عبدالرسول قاسمی

In this paper, the effect of behavioral economics policy tools on increasing the share of public transportation in the conditions of increasing gasoline prices has been investigated. For this issue, the MINDSPACE conceptual model, one of the common models for behavioral economics policymaking, has been used. In this regard, out of the nine tools recommended by this framework, three tools "Incentives", "Messenger" and a combined tool "Norms" and "Ego" were selected in consultation with experts. The stated preference method and questionnaire were used to investigate the effect of these tools. Also, the optimal questionnaire was prepared using the Taguchi method and Minitab software, and then the data was modeled with the logit model and in the NLogit software. Based on Cochran's formula, 384 people from Tehran using personal transportation were selected as a sample, 420 questionnaires were distributed in January 1402, and 403 questionnaires were found to be acceptable. According to the results obtained from the logit model, the combined tool of "Norms" and "Ego" had the greatest effect on changing the behavior of passengers in determining the mode of transportation, and the "Messenger" tool had no noticeable effect in this regard.

The National Development Fund was established as a development fund with the aim of providing intergenerational benefits, preventing the spread of fluctuations in oil revenues to the economy, and also supporting the country's development plans. Despite this, until now, there has not been a detailed evaluation of how the allocation of resources of this fund affects macroeconomic variables. However, by studying and examining the successful global models of such funds, in addition to the limited impact of this fund on the macro-economic variables in Iran, there are also flaws in the way its resources are allocated. Based on this, the main goal of this research is to design a dynamic stochastic general equilibrium model to evaluate the impact of the allocation of National Development Fund resources on macroeconomic variables with the Bayesian estimation approach using quarterly data for the period 2011-2021. The results of the simulation show that if the National Development Fund spends part of its resources on direct and indirect investment, although at the beginning of the period its effects are the same as before (only facilities), but after that the level of production, capital and investment will increase, which will lead to higher economic growth. Also, the results obtained from the minimum variance portfolio method show that among the existing methods, buying shares of capital market companies directly and investing in various types of investment funds, can bring higher returns than the current method (facilities) for the Fund at a certain level of risk

Renewable energies are more compatible with the environment and their preparation and production have less pollution. In addition, since there is no end in sight for this type of energy, renewable energies take on a greater share in the world's energy supply system day by day, even in countries with fossil energy. The purpose of this research is to investigate the factors affecting the consumption of renewable energy in OPEC member countries using the panel data approach in the period from 2004 to 2018. In this research, the effects of factors such as good governance index, human capital, carbon dioxide emission intensity, income (GDP) and crude oil price were investigated. The results of estimating the model using the feasible generalized least squares (FGLS) method indicated that the good governance index, human capital, carbon dioxide emission intensity and income (GDP) have a positive and significant effect on the consumption of renewable energy in OPEC member countries, but the Crude oil price Does not have a significant effect on the consumption of this group of energies in the mentioned countries. Factors such as the high cost of establishing renewable industries in OPEC member countries and the dependence of these countries' economies on oil revenues can be considered among the reasons for this result.

Electricity is a unique commodity, and forecasting its price is challenging due to its distinct characteristics. Accurate electricity price forecasting is essential for market participants, as it can help reduce risk, increase economic profitability, and enhance power system stability. In the electricity price forecasting literature, machine learning models have been preferred over other models due to their ability to capture the nonlinear behavior of market data, ease of implementation, and good performance. In recent years, the emphasis on the importance of the number of hidden layers in machine learning structures has led to the emergence of deep learning. However, the performance of these models is significantly influenced by optimal feature selection and appropriate hyperparameter tuning. Therefore, this study aims to forecast the market clearing price in Iran’s electricity market by employing two deep learning-based hybrid models: deep neural networks (DNN) and long short-term memory (LSTM) networks, using feature selection and hyperparameter optimization techniques. The forecasting accuracy of the models is then compared. The results indicate that the LSTM-based hybrid model outperforms the DNN-based model.

