State greenhouse gas emissions projections and pathways to meet statewide goals: CALGAPS results Jeffery B. Greenblatt, Ph.D. Staff Scientist Presentation to Silicon Valley Leadership Group SunPower Headquarters, San Jose, CA April 21, 2016

Background CARB funded study in summer 2013 LBNL report published November 2013 Energy Policy paper published January 2015 Being adapted for new work with BAAQMD, CEC 2Footer

What we did: CALGAPS Comprehensive CA energy and GHG model Four scenarios modeled (49 policies in all): Committed Policies (S1) – All policies underway or extremely likely by 2020 Uncommitted Policies (S2) - Existing policies and targets lacking detailed implementation or financial plans Potential Policy and Technology Futures (S3) - Speculative policies (includes extensions of S1/S2) “If We Do Nothing” Scenario (S0) - Disables all policies in S1 3Footer

Statewide annual GHG emissions 4Footer Greenblatt, J. B., “Modeling California policy impacts on greenhouse gas emissions,” Energy Policy, 78, 158–172, 14 January. DOI: /j.enpol (Uncertainty driven primarily by population, GDP, building efficiency assumptions) Governor’s 2030 target (40% below 1990)

Cumulative GHG emissions 5Footer Greenblatt, J. B., “Modeling California policy impacts on greenhouse gas emissions,” Energy Policy, 78, 158–172, 14 January. DOI: /j.enpol Same pathway as “straight line” in previous figure

Policy GHG sensitivities: S1 6Footer Greenblatt, J. B., “Modeling California policy impacts on greenhouse gas emissions,” Energy Policy, 78, 158–172, 14 January. DOI: /j.enpol Top 3 GHG policies Removed single policies and observed change in GHG emissions (usually positive) by decade

Policy GHG sensitivities: S2 7Footer Greenblatt, J. B., “Modeling California policy impacts on greenhouse gas emissions,” Energy Policy, 78, 158–172, 14 January. DOI: /j.enpol Next 3 most important GHG policies

Governor Brown’s Inaugural Policies Footer8 Policy area 50% Renewable Electricity 50% Reduced Petroleum Use Doubled Building Efficiency Cleaner Heating Fuels Reduced Methane Emissions Reduced Other Potent Emissions Boosted Land CO 2 Sequestration Greenblatt, J. B., W. R. Morrow III, S. M. Donovan, “Modeling impacts of California Governor’s policy actions on 2030 greenhouse gas emissions,” in preparation. Collective reductions may be significant relative to S1 SB350

Conclusions and Ongoing Work California is on track to meet 2020 GHG target Potential for much greater 2030 emissions reductions Existing policies are insufficient to meet 2050 target— even the most aggressive scenario (S3) is 2x too high – Must explore additional policy & technology options Developing updated demand forecasts and SB350 impacts for new CEC project Parallel work in progress using CALGAPS for Bay Area emissions inventory and policy analysis Footer9

Extra materials Footer10

Research Questions and Policy Gaps Footer11 Transpor- tation Can we accelerate ZEV adoption, higher vehicle efficiencies? Assess automation, ride-sharing trends, VMT reduction strategies More focus on heavy truck GHG reduction, airplane GHG policy? Fuel supply How can we improve LCFS to maximize low-carbon biofuels? Can low-carbon hydrogen play a significant role in California? Buildings Monitor AB758 implementation for research and policy gaps Policies needed for electrification/solar-assisted heating Electricity Get renewable integration right at lowest cost & GHG emissions Long-term role for CO 2 capture/sequestration in electricity (fuels)? Industry Explore industrial efficiency, fuel switching, CCS opportunities? Consider GHG policies for refinery emissions, imported goods? Beyond energy Need to assess methane, HCFC reduction potentials Need to understand land-use carbon management opportunities Criteria pollutant impacts of transportation, electricity policies?

Policy GHG sensitivities: S3 Footer12 Greenblatt, J. B., “Modeling California policy impacts on greenhouse gas emissions,” Energy Policy, 78, 158–172, 14 January. DOI: /j.enpol

CALGAPS model overview 13Footer Greenblatt, J. B., “Modeling California policy impacts on greenhouse gas emissions,” Energy Policy, 78, 158–172, 14 January. DOI: /j.enpol

Footer14 GHG breakdown by sector S2 S3 S0 S1 Greenblatt, J. B., “Modeling California policy impacts on greenhouse gas emissions,” Energy Policy, 78, 158–172, 14 January. DOI: /j.enpol

Criteria air pollutants 15Footer Emissions modeled for transportation and electricity Regionally disaggregated into SCAB, SJV, rest of CA Found that NOx levels are ~2x higher than 2023 and 2032 targets even in most aggressive scenario (S3), resulting in unacceptable ground-level ozone levels Greenblatt, J. B., “Modeling California policy impacts on greenhouse gas emissions,” Energy Policy, 78, 158–172, 14 January. DOI: /j.enpol

Uncertainty analysis 16Footer Estimated 95% confidence levels on 10 parameters Calculated GHG sensitivities to each parameter Ran 1,000-iteration Monte Carlo simulation to estimate total scenario uncertainties Greenblatt, J. B., “Modeling California policy impacts on greenhouse gas emissions,” Energy Policy, 78, 158–172, 14 January. DOI: /j.enpol

Model comparison to inventory data Footer17 Greenblatt, J. B., “Modeling California policy impacts on greenhouse gas emissions,” Energy Policy, 78, 158–172, 14 January. DOI: /j.enpol

Electricity sector Sector GHG variabilityHydropower variability 18Footer Greenblatt, J. B., “Modeling California policy impacts on greenhouse gas emissions,” Energy Policy, 78, 158–172, 14 January. DOI: /j.enpol

UNIVERSITY OF CALIFORNIA