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Economic growth planning and policy making is one of the macrocosmic goals which it need to pay specific attention to energy and environmental sector and their relationship with production. Therefore, this study has conducted to investigate the relationship between economic growth, energy consumption and environmental pollution using a Spatial Panel Simultaneous-Equations model for 9 developing countries during 2000-2011. Empirical results of this method show that energy consumption, economic growth and environmental pollution in each country is affected by these factors in neighboring countries. The results of research confirm there exists bidirectional causality between energy consumption and environmental pollution, economic growth and environmental pollution. Thus, there is a bidirectional causal relationship between energy consumption and economic growth. Regarding to result of this study suggests to achieve the sustainable economic growth should be used tax tools for controlling the emissions of CO2 and replacement of the renewable energies with fossil fuels.

Caspian Sea is one of the most low-lying areas of the region which is located between latitudes 36.34 and 47.13 degrees north. Its considerable vast area and depth have provided an opportunity to gain renewable energy by different methods. This paper analyzes the performance and mechanism of a floating wave energy converter known as WaveStar, in the above-mentioned sea. Different parts of mechanism are examined under hydrodynamic forces of waves with certain periods and amplitudes. By using the frequency parameters, profile and velocity of the waves; and solving the governing dynamic equations for the model, the vibration response of system has been derived. The main part of this study is the investigation of the effect of changing the arm length, float diameter, wave period and wave amplitude on the structure using regular wave with Froude-Krylov force.

This study aims at observing the long-term trends of meteorological parameters and ET0in Caspian region, North of Iran. The long-term trends of meteorological data (1961-2008) were obtained from five synoptic meteorological stations, i.e. Gorgan, Qaem Shahr, Babolsar, Ramsar, and Anzali located throughout the Caspian region. In order to observe the trends of meteorological parameters, the region was primarily classified into five climatic zones based on De Martonne climate classification index (IA). The FAO Penman–Monteith combination equation was applied to calculate the ET0. The Caspian region was categorized in five climatic classifications as Mediterranean (Gorgan), semi-humid (Qaem­ Shahr), humid (Babolsar), very humid, type 1 (Ramsar), and very humid, type 2 (Anzali). Our results indicated that trends of air temperature were significant within the past half-decade so that it was increased (0.74ºC) during the two past decades. As well, wind speed showed significant increasing trends in all stations and increased 1.1 m.s-1 as average. The ET0 has been raised 0.4 mm.d-1 in Caspian region since 1988 and IA decreased from 39.5 to 36.8 showing the region is getting warmer and drier. Changes in meteorological parameters and ET0 will definitely affect the function of natural and artificial ecosystems. It is essential for managers to think of expected changes while planning for future development in Caspian region.

In spite the global efforts to reduce energy intensity; Iran’s energy intensity has been increasing during the recent decades. To get a more detailed investigation of energy intensity, this study aims at decomposing energy intensity into its components including efficiency and structural change as well as at examining driving forces behind Iran’s energy intensity components during 1973-2011. Energy intensity decomposition showed that efficiency changes accounts for the most of increased energy intensity. It is found in this study that income (GDP), capital- labor ratio and urbanization are the most determinants of energy intensity and its components. Regarding the non-linear relationship between energy intensity and driving forces of income and capital-labor ratio as well as the estimated turning points, income plays a significant role in increase of energy intensity while capital-labor ratio tends to induce a reduction in energy intensity. Although urbanization has a positive contribution to energy intensity via structural changes component, its dominant effect on improved energy efficiency leads to an overall effect of reduced energy intensity by more than 1.8% as 1% increase in urbanization. The results showed a limited effect for price and share of industry in GDP and left no significant role for economic integration and foreign direct investment. The corresponding value for these variables remain less than 0.05%. 1.0pt;line-height:85%;font-family:"B Zar";letter-spacing: -.2pt;mso-bidi-language:FA;mso-ansi-font-style:italic'>کار با شاخص‌های شدت انرژی و نقطه عطف مترتب بر آنها در مجموع اثر درآمد در جهت افزایش شدت انرژی و اثر سرمایه در جهت کاهش شدت انرژی ارزیابی شد. اما شهرنشینی با وجود افزایش شدت انرژی از طریق تغییرات ساختاری از طریق بهبود کارایی در مجموع موجب کاهش شدت انرژی فراتر از 8/1 درصد به ازای 1 درصد افزایش شهرنشینی خواهد شد. اثر قیمت و سهم صنعت از تولید ناخالص داخلی بر شاخص‌های فوق محدود و اثر متغیرهای شاخص ادغام تجاری و سرمایه‌گذاری خارجی قابل اغماض ارزیابی شد. رقم متناظر برای متغیرهای یاد شده بیشتر کمتر از 05/0 درصد به دست آمد.