Permanent Magnet Synchronous Motors are used in military applications due to their numerous advantages, including high power density, high efficiency, low noise, and simple and inexpensive maintenance. This paper, a model-independent adaptive fuzzy robust controller is presented for a mechanical robot arm driven by permanent magnet synchronous motors. Although robust control methods have been proposed in order to overcome the uncertainties that include parametric uncertainty, unmodeled dynamics, and external disturbances, they face problems due to the complexity of robot dynamics. A fuzzy system can be used as a general approximator to approximate any nonlinear function. In this article, considering that the joints of the robot arm are the load torques of PMSMs, the uncertainties related to the dynamics of the robot arm have been transferred to the voltage equations of PMSMs. Uncertainties in the control law are estimated and compensated using fuzzy systems. Unlike many previous related works that ignore the electrical equations of PMSM, both electrical and mechanical equations are considered. Based on Barblat's lemma, the asymptotic convergence of the tracking error to zero is guaranteed. A case study is an artist robot arm driven by PMSMs. Compared to a robust controller for a single-joint robot arm actuated by PMSM, the proposed method is superior due to faster and smoother responses. The simulation results show that the regulation error quickly drops to zero (less than 1.2 sec).

As societies grow and develop, pollution has emerged as a fundamental management challenge in the contemporary world. This issue holds particular significance in developing countries where economic growth and environmental quality often clash in policy-making. Urban solid waste is a prevalent form of pollution afflicting cities, including Tehran. The pivotal question is whether the production of urban solid waste increases in tandem with citizens' incomes. This article delves into the relationship between the per capita income of Tehran residents and the volume of urban solid waste they generate within the Environmental Kuznets Curve (EKC) framework. Given that household activities directly drive municipal solid waste production, the findings of this study shed light on citizens' environmental preferences. Utilizing panel data from 2007 to 2016 across 22 districts of Tehran, the study confirms that the quadratic model hypothesis of the EKC adequately analyzes this relationship based on Wald's test. Additionally, utilizing the fixed effects method, the study demonstrates the significant impact of per capita income on waste generation. Moreover, the analysis includes population density and household size as control variables, revealing a negative correlation between population density and waste production, while the impact of household size remains statistically insignificant.

The escalating population growth, urbanization expansion, and industrial development witnessed in recent decades have significantly elevated environmental concerns. A paramount issue among these concerns is the discharge of diverse pollutants, posing a pressing challenge for contemporary societies and garnering considerable attention from governmental entities, academia, and researchers. Waste, as a by-product of human endeavors, manifests in various dimensions and necessitates thorough examination. This study delves into the production of municipal solid waste (MSW) and its correlation with citizen income.
The primary focus of this research is to scrutinize the interplay between citizens' income levels and waste generation, particularly waste directly emanating from households. The underlying research hypothesis posits that as citizens' income rises, there is an initial upsurge in consumption and subsequent waste production. However, as per capita income levels advance, it is anticipated that individuals' preferences will undergo a transformation. This evolution is envisaged to manifest as a heightened environmental conscientiousness, leading to a shift towards prioritizing environmental quality and consequently diminishing waste generation.

Within the realm of environmental economics, scholarly attention has been directed towards exploring the intricate relationship between waste generation and per capita income through the lens of the Environmental Kuznets Curve theory. This hypothesis posits an inverted U-shaped association between per capita income and environmental degradation, with a specific focus on urban solid waste within the scope of the present study. Over the recent years, the Environmental Kuznets Curve theory has been employed as a theoretical framework to elucidate the link between per capita income levels and the proliferation of diverse environmental pollutants.
The term "Environmental Kuznets Curve" (EKC) was coined by (Dasgupta and Maler, 1994) owing to the resemblance of this curve to the fundamental Kuznets curve. Subsequently, (Shafik and Bandyopadhyay, 1992) posited, under the assumption of technological stability, consistent consumer preferences, and continued investment in environmental conservation, that escalating economic activities serve as a catalyst for environmental deterioration. Expounding on this notion, (Beckerman, 1992) delineated the presence of evidence showcasing environmental pollution during the early stages of economic advancement. Ultimately, Beckerman advocated for the augmentation of societal income as the paramount and most effective strategy to safeguard and ameliorate the environment.

