# Presentation: 223383

## The Contribution of Economic Models to the Oil Transition and to Climate Policy

***A Review of the AMIGA Modeling System***
- John A. “Skip” Laitner
- EPA Office of Atmospheric Programs
- and
- Donald A. Hanson
- Argonne National Laboratory
- Modeling the Oil Transition
- April 20-21, 2006
- Washington DC

## The Economy is, indeed, a Complex Territory*

***The Economy is, indeed, a Complex Territory****

- *Adapted from a presentation by Stephen J. DeCanio, Presentation for the 6th National Conference on Science, Policy, and the Environment, January 26-27, 2006, Washington DC.

## Conventional View of a General Equilibrium Economic Model

-  An economic model provides a “descriptive” characterization that approximates reality;
-  Adding detail and complexity can fill out the model to improve the approximation; and
-  The dynamic behavior of the model corresponds to evolution of the economy.

## But What if the Model and the Economy Have Very Little Overlap?

## Room for Improvement: Increasing the Value of Energy Modeling for Policy Analysis*

- There is a need for an in-depth review of critical model shortcomings as they might impact the evaluation of different oil/energy transition scenarios.  We highlight five areas here today:
- Technology characterization is often limited or even inappropriate – for both the demand and the supply-side of the equation;
- Capital flows are not sufficiently disaggregated to provide meaningful policy assessments;
- Many model assumptions about consumers and firms may be unrealistic and give misleading insights about policy options;
- The economic accounting of investments and technology choices are limited or poorly represented; and
- All models reviewed in this workshop, including our own AMIGA model, have no ability to anticipate unexpected outcomes or disruptions in ways that inform policymakers about appropriate steps for more robust policy initiatives.

- *Adapted from Laitner et al (2003).

## Given Our Rather Large “Room for Improvement”

***The question might then be asked in terms of modeling the oil transition: “How do we address these potential shortcomings or weaknesses within the AMIGA Modeling System?”***

***To answer that question, we first offer a brief description of AMIGA; ***

***                                       ******and we then highlight several of the key problem areas with a quick review of how they are currently handled within the model.***

## AMIGA

- Examines the impact of changes in more than 200 individual sectors (in terms of both dollar measures and physical units).
- Integrates a detailed energy market specification within a structural economic model.
- Allows firms to maximize net wealth and consumers to maximize intertemporal utility.  In the absence of perfect foresight, agents act on approximate intertemporal rules.** **
- Calculates both prices and macroeconomic
-      variables as consumption, investment, GDP
-      government spending, and employment.
- Provides equilibrium paths for 21 world regions,
-      from the present through the year 2050, with the
-      capability of extending the time horizon out to
-      2100, including GHG concentrations, atmospheric
-      feedback, and temps using the MAGICC model.

***AMIGA Is a Comprehensive Model Integrating Energy Markets, Technologies, and Policies***

***AMIGA***

## AMIGA Modeling System, Version 4.2

- Version 1.0 was a strictly energy-economic framework developed for DOE/EERE Office of Transportation Technologies (OTT) with a 2020 time horizon and used for preparing the OTT R&D Report to Congress.
- Version 2.0 included carbon emissions with tradable permits.  It tracked the NEMS Annual Energy Outlook and was used for Clean Energy Future (CEF) analysis.
- Version 3.0 incorporated the Argonne Unit Planning and Compliance model.  SO2, NOx, and Hg emissions were added for the Jeffords-Lieberman analysis and EMF-19 scenarios.
- Version 3.1 increased representation of the transportation sector with a 2050 time horizon and was used for Pew Climate Center, Keystone scenario analysis, and EPA-Argonne Energy Future Scenarios.
- Version 4.2 adds the other (Non-CO2) greenhouse gases and 20 other regions of the world with a time horizon out to 2100.  This model was being used for the IEA *World Energy Outlook 2004* and the WEO 2006, as well as a contributing model to the EMF-21 and EMF-22 multi-gas emission scenario exercises.

