economists of all
persuasions - Adam Smith, Karl Marx, Joseph
Schumpeter, ... – but ignored in traditional economic
equlibrium models)
BAU / MM (Moderate Mitigation)
28
800
CO2 emissions
3,0
CO2 concentration
Global Mean
Temperature Change
24
8
1,5
1,0
MM
400
MM
time [y]
0
20
40
time [y]
300
60
80
100
18
Production
0
20
40
60
80
100
10
8
6
4
Profits
16
12
BAU
10
MM
8
6
0
10
BAU
8
MM
0
20
40
60
80
100
6
0
20
40
60
8
6
4
100
80
60
MM
40
2
time [y]
0
20
40
60
80
100
0
100
20
40
60
80
100
Savings
16
12
10
BAU
8
6
BAU
MM
4
MM
2
time [y]
0
80
MM
time [y]
14
20
BAU
0
normalized climate damages
10
BAU
0
Climate damages
120
normalized net carbon eff.
12
100
18
Net carbon efficiency
14
80
2
time [y]
140
16
60
4
0
18
40
Wages
12
2
time [y]
20
16
4
2
0
14
normalized profits
12
time [y]
18
14
normalized production
14
MM
0,0
18
16
BAU
0,5
4
0
Temperature [C]
500
2,0
BAU
normalized wages
12
600
2,5
normalized savings
Emissions [Gt C]
16
BAU
Concentration [ppmV]
700
20
0
20
40
60
80
100
time [y]
0
0
20
40
60
80
100
ITC (Induced Technological Change)
28
800
CO2 emissions
3,0
CO2 concentration
Global Mean
Temperature Change
Emissions [Gt C]
BAU
8
MM
s averate 10%
500
Poly. (BAU)
MM
1,5
ITC
1,0
Poly. (MM)
Poly. (s averate 10%)
MM
400
ITC
4
time [y]
0
0
2,0
BAU
20
40
60
80
100
time [y]
0
20
40
Production
60
80
100
6
5
4
3
7
6
5
4
BAU
3
ITC
2
1
-1
20
40
6
5
4
3
60
0
20
40
80
100
0
150
MM
100
deviation from
MM scenario[%]
-20
-40
-60
-80
0
50
20
40
60
100
12
10
8
6
60
80
100
Savings
BAU
ITC
4
MM
2
ITC
0
20
40
60
MM
0
time [y]
-100
80
40
14
-50
time [y]
20
16
BAU
0
BAU
0
18
Climate damages
deviation from
MM scenario[%]
200
time [y]
-2
60
250
ITC
20
MM
-1
time [y]
300
40
100
ITC
0
350
Net carbon efficiency
80
BAU
1
MM
0
100
60
2
-2
80
7
ITC
-1
60
40
Wages
8
BAU
1
time [y]
-2
20
9
2
MM
0
0
Profits
9
deviation from
MM scenario[%]
7
0
10
8
deviation from
MM scenario[%]
8
time [y]
0,0
10
9
ITC
0,5
300
10
MM
deviation from
MM scenario[%]
12
600
BAU
deviation from
MM scenario[%]
16
2,5
BAU
Temperature [C]
700
20
Concentration [ppmV]
24
time [y]
-2
80
100
0
20
40
60
80
100
Relative demand
Good1(climate-friendly)/Good2(climate-hostile)
600
11
2,5
550
500
1/2
6
1/6
1/2
1/1
BAU
1/1
Emissions [Gt C]
5
4
3
2
1
0
0
450
MM
1,5
saverate 10%
Poly. (BAU)
1,0
Poly. (MM)
400
Poly. (saverate 10%)
time [y]
20
40
60
time [y]
300
80
100
100
0
20
40
60
80
100
60
50
40
30
1/1
15
1/2
10
1/5
20
15
MM
Series2
1/5
Poly. (Series2)
Poly. (MM)
10
Poly. (BAU)
1/2
1/1
time [y]
0
20
40
60
80
100
40
60
80
100
Production
BAU
time [y]
0
0
20
20
5
10
0
25
normalized production
70
time [y]
Production Good 2
20
normalized production
80
1/2
1/1
0,0
25
Production Good 1
90
1/6
0,5
350
0
normalized production
7
2,0
Temperature [C]
1/6
Concentration [ppmV]
9
8
Global Mean
Temperature Change
CO2 concentration
CO2 emissions
10
20
40
60
80
100
1/1
1/2
BAU
MM
Series2
1/6
Poly. (Series2)
Poly. (MM)
Poly. (BAU)
5
time [y]
0
0
20
40
60
80
100
mitigation measures:
w: weak,
30
20
(a) Emissions
20
15
10
(b) Production
15
BAU
w
10
m
5
5
normalized production
Emissions [Gt C]
25
m: moderate, s: strong
BAU
s
time [y]
w
m
s
time [y]
0
0
0
20
40
60
80
100
0
20
40
60
80
100
Estimates of the costs of climate change mitigation:
1 % of GDP
Consistent with: IPCC 4th Assessment Report; macroeconomic model intercomparison, The Energy
Journal, Special Issue, 2006; the Stern Review, 2006).