The focal point of this study revolves around the interplay between the income levels of Tehran's residents and the volume of solid waste generated by them. Urban solid waste emanates directly from household sources or urban establishments, indicative of the significant influence citizens possess over the production of this particular form of pollution. This investigation draws upon panel data encompassing 22 districts within Tehran. Specifically, the study incorporates data from the 22 districts under the purview of Tehran Metropolitan Municipality over a temporal span stretching from the year 2006 to 2016, totaling a duration of 10 years. The principal dependent variable under scrutiny pertains to the per capita quantity of waste generated by individuals, with this data sourced from official statistics released by the Municipality of Tehran through the Information and Communication Technology Organization.

To initiate the model estimation process, the F-Limer's test was employed to ascertain the distinct impacts of individual sections. The test results unequivocally rejected the null hypothesis with complete certainty, signifying the inadequacy of assuming uniform origins across all regions. Consequently, the data conveys a nuanced perspective by embracing the notion of unique regional effects as opposed to a homogeneous data distribution. Subsequently, the Hausman test was utilized to discern the most appropriate estimation method between fixed effects and random effects.
Upon a thorough examination of the statistical values within the table and the associated probabilities, it was determined that the random effect estimator should be disregarded with a probability exceeding 95%, thereby endorsing the utilization of the fixed effects method. Subsequently, the quadratic model was estimated employing the fixed effects approach.
The estimated coefficients for the variables exhibit significance, barring the household dimension variable. Remarkably, the coefficient of the squared per capita income variable is not only negative but also statistically significant, underscoring the presence of an inverse U-shaped correlation between per capita income and urban solid waste production. This observation aligns with the Environmental Kuznets Curve (EKC) hypothesis, indicating that as the income of citizens in the 22 districts of Tehran rises, the initial surge in solid waste generation eventually plateaus and diminishes beyond a certain threshold. Moreover, the estimated coefficient for population density displays a negative and statistically significant relationship with urban solid waste production, elucidating that heightened population density inversely correlates with the amount of solid waste yielded by inhabitants.

In specific districts within Tehran, the initiation of efforts to diminish urban waste generation has been spurred by income escalations, while in alternate areas; a decline in waste output is projected to transpire once a particular threshold of per capita income is reached. It is imperative to emphasize that these outcomes should not be construed to imply a passive reliance on income augmentation alone to address urban waste management issues, thereby underscoring the continued significance of espousing efficient policies within this domain.

Since most space-based synthetic aperture radar systems have an accuracy of less than one meter in the resolution of images, very accurate processing of synthetic aperture radar data to produce images with high-resolution accuracy is of particular importance. In this article, methods for actual modeling and simulation of the space-based synthetic aperture radar system are presented and the raw data were obtained. For simulation and modeling, the main characteristics of the real satellite synthetic aperture radar system related to sensor mode/dynamics, target observation, antenna beam patterns, pointing errors on the antenna beam, and raw data generation are reflected. Analyzes based on simulations show the effectiveness of the presented methods. In the simulation, the presented method compensates for the phase errors induced by the aiming errors of the antenna beam. The results of the centralization of raw data, the calculated value of the resolution accuracy of the slant range is equal to 1.89 meters. Also, the average values of the measured slant range resolution accuracy, peak side-lobe ratio (PSLR), and integrated side-lobe ratio (ISLR) for the Interrogation Rate Frequency (IRF). An unweighted point in the focused image was obtained around 1.94 m, 13.57 dB and -10.26 dB respectively. The calculated value of azimuth resolution accuracy is 2.24 meters and the average values of measured azimuth resolution accuracy, PSLR, and ISLR for unweighted point target IRFs are 2.29 meters, -12.57 dB and -9.68 dB, respectively. These results show the effectiveness of the proposed method. In other words, the performance of space-based synthetic aperture radar image formation using the proposed method for raw data is very good, so the various effects induced by the real synthetic aperture radar sensor are reflected. Therefore, these results confirm the proposed methods for forming the space-based synthetic aperture radar image.