## Overview of the AMIGA CGE Modeling System

- Source: Hanson and Laitner (2006)

## AMIGA Calculates Equilibrium Prices, and Functions that Depend on Prices and Quantities

***AMIGA Calculates Equilibrium Prices, and Functions that Depend on Prices and Quantities***

## How, then, do we handle the critical modeling issues within the AMIGA Modeling System?

## First Problem Area

***A limited technology characterization for both demand and supply-side resources***

## Without New Efficiency Technology,** Energy Use Would Be Almost 3 Times 1970 Levels

-    **Contrast 3 Energy Patterns**
- Using 1970 Technology
- Standard 1970s Forecast
- Actual energy use since 1970

**An increase to ~196 quads based on 1970 technology and market structure**

**Typical forecasts**
**to ~160 quads**

**Actual use of ~100 quads in 2004**

**** Where “energy efficiency” is broadly defined as the difference between the 1970 and 2004 energy intensities.**

***Since 1970, energy efficiency has met 75% of new energy service demands in the U.S, ***

***while new energy supplies have perhaps contributed only 25% of new energy service demands.***

## Other Useful Perspectives on Those Historical and Prospective Efficiency Gains

- By 2004, improved energy efficiency (compared to 1970 technologies and market structure) was already providing 75 percent of all U.S. energy services, which was:
- 1.4 times our total energy production
- 8.3 times our total domestic crude oil production
- 3.7 times our net petroleum imports
- Indeed, the evidence suggests – that despite the impressive efficiency gains following the oil crises in the 1970s and early 1980’s, efficiency resources remain an impressive investment opportunity (Shipley and Elliott 2006, and Laitner 2004); and with large resources to reduce oil consumption beyond cars and light trucks (Elliott et al 2006).
***So the question as we model or evaluate transition scenarios: why is it that we always gravitate toward supply side solutions with the implied assumption that efficiency reserves are either already used up?  Or that they cannot be deployed quickly or cost-effectively?***

## The AMIGA Modeling System offers a detailed technology representation within a CGE framework

## The Model Adds Capacity in New Energy Supply Technologies

- Starting with 2000 existing power plants from the E-GRID database, and adding as needed:
- New coal units – 4 types of re-powering and gasification technologies, with SO2, NOx, and mercury controls
- Nuclear – evolutionary and advanced designs,
- Gas – both conventional and advanced natural gas combined Cycle (NGCC) units, peakers, and advanced turbines,
- Municipal solid waste,
- Large industrial combined heat and power (CHP) units by fuel type, such as natural gas or biomass,
- Fuel cells systems,
- Other distributed generation, and
- Renewable technologies, including
- Wind, Geothermal, Hydroelectric, Biomass co-firing, Biomass gasification combined cycle, Building Integrated PV, Solar, and other

## AMIGA’s Newest Simulation Module:The Macro Analysis of Refinery Systems (MARS)*

- MARS simulation module
- Represents 26 primary & secondary refinery processes
- Assays for six crude types represented: _L_ow-_S_ulfur Light, MS Heavy, HS Light, HS Heavy, HS Very-Heavy, _S_yn_C_rude
- Includes processes for upgrading heavy oils and oil sands (including natural gas consumption and hydrogen production and use)
- Also includes representation of coal-to-liquids (CTL) co-production
- Includes eight major refinery products including gasoline, diesel & jet fuel, and evaluates range of product shares
- Refineries can become more efficient using an array of technologies to capture waste energy including Combined Heat and Power (CHP)
- MARS Interfaces with AMIGA
- Transportation and other demand drivers for refinery products
- Refining feedstocks, energy use, emissions, variable costs, and capital investment spending are used in economy-wide analysis

****With funds and support made available from the National Energy Technology Laboratory***

## Designed to satisfy discrete and well-defined service demands

With technology characterization based on
Engineering cost-of-energy estimates,
Supply function analysis, and
Consumer preferences based on a distribution of hurdle rates unique to end-use sector, energy service, and fuel type.

Including
Light and heavy duty vehicles for both passenger and freight services,
Industrial processes, and
Building service demands.

The model stores a linear list of technologies with a variety of cost and performance attributes.

- With technology characterization based on
- Engineering cost-of-energy estimates,
- Supply function analysis, and
- Consumer preferences based on a distribution of hurdle rates unique to end-use sector, energy service, and fuel type.
- Including
- Light and heavy duty vehicles for both passenger and freight services,
- Industrial processes, and
- Building service demands.
- The model stores a linear list of technologies with a variety of cost and performance attributes.

