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Spatial Panel Data Analysis of Electricity Demand in Turkey

This study analyzes spatial dependency in electricity consumption across Turkey's provinces and regions using dynamic spatial panel data techniques. The research aims to investigate determinants of electricity demand and obtain price and income elasticities to inform policy-making.

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Spatial Panel Data Analysis of Electricity Demand in Turkey

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  1. Regional Electricity Demand in Turkey: Dynamic Spatial Panel Data AnalysisbyGülsüm AkarsuFacultyof EconomicandAdministrative Sciences, OndokuzMayıs University, SAMSUN, TURKEY8th INTERNATIONAL SCIENTIFIC CONFERENCEON ENERGY AND CLIMATE CHANGE7-9 OCTOBER 2015, ATHENS (HELLAS), GREECE

  2. Outline • Introduction • Model and Methodology • Analysis at NUTS-3 Level • Data and Empirical Results • Analysis at NUTS-2 Level • Data and Empirical Results • Conclusion

  3. Introduction • Spatial effects as an important type of interaction effects econometrically since the seminal work of Anselin (1988) • Many theoretical and empirical contributions in the spatial econometrics (Elhorst, 2013) • For the electricity demand estimation, more than 450 studies (Dahl, 2011) but not so much study including the spatial effects • Our aims: (1) to investigate the spatial dependency among the provinces and regions of Turkey due to the socio-economic relations between the provinces and regions, (2) to analyze the determinants of electricity demand, (3) to obtain the price and income elasticities

  4. Introduction • Uptoourknowledge, noanyotherstudyexaminingthespatialdependencyfortheelectricity consumption of Turkey’sprovincesandregions • Annual balanced panel data on 65 provinces of Turkey between the years 1990 and 2001 as well as 26 regionsfrom 1990 to 2001 andfrom 2004 to 2011 • Spatial panel data techniques to capture cross-section heterogeneity, dynamics, trends, spatial spillovers, and spatial clusters

  5. Introduction Understanding the determinants of electricity demand are important • electricity demand forecasting, • investment planning, • the regulation of the sector, • the formulation of policies on demand management, restructuring of electricity sector, the determination of the social, economic, and environmental impacts of policies.

  6. Literature Review Houthakker (1951): pioneering study Between years 1951 and 2008, more than 450 studies for the electricity demand estimation (Dahl (2011)) Our focus on the aggregate electricity demand based on the arguments suggested by Pouris (1989) in order to obtain unbiased elasticity estimates for the total economy. For aggregate electricity demand, some studies at the regional level employing panel data, such as, Hsiao et al. (1989), Diabi (1998), Atakhanova and Howie (2007), Ma et al. (2008), Ohtsuka et al. (2010), and Ohtsuka and Kakamu (2013). Only Ohtsuka et al. (2010) and Ohtsuka and Kakamu (2013) incorporate spatial interactions into their models. Ohtsuka and Kakamu (2013) compare the forecasting performance of VAR(1) model and SAR-ARMA(1,1) model and their results show that VAR model have better forecasting performance.

  7. Literature Review • For residential electricity demand, Gomez et al. (2013)  presence of the significant spatial effects from the estimation of static SARAR model. • For Turkey, only few studies analyzing the total electricity demand. • Basedon the explanatory variables, time period and method employed, different results for elasticity estimates. • aim to contribute to the literature by considering spatial interactions among the provinces ofTurkey.

  8. Total Electricity Demand Studies for Turkey

  9. ModelDistinction between long run and short run effects of economic factors • In the short run, as stocks of electrical appliances, equipment, and machines, and other factors of production are fixed, only the factors that lead to changes in utilization rate of fixed electrical equipment stock determine the electricity demand; • In the long run, size of stock and efficiency of electrical appliances, equipment, and machines can change as a result of change in the economic and technological factors. • “This recognition actually calls for a dynamic model, where the difference between the short run and the long run is tackled explicitly” (Olsen and Roland, 1988: 16).

