MIT OpenCourseWare
http://ocw.mit.edu
15.023J / 12.848J / ESD.128J Global Climate Change: Economics, Science, and Policy
Spring 2008
For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.
Climate Policy Analysis
• What long-term stabilization target?
• How strong a mitigation effort to
undertake NOW?
– Quantity target, say for 2008-2015?
– Social cost of carbon?
• Need more information?
– What specifically?
– How to frame the issue for public/policy
discussion?
Path for Today
• Structure of the assessment task
– The handling of uncertainty
– Representation of decision-making process
– Areas of policy choice
• Examples under Certainty
– Benefit-cost analysis
– Cost-effectiveness analysis
– Tolerable windows analysis
• Examples under Uncertainty (preview)
– Probabilistic forecasts
– Sequential decision
Certainty vs. Uncertainty
• Assuming certainty
– Once-and-for-all decision now
• Near-term choice (e.g., Kyoto-type analyses)
• Path over time
(e.g., B/C, stabilization studies)
• Considering uncertainty
– Once-and-for-all decision now
• Scenario analysis
• With probability distributions
– Sequential choice, with learning
How important to include uncertainty?
Representation of the DecisionMaker or Process
• Single decision-maker
• Multiple decision-makers and gaming
behavior
• Negotiation among parties
What is the value/limits of single-actor analysis?
Areas of Policy Choice
• Emissions control (what to do now?) Ã
– Single decision-maker (global welfare)
– [Multiple parties and negotiation]
• Anticipatory adaptation
• Actions to open options
– R&D & technology push
– “Architecture” of climate negotiations
• Geo-engineering
Examples under Certainty
(1)
B/C
(2)
What control to
take today?
Who does what?
Climate target
(Article 2)
(3)
(4)
CE TWA
Benefit-Cost Analysis
• Cost function & benefit relationship
• Alternative applications
– Calculate optimal path, unconstrained
– Constrain by long-term target
– Apply policy scenarios (e.g., burden sharing)
• Difficult issues
– Valuation & aggregation
– Discounting
– Institutional assumptions
Example: Nordhaus DICE Model
• Growth, emissions, and ΔT
– Like Homework's 2 & 3
• Climate change effects
– A damage function of form in last lecture
• Forward-looking, optimizing model
• Policy assumptions
– Optimal path (by their valuations)
– Stabilize concentrations at 2xCO2
– Hold ΔT to 2.5°C
– Stabilize emissions at 1990 levels (E90)
Efficient Policies
Efficient Climate-Change Policies
70
Control Rates - % of baseline
60
50
E90
40
2XCO2
30
T < 2.5
20
Optimal
10
0
1995
2005
2015
2025
2035
2045
2055
2065
2075
2085
2095
2105
-10
Emission control rates: Alternative policies
Optimal
E90
2xCO2
T < 2.5 deg C
Figure by MIT OpenCourseWare.
Social Cost of Carbon
100
2XCO2
E90
90
T<2.5
80
Carbon taxes (1990 $ per ton)
70
60
Optimal
50
40
30
20
10
0
1995
2005
2015
2025
2035
2045
2055
2065
2075
2085
Carbon taxes: Alternative policies
Optimal
E90
2xCO2
T < 2.5 deg C
Figure by MIT OpenCourseWare.
2095
2105
Insights/Evaluation?
• What think of the analysis?
• Insights gained?
– About paths of stringency?
– Other?
• What assumptions dominate?
–
–
• What is missing?
–
• US EPA task under Court ruling on CO2
• Debates surrounding Warner-Lieberman
Cost Effectiveness Analysis
B/C
(?)
What control to
take today?
Who does what?
