ENERGY ECONOMICS REVIEW
Year:2010 | Volume:6 | Issue:23|Papers Count:10

This study investigates the cointegration relationship between energy consumption and GDP in a panel of 78 selected developed and developing countries from 1970 to 2006. To that end, different panel unit root tests, panel cointegration and pooled least square tests are used.The existence of a cointegration relationship between GDP and energy consumption is confirmed both for developed and developing countries. However, the cointegrating vectors are not the same. During the study period, developed countries had higher levels of energy consumption, while the long run impact of energy use on GDP is less pronounced for developing countries.

In Iran, energy consumption in different sectors, including industry, is high. Thus it is important to consider use of technologies which decrease energy consumption.We use a Cobb-Douglas production function with energy, labor and capital as independent variables to study the relationship between energy consumption and choice of technology. Since increased production requires increased energy use, the growth of output per unit of capital and labor will increase energy consumption. On the other hand, technological improvement decreases energy consumption. Empirical results of the study show that technological improvement decreases the growth rate of energy consumption in the industrial sector by an average of 1.53 per cent and that of agriculture by an average of 1.32 per cent.

In this paper we design a new model that integrates linear and non linear models to forecast the trend natural gas daily demand in the residential sector of the country and isolate the factors that impact daily demand. We use the model to forecast the natural gas daily demand of the residential sector for the next 10 days in a “step by step” manner, using the ARIMA, Feed forward Artificial Neural Network, and Neural Network - Wavelet Transform models and the new proposed model. The results indicate that the proposed model has fewer forecasting errors and higher accuracy in forecasting the natural gas daily demand of the residential sector of the country. The Neural Network-Wavelet Transform, Feed forward Artificial Neural Network and ARIMA models rank lower than the proposed model in the given order.

This article studies the dynamic effects of oil price, supply and demand shocks on Iran’s GDP and inflation. It uses an AD-AS model to identify a structural VAR for Iran. Unlike the standard Blanchard-Quah decomposition, the method proposed in this paper does not assume the correlation between demand and supply shocks to be zero. The study is based on quarterly data for the period 1367:1 to1386:4. This paper finds the oil income shocks explain 0/5-1/5 per cent of the variation in output. If demand shocks have an effect on the aggregate supply curve, in the short term, aggregate demand shocks can account for approximately 36 per cent of the forecast-error variance in output .On the other hand, in the long run, aggregate demand shocks account for approximately 72/9 per cent of changes in inflation. The variance decompositions also indicated that aggregate supply shocks are the most important sources of output fluctuations in Iran.

High electricity consumption in the industrial sector of Iran is an important issue. Price liberalization and elimination of electricity subsidy is one way of reducing electricity consumption. In this study we estimated the demand function of electricity in the industrial sector for 1975 to 2007, using the ARDL method. The results indicated short and long-run price elasticities of -0.05 and -0.28 and short and long-run income elasticities of 0.27 and 1.52. We use these elasticities to consider two potential scenarios for removal of subsidies. The first scenario would adopt a three year time horizon of 2010 to 2012, while the second one allows for five years (2010-2014). In each scenario we considered both a monotonous increase of nominal prices and a monotonous increase of real prices. The results indicate that in the first scenario the price will be 354.9, 557.4 and 833.2 rials per kilowatt / h, respectively in 2010, 2011 and 2012, while under the second scenario price will be 310.55, 415.24, 519.93, 624.62 and 729.31 rials per kilowatt / h respectively in 2010, 2011, 2012, 2013 and 2014.

This paper examines the impact of international financial integration on macroeconomic volatility in a group of oil exporting developing countries over the period 1960-2005. The study introduces an index of financial integration into a standard macro - economic model that uses such variables as government expenditures. The study finds that in the same manner that these countries did not succeed to use financial integration to smooth out consumption through spreading risks, financial integration did not act as an effective factor in creating macroeconomic volatility.

An examination of various sources indicate that most of the non – metal inorganic industries, especially the tile, ceramic, clay and machine- made brick industries, have higher energy consumption than the global average. Inefficient consumption of energy and wastage of approximately one - third of the total energy in the industrial process, leading to adverse environmental consequences, makes it imperative to optimize energy consumption.This paper aims to study the role of energy management systems in optimizing energy consumption in the non - metal inorganic industries, through the case study of the Tabriz Machine - made Clay and Brick Factory. Data has been collected from the technical documents and papers of the company. In order to test the hypotheses, we have calculated the energy consumption rate in terms of production unit, to take into account fluctuations in production and consumption of energy in different months of the year and to reduce the probability of ambiguities and for greater comparability of data. We have then analyzed the data using the pair - comparison test. The results show that applying an energy management system leads to a decrease in energy consumption and production costs and a decrease in the amount of pollutant gases.

Among various users of energy, the household sector has a major role accounting for around one-third of total energy consumption. We employ the MADE-II energy forecasting model in which the level of household income affects energy demand to estimate household effective energy demand. The study forecasts effective energy demand for seven uses across ten expenditure groups in rural and urban area of every province. The results show that effective energy demand for space-heating constitutes around 70 percent of total energy demand, amounting to 764 million GJs. Amongst provinces, Tehran, Khorasan-E-Razavi, and Esfahan have the highest effective energy demand. Meanwhile, Chaharmahal-E- Bakhtiari has the highest per family effective energy demand at 91.88 GJs while Hormozghan has the least per family demand at 37.57 GJs.