  10. Model Dynamic spatial panel data model [Elhorst (2005)]; (1) Where X = (lnpcgdp lnrep uratio hdd cdd ), lnpcec, lnpcgdp, and lnrep: natural logarithms of per capita electricity consumption, per capita gross domestic product, real electricity price; uratio: urbanization ratio; hdd and cdd: heating and cooling degree days, respectively;

  11. Model W: weightmatrix • different types of weight matrices: • binary (queen) contiguity weight matrix, • symmetric spatial weight matrix, • row-stochastic spatial weight matrix, • diagonal weight matrix with (i, i) equal to (1/sqrt(sum of ithrow)), • weight matrices based on nearest 2, 3 and 4 neighbors • weight matrix based on power distance weights given by (wij=dij-1) where dijis the distance between ith and jth provinces.

  12. Analysis at NUTS-3 level

  13. Data • annual balanced panel data on 65 provinces of Turkey between the years 1990 and 2001 1 Average daily temperature is used for the calculation of hdd and cdd variables. 2 İstanbul Chamber of Commerce (İTO) wholesale price index (general, 1968=100) is used for deflation of GDP and electricity end-use prices.

  14. Data • Average daily temperatures for each province from Turkish State Meteorological Service for calculation of hdd and cddvariables where,is the average daily temperature.

  15. Data • interpolate gaps in the total population and urban population by using exponential function method as described in Kocaman (2002). • After 1989, some towns have gained province status. rearrange the data on total population, urban population, total and sectoral electricity consumption and GDP by the data values on new provinces to the provinces that they were disjoined based on the information obtained from the website of Ministry of Justice. • the electricity end-use price by taking the weighted average of each sector’s electricity end-use prices. Weights: electricity consumption share of each sector out of total electricity consumption. • Urbanization ratio: ratio of urban population to the total population of each province. • For per capita values, we use populations of each province over the period.

  16. Data Moran’s I of per capita Electricity Consumption for years 1990 and 2001

  17. Estimation of the Dynamic Spatial Panel Data Model • Maximum Likelihood (ML) estimator as suggested by Das, Kelejian, and Prucha (2003) and Elhorst (2005) assuming that all the variables are exogenous • Time dimension is short, it is mentioned that first cross section of observations contains important amount of information. • ML estimation based on unconditional likelihood function which requires the specification of the marginal distribution of initial values and thus, pre-sample values of exogenous explanatory variables. • Elhorst (2005) considers two approximations: Bhargava and Sargan (1983) (BS) approximation and another approximation proposed by Nerlove and Balestra (1996) (NB) and Nerlove (1999) or Nerlove (2000). • Parameter estimates, obtained from the unconditional likelihood function of the first differenced fixed effects dynamic panel data model which is further extended to include spatial error autocorrelation, for the lagged dependent variable and exogenous explanatory variables are consistent. • Numerical iterative optimization based on golden section search and parabolic interpolation.

  18. Estimation Results of Electricity Demand Model for Turkey (Panel data on 65 provinces over the period from 1990 to 2001)

  19. Empirical Results • Our results based on the estimation using binary (queen) contiguity weight matrix • Differ according to the approximation for the first observations • Results show evidence of significant spatial effects for both approximations • Elhorst (2005): Nerlove and Balestra (1996) approximation is better than the Bhargava and Sargan (1983) approximation based on the comparison of the forecast performance of the estimator using the first cross section of observations according to each approximation. • Negative spatial correlation for Nerlove and Balestra (1996) approximation contrary to our expectation. • Conclusion based on the estimation results for Bhargava and Sargan (1983) approximation. • cannot find statistically significant effects of any economic factor.

  20. Empirical Results • shortrunelectricitydemand is priceinelastic (shortrunelasticity is -0.30348), longrunelectricitydemand is foundto be elasticwithrespecttoprice (longrunelasticity is -1.13231). • electricitydemand is inelasticwithrespecttoincomeboth in theshortrunand in thelongrunwiththefollowingshortrunandlongrunelasticities, 0.16734 and 0.62437, respectively. • Comparison of our results with the previous studies: results in line with the findings of the past studies employing partial adjustment model Hsiao et al. (1989), Diabi (1998), Erdoğdu (2007), and Bhargava et al. (2009) have found that short run electricity demand is income and price inelastic. However, findings of Hsiao et al. (1989) and Bhargava et al. (2009) indicate that long run electricity demand is elastic with respect to income and inelastic with respect to price.