Climate change is becoming a global crisis that can affect all aspects of human life on Earth, including the economy. So, protecting the environment and achieving sustainable development goals are now on the agenda of many governments. This article uses the Panel Smooth Transition Regression Model to test the role of information communication technology as a transition variable. In this research, it is investigated whether ICT can decouple the economic growth from the increase in pollution by changing the effect of productivity on CO2 emission. The goal is to show whether policymakers can use the development of the digital economy around the world to achieve better environmental and economic outcomes. This study was conducted on a global scale and data from 108 countries was used from 2003 to 2019. The results show that the co2 emission follows a PSTR model with ICT as a transition variable that changes the effect of total factor productivity on co2 emission in favor of the environment. ICT development will improve carbon efficiency and reduce climate change.

Oil revenue management in the Iranian economy has been an important topic in recent studies. This study addresses the effects of shocks propagating from oil sector to other sectors by pplying proposed fiscal rules. Given the key role of governments and the financial system in propagating oil shocks in resource-rich economies, this study considers both sectors. A dynamic stochastic general equilibrium (DSGE) model in which government expenditures are dependent on oil revenues has been designed and simulated for Iran's economy. The proposed fiscal rules are the setting a floor for the oil fund (OF) oil inflow and imposing the fund balance floor. To examine the importance of the oil revenues coefficient, we treat it as a variable in the model and examine the impact of shocks on it. The results show that oil revenue coefficient is an effective tool for applying fiscal rules and has a large impact on macroeconomic variables. Applying a floor to the OF oil revenues coefficient is more effective at mitigating the effect of oil shock on macro variables fluctuations than setting a floor on the fund's balance. Both policies help to stabilize macroeconomic variables and preserve the share of oil revenues for future.

This paper focuses on the analysis of the petrochemical products manufacturing value chain and the presentation of petrochemical products' optimal manufacturing pattern in petrochemical industries affiliates of pension funds. The case study in this research is JAM and MASJIDSOLEYMAN Petrochemical companies. Period of this research is 5 years. In this case study, the determiner faces paradoxical objectives simultaneously. The fuzzy multi-objective linear programming (FMOLP) method has been used based on the α-cut of fuzzy parameters in this research. The goal of this research is to extract the amount of products that have maximized profit and value-added and minimized risk simultaneously which face technological, market, financial, budget, legal, and national interest-related limitations. This method is often interested in the proper POSs that have finite tradeoffs between objective functions. One numerical sample has been used to show the possibility of using the said method in multi-objective optimizing issues for the production of propylene. Because of the efficiency and satisfaction of the obtained new solutions for this method, this method undoubtedly can be useful to solve the FMOLP problems. A decrease in the production of ethylene and light polyethylene and an increase in propylene and butadiene production is the most important policy recommendation of this research.

Wireless systems have important applications in various scientific fields, including medical applications and military fields. In this paper, a ring voltage control oscillator is implemented by combining direct bulk biasing techniques and oscillating frequency current control and tunability in a wide frequency range for behind-the-wall live target detection applications. In this paper, three new structures are proposed to improve phase noise and frequency limitation. The simulations related to the first proposed circuit in 0.18µm CMOS technology using bulk voltage bias technique show that the amount of phase noise at 1 MHz frequency is equal to -138dBc/Hz and its power consumption is -45dB with a frequency range of 2 to 12 gigahertz, which represents the efficiency of the provided circuit. In the second proposed structure, by using the inductor in these control transistors, the frequency range has been improved. Circuit simulations of the second structure show that the phase noise for this circuit is equal to -123.26dBc/Hz and the frequency range is between 6 and 16 GHz with a power consumption of -90dB. By combining the first and second circuits, another circuit was proposed, which has a wider frequency range of 7 to 22 GHz. The simulation results show that the three proposed circuits have a much better performance than the previous ones. In this article, all the results were analyzed and simulated with Advance Design System (ADS) software.

