residential consumption of gas and electricity in the.pptx

7/28/2019 Residential Consumption of Gas and Electricity in the.pptx

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By

Anna Alberini and Daniel Velez-Lopez


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Agenda Introduction

Econometric models

Sample and the data Results

Conclusion


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Introduction


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Introduction Why is it important to measure the responivness of residential

energy demand to the prices of electricity/gas Purpose of forecasting demand Planning for generation

Transmission and distribution capacity Energy policy purposes

Three research questions: What are the price elasticities of residential electricity and gas

demand Is such responsivness sensitive to equipement and energy choices

which are not easily reversed How does houshold income influence demand and the price

elasticities


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Introduction Nationalwide sample with unprecedented detail and

breadth of coverage to study residential energyconsumption in U.S.

Attention focused to single-family homes and duplexes

Assembling of large and comprehensive dataset that

documents energy usage for over 69000 dwellings (74000households) in U.S.

Time period 1997-2005 and 2007


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Econometric models


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Econometric models Individuals and households do not derive utility directly from energy: they use

energy to produce goods (warm home, meals,heating, lighting etc.)

Demand for energy on residental level depends on: energy prices, prices of other goods, income other chacterisics of the household

Focus on electricity and gas -> most important fuels used in U.S. (100% / 60%)- Fuel oil 7 %

LPG 1.5 % Less important Kerosene 1.5 %

Estimation of two set of models. In the first set the regression equations arevariants of the static energy demand model:


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Econometric models

Q consumption

j=E,G (electricity,gas)

i denotes the dwelling

t denotes time period

P

x

z

s Year effects

price (coefficients on the log

prices are the short-termown- and cross-priceelasticity)

includes weather, size/age ofhome, heating/cooling

equipment dummies andappliances

number and age ofoccupants, income, presenceof children and elderly andhomeownership dummy


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Econometric models Partial adjustment model lets individuals adjust their stock

of appliances / make energy-efficiency and conservationinvestments

Desired energy consumption can be expressed as:

After inserting and re-arranging we obtain the regressionequation:

Shows that the short run elasticities are the regression coefficients on the log prices and longrun elasticities can be computed by dividing short run elasticities by the estimate of


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Econometric models Estimation of the dynamic model includes partial adjustment model

with fixed, dwelling spedific effects

Concern: lagged dependent variable serially correlated with error term makes estimators bisaed and inconsistent

Alternative approach swipping out the state-specific effects:

wdenotes all exogeneous regressors in the right-hand side of thepartial adjustment regression equation


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Econometric models Price of energy measured with error

Mismeasured prices enter in the dependent variable as well as in right-hand side as aregressor:

Assume:

Then:

i denotes dwelling A is the utility bill at time t P*it is the nominal price (mismeasured)

CPI index (converts nominal to real prices) is the own price elasticity

This equation shows that the price elasticity is thenegative of the coefficient on ln CPI. In our case ln CPIis positively correlated with the log price of electricity.

How to get around the mismeasurement problem?

Restrict estimation to areas with only one utility Use state-level electricity and gas prices


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Sample and data


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Sample and data Large and comprehensive dataset merged out of several sources

American Housing Survey (department of housing and urbandevelopement)contains extensive informations about :

Structural characteristics Home ownership Financial aspects Appliances Socio-demographic circumstances of occupants

98 772 observations

50 largest metro areas locations should ensure: Considerable variation in climate Age of the stock of housing Construction material Utility prices


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Sample and data No AHS reports of E/G Tariffs = Construct consumption

through dividing the utility bills by unit price

Identification of relevant E/G utilities Listings provided by state public utility commission

Variety of on-line city services

If more than one utility

computed average price

weighted average in three ways

Residential price 1,2,3 RP 1 (utilitys customer base)


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Variation across states:

E:Indiana(L)/NY (H)G: Georgia(L)/Florida(H)


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Sample and data Key regressors (table 5)

Weather (important determinant of energy use)

Heating and cooling degree-days

Regression controls for dwelling characteristics Age and size of home Number of rooms Number of floors

Heating and cooling equipment andappliances (table 6)


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E.Heat: mild or warmclimates ( Arizona,Tenessee, Texas,Washington)


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Results:

Static and Dynamic

Electricity; S


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Electricity; S

Gas; S


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Gas; S

Measurement error smallerin single utility areas

(mentioned)

Results for this runs arereported in (D) und (E)

Close to counterparts in

(B) and (C)

Columns(F) and (G)present regression resultsfor the subsamples with

E/G heating

Only for city specific-result brevity- hold forD.S. and D.H.S.E.


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Dynamic

In (B) and (D) we excludeheating/cooling, appliancedummies from regression.

Interpret result as if thechoices of thesetechnologies werereversible

E 2% / G 6%


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Conclusion


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Strong household response to energy prices

(short/long run)

Static model contrary to earlier literature noevidence of significantly different elasticities across

households with electric and gas heating

Dynamic model similar estimates in short-run

(long-run)

Considerable potential for policies which affectenergy price


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residential consumption of gas and electricity in the.pptx

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