Introduction Directory UMM :Data Elmu:jurnal:E:Energy Economics:Vol22.Issue6.2000:

G.E. Nasr et al. r Energy Economics 22 2000 627]640 628

1. Introduction

Econometric models have been developed to forecast energy in many countries. A survey of statistical methods to evaluate urban energy needs is presented by Ž . Balocco and Grazzini 1997 . Determinants of energy demand in the literature Ž . Ž . include degree days DD temperature by Al-Zayer and Al-Ibrahim 1996 , popula- Ž . tion growth, and oil prices by Majumdar and Parikh 1996 and GDP by Dincer and Ž . Ž . Dost 1997 . Moreover, Connor 1996 used neural network models to simulate the impact of various physical and economical variables on electricity and energy Ž . production levels. Dahl 1994 , after reviewing 50 studies for energy demand in developing countries, found that the most commonly used modeling techniques for aggregate energy demand are the simple static and the partial adjustment models Ž . Ž . PAM . Erdogan and Dahl 1997 investigated the impact of income, price and population on the aggregate, industrial, manufacture and mining sectors of energy Ž . in Turkey. Eltony and Hosque 1997 presented a cointegrating relationship for the demand of electricity in Kuwait. Moreover, cointegrating relationships were also Ž developed for natural gas and gasoline demand in Kuwait Eltony and Al-Mutairi, . 1995; Eltony, 1996 , cointegration models included price, income and population as explanatory variables. This work is the first to investigate the determinants of electricity consumption in Lebanon and focuses on the post-war period, 1993]1997. The Lebanon experienced economic growth rates averaging approximately 5 in the period from 1993 to 1997. Economic growth was concentrated in the construc- tion and public investment sectors. This concentration is evidenced in the large increases in public investments during this period which averaged approximately 6.7 of GDP in 1994]1996 and in the growth of construction permits that Ž increased from 10 614 in 1992 to 17 433 in 1996 Central Bank of Lebanon, . 1993]1997 . This increase in economic activities was also accompanied by renova- tion of war-damaged buildings and facilities both in the public and private sectors. These developments were accompanied by a major drive to renovate and expand existing national capabilities in electricity production. The Electricite Du Liban Ž . EDL electric utility company has developed a comprehensive plan of investments costing approximately 330 million for the renovation and expansion of production capacity up to approximately 1300 MW in order to meet peak demand levels. Moreover, power restructuring and expansion projects have been agreed upon between the government and the World Bank with investment total cost of 486 million. Given these large investment outlays, it is important to study the determi- nants in electricity consumption in the Lebanon in order to provide a better planning and execution of power generation expansion in the future. The electrical Ž energy consumption barely balanced the production in pre-war Lebanon prior to . 1975 . During the war, from 1975 to 1992, Lebanon halted all infrastructure expansion and restructuring and was only maintaining pre-war facilities. During and after the war, electricity demand exceeded production capacity, consequently, the EDL implemented a rationing policy. Black outs reached approximately 12 h a day in 1993]1994 and 6.5 h during 1995. Rationing was almost non-existent starting Ž Ž .. 1996 in most of the country Electricite Du Liban 1996 . G.E. Nasr et al. r Energy Economics 22 2000 627]640 629 Fig. 1. Electrical energy consumption for the period 1993]1997. Moreover, electricity demand varies during different periods of the year. During the hot and humid summer, substantial use of ArC equipments is required in the coastal and heavily populated areas to maintain comfortable indoor spaces. In this Ž . paper, the impact and statistical significance of TI GDP proxy and DD on electrical energy consumption is investigated over different time spans of the 1993]1997 period. Furthermore, the use of the models in forecasting future electrical energy consumption is investigated.

2. Data and explanatory variables