In today's electronic and digital world, increasing demand for portable systems has led the electronics industry and chip design technology to reduce power consumption methods, and therefore power consumption has become an important criterion in this field. Also, increasing the speed of chips and reducing the propagation delay of circuits has always been an important goal of digital design engineers. Since the Adder element is one of the important elements in many digital systems, so today various Adders with different technologies and design approaches have been proposed, each of which has certain advantages and disadvantages. This paper presents a low-power single-bit full-adder cell design that is based on pass-transistor logic.This circuit is used in the arithmetic logic units of digital signal processors and also in several electronic and digital communication systems that operate within the frequency range of in 1GHz. The proposed cell exploits the pass transistor techniques and XOR-XOR structures to improve the design parameters namely power consumption, propagation delay, power–delay product, and the number of transistors. The proposed circuit is designed using 180nm CMOS technology and the simulation results show that for a supply voltage of 1.8V, the power consumption, delay, and power–delay product have been achieved as 83 W, 89ps, and 7fJ respectively.

This paper comprehensively investigates how the control voltage settles in Voltage Controlled Oscillators (VCO) by considering all non-ideal factors in phase lock loop circuits. Also, the different structures of the phase detector and their effect on the locking speed of the phase-locking loop circuit, the control voltage ripple and the locking frequency range will be compared. Three phase locked loop circuits with XOR detector, RS-FF detector and dynamic phase detector were investigated in this paper. The simulations were performed on 0.18 µm-CMOS technology with a 1.8V power supply. The simulation results show that the operating range of the phase-locked loop circuit including dynamic phase detector with charge pump circuit has less ripple for the non-ideal effects compared to the phase-locked loop circuit including XOR phase detector and the phase-locked loop circuit including RS-FF phase detector. of the phase-locked loop circuit including dynamic phase detector is designed using 180nm CMOS technology and the simulation results show that for a supply voltage of 1.8V, frequency range is 0.284-3.33GHz, power consumption is 2.86mW and phase noise is -118.8dBc/Hz.

Fluctuations in crude oil prices are among the factors affecting other economic indicators, and therefore these fluctuations have always been of interest to economists. In this paper, the effect of fundamental market factors (supply and demand of crude oil) on changes in world oil prices is addressed and supply and demand elasticity are estimated using the simultaneous equations system considering world crude oil prices, US dollar index, natural gas price, global military and security tensions, reserves of OECD countries, operating capacity of the world's refineries as well as world gross domestic product (GDP). Then, the proposed model is utilized to forecast the world crude oil prices. Using the proposed model, supply elasticity in relation to world crude oil price, reserves of OECD countries, and operating capacity of the world's refineries are estimated. Moreover, demand elasticity in relation to world crude oil prices, world GDP, US dollar index, natural gas prices, and military and security tensions in the world are estimated. based on the results, the highest impact belongs to the world GPD with the demand elasticity of 0.6039, and military and security tensions in the world have the minimum impact with a demand elasticity of -0.0110.

In this paper, a robust dynamic slip mode controller for an electrical robot manipulator is presented. The control law calculates the motor voltage based on the voltage control strategy. Uncertainties are estimated using the Fourier series expansion and the cutting error is compensated. Fourier coefficients are adjusted based on stability analysis. Also in this paper is the design of a robust controller using a new adaptive Fourier series extension. Compared to previous related works based on the Fourier series expansion, the advantage of this paper is that it provides a matching law for the main frequency of the Fourier series expansion and thus eliminates the need for trial and error in its regulation. A case study of a Scara robot powered by DC magnet electric motors. The effect of uncertainty estimation based on the Fourier series expansion is studied instead of using the sign function. The proposed method is also compared with Legendre polynomials. The simulation results confirm the robust and satisfactory performance of the proposed controller.