***But AMIGA Also Adds Capacity for Many Energy End-Use Technologies***

## Transportation Technologies: Size Classes

- Sub compact
- Compact car
- Medium car
- Large car
- Mini vans
- SUV – regular
- SUV – large
- Pickup trucks and large vans
- Commercial trucks, Medium weight trucks, and Buses

- With all technologies designed to meet specific and well-defined end-use service demands such as as vehicle miles or ton-miles traveled

## Vehicle Attributes

- Vehicles have multiple attributes
- Performance
- Size
- Luxury features
- MPG
- Consumers demand these attributes, yet there is a tradeoff in supplying these attributes:
- Fuel economy versus performance
- Cost versus light-weight materials
- Styling versus aerodynamics
- While AMIGA does maintain “default assumptions” about vehicle attributes within its standard reference case, its highly modular and database design will allow _*any of these attributes can be changed or modified*_ to enable users to explore a variety of scenarios and policy tools.

## Transportation Technological Change

- Vehicle technology has been undergoing relatively continuous technical change. We represent this evolutionary technical change as improvements in the characteristics of conventional and advanced gasoline and diesel vehicle types.
- Practical, economic fuel cell technology and hydrogen storage technology would represent engineering breakthroughs. The model takes as an input assumption the dates for availability of breakthrough technologies. Their importance can be examined through sensitivity testing.
- Hybrid vehicles with advanced power electrics and controls represents a mid case. This is foreseeable technology that is developing very quickly.
- We maintain current vehicle cost estimates from DOE’s former Office of Transportation Technologies, Argonne’s Center for Transportation Research, and EPRI Hybrid Electric Vehicle Study Reports.

## Second Problem Area

***Capital flows not sufficiently disaggregated***

## Hierarchical Structure of Output Within the AMIGA Modeling System

***Energy-Related Capital***

***Non-Energy Productive Capital***

## Example of Investment Patterns Needed to Achieve a ~25% Reduction in Energy Use by 2020

- Source: Hanson et al (2004) with monetary values in billions of 2000 dollars.  For this example, the reduction in energy consumption assumed a start year of 2002 with results out to 2050.  Note that the AMIGA report writer can provide greater level of sectoral and end-use use detail than summarized here.

## Third Problem Area

***Third Problem Area***

## Economics Science Has Not Solved. . . .

-   Its first problem – namely, what determines the price of a commodity?  (Robinson 1947)
-  Among things that can influence commodity prices:
-   Belief
-  Value
-  Habit
-  Alternatives
-  Necessity
-  Income
-  All of which can be shaped by changed perceptions, clear and persistent policy signals, as well as new or expanding programs (Brown 2001).

## Highly Imperfect Markets with Search, Information, and Other Transaction Costs; and with all Choices Impacted by Perceived Risks and Existing Preferences and Beliefs

***Impacted by policies, programs, awareness, and by shifting preferences***

***Impacted by policies, R&D programs, experience, and growing expectations***

- Adapted from Koomey (2006)

## Market Imperfections = Efficiency Opportunities in U.S. Refrigerators

- Source: David Goldstein

- Slide courtesy of Art Rosenfeld, California Energy Commission.
- Celebrating his 80th Birthday this month. . . .

***Market Imperfections = Efficiency Opportunities in U.S. Refrigerators***

***A 75% reduction in 30 yrs ~ 5%/year***

## In AMIGA the Energy-Efficient Investment Decision

- Is determined by the condition:

- which is the point on the isoquant at which its slope and the factor price ratio are equal, i.e., the tangent point. A high value for the hurdle rate, *r*, implies that only energy-efficiency investments with a short payback will be undertaken.
- But we also allow *r* to be impacted by program expenditures that we track, and under specific scenarios which we might explore, by changing consumer preferences as households and businesses become more aware of pending energy shortages and/or climate change.
- At the same time, we can also incorporate equipment and appliance performance standards as well as flexible and/or tradable CAFE permits and similar policies.

