Greenhouse gas emissions from oil substitutes:Dynamics, resources, and systems behavior

Adam Brandt

Dept. of Energy Resources Engineering

Stanford Energy Seminar, September 23rd, 2009

abrandt@stanford.edu

Overview

• The risks of the oil transition

– Environmental, economic, and strategic problems

• Modeling the oil transition

– System behavior and dynamics

– Using the model for improving decisions

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The “oil transition” begins

• Oil depletion

– Petroleum production will eventually peak and decline

– Peak will be determined by geologic, economic, and political factors.

– Skeptical about ability to predict peak date (Brandt 2007)

• Response: substitutes for conventional petroleum

– Fossil and bio-based liquids

– Other energy carriers

The oil transition is already underway

4Sources: Various

Will availability present a problem?

• Fossil fuels are abundant

– Total liquid fuel potential >15X total historical consumption

• Biofuels are more difficult

– Ultimate limit: net primary productivity (NPP)• Cannot come close to this limit (NPP is our “fuel”)

• Current appropriation ≈ 0.2 → 0.4 of terr. NPP

– Opinions differ widely• 30 EJ/y sustainable yield

• “Energy farming in current agricultural land alone could contribute over 800 EJ without affecting the world’s food supply” (Rosillo-Calle,

2007)

5Sources: Farrell and Brandt 2006, Haberl et al. 2007, Field et al. 2008

Environmental risks

• The risk: Producing liquid fuels will worsen environmental problems such as climate change or biodiversity loss

• Liquid SCPs have higher GHG emissions

– Cellulosic fuels are the big exception

– Non-liquid SCPs lower emissions (CNG, e-)

• Biofuels shift environmental burdens of transport

– Ecosystem change, deforestation, water, fertilizer

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GHG impacts of liquid oil substitutes

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Sources: Gerdes and Skone, 2009; Charpentier et al., 2009; Jaramillo et al., 2008;

Brandt, 2008; Brandt, 2009; Farrell et al., 2006, Searchinger et al. 2008

Economic risks

• The risk: Transport fuels will become more expensive, reducing economic growth and development

• Short-term risks

– Oil price volatility and recessions

– Debate about cause and effect

• Long-term risks

– Cheap, reliable transportation is a primary economic good

– Saturation in rich countries, but not in poor ones

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Energy security risks

• Over 95% of transport fuels from petroleum

– Causes strategic concerns

• Large oil revenues:

– Ignore/suppress democratic movements

– Incentives for corruption (resource curse)

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•The risk: Securing supply of liquid fuels will make the

world less peaceful and just

Source: http://www.edwards.af.mil/

What can we do to mitigate these risks?

Are there solutions that address multiple risks at once?

Can quantitative models point to these solutions?

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Modeling the oil transition

• Regional Optimization Model for Emissions from Oil substitutes (ROMEO)

• Collaborators: Deepak Rajagopal, Rich Plevin, Alex Farrell

• Funding: NSF Climate Decision Making Center (Carnegie Mellon Univ., SES-0345798) and UC Energy Institute

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ROMEO overview

• Models liquid fuel production

– 5 fossil fuels, 2 biofuels

• 17 world regions, 2000-2050

• Economic optimization model

– Each year optimized independently without foresight

• Three non-linear modules

– Demand response, technological learning, and depletion

• Demand model

– Partial adjustment model of Cooper (2003)

– Short & long-run impacts of price and income

Strengths and weaknesses

• Strengths

– Resource data are strong• Country-level data (low, med, high)

– Flexible structure allows inclusion of new fuels and technologies, regional definitions

• Weaknesses

– Technologies are simple• Defined by a few parameters (costs, GHG emissions, efficiency)

– Market is simple (Cooper, 2003)

• Does not have separate market for food and bioproducts

• No “wrinkles”: non-competitive behavior, politics, or regulation

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Depletion module

• Variable costs increase with depletion

• Fossil fuels (Greene et al. 2003)

– Logistic depletion multiplier: costs → ∞ as depletion reaches 100%

• Biofuels

– Resource: terrestrial above-ground NPP

– Includes exogenous NPP consumption (Imhoff et al. 2004)

• Veg, meat, grain, fiber, milk, wood

• Grows with economy

– Constraints on grain production and frac. NPP

(15% to biofuels, 75% overall limit)

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Typical results: Oil production

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Typical results: Total fuel production

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Typical results: Oil price

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Divergent futures using ROMEO

Per cap. GDP growth =

1% developing, 0% developed

Oil = High USGS

Per cap. GDP growth =

4% developing, 2% developed

Oil = Low USGS

Effect of policies or technologies

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Carbon Tax = 25$/ton in 2010 →

105$/ton in 2050

Cheap cellulosic ethanol

(1/2 capital and operating costs)

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Pause to consider the problems…

• Large-scale energy systems modeling is frequently criticized

– IPCC models: “computerized fairy tales” (Smil 2008)

– Failure of imagination results in huge errors

• How do models help?

– Calculate complex causal chains

– Tie together disparate bits of knowledge about system

• How do models hurt?

– Prediction is problematic

– Range is larger than we imagine

How can we act despite this uncertainty?