Today, geo-economics strategies play a significant role in the global economy and new dimensions have been considered, including the distribution of energy resources and their trade movements. In this study, evaluating the factors affecting the volume of oil trade in the corridors in the current period and till 2040 perspective is sought through the gravity approach and using the panel data model in the period (2008- 2020). Reviewed oil trade flows of the area include: Middle East-Southeast Asia, Middle East-China, Middle East-Japan, Middle East-Europe, Middle East-India, Middle East-Singapore, Middle East-Africa, Middle East-Australia, Russia and CIS-India and Russia and CIS-Middle East. The required data were acquired from IEA, BP and WDI databases. The results obtained from this model reveal that the variables affecting oil trade between countries include the amount of GDP of the importing country, the share of oil in the energy consumption of exporting countries, and the share of oil in the energy consumption of importing countries. On the horizon of 2040, the Middle East-China oil trade is projected to rank first, the Middle East-Southeast Asia oil trade to be ranked second, and the Middle East-India oil trade to be ranked third.

Nowadays, environmental performance has attracted widespread attention, and it is mentioned that issues such as air pollution, global warming, habitat destruction, biodiversity threat, water scarcity crisis and desertification drastically threaten human future. The volume of oil and gas activities in the Persian Gulf countries are among the platforms that also highlight the attention to environmental performance in this area. Therefore, this article examines the effect of economic and non-economic factors affecting the environmental performance of the Persian Gulf countries. The economic factors that we selected to investigate are growth rate, degree of economic openness, energy intensity; and the non-economic factors we took into account are urbanization, democracy, population and financial development. Considering trade relations between the countries of the Persian Gulf, the spatial panel data model is used to take into account the effects of such interaction. We selected 8 Persian Gulf countries including Iran, United Arab Emirates, Bahrain, Iraq, Oman, Saudi Arabia, Qatar and Kuwait for this article. To demonstrate the relationship between economic and non-economic factors for selected countries, we chose the period 1998-2018. The findings indicate significant spatial effects related to financial development, business openness and urbanization. Findings showed significant spatial effects related to financial development, business openness and urbanization.

Emissions of more than three-quarters of greenhouse gases by the energy sector have affected climate change in various economic, political and social sectors. Climate change is affecting energy consumption, including natural gas. This article deals with the future of natural gas consumption in the country with regard to climate change until 2030 using a combined approach of econometrics and scenario building. Natural gas consumption is projected to reach 337,113 million cubic meters by 2030 within framework of the baseline scenario, which is based on the continuation of the current trend and the results of the censored regression model (Tobit). Also, the future research of natural gas consumption in the form of eight scenarios, based on two key drivers of changes in temperature and price of natural gas, as well as the assumption of stability of trends: GDP, electricity consumption, energy intensity, consumption of petroleum products, population, has been examined. Findings show that a 4 degree increase in temperature will increase natural gas consumption to 358669 million cubic meters (with 78.7% growth compared to 2016 and 46.8% growth compared to 2020) in 2030, but rising natural gas prices could moderate that trend to some extent.

Heavy oil as an unconventional oil resource contains 15 and 10.5 percentage of global hydrocarbon proven reserves and production respectively. Since the share of conventional and tight oil in world oil production is dwindling, it is expected that heavy oil play an important role in global market in the long-term. Therefore, countries with massive heavy oil reserve such as Iran would be significant players in the future. This paper aims to identify the national and global key factors and driving forces which play significant roles in reshaping the Iran’s heavy oil future, then presents Iran’s heavy oil export plausible scenarios by 2040. The 27 key factors were extracted using the Delphi method through questionnaires and environment scanning based on the STEEP framework using mathematical and statistical analysis. In the next step, the application of Cross impact analysis identifies the 16 driving forces of the Iran’s future heavy oil export using the MicMac software.Then based on academic literature and expert panel, 60 variants identified for all descriptors. In the next step, 16 consistence and plausible scenarios have been chosen using scenario wizard software. At the end, after evaluating and endorsing all the 16 scenarios by the expert panel, 5 final scenarios named “In the Sanction’s Labyrinth”, “East Without West”, “The Great Virus”, “The bond with the World” and “The Lost Opportunities” have been written.

