Climate Change Technology R&D Portfolio Analysis under Uncertainty Erin Baker, UMass Amherst Presented at The International Energy Workshop Venice, Italy June 18, 2009

2 What to do about climate change? What is the optimal path for a carbon tax and/or an emissions path?  Emissions taxes  Cap and trade  Emissions standards What is the optimal investment path in a portfolio of technology R&D projects?  Government funded R&D  R&D subsidies  Technology standards

3 Today’s Talk Background: Decision Making Under Uncertainty Motivation: Optimal R&D investment is impacted by:  The choice of R&D program (greener vs. cleaner);  The riskiness of R&D program; and  Uncertainty and learning about climate damages. Collecting and implementing data on R&D programs: a decision-analytic approach  Advanced Solar PVs, nuclear, CCS, batteries for vehicles Current Work: Optimal R&D portfolios  CCS & nuclear

4 Paradigm: Act – Learn – Act Question: How does uncertainty impact optimal near term actions? R&D Funding Technical Success Abatement Cost Curve Damage Curve Abatement Level Societal Cost

5 Optimal Abatement $ µ L0 = µ L1 µ L2 µ H0 µ H2 µ H1 Abatement MD H MAC MAC 2 MAC 1 MD L Technical change can reduce the cost of abatement PLUS change the optimal level of abatement

Collecting and implementing data on R&D programs: a decision-analytic approach This research is being supported by the Office of Science (BER) U.S. Department of Energy, Grant No.DE-FG02- 06ER64203 Baker, E., Chon, H. and Keisler, J. Carbon Capture and Storage: Applying Expert Elicitations to Inform Climate Policy. Revision under review at Climatic Change. Baker, E., Chon, H. and Keisler, J. Advanced Solar R&D: Applying Expert Elicitations to Inform Climate Policy. Energy Economics Forthcoming.

7 What is needed? What is the probability distribution over different outcomes of technical change? How will different technologies impact the MAC, if successful? R&D FUNDING TECH SUCCESS ABATEMENT CURVE DAMAGE CURVE ABATEMENT LEVEL SOCIETAL COST

8 Research Plan Collect Expert Assessments of Potential R&D Projects Determine Impact on MAC, using MiniCAM Develop portable representations of the probabilistic impact of technical change. MiniCAM calculations of impact on MAC probabilities Expert Elicitations MACs definitions of success Random Returns to R&D Baker, Chon, & Keisler (2007)

9 Comments on Elicitation results Other than nuclear, experts gave us relatively small budgets. We found considerable disagreement among experts  Optimists versus pessimists  Disagreement over cost targets  Fundamental technological disagreement The average expert is often close to the median expert

Probability distributions over MACs

11 Solar, free storage Nuclear Low Abatement Low R&D High R&D High Abatement abatement CCS Battery for vehicles Solar, Ref The expected MAC for different technologies

Current Work: Optimal R&D Portfolios

13 Issues to explore by using this data in policy models (DICE and a DA Portfolio Model) zero carbon coal & ccs biomass vehicles ? Interactions between individual technologies Increasing risk in damages vs. optimal portfolio Areas where riskier technologies are more attractive Impact of portfolio on optimal abatement Impact of portfolio’s risk on optimal abatement Robustness of portfolios Value of information on technical success

14 Mixed Integer Non-linear Stochastic Program Choose individual projects Success or not Abatement Cost Curve Three damages levels Abatement Level Societal Cost

15 Different Representations of Risk Zhigh1314 Pl02/313/14 Ph11/31/14 Abatement if Z high46%80%100% Z low is 0; optimal abatement is 0%

16 The optimal portfolio did not change with damage risk.

17 Nuclear dominates at large budgets

18 Total Social Cost

19 Total Social Cost In no- risk case, expected abatement cost INCREASES with R&D. The benefits are on the environmental side. In the high-risk case, the expected damages aren’t effected by R&D. All benefits are in cost reduction.

20 Total Social Cost High Chem Looping, Inorg. solar; & Nuc; Med post-comb; Low pre-comb Increase pre-comb to High.

21 The value of the portfolio depends on the risk level Moderate LWR. Reduce solar, increase pre-combustion to high

22 Some projects have very little value. pre-combustion med + organic Add 3 rd gen solar

23 The value of technology is non-monotonic in risk.

24 Moderate damages: technical change increases abatement, but increases cost of abatement

25 Medium high damages: technical change increases abatement and decreases cost of abatement

26 Increasing risk has an ambiguous effect on the value of R&D

27 Summary Expert Elicitations  A great deal of disagreement on emerging technologies. Impacts on the MAC  Nuclear and Solar w/storage are promising for low abatement levels  CCS is promising for high abatement levels  Batteries provide a steady benefit at all levels R&D Portfolio  Technology has less value when emissions are fixed.  CCS has more value when emissions are flexible  Very high risk favors: Technologies that reduce total cost (MAC is unimportant) Low risk technologies  Given current data, the optimal portfolio is robust to climate damages

Backup slides

29 Probability of success: CCS

30 National Academy Study The probability that CCS will be viable ranged from 66% to 77% in the NAS

31 Maximum Mean Central Minimum Probability a. Inorganic Low 2b. Inorg. Medium 1a. Organic Low 3. CIGS 4. 3 rd Gen. 1b. Organic High Probability of success: Solar PV

32

33 The expected MAC for different technologies. Investment NPV $38M; about $3.7M/yr for 15 years

34 The expected MAC for different technologies. Investment NPV $38M; about $3.7M/yr for 15 years

35 The expected MAC for different technologies. Investment NPV $2.2B; about $215M/yr for 15 years

36 The expected MAC for different technologies. Investment NPV $2.2B; about $215M/yr for 15 years

37 Solar, free storage Nuclear CCS Battery for vehicles Solar, Ref If we combine the CCS probabilities differently, they imply that a great deal of the benefit from CCS will accrue without any government R&D Low Abatement Very High R&D High Abatement Ref, no CCS

38 Conclusions Baker & Adu-Bonnah (2006) Optimal investment is significantly higher in R&D programs aimed at reducing the cost of low-carbon technologies when the program is riskier.  Policies should be aimed at increasing the probability of a breakthrough.  Investment in alternative technologies should be higher than deterministic studies would indicate.  Rationale for government policy, since private sector tends to be risk-averse.  Result is robust to many different probability distributions over climate damages.

39 Conclusions Baker & Adu-Bonnah (2006) The risk-profile of R&D programs aimed at reducing emissions in conventional technologies is largely unimportant.  Policies should be aimed at maximizing expected value of technical change.  Deterministic studies should give a good approximation of appropriate level of investment.  Less rationale for government policy.  If the probability of full abatement is high, then investment in risky program increases.

40 General Conclusions From Past Work Care should be taken in the representation of technical change in theoretical and applied environmental models  There is no general effect of technical change on the cost of abatement  In fact, environmental technical change may increase the marginal cost of abatement A portfolio of technologies should be modeled, with different impacts on the MAC We need more information about how improvements in real-world technologies impacts the MAC