  21. Analysis at NUTS-2 level

  22. Data • annual balanced panel data on 26regionsof Turkey between the years 1990 and 2001andbetween 2004 and 2011 1 Average daily temperature is used for the calculation of hdd and cdd variables. 2 used for deflation of GVA and electricity end-use prices for 2004-2011 and use İstanbul Chamber of Commerce (İTO) wholesale price index (general, 1968=100) for years between 1990 and 2001.

  23. Data • Average daily temperatures for each province from Turkish State Meteorological Service for calculation of hdd and cddvariables where,is the average daily temperature. • Foreachregion, calculatethe HDD and CDD bytakingweightedaverage of eachprovinces’ HDD and CDD (weights: surfacearea of eachprovince)

  24. Data • interpolate gaps in the total population and urban population by using exponential function method as described in Kocaman (2002). • the electricity end-use price by taking the weighted average of each sector’s electricity end-use prices. Weights:electricity consumption share of each sector out of total electricity consumption. • Urbanization ratio:ratio of urban population to the total population of each province. • Forper capita values, we use populations of each regionover the period.

  25. Data Moran’s I of per capita Electricity Consumption

  26. Estimation Results of Electricity Demand Model for Turkey (Panel data on 26regions over the period from 1990 to 2001)

  27. Estimation Results of Electricity Demand Model for Turkey (Panel data on 26regions over the period from 2004 to 2011)

  28. Empirical Results • Our results based on the estimation using twodifferentweight matricesselectedbymaximizingloglikelihoodvalue • Differ according to the approximation for the first observations • Negativespatialcorrelationcontrarytoourexpectation. • Spatialeffectsare not statisticallysignificant • Fortheperiodbetween 1990 and 2001, cannotfindstatisticallysignificanteffects of anyeconomicfactorforboth of theestimationsusingdifferentweightmatrices at theprovincelevel. • Fortheperiodbetween1990 and2001, foundsignificanteffects of incomeandcoolingdegreedaysvariablesusingdiagonalweightmatrix. • Fortheperiodbetween 2004 and 2011, foundsignificanteffects of priceandcoolingdegreedaysvariablesusingdiagonalweightmatrix.

  29. Empirical Results • For the period from 1990 to 2001 results indicate that electricity demand is inelastic with respect to price (S:(0.046437;-0.243297), L:(0.156823;-0.9077704)) and income (S:(0.107522;0.163487), L:(0.363114;0.6099897)) both in the short run and in the long run • For the period between 2004 and 2011, short run and long run electricity demand is income inelastic ((S:(0.1003;0.1560), L:(0.2528;0.5818)), short run electricity demand is found to be price inelastic (-0.3879; -0.4248)but in the long run, price elastic based on the results using weights formed by considering nearest three neighbours (-0.9778; -1.5845). • Comparison of our results with the previous studies: results in line with the findings of the past studies employing partial adjustment model Hsiao et al. (1989), Diabi (1998), Erdoğdu (2007), and Bhargava et al. (2009) have found that short run electricity demand is income and price inelastic. However, findings of Hsiao et al. (1989) and Bhargava et al. (2009) indicate that long run electricity demand is elastic with respect to income and inelastic with respect to price.

  30. Conclusion • Determinants of electricity demandaccounting for spatial effects • Spatial panel data techniques. • Application for the provinces of Turkey • Estimate the dynamic spatial electricity demand model by Maximum Likelihood Estimation method • Results show evidence of spatial effects for the estimations performed at province level • As introduce the spatial effects by using spatial error model, cannot obtain the indirect effects (spatial spillover)

  31. Conclusion • Electricity demand is inelastic with respect to income and price in the short run, and therefore electricity is a normal good and a necessity. • Long run electricity demand is income inelastic and price elastic. • Pricing policies alone cannot be so much effective to decrease electricity consumption • Spatial effects should be considered while making energy policies. • Policy makers should support pricing policies by the diversification across energy resources and energy efficiency programs.

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  36. Thank you for your attention Gülsüm Akarsu gulsum.akarsu@omu.edu.tr

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