Climate target
(Article 2)
CE
9
8
8
7
7
6
6
5
5
W /m 2
W /m 2
9
4
3
IGSM_Level2
Radiative Forcing
MERGE_Level2
Level 2 (550 MINICAM_Level2
ppmv)
Level
2
1
0
2020
2040
2060
2080
2100
1
2000
2020
2040
2060
2080
Stabilization
8
7
6
W /m 2
IGSM_Level3
MERGE_Level3
MINICAM_Level3
Level 3
2
0
9
5
4
3
IGSM_Level1
MERGE_Level1
MINICAM_Level1
Level 1
2
Radiative Forcing
Level 1 (450 ppmv)
1
0
2000
4
3
2
2000
Radiative Forcing
Level 3 (650 ppmv)
2020
2040
2060
2080
2100
• Forcing trajectories are
similar across the models
• 550 and 650 ppmv cases
stabilize in next century
• 450 case must stabilize
with 50 t0 75 years
2100
25
20
20
IGSM
Fossil & Industrial CO2
Level 2 (550 ppmv)
15
GtC/yr
GtC/yr
25
IGSM_Level1
IGSM_Level2
IGSM_Level3
IGSM_Level4
IGSM_REF
10
0
0
2040
2060
2080
2100
25
20
GtC/yr
10
5
2020
MERGE_Level1
MERGE_Level2
MERGE_Level3
MERGE_Level4
MERGE_REF
2000
•
•
10
•
5
0
2020
2040
2060
2080
2020
2040
2060
2080
2100
Required CO2 Reduction
MERGE
15
2000
MiniCAM
15
5
2000
MINICAM_Level1
MINICAM_Level2
MINICAM_Level3
MINICAM_Level4
MINICAM_REF
2100
To stabilize, emissions must decline
to the rate of natural removal (EJ0)
Higher stabilization targets only
delay this ultimate condition
Monotonic increase in effort over
time, with only technology to
moderate
% Loss in Global World Product
550 ppmv case (MER)
8%
IGSM_Level1
IGSM_Level2
IGSM_Level3
IGSM_Level4
1200
800
400
0
2020
2040
2060
2080
2100
Year
2000
$/tonne (2000$)
1600
MERGE_Level1
MERGE_Level2
MERGE
MERGE_Level3
MERGE_Level4
1200
800
400
0
2020
2040
2060
2080
2100
Year
2000
$/tonne (2000$)
1600
MINICAM_Level1
MINICAM_Level2
MINICAM_Level3
MiniCAM
MINICAM_Level4
1200
800
400
0
2020
2040
2060
Year
2080
2100
CO2 Price Paths
$/tonne (2000$)
1600
Emissions
price and
IGSM
economic
cost
Percentage Loss in Gross W orld Product
2000
7%
6%
IGSM_Level2
MERGE_Level2
MINICAM_Level2
5%
4%
3%
2%
1%
0%
2000
2020
2040
2060
2080
2100
Origin of the Differences
• Required CO2 reduction
• Assumptions about post-2050
technology
Cost-Effectiveness Analysis
• Maybe no direct benefit estimate
– Least-cost path in stabilization studies
– Examples: CCSP & HW #3
• Explore what, where & when flexibility
• Input to “meta” benefit-cost analysis
– Combine with benefits of stabilization level
– Example: Stern Review
• Difficult issues
– Aggregation
– Discounting
– Institutional assumptions
Tolerable Windows
B/C
(?)
What GHGs are
allowed today?
Who does what?
Climate target
(Article 2)
TWA
Tolerable Windows
• No explicit benefit function
– Represented in form of constraints
• No explicit cost function
– Represented by some limit on effort
• Question: what must we do to preserve
the option of some future climate state?
– Capable of multiple attributes
Sequence of Windows
Climate Space
0.20
0.15
0.10
0.05
460
440
420
400
300
360
340
0.00
15.0
15.5
16.0
o
Composition Space
480
CO2 Concentration [ppm]
Rate of Change (oC/dec)
0.25
16.5
0
50
17.0
100
150
200
Years After 1995
Temperature ( C)
Emission Space (2)
15
1000
Emissions/Year [Gt C/a]
Cumulative Emissions [Gt C]
Emission Space (1)
800
600
400
200
0
10
5
0
0
50
100
150
200
0
50
100
150
200
Years After 1995
Years After 1995
Figure by MIT OpenCourseWare.
