mohammad hossin nekooei; reza zeinalzadeh; Zeinolabedin Sadeghi
Abstract
The relationship between democracy and environment has always been controversial. Some scientists found that democracy had a positive impact on reducing environmental disruption. Other scholars claimed that democracy tends to accelerate environmental degradation. Ther are many studies focusing on main ...
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The relationship between democracy and environment has always been controversial. Some scientists found that democracy had a positive impact on reducing environmental disruption. Other scholars claimed that democracy tends to accelerate environmental degradation. Ther are many studies focusing on main determinants of environmental degradation. More recently, democracy is considered to be one of factors affecting environmental quality. This research studies the relationship between democracy and environment quality in selected Organization of Islamic Cooperation (OIC) countries by using panel data model for the period 2000-2010. The results of estimation show that democracy affects environment quality directly in these countries. Moreover, we find that economic growth and trade have positive effect on environmental quality. However, energy consumption and population have negative effect on environment in selected OIC countries.
Abstract
Forecasting energy price and consumption is essential in making effective managerial decisions and plans. While there are many sophisticated mathematical methods developed so far to forecast, some nature-based intelligent algorithms with desired characteristics have been developed recently. The main ...
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Forecasting energy price and consumption is essential in making effective managerial decisions and plans. While there are many sophisticated mathematical methods developed so far to forecast, some nature-based intelligent algorithms with desired characteristics have been developed recently. The main objective of this research is short term forecasting of energy price and consumption in Iranian industrial sector using artificial intelligence including an Adaptive Neuro-Fuzzy Inference System (ANFIS) and an Artificial Neural Networks (ANN). The dataset contains monthly price and consumption of gas oil, petrol, and liquid petroleum gas in the period between March 1996 and March 2010. Based on dataset, energy price and consumption for 2011 and 2012 are forecasted. The results obtained utilizing the two methods show that while both are appropriate tools to forecast price and consumption, most of the time ANFIS has lower error than ANN in terms of the mean squared error criterion