# Presentation: 739007

## A Heuristic Multi-Agent, Multi-Attribute Policy Model for Analyzing the Adoption of Energy-Efficiency Technologies*

**John A. “Skip” Laitner**
**Economic Analysis Director**
**American Council for an Energy-Efficient Economy (ACEEE)**
***NREL Energy Collaborative Analysis Initiative***
**Web Forum: Improving Behavior Modeling**
**August 28, 2008**

***adapted from Sanstad and Laitner 2004 (see bibliography).**

## Overview

- (A) What factors influence technology adoption?
- (B) What factors actually tend to get modeled in practice (in quantitative energy and climate policy analysis)?
- Why A     B?
- A heuristic model of multiple influences on technology adoption
- Illustrative results of said model, and then
- Concluding remarks

## Perhaps adding a little perspective to our discussion this afternoon. . . .

**The picture’s pretty bleak, gentlemen. . . .**
**The world’s climates are changing, the mammals are taking over, and we all have a brain about the size of a walnut.**

## What Factors Influence Technology Adoption?

- Among other things:
- Direct costs and available financial resources
- Expected financial benefits - savings, profits, etc.
- "Transaction costs" and/or "hassles"
- Information/knowledge about technology performance
- Habits and inertia
- Perception of adoption in the marketplace
- Government policies - taxes, subsidies, R&D, information dissemination, etc., etc....

## What Factors Actually Tend to Get Modeled in Practice?

- Direct costs and available financial resources
- Perhaps, although not necessarily, some expected financial benefits - savings, profits, etc.

## Why?

- Short version of long story:
- Across various parts of the field of economics, a thousand flowers bloom, but...
- In quantitative energy and climate policy modeling specifically, there is mono-culturing
- Specifically: Models of this ilk primarily built on standard neo-classical foundations - the basic cost minimization/profit maximization story is the only one included

## Progress of Sorts, But. . .

- In recent years there has been an acceleration of work on the influence of technological change in energy modeling and climate analysis.
- In general, the findings to date indicate that improved representations of technological change - such as “endogenous/induced” and "learning-by-doing" - may have substantial implications for estimating costs and benefits of energy and/or pollution abatement policies.
- However, still relatively little attention to the marketing and behavioral characterizations that also influence the adoption of new energy technologies.
- The heuristic model described in the next set of slides is intended to illustrate the results of representing a more dynamic behavioral and technological diffusion process that encourages new policy development.

## About the Heuristic Model

## Would this necessarily be true?  Where the fort might symbolize the gathering of energy efficiency and clean energy technologies, and where the single (but admittedly very big) arrow had been launched from an inappropriate behavioral and technological characterization of the U.S. economy?

***Would this necessarily be true?  Where the fort might symbolize the gathering of energy efficiency and clean energy technologies, and where the single (but admittedly very big) arrow had been launched from an inappropriate behavioral and technological characterization of the U.S. economy?***

## The Overall Analytical Framework

- The heuristic model is a Monte Carlo simulation that evaluates and selects a mix of four competing technologies which might be required to meet the growth in electricity demand, and to replace retired generating units — all in some future year (call it 2013).
- However, rather than assume a single “representative agent” to choose the technologies, we allow 100 different agents (whether firms or households, all with different preferences) to choose within a random pattern of influences affecting their choice.
- Among the influences on technology selection are economic cost (of course), but also the assumption of a general familiarity with the emerging technologies, an          awareness of non-energy benefits, and                      valuation for market externalities.

## Several Important Caveats

- The focus of the analysis that follows is on the influence of both price and non-price market drivers as they may affect technology choice — not on the actual characterization of any individual technology or energy scenario.
- All values or attributes in this exercise are expressed in $/kWh for comparative purposes and ease of use.  A more rigorous modeling exercise would also include financial hurdle rates, substitution and income elasticities, revealed preferences, and the influence of networks, advertising, standards and other market drivers.
- An entirely different set of assumptions, or range of assumptions, would produce an entirely different set of results.
- Finally, while the analysis does provide useful insights into understanding the market potential of renewable energy or green power technologies, in many cases the data do not yet exist to properly reflect the many factors that might        influence consumer and business decisions.

## Illustrative Values of Cost Parameters ($/kWh)

| Technology |  | Cost | Benefits | Awareness | See Others | Externalities |
| --- | --- | --- | --- | --- | --- | --- |
| Defender |  | 0.040 | 0.000 | 0.000 | 0.000 | 0.000 |
| Challenger |  | 0.060 | 0.002 | 0.004 | 0.002 | 0.004 |
| Renewables |  | 0.080 | 0.008 | 0.015 | 0.008 | 0.008 |
| Efficiency |  | 0.060 | 0.004 | 0.012 | 0.004 | 0.010 |
| 
Range of the Value Influence |  |  |  |  |  |  |
| Lower Weight | 80 |  | 60 | 40 | 40 | 20 |
| Upper Weight | 100 |  | 100 | 80 | 80 | 80 |

## Market Share Algorithm

- Where:
*MS**kt* = market share of technology *k* at time *t*
*COST**kt* = amortized capital and operating costs (net of benefits and/or
-                 incentives) of technology *k* at time *t*
*v* = variance parameter representing cost homogeneity
*J* = number of technologies competing to provide the same service as *k*.