National Assessment Overview, Chapter
2
National Assessment Synthesis Team, Climate Change Impacts on the United States: The Potential
Consequences of Climate Variability and Change (Washington, DC: U.S. Global Change Research Program, 2000).
Courtesy of The U.S. Global Change Research Program (USGCRP). Used with permission.
Figure
2. Corridors for energy-related CO2 emissionsCorridors for energy-related CO2 emissions
(a) Variation of the impact constraint
24
Maximum global
ecosystem
transformation
50%
45%
40%
35%
30%
Maximum regional income loss: 2.0%
CO2 emissions ( Gt C/yr)
20
16
Inner structure
(for 35% limit)
12
Max. 2035
Min. 2035
Max. 2065
Min. 2065
Max. 2100
Min. 2100
8
4
0
2000
2020
2040
Year
2060
2080
2100
Cost-effective
emission path
Figure by MIT OpenCourseWare.
Insights/Evaluation?
• What think of the analysis?
• Insights gained?
–
–
• What assumptions dominate?
– Structure of solution algorithm
–
• What is missing?
–
–
Assessing an Atmospheric
Target Under Uncertainty
Meta
B/C
What control
action today?
Climate target
(Article 2)
What would
we gain with
stabilization
& 550 ppm?
Low probability, high
consequence events?
Benefit-Cost Under Uncertainty
What control
action today?
B/C
Climate target
(Article 2)
The
“Wait to Learn”
Debate
April 23 & 28
Ongoing Research
• Upper tail the distribution of outcomes
– Missing (extreme) events
• Methodology
– Elicitation of parameter PDFs
– Cascading uncertainties through models of
several stages of the climate issue
• The real (sequential) decision problem
– Partial learning
– Institutions and path dependency
– Capturing risk aversion (precaution)
– Multiple players & “who does what?”
• Lay communication
Final Thoughts
• At best, gain rough insight to today’s
decision
– Damage functions are inadequate to the task
– Necessary simplification of choices
– Thus far: single decision-maker model, or very
simple decision theory representations
• Much work needed to do better, even for
“expert” understanding
• Lay audiences deserve our sympathy
McKenzie - 2007
Cost basis
Discount
rate
What is in
baseline?
What use?
Courtesy of McKinsey & Company. Used with permission.
Explaining Why Technologies Are
Not Used
• Market failures: decision-makers don’t
see correct price signals
– Lack of information
– Principal-agent problems (e.g., landlordtenant)
– Externalities & public goods
• Market barriers
– Hidden costs (e.g., transactions costs)
– Disadvantages perceived by users
– “High” discount rates
Alternative Views of the Options
Increasing energy efficiency
Alternative Notions of the Energy Efficiency Gap
Technologists'
optimum
Eliminate
"market barriers" to
energy efficiency, such
as high discount rates
and inertia; ignore
heterogeneity
Theoretical
social optimum
Eliminate
environmental
externalities and
market failures in
energy supply
Economists'
narrow optimum
Eliminate market failures in
the market for energy-efficient
technologies
Baseline
Set aside corrective
policies that cannot be
implemented at
acceptable cost
True social
optimum
Net effect of corrective
policies that can pass a
cost-benefit test
Increasing economic efficiency
Figure by MIT OpenCourseWare, adapted from Resources for the Future.
Thinking about Technology
• What is technology, and tech. change?
• What leads to change?
– Does change tend to economize on one
factor or another, in response to prices?
– What is the role of R&D expenditure?
– To what degree is it ad hoc or random?
• Role of “learning by doing”
P
• How to distinguish tech change from
– Change in inputs (in response to price)
– Economies of scale
∑Q
“New” Technologies
• Carbon capture and storage
– From electric power plants
– From the air
• Renewables
– Wind & solar
– Biomass
– Tidal power
– Geothermal
What determines the
likely contribution of
each?
• New generation of fission, and fusion
• Solar satellites
• Demand-side technology
– Fuel cells and H2 fuel
– Other? (lighting, buildings, ind. process, etc.)