Range of other estimates:
-1 % to + 4% of GDP
Is climate change mitigation affordable?
4-
3-
1% BAU growth
2GDP
(log
Scale)
1-
2000
2100
Is climate change mitigation affordable?
4-
3-
1% BAU growth
2BSP
(log
Skala)
1-
2000
1% GDP loss corresponds to a
delay of 1 year over a period of 100
years–an affordable insurance
premium to avoid the risk of
dangerous climate change!
2100
Overview
• Available technologies for closing the wedge
between the BAU (Business as Usual) emissions
trajectory and the sustainable emissions goal
• Proposed strategies for closing the wedge
• Traditional versus multi-agent economic models
- with three examples of the latter:
1) Ginti’s model of the “invisible hand”
2) A Multi-Actor Dynamic Integrated Assessment
Model (MADIAM) of climate policies
3) A climate-policy evolution model
• Conclusions
Third example: A multi-actor model of the
evolution and implementation of climate policy
Scenarios from
1970 (first serious warnings of climate
change) to
2100 (end of IPCC scenarios)
Simplified MADIAM, extended to include
• interaction of scientific knowledge, interest
groups, media, etc in climate policy
development and implementation
• identification of critical policy parameters
(e.g. whip and carrot policies)
(8)
global
warming
warming rate
GDP
emissions
research
low fruits
technology
policy
concepts
policy information
(1)
scientific
knowledge
(3)
(2)
Climate policies
carbon price whip
policy implementation
T1
T2
glatt 1
IPCC
media
NGOs
extreme events
public
low fruits
investments
(4)
(5)
solar technology
subsidies carrot
solar investments
vested interests
A Vensim model of the climate-policy obstacle course:
from scientific knowledge (1) to reduced global warming (8)
(7)
carbon
intensity
(6)
Three stages: 1: scientific knowledge (1 ) to policy information (2)
2: Information (2) to mitigation technology (5)
3: mitigation technology (5) to global warming (8)
global
warming
warming rate
(8)
GDP
(2)
Climate policies
research
carbon price whip
(4)
policy
concepts
policy information
(1)
scientific
knowledge
emissions
(3)
policy implementation
T1
media
NGOs
extreme events
low fruits
investments
T2
glatt 1
IPCC
low fruits
technology
public
(5)
solar technology
subsidies carrot
solar investments
vested interests
A Vensim model of the climate-policy obstacle course:
from scientific knowledge (1) to reduced global warming (8)
(7)
carbon
intensity
(6)
Stage 1: From scientific knowledge (1) (IPCC) to policy information (2)
via the media, vested interests, extreme events, etc.
Climate policies
(2)
policy
concepts
policy information
(1)
research
scientific
knowledge
IPCC
media
NGOs
extreme events
public
vested interests
Step 1: From scientific knowledge (1) (IPCC) to policy information (2)
via the media, vested interests, extreme events, etc.