***A focus on the factor price ratio***

## Some of the Policy Drivers in AMIGA that Now Affect Investment Decisions

- Voluntary and information programs that impact both service demands as vehicle or ton-miles traveled, or technology characterization as Energy Star products
- Experience curves or learning from adoption
- Research and Development programs
- Renewable portfolio standards
- Appliance, Equipment and CAFE standards (including tradable permits for standards)
- System of tradable emission permits (including banking, borrowing, and interpollutant trading)

## Fourth Problem Area

***Fourth Problem Area***

## Fuel Economy Cost Curve I Medium-Sized Cars in the Year 2020

- Adapted from the AMIGA Modeling System with technology characterization as described in Laitner et al (2005) and Hanson et al (2004).  Note that all financial values are expressed in 2000 US dollars.

## Fuel Economy Cost Curve IIMedium-Sized Cars in the Year 2020

***Fuel Economy Cost Curve II******Medium-Sized Cars in the Year 2020***

- Adapted from the AMIGA Modeling System with technology characterization as described in Laitner et al (2005) and Hanson et al (2004).  Note that all financial values are expressed in 2000 US dollars.

## The Importance of Both Technological Detail and Economic Accounting

- At today’s prices, if the cost of gasoline rose by (say) $100/tC, or 24 cents/gallon, it might require the purchase of 4 new cars to save one ton of carbon.  Many would think of the resulting carbon charge as a $100 resource cost.  But if we “follow the money,” the possibility emerges for a small but net positive economic gain.
- A full accounting might reveal the following changes in economic activity:
- Spending for consumer durables might increase by $8,000 (or some other amount)
- Additional business in the banking sector might also with added annual loan payments of perhaps $2,000 (although there may be less investment in energy production)
- Gasoline savings of about $1,200 per year might increase spending for other goods
- Gasoline savings might prompt a $300 reduction in the national oil import bill
- $100 in carbon transfer payments can offset other government revenue sources
- This detail offers a significantly different result than many of the models now portray, especially as the savings continue over the life of the cars.

***The Importance of Both Technological ******Detail and Economic Accounting***

## Potential for Pareto Gains: World Oil Markets

**Now Reflected within the AMIGA Modeling System**
- Reduced energy expenditures, making income available to spend on goods and services in other sectors of the economy
- Lower world oil prices benefiting most countries
- Decrease in the aggregate price index for both domestic and imported goods
**Other Potential Economic Gains (for future mapping)**
- Increased productivity associated with many energy efficiency technologies
- Increased energy and national security
- Possibly reduced military expenditures
- Value of fuel diversity
- Improved trade balance with the prospect of less foreign debt

## Fifth Problem Area

***Fifth Problem Area***

## Scenario Analysis

- Current energy supply markets are now straining to maintain such high levels of output that almost any disruption may generate significant economic impacts (Elliott 2006).  Models cannot predict such events with certainty.  But they can be used to help understand the range of possible outcomes that are likely to emerge.  This knowledge can help policymakers identify different paths that might avoid or minimize those disruptions.
- One tool that can assist in this area is scenario analysis.
- As technologists we may have our own insights about the core assumptions embedded within our models; but as modelers we should invite a greater diversity of technologists, sociologists, and policy analysts to help us understand the full range of “fitness landscapes” – given the potential for transition difficulties and the likely but unexpected outcomes.
- Lempert et al (2003) describe robust quantitative methods that enable decision-makers to explore the set of plausible actions that might shape the long-term future to one that is more to our liking.  Recalling the warning published by Elliott (2006), perhaps more important in the short run is that such efforts can help us better position our economies to ease the inevitable transitions; and/or to minimize the negative impacts of the unexpected.
- Hanson et al (2004) undertook one such exercise using the AMIGA model with the results recently described in Laitner et al (2005).

## The Really “Not So Big” Insights

- Admittedly, low energy prices can sustain a higher level of economic growth; but so can a smart investment path – one that emphasizes a balanced portfolio of both energy efficiency and advanced energy supply or energy conversion technologies.
- There is little question that today’s public and private choices, along with the external events (the so-called Black Swan), will affect the cost of managing the oil transition and responding to the unexpected future.
- In our study we found, however, that ***a productive technology investment*** strategy – begun early – will better position the economy and the environment to respond to unexpected challenges ahead.
- Hence, one task of near-term modeling exercises will be to identify the mix of early technology investments that will satisfy multiple social goals (national security, environmental quality, equity, and a robust economy) ― given conditions of deep uncertainty.
- To accommodate these scenario evaluations, the pressing need is to complete an assessment of model shortcomings.  The improved model performance can provide useful rather than misleading policy insights.

