# Presentation: 510295

## Avoiding “The Big Mistake” in Forecasting Technology Adoption

- Jonathan Koomey, Ph.D.
- Lawrence Berkeley National Laboratory
- JGKoomey@lbl.gov, 510/486-5974
- http://enduse.lbl.gov/Projects/forecasting.html
- Presented at the Energex Conference
- Las Vegas, NV
- July 26, 2000

## Hazards of predicting the future

- 	"There is no documented evidence that indicates the long-lasting, consistent relationship between energy use and GNP will change in the future.  There is no sound, proven basis for believing a billion dollars of GNP can be generated with less energy in the future.”
- The 1976 Chase Manhattan Energy Report

## The Big Mistake

- Creating a scenario that involves large shifts in policy, but continuing to use a model’s Business-As-Usual parameters for technology costs and market behavior
- Large shifts in policy could include $250/t carbon taxes or substantial increases in programs and policies that directly promote energy efficiency & renewables

## Who makes this mistake?

- Virtually all top-down analysts who rely largely or solely on carbon taxes to reduce carbon emissions
- Bottom-up analysts who rely on current or Business-As-Usual technology costs, elasticities, or implicit discount rates in aggressive policy cases
- Any analyst who relies on an incomplete technology portfolio

## Analyses based on historically-prevalent parameters can be useful

- Business-As-Usual forecasts
- Short term macroeconomic forecasts
- Small price changes

## Background on tax credits analysis

- Commissioned by EPA and DOE in Fall 1997 to assist in design of Administration’s proposed tax credits
- LBNL analyzed central air conditioners, heat pumps,  heat pump water heaters, fuel cells, natural gas water heaters, and natural gas heat pumps (preliminary analysis on others)
- Used empirical data on rebate effectiveness and on cost reductions from increased production experience
- Fully documented

## Effects of tax credits on adoption of efficient technologies

- Direct price effect:  the price reduction from the credits induces some people to buy the more efficient product
- Announcement effect:  the mere existence of a rebate establishes the credibility of the more efficient technology, changes purchasing behavior, and encourages adoption
- Increased production experience:  learning, as embodied in the progress ratio, causes price reductions that lead even more people to buy the technology (this effect persists after the credits end).

## Empirical data on rebate response

- CAC = Central Air Conditioner;  Price response from Cambridge Systematics 1994, *Customer Decision Study:  Analysis of Residential Customer Equipment Purchase Decisions*;  Technology data from Wenzel et al. 1997.

## How do tax credits work?

- Source:  LBNL analysis of administration’s CCTI tax credits, memo dated 13 Feb 1998.  http://enduse.lbl.gov/Projects/TaxCredits.html

## Policy context matters

- Our choices affect how the future unfolds
- A world of aggressive low-carbon policies will change technology costs and adoption behavior compared to the Business-As-Usual case

## Avoiding The Big Mistake

- Ask if the forecasters changed the key historical relationships to reflect the effect of policy choices and technological change
- Focus on data and scenario creation, NOT modeling methodologies
- Learn from past forecasting mistakes
- Adopt strategies that are robust in the face of inevitably imperfect forecasts

## Conclusions

- Technology costs and behavioral parameters are affected by aggressive policies and programs
- The Big Mistake is pervasive in analyses of technology adoption, but you can avoid it in your own work
- Analyses that succumb to The Big Mistake underestimate the effects of policies and overestimate the costs of reducing carbon emissions