1 Optimal Technology R&D in the Face of Climate Uncertainty Erin Baker University of Massachusetts, Amherst Presented at Umass INFORMS October 2004

2 Today’s Talk Background on Climate Change How to model R&D programs in top-down models? Theoretical results indicate that  How R&D is modeled matters, and  How increasing risk is modeled matters. Results and insights from numerical model. Including uncertainty in the returns from R&D

3 Climate Change Humans are changing the climate, through the accumulation of greenhouse gasses (GHG). GHG are mainly emitted through the combustion of fossil fuels.

4 Human Contributions to the Greenhouse Effect Carbon Dioxide 55% Nitrous Oxide 6% CFCs 11 and 12 17% Methane 15% Other CFCs 7%

5 Carbon Emissions Due to Fossil Fuel Consumption

6 Reference Carbon Projections

7 Climate Change We have seen an increase of about 1°C over the last 100 years. A doubling of CO 2 from pre-industrial levels would increase global average temperatures by about 1.5 – 4.5°C A 1.5°C rise would be warmest temperatures in last 6000 years. A 4.5°C rise would raise temperatures to those last seen in time of dinosaurs.

8 Climate Change - Uncertainty 100 years is a blip in geologic time. Climate models are still in infancy. Regional climate impacts are highly uncertain. Human impacts of climate changes are uncertain. Potential for catastrophic damages  Runaway greenhouse effect  Failure of the Gulf stream

9 Climate Change Uncertainty about how emissions today will cause damages tomorrow. But, we are learning more and more. Uncertainty, learning, and adaptation impact current decisions Conclusion: Uncertainty + Learning = less control of emissions.  Kolstad  Ulph & Ulph  Manne & Richels  Baker

10 What about R&D? R&D planning is complicated by different programs  Solar PVs versus efficiency of coal-fired electricity We consider optimal R&D  uncertainty and learning about climate damages  choice of R&D program

11 How to represent R&D? Climate change is a complex problem, involving multiple variables. In order to get insights about the best policy, we need a simple representation of alternative R&D programs. What matters for climate change is how the technology that results from the R&D impacts the cost of abatement.

12 Climate Change Policy We would like to choose a carbon emissions level that equated the marginal cost of abatement with the marginal damages from climate change. MC = MD Technical change impacts the marginal cost of abatement.

13 The Production Function **   0 Q = f(  )  = standard inputs  = emissions

14   Cost **   0 Q = f(  ) 0 $ Production Function Abatement Cost Curve From production function to abatement cost curve  = emission reductions  = emissions

15 Multiplicative Shift: Cost Reduction of No-Carbon Alternatives   max   min 1-    0 01  Production Function Cost The abatement cost curve pivots downward

16      Production Function Cost Emissions Reduction of Currently Economic Alternatives The abatement cost curve pivots to the right

17 Define Risk How does optimal investment in R&D change with an increase in risk? “Risk” – “uncertainty” – “Mean-preserving- spread” See for example Rothschild & Stiglitz 1970,1971.

18 Theory Results Proposition: Optimal R&D decreases with some increases in risk.

19 Theory Results Proposition: Optimal R&D decreases with some increases in risk.  “Full abatement”

20 Theory Results Proposition: Optimal R&D decreases with some increases in risk.  “Full abatement”  Fundamentally different from abatement result

21 Theory Results The converse is not true – some R&D programs will always decrease in risk. Individual R&D programs will react differently to an increase in risk. It is crucial to model the specific program.

22 R&D impacts convexity of cost curve / production function Cost Reduction   Emissions reduction   Flatter  R&D increases in risk More convex  R&D decreases in risk

23 Integrated Assessment Model William Nordhaus’s DICE Optimal Growth + Climate Model  Social Planner chooses how to divide income between consumption, investment, and emissions reduction. Added uncertainty, using stochastic programming.  First 5 periods decisions are made under uncertainty  After 5 periods the world splits into two damage scenarios.

24 Integrated Assessment Model William Nordhaus’s DICE Added R&D as a decision variable.  One time decision in 1 st period before learning  Cost reduction implemented in 50 years, after learning about damages.  No uncertainty in the returns to R&D.

25 Increasing Damage certain low medium high Probability of high damage Value of high damage Value of low damage Increasing Probabilitycertain low medium high Probability of high damage Value of high damage Value of low damage Types of increasing risk

Increasing Probability Damage is on x-axis, Probability is on y-axis Increasing Damage

Damage is on x-axis, Probability is on y-axis Increasing Probability Increasing Damage

Damage is on x-axis, Probability is on y-axis Increasing Probability Increasing Damage

29 Results – Increasing Probability Probability of high damage Billions of US$ Cost ReductionEmissions Reduction Probability of high damage Optimal R&D

30 Results – Increasing Damages % GDP Loss Billions of US $ % GDP Loss Optimal R&D Cost ReductionEmissions Reduction

31 Conclusions R&D can be a hedge against uncertainty. But, it depends on what kind of R&D.  R&D into reducing the cost of low carbon alternatives And what kind of risk.  Increasing the probability of needing very low carbon technologies, rather than considering higher levels of damages.

32 Unknowns We need to estimate the relationship between investment in and R&D program, and the expected impact on the abatement cost curve. We need to estimate the amount of uncertainty surrounding R&D programs.

33 Uncertain Returns to R&D

34 DICE equations