Nowadays, in water resources management, climate change is one of the main challenges. Changes in the water cycle are one of the most important of ground responses to warming it (IPCC 2014). Changes of precipitation and temperature caused by climate change, will damage to the products of garden and agricultural. Therefore, in order to increase the food security in future periods, it is necessary to evaluate the climate change impacts on the agricultural of regions and provide adaptation strategies its. So Regarding the importance of climate change, the purpose of this study is simulating the performance of the agricultural sector Hashtgerd Plain under climate change impacts in the future period (2020-2049).

This study aims at analyzing the energy intensity and also the effect of changes in the production technology on the efficiency of energy consumption in Iranian manufacturing sector. To this end, a regression method entitled the Translog Cost Equation Function is used to evaluate the energy consumption. The period of investigation is 1999- 2011. The results show that the energy intensity in the period of investigation is equal to 0.08 percent which indicates the effectiveness of this variable in the industrial sector. Findings also demonstrate that the technology had the lowest effect, while the small change in the price of energy (i.e. substitution and budgetary effects) had the highest effect on the energy intensity. This means that due to the structure of the industrial sector of the Iranian economy and the low price for energy as well as its adequate supply has led to the utilization of energy intensive components.

Energy intensity is one of the important and attractive indicators in energy economics. According to the abundance of energy resources in Iran, these resources are not used properly and therefore energy intensity is very high compared to other countries. Hence in this paper using a nonlinear regression method, we study the factors affecting energy intensity in Iran during the period 1979-2013. The results indicate that the existence of two regimes by considering relative price of energy as transition variable with the threshold about 1.58. The rate of urbanization and industrialization had positive effect and the level of technology and relative price of energy had negative effect on energy intensity in Iran. The effectiveness of relative price in the high price regime is intensified and the effectiveness of industrialization and technology is dropped. These results would suggest the important role of price regime on the effectiveness of energy intensity determinants in Iran and leads policy makers to prevent the decrease in the relative price of energy in the years following the implementation of targeted subsidies.

Renewable energy technologies convert renewable resources into forms of energy that can complement or replace conventional energy sources such as fossil fuels. Wind, solar, earth energy systems, small-scale hydro systems and biomass (eg. Straw, wood, corn) are all forms of renewable energy. Wind, solar and small-scale hydro systems have zero Greenhouse Gas Emissions. For example, for every kilowatt-hour generated by a wind turbine instead of by burning fossil fuels, about one kilogram of co2 is not emitted into the atmosphere (Alberta Environmentally Sustainable Agriculture Council, 2001). Wind turbines capture wind energy and convert it to electricity. Wind energy systems can either be small, stand-alone “off-grid” systems, or connected to the provincial power grid. Because wind is an intermittent resource, a back-up system is needed. Wind systems require an average annual wind speed greater then 15 kilometers per hour may only be feasible in some part Earth. Electricity generating coasts are reported to have dropped from $0.25 per kilowatt-hour (kWh) in the 1980’s to below $0.10 per kWh in 2001. One opportunity for farmers is the potential to lease land to wind energy producers. Every ten days, the earth receives solar energy of an amount equal to the world’s entire fossil fuel reserves, and approximately one precent of this is converted to wind energy (Freris, 1990). This solar radiation is converted to wind energy as a result of the unequal heating of the equator as compared to the poles, and of the oceans as compared to the continents. This unequal heating leads to motion within the atmosphere as it tries to equalize its pressure- resulting in what we know as wind. A second cause wind is the motion of the earth. Many meteorological quantities are transported via air currents. In fact, because of the winds role in the transmission of physical and meteorological parameters atmospheric, are very important. Further movement of wind as a source of new and inexhaustible energy is considered. In recent years, the kinetic energy of wind as a source of new and inexhaustible energy is considered by many countries. Recently use of wind energy as one of the most popular renewable power resources for producing electrical energy, is growing up. The purpose of this study is evaluating the amount of wind energy production and finding the windward areas in the Ilam province. In this study, the wind speed and wind direction daily data from 7 weather stations in the state (from established year to 2013) and 15 weather stations from outside of the state boundary are collected. At First, the days with incomplete data are eliminated, then for unifying dimensions of each data base, the average of long-term daily data are calculated. For averaging the long-term daily data related to wind speed and wind direction, two data bases with the 366*22 dimensions are established separately. By utilizing the two data bases, the orbital and meridional components are calculated. Based on the orbital and meridional components, and using krigingchr('39')s geostatistics method, the orbital and meridional components of wind speed of the area study, are estimated. Finally, tow data bases with the 896*366 dimensions are created for the Ilam province that 366 of data are belonged to the orbital and meridional components of wind speed average and 896 of data are belonged to estimated cells in the Ilam province. The dimensions of each cell were 4.7*4.7 square kilometers. With cluster analysis of the two data bases, the windward areas of Ilam province are specified. For better understanding of wind speed characteristics, the monthly maps of windward areas of Ilam province and the annual coefficient of variability of wind speed are plotted. Based on wind speed, wind density, and the size of utilized wind turbine (rotatory radius 5, 10, 15, 20 meters), the amount of wind power generations (from 896 cells), are estimated. The annually and monthly equipotential maps of wind power generation are plotted. The results show that Mehran is the most windward area in the Ilam province. Also the western areas of Ilam province have more wind speed availability compared to the eastern areas of the Ilam province. Darehshahr has minimum average wind speed in all months of the year. The variability coefficient of wind speed in the Ilam province is between 17.2 and 40.6. The northern areas of the state have less variability coefficient compared to other areas of the state. The evaluation findings of the four mentioned wind turbines show that the wind turbines can produce maximum wind power generation at the west areas of the state (Mehran). Among all of the months in a year, the July has the most wind power generation, as the time viewpoints. By utilizing the wind turbines with five meters blades, the amount of wind production threshold is about 1 to 11 million watts per squared meters. Also by utilizing the wind turbines with 10 and 15 meters blades, the amount of minimum and maximum annual wind energy generation can be about 5 to 45, and 12 to 101, million watts per squared meters, respectively.

