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15.023J / 12.848J / ESD.128J Global Climate Change: Economics, Science, and PolicySpring 2008

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• 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?

Climate Policy Analysis

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

How important to include uncertainty?

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

Representation of the Decision-Maker 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?)– 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

Ã

Examples under Certainty

Who does what?

What control to take today?

B/C

(1)

Climate target (Article 2)

(2)

TWA

(4)

CE(3)

• 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

Benefit-Cost Analysis

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

1995

70

60C

ontro

l Rat

es -

% o

f bas

elin

e

50E90

40

2XCO2

30

T < 2.5

20

Optimal

10

02005 2015 2025 2035 2045 2055 2065 2075 2085 2095 2105

-10

Emission control rates: Alternative policies

Optimal E90 2xCO2 T < 2.5 deg C

Efficient Climate-Change Policies

Figure by MIT OpenCourseWare.

Social Cost of Carbon

100

90

80

70

Car

bon

taxe

s (19

90 $

per

ton)

60

50

40

30

20

10

01995 2005 2015 2025 2035 2045 2055 2065 2075 2085 2095 2105

Carbon taxes: Alternative policies

Optimal E90 2xCO2 T < 2.5 deg C

2XCO2

E90T<2.5

Optimal


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

Who does what?

What control to take today?

B/C

Cost Effectiveness Analysis


(?)

CE

0

1

2

3

4

5

6

7

8

9

2000 2020 2040 2060 2080 2100

W/m

2

IGSM_Level3MERGE_Level3MINICAM_Level3Level 3

Radiative ForcingLevel 3 (650 ppmv)

0

1

2

3

4

5

6

7

8

9

2000 2020 2040 2060 2080 2100

W/m

2


Radiative Forcing Level 1 (450 ppmv)

0

1

2

3

4

5

6

7

8

9

2000 2020 2040 2060 2080 2100

W/m

2


Radiative ForcingLevel 2 (550 ppmv)

Stabilization• 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

• 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

Fossil & Industrial CO2Level 2 (550 ppmv)

0

5

10

15

20

25

2000 2020 2040 2060 2080 2100

GtC

/yr

IGSM_Level1IGSM_Level2IGSM_Level3IGSM_Level4IGSM_REF

0

5

10

15

20

25

2000 2020 2040 2060 2080 2100

GtC

/yr

MERGE_Level1MERGE_Level2MERGE_Level3MERGE_Level4MERGE_REF

0

5

10

15

20

25

2000 2020 2040 2060 2080 2100

GtC

/yr

MINICAM_Level1MINICAM_Level2MINICAM_Level3MINICAM_Level4MINICAM_REFIGSM

MiniCAM

MERGE Required CO2 Reduction

Emissions price and economic cost

0

400

800

1200

1600

2000

2020 2040 2060 2080 2100

Year

$/to

nne

(200

0$)

IGSM_Level1IGSM_Level2IGSM_Level3IGSM_Level4

IGSM

0

400

800

1200

1600

2000

2020 2040 2060 2080 2100

Year

$/to

nne

(200

0$)

MERGE_Level1MERGE_Level2MERGE_Level3MERGE_Level4

MERGE

0

400

800

1200

1600

2000

2020 2040 2060 2080 2100

Year

$/to

nne

(200

0$)

MINICAM_Level1MINICAM_Level2MINICAM_Level3MINICAM_Level4

MiniCAM

CO

Pric

e P

aths

0%

1%

2%

3%

4%

5%

6%

7%

8%

2000 2020 2040 2060 2080 2100

Perc

enta

ge L

oss i

n G

ross

Wor

ld P

rodu

ct

IGSM_Level2

MERGE_Level2

MINICAM_Level2

% Loss in Global World Product 550 ppmv case (MER)

Origin of the Differences• Required CO2 reduction• Assumptions about post-2050

technology

2

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

Who does what?

What GHGs are allowed today?

B/C

Tolerable Windows


(?)

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 WindowsClimate Space

0.25C

/dec

)

0.20

oR

ate

of C

hang

e ( 0.15

0.10

0.05

0.0015.0 15.5 16.0 16.5 17.0

oTemperature ( C)

Composition Space480

Con

cent

ratio

n [p

pm] 460

440

420

400

300

2C

O 360

3400 50 100 150 200

Years After 1995

Emission Space (1)

Cum

ulat

ive

Emis

sion

s [G

t C] 1000

800

600

400

200

00 50 100 150 200

Years After 1995

Emission Space (2)15

ear [

Gt C

/a]

10

Emis

sion

s/Y

5

00 50 100 150 200

Years After 1995


National Assessment Overview, Chapter2

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 CO 2 emissions-

Maximum regional income loss: 2.0% Maximum globalecosystemtransformation

50%45%40%35%30%

Inner structure(for 35% limit)

Max. 2035Min. 2035Max. 2065Min. 2065Max. 2100Min. 2100

2000

Corridors for energy-related CO2 emissions

(a) Variation of the impact constraint

24

20

em

issi

ons (

Gt C

/yr)

16

12

2

8

CO

4

0 Cost-effective2020 2040 2060 2080 2100 emission path

Year


Insights/Evaluation?

• What think of the analysis?

• Insights gained?––

• What assumptions dominate?– Structure of solution algorithm–

• What is missing?––

What control action today?


Meta B/C

Assessing an Atmospheric Target Under Uncertainty

What would we gain with stabilization & 550 ppm?

Low probability, high consequence events?

What control action today?


B/C

Benefit-Cost Under Uncertainty

The “Wait to Learn”

DebateApril 23 & 28

• 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

Ongoing Research

• 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

Final Thoughts

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., landlord-

tenant)– Externalities & public goods

• Market barriers– Hidden costs (e.g., transactions costs)– Disadvantages perceived by users– “High” discount rates

Alternative Views of the Options

Figure by MIT OpenCourseWare, adapted from Resources for the Future.

Baseline Increasing economic efficiency

Incr

easi

ng e

nerg

y ef

ficie

ncy

Technologists'optimum

Theoreticalsocial optimum

True socialoptimum

Economists' narrow optimum Net effect of corrective

policies that can pass acost-benefit test

Set aside correctivepolicies that cannot be

implemented at acceptable cost

Eliminate environmental

externalities andmarket failures in

energy supply

Eliminate "market barriers" to

energy efficiency, suchas high discount rates

and inertia; ignoreheterogeneity

Alternative Notions of the Energy Efficiency Gap

Eliminate market failures inthe market for energy-efficient

technologies

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”

• How to distinguish tech change from– Change in inputs (in response to price)– Economies of scale

P

∑Q

“New” Technologies• Carbon capture and storage

– From electric power plants– From the air

• Renewables– Wind & solar– Biomass– Tidal power– Geothermal

• New generation of fission, and fusion• Solar satellites• Demand-side technology

– Fuel cells and H2 fuel– Other? (lighting, buildings, ind. process, etc.)

What determines the likely contribution of each?

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