## Explaining the Variance Parameter

- For the variance parameter, *v*, an extremely low value, such as 1, means that new equipment market shares are distributed almost evenly among all competing technologies, even if their annual costs differ significantly.
- An extremely high value, such as 10, means that the most cost effective equipment gains a proportionately higher market share.
- For example, a technology with a 25 percent cost advantage would grab 90 percent of market share.
- In this exercise, we adopt a value of 4.
- In this case, a technology with a 25 percent cost advantage would grab 71 percent of the market    share.

## Illustrative Scenario Impacts

## Exploring Market Share(Based only on Cost Parameters as Conventional Models Generally Reflect)

- Note:  Perhaps one way to help us understand these results is to think of the market share as the contribution of each technology in meeting an estimated 100 billion kWh of new electricity demand in some future year.

## Exploring Market Share(Including an Increased Technology Awareness)

## Exploring Market Share(Now Adding Information Programs on Larger Technology Benefits)

## Exploring Market Share(And Finally, Seeing Others Implement and Reflecting Market Externalities)

## Some Conclusions

- Recall the caveats: the heuristic model is indeed just heuristic
- However, it does serve to illustrate that
- Modeling exercises that depend only on the cost characterization of technologies are likely to imperfectly represent new or emerging technologies under different price regimes, behavioral assumptions, and policy scenarios.
- Perhaps just as important, models that fail to include a richer behavioral backdrop and market characterization tend to reinforce or preserve the status quo rather than provide meaningful insights for policy analysts and decision makers.

## A Selected Bibliography of Behavior and Technology Policy Modeling

**DeCanio, Stephen J. and John A. "Skip” Laitner.  1997.  **"Modeling Technological Change in Energy Demand Forecasting: A Generalized Approach." Technological Forecasting and Social Change, Volume 55, pp. 249-53.
**DeCanio, Stephen J. and John A. "Skip” Laitner.  2003.  **"The Role of a Dynamic Marketplace in the Adoption of Industrial Efficiency Innovations," *Proceedings of the 2003 ACEEE Summer Study on Energy Efficiency in Industry. *Washington, DC: American Council for an Energy-Efficient Economy, pp. 51-65.
**Geller, Howard, Philip Harrington, Arthur H. Rosenfeld, Satoshi Tanishima, and Fridtjof Unander.** “Polices for increasing energy efficiency: Thirty years of experience in OECD countries,” *Energy Policy*, 34 (2006) 556–573.
**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.
**Laitner, John A. "Skip“ and Donald A. Hanson.  **2006.  “Modeling Detailed Energy-Efficiency Technologies and Technology Policies within a CGE Framework,” *Energy Journal*, Hybrid Modelling: New Answers to Old Challenges.
**Laitner, John A. "Skip" and Alan H. Sanstad.** 2004. "Learning by Doing on Both the Demand and the Supply Sides: Implications for Electric Utility Investments in a Heuristic Model." *International Journal of Energy Technology and Policy*, 2004, 2(1/2), pp. 142-152.
**Laitner, John A. "Skip, “Stephen J. DeCanio, and Irene Peters.** 2000. "Incorporating Behavioral, Social, and Organizational Phenomena in the Assessment of Climate Change Mitigation Options," E. Jochem, J. Sathaye and D. Bouille, *Society, Behavior, and Climate Change Mitigation*. Dordrecht, The Netherlands: Kluwer Academic Press, pp. 1-64.
**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*, Volume 11, pp. 87-94.
**Sanstad, Alan H. and John A. "Skip” Laitner.  **2004.  “A Multi-Agent, Multi-Attribute Policy Model Reflecting a Dynamic Marketplace in the Adoption of Energy Efficiency Technologies,” *Proceedings of the 2004 ACEEE Summer Study on Energy Efficiency in Buildings*. Washington, DC: American Council for an Energy Efficient Economy..

## The difficulty lies not with the new ideas, but in escaping the old ones

***John Maynard Keynes***

## Contact Information

**John A. “Skip” Laitner**
- Director of Economic Analysis
**American Council for an Energy-Efficient Economy (ACEEE)**
- 4372 Shooting Star Drive
- Island Lake, IL 60042
- (847) 865-5106
- jslaitner@aceee.org
- For more information and updates visit:
- http://www.aceee.org