Introduction: Precipitation is one of the most important and sensitive parameters of the tropical climate that influence the catchments hydrological regime. The prediction of rainfall is vital for strategic planning and water resources management. Despite its importance, statistical rainfall forecasting, especially for long-term, has been proven to be a great challenge due to the dynamic nature of climate phenomena and random fluctuations involved in the process. Various methods, such as time series and artificial neural network models, have been proposed to predict the level of rainfall. But there is not enough attention to global warming and climate change issues. The main aim of this study is to investigate the conformity of artificial neural network and time series models with climate scenarios. Materials and Methods: For this study, 50 years of daily rainfall data (1961 to 2010) of the synoptic station of Urmia, Tabriz and Khoy was investigated. Data was obtained from Meteorological Organization of Iran. In the present study, the results of two Artificial Neural Network (ANN) and Time Seri (TS) methods were compared with the result of the Emission Scenarios (A2 & B1). HadCM3 model in LARS-WG software was used to generate rainfall for the next 18 years (2011-2029). The results of models were compared with climate scenarios over the next 18 years in the three synoptic stations located in the basin of the Lake Urmia. At the first stage, the best model of time series method was selected. The precipitation was estimated for the next 18 years using these models. For the same period, precipitation was forecast using artificial neural networks. Finally, the results of two models were compared with data generated under two scenarios (B1 and A2) in LARS-WG. Results and Discussion: Different order of AR, MA and ARMA was examined to select the best model of TS The results show that AR(1) was suitable for Tabriz and Khoy stations .In the Urmia station MA(1) was the best performance. Multiple Layer Perceptron with a 10 neurons in hidden layer and the output layer consists of five neurons had the lowest MSE and the highest correlation coefficient in modeling the values of annual precipitation. So MLP was determined as the best structure of neural network for rainfall prediction. According to results, precipitation predicted by the ANN model was very close to the results of A2 and B1 scenario, whereas TS has a significant difference with these scenarios. Average rainfall predicted by two A2 and B1 scenarios in Urmia station has more difference than other stations. Based on the B1 scenario, precipitation will increase 11 percent over the next two decades. It will decrease 10.7 percent according to A2 emissions scenario. According to ANN models and two A2 and B1 scenarios, the rates of rainfall will increase in Tabriz and Khoy stations. However, according to TS model, rainfall will decline 5.94 and 3.63 percent for these two stations, respectively. Conclusion: Global warming and climate change should have adverse effects on groundwater and surface water resources. Different models are used for simulating of thes effects. But, conformity of these models with the results of climate scenarios is an issue that has not been addressed. In the present research coincidence of TS model, ANN model and climate change scenarios was investigated. Results show under emissions scenarios, during the next two decades in Tabriz and Khoy stations, precipitation will increase. In Urmia station B1 and A2 scenario percent increase by 11 percent and 10.5 percent decline predicted, respectively. The results of Roshan and et al (4) and Golmohammad and et al, (7) investigations show increasing trend in the rainfall rate and confirming the results of this study According to results, the performance of ANN model is better than TS model for rainfall prediction and its result is similar to climate change scenarios. Similar results have been reported by Wang et al (29) and the Norani et al (20). Due to the significant difference between the TS and climate scenarios used in the study area, is not recommended, though it can be used as a plausible climate scenario to predict the precipitation of stations in the future studied. At the end, it is suggested that the similar studies carried out in a larger number of stations in the country with respect to global warming and climate change, to determine the validity of the methods used to the predicted rainfall.