## Bibliography

**Brown, Marilyn A.** 2001. “Market Failures and Barriers as a Basis for Clean Energy Policies.” *Energy Policy*, 2001, *29*(14), pp. 1197–207.
**DeCanio, Stephen J. **2006.  “Presentation for the 6th National Conference on Science, Policy, and the Environment,” January 26-27, 2006, Washington DC.  See also,
**DeCanio, Stephen J. **2003**.**  *Economic Models of Climate Change: A Critique.*  New York, NY: Palgrave Macmillan.
**Elliott, R. Neal, Therese Langer, and Steven Nadel. ** 2006.  “Reducing Oil Use through Energy Efficiency: Opportunities Beyond Cars and Light Trucks,” Washington, DC: American Council for an Energy Efficient Economy, January.
**Elliott, R. Neal. ** 2006.  “America’s Energy Straightjacket,” Washington, DC: American Council for an Energy Efficient Economy, April.
**Hanson, Donald A. and Laitner, John A. "Skip".**  2006.  “The AMIGA Modeling System, Version 4.2: Disaggregated Capital and Physical Flows of Energy within a General Equilibrium Framework,” Argonne, IL: Argonne National Laboratory (in review).
**Hanson, Donald A., Irving Mintzer, John A. "Skip" Laitner, and Amber J. Leonard.** 2004.  “Engines of Growth: Energy Challenges, Opportunities, and Uncertainties in the 21st Century,” Argonne, IL: Argonne National Laboratory.
**Koomey, Jonathan G., Paul Craig, Ashok Gadgil, and David Lorenzetti.** 2003. “Improving long-range energy modeling:  A plea for historical retrospectives.” *The Energy Journal*, vol. 24, no. 4. October. pp. 75-92.
**Koomey, Jonathan G.**  2006.  “Long-term Energy Forecasts: Is Useful Learning Possible?”  A presentation at the IIASA Conference on Learning and Climate Change, Laxenberg, Austria.  April 10-11.
**Laitner, John A. "Skip.“** 2004. “How Far Energy Efficiency?” *Proceedings of the 2004 ACEEE Summer Study on Energy Efficiency in Buildings.* Washington, DC: American Council for an Energy Efficient Economy.
**Laitner, John A. "Skip", Donald A. Hanson, Irving Mintzer, and Amber J. Leonard.**  2005. “Adapting in Uncertain Times: A Scenario Analysis of U.S. Energy and Technology Futures.” *Energy Studies Review*, Vol. 14, No.1, 2005 pp120-135.
**Laitner, John A., Stephen J. DeCanio, Jonathan G. Koomey, and Alan H. Sanstad.** 2003. “Room for Improvement: Increasing the Value of Energy Modeling for Policy Analysis.” *Utilities Policy*, 11, pp. 87-94.
**Lempert, Robert J., Steven W. Popper, and Steven C.** **Bankes.  **2003**.  ***Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis*. Santa Monica, CA: RAND.
**Robinson, Joan. **1947. *An Essay on Marxian Economics*. London, England: MacMillan.
**Shipley, Anna Monis and R. Neal Elliott.  **2006**.  **“Ripe for the Picking: Have We Exhausted the Low-Hanging Fruit in the Industrial Sector?”  Washington, DC: American Council for an Energy-Efficient Economy, April.

## For more information on the material referenced in this summary, contact:

**John A. “Skip” Laitner**
**EPA Office of Atmospheric Programs**
**o: (202) 343-9833**
**f: (202) 343-2210**
**email: Laitner.Skip@epa.gov**
**Donald A. Hanson**
**Argonne National Laboratory**
**o: (630) 252-9061**
**f: (630) 252-4498**
**email: dhanson@anl.gov**

**For more information on the material referenced in this summary, contact:**

- The preliminary findings contained in this workshop presentation are believed to rely on credible and accurate sources of information.  Any errors in the analysis are solely the responsibility of the analytical team presenting the information.  The recommendations or results of the energy scenarios described herein should not be construed as reflecting the official views of the U.S. Environmental Protection Agency, Argonne National Laboratory, or the U.S. Government.