# Presentation: 521159

## State Energy Efficiency Commitment and It’s Effects on Aggregate BehaviorPresented to:Energy Collaborative Analysis Initiative (ECAI)Web Forum: “Improving Modeling Behavior”

- _Preface_
- Many modeling efforts often overlook good information that is readily available that can improve our understanding of energy use
- Most of the time *behavior* plays an important role in saving energy.  So it is useful to inquire into whether it’s possible to measure some of the main factor that influence energy use, and to see how these factors may have changed over time and over policy regimes

## Y = b0 + b1(X) + b2(I) + b3(T) +... + e

- Suppose energy use (Y) is a function of energy prices (X), income (I), and a time trend (T).  With several data points for each state, we estimate a regression model:
- Y = ***b******0*** + ***b******1***(X) +*** b******2***(I) +*** b******3***(T) +... + e
- The ***b***’s give us very important pieces of information about aggregate energy use behavior. For example, ***b******1*** tells us how energy use will change for a small change in energy prices

## Questions that are important for understanding energy-related behavior...

- which ***b***’s have the largest effects on energy use
- what’s the difference in the ***b***’s between states with strong and states with weak commitments to energy efficiency
- 2)  how does an energy efficiency commitment change the ***b***’s*** ***for the different states

## I produced a recent study that looks into

-  energy demand behavior:
**Changes in Electricity Demand**
**in the United States from the 1970s to 2003**
*The Energy Journal*, Vol. 28(3), Summer 2007, pp. 93 – 119

## Data:  combine 48 states (cross sections) with 27 years (time series)

- Base Period = 1977 to 1991 (15 yrs.)
- Treatment Period = 1992 to 2003 (12 yrs.)
- Dependent variable (Y) is electricity intensity, e.g. kWh/$, and independent variables are electricity price, natural gas price, per capita income/GDP, FRB technology index, HDD, and CDD

## Separate models for residential, commercial, and industrial:

**general comments on differences between S & W quartiles**

- In the commercial sector, both models appear to be closer to each other in the treatment period than in the base period, and they both change in the direction of better energy efficiency behavior
- In the industrial sector, the models differ in both periods, and the *S* quartile has changed in the direction of better energy efficiency behavior
- In the residential sector, both models appear to be similar in both periods, and both quartiles appear to be moving in the direction of better energy efficiency behavior

## Commercial sector: strong quartile’s

** **_***b***_**’s from base to treatment period shows improvement in 3 major economic variables**

## Here’s the practical meaning...comparing across periods, commitment seems to affect behavior

- Suppose electricity prices increased by 50% in the commercial sector...
- In the base period, the *S* quartile model indicates that energy demand in the commercial sector would have decreased by 2.6%:
- In the treatment period, the *S* quartile model indicates that energy demand in the commercial sector would have decreased by 8%:

## Commercial sector: weak quartile’s

** **_***b***_**’s from base to treatment period**
**shows improvement in 2 of 3 economic variables**

**worsened**

## Commercial sector: S & W’s b’s in the treatment period – S’s demand is more negatively income elastic (marginal increase in income leads to larger decrease in energy use)

**a good thing**

## Comparing between groups, commitment appears to affect behavior...

- Suppose per capita GSP (income) increased by 25%
- In the commercial sector in the treatment period, the models for the *S* and *W* quartiles indicate that energy use in the *S* states would decrease by
- _2.5 times_ the amount it would decrease in the *W* quartile
- Why would this be so?  Perhaps because in states with strong energy efficiency program commitment, as consumers become wealthier they are relatively more willing to invest in better equipment, more maintenance, and better energy management

## Conclusions

- The marginal costs of acquiring information on coefficient changes are not high
- Forecasting with coefficients that are assumed to be unchanging could lead to serious errors
- Erroneous forecasts can lead to flawed investment and public policy strategies – and probaby stacks the deck away from energy efficiency and towards new supply resources