Introduction: Due to the effects of climate change on water resources and hydrology, Changes in low flow as an important part of the water cycle, is of interest to researchers, water managers and users in various fields. Changes in characteristics of low flows affected by climate change may have important effects on various aspects of socioeconomic , environmental, water resources and governmental planning. There are several indices to assess the low flows. The used low flow indices in this research for assessing climate change impacts, is include the extracted indices from flow duration curve (Q70, Q90 and Q95), due to the importance of these indices in understanding and assessing the status of river flow in dry seasons that was investigated in Tang Panj Sezar basin in the west of Iran. Materials and methods: In this paper, the Tang Panj Sezar basin with an area of 9410 km2 was divided into 6 smaller sub catchments and the changes of low flow indices were studied in each of the sub catchments. In order to consider the effects of climate change on low flow, scenarios of temperature and precipitation using 10 atmospheric general circulation models (to investigate the uncertainty of GCMs) for both the baseline (1971-2000) and future (2011-2040) under A2 emission scenario was prepared. These scenarios, due to large spatial scale need to downscaling. Therefore, LARS-WG stochastic weather generator model was used. In order to consider the effects of climate change on low flows in the future, a hydrologic model is required to simulate daily flow for 2011-2040. The IHACRES rainfall-runoff model was used for this purpose . After simulation of daily flow using IHACRES, with two time series of daily flow for the observation and future period in each of the sub catchment, the low flow indices were compared. Results Discussion: According to results, across the whole year, the monthly temperature in the future period has increased while rainfall scenarios show different variations for different months, also within a month for different GCMs. Based on the results of low flow indices, in most cases, the three indices of Q70, Q90, and Q95 will show incremental changes in the future compared to the past. Also, the domain simulation by 10 GCMs for all three indices is maximum in Tang Panj Sezar and less for other sub catchments, which is related to better performance of IHACRES model in smaller sub catchments. In order to investigate the uncertainty of type changes in different indices in every sub catchment, changes in any of the indices were considered based on the median of GCMs. To achieve the correct type of changes in low flow indices, the amount of error in a simulation of the indices of IHACRES rainfall-runoff model should also be taken into consideration. Therefore, considering the error, the three indices Q70, Q90 and Q95 in all sub catchments (except for Tang Panj Sezar) will have the relative increase in the future period. The improvement of low flow state in the future period is related to the changes occurred in the state of climate scenarios. As the results indicated, most often, there is an increase in rainfall in dry seasons. Also, in different months of the wet season wet season, if the result of changes in quantity of rainfall is incremental, it can lead to an increase in river flow through groundwater recharge. On the other hand due to the limestone and karst forms in most of the basin area, water storage ability and increase the amount of river flow during low water season in this area is expected. The study on rainfall quantity in Tang Panj Sezar sub catchment also indicated that, there will be no significant increase or decrease in the quantity of rainfall in the dry season. Thus, it is expected that there will not be significant changes in low flow indices. In this sub catchment, changes in various low flow indices do not match perfectly, so more difficult to obtain reliable results. With regard to incremental changes of Q95, low flow index with less uncertainty, as well as improving indices of low flow in other sub-basins, it is possible to predict a relatively better state for low flow indices of Tang Panj Sezar in the future period. Conclusion: Using temperature and rainfall scenarios to simulate river flow in the future, a relative increase of all three low flow indices Q70, Q90 and Q95 was predicted compared with the past period. Although all three of mentioned indices show the amount of low flow in the dry season, it is recommended that only two indices of Q90 and Q95 to assess the effects of climate change be considered. Q90 and Q95 indices are more suitable indices than Q70 for studying the effects of climate change on low flow state. These two indices indicate less quantity of flow in dry seasons; therefore, the changes of the two indices are more important in identifying the low flow state. However, there is less uncertainty in the estimation of the two Q90 and Q95 indices than Q70.