• Avoid “predict-then-act” form of systems modeling (Lempert et al. 2003)

• Optimization is problematic under deep uncertainty or disagreement

– If you optimize for a given future, the result is often sub-optimal

• Multi-objective and robust optimization

– Account for varying priorities and uncertainty

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“Rather than first predicting the future

in order to act on it, gain a systematic

understanding of [the] best near-term

options for shaping [the] future in the

absence of any reliable predictions.”

Lempert et al., 2003

Characteristics of robust “actions”

1. An action causes improvement despite variation in uncertain inputs

2. An action causes improvement despite variation in beliefs about how the world works

3. An action causes improvement despite variation in goals

23Sources: Lempert et al. 2003, Kann and Weyant 2000

Robustness/multiobjective analysis in ROMEO

• Perform 2-D “slice” through parameter space

– Vary amount of conventional oil (x-axis) and growth in global income (y-axis)

• Test actions using multiple metrics

– Economic: Average oil price, highest oil price

– Environmental: GHGs, fraction NPP consumed

– Security: Fraction of fuel from largest producing region, fraction from most important fuel type

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Baseline results – Average price per bbl

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Baseline results – Total emissions

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Impacts of actions

• How would landscape of futures be changed by near-term actions (technologies or policies)?

– Cheap cellulosic technology: ½ initial capital and operating costs

– Demand: + 0.5% yearly reduction oil/ GDP

– Carbon tax: 25$/t CO2 2010 → 105$/t CO2 in 2050

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Effects of cheap cellulosic ethanol – GHG emissions

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Cheap cellulosic ethanol

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% Change

Demand reduction

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% Change

Carbon Tax

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% Change

Conclusions and next steps

• Potential for modeling to aid in beneficial oil transition

– Models can help us understand complex problems

– Modeling methods should address uncertainty and varying goals

• Next steps:

– Improve modeling of markets (biomass mkt.)

– Refine metrics of comparison

– Work on visualization

– Add electricity as oil substitute

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Thank you

• Woods Energy Seminar organizers and attendees

• Collaborators and funding sources:– Deepak Rajagopal, Rich Plevin, Alex Farrell

– NSF Climate Decision Making Center (Carnegie Mellon Univ., SES-0345798) and UC Energy Institute

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Works cited - 1

• Brandt, A. R. (2007). "Testing Hubbert." Energy Policy 35(5): 3074-3088.

• Brandt, A. R. (2008). "Converting oil shale to liquid fuels: Energy inputs and greenhouse gas emissions of the Shell in situ conversion process." Environmental Science & Technology 42(19): 7489-7495.

• Brandt, A. R. (2009). "Converting Oil Shale to Liquid Fuels with the Alberta Taciuk Processor: Energy Inputs and Greenhouse Gas Emissions." Energy & Fuels.

• Charpentier, A. D., J. Bergerson, et al. (2009). "Understanding the Canadian oil sands industry's greenhouse gas emissions." Environmental Research Letters 4(014005): 14.

• Cooper, J. C. B. (2003). "Price elasticity of demand for crude oil: Estimates for 23 countries." OPEC Review 2003(March): 8.

• Farrell, A., R. J. Plevin, et al. (2006). "Ethanol can contribute to energy and environmenal goals." Science 311(27 January): 506-508.

• Farrell, A. E. and A. R. Brandt (2006). "Risks of the oil transition." Environmental Research Letters 1(1): 014004 (014006pp).

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Works cited - 2

• Field, C. B., J. E. Campbell, et al. (2008). "Biomass energy: the scale of the potential resource." Trends in Ecology & Evolution 23(2): 65-72.

• Gerdes, K. J. and T. J. Skone (2009). An evaluation of the extraction, transport and refining of imported crude oils and the impact on life cycle greenhouse gas emissions, Office of Systems, Analysis and Planning, National Energy Technology Laboratory.

• Greene, D. L., J. L. Hopson, et al. (2003). Running out of and into oil: Analyzing global oil depletion and transition through 2050. Oak Ridge, TN, Oak Ridge National Laboratory: 124.

• Haberl, H., K. H. Erb, et al. (2007). "Quantifying and mapping the human appropriation of net primary production in earth's terrestrial ecosystems." Proceedings of the National Academy of Sciences 104(31): 12942-12947.

• Imhoff, M. L., L. Bounoua, et al. (2004). "Global patterns in human consumption of net primary production." Nature 429(6994): 870-873.

• Jaramillo, P., W. M. Griffin, et al. (2008). "Comparative Analysis of the Production Costs and Life-Cycle GHG Emissions of FT Liquid Fuels from Coal and Natural Gas." Environmental Science & Technology 42(20): 7559-7565. 35

Works cited - 3

• Kann, A. and J. P. Weyant (2000). "Approaches for performing uncertainty analysis in large-scale energy/economic policy models." Environmental Modeling and Assessment 5: 29-46.

• Lempert, R. J., S. W. Popper, et al. (2003). Shaping the next one hundred years: New methods for quantitiative, long-term policy analysis. Santa Monica, CA, RAND.

• Searchinger, T., R. Heimlich, et al. (2008). "Use of U.S. Croplands for Biofuels Increases Greenhouse Gases Through Emissions from Land Use Change." Science 319(February 29): 1238-1240.

• Smil, V. (2008). "Long-range energy forecasts are no more than fairy tales." Nature Vol. 253(8 May): 154.

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