(1) IPCC
(2)Policy information
4
4
2
2
0
1970
0
1970
1990
2010
2030
2050
Time (Year)
2070
IPCC : reference 2
2090
SK
2010
2030 2060
Time (Year)
extreme events : reference 2
0
1970
2090
SK
2070
2090
policy information : reference 2
SK
Vested interests
4
10
2000
2030
2050
Time
Media
extreme events
0
1970
1990
0
2000
2030 2060
Time (Year)
media : reference 2
-4
1970
2090
SK
2000
2030 2060
Time (Year)
vested interests : reference 2
2090
SK
Stage 2: The delay cascade: Information (2) to mitigation technology (5)
(2)
policy information
(3)
policy
concepts
carbon price whip
low fruits
technology
(4)
policy implementation
low fruits
investments
(5)
solar technology
subsidies carrot
solar investments
Step 2: The delay cascade: Information (2) to mitigation technology (5)
Policy concepts
Policy information
4
0.4
2
0.2
0
1970 1990 2010 2030 2050 2070 2090
Time
(2)
policy information : Test 23 Mar
SK
0
1970
2000
(3)
2030
2060
Time (Year)
policy concepts : Test 23 Mar
Low fruits/solar technology (5)
2090
WGDP
(4) Policy implementation
0.4
20 Year*WGDP
20 Year*WGDP
0.2
0 Year*WGDP
0 Year*WGDP
1970
2010
2050
Time (Year)
low fruits technology : Test 23 Mar
solar technology : Test 23 Mar
2090
Year*WGDP
Year*WGDP
0
1970
2000
2030
2060
Time (Year)
policy implementation : Test 23 Mar
2090
WGDP
Stage 3: From mitigation technology (5) to global warming (8)
warming rate
global
warming
GDP
emissions
(7)
low fruits
technology
low fruits
investments
(5)
solar technology
solar investments
carbon
intensity
(6)
(8)
Step 3: From mitigation technology (5) to global warming (8)
(5)
Low fruits/solar technology
solar
0 Year*WGDP
0 Year*WGDP
low fruits
2000
2030
2060
Time (Year)
low fruits technology : run Alcala
solar technology : run Alcala
2090
Year*WGDP
Year*WGDP
8 WGDP
8 WGDP
BAU GDP : run Alcala
GDP : run Alcala
2030 2050
Time (Year)
BAU emissions : run Alcala
emissions : run Alcala
2070
GTC/Year
GTC/Year
global warming
BAU
------2 degree limit --------------------------------
policy
2010
0 GTC/Year
policy
0 GTC/Year
1970 1990 2010 2030 2050 2070 2090
Time
4 degC
4 degC
BAU
1990
BAU
20 GTC/Year
20 GTC/Year
(8)
GDP
0 WGDP
0 WGDP
1970
Emissions
40 GTC/Year
40 GTC/Year
20 Year*WGDP
20 Year*WGDP
1970
(7)
2090
WGDP
WGDP
0 degC
0 degC
1970
policy
1990
2010
2030 2050
Time (Year)
BAU global warming : run Alcala
global warming : run Alcala
2070
2090
degC
degC
What are the critical parameters that govern
the effectiveness of climate policy?
• the whip factor: the emissions cap in a cap
and trade system
• the carrot factor: the subsidies level, in
particular for solar energy
• the delay factor: time delay between policy
concepts and implementation
See Vensim sensitivity simulation….
Overview
• Available technologies for closing the wedge
between the BAU (Business as Usual) emissions
trajectory and the sustainable emissions goal
• Proposed strategies for closing the wedge
• Traditional versus multi-agent economic models
- with three examples of the latter:
1) Ginti’s model of the “invisible hand”
2) A Multi-Actor Dynamic Integrated Assessment
Model (MADIAM) of climate policies
3) A climate-policy evolution model
• Conclusions
Consider again the two basic questions raised by
the paradigm shift from traditional economic
equilibrium theory to “complexity economics”:
1) The emergence problem;
How do macro-economic structures emerge from
the complex micro-economic interactions of
many agents pursuing different goals?
2) The parametrization problem:
How can one represent the dynamics of macroeconomic systems in terms of the interactions
between a small set of aggregated agents?
Proponents of the paradigm shift have focused
on the first problem:
the emergence problem:
e.g. Eric Beinhocker, 2006:
“The ultimate accomplishment of Complexity
Economics [is to take us] from theories of
agents, networks and evolution all the way up to
the macro-economic patterns we see in realworld economies. Such a comprehensive theory
does not yet exist, but we can begin to see
glimmers of hope….”
However, if we wish to close the present
gap between useful scientific advice and
climate policy, we need also to urgently
address the second problem:
the parametrization problem
However, if we wish to close the present
gap between useful scientific advice and
climate policy, we need also to urgently
address the second problem:
the parametrization problem
I hope I have provided you also with a
“glimmer of hope”
Thank you for listening