The characteristics of crude oil and the factors affecting the price of this energy carrier have made its price forecast always considered by researchers, oil market participants, governments, and policymakers. Because the price of crude oil is affected by many factors, ongoing studies should be done to make more accurate and reliable estimates over time. In this paper, a combination of GM (1,1) and ARIMA models and a hybrid model (GM-ARIMA) for crude oil price forecasting is proposed. The Brent crude oil price data for seasonal (2015Q1-2021Q2), monthly(2020m3-2020m12), and weekly(w12-2020: w16-2021) periods were used to examine this method. The results show that based on the evaluation criteria of mean absolute error percentage (MAPE) and square mean square error (RMSE), the evaluation criteria of MAPE and RMSE in the combined GM-ARIMA model are always lower than the GM and ARIMA models alone. Therefore, the GM-ARIMA hybrid model will be able to predict more accurately than the GM and ARIMA models. Therefore, for more accurate prediction, the GM-ARIMA hybrid model can be used instead of single models.

The characteristics of crude oil and the factors affecting the price of this energy carrier have caused its price forecast to always be considered by researchers, oil market activists, governments and policy makers. Since the price of crude oil is affected by many factors, therefore, continuous studies should be done in this way so that the estimates made over time, the results are more accurate and more reliable. In this paper, a combination of nonlinear grey model and Arima is used to forecast the price of crude oil and a combined nonlinear grey-Arima model is proposed. Brent crude oil price data for seasonal, monthly and weekly periods have been used to investigate this technique. In the quarterly forecast of data for the first quarter of 2015 to the fourth quarter of 2021, in the monthly forecast of data for January 2020 to December 2021 and in the weekly forecast of data for the second quarter of March 2020 until the first week of December 2021 has been used. The results showed that the mean absolute value of the error percentage and the square root of the mean square error in the hybrid model are always lower than the single theory-based model or the nonlinear grey single theory. Also, the hybrid model has a higher ability to explain and cover price fluctuations in different time periods and is more reliable than the single model. Therefore, a hybrid model can be used instead of single and single theory-based models for more accurate forecasting.

In this study, we employed the CGARCH model, the vector auto-regression and graph analysis, to analyze permanent and temporary fluctuations of Brent oil, gold, currency, petrochemical and petroleum industries index and Tehran stock exchange index. Data of this study includes the daily and weekly observations of the above variables from 2008 to 2018. The results showed that the fluctuations of Brent oil prices, oil and petrochemical industries were the most affected. Therefore, in the short term, these fluctuations in oil and related industries have the greatest impact on fluctuations in the stock market, currency and gold in Iran. However, according to daily and weekly data, permanent gold fluctuations and Tehran Stock Exchange index have the most impact on the volatility of other variables. In addition, based on daily data, the fluctuations of the oil products index, exchange rate and gold are the most important given the criteria presented.

Economic sanctions in the field of international relations affect the trade pattern of countries. One of the effective channels of sanctions is exports (as the most important source of foreign exchange supply for countries). In this regard, oil exports to OPEC member countries, especially Iran, are among the most important economic revenues and have a high share in the country's budget. In the present study, the effect of sanctions on the pattern of trade in Iranian crude oil and petroleum products and also the effect of these sanctions on OPEC member countries in the framework of the generalized gravity model using panel data econometric models have been investigated. The research model is based on statistical data from 1988 to 2018 in the form of four periods of sanctions, including the first period of US sanctions, EU sanctions, UN sanctions, and the second period of US sanctions. The findings show that US-era sanctions and EU sanctions have had less of an impact on Iran's oil exports, but UN sanctions have had a significant impact on these oil exports and Iran's share of OPEC exports. According to research findings, other OPEC member countries have not had a significant impact on the replacement of the Iranian oil market, and this shortage has been mainly met by countries outside the OPEC.

Investigating the effects of climate change on different aspects of social life has been the focus of research in recent decades. The importance of energy for development and growth as well as pollution caused by energy carriers has made it necessary to assess the impact of climate change on the consumption of various energy carriers including natural gas. This study designed a model for natural gas demand in which in addition to the conventional economic factors, climate variables are considered. Then, we estimate this model for Iran during the years 2003-2015. In addition, variables of GDP and electricity consumption as well as trend variable had the greatest effect on natural gas consumption in the country.